分子进化树

load_skill (state: result)

Args

{
  "name": "distance-calculations"
}

Result

{
  "instructions": "## Version Compatibility\n\nReference examples tested with: BioPython 1.83+. Model-corrected alternatives: ape 5.8+, phangorn, FastME, scikit-bio.\n\nBefore using code patterns, verify installed versions match. If versions differ:\n- Python: `pip show biopython` then `help(module.function)` to check signatures\n- R: `packageVersion('ape')` then `?dist.dna` to verify model strings\n- CLI: `fastme --version` then `fastme --help` to confirm flags\n\nIf code throws ImportError, AttributeError, or TypeError, introspect the installed\npackage and adapt the example to match the actual API rather than retrying.\n\nBio.Phylo `DistanceCalculator` offers only identity / substitution-matrix distances and has NO model-based JC/K80/TN93; for model-corrected DNA distances use ape `dist.dna(model=...)` (introspect its model list and `gamma=` support if the version differs).\n\n# Distance Calculations -- Distance Erases the Information ML Keeps; the Correction Is the Model\n\n**\"Build a tree from my alignment\"** -> Estimate model-corrected pairwise distances, then cluster them into a tree, knowing the correction is where the biology lives and the matrix discards what ML uses.\n- Python: `DistanceCalculator(...).get_distance(aln)` then `DistanceTreeConstructor().nj(dm)` (Bio.Phylo, identity-only)\n- R: `dist.dna(x, model='TN93')` then `fastme.bal(d)` / `nj(d)` (ape, model-corrected)\n\nScope: computing evolutionary distance matrices and building/bootstrapping distance trees (NJ, BIONJ, FastME, UPGMA), and testing saturation before trusting them. ML inference, model selection, and where NJ seeds an ML search -> modern-tree-inference. Alignment quality and trimming, which gate every distance -> alignment/alignment-io. Reading/writing/converting the trees these methods emit -> tree-io. Rooting and pruning -> tree-manipulation.\n\n## The Single Most Important Modern Insight\n\nA distance-based phylogeny is built in two lossy steps and both losses are silent. First, a distance is a model-corrected estimate of expected substitutions per site, not the observed proportion of differing sites; second, the matrix collapses each sequence pair to one scalar and discards the site-pattern information that maximum likelihood evaluates. Distance methods are therefore fast but doubly limited -- by how good the correction is, and by what a matrix of scalars can encode -- and they are structurally incapable of using the per-site signal that makes ML the gold standard for hard problems. Three load-bearing facts:\n\n1. **The correction is the whole biological content.** The raw p-distance (proportion of differing sites) systematically UNDERCOUNTS true divergence because every site that mutated twice (back-substitution), in parallel on both lineages, or to a third state (multiple hit) is missed, and the gap grows without bound toward saturation. The algorithm downstream (NJ, FastME) is just arithmetic on the numbers handed to it; feed it p-distances on divergent data and it returns a confidently wrong tree, fast.\n2. **The matrix throws away what ML keeps.** Two completely different alignments can produce the same distance matrix and therefore the same tree. ML never collapses the data, so it cannot be fooled this way; once the matrix exists, which sites drove the divergence, among-site rate heterogeneity, and competing site-pattern signal are gone.\n3. **Consistency is conditional, not a guarantee.** NJ is statistically consistent -- it returns the true tree as sequence length grows -- but only if the input distances are additive and correctly estimated. A consistent algorithm fed biased (saturated, misspecified) distances converges confidently on the WRONG tree. \"I used NJ so it is consistent\" is not a defense; the correction is exactly what most users get wrong.\n\n## Distance Corrections\n\nEvery nucleotide correction inverts the probability that a site differs under a model, so as p approaches the equilibrium difference (0.75 for equal base frequencies under JC) the correction diverges to infinity and its variance explodes -- the mathematical face of saturation.\n\n| Correction | Corrects for | When to use | ape model |\n|---|---|---|---|\n| p-distance (raw / Hamming) | nothing | very shallow / barcoding; saturation plots | `'raw'`, `'N'` |\n| Jukes-Cantor (JC69) | multiple hits, one rate | minimal correction, quick sanity tree | `'JC69'` |\n| Kimura 2-param (K80) | + transition/transversion bias | default DNA quick distance | `'K80'` (default) |\n| Tamura-Nei (TN93) | + two transition rates + unequal base freqs | mtDNA, richest closed-form | `'TN93'` |\n| LogDet / paralinear | COMPOSITIONAL heterogeneity (non-stationary, general Markov) | GC drift across taxa; compositional attraction suspected | `'logdet'`, `'paralin'` |\n| Gamma-corrected | among-site rate variation (ASRV) | alpha small (< ~1); long branches | `dist.dna(..., gamma=alpha)` |\n| Protein matrix (LG/WAG/JTT/PAM) | amino-acid replacement, multiple hits | protein data (use phangorn `dist.ml`) | n/a (LG/WAG modern; PAM legacy) |\n\nLogDet / paralinear (Lockhart et al. 1994; Lake 1994) is the non-obvious one: JC/K80/TN93 all assume stationarity (constant base composition across the tree), and when composition drifts (thermophiles, AT-rich insect mtDNA, GC-rich chloroplasts) every stationary correction groups taxa by base composition rather than ancestry -- compositional attraction, a sibling of long-branch attraction that fools NJ and stationary-model ML alike. LogDet is computed from the determinant of the 4x4 pairwise divergence matrix and is consistent under the general Markov model, at the cost of needing long sequences and not easily taking gamma. If a topology flips between JC and LogDet, suspect compositional heterogeneity.\n\n## Algorithm Decision\n\n| Algorithm | Criterion | Clock? | Cost | Use / pitfall |\n|---|---|---|---|---|\n| UPGMA | average-linkage clustering | ASSUMES strict clock (ultrametric) | O(n^2) | avoid for molecular phylo; wrong TOPOLOGY under rate variation; serotyping/dendrograms only |\n| NJ (Saitou & Nei 1987) | greedy balanced minimum evolution (Q-matrix) | no | O(n^3) | fast default; consistent ONLY if distances correct/additive; barcoding, large n |\n| BIONJ (Gascuel 1997) | NJ + variance-weighted reduction | no | O(n^3) | better than NJ on large/noisy distances; a common ML starting-tree generator (IQ-TREE2/RAxML-NG default to parsimony starts) |\n| FastME (Lefort et al. 2015) | balanced minimum evolution + NNI/SPR search | no | ~NJ-speed | the modern best distance tree; actively searches, not a single greedy pass |\n\nUPGMA is the key warning: it forces every tip equidistant from the root, which molecular data essentially never satisfies because lineages evolve at different rates. A fast-evolving lineage gets pulled toward the tips and grouped by total divergence rather than true ancestry -- a clustering-by-rate artifact analogous to long-branch attraction. Flat rule: do not use UPGMA for molecular phylogeny unless a clock is independently established. NJ does not assume a clock because its Q-matrix corrects each pairwise distance for that taxon's average divergence to everyone else. NJ/BIONJ/FastME can seed ML searches: some ML programs build a BIONJ or ME starting tree in O(n^3) before likelihood NNI/SPR (though IQ-TREE2 and RAxML-NG default to parsimony starting trees), so this machinery still has a place in an all-ML pipeline.\n\n## Tool Taxonomy\n\n| Tool (lang) | Distance step | Tree step | Note |\n|---|---|---|---|\n| Bio.Phylo.TreeConstruction (Py) | `DistanceCalculator` identity / BLOSUM / PAM matrices ONLY | `DistanceTreeConstructor().nj()` / `.upgma()` | NO JC/K80/TN93; `'identity'` is p-distance, named matrices are score distances |\n| ape (R) | `dist.dna(model=, gamma=)` -- full menu incl. LogDet, gamma | `nj()`, `bionj()`, `fastme.bal()`, `fastme.ols()` | the reference engine for real corrections |\n| phangorn (R) | `dist.ml(model=)` -- DNA JC69/F81 only, but all protein matrices | `upgma()`, `NJ()` | the natural choice for protein distances |\n| FastME (CLI) | JC/K2P/F84/TN93/LogDet/protein | NJ/BIONJ/BME/OLS-ME + NNI/SPR + bootstrap | standalone for very large n; `-m B` = balanced ME |\n| scikit-bio (Py) | none (requires a pre-corrected matrix) | `skbio.tree.nj(dm)` | pure-Python NJ; `neg_as_zero=` since 0.6.3 |\n\nLoud flag, the second flat rule: Bio.Phylo and scikit-bio do NOT correct for multiple hits -- their built-in distance is p-distance or score-based. Presenting Bio.Phylo's `'identity'` distance as a \"Jukes-Cantor tree\" is a common and wrong shortcut. For a real correction, compute the matrix in ape/FastME (or compute it yourself) and pass it in.\n\n## Build a Model-Corrected Distance Matrix and NJ Tree\n\n**Goal:** Turn an alignment into a tree, contrasting Bio.Phylo's identity-only path against ape's model-corrected path.\n\n**Approach:** In Python use Bio.Phylo for the NJ/UPGMA algorithm and pure-Python I/O, but treat its distance as an uncorrected p-distance; for any divergent data move the distance step to ape `dist.dna`, then cluster with FastME (best) or NJ.\n\n```python\nfrom Bio import AlignIO\nfrom Bio.Phylo.TreeConstruction import DistanceCalculator, DistanceTreeConstructor\n\naln = AlignIO.read('alignment.fasta', 'fasta')\ncalc = DistanceCalculator('identity')        # identity-only: this is a p-distance, NOT a JC/K80 correction\ndm = calc.get_distance(aln)                   # multiple/back/parallel hits are NOT corrected here\ntree = DistanceTreeConstructor().nj(dm)       # NJ algorithm is correct; the DISTANCES are the limitation\n# For a model-corrected distance, build the matrix in ape (below) and pass it to skbio/Bio.Phylo, or stay in R.\n```\n\n```r\nlibrary(ape)\naln <- read.dna('alignment.fasta', format = 'fasta')\nd <- dist.dna(aln, model = 'TN93', gamma = 0.5)   # model-corrected; gamma applies ASRV (alpha < 1 = strong)\ntree <- fastme.bal(d, nni = TRUE, spr = TRUE)     # balanced minimum evolution: the modern best distance tree\n# nj(d) / bionj(d) are the faster single-pass alternatives; bionj seeds ML searches\n```\n\n## Pre-flight: Test Substitution Saturation\n\n**Goal:** Decide whether the data retain phylogenetic signal before trusting any deep distance tree.\n\n**Approach:** Compute Xia's index of substitution saturation Iss and compare it to the simulation-derived critical value Iss.c; a saturation plot of transitions against a corrected distance is the visual companion.\n\n```r\nlibrary(ape)                    # the formal entropy-based Iss vs Iss.c test lives in DAMBE; this is the ape/base-R saturation plot\nd_jc  <- dist.dna(aln, model = 'JC69')\nts_tv <- dist.dna(aln, model = 'TS')   # transitions; plot against d_jc -- a PLATEAU means saturated, signal erased\nplot(d_jc, ts_tv)                       # unsaturated = roughly linear; bent-over transition curve = drop those sites\n# Interpretation gate (Xia 2003): Iss < Iss.c => signal retained (usable); Iss >= Iss.c => substantially saturated, do not use.\n```\n\n## Bootstrap a Distance Tree\n\n**Goal:** Quantify how reproducible each clade is under resampling -- precision, not accuracy.\n\n**Approach:** Resample alignment columns with replacement, recompute the matrix with the SAME correction, rebuild with the SAME algorithm, and summarize clade frequencies on a majority-rule consensus.\n\n```r\nlibrary(ape)\nboot <- boot.phylo(tree, aln, function(x) fastme.bal(dist.dna(x, model = 'TN93')), B = 500)\n# 100-1000 replicates standard; support measures SAMPLING stability only -- it cannot detect a bias in the distances\n```\n\nIn Python, Bio.Phylo's `bootstrap_consensus(aln, 100, DistanceTreeConstructor(calc, 'nj'), majority_consensus)` does the same on the identity distance (same correction caveat).\n\n## When Distance Is Legitimate vs a Trap\n\nLegitimate or preferred: a quick exploratory / sanity tree before a long ML run; very large n (thousands+ tips) where ML is infeasible (large-scale barcoding, OTU/pangenome trees); barcoding and population-level shallow data, where saturation is negligible and the per-site information ML keeps adds little (NJ on K2P distances is the literal DNA-barcoding standard); and as the starting tree for ML/Bayesian search.\n\nA trap, do not: publication-grade deep phylogeny or formal hypothesis testing (dating, selection, contested deep nodes) -- use ML or Bayesian with model selection; any dataset that fails a saturation test (Xia Iss >= Iss.c), which no algorithm rescues; data with strong compositional heterogeneity unless using LogDet/paralinear; and mistaking consistency for a guarantee -- NJ is consistent only with correct distances, and saturation/misspecification break it exactly as they break ML.\n\n## Per-Method Failure Modes\n\n### Saturation Makes Distances Plateau and Mislead at Depth\n**Trigger:** Deep divergences with many pairwise p-distances above ~0.5; transitions exhausted while transversions still climb.\n**Mechanism:** So many superimposed substitutions accumulate that observed differences approach the random expectation; the correction inflates violently near its singularity and carries no remaining signal about deep splits.\n**Symptom:** Corrected distances explode and become unstable; the saturation plot's transition curve flattens; deep nodes are unstable across models.\n**Fix:** Run the Xia Iss test; exclude transitions, third codon positions, or saturated partitions; do not build a deep distance (or ML) tree on saturated data.\n\n### UPGMA Returns the Wrong Topology Under Rate Variation\n**Trigger:** Lineages evolving at different rates analyzed with UPGMA.\n**Mechanism:** UPGMA forces an ultrametric tree (clock), so a fast-evolving lineage is pushed toward the tips and grouped by total divergence, not ancestry.\n**Symptom:** Topology differs from an NJ tree on the same matrix; fast lineages cluster together.\n**Fix:** Use NJ or FastME; never UPGMA for molecular phylogeny without an independently established clock.\n\n### Identity / Uncorrected Distance Model on Divergent Data\n**Trigger:** Bio.Phylo `'identity'` or ape `'raw'` on anything beyond shallow divergence; calling it a \"JC tree\".\n**Mechanism:** No multiple-hit correction, so divergence is undercounted nonlinearly and worst on the longest branches, amplifying long-branch artifacts.\n**Symptom:** Branch lengths and topology shift when a real correction is applied; long-branch taxa attract.\n**Fix:** Use ape `dist.dna(model='TN93')` or a gamma correction; for compositional skew use LogDet; for protein use phangorn `dist.ml(model='LG')`.\n\n### Bootstrap on a Method That Discarded the Per-Site Information\n**Trigger:** High distance-NJ bootstrap on saturated or compositionally biased data.\n**Mechanism:** Resampling removes sampling noise, not a systematic bias baked into the distances, so the wrong split reproduces every replicate.\n**Symptom:** Confident, reproducible support on a clade that moves when the correction or saturated sites change.\n**Fix:** Bootstrap measures precision, not accuracy; fix the distances (model + saturation) first.\n\n## Quantitative Thresholds\n\n| Quantity | Threshold | Source / rationale |\n|---|---|---|\n| p-distance saturation onset | unstable as nucleotide p -> ~0.5-0.6; singularity at 0.75 (equal base freqs) | many pairwise p > ~0.5 = red flag, run a saturation test |\n| Xia substitution-saturation test | Iss < Iss.c => usable; Iss >= Iss.c (esp. asymmetric Iss.c, the stricter bar) => do not use | Xia et al. 2003 (DAMBE) |\n| JC/correction singularity | log argument -> 0 as p -> 0.75 | d and its variance diverge to infinity |\n| Gamma shape | alpha < ~1 = strong ASRV, materially changes distances/topology | ignoring ASRV inflates long-branch artifacts |\n| ts/tv ratio | commonly 2-15 (~15 primate mtDNA control region) | large ratios make K80/TN93 over JC matter (Tamura & Nei 1993) |\n| Bootstrap replicates | 100-1000; clades < ~70% conventionally unsupported | precision not truth (Hillis & Bull 1993) |\n\n## Common Errors\n\n| Error / symptom | Cause | Solution |\n|---|---|---|\n| NJ tree on `model='identity'` called a Jukes-Cantor tree | DistanceCalculator does NOT do JC/K80/TN93 | compute the corrected matrix in ape/FastME, pass it as a matrix |\n| Corrected distances explode / `NaN` | p near or above 0.75 singularity (saturation) | test saturation; drop saturated sites; do not trust deep distances |\n| UPGMA tree disagrees with NJ | UPGMA clock assumption violated by rate variation | use NJ/FastME for molecular data |\n| Unrelated GC-rich taxa group together; topology flips JC vs LogDet | non-stationary composition under a stationary correction | use LogDet/paralinear or a non-stationary ML model |\n| LogDet returns `NaN` / undefined | short sequences drive det(F) <= 0 | use longer alignments or a stationary correction |\n| High bootstrap on a wrong clade | resampling masks a systematic bias in the distances | fix the model/saturation; support is precision, not accuracy |\n\n## References\n\nSaitou N, Nei M. 1987. The neighbor-joining method: a new method for reconstructing phylogenetic trees. *Molecular Biology and Evolution* 4(4):406-425.\nGascuel O. 1997. BIONJ: an improved version of the NJ algorithm based on a simple model of sequence data. *Molecular Biology and Evolution* 14(7):685-695.\nLefort V, Desper R, Gascuel O. 2015. FastME 2.0: a comprehensive, accurate, and fast distance-based phylogeny inference program. *Molecular Biology and Evolution* 32(10):2798-2800.\nXia X, Xie Z, Salemi M, Chen L, Wang Y. 2003. An index of substitution saturation and its application. *Molecular Phylogenetics and Evolution* 26(1):1-7.\nLockhart PJ, Steel MA, Hendy MD, Penny D. 1994. Recovering evolutionary trees under a more realistic model of sequence evolution. *Molecular Biology and Evolution* 11(4):605-612.\nLake JA. 1994. Reconstructing evolutionary trees from DNA and protein sequences: paralinear distances. *PNAS* 91(4):1455-1459.\nTamura K, Nei M. 1993. Estimation of the number of nucleotide substitutions in the control region of mitochondrial DNA in humans and chimpanzees. *Molecular Biology and Evolution* 10(3):512-526.\nSchliep KP. 2011. phangorn: phylogenetic analysis in R. *Bioinformatics* 27(4):592-593.\nHillis DM, Bull JJ. 1993. An empirical test of bootstrapping as a method for assessing confidence in phylogenetic analysis. *Systematic Biology* 42(2):182-192.\n\n## Related Skills\n\n- modern-tree-inference - ML inference, model selection, and where a BIONJ/ME distance tree seeds the ML search\n- tree-manipulation - rooting and pruning the unrooted trees NJ/FastME emit\n- tree-io - reading, writing, and converting the trees these methods produce\n- alignment/alignment-io - the alignment whose quality gates every distance; filter ambiguous blocks first",
  "skillRoot": "/home/wangzg/.config/xyz.chatboxapp.app/skills/distance-calculations",
  "files": [
    "usage-guide.md"
  ]
}

run_command (state: result)

Args

{
  "command": "ls -la dataset/ && echo \"---\" && grep -c \">\" dataset/H7N9_HA.fa && echo \"--- 前两条序列头 ---\" && grep \">\" dataset/H7N9_HA.fa | head -5"
}

Result

{
  "success": true,
  "exitCode": 0,
  "stdout": "total 28\ndrwxrwxr-x 2 wangzg wangzg  4096 Sep 15 09:00 .\ndrwxrwxr-x 4 wangzg wangzg  4096 Sep 15 09:00 ..\n-rw-rw-r-- 1 wangzg wangzg 11453 Aug 11  2013 H7N9_HA.fa\n---\n22\n--- 前两条序列头 ---\n>gi|485160872|gb|AGK82158|hemagglutinin[Influenza A virus (A/Fujian/1/2013(H7N9))]\n>gi|475662454|gb|AGI60301|hemagglutinin[Influenza A virus (A/Hangzhou/1/2013(H7N9))]\n>gi|485649824|gb|AGK84857|hemagglutinin[Influenza A virus (A/Hangzhou/2/2013(H7N9))]\n>gi|485649830|gb|AGK84860|hemagglutinin[Influenza A virus (A/Hangzhou/3/2013(H7N9))]\n>gi|513857263|gb|AGO28204|hemagglutinin[Influenza A virus (A/Nanchang/1/2013(H7N9))]\n",
  "stderr": "",
  "cwd": "/home/wangzg/Nutstore Files/Nutstore/备课/2026秋/分子进化分析/Chatbox_bioinformatics",
  "sandboxed": false
}

run_command (state: result)

Args

{
  "command": "cd dataset && head -c 400 H7N9_HA.fa && echo && echo \"=== 统计 ===\" && awk '/^>/{if(n)print n; n=0; next}{n+=length($0)}END{print n}' H7N9_HA.fa | sort | uniq -c && echo \"=== 字符集 ===\" && grep -v \"^>\" H7N9_HA.fa | tr -d '\\n' | fold -w1 | sort -u | tr -d '\\n' && echo"
}

Result

{
  "success": true,
  "exitCode": 0,
  "stdout": ">gi|485160872|gb|AGK82158|hemagglutinin[Influenza A virus (A/Fujian/1/2013(H7N9))]\nMNTQILVFALIAIIPTNADKICLGHHAVSNGTKVNTLTERGVEVVNATETVERTNIPRICSKGKRTVDLG\nQCGLLGTITGPPQCDQFLEFSADLIIERREGSDVCYPGKFVNEEALRQILRESGGIDKEAMGFTYSGIRT\nNGATSACRRSGSSFYAEMKWLLSNTDNAAFPQMTKSYKNTRKSPALIVWGIHHSVSTAEQTKLYGSGNKL\nVTVGSSNYQQSFVPSPGARPQVNGLSGRIDFHWLMLNPNDTVTFSFNGAFIAPDRASFLRGKSMGIQSGV\nQVDANCEGDCYHSGGTIISNLPFQNIDSRAVGK\n=== 统计 ===\n      1 191\n      1 192\n      2 193\n      1 196\n      3 206\n      1 549\n      1 552\n     12 560\n=== 字符集 ===\nACDEFGHIKLMNPQRSTVWY\n",
  "stderr": "",
  "cwd": "/home/wangzg/Nutstore Files/Nutstore/备课/2026秋/分子进化分析/Chatbox_bioinformatics",
  "sandboxed": false
}

run_command (state: result)

Args

{
  "command": "for t in mafft iqtree2 iqtree raxml-ng FastME fastme clustalo muscle trimal; do printf \"%-10s \" \"$t\"; command -v $t 2>/dev/null || echo \"未安装\"; done; echo \"--- python 包 ---\"; python3 -c \"import Bio; print('biopython', Bio.__version__)\" 2>/dev/null || echo \"biopython 未装\""
}

Result

{
  "success": true,
  "exitCode": 0,
  "stdout": "mafft      未安装\niqtree2    未安装\niqtree     未安装\nraxml-ng   未安装\nFastME     未安装\nfastme     未安装\nclustalo   未安装\nmuscle     未安装\ntrimal     未安装\n--- python 包 ---\nbiopython 未装\n",
  "stderr": "",
  "cwd": "/home/wangzg/Nutstore Files/Nutstore/备课/2026秋/分子进化分析/Chatbox_bioinformatics",
  "sandboxed": false
}

run_command (state: result)

Args

{
  "command": "cd dataset && awk '/^>/{name=$0; n=0; next}{n+=length($0)} /^>/{next} {if(n>0 && prev!=\"\"){} } END{}' H7N9_HA.fa; awk '/^>/{if(name)printf \"%4d  %s\\n\", len, substr(name,1,70); name=$0; len=0; next}{len+=length($0)}END{printf \"%4d  %s\\n\", len, substr(name,1,70)}' H7N9_HA.fa | sort -n"
}

Result

{
  "success": true,
  "exitCode": 0,
  "stdout": " 191  >gi|507593992|gb|AGM53886|hemagglutinin[Influenza A virus (A/Shanghai/\n 192  >gi|507593990|gb|AGM53885|hemagglutinin[Influenza A virus (A/Shanghai/\n 193  >gi|507593988|gb|AGM53884|hemagglutinin[Influenza A virus (A/Shanghai/\n 193  >gi|507593994|gb|AGM53887|hemagglutinin[Influenza A virus (A/Shanghai/\n 196  >gi|507593986|gb|AGM53883|hemagglutinin[Influenza A virus (A/Shanghai/\n 206  >gi|491874175|gb|AGL33692|hemagglutinin[Influenza A virus (A/Shanghai/\n 206  >gi|491874186|gb|AGL33693|hemagglutinin[Influenza A virus (A/Shanghai/\n 206  >gi|491874193|gb|AGL33694|hemagglutinin[Influenza A virus (A/Shanghai/\n 549  >gi|501485301|gb|AGL95088|hemagglutinin[Influenza A virus (A/Taiwan/S0\n 552  >gi|501485319|gb|AGL95098|hemagglutinin[Influenza A virus (A/Taiwan/T0\n 560  >gi|475662454|gb|AGI60301|hemagglutinin[Influenza A virus (A/Hangzhou/\n 560  >gi|476403560|gb|AGI60292|hemagglutinin[Influenza A virus (A/Shanghai/\n 560  >gi|478735269|gb|AGJ51953|hemagglutinin[Influenza A virus (A/Zhejiang/\n 560  >gi|479285761|gb|AGJ73503|hemagglutinin[Influenza A virus (A/Nanjing/1\n 560  >gi|485160872|gb|AGK82158|hemagglutinin[Influenza A virus (A/Fujian/1/\n 560  >gi|485649824|gb|AGK84857|hemagglutinin[Influenza A virus (A/Hangzhou/\n 560  >gi|485649830|gb|AGK84860|hemagglutinin[Influenza A virus (A/Hangzhou/\n 560  >gi|496493389|gb|AGL44438|hemagglutinin[Influenza A virus (A/Shanghai/\n 560  >gi|506460794|gb|AGM16242|hemagglutinin[Influenza A virus (A/Zhejiang/\n 560  >gi|511105778|gb|AGN69462|hemagglutinin[Influenza A virus (A/Wuxi/2/20\n 560  >gi|511105798|gb|AGN69474|hemagglutinin[Influenza A virus (A/Wuxi/1/20\n 560  >gi|513857263|gb|AGO28204|hemagglutinin[Influenza A virus (A/Nanchang/\n",
  "stderr": "",
  "cwd": "/home/wangzg/Nutstore Files/Nutstore/备课/2026秋/分子进化分析/Chatbox_bioinformatics",
  "sandboxed": false
}

web_search (state: result)

Args

{
  "query": "MEGA 11 distance based methods neighbor-joining minimum evolution UPGMA protein model"
}

Result

{
  "query": "MEGA 11 distance based methods neighbor-joining minimum evolution UPGMA protein model",
  "searchResults": [
    {
      "title": "Login - MEGA",
      "snippet": "Log in to your MEGA account. Access the world’s most trusted, protected cloud storage.",
      "link": "https://mega.nz/login"
    },
    {
      "title": "登录 - MEGA",
      "snippet": "MEGA provides free cloud storage with convenient and powerful always-on privacy. Claim your free 20GB now",
      "link": "https://mega.nz/login/lang=cn"
    },
    {
      "title": "理想汽车丨理想，给车和家赋予生命。 - LIXIANG",
      "snippet": "理想汽车致力于成为全球领先的具身智能企业，产品布局包含智能汽车、智能眼镜、空间机器人和人形机器人，提供家一样的 ...",
      "link": "https://www.lixiang.com/mega.html"
    },
    {
      "title": "关于Mega网盘的研究【工具篇】 - 知乎",
      "snippet": "2020年5月19日 · 本教程是写给想用Mega网盘的小伙伴和本来就知道的小伙伴，也是萌新入坑Mega的指导帖。 相信能找到此专栏的你 …",
      "link": "https://zhuanlan.zhihu.com/p/141974000"
    },
    {
      "title": "Home [www.megasoftware.net]",
      "snippet": "MEGA is an integrated tool for conducting automatic and manual sequence alignment, inferring phylogenetic trees, mining web-based …",
      "link": "https://www.megasoftware.net/"
    },
    {
      "title": "常用生物学软件的安装与应用（四）—MEGA - 知乎",
      "snippet": "2020年12月16日 · megasoftware.net/ MEGA的全称为Molecular Evolutionary Genetics Analysis，也就是专门用于分子进化 遗传分析  …",
      "link": "https://zhuanlan.zhihu.com/p/337236253"
    },
    {
      "title": "【理想MEGA】理想汽车_理想MEGA报价_理想MEGA图片 ...",
      "snippet": "2026年8月14日 · 汽车之家理想MEGA频道,提供理想MEGA报价,理想汽车在售理想MEGA图片,理想汽车全部理想MEGA参数配置,理 …",
      "link": "https://www.autohome.com.cn/6939/"
    },
    {
      "title": "mega网盘吧-百度贴吧--友好和谐文明自由--仅限讨论网盘安装 ...",
      "snippet": "用了多年这网盘，总是傻傻的被它限制5G的量，换过节点，也不大管事，只能等5、6小时候再下。但现在它好像也不再客气了，不提 …",
      "link": "https://tieba.baidu.com/f?kw=mega%E7%BD%91%E7%9B%98"
    },
    {
      "title": "Download Mega (free) for Windows, macOS, Android, APK, iOS",
      "snippet": "5 天之前 · MEGA provides you with secure cloud storage with end-to-end encryption, safe online meetings, and a VPN.",
      "link": "https://gizmodo.com/download/mega"
    }
  ]
}

save_memory (state: result)

Args

{
  "content": "用户偏好：描述建树方法对数据的适配性时，用「适用 / 不适用」，不要用「合法 / 不合法」（后者易误解为法律/规则含义）。用于其《分子进化分析》课程讲义。"
}

Result

{
  "message": "Memory saved with id mmu27msa3wk3j. It will be loaded in future agent sessions."
}

load_skill (state: result)

Args

{
  "name": "multiple-alignment"
}

Result

{"instructions":"## Version Compatibility\n\nReference examples tested with: MAFFT 7.520+, MUSCLE 5.1+, ClustalOmega 1.2.4+, T-Coffee 13+, PAL2NAL 14+, BioPython 1.83+\n\nBefore using code patterns, verify installed versions match. If versions differ:\n- CLI: `mafft --version`, `muscle -version`, `clustalo --version`\n- Python: `pip show biopython` then `help(module.function)` to check signatures\n\nIf code throws errors, introspect the installed tool and adapt the example to match the actual CLI flags rather than retrying.\n\n# Multiple Sequence Alignment\n\n**\"Align multiple sequences\"** -> Compute an optimal alignment of three or more homologous sequences using progressive, iterative, or consistency-based methods.\n- CLI: `mafft` (most versatile), `muscle` (highest accuracy), `clustalo` (scales well), `t_coffee` (consistency-based)\n- Python: `subprocess.run()` wrapping CLI tools; BioPython `Bio.Align.Applications` was removed in BioPython 1.86 (verify with `pip show biopython`); use `subprocess` directly\n\n## MSA Algorithm Taxonomy\n\nWhen a tool is failing on a dataset, switch to a tool from a different algorithmic family rather than tuning flags. The six families and their characteristic failure modes:\n\n| Family | Representative tools | Best at | Fails when |\n|--------|---------------------|---------|-----------|\n| Progressive | ClustalW, MAFFT FFT-NS-2 | Fast, large datasets, similar lengths | Early-stage gap errors propagate; no recovery |\n| Iterative refineme

run_command (state: result)

Args

{
  "command": "echo \"== PATH tools ==\"; for t in mafft muscle muscle5 clustalo t_coffee prank macse famsa; do printf \"%-10s \" \"$t\"; command -v $t 2>/dev/null || echo \"-\"; done\necho \"== python/pip/conda ==\"; for t in python3 pip3 conda mamba; do printf \"%-10s \" \"$t\"; command -v $t 2>/dev/null || echo \"-\"; done\necho \"== conda envs ==\"; conda env list 2>/dev/null || echo \"no conda\"\necho \"== biopython ==\"; python3 -c \"import Bio,sys;print('Bio',Bio.__version__)\" 2>/dev/null || echo \"no biopython\"\necho \"== pip list grep ==\"; pip3 list 2>/dev/null | grep -iE \"bio|align|muscle\" || echo \"none\""
}

Result

{
  "success": true,
  "exitCode": 0,
  "stdout": "== PATH tools ==\nmafft      -\nmuscle     -\nmuscle5    -\nclustalo   -\nt_coffee   -\nprank      -\nmacse      -\nfamsa      -\n== python/pip/conda ==\npython3    /usr/bin/python3\npip3       /usr/bin/pip3\nconda      /home/wangzg/miniconda3/bin/conda\nmamba      -\n== conda envs ==\n\n# conda environments:\n#\n# * -> active\n# + -> frozen\nbase                     /home/wangzg/miniconda3\nbiosoft                  /home/wangzg/miniconda3/envs/biosoft\nr-4.6                    /home/wangzg/miniconda3/envs/r-4.6\n\n== biopython ==\nno biopython\n== pip list grep ==\nnone\n",
  "stderr": "",
  "cwd": "/home/wangzg/Nutstore Files/Nutstore/备课/2026秋/分子进化分析/Chatbox_bioinformatics",
  "sandboxed": false
}

run_command (state: result)

Args

{
  "command": "B=/home/wangzg/miniconda3/envs/biosoft/bin\necho \"== biosoft bin 里的相关工具 ==\"\nls $B 2>/dev/null | grep -iE \"mafft|muscle|clustal|coffee|prank|macse|famsa|iqtree|raxml|fastme|trimal|clipkit|bmge|guidance|pal2nal|dendropy|seqkit|phyml|model\" || echo \"未找到\"\necho \"== biosoft mafft 版本 ==\"\n$B/mafft --version 2>&1 | head -1 || echo \"no mafft\"\necho \"== r-4.6 里的系统发育 R 包 ==\"\n/home/wangzg/miniconda3/envs/r-4.6/bin/Rscript -e 'ip<-rownames(installed.packages()); cat(intersect(ip,c(\"ape\",\"phangorn\",\"Biostrings\",\"treeio\",\"ggtree\",\"ips\",\"phytools\")),sep=\"\\n\")' 2>/dev/null || echo \"Rscript 不可用\""
}

Result

{
  "success": true,
  "exitCode": 0,
  "stdout": "== biosoft bin 里的相关工具 ==\n未找到\n== biosoft mafft 版本 ==\nbash: line 9: /home/wangzg/miniconda3/envs/biosoft/bin/mafft: No such file or directory\nno mafft\n== r-4.6 里的系统发育 R 包 ==\nape\nBiostrings\nggtree\nphangorn\nphytools\ntreeio\n",
  "stderr": "",
  "cwd": "/home/wangzg/Nutstore Files/Nutstore/备课/2026秋/分子进化分析/Chatbox_bioinformatics",
  "sandboxed": false
}

run_command (state: result)

Args

{
  "command": "echo \"== biosoft/bin 全部内容 ==\"; ls /home/wangzg/miniconda3/envs/biosoft/bin 2>/dev/null | head -80\necho; echo \"== biosoft 里的可执行(非 python) ==\"; find /home/wangzg/miniconda3/envs/biosoft -maxdepth 3 -type f -executable 2>/dev/null | grep -viE \"python|/lib/|/include/\" | head -40\necho; echo \"== r-4.6 是否有 DECIPHER/msa ==\"; /home/wangzg/miniconda3/envs/r-4.6/bin/Rscript -e 'cat(intersect(rownames(installed.packages()),c(\"DECIPHER\",\"msa\",\"seqinr\",\"pegas\")),sep=\"\\n\")' 2>/dev/null || echo \"none\""
}

Result

{
  "success": true,
  "exitCode": 0,
  "stdout": "== biosoft/bin 全部内容 ==\n2to3\n2to3-3.10\nbunzip2\nbzcat\nbzcmp\nbzdiff\nbzegrep\nbzfgrep\nbzgrep\nbzip2\nbzip2recover\nbzless\nbzmore\ncaptoinfo\nclear\nc_rehash\nf2py\nfonttools\nidle3\nidle3.10\ninfocmp\ninfotocap\nlzcat\nlzcmp\nlzdiff\nlzegrep\nlzfgrep\nlzgrep\nlzless\nlzma\nlzmadec\nlzmainfo\nlzmore\nncursesw6-config\nnumpy-config\nopenssl\npip\npip3\npydoc\npydoc3\npydoc3.10\npyftmerge\npyftsubset\npython\npython3\npython3.1\npython3.10\npython3.10-config\npython3-config\nreset\nsqlite3\nsqlite3_analyzer\ntabs\ntclsh\ntclsh8.6\ntic\ntoe\ntput\ntset\nttx\nunlzma\nunxz\nwheel\nwish\nwish8.6\nx86_64-conda-linux-gnu-ld\nxz\nxzcat\nxzcmp\nxzdec\nxzdiff\nxzegrep\nxzfgrep\nxzgrep\nxzless\nxzmore\n\n== biosoft 里的可执行(非 python) ==\n/home/wangzg/miniconda3/envs/biosoft/ssl/misc/CA.pl\n/home/wangzg/miniconda3/envs/biosoft/ssl/misc/tsget.pl\n/home/wangzg/miniconda3/envs/biosoft/bin/tclsh8.6\n/home/wangzg/miniconda3/envs/biosoft/bin/tic\n/home/wangzg/miniconda3/envs/biosoft/bin/toe\n/home/wangzg/miniconda3/envs/biosoft/bin/lzmadec\n/home/wangzg/miniconda3/envs/biosoft/bin/pyftsubset\n/home/wangzg/miniconda3/envs/biosoft/bin/fonttools\n/home/wangzg/miniconda3/envs/biosoft/bin/xzless\n/home/wangzg/miniconda3/envs/biosoft/bin/xz\n/home/wangzg/miniconda3/envs/biosoft/bin/wish8.6\n/home/wangzg/miniconda3/envs/biosoft/bin/bzip2recover\n/home/wangzg/miniconda3/envs/biosoft/bin/tput\n/home/wangzg/miniconda3/envs/biosoft/bin/sqlite3\n/home/wangzg/miniconda3/envs/biosoft/bin/bzcat\n/home/wangzg/miniconda3/envs/biosoft/bin/pyftmerge\n/home/wangzg/miniconda3/envs/biosoft/bin/pip3\n/home/wangzg/miniconda3/envs/biosoft/bin/openssl\n/home/wangzg/miniconda3/envs/biosoft/bin/xzdiff\n/home/wangzg/miniconda3/envs/biosoft/bin/c_rehash\n/home/wangzg/miniconda3/envs/biosoft/bin/2to3-3.10\n/home/wangzg/miniconda3/envs/biosoft/bin/f2py\n/home/wangzg/miniconda3/envs/biosoft/bin/pip\n/home/wangzg/miniconda3/envs/biosoft/bin/wheel\n/home/wangzg/miniconda3/envs/biosoft/bin/x86_64-conda-linux-gnu-ld\n/home/wangzg/miniconda3/envs/biosoft/bin/numpy-config\n/home/wangzg/miniconda3/envs/biosoft/bin/bunzip2\n/home/wangzg/miniconda3/envs/biosoft/bin/pydoc3.10\n/home/wangzg/miniconda3/envs/biosoft/bin/bzgrep\n/home/wangzg/miniconda3/envs/biosoft/bin/idle3.10\n/home/wangzg/miniconda3/envs/biosoft/bin/xzgrep\n/home/wangzg/miniconda3/envs/biosoft/bin/ncursesw6-config\n/home/wangzg/miniconda3/envs/biosoft/bin/infocmp\n/home/wangzg/miniconda3/envs/biosoft/bin/sqlite3_analyzer\n/home/wangzg/miniconda3/envs/biosoft/bin/clear\n/home/wangzg/miniconda3/envs/biosoft/bin/bzip2\n/home/wangzg/miniconda3/envs/biosoft/bin/bzmore\n/home/wangzg/miniconda3/envs/biosoft/bin/xzmore\n/home/wangzg/miniconda3/envs/biosoft/bin/lzmainfo\n/home/wangzg/miniconda3/envs/biosoft/bin/bzdiff\n\n== r-4.6 是否有 DECIPHER/msa ==\n\n",
  "stderr": "",
  "cwd": "/home/wangzg/Nutstore Files/Nutstore/备课/2026秋/分子进化分析/Chatbox_bioinformatics",
  "sandboxed": false
}

run_command (state: result)

Args

{
  "command": "cd \"$HOME\" && timeout 110 /home/wangzg/miniconda3/bin/conda install -n biosoft -y -c conda-forge -c bioconda mafft 2>&1 | tail -25",
  "timeout": 120000
}

Result

{
  "success": true,
  "exitCode": 0,
  "stdout": "\rgawk-5.4.1           | 1.5 MB    | ########## | 100% \u001b[A\n\rgawk-5.4.1           | 1.5 MB    | ########## | 100% \u001b[A\rmafft-7.526          | 2.5 MB    | #7         |  18% \rmafft-7.526          | 2.5 MB    | ##         |  20% \rmafft-7.526          | 2.5 MB    | ##3        |  23% \rmafft-7.526          | 2.5 MB    | ##5        |  26% \rmafft-7.526          | 2.5 MB    | ##8        |  29% \rmafft-7.526          | 2.5 MB    | ###1       |  31% \rmafft-7.526          | 2.5 MB    | ###4       |  35% \rmafft-7.526          | 2.5 MB    | ###7       |  37% \rmafft-7.526          | 2.5 MB    | ###9       |  40% \rmafft-7.526          | 2.5 MB    | ####2      |  43% \rmafft-7.526          | 2.5 MB    | ####5      |  46% \rmafft-7.526          | 2.5 MB    | ####8      |  48% \rmafft-7.526          | 2.5 MB    | #####1     |  51% \rmafft-7.526          | 2.5 MB    | #####4     |  55% \rmafft-7.526          | 2.5 MB    | #####7     |  58% \rmafft-7.526          | 2.5 MB    | ######     |  61% \rmafft-7.526          | 2.5 MB    | ######4    |  64% \rmafft-7.526          | 2.5 MB    | ######7    |  67% \rmafft-7.526          | 2.5 MB    | #######    |  70% \rmafft-7.526          | 2.5 MB    | #######3   |  73% \rmafft-7.526          | 2.5 MB    | #######6   |  77% \rmafft-7.526          | 2.5 MB    | #######9   |  80% \rmafft-7.526          | 2.5 MB    | ########2  |  83% \rmafft-7.526          | 2.5 MB    | #########1 |  91% \rmafft-7.526          | 2.5 MB    | #########5 |  95% \rmafft-7.526          | 2.5 MB    | #########9 |  99% \rmafft-7.526          | 2.5 MB    | ########## | 100% \rmafft-7.526          | 2.5 MB    | ########## | 100% \r                                                     \r\n\r                                                     \u001b[A\n\n\r                                                     \u001b[A\u001b[A\n\n\n\r                                                     \u001b[A\u001b[A\u001b[A\n\n\n\n\r                                                     \u001b[A\u001b[A\u001b[A\u001b[A\n\n\n\n\n\r                                                     \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A done\nPreparing transaction: / \b\bdone\nVerifying transaction: \\ \b\bdone\nExecuting transaction: / \b\bdone\nWARNING conda.conda_pypi.main:notify_externally_managed_future(156): \n  Did you know? You can install many PyPI packages with conda\n  using the conda-pypi beta. Get started:\n    https://docs.conda.io/projects/conda/en/stable/new-features.html\n\n",
  "stderr": "",
  "cwd": "/home/wangzg/Nutstore Files/Nutstore/备课/2026秋/分子进化分析/Chatbox_bioinformatics",
  "sandboxed": false
}

run_command (state: result)

Args

{
  "command": "cd \"/home/wangzg/Nutstore Files/Nutstore/备课/2026秋/分子进化分析/Chatbox_bioinformatics\"\nMAFFT=/home/wangzg/miniconda3/envs/biosoft/bin/mafft\nmkdir -p results\n$MAFFT --version 2>&1 | head -1\necho \"== 开始 L-INS-i 比对 ==\"\n$MAFFT --amino --localpair --maxiterate 1000 --thread 4 dataset/H7N9_HA.fa > results/H7N9_HA.aln.fasta 2> results/mafft.log\necho \"exit=$?\"\necho \"== 比对结果概览 ==\"\ngrep -c \">\" results/H7N9_HA.aln.fasta\nawk '/^>/{if(n)print n; n=0; next}{n+=length($0)}END{print n}' results/H7N9_HA.aln.fasta | sort -n | uniq -c"
}

Result

{
  "success": true,
  "exitCode": 0,
  "stdout": "v7.526 (2024/Apr/26)\n== 开始 L-INS-i 比对 ==\nexit=0\n== 比对结果概览 ==\n22\n     22 560\n",
  "stderr": "",
  "cwd": "/home/wangzg/Nutstore Files/Nutstore/备课/2026秋/分子进化分析/Chatbox_bioinformatics",
  "sandboxed": false
}

run_command (state: result)

Args

{
  "command": "cd \"/home/wangzg/Nutstore Files/Nutstore/备课/2026秋/分子进化分析/Chatbox_bioinformatics\"\npython3 - <<'PY'\nseqs={}\nname=None\nfor line in open('results/H7N9_HA.aln.fasta'):\n    line=line.rstrip('\\n')\n    if line.startswith('>'):\n        name=line[1:]; seqs[name]=''\n    else:\n        seqs[name]+=line\nL=len(next(iter(seqs.values())))\nprint(\"比对长度(列数):\",L,\" 序列数:\",len(seqs))\nprint(f\"{'序列(简)':<42}{'有效残基':>8}{'gap数':>7}{'gap%':>7}\")\nrows=[]\nfor k,v in seqs.items():\n    label=k.split('|')[-1][:40]\n    r=v.count('-'); eff=len(v)-r\n    rows.append((eff,r,label))\nfor eff,r,label in sorted(rows):\n    print(f\"{label:<42}{eff:>8}{r:>7}{100*r/len(v):>6.1f}%\")\n# 每列 gap 比例\nimport collections\ncols=len(seqs)\nfullgap=sum(1 for i in range(L) if all(s[i]=='-' for s in seqs.values()))\nhighgap=sum(1 for i in range(L) if sum(1 for s in seqs.values() if s[i]=='-')/cols>0.5)\nprint(\"全gap列:\",fullgap,\"  >50%gap列:\",highgap)\nPY"
}

Result

{
  "success": true,
  "exitCode": 0,
  "stdout": "比对长度(列数): 560  序列数: 22\n序列(简)                                         有效残基   gap数   gap%\nhemagglutinin[Influenza A virus (A/Shang       191    369  65.9%\nhemagglutinin[Influenza A virus (A/Shang       192    368  65.7%\nhemagglutinin[Influenza A virus (A/Shang       193    367  65.5%\nhemagglutinin[Influenza A virus (A/Shang       193    367  65.5%\nhemagglutinin[Influenza A virus (A/Shang       196    364  65.0%\nhemagglutinin[Influenza A virus (A/Shang       206    354  63.2%\nhemagglutinin[Influenza A virus (A/Shang       206    354  63.2%\nhemagglutinin[Influenza A virus (A/Shang       206    354  63.2%\nhemagglutinin[Influenza A virus (A/Taiwa       549     11   2.0%\nhemagglutinin[Influenza A virus (A/Taiwa       552      8   1.4%\nhemagglutinin[Influenza A virus (A/Fujia       560      0   0.0%\nhemagglutinin[Influenza A virus (A/Hangz       560      0   0.0%\nhemagglutinin[Influenza A virus (A/Hangz       560      0   0.0%\nhemagglutinin[Influenza A virus (A/Hangz       560      0   0.0%\nhemagglutinin[Influenza A virus (A/Nanch       560      0   0.0%\nhemagglutinin[Influenza A virus (A/Nanji       560      0   0.0%\nhemagglutinin[Influenza A virus (A/Shang       560      0   0.0%\nhemagglutinin[Influenza A virus (A/Shang       560      0   0.0%\nhemagglutinin[Influenza A virus (A/Wuxi/       560      0   0.0%\nhemagglutinin[Influenza A virus (A/Wuxi/       560      0   0.0%\nhemagglutinin[Influenza A virus (A/Zheji       560      0   0.0%\nhemagglutinin[Influenza A virus (A/Zheji       560      0   0.0%\n全gap列: 0   >50%gap列: 0\n",
  "stderr": "",
  "cwd": "/home/wangzg/Nutstore Files/Nutstore/备课/2026秋/分子进化分析/Chatbox_bioinformatics",
  "sandboxed": false
}

run_command (state: result)

Args

{
  "command": "cd \"/home/wangzg/Nutstore Files/Nutstore/备课/2026秋/分子进化分析/Chatbox_bioinformatics\"\npython3 - <<'PY'\nseqs={};name=None\nfor line in open('results/H7N9_HA.aln.fasta'):\n    line=line.rstrip('\\n')\n    if line.startswith('>'): name=line[1:]; seqs[name]=''\n    else: seqs[name]+=line\nL=len(next(iter(seqs.values())))\n# 片段占据的列区间（首末非gap位置）\nprint(\"== 片段序列在比对中的列区间 ==\")\nfor k,v in seqs.items():\n    if v.count('-')>100:\n        first=v.index(next(c for c in v if c!='-')); last=L-1-v[::-1].index(next(c for c in v[::-1] if c!='-'))\n        print(f\"  {k.split('|')[-1][:38]:<40} 列 {first+1:>3}-{last+1:>3}  (长度 {last-first+1})\")\n# 成对比对身份（去gap后）\nimport itertools\nfull=[v for v in seqs.values() if '-' not in v]\nprint(f\"\\n== 12 条全长序列的两两一致度（去gap, {len(full)}条）==\")\nids=[]\nfor a,b in itertools.combinations(full,2):\n    same=sum(1 for x,y in zip(a,b) if x==y)\n    ids.append(same/len(a))\nprint(f\"  最小 {min(ids)*100:.1f}%  平均 {sum(ids)/len(ids)*100:.1f}%  最大 {max(ids)*100:.1f}%\")\n# 列的守恒性\nprint(\"\\n== 比对整体守恒分布（基于12条全长序列）==\")\nsub=[v for v in seqs.values() if '-' not in v]\nbest=0\nfor i in range(L):\n    col=[s[i] for s in sub]\n    top=max(col.count(c) for c in set(col))\n    if top==len(sub): best+=1\nprint(f\"  完全保守列: {best}/{L} = {100*best/L:.1f}%\")\nPY\necho \"== 输出文件 ==\"\nls -l results/"
}

Result

{
  "success": true,
  "exitCode": 0,
  "stdout": "== 片段序列在比对中的列区间 ==\n  hemagglutinin[Influenza A virus (A/Sha   列 355-560  (长度 206)\n  hemagglutinin[Influenza A virus (A/Sha   列 355-560  (长度 206)\n  hemagglutinin[Influenza A virus (A/Sha   列 355-560  (长度 206)\n  hemagglutinin[Influenza A virus (A/Sha   列 367-559  (长度 193)\n  hemagglutinin[Influenza A virus (A/Sha   列 367-557  (长度 191)\n  hemagglutinin[Influenza A virus (A/Sha   列 362-557  (长度 196)\n  hemagglutinin[Influenza A virus (A/Sha   列 368-560  (长度 193)\n  hemagglutinin[Influenza A virus (A/Sha   列 366-557  (长度 192)\n\n== 12 条全长序列的两两一致度（去gap, 12条）==\n  最小 99.3%  平均 99.7%  最大 100.0%\n\n== 比对整体守恒分布（基于12条全长序列）==\n  完全保守列: 552/560 = 98.6%\n== 输出文件 ==\ntotal 20\n-rw-rw-r-- 1 wangzg wangzg 14453 Sep 15 13:14 H7N9_HA.aln.fasta\n-rw-rw-r-- 1 wangzg wangzg  3825 Sep 15 13:14 mafft.log\n",
  "stderr": "",
  "cwd": "/home/wangzg/Nutstore Files/Nutstore/备课/2026秋/分子进化分析/Chatbox_bioinformatics",
  "sandboxed": false
}

run_command (state: result)

Args

{
  "command": "cd \"/home/wangzg/Nutstore Files/Nutstore/备课/2026秋/分子进化分析/Chatbox_bioinformatics\"\necho \"== mafft 日志尾部（错误/警告检查）==\"; tail -8 results/mafft.log\necho \"== 比对预览（前6列+保守区片段）==\"\nawk '/^>/{printf \"%-34s \", substr($1,1,32)} !/^>/{print substr($0,345,40)}' results/H7N9_HA.aln.fasta | head -6"
}

Result

{
  "success": true,
  "exitCode": 0,
  "stdout": "== mafft 日志尾部（错误/警告检查）==\n\nIf unsure which option to use, try 'mafft --auto input > output'.\nFor more information, see 'mafft --help', 'mafft --man' and the mafft page.\n\nThe default gap scoring scheme has been changed in version 7.110 (2013 Oct).\nIt tends to insert more gaps into gap-rich regions than previous versions.\nTo disable this change, add the --leavegappyregion option.\n\n== 比对预览（前6列+保守区片段）==\n>gi|485160872|gb|AGK82158|hemagg   \n\n\n\n\n\n",
  "stderr": "",
  "cwd": "/home/wangzg/Nutstore Files/Nutstore/备课/2026秋/分子进化分析/Chatbox_bioinformatics",
  "sandboxed": false
}

run_command (state: result)

Args

{
  "command": "cd \"/home/wangzg/Nutstore Files/Nutstore/备课/2026秋/分子进化分析/Chatbox_bioinformatics\"\npython3 - <<'PY'\nseqs={};name=None\nfor line in open('results/H7N9_HA.aln.fasta'):\n    line=line.rstrip('\\n')\n    if line.startswith('>'): name=line[1:]; seqs[name]=''\n    else: seqs[name]+=line\nsub={k:v for k,v in seqs.items() if '-' not in v}\nL=len(next(iter(sub.values())))\n# 可变位点\nvar=[]\nfor i in range(L):\n    col=set(s[i] for s in sub.values())\n    if len(col)>1: var.append(i)\nprint(f\"全长序列间可变位点数: {len(var)}/{L}\")\nprint(\"可变位点(1-based列号:残基集合):\")\nfor i in var:\n    print(f\"  列{i+1:>4}  \"+\" \".join(sorted(set(s[i] for s in sub.values())))+f\"   (出现次数: { {c:[s[i] for s in sub.values()].count(c) for c in set(s[i] for s in sub.values())} })\")\n# 预览：取第一条全长序列头尾各20列\nfirst=next(iter(sub.values()))\nprint(\"\\n示例(第1条全长序列, 头40列):\", first[:40])\nprint(\"示例(第1条全长序列, 尾40列):\", first[-40:])\nPY"
}

Result

{
  "success": true,
  "exitCode": 0,
  "stdout": "全长序列间可变位点数: 8/560\n可变位点(1-based列号:残基集合):\n  列  16  A T   (出现次数: {'A': 1, 'T': 11})\n  列  65  M R   (出现次数: {'R': 9, 'M': 3})\n  列 143  A S   (出现次数: {'A': 11, 'S': 1})\n  列 173  I M   (出现次数: {'I': 1, 'M': 11})\n  列 208  N S   (出现次数: {'N': 11, 'S': 1})\n  列 235  I L   (出现次数: {'L': 11, 'I': 1})\n  列 291  H Y   (出现次数: {'Y': 11, 'H': 1})\n  列 358  D N   (出现次数: {'D': 11, 'N': 1})\n\n示例(第1条全长序列, 头40列): MNTQILVFALIAIIPTNADKICLGHHAVSNGTKVNTLTER\n示例(第1条全长序列, 尾40列): YKDVILWFSFGASCFILLAIVMGLVFICVKNGNMRCTICI\n",
  "stderr": "",
  "cwd": "/home/wangzg/Nutstore Files/Nutstore/备课/2026秋/分子进化分析/Chatbox_bioinformatics",
  "sandboxed": false
}

save_memory (state: result)

Args

{
  "content": "分子进化分析课程环境（/home/wangzg/Nutstore Files/Nutstore/备课/2026秋/分子进化分析/Chatbox_bioinformatics）：miniconda 的 biosoft 环境已装 MAFFT 7.526（及 gawk）；r-4.6 环境含 ape/phangorn/Biostrings/ggtree/phytools/treeio 等 R 包。数据集 dataset/H7N9_HA.fa 为 22 条 H7N9 HA 蛋白序列（12条全长560aa + 2条台湾549/552aa + 8条上海片段191-206aa），比对结果存于 results/H7N9_HA.aln.fasta（560列），全长序列间仅8个可变位点、一致度99.7%。"
}

Result

{
  "message": "Memory saved with id mmu27wvg8msds. It will be loaded in future agent sessions."
}

run_command (state: result)

Args

{
  "command": "R=/home/wangzg/miniconda3/envs/r-4.6/bin/Rscript\n$R -e 'suppressMessages({library(phangorn);library(ape)});\ncat(\"phangorn\",as.character(packageVersion(\"phangorn\")),\"ape\",as.character(packageVersion(\"ape\")),\"\\n\");\ncat(\"dist.ml models(AA):\", paste(grep(\"LG|WAG|JTT|Dayhoff|Blosum|mt\", names(phangorn:::.aamodels), value=TRUE), collapse=\",\"),\"\\n\");\ncat(\"has fastme.bal:\", exists(\"fastme.bal\"), \" has bionj:\", exists(\"bionj\"), \" has boot.phylo:\", exists(\"boot.phylo\"),\"\\n\")' 2>&1 | tail -20"
}

Result

{
  "success": true,
  "exitCode": 0,
  "stdout": "phangorn 2.12.1 ape 5.8.1 \ndist.ml models(AA):  \nhas fastme.bal: TRUE  has bionj: TRUE  has boot.phylo: TRUE \n",
  "stderr": "",
  "cwd": "/home/wangzg/Nutstore Files/Nutstore/备课/2026秋/分子进化分析/Chatbox_bioinformatics",
  "sandboxed": false
}

run_command (state: result)

Args

{
  "command": "R=/home/wangzg/miniconda3/envs/r-4.6/bin/Rscript\n$R -e 'suppressMessages(library(phangorn))\nm <- tryCatch(phangorn:::.aamodels, error=function(e) NULL)\nif(is.null(m)) m <- tryCatch(get(\".aamodels\", envir=asNamespace(\"phangorn\")), error=function(e) NULL)\ncat(\"AA models:\", paste(names(m), collapse=\", \"), \"\\n\")\ncat(\"formals dist.ml:\\n\"); print(args(dist.ml))' 2>&1 | tail -20"
}

Result

{
  "success": true,
  "exitCode": 0,
  "stdout": "AA models:  \nformals dist.ml:\nfunction (x, model = \"JC69\", exclude = \"none\", bf = NULL, Q = NULL, \n    k = 1L, shape = 1, ...) \nNULL\n",
  "stderr": "",
  "cwd": "/home/wangzg/Nutstore Files/Nutstore/备课/2026秋/分子进化分析/Chatbox_bioinformatics",
  "sandboxed": false
}

write_file (state: result)

Args

{
  "file_path": "scripts/distance_tree.R",
  "content": "## 距离法建树 —— H7N9 HA (蛋白)\n## 依据 distance-calculations skill：蛋白数据用 dist.ml(model=\"LG\") 算校正距离，\n## 再用 NJ / BIONJ / FastME(平衡最小进化) / UPGMA 建树 + bootstrap。\nsuppressMessages({library(phangorn); library(ape)})\nset.seed(1)\n\naln <- read.phyDat(\"results/H7N9_HA.aln.fasta\", format = \"fasta\", type = \"AA\")\ncat(sprintf(\"读入: %d 条序列, %d 列\\n\", length(aln), length(aln[[1]])))\n\nlab <- function(nm) sub(\".*(A/[^()]+).*\", \"\\\\1\", nm)   # 缩短为株名 A/Province/n/Year\n\n## ---------- 1) 全长(无gap)子集 ----------\nmat  <- as.character(as.matrix(aln))\nnogap <- rownames(mat)[apply(mat, 1, function(r) !any(r == \"-\"))]\ncat(sprintf(\"无gap全长序列: %d 条\\n\", length(nogap)))\naln2 <- aln[nogap]\n\n## ---------- 2) LG 蛋白校正距离 ----------\ndm <- dist.ml(aln2, model = \"LG\")\ncat(sprintf(\"LG距离范围: %.4f - %.4f  (平均 %.4f)\\n\",\n            min(dm), max(dm), mean(dm)))\n\n## ---------- 3) 四种距离树 ----------\nnj  <- NJ(dm)                 # 邻接法\nbio <- bionj(dm)              # BIONJ\nme  <- fastme.bal(dm, nni = TRUE, spr = TRUE)   # FastME 平衡最小进化 + 拓扑搜索\nup  <- upgma(dm)              # UPGMA (假设分子钟)\n\n## 拓扑一致性 (Robinson-Foulds)\nrf <- function(a, b) dist.topo(unroot(a), unroot(b))[1]\ncat(sprintf(\"RF 距离  NJ-vs-FastME=%d  NJ-vs-BIONJ=%d  NJ-vs-UPGMA=%d (0=完全一致)\\n\",\n            rf(nj, me), rf(nj, bio), rf(nj, up)))\n\n## ---------- 4) Bootstrap (500) ----------\nFUN <- function(x){ ph <- phyDat(x, type = \"AA\"); fastme.bal(dist.ml(ph, model = \"LG\"), nni = TRUE, spr = TRUE) }\nbs_me <- boot.phylo(me, mat[nogap, , drop = FALSE], FUN, B = 500, quiet = TRUE)\nFUN2 <- function(x){ ph <- phyDat(x, type = \"AA\"); NJ(dist.ml(ph, model = \"LG\")) }\nbs_nj <- boot.phylo(nj, mat[nogap, , drop = FALSE], FUN2, B = 500, quiet = TRUE)\n\n## ---------- 5) 输出 ----------\nout <- function(tr, file, bs=NULL){ if(!is.null(bs)) tr$node.label <- bs;\n  tr$tip.label <- lab(tr$tip.label); write.tree(tr, file) }\nout(nj,  \"results/tree_nj.nwk\",  bs_nj)\nout(me,  \"results/tree_fastme.nwk\", bs_me)\nout(bio, \"results/tree_bionj.nwk\")\nout(up,  \"results/tree_upgma.nwk\")\n\npng(\"results/tree_fastme.png\", width = 1100, height = 800, res = 130)\nplot(me, main = \"H7N9 HA — FastME (LG distance, 500 boot)\", cex = 0.85)\naxisPhylo(side = 1)\ndev.off()\n\ncat(\"\\n== FastME 树 (Newick, tip=株名) ==\\n\")\nwrite.tree(me, \"\")\ncat(\"\\n== UPGMA 树 ==\\n\"); write.tree(up, \"\")\ncat(\"\\n输出: results/tree_{nj,fastme,bionj,upgma}.nwk, results/tree_fastme.png\\n\")\n"
}

Result

{
  "error": "Relative paths require an active session sandbox"
}

write_file (state: result)

Args

{
  "content": "## 距离法建树 —— H7N9 HA (蛋白)\n## 依据 distance-calculations skill：蛋白数据用 dist.ml(model=\"LG\") 算校正距离，\n## 再用 NJ / BIONJ / FastME(平衡最小进化) / UPGMA 建树 + bootstrap。\nsuppressMessages({library(phangorn); library(ape)})\nset.seed(1)\n\naln <- read.phyDat(\"results/H7N9_HA.aln.fasta\", format = \"fasta\", type = \"AA\")\ncat(sprintf(\"读入: %d 条序列, %d 列\\n\", length(aln), length(aln[[1]])))\n\nlab <- function(nm) sub(\".*(A/[^()]+).*\", \"\\\\1\", nm)   # 缩短为株名 A/Province/n/Year\n\n## ---------- 1) 全长(无gap)子集 ----------\nmat  <- as.character(as.matrix(aln))\nnogap <- rownames(mat)[apply(mat, 1, function(r) !any(r == \"-\"))]\ncat(sprintf(\"无gap全长序列: %d 条\\n\", length(nogap)))\naln2 <- aln[nogap]\n\n## ---------- 2) LG 蛋白校正距离 ----------\ndm <- dist.ml(aln2, model = \"LG\")\ncat(sprintf(\"LG距离范围: %.4f - %.4f  (平均 %.4f)\\n\",\n            min(dm), max(dm), mean(dm)))\n\n## ---------- 3) 四种距离树 ----------\nnj  <- NJ(dm)                 # 邻接法\nbio <- bionj(dm)              # BIONJ\nme  <- fastme.bal(dm, nni = TRUE, spr = TRUE)   # FastME 平衡最小进化 + 拓扑搜索\nup  <- upgma(dm)              # UPGMA (假设分子钟)\n\n## 拓扑一致性 (Robinson-Foulds)\nrf <- function(a, b) dist.topo(unroot(a), unroot(b))[1]\ncat(sprintf(\"RF 距离  NJ-vs-FastME=%d  NJ-vs-BIONJ=%d  NJ-vs-UPGMA=%d (0=完全一致)\\n\",\n            rf(nj, me), rf(nj, bio), rf(nj, up)))\n\n## ---------- 4) Bootstrap (500) ----------\nFUN <- function(x){ ph <- phyDat(x, type = \"AA\"); fastme.bal(dist.ml(ph, model = \"LG\"), nni = TRUE, spr = TRUE) }\nbs_me <- boot.phylo(me, mat[nogap, , drop = FALSE], FUN, B = 500, quiet = TRUE)\nFUN2 <- function(x){ ph <- phyDat(x, type = \"AA\"); NJ(dist.ml(ph, model = \"LG\")) }\nbs_nj <- boot.phylo(nj, mat[nogap, , drop = FALSE], FUN2, B = 500, quiet = TRUE)\n\n## ---------- 5) 输出 ----------\nout <- function(tr, file, bs=NULL){ if(!is.null(bs)) tr$node.label <- bs;\n  tr$tip.label <- lab(tr$tip.label); write.tree(tr, file) }\nout(nj,  \"results/tree_nj.nwk\",  bs_nj)\nout(me,  \"results/tree_fastme.nwk\", bs_me)\nout(bio, \"results/tree_bionj.nwk\")\nout(up,  \"results/tree_upgma.nwk\")\n\npng(\"results/tree_fastme.png\", width = 1100, height = 800, res = 130)\nplot(me, main = \"H7N9 HA - FastME (LG distance, 500 boot)\", cex = 0.85)\naxisPhylo(side = 1)\ndev.off()\n\ncat(\"\\n== FastME 树 (Newick, tip=株名) ==\\n\")\nwrite.tree(me, \"\")\ncat(\"\\n== UPGMA 树 ==\\n\"); write.tree(up, \"\")\ncat(\"\\n输出: results/tree_{nj,fastme,bionj,upgma}.nwk, results/tree_fastme.png\\n\")\n",
  "file_path": "/home/wangzg/Nutstore Files/Nutstore/备课/2026秋/分子进化分析/Chatbox_bioinformatics/scripts/distance_tree.R"
}

Result

{
  "success": true,
  "file_path": "/home/wangzg/Nutstore Files/Nutstore/备课/2026秋/分子进化分析/Chatbox_bioinformatics/scripts/distance_tree.R"
}

run_command (state: result)

Args

{
  "command": "cd \"$HOME\" && timeout 115 /home/wangzg/miniconda3/bin/conda install -n biosoft -y -c conda-forge -c bioconda biopython scikit-bio 2>&1 | tail -15",
  "timeout": 120000
}

Result

{
  "success": true,
  "exitCode": 0,
  "stdout": "\n\n\n\n\n\n\r\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A done\nPreparing transaction: \\ \b\b| \b\bdone\nVerifying transaction: - \b\b\\ \b\b| \b\b/ \b\b- \b\bdone\nExecuting transaction: | \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\bdone\nWARNING conda.conda_pypi.main:notify_externally_managed_future(156): \n  Did you know? You can install many PyPI packages with conda\n  using the conda-pypi beta. Get started:\n    https://docs.conda.io/projects/conda/en/stable/new-features.html\n\n",
  "stderr": "",
  "cwd": "/home/wangzg/Nutstore Files/Nutstore/备课/2026秋/分子进化分析/Chatbox_bioinformatics",
  "sandboxed": false
}

run_command (state: result)

Args

{
  "command": "PY=/home/wangzg/miniconda3/envs/biosoft/bin/python\n$PY - <<'PY'\nimport Bio, skbio\nprint(\"biopython\", Bio.__version__)\nprint(\"scikit-bio\", skbio.__version__)\nfrom Bio.Phylo.TreeConstruction import DistanceCalculator, DistanceTreeConstructor\nc = DistanceCalculator(\"blosum62\")\nprint(\"DistanceCalculator models:\", c._matrices.keys() if hasattr(c,'_matrices') else \"?\")\nimport inspect\nprint(\"bio NJ sig:\", str(inspect.signature(DistanceTreeConstructor.nj)))\nfrom skbio.tree import nj\nprint(\"skbio nj sig:\", str(inspect.signature(nj)))\nPY"
}

Result

{
  "success": true,
  "exitCode": 0,
  "stdout": "biopython 1.88\nscikit-bio 0.7.3\nDistanceCalculator models: ?\nbio NJ sig: (self, distance_matrix)\nskbio nj sig: (dm, neg_as_zero=True, result_constructor=None, inplace=False)\n",
  "stderr": "",
  "cwd": "/home/wangzg/Nutstore Files/Nutstore/备课/2026秋/分子进化分析/Chatbox_bioinformatics",
  "sandboxed": false
}

write_file (state: result)

Args

{
  "content": "#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\"\"\"\n距离法建树 (纯 Python) —— H7N9 HA 蛋白\n依据 distance-calculations skill:\n  * Bio.Phylo 的 DistanceCalculator 只有 identity/BLOSUM 等\"分数距离\", 没有 LG/WAG 进化距离;\n    这里自己实现\"多重命中校正\"(p-distance -> Poisson 校正)得到真正的蛋白进化距离。\n  * 建树用 Bio.Phylo( NJ/UPGMA ) 与 scikit-bio( nj ) 两种引擎交叉验证。\n输入 : results/H7N9_HA.aln.fasta  (由 MAFFT L-INS-i 生成)\n输出 : results/tree_*_py.nwk, 距离矩阵对比\n\"\"\"\nimport math, itertools\nfrom Bio import AlignIO\nfrom Bio.Phylo.TreeConstruction import DistanceMatrix, DistanceTreeConstructor\nfrom skbio import DistanceMatrix as SkDM\nfrom skbio.tree import nj as sk_nj\nimport pandas as pd\n\nALN = \"results/H7N9_HA.aln.fasta\"\n\n# ---------- 读比对 ----------\naln = AlignIO.read(ALN, \"fasta\")\ndef strain(r):                      # 缩短为株名 A/Province/n/Year\n    import re\n    m = re.search(r\"(A/[^()]+)\", r.description)\n    return m.group(1) if m else r.id[:30]\n\nprint(f\"读入: {len(aln)} 条序列, {aln.get_alignment_length()} 列\")\n\n# ---------- 选取无 gap 的全长序列 ----------\ndef has_gap(rec):\n    return any(c == '-' for c in str(rec.seq))\nfull = [r for r in aln if not has_gap(r)]\nprint(f\"无 gap 全长序列: {len(full)} 条  (其余 {len(aln)-len(full)} 条为含 gap 的片段/近全长的)\")\n\nnames = [strain(r) for r in full]\nseqs  = [str(r.seq) for r in full]\nL     = len(seqs[0])\n\n# ---------- 距离: p-distance 与 Poisson 校正 ----------\ndef p_dist(a, b):\n    \"\"\"观测差异比例 (含两两删除: 跳过任一方为 gap 的列)\"\"\"\n    diff = tot = 0\n    for x, y in zip(a, b):\n        if x == '-' or y == '-':\n            continue\n        tot += 1\n        if x != y:\n            diff += 1\n    return diff / tot if tot else float('nan')\n\ndef poisson(p):\n    \"\"\"Poisson 校正: d = -ln(1-p)  (多重命中校正; p->1 时发散=饱和)\"\"\"\n    if p >= 1:\n        return float('inf')\n    return -math.log(1.0 - p)\n\nn = len(full)\nP  = [[0.0]*n for _ in range(n)]\nD  = [[0.0]*n for _ in range(n)]\nfor i in range(n):\n    for j in range(i+1, n):\n        p = p_dist(seqs[i], seqs[j])\n        P[i][j] = P[j][i] = p\n        D[i][j] = D[j][i] = poisson(p)\n\nps   = [P[i][j] for i in range(n) for j in range(i+1, n)]\nds   = [D[i][j] for i in range(n) for j in range(i+1, n)]\nprint(f\"\\np-distance  范围: {min(ps):.4f} - {max(ps):.4f}  (平均 {sum(ps)/len(ps):.4f})\")\nprint(f\"Poisson校正 范围: {min(ds):.4f} - {max(ds):.4f}  (平均 {sum(ds)/len(ds):.4f})\")\nprint(f\"校正平均放大倍数: { (sum(ds)/len(ds)) / (sum(ps)/len(ps)):.4f}  (接近1 => 分歧极浅, 校正几乎无影响)\")\n\n# ---------- 建树: Bio.Phylo (下三角矩阵) ----------\ndef lower(m):\n    return [[m[i][j] for j in range(i+1)] for i in range(len(m))]\ndm_bio = DistanceMatrix(names, lower(D))\ncon = DistanceTreeConstructor()\nt_nj_bio   = con.nj(dm_bio)\nt_upgma    = con.upgma(dm_bio)\n\n# ---------- 建树: scikit-bio ----------\ndf = pd.DataFrame(D, index=names, columns=names)\nskdm = SkDM(df.values, ids=names)\nt_nj_sk = sk_nj(skdm)\n\n# ---------- 输出 ----------\nt_nj_bio.write(\"results/tree_nj_py.nwk\",   format=\"newick\")\nt_upgma .write(\"results/tree_upgma_py.nwk\", format=\"newick\")\nwith open(\"results/tree_nj_skbio.nwk\", \"w\") as f:\n    f.write(str(t_nj_sk))\n\nprint(\"\\n== NJ (Bio.Phylo) ==\\n\" + t_nj_bio.format(\"newick\").strip())\nprint(\"\\n== NJ (scikit-bio) ==\\n\" + str(t_nj_sk).strip())\nprint(\"\\n== UPGMA (Bio.Phylo) ==\\n\" + t_upgma.format(\"newick\").strip())\nprint(\"\\n输出: results/tree_nj_py.nwk  tree_nj_skbio.nwk  tree_upgma_py.nwk\")\n",
  "file_path": "/home/wangzg/Nutstore Files/Nutstore/备课/2026秋/分子进化分析/Chatbox_bioinformatics/scripts/distance_tree.py"
}

Result

{
  "success": true,
  "file_path": "/home/wangzg/Nutstore Files/Nutstore/备课/2026秋/分子进化分析/Chatbox_bioinformatics/scripts/distance_tree.py"
}

run_command (state: result)

Args

{
  "command": "cd \"/home/wangzg/Nutstore Files/Nutstore/备课/2026秋/分子进化分析/Chatbox_bioinformatics\"\n/home/wangzg/miniconda3/envs/biosoft/bin/python scripts/distance_tree.py"
}

Result

{
  "success": false,
  "exitCode": 1,
  "stdout": "读入: 22 条序列, 560 列\n无 gap 全长序列: 12 条  (其余 10 条为含 gap 的片段/近全长的)\n\np-distance  范围: 0.0000 - 0.0071  (平均 0.0028)\nPoisson校正 范围: -0.0000 - 0.0072  (平均 0.0028)\n校正平均放大倍数: 1.0020  (接近1 => 分歧极浅, 校正几乎无影响)\n",
  "stderr": "Traceback (most recent call last):\n  File \"/home/wangzg/Nutstore Files/Nutstore/备课/2026秋/分子进化分析/Chatbox_bioinformatics/scripts/distance_tree.py\", line 87, in <module>\n    t_nj_bio.write(\"results/tree_nj_py.nwk\",   format=\"newick\")\nAttributeError: 'Tree' object has no attribute 'write'\n",
  "cwd": "/home/wangzg/Nutstore Files/Nutstore/备课/2026秋/分子进化分析/Chatbox_bioinformatics",
  "sandboxed": false
}

edit_file (state: result)

Args

{
  "file_path": "/home/wangzg/Nutstore Files/Nutstore/备课/2026秋/分子进化分析/Chatbox_bioinformatics/scripts/distance_tree.py",
  "old_text": "t_nj_bio.write(\"results/tree_nj_py.nwk\",   format=\"newick\")\nt_upgma .write(\"results/tree_upgma_py.nwk\", format=\"newick\")",
  "new_text": "from Bio import Phylo\nPhylo.write(t_nj_bio, \"results/tree_nj_py.nwk\",   \"newick\")\nPhylo.write(t_upgma,  \"results/tree_upgma_py.nwk\", \"newick\")"
}

Result

{
  "success": true,
  "file_path": "/home/wangzg/Nutstore Files/Nutstore/备课/2026秋/分子进化分析/Chatbox_bioinformatics/scripts/distance_tree.py",
  "edits": 1
}

run_command (state: result)

Args

{
  "command": "cd \"/home/wangzg/Nutstore Files/Nutstore/备课/2026秋/分子进化分析/Chatbox_bioinformatics\"\n/home/wangzg/miniconda3/envs/biosoft/bin/python scripts/distance_tree.py 2>&1"
}

Result

{
  "success": true,
  "exitCode": 0,
  "stdout": "读入: 22 条序列, 560 列\n无 gap 全长序列: 12 条  (其余 10 条为含 gap 的片段/近全长的)\n\np-distance  范围: 0.0000 - 0.0071  (平均 0.0028)\nPoisson校正 范围: -0.0000 - 0.0072  (平均 0.0028)\n校正平均放大倍数: 1.0020  (接近1 => 分歧极浅, 校正几乎无影响)\n\n== NJ (Bio.Phylo) ==\n(((((A/Shanghai/02/2013:-1.336288e-06,(A/Shanghai/4664T/2013:0.0035802272,A/Fujian/1/2013:-2.4058939e-06)Inner3:1.336288e-06)Inner5:1.0251809e-07,A/Hangzhou/1/2013:0.001788811)Inner7:6.6431971e-08,A/Hangzhou/2/2013:0.0017888442)Inner8:4.9823978e-08,(A/Zhejiang/HZ1/2013:0,(A/Nanjing/1/2013:0,A/Hangzhou/3/2013:0)Inner1:0)Inner2:0.0017888608)Inner9:4.9823978e-08,(A/Zhejiang/DTID-ZJU01/2013:-1.4429608e-06,(A/Wuxi/2/2013:0.0035801121,A/Nanchang/1/2013:-2.2907794e-06)Inner4:1.4429608e-06)Inner6:5.2694115e-08,A/Wuxi/1/2013:0.0017888608)Inner10:0;\n\n== NJ (scikit-bio) ==\n((((((A/Fujian/1/2013:0.0,A/Shanghai/4664T/2013:0.003580227241794543):1.3362879644551625e-06,A/Zhejiang/DTID-ZJU01/2013:0.0):1.0251809339119344e-07,A/Hangzhou/1/2013:0.0017888110259851498):6.643197122155158e-08,A/Hangzhou/2/2013:0.0017888442419707615):4.9823978417518934e-08,A/Wuxi/1/2013:0.0017888608499635659):4.982397841697683e-08,((A/Hangzhou/3/2013:0.0,A/Nanjing/1/2013:-0.0):0.0,A/Zhejiang/HZ1/2013:1.927470528863119e-19):0.0017888608499635663,((A/Nanchang/1/2013:0.0,A/Wuxi/2/2013:0.0035801121272516045):1.4429607717888863e-06,A/Shanghai/02/2013:0.0):5.269411497465029e-08);\n\n== UPGMA (Bio.Phylo) ==\n(A/Shanghai/4664T/2013:0.0031069814,(A/Wuxi/2/2013:0.0026297181,(A/Hangzhou/1/2013:0.0016770038,(A/Hangzhou/2/2013:0.0015650968,(((A/Zhejiang/HZ1/2013:0,A/Nanjing/1/2013:0)Inner1:0,A/Hangzhou/3/2013:0)Inner4:0.001341283,(A/Wuxi/1/2013:0.00089365529,(((A/Zhejiang/DTID-ZJU01/2013:0,A/Shanghai/02/2013:0)Inner2:0,A/Nanchang/1/2013:0)Inner3:0,A/Fujian/1/2013:0)Inner5:0.00089365529)Inner6:0.00044762769)Inner7:0.00022381385)Inner8:0.00011190692)Inner9:0.00095271432)Inner10:0.00047726333)Inner11:0;\n\n输出: results/tree_nj_py.nwk  tree_nj_skbio.nwk  tree_upgma_py.nwk\n",
  "stderr": "",
  "cwd": "/home/wangzg/Nutstore Files/Nutstore/备课/2026秋/分子进化分析/Chatbox_bioinformatics",
  "sandboxed": false
}

run_command (state: result)

Args

{
  "command": "cd \"/home/wangzg/Nutstore Files/Nutstore/备课/2026秋/分子进化分析/Chatbox_bioinformatics\"\n/home/wangzg/miniconda3/envs/biosoft/bin/python - <<'PY'\nfrom Bio import Phylo\na=Phylo.read(\"results/tree_nj_py.nwk\",\"newick\")\nb=Phylo.read(\"results/tree_nj_skbio.nwk\",\"newick\")\n# 用 clade 集合比较拓扑(忽略分支长)\ndef clades(t): return set(frozenset(c.get_terminals()) for c in t.get_nonterminals())\nca,cb=clades(a),clades(b)\nprint(\"NJ(Bio) 与 NJ(scikit-bio) 拓扑一致:\", ca==cb)\nprint(\"共有 clade 数:\", len(ca&cb), \" 各自:\", len(ca), len(cb))\n# 列出非平凡 clade (大小>=2 且 <=10)\nfor s in sorted([c for c in ca&cb if 1<len(c)<=10], key=len):\n    print(\"   clade:\", sorted(x.split('/')[1] for x in s))\nPY\necho \"== 结果文件 ==\"; ls -l results/*.nwk"
}

Result

{
  "success": true,
  "exitCode": 0,
  "stdout": "NJ(Bio) 与 NJ(scikit-bio) 拓扑一致: False\n共有 clade 数: 0  各自: 10 10\n== 结果文件 ==\n-rw-rw-r-- 1 wangzg wangzg 546 Sep 15 13:19 results/tree_nj_py.nwk\n-rw-rw-r-- 1 wangzg wangzg 582 Sep 15 13:19 results/tree_nj_skbio.nwk\n-rw-rw-r-- 1 wangzg wangzg 496 Sep 15 13:19 results/tree_upgma_py.nwk\n",
  "stderr": "",
  "cwd": "/home/wangzg/Nutstore Files/Nutstore/备课/2026秋/分子进化分析/Chatbox_bioinformatics",
  "sandboxed": false
}

run_command (state: result)

Args

{
  "command": "cd \"/home/wangzg/Nutstore Files/Nutstore/备课/2026秋/分子进化分析/Chatbox_bioinformatics\"\n/home/wangzg/miniconda3/envs/biosoft/bin/python - <<'PY'\nimport re, math\nfrom Bio import AlignIO\naln=AlignIO.read(\"results/H7N9_HA.aln.fasta\",\"fasta\")\ndef strain(r): \n    m=re.search(r\"(A/[^()]+)\",r.description); return m.group(1) if m else r.id\nfull=[r for r in aln if '-' not in str(r.seq)]\nnames=[strain(r) for r in full]; seqs=[str(r.seq) for r in full]\nn=len(full); L=len(seqs[0])\nzero=0; tot=0; same_as_pair={}\nfor i in range(n):\n    for j in range(i+1,n):\n        d=sum(1 for a,b in zip(seqs[i],seqs[j]) if a!=b)\n        tot+=1\n        if d==0: zero+=1\nprint(f\"两两序列对共 {tot} 对, 其中差异=0 的有 {zero} 对 ({100*zero/tot:.0f}%)\")\n# 差异位点分布：每条序列相对\"多数序列\"的差异\nprint(\"\\n每条全长序列的差异位点(相对共同序列):\")\nbase=seqs[0]\nfor nm,s in zip(names,seqs):\n    diff=[k+1 for k,(a,b) in enumerate(zip(base,s)) if a!=b]\n    print(f\"  {nm:<28} 差异位点 {diff if diff else '无(与基准一致)'}\")\n# 检查 Bio vs skbio 拓扑差异根源\nfrom Bio import Phylo\na=Phylo.read(\"results/tree_nj_py.nwk\",\"newick\"); b=Phylo.read(\"results/tree_nj_skbio.nwk\",\"newick\")\nprint(\"\\nBio NJ   terminals:\", sorted(x.name for x in a.get_terminals())[:3],\"...\")\nprint(\"skbio NJ terminals:\", sorted(x.name for x in b.get_terminals())[:3],\"...\")\ndef clades(t): return set(frozenset(x.name for x in c.get_terminals()) for c in t.get_nonterminals())\nprint(\"Bio clades:\", len(clades(a)), \" skbio clades:\", len(clades(b)), \" 共有:\", len(clades(a)&clades(b)))\nPY"
}

Result

{
  "success": true,
  "exitCode": 0,
  "stdout": "两两序列对共 66 对, 其中差异=0 的有 9 对 (14%)\n\n每条全长序列的差异位点(相对共同序列):\n  A/Fujian/1/2013              差异位点 无(与基准一致)\n  A/Hangzhou/1/2013            差异位点 [235]\n  A/Hangzhou/2/2013            差异位点 [173]\n  A/Hangzhou/3/2013            差异位点 [65]\n  A/Nanchang/1/2013            差异位点 无(与基准一致)\n  A/Nanjing/1/2013             差异位点 [65]\n  A/Shanghai/02/2013           差异位点 无(与基准一致)\n  A/Shanghai/4664T/2013        差异位点 [16, 291]\n  A/Wuxi/1/2013                差异位点 [358]\n  A/Wuxi/2/2013                差异位点 [143, 208]\n  A/Zhejiang/DTID-ZJU01/2013   差异位点 无(与基准一致)\n  A/Zhejiang/HZ1/2013          差异位点 [65]\n\nBio NJ   terminals: ['A/Fujian/1/2013', 'A/Hangzhou/1/2013', 'A/Hangzhou/2/2013'] ...\nskbio NJ terminals: ['A/Fujian/1/2013', 'A/Hangzhou/1/2013', 'A/Hangzhou/2/2013'] ...\nBio clades: 10  skbio clades: 10  共有: 5\n",
  "stderr": "",
  "cwd": "/home/wangzg/Nutstore Files/Nutstore/备课/2026秋/分子进化分析/Chatbox_bioinformatics",
  "sandboxed": false
}

run_command (state: result)

Args

{
  "command": "cd \"/home/wangzg/Nutstore Files/Nutstore/备课/2026秋/分子进化分析/Chatbox_bioinformatics\"\n/home/wangzg/miniconda3/envs/biosoft/bin/python - <<'PY'\nfrom Bio import Phylo\nimport sys\nfor f,tag in [(\"results/tree_nj_skbio.nwk\",\"NJ (scikit-bio, Poisson距离)\"),\n              (\"results/tree_upgma_py.nwk\",\"UPGMA (Bio.Phylo)\")]:\n    print(\"=\"*60); print(tag); print(\"=\"*60)\n    t=Phylo.read(f,\"newick\"); t.ladderize()\n    Phylo.draw_ascii(t, column_width=34)\n    print()\nPY"
}

Result

{
  "success": true,
  "exitCode": 0,
  "stdout": "============================================================\nNJ (scikit-bio, Poisson距离)\n============================================================\n  , A/Zhejiang/HZ1/2013\n ,|\n |, A/Hangzhou/3/2013\n ||\n || A/Nanjing/1/2013\n |\n , A/Shanghai/02/2013\n_|\n , A/Nanchang/1/2013\n |\n |_ A/Wuxi/2/2013\n |\n , A/Wuxi/1/2013\n |\n , A/Hangzhou/2/2013\n |\n , A/Hangzhou/1/2013\n |\n , A/Zhejiang/DTID-ZJU01/2013\n |\n , A/Fujian/1/2013\n |\n |__ A/Shanghai/4664T/2013\n\n\n============================================================\nUPGMA (Bio.Phylo)\n============================================================\n  _ A/Shanghai/4664T/2013\n |\n_|_ A/Wuxi/2/2013\n |\n | _ A/Hangzhou/1/2013\n ||\n |, A/Hangzhou/2/2013\n  |\n  |, A/Hangzhou/3/2013\n  ,|\n  |, A/Zhejiang/HZ1/2013\n  ||\n  || A/Nanjing/1/2013\n  |\n  |, A/Wuxi/1/2013\n  ||\n   , A/Fujian/1/2013\n   |\n   , A/Nanchang/1/2013\n   |\n   , A/Zhejiang/DTID-ZJU01/2013\n   |\n   | A/Shanghai/02/2013\n\n\n",
  "stderr": "",
  "cwd": "/home/wangzg/Nutstore Files/Nutstore/备课/2026秋/分子进化分析/Chatbox_bioinformatics",
  "sandboxed": false
}

load_skill (state: result)

Args

{
  "name": "tree-visualization"
}

Result

{
  "instructions": "## Version Compatibility\n\nReference examples tested with: BioPython 1.83+, matplotlib 3.8+. Rich-figure alternatives: ggtree 3.12+ / treeio 1.28+ / ggtreeExtra 1.14+ (Bioconductor), ete4 4.x (Python), iTOL v6 (web), FigTree (desktop).\n\nBefore using code patterns, verify installed versions match. If versions differ:\n- Python: `pip show biopython` then `help(Phylo.draw)` to check signatures\n- R: `packageVersion('ggtree')` then `?ggtree` to verify layout and geom arguments\n\nIf code throws ImportError, AttributeError, or TypeError, introspect the installed\npackage and adapt the example to match the actual API rather than retrying.\n\nBio.Phylo `draw()` renders rectangular layouts only and parses no BEAST/MrBayes annotation block; circular/fan/unrooted layouts and HPD bars require ggtree, ETE4, or iTOL. The ETE3->ETE4 migration changed APIs; introspect rather than assume ETE3 signatures.\n\n# Tree Visualization -- A Figure Is an Argument\n\n**\"Draw and export my tree figure\"** -> Render a topology under a chosen layout and branch-length encoding, decide which support to show and how to label it, and export in a format that survives print.\n- Python quick look: `Phylo.draw(tree, axes=ax)` (Bio.Phylo + matplotlib)\n- Publication with metadata, rich support, or BEAST HPD bars: ggtree + treeio (R)\n\nScope: drawing, styling, annotating, and exporting tree figures, and choosing the drawing tool. Reading/converting files and preserving `[&...]` annotations before plotting -> tree-io. Rooting, ladderizing, pruning, collapsing low-support nodes -> tree-manipulation. ggplot2 grammar, themes, and `ggsave` underlying ggtree -> data-visualization/ggplot2-fundamentals. Composing a tree plus aligned panels into one figure -> data-visualization/multipanel-figures.\n\n## The Single Most Important Modern Insight\n\nA phylogenetic tree figure is a rhetorical and modeling artifact, not a neutral picture of the data. A Newick or Nexus file encodes a topology, optionally branch lengths, optionally one node label; it does NOT encode which child is drawn on top, whether the horizontal axis means evolutionary distance or just nesting depth, where the root sits, the aspect ratio, or which of several support measures a bare \"95\" refers to. Every one of those is supplied by the person drawing the tree, so the figure is the data filtered through a dozen interpretive choices the reader cannot see. Four argument-level decisions dominate, and the same file can be drawn to tell mutually contradictory stories that are all technically correct renderings of one topology:\n\n1. **The geometry is a claim about what the lengths mean.** A cladogram asserts only nesting; a phylogram asserts nesting plus amount of change (substitutions per site); a chronogram asserts timing and demands a clock model plus calibrations. Drawing a phylogram as a cladogram erases rate variation, the very signal that flags long-branch attraction; drawing untrusted branch lengths as a phylogram fabricates a quantitative claim.\n2. **Ladderization implies a directionality that is not there.** Sibling subclades are unordered, so any node rotation is the same tree, but the eye reads top-to-bottom ordering as a march of progress toward the bottom-most tip. Rotation can also bury non-monophyly: a paraphyletic group can be rotated to look contiguous, or a clean clade made to look scattered.\n3. **Which support is shown, and whether it is labeled, changes the conclusion.** Support is not accuracy, and the measures are not interchangeable: a posterior of 0.95 is generally weaker evidence than a bootstrap of 95 for the same data, and UFBoot 95, SH-aLRT 80, and TBE live on different scales. A bare integer with no legend is the quietest lie because the reader assumes bootstrap and the ambiguity always flatters the result.\n4. **The drawing tool is decided by the I/O layer, not the surrounding language.** If the tree carries BEAST/MrBayes HPD intervals and posteriors, only a tool whose data model can carry those annotations (treeio + ggtree) can plot them; Bio.Phylo flattens the tree to topology + length + one label and silently drops the uncertainty that was the result.\n\nThe skill's job is to make these choices conscious and DECLARE them in the caption, not to default into them. Default ladderization plus cladogram-ish rendering plus unlabeled nodes is the path of least resistance, and it is an argument the user did not intend to make.\n\n## Tool Taxonomy\n\nThe central decision is which drawing tool to reach for, and it is coupled to the I/O decision: a tool that flattens the tree to topology plus one label cannot draw BEAST HPD bars or posteriors it cannot represent. The plotting path through treeio + ggtree is the same I/O choice made in tree-io.\n\n| Tool (lang) | Citation | Layouts and annotation power | When to choose |\n|-------------|----------|------------------------------|----------------|\n| Bio.Phylo + matplotlib (Py) | Talevich 2012 | rectangular phylogram/cladogram only; one label per node via `label_func`/`branch_labels`; no circular/unrooted, no BEAST-block parse | a fast scripted look inside a Python pipeline; headless/CI rendering of simple trees; NOT publication figures with metadata or HPD bars |\n| ggtree + treeio + ggtreeExtra (R) | Yu 2017; Wang 2020; Xu 2021 | rectangular, slanted, circular, fan, radial/unrooted; `%<+%` attaches a data.frame, `geom_tiplab`/`geom_nodelab`/`geom_cladelab`, `gheatmap`, `geom_facet` (Cartesian) or `geom_fruit` rings (circular); treeio reads BEAST/MrBayes/IQ-TREE so HPD bars and posterior plot directly | the publication standard whenever a figure integrates metadata, rich/dual support, HPD bars, or aligned panels; the only clean path for BEAST HPD bars and posterior |\n| ETE4 (Py; ETE3 paper) | Huerta-Cepas 2016 | rectangular and circular; programmatic per-node `NodeStyle`/faces and layout functions; integrated NCBI taxonomy | styling thousands of nodes by rule, taxonomy-driven annotation, or rendering many trees headlessly in Python |\n| iTOL v6 (web) | Letunic 2024 | rectangular, circular, unrooted, very large trees; tab-delimited dataset templates for color strips, heatmaps, bars, binary symbols, clade collapse; SVG/PDF/PNG/EPS export | large trees and template-based metadata with no code; collaborative web-driven polished figures |\n| FigTree (desktop GUI) | Rambaut (software) | rectangular, polar, radial; reads BEAST NEXUS node annotations to display HPD bars and posterior on screen; manual rotate/collapse/color | interactive inspection of a single BEAST/MrBayes tree before scripting the final figure; not reproducible, use for exploration not the pipeline |\n\nDecision rule: large tree or no-code web annotation -> iTOL v6. Publication figure with metadata, dual support, HPD bars, or aligned heatmaps -> ggtree + treeio (+ ggtreeExtra for circular rings). Programmatic styling of thousands of nodes or NCBI-taxonomy annotation -> ETE4. Fast topology look from a Python script -> Bio.Phylo. Interactive rooting/inspection of one BEAST tree -> FigTree, then move the reproducible figure to ggtree. The recurring mistake is defaulting to Bio.Phylo because the pipeline is Python, then discovering it cannot draw a circular layout, place a heatmap, or show the BEAST HPD bars that are the whole point.\n\n## What Each Layout Reveals and Hides\n\n| Layout | Reveals | Hides or distorts | Good for | Bad for |\n|--------|---------|-------------------|----------|---------|\n| Rectangular phylogram | branch lengths read directly off a linear axis; honest distance comparison | tip labels overplot past a few hundred tips; uses vertical space | the default when length matters and tip count is moderate (<~150) | very large trees |\n| Slanted / triangular | compactness; quick topology read | the diagonal implies a ladder/progression; imprecise length reading | quick schematic topology views | precise branch-length comparison |\n| Circular / fan | hundreds to thousands of tips in one panel; metadata rings | radial distortion -- the same length subtends a larger arc near the rim, so distance is perceptually compressed near the root; near-root structure cramped | big trees where the message is broad structure plus aligned metadata | quantitative branch-length comparison |\n| Unrooted / radial | honestly shows NO assumed root or time direction; overall shape and long-branch outliers | no time direction; clade membership harder to trace; clusters over-read as clades | exploratory views, \"no root committed\" | directional stories; any \"basal/early-diverging\" claim |\n| Chronogram (time-tree) | node timing on a time axis | requires a clock model and calibrations the reader must trust; without HPD bars timing looks falsely precise | dated BEAST/treePL/MCMCtree trees where timing is the message | any tree lacking a clock; never draw without age uncertainty bars |\n\nCircular is still a ROOTED tree bent into a ring (center = root = past); unrooted/radial explicitly refuses a root. Presenting an unrooted radial tree and narrating \"X is basal\" is a contradiction.\n\n## Bio.Phylo + matplotlib Recipes\n\nQuick text and ASCII inspection, no figure needed:\n\n```python\nfrom Bio import Phylo\n\ntree = Phylo.read('tree.nwk', 'newick')\nprint(tree)                  # indented text summary\nPhylo.draw_ascii(tree)       # ASCII-art diagram, useful in a terminal or log\n```\n\nDraw to a vector file (always pass an axes and `do_show=False` for headless/scripted use):\n\n```python\nfrom Bio import Phylo\nimport matplotlib.pyplot as plt\n\ntree = Phylo.read('tree.nwk', 'newick')\ntree.ladderize()             # legibility only; ordering carries NO phylogenetic meaning -- say so in the caption\n\nfig, ax = plt.subplots(figsize=(10, 8))\nPhylo.draw(tree, axes=ax, do_show=False)\nax.set_title('Phylogenetic tree (phylogram, branch length = subs/site)')\nfig.savefig('tree.pdf', bbox_inches='tight')   # vector: text and lines stay sharp at any size\nplt.close(fig)\n```\n\nLabel tips, and show support with its measure named (never a bare integer):\n\n```python\ndef tip_only(clade):\n    return clade.name if clade.is_terminal() else ''\n\ndef support_label(clade):\n    # the measure MUST be stated in the legend/caption; here values are bootstrap percentages\n    if not clade.is_terminal() and clade.confidence is not None:\n        return f'{clade.confidence:.0f}'\n    return ''\n\nfig, ax = plt.subplots(figsize=(12, 10))\nPhylo.draw(tree, axes=ax, do_show=False, label_func=tip_only, branch_labels=support_label)\nax.set_title('Bootstrap support shown at internal nodes')\nfig.savefig('supported_tree.svg', bbox_inches='tight')\nplt.close(fig)\n```\n\nColor branches by group (convert to phyloXML for native color support):\n\n```python\nfrom Bio.Phylo.PhyloXML import BranchColor\n\nxtree = tree.as_phyloxml()                     # phyloXML carries branch color through draw()\nfor clade in xtree.find_clades():\n    if clade.name and clade.name.startswith('Homo'):\n        clade.color = BranchColor.from_name('red')\n\nfig, ax = plt.subplots(figsize=(10, 8))\nPhylo.draw(xtree, axes=ax, do_show=False)\nfig.savefig('colored_tree.pdf', bbox_inches='tight')\nplt.close(fig)\n```\n\nScale the panel to tip count so labels stay legible, and drop the axis frame:\n\n```python\nn_tips = len(tree.get_terminals())\nheight = max(8, n_tips * 0.25)                  # ~0.25 in/tip keeps ~6-8 pt labels from colliding\n\nfig, ax = plt.subplots(figsize=(10, height))\nPhylo.draw(tree, axes=ax, do_show=False)\nax.axis('off')\nfig.savefig('scaled_tree.pdf', bbox_inches='tight')\nplt.close(fig)\n```\n\nFor circular/fan/unrooted layouts, metadata heatmaps, dual support, or BEAST HPD bars, Bio.Phylo cannot help -- route to ggtree + treeio (R), ETE4, or iTOL.\n\n## Per-Method Failure Modes\n\n### Drawing Meaningless Branch Lengths as a Phylogram\n**Trigger:** Rendering a constraint tree, a supertree, or a tree whose lengths are non-comparable with the horizontal axis proportional to length.\n**Mechanism:** The phylogram geometry asserts a quantitative claim about evolutionary distance that the lengths do not support.\n**Symptom:** A topology-only or arbitrary-length tree appears to make precise distance statements.\n**Fix:** Draw as a cladogram and SAY so (`branch.length='none'` in ggtree); or, if lengths are real, keep them and state the unit (subs/site vs time) plus a scale bar.\n\n### Unlabeled or Mislabeled Support\n**Trigger:** A node shows \"98\" with no legend, or two measures are printed without saying which is which.\n**Mechanism:** The reader defaults to assuming bootstrap, but it may be a posterior (much weaker for the same number), an SH-aLRT (cutoff 80), a UFBoot (cutoff 95, not the BP-70 scale), or a TBE.\n**Symptom:** A weakly resolved bush reads as a confident comb because the displayed number is over-read.\n**Fix:** Always state the measure(s) and their order (e.g. \"SH-aLRT/UFBoot\" at each node); collapse nodes below threshold into polytomies rather than drawing fake resolution, since bootstrap, posterior, SH-aLRT, and UFBoot sit on different scales.\n\n### Tip-Label Overplotting on Large Trees\n**Trigger:** A rectangular layout past a few hundred tips.\n**Mechanism:** Horizontal labels collide into an unreadable black band; authors then shrink the font to illegibility or silently drop labels.\n**Symptom:** Tip labels are unreadable or missing.\n**Fix:** Rotate labels, switch to circular/fan with radial labels, collapse uninformative clades, annotate by colored strips/rings (ggtreeExtra, iTOL datasets) instead of text, or move to iTOL which is built for large trees.\n\n### Non-Monophyly Hidden by Ladderization or Rotation\n**Trigger:** Rotating nodes to make a paraphyletic or polyphyletic group look contiguous (or a clade look scattered).\n**Mechanism:** Node rotation is information-free with respect to the tree, but the eye reads contiguity as a clade and ordering as direction.\n**Symptom:** A group that is not monophyletic appears unified, or a directional narrative is implied.\n**Fix:** Color by group and let the topology speak; never narrate ordering as meaning; state \"tips ladderized for legibility, ordering carries no phylogenetic meaning.\"\n\n### Chronogram Without Age Uncertainty\n**Trigger:** Drawing a BEAST/MrBayes time-tree with point-estimate node ages and no HPD bars.\n**Mechanism:** The 95% HPD intervals on node heights ARE the result; omitting them asserts false precision.\n**Symptom:** Node ages look known to the day; a reviewer asks where the credible intervals went.\n**Fix:** Draw the HPD bars via treeio `read.beast()` -> ggtree `geom_range('height_0.95_HPD')` (introspect the column name; it varies by source program and treeio version), or enable node bars in FigTree; never draw an annotated Bayesian tree with Bio.Phylo, which drops the annotations silently.\n\n### Raster Export for Publication\n**Trigger:** Saving a tree as a 150-dpi PNG for a paper.\n**Mechanism:** Raster pixelates thin branches and small tip labels at print size.\n**Symptom:** Fuzzy labels and pixelated branches; journal rejection or blurry print.\n**Fix:** Export SVG/PDF/EPS (vector) so text and lines stay sharp at any scale; only if forced to rasterize, do so at final size with >=600 dpi for line art.\n\n## Quantitative Thresholds\n\nThese are operational defaults for a standard portrait panel with ~6-8 pt labels; adapt to font, page, and journal specs.\n\n| Quantity | Threshold | Rationale / source |\n|----------|-----------|--------------------|\n| Tips, rectangular horizontal labels comfortable | <=~50 | labels do not collide at legible font |\n| Tips, start rotating/shrinking labels | ~50-150 | label height x tip count approaches panel height |\n| Tips, switch to circular/fan or collapse clades | ~150-500 | rectangular labels collide; radial labels recover space |\n| Tips, per-tip text impractical; use strips/rings | >~500-1000 | annotate by color/groups (iTOL, ggtreeExtra); iTOL is built for large trees |\n| Publication export | vector (SVG/PDF/EPS) | text and lines stay sharp at any scale; the default |\n| Forced raster, line-art/tree | >=600 dpi (many journals 600-1200) | ~300 dpi is the photo floor but too coarse for thin branches and small labels |\n| Scale bar on any phylogram | mandatory (subs/site or a time axis) | without it the reader cannot recover true distances under aspect-ratio distortion |\n| Bootstrap strong | >=95 (>=70 moderate) | Hillis-Bull heuristic; state the measure (Felsenstein 1985) |\n| UFBoot strong | >=95 (not the BP-70 scale) | recalibrated; IQ-TREE joint rule SH-aLRT>=80 AND UFBoot>=95 |\n| SH-aLRT strong | >=80 | IQ-TREE recommendation |\n| Posterior probability strong | >=0.95, but WEAKER than bootstrap 95 | PP systematically higher for the same data; do not equate |\n\nLock the branch-length scale; do not let the figure engine non-uniformly stretch a phylogram to fill a fixed panel, which distorts the lengths the figure exists to communicate.\n\n## Common Errors\n\n| Error / symptom | Cause | Solution |\n|-----------------|-------|----------|\n| BEAST HPD bars absent from a Python figure | drew an annotated tree with Bio.Phylo | route through treeio `read.beast` + ggtree `geom_range` |\n| Figure not saving / blank | `do_show=True` opens a window instead of writing | pass `do_show=False`, then `fig.savefig(...)` |\n| Branch colors not appearing | plain Newick tree has no color slot | convert with `tree.as_phyloxml()` and set `clade.color` |\n| Labels overlap into a black band | too many tips for rectangular layout | increase panel height, rotate labels, or switch to circular/iTOL |\n| \"Basal\" claim on a radial tree | narrated an unrooted layout as if rooted | root explicitly (tree-manipulation) and show the root before any directional claim |\n| Support number misread | bare integer with no measure stated | label the measure(s) and order; collapse sub-threshold nodes to polytomies |\n| `Phylo.draw` has no circular option | Bio.Phylo is rectangular-only | use ggtree `layout='circular'`, ETE4, or iTOL |\n\n## References\n\nTalevich E, Invergo BM, Cock PJA, Chapman BA. 2012. Bio.Phylo: a unified toolkit for processing, analyzing and visualizing phylogenetic trees in Biopython. *BMC Bioinformatics* 13:209.\nYu G, Smith DK, Zhu H, Guan Y, Lam TT-Y. 2017. ggtree: an R package for visualization and annotation of phylogenetic trees with their covariates and other associated data. *Methods in Ecology and Evolution* 8(1):28-36.\nYu G. 2020. Using ggtree to visualize data on tree-like structures. *Current Protocols in Bioinformatics* 69(1):e96.\nWang L-G, Lam TT-Y, Xu S, Dai Z, Zhou L, Feng T, Guo P, Dunn CW, Jones BR, Bradley T, Zhu H, Guan Y, Jiang Y, Yu G. 2020. treeio: an R package for phylogenetic tree input and output with richly annotated and associated data. *Molecular Biology and Evolution* 37(2):599-603.\nXu S, Dai Z, Guo P, Fu X, Liu S, Zhou L, Tang W, Feng T, Chen M, Zhan L, Wu T, Hu E, Jiang Y, Bo X, Yu G. 2021. ggtreeExtra: compact visualization of richly annotated phylogenetic data. *Molecular Biology and Evolution* 38(9):4039-4042.\nHuerta-Cepas J, Serra F, Bork P. 2016. ETE 3: reconstruction, analysis, and visualization of phylogenomic data. *Molecular Biology and Evolution* 33(6):1635-1638.\nLetunic I, Bork P. 2024. Interactive Tree of Life (iTOL) v6: recent updates to the phylogenetic tree display and annotation tool. *Nucleic Acids Research* 52(W1):W78-W82.\nFelsenstein J. 1985. Confidence limits on phylogenies: an approach using the bootstrap. *Evolution* 39(4):783-791.\n\n## Related Skills\n\n- tree-io - parsing and preserving BEAST/MrBayes/IQ-TREE annotations so they survive into the figure; the tightest coupling\n- tree-manipulation - rooting, ladderizing, pruning, and collapsing low-support nodes before drawing\n- data-visualization/ggplot2-fundamentals - the grammar, themes, and ggsave vector export underlying ggtree\n- data-visualization/multipanel-figures - composing a tree plus aligned metadata panels into one figure",
  "skillRoot": "/home/wangzg/.config/xyz.chatboxapp.app/skills/tree-visualization",
  "files": [
    "usage-guide.md"
  ]
}

run_command (state: result)

Args

{
  "command": "echo \"== biosoft: matplotlib / ete ==\"\n/home/wangzg/miniconda3/envs/biosoft/bin/python -c \"import matplotlib;print('matplotlib',matplotlib.__version__)\" 2>/dev/null || echo \"matplotlib 未装\"\n/home/wangzg/miniconda3/envs/biosoft/bin/python -c \"import ete4;print('ete4 ok')\" 2>/dev/null || echo \"ete4 未装\"\necho \"== r-4.6: ggtree / treeio ==\"\n/home/wangzg/miniconda3/envs/r-4.6/bin/Rscript -e 'for(p in c(\"ggtree\",\"treeio\",\"ape\",\"ggplot2\")) cat(p, tryCatch(as.character(packageVersion(p)),error=function(e)\"-\"),\"\\n\")' 2>/dev/null\necho \"== 现有树文件 ==\"; ls -l \"/home/wangzg/Nutstore Files/Nutstore/备课/2026秋/分子进化分析/Chatbox_bioinformatics/results/\"*.nwk 2>/dev/null"
}

Result

{
  "success": true,
  "exitCode": 0,
  "stdout": "== biosoft: matplotlib / ete ==\nmatplotlib 3.10.9\nete4 未装\n== r-4.6: ggtree / treeio ==\nggtree 4.2.0 \ntreeio 1.36.1 \nape 5.8.1 \nggplot2 4.0.3 \n== 现有树文件 ==\n-rw-rw-r-- 1 wangzg wangzg 546 Sep 15 13:19 /home/wangzg/Nutstore Files/Nutstore/备课/2026秋/分子进化分析/Chatbox_bioinformatics/results/tree_nj_py.nwk\n-rw-rw-r-- 1 wangzg wangzg 582 Sep 15 13:19 /home/wangzg/Nutstore Files/Nutstore/备课/2026秋/分子进化分析/Chatbox_bioinformatics/results/tree_nj_skbio.nwk\n-rw-rw-r-- 1 wangzg wangzg 496 Sep 15 13:19 /home/wangzg/Nutstore Files/Nutstore/备课/2026秋/分子进化分析/Chatbox_bioinformatics/results/tree_upgma_py.nwk\n",
  "stderr": "",
  "cwd": "/home/wangzg/Nutstore Files/Nutstore/备课/2026秋/分子进化分析/Chatbox_bioinformatics",
  "sandboxed": false
}

write_file (state: result)

Args

{
  "content": "#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\"\"\"树可视化 (Bio.Phylo + matplotlib) —— H7N9 HA NJ 树\n依 tree-visualization skill:\n  * 枝长是真实的 Poisson 校正距离(subs/site) -> 用 phylogram + 比例尺, 不画成 cladogram。\n  * 树是\"无根\"的 -> 声明定位任意, 不叙述\"基部/祖先\"。\n  * 没有 bootstrap 支持度 -> 不显示节点数字(不伪造解析度)。\n\"\"\"\nfrom Bio import Phylo\nimport matplotlib\nmatplotlib.use(\"Agg\")\nimport matplotlib.pyplot as plt\n\nSRC = \"results/tree_nj_skbio.nwk\"\ntree = Phylo.read(SRC, \"newick\")\ntree.ladderize()                     # 仅为可读; 顺序无系统发育含义\nn = len(tree.get_terminals())\n\nfig, ax = plt.subplots(figsize=(9, max(4.5, n * 0.34)))\nPhylo.draw(tree, axes=ax, do_show=False,\n           label_func=lambda c: c.name if c.is_terminal() else \"\")\n\n# 比例尺 (subs/site)\nxl = ax.get_xlim()\nbar = 0.002\nx0 = xl[1] - bar - 0.03 * (xl[1] - xl[0])\ny0 = -1.3\nax.plot([x0, x0 + bar], [y0, y0], color=\"black\", lw=2)\nax.text(x0 + bar / 2, y0 - 0.25, f\"{bar} subs/site\", ha=\"center\", va=\"top\", fontsize=8)\n\nax.set_title(\"H7N9 HA — NJ tree (Poisson-corrected protein distance)\\n\"\n             \"rectangular phylogram | UNROOTED (root placement arbitrary) | no branch support shown\",\n             fontsize=10)\nax.set_xlabel(\"branch length = substitutions per site (Poisson corrected)\", fontsize=9)\nfig.savefig(\"results/tree_nj_phylogram.pdf\", bbox_inches=\"tight\")   # 矢量, 首选\nfig.savefig(\"results/tree_nj_phylogram.png\", dpi=300, bbox_inches=\"tight\")\nplt.close(fig)\nprint(\"写出: results/tree_nj_phylogram.pdf / .png\")\n\n# 另存 UPGMA 对照\nup = Phylo.read(\"results/tree_upgma_py.nwk\", \"newick\"); up.ladderize()\nfig, ax = plt.subplots(figsize=(9, max(4.5, len(up.get_terminals()) * 0.34)))\nPhylo.draw(up, axes=ax, do_show=False, label_func=lambda c: c.name if c.is_terminal() else \"\")\nax.set_title(\"H7N9 HA — UPGMA tree (assumes a molecular clock)\\n\"\n             \"shown for contrast: UPGMA forces ultrametricity — NOT recommended for molecular data\", fontsize=10)\nfig.savefig(\"results/tree_upgma_phylogram.pdf\", bbox_inches=\"tight\")\nfig.savefig(\"results/tree_upgma_phylogram.png\", dpi=300, bbox_inches=\"tight\")\nplt.close(fig)\nprint(\"写出: results/tree_upgma_phylogram.pdf / .png\")\n",
  "file_path": "/home/wangzg/Nutstore Files/Nutstore/备课/2026秋/分子进化分析/Chatbox_bioinformatics/scripts/visualize_tree.py"
}

Result

{
  "success": true,
  "file_path": "/home/wangzg/Nutstore Files/Nutstore/备课/2026秋/分子进化分析/Chatbox_bioinformatics/scripts/visualize_tree.py"
}

write_file (state: result)

Args

{
  "content": "#!/usr/bin/env Rscript\n# 树可视化 (ggtree) —— H7N9 HA NJ 树\n# 依 tree-visualization skill: 无根树用 unrooted 布局最诚实; 矩形图加比例尺, 声明无支持度。\nsuppressMessages({library(ggtree); library(treeio); library(ggplot2); library(ape)})\ntr <- read.tree(\"results/tree_nj_skbio.nwk\")\ntr$tip.label <- sub(\".*(A/[^()]+).*\", \"\\\\1\", tr$tip.label)\n\n## 1) 矩形 phylogram (枝长=Poisson 距离)\np1 <- ggtree(tr, branch.length = \"branch.length\") +\n  geom_tiplab(size = 3.2) +\n  geom_treescale(x = 0, y = -0.5, width = 0.002,\n                 label = \"0.002 subs/site\", fontsize = 3) +\n  ggtitle(\"H7N9 HA — NJ (Poisson-corrected protein distance)\",\n          subtitle = \"rectangular phylogram | UNROOTED | no branch support shown\") +\n  theme_tree2()\nggsave(\"results/tree_ggtree_rect.pdf\", p1, width = 8, height = 5)\nggsave(\"results/tree_ggtree_rect.png\", p1, width = 8, height = 5, dpi = 300)\n\n## 2) 无根布局 (最符合\"未定根\"这一事实)\np2 <- ggtree(tr, layout = \"unrooted\", branch.length = \"branch.length\") +\n  geom_tiplab(size = 3.2) +\n  geom_treescale(width = 0.002, label = \"0.002 subs/site\", fontsize = 3) +\n  ggtitle(\"H7N9 HA — NJ, unrooted layout\",\n          subtitle = \"no root is implied; overall shape + long-branch outliers only\") +\n  theme(plot.title = element_text(size = 11))\nggsave(\"results/tree_ggtree_unrooted.pdf\", p2, width = 7, height = 7)\nggsave(\"results/tree_ggtree_unrooted.png\", p2, width = 7, height = 7, dpi = 300)\n\ncat(\"写出: results/tree_ggtree_{rect,unrooted}.pdf / .png\\n\")\n",
  "file_path": "/home/wangzg/Nutstore Files/Nutstore/备课/2026秋/分子进化分析/Chatbox_bioinformatics/scripts/visualize_tree.R"
}

Result

{
  "success": true,
  "file_path": "/home/wangzg/Nutstore Files/Nutstore/备课/2026秋/分子进化分析/Chatbox_bioinformatics/scripts/visualize_tree.R"
}

run_command (state: result)

Args

{
  "command": "cd \"$HOME\" && timeout 118 /home/wangzg/miniconda3/bin/conda install -n biosoft -y -c conda-forge -c bioconda r-base r-ggtree r-treeio r-ape 2>&1 | tail -20",
  "timeout": 120000
}

Result

{
  "success": false,
  "exitCode": 1,
  "stdout": "Solving environment: \\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\bfailed\n\nPackagesNotFoundInChannelsError: The following packages are not available from current channels:\n\n  - r-ggtree\n\nCurrent channels:\n\n  - https://conda.anaconda.org/conda-forge\n  - https://conda.anaconda.org/bioconda\n  - defaults\n\nTo search for alternate channels that may provide the conda package you're\nlooking for, navigate to\n\n    https://anaconda.org\n\nand use the search bar at the top of the page.\n\n\n",
  "stderr": "",
  "cwd": "/home/wangzg/Nutstore Files/Nutstore/备课/2026秋/分子进化分析/Chatbox_bioinformatics",
  "sandboxed": false
}

run_command (state: result)

Args

{
  "command": "cd \"$HOME\" && timeout 118 /home/wangzg/miniconda3/bin/conda install -n biosoft -y -c conda-forge -c bioconda r-base bioconductor-ggtree bioconductor-treeio r-ape 2>&1 | tail -20",
  "timeout": 120000
}

Result

{
  "success": false,
  "exitCode": 124,
  "stdout": "\n\n\n\r\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n\n\n\n\n\n\r\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n\n\n\n\n\n\n\r\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\nCondaSignalInterrupt: Signal interrupt SIGTERM\n\n done\n",
  "stderr": "",
  "cwd": "/home/wangzg/Nutstore Files/Nutstore/备课/2026秋/分子进化分析/Chatbox_bioinformatics",
  "sandboxed": false
}

run_command (state: result)

Args

{
  "command": "cd \"$HOME\"\nLOG=/tmp/bioggtree_install.log\nnohup /home/wangzg/miniconda3/bin/conda install -n biosoft -y -c conda-forge -c bioconda r-base bioconductor-ggtree bioconductor-treeio r-ape > $LOG 2>&1 &\necho \"PID=$!  日志: $LOG\"\nsleep 25\necho \"== 25s 后进度 ==\"; tail -3 $LOG"
}

Result

{
  "success": true,
  "exitCode": 0,
  "stdout": "PID=92858  日志: /tmp/bioggtree_install.log\n== 25s 后进度 ==\nPlatform: linux-64\nCollecting package metadata (repodata.json): - \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\bdone\nSolving environment: | \b\b/ \b\b- ",
  "stderr": "",
  "cwd": "/home/wangzg/Nutstore Files/Nutstore/备课/2026秋/分子进化分析/Chatbox_bioinformatics",
  "sandboxed": false
}

run_command (state: result)

Args

{
  "command": "LOG=/tmp/bioggtree_install.log\nsleep 60\necho \"== 进度 ==\"; tail -4 $LOG\necho \"== 是否已装好 Rscript ==\"; ls -l /home/wangzg/miniconda3/envs/biosoft/bin/Rscript 2>/dev/null || echo \"尚未就绪\"\npgrep -f \"conda install -n biosoft\" >/dev/null && echo \"安装进程仍在运行\" || echo \"安装进程已结束\"",
  "timeout": 90000
}

Result

{
  "success": true,
  "exitCode": 0,
  "stdout": "== 进度 ==\n\n\n\n\rr-colorspace-2.1_3   | 2.4 MB    | ########## | 100% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A== 是否已装好 Rscript ==\n尚未就绪\n安装进程仍在运行\n",
  "stderr": "",
  "cwd": "/home/wangzg/Nutstore Files/Nutstore/备课/2026秋/分子进化分析/Chatbox_bioinformatics",
  "sandboxed": false
}

run_command (state: result)

Args

{
  "command": "cd \"/home/wangzg/Nutstore Files/Nutstore/备课/2026秋/分子进化分析/Chatbox_bioinformatics\"\n/home/wangzg/miniconda3/envs/biosoft/bin/python scripts/visualize_tree.py 2>&1 | tail -10\necho \"== 生成图 ==\"; ls -l results/tree_nj_phylogram.* results/tree_upgma_phylogram.* 2>/dev/null"
}

Result

{
  "success": true,
  "exitCode": 0,
  "stdout": "写出: results/tree_nj_phylogram.pdf / .png\n写出: results/tree_upgma_phylogram.pdf / .png\n== 生成图 ==\n-rw-rw-r-- 1 wangzg wangzg  19591 Sep 15 13:25 results/tree_nj_phylogram.pdf\n-rw-rw-r-- 1 wangzg wangzg 222546 Sep 15 13:25 results/tree_nj_phylogram.png\n-rw-rw-r-- 1 wangzg wangzg  19802 Sep 15 13:25 results/tree_upgma_phylogram.pdf\n-rw-rw-r-- 1 wangzg wangzg 209095 Sep 15 13:25 results/tree_upgma_phylogram.png\n",
  "stderr": "",
  "cwd": "/home/wangzg/Nutstore Files/Nutstore/备课/2026秋/分子进化分析/Chatbox_bioinformatics",
  "sandboxed": false
}

run_command (state: result)

Args

{
  "command": "LOG=/tmp/bioggtree_install.log\nsleep 90\necho \"== 进度尾部 ==\"; tail -4 $LOG | tr -d '\\r'\nls -l /home/wangzg/miniconda3/envs/biosoft/bin/Rscript 2>/dev/null && echo \"Rscript 就绪\" || echo \"Rscript 尚未就绪\"\npgrep -f \"conda install -n biosoft\" >/dev/null && echo \"仍在运行\" || echo \"已结束\"",
  "timeout": 110000
}

Result

{
  "success": true,
  "exitCode": 0,
  "stdout": "== 进度尾部 ==\n  Did you know? You can install many PyPI packages with conda\n  using the conda-pypi beta. Get started:\n    https://docs.conda.io/projects/conda/en/stable/new-features.html\n\n-rwxrwxr-x 1 wangzg wangzg 17008 Sep 15 13:27 /home/wangzg/miniconda3/envs/biosoft/bin/Rscript\nRscript 就绪\n仍在运行\n",
  "stderr": "",
  "cwd": "/home/wangzg/Nutstore Files/Nutstore/备课/2026秋/分子进化分析/Chatbox_bioinformatics",
  "sandboxed": false
}

run_command (state: result)

Args

{
  "command": "cd \"/home/wangzg/Nutstore Files/Nutstore/备课/2026秋/分子进化分析/Chatbox_bioinformatics\"\nR=/home/wangzg/miniconda3/envs/biosoft/bin/Rscript\n$R -e 'for(p in c(\"ggtree\",\"treeio\",\"ape\",\"ggplot2\")) cat(p, tryCatch(as.character(packageVersion(p)),error=function(e)\"缺失\"),\"\\n\")' 2>&1 | tail -6\necho \"== 运行 ggtree 可视化 (biosoft) ==\"\n$R scripts/visualize_tree.R 2>&1 | tail -8\necho \"== 生成图 ==\"; ls -l results/tree_ggtree_* 2>/dev/null"
}

Result

{
  "success": true,
  "exitCode": 0,
  "stdout": "ggtree 4.0.5 \ntreeio 1.34.0 \nape 5.8.1 \nggplot2 4.0.3 \n== 运行 ggtree 可视化 (biosoft) ==\nAverage angle change [2] 0.427079555197805\nAverage angle change [3] 0.273316031842569\nAverage angle change [4] 0.260674625684137\nAverage angle change [5] 0.396314305989715\nWarning message:\nIn grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y,  :\n  for 'H7N9 HA — NJ, unrooted layout' in 'mbcsToSbcs': - substituted for — (U+2014)\n写出: results/tree_ggtree_{rect,unrooted}.pdf / .png\n== 生成图 ==\n-rw-rw-r-- 1 wangzg wangzg  5117 Sep 15 13:28 results/tree_ggtree_rect.pdf\n-rw-rw-r-- 1 wangzg wangzg 64470 Sep 15 13:28 results/tree_ggtree_rect.png\n-rw-rw-r-- 1 wangzg wangzg  4776 Sep 15 13:28 results/tree_ggtree_unrooted.pdf\n-rw-rw-r-- 1 wangzg wangzg 23241 Sep 15 13:28 results/tree_ggtree_unrooted.png\n",
  "stderr": "",
  "cwd": "/home/wangzg/Nutstore Files/Nutstore/备课/2026秋/分子进化分析/Chatbox_bioinformatics",
  "sandboxed": false
}

edit_file (state: result)

Args

{
  "file_path": "/home/wangzg/Nutstore Files/Nutstore/备课/2026秋/分子进化分析/Chatbox_bioinformatics/scripts/visualize_tree.R",
  "edits": [
    {
      "old_text": "  ggtitle(\"H7N9 HA — NJ (Poisson-corrected protein distance)\",",
      "new_text": "  ggtitle(\"H7N9 HA - NJ (Poisson-corrected protein distance)\","
    },
    {
      "old_text": "  ggtitle(\"H7N9 HA — NJ, unrooted layout\",",
      "new_text": "  ggtitle(\"H7N9 HA - NJ, unrooted layout\","
    }
  ]
}

Result

{
  "success": true,
  "file_path": "/home/wangzg/Nutstore Files/Nutstore/备课/2026秋/分子进化分析/Chatbox_bioinformatics/scripts/visualize_tree.R",
  "edits": 2
}

run_command (state: result)

Args

{
  "command": "cd \"/home/wangzg/Nutstore Files/Nutstore/备课/2026秋/分子进化分析/Chatbox_bioinformatics\"\n/home/wangzg/miniconda3/envs/biosoft/bin/Rscript scripts/visualize_tree.R 2>&1 | tail -4\necho \"== 全部可视化输出 ==\"\nls -lh results/tree_*phylogram.* results/tree_ggtree_* 2>/dev/null | awk '{print $5\"\\t\"$9}'"
}

Result

{
  "success": true,
  "exitCode": 0,
  "stdout": "Average angle change [3] 0.273316031842569\nAverage angle change [4] 0.260674625684137\nAverage angle change [5] 0.396314305989715\n写出: results/tree_ggtree_{rect,unrooted}.pdf / .png\n== 全部可视化输出 ==\n5.0K\tresults/tree_ggtree_rect.pdf\n63K\tresults/tree_ggtree_rect.png\n4.7K\tresults/tree_ggtree_unrooted.pdf\n23K\tresults/tree_ggtree_unrooted.png\n20K\tresults/tree_nj_phylogram.pdf\n218K\tresults/tree_nj_phylogram.png\n20K\tresults/tree_upgma_phylogram.pdf\n205K\tresults/tree_upgma_phylogram.png\n",
  "stderr": "",
  "cwd": "/home/wangzg/Nutstore Files/Nutstore/备课/2026秋/分子进化分析/Chatbox_bioinformatics",
  "sandboxed": false
}

save_memory (state: result)

Args

{
  "content": "分子进化分析课程环境统一用 biosoft conda 环境（/home/wangzg/miniconda3/envs/biosoft）：除 MAFFT 7.526 外，Python 侧有 biopython 1.88、scikit-bio 0.7.3、matplotlib 3.10.9；R 侧已装 r-base、ggtree 4.0.5、treeio 1.34.0、ape 5.8.1、ggplot2 4.0.3。用户偏好把所需工具都装进 biosoft，而不是分散到 r-4.6 等其他环境。"
}

Result

{
  "message": "Memory saved with id mmu28efm6whqd. It will be loaded in future agent sessions."
}

run_command (state: result)

Args

{
  "command": "cd \"$HOME\"\nLOG=/tmp/iqtree_install.log\nnohup /home/wangzg/miniconda3/bin/conda install -n biosoft -y -c conda-forge -c bioconda iqtree > $LOG 2>&1 &\necho \"IQ-TREE 安装 PID=$!  日志=$LOG\"\nsleep 20; echo \"== 20s 进度 ==\"; tail -2 $LOG | tr -d '\\r'"
}

Result

{
  "success": true,
  "exitCode": 0,
  "stdout": "IQ-TREE 安装 PID=99859  日志=/tmp/iqtree_install.log\n== 20s 进度 ==\n    https://docs.conda.io/projects/conda/en/stable/new-features.html\n\n",
  "stderr": "",
  "cwd": "/home/wangzg/Nutstore Files/Nutstore/备课/2026秋/分子进化分析/Chatbox_bioinformatics",
  "sandboxed": false
}

web_search (state: result)

Args

{
  "query": "H7N9 hemagglutinin phylogenetic tree outgroup sequence A/duck H7 Eurasian lineage root"
}

Result

{
  "query": "H7N9 hemagglutinin phylogenetic tree outgroup sequence A/duck H7 Eurasian lineage root",
  "searchResults": [
    {
      "title": "H7N9型禽流感_百度百科",
      "snippet": "H7N9型禽流感是一种新型禽流感，于2013年3月底在上海和安徽两地率先发现。 H7N9型禽流感是全球首次发现的新亚型流感病毒，尚 …",
      "link": "https://baike.baidu.com/item/H7N9%E5%9E%8B%E7%A6%BD%E6%B5%81%E6%84%9F/454515"
    },
    {
      "title": "甲型流感病毒H7N9亚型_百度百科",
      "snippet": "2013年4月4日 · 我国科研团队于2013年10月成功研发全球首支H7N9疫苗株，采用反向遗传技术构建并通过安全性评估。 世界卫生组 …",
      "link": "https://baike.baidu.com/item/%E7%94%B2%E5%9E%8B%E6%B5%81%E6%84%9F%E7%97%85%E6%AF%92H7N9%E4%BA%9A%E5%9E%8B/487365"
    },
    {
      "title": "中国疾病预防控制中心",
      "snippet": "2018年1月4日 · 人感染H7N9禽流感是由新型流感病毒A（H7N9）导致的一种新发传染病。 2013年2月以来，我国已发生了两波人感 …",
      "link": "https://www.chinacdc.cn/jkyj/crb2/yl/H7N9/jswj_h7n9/"
    },
    {
      "title": "中国疾病预防控制中心",
      "snippet": "2017年2月21日 · 动物疫情地理分布： 因H7N9病毒对家禽几乎不致病，一般只能通过主动病原学监测发现禽间病毒。 中国建立了几个 …",
      "link": "https://www.chinacdc.cn/jkyj/crb2/yl/H7N9/yqjz_h7n9/202409/t20240909_299334.html"
    },
    {
      "title": "Avian influenza A (H7N9) virus outbreak - World Health …",
      "snippet": "1 天前 · Avian influenza A (H7N9) is a subtype of influenza viruses that have been detected in birds in the past. This particular A …",
      "link": "https://www.who.int/emergencies/situations/avian-influenza-a-(h7n9)-virus-outbreak"
    },
    {
      "title": "Avian influenza A (H7N9) virus - World Health Organization (WHO)",
      "snippet": "2026年8月21日 · Avian influenza A (H7N9) is a subtype of influenza virus that has been detected in birds in the past. This particular A …",
      "link": "https://www.who.int/teams/global-influenza-programme/avian-influenza/avian-influenza-a-(h7n9)-virus"
    },
    {
      "title": "h7n9 | 百度健康·医学科普",
      "snippet": "H7N9是甲型禽流感病毒的一种亚型，2013年首次在中国发现，主要感染禽类，人类通过接触感染禽类或其分泌物偶发感染，可引起高 …",
      "link": "https://health.baidu.com/m/detail/ar_5766560562408188149"
    },
    {
      "title": "h7n9 = | 百度健康·医学科普",
      "snippet": "一、H7N9的基本特性 H7N9属于 人畜共患病毒，由禽类（如鸡、鸭）携带，病毒基因重组可能导致跨物种传播。 感染者多因直接接触 …",
      "link": "https://health.baidu.com/m/detail/ar_6943972619196878754"
    },
    {
      "title": "H7N9防控知识问答 - 中华人民共和国国家卫生健康委员会",
      "snippet": "2017年2月15日 · 一、什么是H7N9疫情？ H7N9疫情是由H7N9病毒引起的，自2013年我国华东地区首次发现人感染H7N9病例以来， …",
      "link": "https://www.nhc.gov.cn/zwgk/bmts/201702/345d393fb1434701b8ee29e4fc3f26b7.shtml"
    },
    {
      "title": "H7N9 _ 百科",
      "snippet": "经过多方努力，查找病毒来源的工作终于有了眉目，农业部通报国家禽流感参考实验室从上海市松江区沪淮农副产品批发市场的鸽子中 …",
      "link": "https://baike.sogou.com/v59566880.htm"
    }
  ]
}

run_command (state: result)

Args

{
  "command": "echo \"== IQ-TREE 安装进度 ==\"; tail -3 /tmp/iqtree_install.log | tr -d '\\r'\nls /home/wangzg/miniconda3/envs/biosoft/bin/iqtree* 2>/dev/null || echo \"iqtree 尚未就绪\"\necho; echo \"== 测试 NCBI E-utilities 连通性 ==\"\ncurl -s --max-time 20 \"https://eutils.ncbi.nlm.nih.gov/entrez/eutils/esearch.fcgi?db=protein&term=hemagglutinin%5BProtein%20Name%5D+AND+influenza+A+virus%5BOrganism%5D+AND+H7&retmax=5&retmode=json\" | head -c 600\necho"
}

Result

{
  "success": true,
  "exitCode": 0,
  "stdout": "== IQ-TREE 安装进度 ==\n  using the conda-pypi beta. Get started:\n    https://docs.conda.io/projects/conda/en/stable/new-features.html\n\n/home/wangzg/miniconda3/envs/biosoft/bin/iqtree\n/home/wangzg/miniconda3/envs/biosoft/bin/iqtree3\n\n== 测试 NCBI E-utilities 连通性 ==\n{\"header\":{\"type\":\"esearch\",\"version\":\"0.3\"},\"esearchresult\":{\"count\":\"744\",\"retmax\":\"5\",\"retstart\":\"0\",\"idlist\":[\"3330916409\",\"3264462069\",\"3221147280\",\"29294609\",\"3915726\"],\"translationset\":[{\"from\":\"influenza A virus[Organism]\",\"to\":\"\\\"Influenza A virus\\\"[Organism]\"}],\"translationstack\":[{\"term\":\"hemagglutinin[Protein Name]\",\"field\":\"Protein Name\",\"count\":\"361746\",\"explode\":\"N\"},{\"term\":\"\\\"Influenza A virus\\\"[Organism]\",\"field\":\"Organism\",\"count\":\"2291565\",\"explode\":\"Y\"},\"AND\",{\"term\":\"H7[All Fields]\",\"field\":\"All Fields\",\"count\":\"5720943\",\"explode\":\"N\"},\"AND\"],\"querytranslation\":\"hemagglut\n",
  "stderr": "",
  "cwd": "/home/wangzg/Nutstore Files/Nutstore/备课/2026秋/分子进化分析/Chatbox_bioinformatics",
  "sandboxed": false
}

run_command (state: result)

Args

{
  "command": "echo \"== IQ-TREE 版本 ==\"; /home/wangzg/miniconda3/envs/biosoft/bin/iqtree --version 2>&1 | head -1\necho; echo \"== 搜禽源 H7 血凝素候选 ==\"\nIDS=$(curl -s --max-time 25 \"https://eutils.ncbi.nlm.nih.gov/entrez/eutils/esearch.fcgi?db=protein&term=hemagglutinin%5BProtein+Name%5D+AND+influenza+A+virus%5BOrganism%5D+AND+duck%5BAll+Fields%5D+AND+H7N3%5BAll+Fields%5D&retmax=8&retmode=json\" | python3 -c \"import sys,json;print(','.join(json.load(sys.stdin)['esearchresult']['idlist']))\")\necho \"IDs: $IDS\"\ncurl -s --max-time 25 \"https://eutils.ncbi.nlm.nih.gov/entrez/eutils/esummary.fcgi?db=protein&id=$IDS&retmode=json\" | python3 -c \"\nimport sys,json\nd=json.load(sys.stdin)['result']\nfor u in d['uids']:\n    r=d[u]; print(u, '|', r.get('accessionversion'), '|', r.get('title')[:95])\n\""
}

Result

{
  "success": true,
  "exitCode": 0,
  "stdout": "== IQ-TREE 版本 ==\nIQ-TREE version 3.1.3 for Linux x86 64-bit built Jul 26 2026\n\n== 搜禽源 H7 血凝素候选 ==\nIDs: 3242609092,3242609052,3242609012,3242608992,3242608972,3196243238,3196243218,3196243158\n3242609092 | YDJ21307.1 | hemagglutinin [Influenza A virus]\n3242609052 | YDJ21283.1 | hemagglutinin [Influenza A virus]\n3242609012 | YDJ21259.1 | hemagglutinin [Influenza A virus]\n3242608992 | YDJ21247.1 | hemagglutinin [Influenza A virus]\n3242608972 | YDJ21235.1 | hemagglutinin [Influenza A virus]\n3196243238 | YDD18826.1 | hemagglutinin [Influenza A virus]\n3196243218 | YDD18814.1 | hemagglutinin [Influenza A virus]\n3196243158 | YDD18778.1 | hemagglutinin [Influenza A virus]\n",
  "stderr": "",
  "cwd": "/home/wangzg/Nutstore Files/Nutstore/备课/2026秋/分子进化分析/Chatbox_bioinformatics",
  "sandboxed": false
}

run_command (state: result)

Args

{
  "command": "cd \"/home/wangzg/Nutstore Files/Nutstore/备课/2026秋/分子进化分析/Chatbox_bioinformatics\"\nmkdir -p data\ncurl -s --max-time 30 \"https://eutils.ncbi.nlm.nih.gov/entrez/eutils/efetch.fcgi?db=protein&id=3242609092,3242609052,3196243238,3196243218&rettype=fasta&retmode=text\" > data/outgroup_candidates.fa\necho \"== 候选外群 ==\"\nawk '/^>/{if(n)print \"   长度=\"n; print; n=0; next}{n+=length($0)}END{print \"   长度=\"n}' data/outgroup_candidates.fa | grep -v \"^[A-Z]\" | head -30\necho \"== 序列行长度分布 ==\"\nawk '/^>/{next}{print length($0)}' data/outgroup_candidates.fa | sort -n | uniq -c"
}

Result

{
  "success": true,
  "exitCode": 0,
  "stdout": "== 候选外群 ==\n>YDJ21307.1 hemagglutinin [Influenza A virus]\n   长度=560\n>YDJ21283.1 hemagglutinin [Influenza A virus]\n   长度=560\n>YDD18826.1 hemagglutinin [Influenza A virus]\n   长度=560\n>YDD18814.1 hemagglutinin [Influenza A virus]\n   长度=560\n== 序列行长度分布 ==\n      4 0\n     32 70\n",
  "stderr": "",
  "cwd": "/home/wangzg/Nutstore Files/Nutstore/备课/2026秋/分子进化分析/Chatbox_bioinformatics",
  "sandboxed": false
}

run_command (state: result)

Args

{
  "command": "cd \"/home/wangzg/Nutstore Files/Nutstore/备课/2026秋/分子进化分析/Chatbox_bioinformatics\"\nE=\"https://eutils.ncbi.nlm.nih.gov/entrez/eutils\"\nfor Q in \"hemagglutinin%5BProtein+Name%5D+AND+A%2Fduck%2FZhejiang%5BAll+Fields%5D+AND+H7%5BAll+Fields%5D\" \"hemagglutinin%5BProtein+Name%5D+AND+A%2Fmallard%5BAll+Fields%5D+AND+H7+N3%5BAll+Fields%5D\" \"hemagglutinin%5BProtein+Name%5D+AND+wild+bird%5BAll+Fields%5D+AND+H7N7%5BAll+Fields%5D\"; do\n  IDS=$(curl -s --max-time 20 \"$E/esearch.fcgi?db=protein&term=$Q&retmax=6&retmode=json\" | python3 -c \"import sys,json;print(','.join(json.load(sys.stdin)['esearchresult']['idlist']))\")\n  [ -z \"$IDS\" ] && continue\n  echo \"=== query: $Q ===\"\n  curl -s --max-time 25 \"$E/efetch.fcgi?db=protein&id=$IDS&rettype=fasta&retmode=text\" | awk '/^>/{if(seq)print \"     len=\"seqlen\"  \"hdr; seq=0; seqlen=0; hdr=$0; next}{seq=1; seqlen+=length($0)}END{if(seq)print \"     len=\"seqlen\"  \"hdr}'\ndone"
}

Result

{
  "success": true,
  "exitCode": 0,
  "stdout": "=== query: hemagglutinin%5BProtein+Name%5D+AND+wild+bird%5BAll+Fields%5D+AND+H7N7%5BAll+Fields%5D ===\n     len=560  >XAI43452.1 hemagglutinin [Influenza A virus]\n     len=214  >WQM30213.1 hemagglutinin, partial [Influenza A virus]\n     len=560  >WOB13068.1 hemagglutinin [Influenza A virus]\n     len=560  >UQK90271.1 hemagglutinin [Influenza A virus]\n     len=560  >UQK90259.1 hemagglutinin [Influenza A virus]\n     len=557  >ACN39307.1 hemagglutinin, partial [Influenza A virus (A/wild bird feces/Korea/ESD07/2005(H7N7))]\n",
  "stderr": "",
  "cwd": "/home/wangzg/Nutstore Files/Nutstore/备课/2026秋/分子进化分析/Chatbox_bioinformatics",
  "sandboxed": false
}

run_command (state: result)

Args

{
  "command": "cd \"/home/wangzg/Nutstore Files/Nutstore/备课/2026秋/分子进化分析/Chatbox_bioinformatics\"\ncurl -s --max-time 30 \"https://eutils.ncbi.nlm.nih.gov/entrez/eutils/efetch.fcgi?db=protein&id=ACN39307.1&rettype=fasta&retmode=text\" > data/outgroup_H7N7_Korea2005.fa\necho \"== 外群 ==\"; head -1 data/outgroup_H7N7_Korea2005.fa\nawk '/^>/{next}{n+=length($0)}END{print \"长度:\",n}' data/outgroup_H7N7_Korea2005.fa\necho; echo \"== IQ-TREE 关键参数 ==\"\n/home/wangzg/miniconda3/envs/biosoft/bin/iqtree -h 2>&1 | grep -iE \"^\\s*-(m|s|B|alrt|bb|T|pre|nt|o|redo) \" | head -20"
}

Result

{
  "success": true,
  "exitCode": 0,
  "stdout": "== 外群 ==\n>ACN39307.1 hemagglutinin, partial [Influenza A virus (A/wild bird feces/Korea/ESD07/2005(H7N7))]\n长度: 557\n\n== IQ-TREE 关键参数 ==\n  -s FILE[,...,FILE]   PHYLIP/FASTA/NEXUS/CLUSTAL/MSF alignment file(s)\n  -s DIR               Directory of alignment files\n  -t FILE|PARS|RAND    Starting tree (default: 99 parsimony and BIONJ)\n  -o TAX[,...,TAX]     Outgroup taxon (list) for writing .treefile\n  -T NUM|AUTO          No. cores/threads or AUTO-detect (default: 1)\n  -S FILE|DIR          Like -p but separate tree inference\n  -m TESTONLY          Standard model selection (like jModelTest, ProtTest)\n  -m TEST              Standard model selection followed by tree inference\n  -m MF                Extended model selection with FreeRate heterogeneity\n  -m MFP               Extended model selection followed by tree inference\n  -m ...+LM            Additionally test Lie Markov models\n  -m ...+LMRY          Additionally test Lie Markov models with RY symmetry\n  -m ...+LMWS          Additionally test Lie Markov models with WS symmetry\n  -m ...+LMMK          Additionally test Lie Markov models with MK symmetry\n  -m ...+LMSS          Additionally test strand-symmetric models\n  -m STRING            Model name string (e.g. GTR+F+I+G)\n  -m ...+F             Empirically counted frequencies from alignment\n  -m ...+FO            Optimized frequencies by maximum-likelihood\n  -m ...+FQ            Equal frequencies\n  -m ...+FRY           For DNA, freq(A+G)=1/2=freq(C+T)\n",
  "stderr": "",
  "cwd": "/home/wangzg/Nutstore Files/Nutstore/备课/2026秋/分子进化分析/Chatbox_bioinformatics",
  "sandboxed": false
}

write_file (state: result)

Args

{
  "content": "#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\"\"\"准备 ML/距离法数据集: 干净株名 + 外群, 生成 14条 与 22条 两个版本。\n- 外群: A/wild bird feces/Korea/ESD07/2005(H7N7)  (禽源 H7, 用于定根)\n- 14条版 = 12 条全长(>=500aa) + 2 条台湾(549/552) + 外群\n- 22条版 = 全部 22 条 + 外群\nNewick 安全标签: 去括号/空格, 保留 '/' 与字母数字.\n\"\"\"\nimport re\nfrom Bio import SeqIO\n\ndef clean(desc):\n    # 抽出 A/... 株名\n    m = re.search(r'A/([A-Za-z_ ]+/\\d+/\\d+)', desc)\n    if m:\n        label = \"A/\" + m.group(1)\n    else:\n        label = desc.split()[0]\n    # 亚型 (H7N9) / (H7N7)\n    sub = re.search(r'\\((H\\d+N\\d+)\\)', desc)\n    if sub:\n        label += \"_\" + sub.group(1)\n    label = label.replace(\" \", \"_\")\n    return label\n\nsrc = \"dataset/H7N9_HA.fa\"\nh7n9 = list(SeqIO.parse(src, \"fasta\"))\nout = list(SeqIO.parse(\"data/outgroup_H7N7_Korea2005.fa\", \"fasta\"))[0]\n\nrecs = []\nfor r in h7n9:\n    r.id = clean(r.description); r.name = r.id; r.description = r.id\n    recs.append(r)\nout.id = \"A/wild_bird_feces/Korea/ESD07/2005_H7N7\"; out.name = out.id; out.description = out.id\n\nprint(\"== 株名 (22 条 H7N9) ==\")\nfor r in recs:\n    print(f\"  {len(r.seq):>4}  {r.id}\")\n\n# 14 条版: 长度 >= 500 (全长 + 台湾)\nfull = [r for r in recs if len(r.seq) >= 500]\n# 22 条版: 全部\ndef write(path, group):\n    with open(path, \"w\") as f:\n        for r in group + [out]:\n            f.write(f\">{r.id}\\n{str(r.seq)}\\n\")\n\nwrite(\"data/set14_plus_outgroup.fa\", full)\nwrite(\"data/set22_plus_outgroup.fa\", recs)\nprint(f\"\\n14条版: {len(full)} H7N9 + 1 外群 = {len(full)+1}  -> data/set14_plus_outgroup.fa\")\nprint(f\"22条版: {len(recs)} H7N9 + 1 外群 = {len(recs)+1}  -> data/set22_plus_outgroup.fa\")\nprint(f\"外群: {out.id}\")\n",
  "file_path": "/home/wangzg/Nutstore Files/Nutstore/备课/2026秋/分子进化分析/Chatbox_bioinformatics/scripts/prep_datasets.py"
}

Result

{
  "success": true,
  "file_path": "/home/wangzg/Nutstore Files/Nutstore/备课/2026秋/分子进化分析/Chatbox_bioinformatics/scripts/prep_datasets.py"
}

run_command (state: result)

Args

{
  "command": "cd \"/home/wangzg/Nutstore Files/Nutstore/备课/2026秋/分子进化分析/Chatbox_bioinformatics\"\n/home/wangzg/miniconda3/envs/biosoft/bin/python scripts/prep_datasets.py"
}

Result

{
  "success": true,
  "exitCode": 0,
  "stdout": "== 株名 (22 条 H7N9) ==\n   560  A/Fujian/1/2013_H7N9\n   560  A/Hangzhou/1/2013_H7N9\n   560  A/Hangzhou/2/2013_H7N9\n   560  A/Hangzhou/3/2013_H7N9\n   560  A/Nanchang/1/2013_H7N9\n   560  A/Nanjing/1/2013_H7N9\n   560  A/Shanghai/02/2013_H7N9\n   206  gi|491874175|gb|AGL33692|hemagglutinin[Influenza_H7N9\n   206  gi|491874186|gb|AGL33693|hemagglutinin[Influenza_H7N9\n   560  gi|476403560|gb|AGI60292|hemagglutinin[Influenza_H7N9\n   206  gi|491874193|gb|AGL33694|hemagglutinin[Influenza_H7N9\n   193  gi|507593994|gb|AGM53887|hemagglutinin[Influenza_H7N9\n   191  gi|507593992|gb|AGM53886|hemagglutinin[Influenza_H7N9\n   196  gi|507593986|gb|AGM53883|hemagglutinin[Influenza_H7N9\n   193  gi|507593988|gb|AGM53884|hemagglutinin[Influenza_H7N9\n   192  gi|507593990|gb|AGM53885|hemagglutinin[Influenza_H7N9\n   549  gi|501485301|gb|AGL95088|hemagglutinin[Influenza_H7N9\n   552  gi|501485319|gb|AGL95098|hemagglutinin[Influenza_H7N9\n   560  A/Wuxi/1/2013_H7N9\n   560  A/Wuxi/2/2013_H7N9\n   560  gi|478735269|gb|AGJ51953|hemagglutinin[Influenza_H7N9\n   560  gi|506460794|gb|AGM16242|hemagglutinin[Influenza_H7N9\n\n14条版: 14 H7N9 + 1 外群 = 15  -> data/set14_plus_outgroup.fa\n22条版: 22 H7N9 + 1 外群 = 23  -> data/set22_plus_outgroup.fa\n外群: A/wild_bird_feces/Korea/ESD07/2005_H7N7\n",
  "stderr": "",
  "cwd": "/home/wangzg/Nutstore Files/Nutstore/备课/2026秋/分子进化分析/Chatbox_bioinformatics",
  "sandboxed": false
}

run_command (state: result)

Args

{
  "command": "cd \"/home/wangzg/Nutstore Files/Nutstore/备课/2026秋/分子进化分析/Chatbox_bioinformatics\"\ngrep \">\" dataset/H7N9_HA.fa | sed 's/[0-9]*//g' | head -25\necho \"---- 完整原始头部 ----\"\ngrep \">\" dataset/H7N9_HA.fa"
}

Result

{
  "success": true,
  "exitCode": 0,
  "stdout": ">gi||gb|AGK|hemagglutinin[Influenza A virus (A/Fujian//(HN))]\n>gi||gb|AGI|hemagglutinin[Influenza A virus (A/Hangzhou//(HN))]\n>gi||gb|AGK|hemagglutinin[Influenza A virus (A/Hangzhou//(HN))]\n>gi||gb|AGK|hemagglutinin[Influenza A virus (A/Hangzhou//(HN))]\n>gi||gb|AGO|hemagglutinin[Influenza A virus (A/Nanchang//(HN))]\n>gi||gb|AGJ|hemagglutinin[Influenza A virus (A/Nanjing//(HN))]\n>gi||gb|AGL|hemagglutinin[Influenza A virus (A/Shanghai//(HN))]\n>gi||gb|AGL|hemagglutinin[Influenza A virus (A/Shanghai/T/(HN))]\n>gi||gb|AGL|hemagglutinin[Influenza A virus (A/Shanghai/T/(HN))]\n>gi||gb|AGI|hemagglutinin[Influenza A virus (A/Shanghai/T/(HN))]\n>gi||gb|AGL|hemagglutinin[Influenza A virus (A/Shanghai/T/(HN))]\n>gi||gb|AGM|hemagglutinin[Influenza A virus (A/Shanghai/T/(HN))]\n>gi||gb|AGM|hemagglutinin[Influenza A virus (A/Shanghai/T/(HN))]\n>gi||gb|AGM|hemagglutinin[Influenza A virus (A/Shanghai/T/(HN))]\n>gi||gb|AGM|hemagglutinin[Influenza A virus (A/Shanghai/T/(HN))]\n>gi||gb|AGM|hemagglutinin[Influenza A virus (A/Shanghai/T/(HN))]\n>gi||gb|AGL|hemagglutinin[Influenza A virus (A/Taiwan/S/(HN))]\n>gi||gb|AGL|hemagglutinin[Influenza A virus (A/Taiwan/T/(HN))]\n>gi||gb|AGN|hemagglutinin[Influenza A virus (A/Wuxi//(HN))]\n>gi||gb|AGN|hemagglutinin[Influenza A virus (A/Wuxi//(HN))]\n>gi||gb|AGJ|hemagglutinin[Influenza A virus (A/Zhejiang/DTID-ZJU/(HN))]\n>gi||gb|AGM|hemagglutinin[Influenza A virus (A/Zhejiang/HZ/(HN))]\n---- 完整原始头部 ----\n>gi|485160872|gb|AGK82158|hemagglutinin[Influenza A virus (A/Fujian/1/2013(H7N9))]\n>gi|475662454|gb|AGI60301|hemagglutinin[Influenza A virus (A/Hangzhou/1/2013(H7N9))]\n>gi|485649824|gb|AGK84857|hemagglutinin[Influenza A virus (A/Hangzhou/2/2013(H7N9))]\n>gi|485649830|gb|AGK84860|hemagglutinin[Influenza A virus (A/Hangzhou/3/2013(H7N9))]\n>gi|513857263|gb|AGO28204|hemagglutinin[Influenza A virus (A/Nanchang/1/2013(H7N9))]\n>gi|479285761|gb|AGJ73503|hemagglutinin[Influenza A virus (A/Nanjing/1/2013(H7N9))]\n>gi|496493389|gb|AGL44438|hemagglutinin[Influenza A virus (A/Shanghai/02/2013(H7N9))]\n>gi|491874175|gb|AGL33692|hemagglutinin[Influenza A virus (A/Shanghai/4655T/2013(H7N9))]\n>gi|491874186|gb|AGL33693|hemagglutinin[Influenza A virus (A/Shanghai/4659T/2013(H7N9))]\n>gi|476403560|gb|AGI60292|hemagglutinin[Influenza A virus (A/Shanghai/4664T/2013(H7N9))]\n>gi|491874193|gb|AGL33694|hemagglutinin[Influenza A virus (A/Shanghai/4665T/2013(H7N9))]\n>gi|507593994|gb|AGM53887|hemagglutinin[Influenza A virus (A/Shanghai/4701T/2013(H7N9))]\n>gi|507593992|gb|AGM53886|hemagglutinin[Influenza A virus (A/Shanghai/4842T/2013(H7N9))]\n>gi|507593986|gb|AGM53883|hemagglutinin[Influenza A virus (A/Shanghai/5083T/2013(H7N9))]\n>gi|507593988|gb|AGM53884|hemagglutinin[Influenza A virus (A/Shanghai/5180T/2013(H7N9))]\n>gi|507593990|gb|AGM53885|hemagglutinin[Influenza A virus (A/Shanghai/5240T/2013(H7N9))]\n>gi|501485301|gb|AGL95088|hemagglutinin[Influenza A virus (A/Taiwan/S02076/2013(H7N9))]\n>gi|501485319|gb|AGL95098|hemagglutinin[Influenza A virus (A/Taiwan/T02081/2013(H7N9))]\n>gi|511105798|gb|AGN69474|hemagglutinin[Influenza A virus (A/Wuxi/1/2013(H7N9))]\n>gi|511105778|gb|AGN69462|hemagglutinin[Influenza A virus (A/Wuxi/2/2013(H7N9))]\n>gi|478735269|gb|AGJ51953|hemagglutinin[Influenza A virus (A/Zhejiang/DTID-ZJU01/2013(H7N9))]\n>gi|506460794|gb|AGM16242|hemagglutinin[Influenza A virus (A/Zhejiang/HZ1/2013(H7N9))]\n",
  "stderr": "",
  "cwd": "/home/wangzg/Nutstore Files/Nutstore/备课/2026秋/分子进化分析/Chatbox_bioinformatics",
  "sandboxed": false
}

edit_file (state: result)

Args

{
  "file_path": "/home/wangzg/Nutstore Files/Nutstore/备课/2026秋/分子进化分析/Chatbox_bioinformatics/scripts/prep_datasets.py",
  "old_text": "def clean(desc):\n    # 抽出 A/... 株名\n    m = re.search(r'A/([A-Za-z_ ]+/\\d+/\\d+)', desc)\n    if m:\n        label = \"A/\" + m.group(1)\n    else:\n        label = desc.split()[0]",
  "new_text": "def clean(desc):\n    # 抽出 A/... 株名 (允许字母数字与连字符的编号)\n    m = re.search(r'(A/[A-Za-z0-9\\-]+(?:/[A-Za-z0-9\\-]+)+)', desc)\n    label = m.group(1) if m else desc.split()[0]"
}

Result

{
  "success": true,
  "file_path": "/home/wangzg/Nutstore Files/Nutstore/备课/2026秋/分子进化分析/Chatbox_bioinformatics/scripts/prep_datasets.py",
  "edits": 1
}

run_command (state: result)

Args

{
  "command": "cd \"/home/wangzg/Nutstore Files/Nutstore/备课/2026秋/分子进化分析/Chatbox_bioinformatics\"\n/home/wangzg/miniconda3/envs/biosoft/bin/python scripts/prep_datasets.py\necho \"== 校验 14 条版头部 ==\"; grep \">\" data/set14_plus_outgroup.fa"
}

Result

{
  "success": true,
  "exitCode": 0,
  "stdout": "== 株名 (22 条 H7N9) ==\n   560  A/Fujian/1/2013_H7N9\n   560  A/Hangzhou/1/2013_H7N9\n   560  A/Hangzhou/2/2013_H7N9\n   560  A/Hangzhou/3/2013_H7N9\n   560  A/Nanchang/1/2013_H7N9\n   560  A/Nanjing/1/2013_H7N9\n   560  A/Shanghai/02/2013_H7N9\n   206  A/Shanghai/4655T/2013_H7N9\n   206  A/Shanghai/4659T/2013_H7N9\n   560  A/Shanghai/4664T/2013_H7N9\n   206  A/Shanghai/4665T/2013_H7N9\n   193  A/Shanghai/4701T/2013_H7N9\n   191  A/Shanghai/4842T/2013_H7N9\n   196  A/Shanghai/5083T/2013_H7N9\n   193  A/Shanghai/5180T/2013_H7N9\n   192  A/Shanghai/5240T/2013_H7N9\n   549  A/Taiwan/S02076/2013_H7N9\n   552  A/Taiwan/T02081/2013_H7N9\n   560  A/Wuxi/1/2013_H7N9\n   560  A/Wuxi/2/2013_H7N9\n   560  A/Zhejiang/DTID-ZJU01/2013_H7N9\n   560  A/Zhejiang/HZ1/2013_H7N9\n\n14条版: 14 H7N9 + 1 外群 = 15  -> data/set14_plus_outgroup.fa\n22条版: 22 H7N9 + 1 外群 = 23  -> data/set22_plus_outgroup.fa\n外群: A/wild_bird_feces/Korea/ESD07/2005_H7N7\n== 校验 14 条版头部 ==\n>A/Fujian/1/2013_H7N9\n>A/Hangzhou/1/2013_H7N9\n>A/Hangzhou/2/2013_H7N9\n>A/Hangzhou/3/2013_H7N9\n>A/Nanchang/1/2013_H7N9\n>A/Nanjing/1/2013_H7N9\n>A/Shanghai/02/2013_H7N9\n>A/Shanghai/4664T/2013_H7N9\n>A/Taiwan/S02076/2013_H7N9\n>A/Taiwan/T02081/2013_H7N9\n>A/Wuxi/1/2013_H7N9\n>A/Wuxi/2/2013_H7N9\n>A/Zhejiang/DTID-ZJU01/2013_H7N9\n>A/Zhejiang/HZ1/2013_H7N9\n>A/wild_bird_feces/Korea/ESD07/2005_H7N7\n",
  "stderr": "",
  "cwd": "/home/wangzg/Nutstore Files/Nutstore/备课/2026秋/分子进化分析/Chatbox_bioinformatics",
  "sandboxed": false
}

run_command (state: result)

Args

{
  "command": "cd \"/home/wangzg/Nutstore Files/Nutstore/备课/2026秋/分子进化分析/Chatbox_bioinformatics\"\nMAFFT=/home/wangzg/miniconda3/envs/biosoft/bin/mafft\n$MAFFT --amino --localpair --maxiterate 1000 --thread 4 data/set14_plus_outgroup.fa > results/aln14.fasta 2>/dev/null && echo \"aln14 done\"\n$MAFFT --amino --localpair --maxiterate 1000 --thread 4 data/set22_plus_outgroup.fa > results/aln22.fasta 2>/dev/null && echo \"aln22 done\"\nfor f in results/aln14.fasta results/aln22.fasta; do\n  echo \"== $f ==\"; grep -c \">\" $f; awk '/^>/{if(n)print n;n=0;next}{n+=length($0)}END{print n}' $f | sort -n | uniq -c\ndone"
}

Result

{
  "success": true,
  "exitCode": 0,
  "stdout": "aln14 done\naln22 done\n== results/aln14.fasta ==\n15\n     15 560\n== results/aln22.fasta ==\n23\n     23 560\n",
  "stderr": "",
  "cwd": "/home/wangzg/Nutstore Files/Nutstore/备课/2026秋/分子进化分析/Chatbox_bioinformatics",
  "sandboxed": false
}

run_command (state: result)

Args

{
  "command": "cd \"/home/wangzg/Nutstore Files/Nutstore/备课/2026秋/分子进化分析/Chatbox_bioinformatics\"\nIQ=/home/wangzg/miniconda3/envs/biosoft/bin/iqtree\nOUT=\"A/wild_bird_feces/Korea/ESD07/2005_H7N7\"\nnohup $IQ -s results/aln14.fasta -m MFP -B 1000 -alrt 1000 -o \"$OUT\" -T 4 -pre results/ml14 > results/ml14.log 2>&1 &\necho \"ml14 PID=$!\"\nnohup $IQ -s results/aln22.fasta -m MFP -B 1000 -alrt 1000 -o \"$OUT\" -T 4 -pre results/ml22 > results/ml22.log 2>&1 &\necho \"ml22 PID=$!\"\nsleep 35\necho \"== ml14 进度 ==\"; tail -3 results/ml14.log\necho \"== ml22 进度 ==\"; tail -3 results/ml22.log",
  "timeout": 60000
}

Result

{
  "success": true,
  "exitCode": 0,
  "stdout": "ml14 PID=101826\nml22 PID=101827\n== ml14 进度 ==\n  Screen log file:               results/ml14.log\n\nDate and Time: Tue Sep 15 13:32:13 2026\n== ml22 进度 ==\n  Screen log file:               results/ml22.log\n\nDate and Time: Tue Sep 15 13:32:30 2026\n",
  "stderr": "",
  "cwd": "/home/wangzg/Nutstore Files/Nutstore/备课/2026秋/分子进化分析/Chatbox_bioinformatics",
  "sandboxed": false
}

run_command (state: result)

Args

{
  "command": "cd \"/home/wangzg/Nutstore Files/Nutstore/备课/2026秋/分子进化分析/Chatbox_bioinformatics\"\nsleep 75\nfor t in 14 22; do\n  echo \"===== ml$t =====\"\n  grep -E \"Best-fit model|BEST SCORE|Optimal|Total wall-clock|Analysis results\" results/ml$t.log | tail -4\n  ls -l results/ml$t.treefile 2>/dev/null || echo \"  treefile 尚未生成\"\n  pgrep -f \"pre results/ml$t\" >/dev/null && echo \"  (仍在运行)\" || echo \"  (已结束)\"\ndone",
  "timeout": 100000
}

Result

{
  "success": true,
  "exitCode": 0,
  "stdout": "===== ml14 =====\nOptimal log-likelihood: -1813.978\nBEST SCORE FOUND : -1813.978\nTotal wall-clock time used: 7.289 sec (0h:0m:7s)\nAnalysis results written to: \n-rw-rw-r-- 1 wangzg wangzg 790 Sep 15 13:32 results/ml14.treefile\n  (已结束)\n===== ml22 =====\nOptimal log-likelihood: -1826.035\nBEST SCORE FOUND : -1826.035\nTotal wall-clock time used: 24.023 sec (0h:0m:24s)\nAnalysis results written to: \n-rw-rw-r-- 1 wangzg wangzg 1263 Sep 15 13:32 results/ml22.treefile\n  (已结束)\n",
  "stderr": "",
  "cwd": "/home/wangzg/Nutstore Files/Nutstore/备课/2026秋/分子进化分析/Chatbox_bioinformatics",
  "sandboxed": false
}

run_command (state: result)

Args

{
  "command": "cd \"/home/wangzg/Nutstore Files/Nutstore/备课/2026秋/分子进化分析/Chatbox_bioinformatics\"\nfor t in 14 22; do\n  echo \"================ ml$t ================\"\n  grep -E \"Best-fit model according|Model of substitution|List of best-fit|AIC|Gamma|Invariable\" results/ml$t.iqtree | head -6\n  echo \"--- 树 (带 SUPPORT: SH-aLRT/UFBoot) ---\"\n  cat results/ml$t.treefile\n  echo\ndone"
}

Result

{
  "success": true,
  "exitCode": 0,
  "stdout": "================ ml14 ================\nBest-fit model according to BIC: FLU\nModel                  LogL         AIC      w-AIC        AICc     w-AICc         BIC      w-BIC\nAIC, w-AIC   : Akaike information criterion scores and weights.\nAICc, w-AICc : Corrected AIC scores and weights.\nModel of substitution: FLU\nAkaike information criterion (AIC) score: 3669.9555\n--- 树 (带 SUPPORT: SH-aLRT/UFBoot) ---\n((((((A/Fujian/1/2013_H7N9:0.0000000000,A/Shanghai/02/2013_H7N9:0.0000000000):0.0000000000,A/Zhejiang/DTID-ZJU01/2013_H7N9:0.0000000000):0.0000010000,(A/Nanchang/1/2013_H7N9:0.0000010000,((A/Taiwan/S02076/2013_H7N9:0.0000010000,A/Taiwan/T02081/2013_H7N9:0.0000010000)0/68:0.0000010000,(A/Wuxi/1/2013_H7N9:0.0017760706,A/Wuxi/2/2013_H7N9:0.0035563636)0/60:0.0000010000)0/38:0.0000010000)0/14:0.0000010000)0/38:0.0000010000,(A/Hangzhou/1/2013_H7N9:0.0017753038,A/Shanghai/4664T/2013_H7N9:0.0035530088)0/50:0.0000010000)0/67:0.0000010000,A/Hangzhou/2/2013_H7N9:0.0017772400)0/62:0.0000010000,((A/Hangzhou/3/2013_H7N9:0.0000000000,A/Zhejiang/HZ1/2013_H7N9:0.0000000000):0.0000010000,A/Nanjing/1/2013_H7N9:0.0000010000)73.6/63:0.0017779963,A/wild_bird_feces/Korea/ESD07/2005_H7N7:0.0334504549);\n\n================ ml22 ================\nBest-fit model according to BIC: FLU+I\nModel                  LogL         AIC      w-AIC        AICc     w-AICc         BIC      w-BIC\nAIC, w-AIC   : Akaike information criterion scores and weights.\nAICc, w-AICc : Corrected AIC scores and weights.\nModel of substitution: FLU+I\nAkaike information criterion (AIC) score: 3728.0707\n--- 树 (带 SUPPORT: SH-aLRT/UFBoot) ---\n((((((((((A/Fujian/1/2013_H7N9:0.0000000000,A/Zhejiang/DTID-ZJU01/2013_H7N9:0.0000000000):0.0000000000,A/Shanghai/02/2013_H7N9:0.0000000000):0.0000010000,(A/Hangzhou/1/2013_H7N9:0.0017564658,A/Shanghai/4664T/2013_H7N9:0.0035272144)0/33:0.0000010000)0/9:0.0000010000,A/Nanchang/1/2013_H7N9:0.0000010000)0/9:0.0000010000,(((A/Shanghai/5240T/2013_H7N9:0.0000010000,A/Wuxi/1/2013_H7N9:0.0017596966)0/44:0.0000011199,A/Wuxi/2/2013_H7N9:0.0035462664)0/43:0.0000010000,(A/Taiwan/S02076/2013_H7N9:0.0000010000,A/Taiwan/T02081/2013_H7N9:0.0000010000)0/59:0.0000010000)0/12:0.0000010000)0/41:0.0000010000,((A/Hangzhou/2/2013_H7N9:0.0017649958,A/Shanghai/5083T/2013_H7N9:0.0000010000)0/36:0.0000014117,A/Shanghai/5180T/2013_H7N9:0.0000010000)0/25:0.0000010610)0/20:0.0000010000,((((A/Hangzhou/3/2013_H7N9:0.0000000000,A/Zhejiang/HZ1/2013_H7N9:0.0000000000):0.0000010000,A/Nanjing/1/2013_H7N9:0.0000010000)0/69:0.0000010330,A/Shanghai/4701T/2013_H7N9:0.0000010000)0/42:0.0000010628,A/Shanghai/4842T/2013_H7N9:0.0000010000)0/20:0.0017698225)83.7/54:0.0064195673,A/Shanghai/4659T/2013_H7N9:0.0000010000)93.2/85:0.0129223659,A/Shanghai/4665T/2013_H7N9:0.0000010000)0/31:0.0000010000,A/Shanghai/4655T/2013_H7N9:0.0000010000,A/wild_bird_feces/Korea/ESD07/2005_H7N7:0.0206993355);\n\n",
  "stderr": "",
  "cwd": "/home/wangzg/Nutstore Files/Nutstore/备课/2026秋/分子进化分析/Chatbox_bioinformatics",
  "sandboxed": false
}

write_file (state: result)

Args

{
  "content": "#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\"\"\"对照: ML (IQ-TREE, FLU/LG+Γ) vs 距离法 (NJ, Poisson) —— 同一比对, 同一外群。\n输出: results/nj14.nwk, results/nj22.nwk (按外群定根) + 拓扑比较 (RF)。\n\"\"\"\nimport math, re\nfrom Bio import AlignIO, Phylo\nfrom Bio.Phylo.TreeConstruction import DistanceMatrix, DistanceTreeConstructor\nfrom skbio import DistanceMatrix as SkDM\nfrom skbio.tree import nj as sk_nj\nimport pandas as pd, io\n\nOUTGROUP = \"A/wild_bird_feces/Korea/ESD07/2005_H7N7\"\n\ndef poisson(p): return float(\"inf\") if p >= 1 else -math.log(1 - p)\n\ndef build_nj(aln_file, tag):\n    aln = AlignIO.read(aln_file, \"fasta\")\n    names = [r.id for r in aln]; seqs = [str(r.seq) for r in aln]; n = len(aln)\n    D = [[0.0]*n for _ in range(n)]\n    for i in range(n):\n        for j in range(i+1, n):\n            diff = tot = 0\n            for x, y in zip(seqs[i], seqs[j]):\n                if x == '-' or y == '-': continue\n                tot += 1\n                if x != y: diff += 1\n            d = poisson(diff/tot) if tot else 0.0\n            D[i][j] = D[j][i] = d\n    df = pd.DataFrame(D, index=names, columns=names)\n    t = sk_nj(SkDM(df.values, ids=names))\n    tr = Phylo.read(io.StringIO(str(t)), \"newick\")\n    tr.root_with_outgroup(OUTGROUP)\n    Phylo.write(tr, f\"results/nj{tag}.nwk\", \"newick\")\n    # 距离范围\n    ds = [D[i][j] for i in range(n) for j in range(i+1, n) if not math.isinf(D[i][j])]\n    print(f\"[NJ{tag}] n={n}  Poisson距离 {min(ds):.4f}-{max(ds):.4f}\")\n    return tr\n\ndef clades(t):\n    terms = {x.name for x in t.get_terminals()}\n    return {frozenset(x.name for x in c.get_terminals())\n            for c in t.get_nonterminals() if 1 < len(c.get_terminals()) < len(terms)}\n\ndef rf(a, b): return len(clades(a) ^ clades(b))\n\ndef supported_nodes(t):\n    \"\"\"统计 IQ-TREE 支持度: SH-aLRT/UFBoot\"\"\"\n    res = []\n    for c in t.get_nonterminals():\n        if c.confidence is not None and isinstance(c.confidence, str):\n            res.append(c.confidence)\n    return res\n\nfor tag in (\"14\", \"22\"):\n    nj = build_nj(f\"results/aln{tag}.fasta\", tag)\n    ml = Phylo.read(f\"results/ml{tag}.treefile\", \"newick\")\n    ml.root_with_outgroup(OUTGROUP)\n    R = rf(ml, nj)\n    print(f\"\\n== set{tag}: ML(FLU) vs NJ(Poisson)  RF={R}  (0=完全相同, 越大差异越大) ==\")\n    print(f\"   ML 共享clade: {len(clades(ml)&clades(nj))}  |  ML={len(clades(ml))}, NJ={len(clades(nj))}\")\n    sup = supported_nodes(ml)\n    strong = [s for s in sup if s.split('/')[0] and float(s.split('/')[0])>=80 and s.split('/')[1] and float(s.split('/')[1])>=95]\n    print(f\"   ML 内部节点 SH-aLRT/UFBoot: {sup}\")\n    print(f\"   ML 强支持(SH-aLRT>=80 且 UFBoot>=95)节点数: {len(strong)} / {len(sup)}\")\n",
  "file_path": "/home/wangzg/Nutstore Files/Nutstore/备课/2026秋/分子进化分析/Chatbox_bioinformatics/scripts/compare_methods.py"
}

Result

{
  "success": true,
  "file_path": "/home/wangzg/Nutstore Files/Nutstore/备课/2026秋/分子进化分析/Chatbox_bioinformatics/scripts/compare_methods.py"
}

run_command (state: result)

Args

{
  "command": "cd \"/home/wangzg/Nutstore Files/Nutstore/备课/2026秋/分子进化分析/Chatbox_bioinformatics\"\n/home/wangzg/miniconda3/envs/biosoft/bin/python scripts/compare_methods.py 2>&1"
}

Result

{
  "success": true,
  "exitCode": 0,
  "stdout": "[NJ14] n=15  Poisson距离 -0.0000-0.0366\n\n== set14: ML(FLU) vs NJ(Poisson)  RF=18  (0=完全相同, 越大差异越大) ==\n   ML 共享clade: 4  |  ML=13, NJ=13\n   ML 内部节点 SH-aLRT/UFBoot: []\n   ML 强支持(SH-aLRT>=80 且 UFBoot>=95)节点数: 0 / 0\n[NJ22] n=23  Poisson距离 -0.0000-0.0366\n\n== set22: ML(FLU) vs NJ(Poisson)  RF=28  (0=完全相同, 越大差异越大) ==\n   ML 共享clade: 7  |  ML=21, NJ=21\n   ML 内部节点 SH-aLRT/UFBoot: []\n   ML 强支持(SH-aLRT>=80 且 UFBoot>=95)节点数: 0 / 0\n",
  "stderr": "",
  "cwd": "/home/wangzg/Nutstore Files/Nutstore/备课/2026秋/分子进化分析/Chatbox_bioinformatics",
  "sandboxed": false
}

edit_file (state: result)

Args

{
  "file_path": "/home/wangzg/Nutstore/files/../Nutstore/备课/2026秋/分子进化分析/Chatbox_bioinformatics/scripts/compare_methods.py",
  "old_text": "def supported_nodes(t):\n    \"\"\"统计 IQ-TREE 支持度: SH-aLRT/UFBoot\"\"\"\n    res = []\n    for c in t.get_nonterminals():\n        if c.confidence is not None and isinstance(c.confidence, str):\n            res.append(c.confidence)\n    return res",
  "new_text": "def supported_nodes(t):\n    \"\"\"统计 IQ-TREE 支持度: SH-aLRT/UFBoot (存于 node.name, 形如 '73.6/63')\"\"\"\n    res = []\n    for c in t.get_nonterminals():\n        lab = c.name if c.name else (str(c.confidence) if c.confidence is not None else None)\n        if lab and \"/\" in lab:\n            res.append(lab)\n    return res"
}

Result

{
  "error": "ENOENT: no such file or directory, open '/home/wangzg/Nutstore/Nutstore/备课/2026秋/分子进化分析/Chatbox_bioinformatics/scripts/compare_methods.py'"
}

edit_file (state: result)

Args

{
  "file_path": "/home/wangzg/Nutstore Files/Nutstore/备课/2026秋/分子进化分析/Chatbox_bioinformatics/scripts/compare_methods.py",
  "edits": [
    {
      "old_text": "def supported_nodes(t):\n    \"\"\"统计 IQ-TREE 支持度: SH-aLRT/UFBoot\"\"\"\n    res = []\n    for c in t.get_nonterminals():\n        if c.confidence is not None and isinstance(c.confidence, str):\n            res.append(c.confidence)\n    return res",
      "new_text": "def supported_nodes(t):\n    \"\"\"统计 IQ-TREE 支持度: SH-aLRT/UFBoot (存于 node.name, 形如 '73.6/63')\"\"\"\n    res = []\n    for c in t.get_nonterminals():\n        lab = c.name if c.name else (str(c.confidence) if c.confidence is not None else None)\n        if lab and \"/\" in lab:\n            res.append(lab)\n    return res"
    },
    {
      "old_text": "    sup = supported_nodes(ml)\n    strong = [s for s in sup if s.split('/')[0] and float(s.split('/')[0])>=80 and s.split('/')[1] and float(s.split('/')[1])>=95]\n    print(f\"   ML 内部节点 SH-aLRT/UFBoot: {sup}\")\n    print(f\"   ML 强支持(SH-aLRT>=80 且 UFBoot>=95)节点数: {len(strong)} / {len(sup)}\")",
      "new_text": "    sup = supported_nodes(ml)\n    def good(s):\n        p = s.split('/')\n        return len(p) == 2 and p[0] not in ('',) and p[1] not in ('',) and float(p[0]) >= 80 and float(p[1]) >= 95\n    strong = [s for s in sup if good(s)]\n    print(f\"   ML 内部节点 SH-aLRT/UFBoot: {sup}\")\n    print(f\"   ML 强支持(SH-aLRT>=80 且 UFBoot>=95)节点数: {len(strong)} / {len(sup)}\")"
    }
  ]
}

Result

{
  "success": true,
  "file_path": "/home/wangzg/Nutstore Files/Nutstore/备课/2026秋/分子进化分析/Chatbox_bioinformatics/scripts/compare_methods.py",
  "edits": 2
}

run_command (state: result)

Args

{
  "command": "cd \"/home/wangzg/Nutstore Files/Nutstore/备课/2026秋/分子进化分析/Chatbox_bioinformatics\"\n/home/wangzg/miniconda3/envs/biosoft/bin/python scripts/compare_methods.py 2>&1"
}

Result

{
  "success": true,
  "exitCode": 0,
  "stdout": "[NJ14] n=15  Poisson距离 -0.0000-0.0366\n\n== set14: ML(FLU) vs NJ(Poisson)  RF=18  (0=完全相同, 越大差异越大) ==\n   ML 共享clade: 4  |  ML=13, NJ=13\n   ML 内部节点 SH-aLRT/UFBoot: ['0/62', '0/67', '0/38', '0/14', '0/38', '0/68', '0/60', '0/50', '73.6/63']\n   ML 强支持(SH-aLRT>=80 且 UFBoot>=95)节点数: 0 / 9\n[NJ22] n=23  Poisson距离 -0.0000-0.0366\n\n== set22: ML(FLU) vs NJ(Poisson)  RF=28  (0=完全相同, 越大差异越大) ==\n   ML 共享clade: 7  |  ML=21, NJ=21\n   ML 内部节点 SH-aLRT/UFBoot: ['0/31', '93.2/85', '83.7/54', '0/20', '0/41', '0/9', '0/9', '0/33', '0/12', '0/43', '0/44', '0/59', '0/25', '0/36', '0/20', '0/42', '0/69']\n   ML 强支持(SH-aLRT>=80 且 UFBoot>=95)节点数: 0 / 17\n",
  "stderr": "",
  "cwd": "/home/wangzg/Nutstore Files/Nutstore/备课/2026秋/分子进化分析/Chatbox_bioinformatics",
  "sandboxed": false
}

write_file (state: result)

Args

{
  "content": "#!/usr/bin/env Rscript\n# 可视化由外群定根的 ML 树 (IQ-TREE FLU+Γ), 标注 SH-aLRT/UFBoot 支持度。\nsuppressMessages({library(ggtree); library(treeio); library(ggplot2); library(ape)})\n\nfor (tag in c(\"14\", \"22\")) {\n  tr <- read.tree(sprintf(\"results/ml%s.treefile\", tag))\n  # 外群高亮\n  tr$tip.label[grepl(\"Korea\", tr$tip.label)] -> og\n  p <- ggtree(tr, size = 0.6) +\n    geom_tiplab(size = 2.8, color = \"black\") +\n    geom_nodelab(aes(label = label), size = 2.4, color = \"blue\", hjust = 1.2, vjust = -0.4) +\n    geom_treescale(width = 0.005, fontsize = 2.8, offset = 0.2) +\n    geom_tippoint(aes(subset = grepl(\"Korea\", label)), color = \"red\", size = 2) +\n    ggtitle(sprintf(\"H7N9 HA — ML tree (IQ-TREE, set%s, model FLU%s)\",\n                    tag, ifelse(tag == \"22\", \"+I\", \"\")),\n            subtitle = \"rooted on outgroup A/wild bird feces/Korea/ESD07/2005(H7N7) (red); blue = SH-aLRT/UFBoot\") +\n    theme(plot.title = element_text(size = 11), plot.subtitle = element_text(size = 8))\n  h <- max(5, length(tr$tip.label) * 0.32)\n  ggsave(sprintf(\"results/ml%s_ggtree.pdf\", tag), p, width = 9, height = h)\n  ggsave(sprintf(\"results/ml%s_ggtree.png\", tag), p, width = 9, height = h, dpi = 300)\n  cat(sprintf(\"写出 results/ml%s_ggtree.pdf/.png  (%d 株)\\n\", tag, length(tr$tip.label)))\n}\n",
  "file_path": "/home/wangzg/Nutstore Files/Nutstore/备课/2026秋/分子进化分析/Chatbox_bioinformatics/scripts/visualize_ml.R"
}

Result

{
  "success": true,
  "file_path": "/home/wangzg/Nutstore Files/Nutstore/备课/2026秋/分子进化分析/Chatbox_bioinformatics/scripts/visualize_ml.R"
}

run_command (state: result)

Args

{
  "command": "cd \"/home/wangzg/Nutstore Files/Nutstore/备课/2026秋/分子进化分析/Chatbox_bioinformatics\"\n/home/wangzg/miniconda3/envs/biosoft/bin/Rscript scripts/visualize_ml.R 2>&1 | tail -6\necho \"== 产物 ==\"; ls -lh results/ml*_ggtree.* results/ml*.treefile results/nj*.nwk 2>/dev/null | awk '{print $5\"\\t\"$9}'"
}

Result

{
  "success": true,
  "exitCode": 0,
  "stdout": "ℹ The deprecated feature was likely used in the ggtree package.\n  Please report the issue at <https://github.com/YuLab-SMU/ggtree/issues>. \n2: In grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y,  :\n  for 'H7N9 HA — ML tree (IQ-TREE, set14, model FLU)' in 'mbcsToSbcs': - substituted for — (U+2014)\n3: In grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y,  :\n  for 'H7N9 HA — ML tree (IQ-TREE, set22, model FLU+I)' in 'mbcsToSbcs': - substituted for — (U+2014)\n== 产物 ==\n5.3K\tresults/ml14_ggtree.pdf\n175K\tresults/ml14_ggtree.png\n790\tresults/ml14.treefile\n5.7K\tresults/ml22_ggtree.pdf\n252K\tresults/ml22_ggtree.png\n1.3K\tresults/ml22.treefile\n681\tresults/nj14.nwk\n1.1K\tresults/nj22.nwk\n",
  "stderr": "",
  "cwd": "/home/wangzg/Nutstore Files/Nutstore/备课/2026秋/分子进化分析/Chatbox_bioinformatics",
  "sandboxed": false
}

edit_file (state: result)

Args

{
  "file_path": "/home/wangzg/Nutstore Files/Nutstore/备课/2026秋/分子进化分析/Chatbox_bioinformatics/scripts/visualize_ml.R",
  "old_text": "    ggtitle(sprintf(\"H7N9 HA — ML tree (IQ-TREE, set%s, model FLU%s)\",",
  "new_text": "    ggtitle(sprintf(\"H7N9 HA - ML tree (IQ-TREE, set%s, model FLU%s)\","
}

Result

{
  "success": true,
  "file_path": "/home/wangzg/Nutstore Files/Nutstore/备课/2026秋/分子进化分析/Chatbox_bioinformatics/scripts/visualize_ml.R",
  "edits": 1
}

run_command (state: result)

Args

{
  "command": "cd \"/home/wangzg/Nutstore Files/Nutstore/备课/2026秋/分子进化分析/Chatbox_bioinformatics\"\n/home/wangzg/miniconda3/envs/biosoft/bin/Rscript scripts/visualize_ml.R 2>&1 | grep -E \"^写出\"\necho \"== 全部产物 ==\"\nls -lh results/ | awk 'NR>1{print $5\"\\t\"$9}'"
}

Result

{
  "success": true,
  "exitCode": 0,
  "stdout": "写出 results/ml14_ggtree.pdf/.png  (15 株)\n写出 results/ml22_ggtree.pdf/.png  (23 株)\n== 全部产物 ==\n8.8K\taln14.fasta\n14K\taln22.fasta\n15K\tH7N9_HA.aln.fasta\n3.8K\tmafft.log\n588\tml14.bionj\n49K\tml14.ckp.gz\n769\tml14.contree\n5.3K\tml14_ggtree.pdf\n175K\tml14_ggtree.png\n29K\tml14.iqtree\n43K\tml14.log\n1.9K\tml14.mldist\n5.6K\tml14.model.gz\n586\tml14.parstree\n3.4K\tml14.splits.nex\n790\tml14.treefile\n7.1K\tml14.uniqueseq.phy\n1020\tml22.bionj\n132K\tml22.ckp.gz\n1.2K\tml22.contree\n5.7K\tml22_ggtree.pdf\n252K\tml22_ggtree.png\n35K\tml22.iqtree\n19K\tml22.log\n4.8K\tml22.mldist\n5.9K\tml22.model.gz\n1.1K\tml22.parstree\n16K\tml22.splits.nex\n1.3K\tml22.treefile\n12K\tml22.uniqueseq.phy\n681\tnj14.nwk\n1.1K\tnj22.nwk\n5.0K\ttree_ggtree_rect.pdf\n63K\ttree_ggtree_rect.png\n4.7K\ttree_ggtree_unrooted.pdf\n23K\ttree_ggtree_unrooted.png\n20K\ttree_nj_phylogram.pdf\n218K\ttree_nj_phylogram.png\n546\ttree_nj_py.nwk\n582\ttree_nj_skbio.nwk\n20K\ttree_upgma_phylogram.pdf\n205K\ttree_upgma_phylogram.png\n496\ttree_upgma_py.nwk\n",
  "stderr": "",
  "cwd": "/home/wangzg/Nutstore Files/Nutstore/备课/2026秋/分子进化分析/Chatbox_bioinformatics",
  "sandboxed": false
}

delete_memory (state: result)

Args

{
  "id": "mmu28efm6whqd"
}

Result

{
  "message": "Memory mmu28efm6whqd deleted."
}

save_memory (state: result)

Args

{
  "content": "分子进化分析课程环境统一用 biosoft conda 环境（/home/wangzg/miniconda3/envs/biosoft）：含 MAFFT 7.526、IQ-TREE 3.1.3（iqtree/iqtree3）；Python 有 biopython 1.88、scikit-bio 0.7.3、matplotlib 3.10.9；R 有 r-base、ggtree 4.0.5、treeio 1.34.0、ape 5.8.1、ggplot2 4.0.3。用户偏好所需工具都装进 biosoft，不分散到 r-4.6。分析产物统一放 results/，脚本放 scripts/，原始数据放 dataset/。"
}

Result

{
  "message": "Memory saved with id mmu28mlroudqb. It will be loaded in future agent sessions."
}

write_file (state: result)

Args

{
  "content": "#!/usr/bin/env Rscript\n# ml22 无根布局图 (ggtree) —— 不承诺任何根\nsuppressMessages({library(ggtree); library(ggtree); library(ggplot2); library(ape)})\ntr_rooted <- read.tree(\"results/ml22.treefile\")\ntr <- unroot(tr_rooted)                      # 先解除定根, 再画无根布局\n\np <- ggtree(tr, layout = \"unrooted\", size = 0.6) +\n  geom_tiplab(size = 2.6) +\n  geom_nodelab(aes(label = label), size = 2.2, color = \"blue\", hjust = 1.1, vjust = -0.4) +\n  geom_tippoint(aes(subset = grepl(\"Korea\", label)), color = \"red\", size = 2.2) +\n  geom_treescale(width = 0.01, fontsize = 2.6, offset = 0.15) +\n  ggtitle(\"H7N9 HA - ML tree (IQ-TREE, set22, FLU+I), UNROOTED layout\",\n          subtitle = \"no root implied; red = the avian H7N7 (shown only as a tip, not a root)\") +\n  theme(plot.title = element_text(size = 11), plot.subtitle = element_text(size = 8))\nggsave(\"results/ml22_unrooted.pdf\", p, width = 9, height = 9)\nggsave(\"results/ml22_unrooted.png\", p, width = 9, height = 9, dpi = 300)\ncat(\"写出 results/ml22_unrooted.pdf/.png\\n\")\n",
  "file_path": "/home/wangzg/Nutstore Files/Nutstore/备课/2026秋/分子进化分析/Chatbox_bioinformatics/scripts/visualize_unrooted.R"
}

Result

{
  "success": true,
  "file_path": "/home/wangzg/Nutstore Files/Nutstore/备课/2026秋/分子进化分析/Chatbox_bioinformatics/scripts/visualize_unrooted.R"
}

write_file (state: result)

Args

{
  "content": "#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\"\"\"对距离树(NJ, Poisson)做 bootstrap 实测, 看它到底给出什么支持度。\nB=500 列重采样; 汇报观测树各 clade 的支持频率。\n\"\"\"\nimport io, math\nimport numpy as np, pandas as pd\nfrom Bio import AlignIO, Phylo\nfrom skbio import DistanceMatrix as SkDM\nfrom skbio.tree import nj as sk_nj\n\nOUT = \"A/wild_bird_feces/Korea/ESD07/2005_H7N7\"\nrng = np.random.default_rng(1)\naln = AlignIO.read(\"results/aln22.fasta\", \"fasta\")\nnames = [r.id for r in aln]\narr = np.array([list(str(r.seq)) for r in aln])     # (n, L)\nn, L = arr.shape\ngap = (arr == \"-\")\nvalid = ~gap[:, None, :] & ~gap[None, :, :]          # (n,n,L)\ndiff  = (arr[:, None, :] != arr[None, :, :]) & valid\n\ndef poisson_matrix(d, v):\n    p = np.divide(d, v, out=np.zeros_like(d, dtype=float), where=v > 0)\n    p = np.clip(p, 0, 0.999999)\n    M = -np.log(1 - p)\n    M = (M + M.T) / 2\n    np.fill_diagonal(M, 0)\n    return M\n\ndef tree_from_M(M):\n    df = pd.DataFrame(M, index=names, columns=names)\n    t = sk_nj(SkDM(df.values, ids=names))\n    tr = Phylo.read(io.StringIO(str(t)), \"newick\")\n    tr.root_with_outgroup(OUT)\n    return tr\n\ndef clades(t):\n    terms = {x.name for x in t.get_terminals()}\n    return {frozenset(x.name for x in c.get_terminals())\n            for c in t.get_nonterminals() if 1 < len(c.get_terminals()) < len(terms)}\n\n# 观测树\nM0 = poisson_matrix(diff.sum(axis=2).astype(float), valid.sum(axis=2).astype(float))\nobs = tree_from_M(M0)\nobs_cl = clades(obs)\n\n# bootstrap\nB = 500\ncounts = {c: 0 for c in obs_cl}\nfor _ in range(B):\n    idx = rng.integers(0, L, L)\n    d = diff[:, :, idx].sum(axis=2).astype(float)\n    v = valid[:, :, idx].sum(axis=2).astype(float)\n    t = tree_from_M(poisson_matrix(d, v))\n    cl = clades(t)\n    for c in obs_cl:\n        if c in cl:\n            counts[c] += 1\n\nsv = sorted(counts.values(), reverse=True)\npct = [100 * x / B for x in sv]\nprint(f\"距离法 (NJ, Poisson) bootstrap 支持度, 观测树共 {len(obs_cl)} 个内部 clade, B={B}\")\nprint(\"  支持度(%):\", [f\"{x:.0f}\" for x in pct])\nprint(f\"  >=95%: {sum(1 for x in pct if x>=95)} 个 | 70-95%: {sum(1 for x in pct if 70<=x<95)} 个 | <70%: {sum(1 for x in pct if x<70)} 个\")\n",
  "file_path": "/home/wangzg/Nutstore Files/Nutstore/备课/2026秋/分子进化分析/Chatbox_bioinformatics/scripts/bootstrap_distance.py"
}

Result

{
  "success": true,
  "file_path": "/home/wangzg/Nutstore Files/Nutstore/备课/2026秋/分子进化分析/Chatbox_bioinformatics/scripts/bootstrap_distance.py"
}

run_command (state: result)

Args

{
  "command": "cd \"/home/wangzg/Nutstore Files/Nutstore/备课/2026秋/分子进化分析/Chatbox_bioinformatics\"\n/home/wangzg/miniconda3/envs/biosoft/bin/Rscript scripts/visualize_unrooted.R 2>&1 | grep -E \"写出|Error|error\" | head -3\nls -lh results/ml22_unrooted.* 2>/dev/null | awk '{print $5\"\\t\"$9}'",
  "timeout": 90000
}

Result

{
  "success": true,
  "exitCode": 0,
  "stdout": "写出 results/ml22_unrooted.pdf/.png\n5.6K\tresults/ml22_unrooted.pdf\n166K\tresults/ml22_unrooted.png\n",
  "stderr": "",
  "cwd": "/home/wangzg/Nutstore Files/Nutstore/备课/2026秋/分子进化分析/Chatbox_bioinformatics",
  "sandboxed": false
}

run_command (state: result)

Args

{
  "command": "cd \"/home/wangzg/Nutstore Files/Nutstore/备课/2026秋/分子进化分析/Chatbox_bioinformatics\"\ntimeout 110 /home/wangzg/miniconda3/envs/biosoft/bin/python scripts/bootstrap_distance.py 2>&1 | tail -8",
  "timeout": 115000
}

Result

{
  "success": true,
  "exitCode": 0,
  "stdout": "距离法 (NJ, Poisson) bootstrap 支持度, 观测树共 21 个内部 clade, B=500\n  支持度(%): ['100', '99', '85', '64', '63', '63', '52', '50', '44', '29', '27', '23', '18', '14', '12', '11', '8', '6', '2', '1', '1']\n  >=95%: 2 个 | 70-95%: 1 个 | <70%: 18 个\n",
  "stderr": "",
  "cwd": "/home/wangzg/Nutstore Files/Nutstore/备课/2026秋/分子进化分析/Chatbox_bioinformatics",
  "sandboxed": false
}

edit_file (state: result)

Args

{
  "file_path": "/home/wangzg/Nutstore Files/Nutstore/备课/2026秋/分子进化分析/Chatbox_bioinformatics/scripts/bootstrap_distance.py",
  "new_text": "sv = sorted(counts.values(), reverse=True)\npct = [100 * x / B for x in sv]\nprint(f\"距离法 (NJ, Poisson) bootstrap 支持度, 观测树共 {len(obs_cl)} 个内部 clade, B={B}\")\nprint(\"  支持度(%):\", [f\"{x:.0f}\" for x in pct])\nprint(f\"  >=95%: {sum(1 for x in pct if x>=95)} 个 | 70-95%: {sum(1 for x in pct if 70<=x<95)} 个 | <70%: {sum(1 for x in pct if x<70)} 个\")\nprint(\"\\n  各 clade 支持度 (高->低):\")\nfor c, v in sorted(counts.items(), key=lambda kv: -kv[1]):\n    lbl = \", \".join(sorted(x.replace(\"_H7N9\", \"\").replace(\"A/\", \"\") for x in c))\n    print(f\"   {100*v/B:5.0f}%  {{{lbl}}}\")",
  "old_text": "sv = sorted(counts.values(), reverse=True)\npct = [100 * x / B for x in sv]\nprint(f\"距离法 (NJ, Poisson) bootstrap 支持度, 观测树共 {len(obs_cl)} 个内部 clade, B={B}\")\nprint(\"  支持度(%):\", [f\"{x:.0f}\" for x in pct])\nprint(f\"  >=95%: {sum(1 for x in pct if x>=95)} 个 | 70-95%: {sum(1 for x in pct if 70<=x<95)} 个 | <70%: {sum(1 for x in pct if x<70)} 个\")"
}

Result

{
  "success": true,
  "file_path": "/home/wangzg/Nutstore Files/Nutstore/备课/2026秋/分子进化分析/Chatbox_bioinformatics/scripts/bootstrap_distance.py",
  "edits": 1
}

run_command (state: result)

Args

{
  "command": "cd \"/home/wangzg/Nutstore Files/Nutstore/备课/2026秋/分子进化分析/Chatbox_bioinformatics\"\ntimeout 115 /home/wangzg/miniconda3/envs/biosoft/bin/python scripts/bootstrap_distance.py 2>&1 | tail -28",
  "timeout": 120000
}

Result

{
  "success": true,
  "exitCode": 0,
  "stdout": "距离法 (NJ, Poisson) bootstrap 支持度, 观测树共 21 个内部 clade, B=500\n  支持度(%): ['100', '99', '85', '64', '63', '63', '52', '50', '44', '29', '27', '23', '18', '14', '12', '11', '8', '6', '2', '1', '1']\n  >=95%: 2 个 | 70-95%: 1 个 | <70%: 18 个\n\n  各 clade 支持度 (高->低):\n     100%  {Fujian/1/2013, Hangzhou/1/2013, Hangzhou/2/2013, Hangzhou/3/2013, Nanchang/1/2013, Nanjing/1/2013, Shanghai/02/2013, Shanghai/4655T/2013, Shanghai/4659T/2013, Shanghai/4664T/2013, Shanghai/4665T/2013, Shanghai/4701T/2013, Shanghai/4842T/2013, Shanghai/5083T/2013, Shanghai/5180T/2013, Shanghai/5240T/2013, Taiwan/S02076/2013, Taiwan/T02081/2013, Wuxi/1/2013, Wuxi/2/2013, Zhejiang/DTID-ZJU01/2013, Zhejiang/HZ1/2013}\n      99%  {Taiwan/S02076/2013, Taiwan/T02081/2013}\n      85%  {Fujian/1/2013, Hangzhou/1/2013, Hangzhou/2/2013, Hangzhou/3/2013, Nanchang/1/2013, Nanjing/1/2013, Shanghai/02/2013, Shanghai/4659T/2013, Shanghai/4664T/2013, Shanghai/4701T/2013, Shanghai/4842T/2013, Shanghai/5083T/2013, Shanghai/5180T/2013, Shanghai/5240T/2013, Taiwan/S02076/2013, Taiwan/T02081/2013, Wuxi/1/2013, Wuxi/2/2013, Zhejiang/DTID-ZJU01/2013, Zhejiang/HZ1/2013}\n      64%  {Fujian/1/2013, Hangzhou/1/2013, Hangzhou/2/2013, Hangzhou/3/2013, Nanchang/1/2013, Nanjing/1/2013, Shanghai/02/2013, Shanghai/4664T/2013, Shanghai/4701T/2013, Shanghai/4842T/2013, Shanghai/5083T/2013, Shanghai/5180T/2013, Shanghai/5240T/2013, Taiwan/S02076/2013, Taiwan/T02081/2013, Wuxi/1/2013, Wuxi/2/2013, Zhejiang/DTID-ZJU01/2013, Zhejiang/HZ1/2013}\n      63%  {Hangzhou/3/2013, Nanjing/1/2013, Zhejiang/HZ1/2013}\n      63%  {Hangzhou/3/2013, Nanjing/1/2013}\n      52%  {Fujian/1/2013, Hangzhou/1/2013, Hangzhou/2/2013, Hangzhou/3/2013, Nanchang/1/2013, Nanjing/1/2013, Shanghai/02/2013, Shanghai/4664T/2013, Shanghai/4701T/2013, Shanghai/4842T/2013, Shanghai/5180T/2013, Shanghai/5240T/2013, Taiwan/S02076/2013, Taiwan/T02081/2013, Wuxi/1/2013, Wuxi/2/2013, Zhejiang/DTID-ZJU01/2013, Zhejiang/HZ1/2013}\n      50%  {Nanchang/1/2013, Taiwan/S02076/2013, Taiwan/T02081/2013}\n      44%  {Fujian/1/2013, Hangzhou/1/2013, Hangzhou/2/2013, Hangzhou/3/2013, Nanchang/1/2013, Nanjing/1/2013, Shanghai/02/2013, Shanghai/4659T/2013, Shanghai/4664T/2013, Shanghai/4665T/2013, Shanghai/4701T/2013, Shanghai/4842T/2013, Shanghai/5083T/2013, Shanghai/5180T/2013, Shanghai/5240T/2013, Taiwan/S02076/2013, Taiwan/T02081/2013, Wuxi/1/2013, Wuxi/2/2013, Zhejiang/DTID-ZJU01/2013, Zhejiang/HZ1/2013}\n      29%  {Nanchang/1/2013, Taiwan/S02076/2013, Taiwan/T02081/2013, Zhejiang/DTID-ZJU01/2013}\n      27%  {Nanchang/1/2013, Shanghai/02/2013, Taiwan/S02076/2013, Taiwan/T02081/2013, Zhejiang/DTID-ZJU01/2013}\n      23%  {Shanghai/4664T/2013, Shanghai/4842T/2013}\n      18%  {Shanghai/5180T/2013, Wuxi/2/2013}\n      14%  {Fujian/1/2013, Nanchang/1/2013, Shanghai/02/2013, Taiwan/S02076/2013, Taiwan/T02081/2013, Zhejiang/DTID-ZJU01/2013}\n      12%  {Hangzhou/2/2013, Shanghai/4701T/2013}\n      11%  {Shanghai/5240T/2013, Wuxi/1/2013}\n       8%  {Fujian/1/2013, Hangzhou/1/2013, Nanchang/1/2013, Shanghai/02/2013, Taiwan/S02076/2013, Taiwan/T02081/2013, Zhejiang/DTID-ZJU01/2013}\n       6%  {Fujian/1/2013, Hangzhou/1/2013, Hangzhou/2/2013, Hangzhou/3/2013, Nanchang/1/2013, Nanjing/1/2013, Shanghai/02/2013, Shanghai/4664T/2013, Shanghai/4701T/2013, Shanghai/4842T/2013, Shanghai/5240T/2013, Taiwan/S02076/2013, Taiwan/T02081/2013, Wuxi/1/2013, Zhejiang/DTID-ZJU01/2013, Zhejiang/HZ1/2013}\n       2%  {Fujian/1/2013, Hangzhou/1/2013, Hangzhou/2/2013, Hangzhou/3/2013, Nanchang/1/2013, Nanjing/1/2013, Shanghai/02/2013, Shanghai/4701T/2013, Shanghai/5240T/2013, Taiwan/S02076/2013, Taiwan/T02081/2013, Wuxi/1/2013, Zhejiang/DTID-ZJU01/2013, Zhejiang/HZ1/2013}\n       1%  {Fujian/1/2013, Hangzhou/1/2013, Nanchang/1/2013, Shanghai/02/2013, Shanghai/5240T/2013, Taiwan/S02076/2013, Taiwan/T02081/2013, Wuxi/1/2013, Zhejiang/DTID-ZJU01/2013}\n       1%  {Fujian/1/2013, Hangzhou/1/2013, Hangzhou/3/2013, Nanchang/1/2013, Nanjing/1/2013, Shanghai/02/2013, Shanghai/5240T/2013, Taiwan/S02076/2013, Taiwan/T02081/2013, Wuxi/1/2013, Zhejiang/DTID-ZJU01/2013, Zhejiang/HZ1/2013}\n",
  "stderr": "",
  "cwd": "/home/wangzg/Nutstore Files/Nutstore/备课/2026秋/分子进化分析/Chatbox_bioinformatics",
  "sandboxed": false
}

write_file (state: result)

Args

{
  "content": "#!/usr/bin/env Rscript\n# ml22 无根 + 无枝长 (cladogram) —— 只展示嵌套关系, 不主张任何长度\nsuppressMessages({library(ggtree); library(ggplot2); library(ape)})\ntr <- unroot(read.tree(\"results/ml22.treefile\"))\n\np <- ggtree(tr, layout = \"unrooted\", branch.length = \"none\", size = 0.6) +\n  geom_tiplab(size = 2.6) +\n  geom_nodelab(aes(label = label), size = 2.2, color = \"blue\", hjust = 1.1, vjust = -0.4) +\n  geom_tippoint(aes(subset = grepl(\"Korea\", label)), color = \"red\", size = 2.2) +\n  ggtitle(\"H7N9 HA - ML tree (IQ-TREE, set22, FLU+I), UNROOTED cladogram\",\n          subtitle = \"cladogram: branch lengths NOT shown (only nesting); blue = SH-aLRT/UFBoot\") +\n  theme(plot.title = element_text(size = 11), plot.subtitle = element_text(size = 8))\nggsave(\"results/ml22_unrooted_cladogram.pdf\", p, width = 9, height = 9)\nggsave(\"results/ml22_unrooted_cladogram.png\", p, width = 9, height = 9, dpi = 300)\ncat(\"写出 results/ml22_unrooted_cladogram.pdf/.png\\n\")\n",
  "file_path": "/home/wangzg/Nutstore Files/Nutstore/备课/2026秋/分子进化分析/Chatbox_bioinformatics/scripts/visualize_unrooted_clado.R"
}

Result

{
  "success": true,
  "file_path": "/home/wangzg/Nutstore Files/Nutstore/备课/2026秋/分子进化分析/Chatbox_bioinformatics/scripts/visualize_unrooted_clado.R"
}

run_command (state: result)

Args

{
  "command": "cd \"/home/wangzg/Nutstore Files/Nutstore/备课/2026秋/分子进化分析/Chatbox_bioinformatics\"\n/home/wangzg/miniconda3/envs/biosoft/bin/Rscript scripts/visualize_unrooted_clado.R 2>&1 | grep -E \"写出|Error|错误\" | head -3\nls -lh results/ml22_unrooted_cladogram.* 2>/dev/null | awk '{print $5\"\\t\"$9}'"
}

Result

{
  "success": true,
  "exitCode": 0,
  "stdout": "写出 results/ml22_unrooted_cladogram.pdf/.png\n5.9K\tresults/ml22_unrooted_cladogram.pdf\n457K\tresults/ml22_unrooted_cladogram.png\n",
  "stderr": "",
  "cwd": "/home/wangzg/Nutstore Files/Nutstore/备课/2026秋/分子进化分析/Chatbox_bioinformatics",
  "sandboxed": false
}

run_command (state: result)

Args

{
  "command": "E=\"https://eutils.ncbi.nlm.nih.gov/entrez/eutils\"\nq() { curl -s --max-time 25 \"$E/esearch.fcgi?db=protein&term=$1&retmax=$2&retmode=json\" | python3 -c \"import sys,json;print(','.join(json.load(sys.stdin)['esearchresult']['idlist']))\"; }\necho \"== H3N2 人 HA 蛋白 ==\"; IDS=$(q \"hemagglutinin%5BProtein+Name%5D+AND+influenza+A+virus%5BOrganism%5D+AND+H3N2%5BAll+Fields%5D+AND+human%5BAll+Fields%5D\" 6); echo \"$IDS\"\ncurl -s --max-time 25 \"$E/efetch.fcgi?db=protein&id=$IDS&rettype=fasta&retmode=text\" | awk '/^>/{if(s)print \"   len=\"ln\"  \"hdr; hdr=$0; s=0; ln=0; next}{s=1; ln+=length($0)}END{if(s)print \"   len=\"ln\"  \"hdr}'"
}

Result

{
  "success": true,
  "exitCode": 0,
  "stdout": "== H3N2 人 HA 蛋白 ==\n3135932044,3384981212,3384981210,3384981208,3384981206,3384981204\n   len=566  >BHM23335.1 hemagglutinin [Influenza A virus]\n   len=566  >YGN38713.1 MAG: hemagglutinin, partial [Influenza A virus]\n   len=524  >YGN38712.1 MAG: hemagglutinin, partial [Influenza A virus]\n   len=538  >YGN38711.1 MAG: hemagglutinin, partial [Influenza A virus]\n   len=543  >YGN38710.1 MAG: hemagglutinin, partial [Influenza A virus]\n   len=566  >YGN38709.1 MAG: hemagglutinin [Influenza A virus]\n",
  "stderr": "",
  "cwd": "/home/wangzg/Nutstore Files/Nutstore/备课/2026秋/分子进化分析/Chatbox_bioinformatics",
  "sandboxed": false
}

run_command (state: result)

Args

{
  "command": "E=\"https://eutils.ncbi.nlm.nih.gov/entrez/eutils\"\nIDS=$(curl -s --max-time 25 \"$E/esearch.fcgi?db=nuccore&term=influenza+A+virus%5BOrganism%5D+AND+H3N2%5BAll+Fields%5D+AND+hemagglutinin%5BTitle%5D+AND+complete+cds%5BTitle%5D+AND+1600%3A1800%5BSLEN%5D&retmax=6&retmode=json\" | python3 -c \"import sys,json;print(','.join(json.load(sys.stdin)['esearchresult']['idlist']))\")\necho \"IDs: $IDS\"\ncurl -s --max-time 25 \"$E/efetch.fcgi?db=nuccore&id=$IDS&rettype=fasta&retmode=text\" | grep \"^>\" | cut -c1-110\necho \"---- 更多关于 H3 的检索 ----\"\ncurl -s --max-time 25 \"$E/esearch.fcgi?db=nuccore&term=influenza+A+virus%5BOrganism%5D+AND+hemagglutinin%5BTitle%5D&retmax=3&retmode=json\" | python3 -c \"import sys,json;d=json.load(sys.stdin)['esearchresult'];print('总记录数:',d['count'])\""
}

Result

{
  "success": true,
  "exitCode": 0,
  "stdout": "IDs: 3391353766,3391353749,3391353729,3391353709,3391353686,3391353666\n>PZ996496.1 Influenza A virus (A/Minnesota/ISC-1070/2026(H3N2)) segment 4 hemagglutinin (HA) gene, complete cd\n>PZ996489.1 Influenza A virus (A/Minnesota/ISC-1069/2026(H3N2)) segment 4 hemagglutinin (HA) gene, complete cd\n>PZ996481.1 Influenza A virus (A/Minnesota/ISC-1066/2026(H3N2)) segment 4 hemagglutinin (HA) gene, complete cd\n>PZ996473.1 Influenza A virus (A/Minnesota/ISC-1064/2026(H3N2)) segment 4 hemagglutinin (HA) gene, complete cd\n>PZ996464.1 Influenza A virus (A/Minnesota/ISC-1063/2026(H3N2)) segment 4 hemagglutinin (HA) gene, complete cd\n>PZ996456.1 Influenza A virus (A/Minnesota/ISC-1062/2026(H3N2)) segment 4 hemagglutinin (HA) gene, complete cd\n---- 更多关于 H3 的检索 ----\n总记录数: 259701\n",
  "stderr": "",
  "cwd": "/home/wangzg/Nutstore Files/Nutstore/备课/2026秋/分子进化分析/Chatbox_bioinformatics",
  "sandboxed": false
}

write_file (state: result)

Args

{
  "content": "#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\"\"\"构建更适合研究\"流感病毒分子进化\"的数据集: \n跨 HA 亚型 (group1: H1,H2,H5,H9 ; group2: H3,H4,H7,H10) x 多宿主,\nCDS 直接翻译为 HA 蛋白; 流感 B HA 作外群 (属间分歧 -> 良好外群).\n\"\"\"\nimport re, time, urllib.parse, urllib.request\nfrom Bio import SeqIO\nfrom Bio.Seq import Seq\nimport io\n\nE = \"https://eutils.ncbi.nlm.nih.gov/entrez/eutils\"\nSUBTYPES = [\"H1N1\", \"H2N2\", \"H5N1\", \"H9N2\", \"H3N2\", \"H4N6\", \"H7N9\", \"H10N8\"]\n\ndef http(url):\n    return urllib.request.urlopen(url, timeout=40).read().decode(\"utf-8\", \"replace\")\n\ndef search(term, n):\n    u = f\"{E}/esearch.fcgi?db=nuccore&term={urllib.parse.quote(term)}&retmax={n}&retmode=json\"\n    import json\n    return json.loads(http(u))[\"esearchresult\"][\"idlist\"]\n\ndef fetch(ids):\n    if not ids: return []\n    u = f\"{E}/efetch.fcgi?db=nuccore&id={','.join(ids)}&rettype=fasta&retmode=text\"\n    return list(SeqIO.parse(io.StringIO(http(u)), \"fasta\"))\n\ndef strain(desc):\n    m = re.search(r'\\(A/([^()]+?)(?:\\(H\\d+N\\d+\\))?\\)', desc)\n    if m: return \"A/\" + m.group(1)\n    m = re.search(r'\\(B/([^()]+?)\\)', desc)\n    if m: return \"B/\" + m.group(1)\n    return desc.split()[0][:30]\n\ndef to_prot(rec):\n    try:\n        p = str(Seq(str(rec.seq)).translate(table=1, cds=True))\n        return p\n    except Exception:\n        return None\n\nrecs = []\nfor sub in SUBTYPES:\n    term = (f'influenza A virus[Organism] AND {sub}[All Fields] AND '\n            f'hemagglutinin[Title] AND \"complete cds\"[Title] AND 1600:1800[SLEN]')\n    ids = search(term, 14)\n    got = 0\n    for r in fetch(ids):\n        p = to_prot(r)\n        if p and p.startswith(\"M\") and p.count(\"*\") == 0 and 540 <= len(p) <= 580:\n            nm = strain(r.description)\n            recs.append((sub, nm, p)); got += 1\n            if got >= 4: break\n    print(f\"  {sub}: 取到 {got} 条\")\n    time.sleep(0.4)\n\n# 外群: 流感 B HA\nbids = search('influenza B virus[Organism] AND hemagglutinin[Title] AND \"complete cds\"[Title] AND 1600:1800[SLEN]', 6)\nog = []\nfor r in fetch(bids):\n    p = to_prot(r)\n    if p and p.startswith(\"M\") and p.count(\"*\") == 0 and 540 <= len(p) <= 590:\n        og.append((\"FluB\", strain(r.description), p)); \n    if len(og) >= 2: break\nprint(f\"  流感B外群: {len(og)} 条\")\n\nwith open(\"data/flu_HA_multisubtype.fa\", \"w\") as f:\n    for sub, nm, p in recs:\n        f.write(f\">{nm}_{sub}\\n{p}\\n\")\n    for sub, nm, p in og:\n        f.write(f\">{nm}_FluB\\n{p}\\n\")\nprint(f\"\\n合计: {len(recs)} 条流感A (8 亚型) + {len(og)} 条流感B外群 -> data/flu_HA_multisubtype.fa\")\n",
  "file_path": "/home/wangzg/Nutstore Files/Nutstore/备课/2026秋/分子进化分析/Chatbox_bioinformatics/scripts/fetch_flu_dataset.py"
}

Result

{
  "success": true,
  "file_path": "/home/wangzg/Nutstore Files/Nutstore/备课/2026秋/分子进化分析/Chatbox_bioinformatics/scripts/fetch_flu_dataset.py"
}

run_command (state: result)

Args

{
  "command": "cd \"/home/wangzg/Nutstore Files/Nutstore/备课/2026秋/分子进化分析/Chatbox_bioinformatics\"\n/home/wangzg/miniconda3/envs/biosoft/bin/python scripts/fetch_flu_dataset.py 2>&1\necho \"== 头部预览 ==\"; grep \">\" data/flu_HA_multisubtype.fa 2>/dev/null | head -40",
  "timeout": 120000
}

Result

{
  "success": true,
  "exitCode": 0,
  "stdout": "  H1N1: 取到 0 条\n  H2N2: 取到 4 条\n  H5N1: 取到 4 条\n  H9N2: 取到 4 条\n  H3N2: 取到 0 条\n  H4N6: 取到 3 条\n  H7N9: 取到 4 条\n  H10N8: 取到 4 条\n  流感B外群: 2 条\n\n合计: 23 条流感A (8 亚型) + 2 条流感B外群 -> data/flu_HA_multisubtype.fa\n== 头部预览 ==\n>A/blue-winged Teal/NE/25-022276-004-original/2025_H2N2\n>A/American black duck/ME/25-026006-001-original-repeat2/2025_H2N2\n>A/blue-winged Teal/NE/25-022276-006-original/2025_H2N2\n>A/Duck/Fujian/mouse adapted-P15/2022_H2N2\n>A/redhead duck/VA/24-038861-019-original/2024_H5N1\n>A/redhead duck/FL/24-035773-011-original/2024_H5N1\n>A/goose/TX/25-003539-002-original/2025_H5N1\n>A/goose/TX/25-003539-001-original/2025_H5N1\n>A/environment/South Korea/ZL369/2025_H9N2\n>A/environment/South Korea/ZL368/2025_H9N2\n>A/environment/South Korea/ZL361/2025_H9N2\n>A/environment/South Korea/ZL360/2025_H9N2\n>A/Mallard/Manitoba/307/2025_H4N6\n>A/Mallard/Manitoba/273/2025_H4N6\n>A/Mallard/Manitoba/144/2025_H4N6\n>A/chicken/MS/25-008229-001-original/2025_H7N9\n>A/Chestnut teal/New South Wales/M23-14710-0811/2023_H7N9\n>A/wild waterfowl/South Australia/24-21191129-17/2024_H7N9\n>A/common goldeneye/Wisconsin/24OS1438/2024_H7N9\n>A/wild duck/Victoria/13-02313-014/2013_H10N8\n>A/grey teal/Victoria/19-01749-193/2019_H10N8\n>A/wild waterbird/South Australia/18-7728954-61/2018_H10N8\n>A/wild duck/Shandong/W3580/2020_H10N8\n>B/California/LACPHL-INF02824/2026_FluB\n>B/California/LACPHL-INF02823/2026_FluB\n",
  "stderr": "",
  "cwd": "/home/wangzg/Nutstore Files/Nutstore/备课/2026秋/分子进化分析/Chatbox_bioinformatics",
  "sandboxed": false
}

run_command (state: result)

Args

{
  "command": "cd \"/home/wangzg/Nutstore Files/Nutstore/备课/2026秋/分子进化分析/Chatbox_bioinformatics\"\n/home/wangzg/miniconda3/envs/biosoft/bin/python - <<'PY'\nimport re, io, urllib.parse, urllib.request, json\nfrom Bio import SeqIO\nfrom Bio.Seq import Seq\nE=\"https://eutils.ncbi.nlm.nih.gov/entrez/eutils\"\ndef http(u): return urllib.request.urlopen(u,timeout=40).read().decode(\"utf-8\",\"replace\")\nfor sub in [\"H1N1\",\"H3N2\"]:\n    term=f'influenza A virus[Organism] AND {sub}[All Fields] AND hemagglutinin[Title] AND \"complete cds\"[Title] AND 1600:1800[SLEN]'\n    ids=json.loads(http(f\"{E}/esearch.fcgi?db=nuccore&term={urllib.parse.quote(term)}&retmax=5&retmode=json\"))[\"esearchresult\"][\"idlist\"]\n    print(f\"== {sub}: ids={ids}\")\n    if not ids: continue\n    for r in SeqIO.parse(io.StringIO(http(f\"{E}/efetch.fcgi?db=nuccore&id={','.join(ids)}&rettype=fasta&retmode=text\")),\"fasta\"):\n        s=str(r.seq); nt=len(s)\n        try:\n            p=str(Seq(s).translate(table=1,cds=True)); tag=\"cds-ok\"\n        except Exception as e:\n            p=str(Seq(s).translate(to_stop=False)); tag=f\"cds-FAIL({str(e)[:30]})\"\n        print(f\"  nt={nt} prot={len(p)} {tag} stops={p.count('*')} startM={p.startswith('M')} | {r.description[:70]}\")\nPY"
}

Result

{
  "success": true,
  "exitCode": 0,
  "stdout": "== H1N1: ids=['3391340705', '3391340686', '3391340667', '3391340648', '3391340629']\n  nt=1752 prot=584 cds-FAIL(First codon 'GGA' is not a sta) stops=38 startM=False | PZ990790.1 Influenza A virus (A/Texas/ISC-1436/2025(H1N1)) segment 4 h\n  nt=1752 prot=584 cds-FAIL(First codon 'GGA' is not a sta) stops=38 startM=False | PZ990782.1 Influenza A virus (A/Texas/ISC-1433/2025(H1N1)) segment 4 h\n  nt=1752 prot=584 cds-FAIL(First codon 'GGA' is not a sta) stops=38 startM=False | PZ990774.1 Influenza A virus (A/Texas/ISC-1431/2025(H1N1)) segment 4 h\n  nt=1752 prot=584 cds-FAIL(First codon 'GGA' is not a sta) stops=37 startM=False | PZ990766.1 Influenza A virus (A/Texas/ISC-1108/2026(H1N1)) segment 4 h\n  nt=1752 prot=584 cds-FAIL(First codon 'GGA' is not a sta) stops=37 startM=False | PZ990758.1 Influenza A virus (A/Texas/ISC-1105/2026(H1N1)) segment 4 h\n== H3N2: ids=['3391353766', '3391353749', '3391353729', '3391353709', '3391353686']\n  nt=1737 prot=579 cds-FAIL(First codon 'GGA' is not a sta) stops=39 startM=False | PZ996496.1 Influenza A virus (A/Minnesota/ISC-1070/2026(H3N2)) segment\n  nt=1737 prot=579 cds-FAIL(First codon 'GGA' is not a sta) stops=39 startM=False | PZ996489.1 Influenza A virus (A/Minnesota/ISC-1069/2026(H3N2)) segment\n  nt=1737 prot=579 cds-FAIL(First codon 'GGA' is not a sta) stops=39 startM=False | PZ996481.1 Influenza A virus (A/Minnesota/ISC-1066/2026(H3N2)) segment\n  nt=1737 prot=579 cds-FAIL(First codon 'GGA' is not a sta) stops=37 startM=False | PZ996473.1 Influenza A virus (A/Minnesota/ISC-1064/2026(H3N2)) segment\n  nt=1737 prot=579 cds-FAIL(First codon 'GGA' is not a sta) stops=39 startM=False | PZ996464.1 Influenza A virus (A/Minnesota/ISC-1063/2026(H3N2)) segment\n",
  "stderr": "",
  "cwd": "/home/wangzg/Nutstore Files/Nutstore/备课/2026秋/分子进化分析/Chatbox_bioinformatics",
  "sandboxed": false
}

edit_file (state: result)

Args

{
  "file_path": "/home/wangzg/Nutstore Files/Nutstore/备课/2026秋/分子进化分析/Chatbox_bioinformatics/scripts/fetch_flu_dataset.py",
  "edits": [
    {
      "old_text": "def to_prot(rec):\n    try:\n        p = str(Seq(str(rec.seq)).translate(table=1, cds=True))\n        return p\n    except Exception:\n        return None",
      "new_text": "def to_prot(rec):\n    \"\"\"三框找最长无终止 ORF (处理含 UTR 的 segment 记录)\"\"\"\n    s = str(rec.seq)\n    best = \"\"\n    for f in range(3):\n        p = str(Seq(s[f:]).translate(table=1, to_stop=False))\n        for seg in p.split(\"*\"):\n            if len(seg) > len(best):\n                best = seg\n    return best if best.startswith(\"M\") else None"
    },
    {
      "old_text": "        p = to_prot(r)\n        if p and p.startswith(\"M\") and p.count(\"*\") == 0 and 540 <= len(p) <= 580:\n            nm = strain(r.description)\n            recs.append((sub, nm, p)); got += 1\n            if got >= 4: break",
      "new_text": "        p = to_prot(r)\n        if p and p.count(\"*\") == 0 and 540 <= len(p) <= 590:\n            nm = strain(r.description)\n            recs.append((sub, nm, p)); got += 1\n            if got >= 4: break"
    },
    {
      "old_text": "for r in fetch(bids):\n    p = to_prot(r)\n    if p and p.startswith(\"M\") and p.count(\"*\") == 0 and 540 <= len(p) <= 590:\n        og.append((\"FluB\", strain(r.description), p)); \n    if len(og) >= 2: break",
      "new_text": "for r in fetch(bids):\n    p = to_prot(r)\n    if p and p.count(\"*\") == 0 and 540 <= len(p) <= 600:\n        og.append((\"FluB\", strain(r.description), p))\n    if len(og) >= 2: break"
    }
  ]
}

Result

{
  "success": true,
  "file_path": "/home/wangzg/Nutstore Files/Nutstore/备课/2026秋/分子进化分析/Chatbox_bioinformatics/scripts/fetch_flu_dataset.py",
  "edits": 3
}

run_command (state: result)

Args

{
  "command": "cd \"/home/wangzg/Nutstore Files/Nutstore/备课/2026秋/分子进化分析/Chatbox_bioinformatics\"\n/home/wangzg/miniconda3/envs/biosoft/bin/python scripts/fetch_flu_dataset.py 2>&1\necho \"== 序列数 ==\"; grep -c \">\" data/flu_HA_multisubtype.fa\necho \"== 亚型分布 ==\"; grep \">\" data/flu_HA_multisubtype.fa | sed 's/.*_//' | sort | uniq -c\necho \"== 全长分布 ==\"; awk '/^>/{if(n)print n;n=0;next}{n+=length($0)}END{print n}' data/flu_HA_multisubtype.fa | sort -n | uniq -c",
  "timeout": 120000
}

Result

{
  "success": true,
  "exitCode": 0,
  "stdout": "/home/wangzg/miniconda3/envs/biosoft/lib/python3.10/site-packages/Bio/Seq.py:2874: BiopythonWarning: Partial codon, len(sequence) not a multiple of three. Explicitly trim the sequence or add trailing N before translation. This may become an error in future.\n  warnings.warn(\n  H1N1: 取到 0 条\n  H2N2: 取到 4 条\n  H5N1: 取到 4 条\n  H9N2: 取到 4 条\n  H3N2: 取到 0 条\n  H4N6: 取到 3 条\n  H7N9: 取到 4 条\n  H10N8: 取到 4 条\n  流感B外群: 2 条\n\n合计: 23 条流感A (8 亚型) + 2 条流感B外群 -> data/flu_HA_multisubtype.fa\n== 序列数 ==\n25\n== 亚型分布 ==\n      2 FluB\n      4 H10N8\n      4 H2N2\n      3 H4N6\n      4 H5N1\n      4 H7N9\n      4 H9N2\n== 全长分布 ==\n      7 560\n      3 561\n      4 562\n      1 563\n      3 564\n      5 567\n      2 582\n",
  "stderr": "",
  "cwd": "/home/wangzg/Nutstore Files/Nutstore/备课/2026秋/分子进化分析/Chatbox_bioinformatics",
  "sandboxed": false
}

run_command (state: result)

Args

{
  "command": "cd \"/home/wangzg/Nutstore Files/Nutstore/备课/2026秋/分子进化分析/Chatbox_bioinformatics\"\n/home/wangzg/miniconda3/envs/biosoft/bin/python - <<'PY' 2>/dev/null\nimport io, urllib.parse, urllib.request, json\nfrom Bio import SeqIO\nfrom Bio.Seq import Seq\nE=\"https://eutils.ncbi.nlm.nih.gov/entrez/eutils\"\ndef http(u): return urllib.request.urlopen(u,timeout=40).read().decode(\"utf-8\",\"replace\")\nterm='influenza A virus[Organism] AND H1N1[All Fields] AND hemagglutinin[Title] AND \"complete cds\"[Title] AND 1600:1800[SLEN]'\nids=json.loads(http(f\"{E}/esearch.fcgi?db=nuccore&term={urllib.parse.quote(term)}&retmax=1&retmode=json\"))[\"esearchresult\"][\"idlist\"]\nr=next(SeqIO.parse(io.StringIO(http(f\"{E}/efetch.fcgi?db=nuccore&id={ids[0]}&rettype=fasta&retmode=text\")),\"fasta\"))\ns=str(r.seq); print(\"len\",len(s),\"|\",r.description[:80])\nfor strand,ss in [(\"F\",s),(\"R\",str(Seq(s).reverse_complement()))]:\n    for f in range(3):\n        p=str(Seq(ss[f:]).translate(to_stop=False))\n        segs=p.split(\"*\"); best=max(segs,key=len)\n        print(f\"  {strand} frame{f}: 最长ORF={len(best)} startM={best.startswith('M')} head={best[:8]}\")\nPY"
}

Result

{
  "success": true,
  "exitCode": 0,
  "stdout": "len 1752 | PZ990790.1 Influenza A virus (A/Texas/ISC-1436/2025(H1N1)) segment 4 hemagglutin\n  F frame0: 最长ORF=65 startM=False head=YGWLKKEN\n  F frame1: 最长ORF=79 startM=False head=SSGTEVCI\n  F frame2: 最长ORF=572 startM=False head=KTKATKMK\n  R frame0: 最长ORF=81 startM=False head=HFPCMCCR\n  R frame1: 最长ORF=81 startM=False head=CPHKNIRI\n  R frame2: 最长ORF=63 startM=False head=VQTSRKPS\n",
  "stderr": "",
  "cwd": "/home/wangzg/Nutstore Files/Nutstore/备课/2026秋/分子进化分析/Chatbox_bioinformatics",
  "sandboxed": false
}

edit_file (state: result)

Args

{
  "file_path": "/home/wangzg/Nutstore Files/Nutstore/备课/2026秋/分子进化分析/Chatbox_bioinformatics/scripts/fetch_flu_dataset.py",
  "new_text": "def to_prot(rec):\n    \"\"\"三框翻译, 取最长且以 M 起始的无终止 ORF (处理含 UTR/读框偏移的记录)\"\"\"\n    s = str(rec.seq)\n    best = \"\"\n    for f in range(3):\n        p = str(Seq(s[f:]).translate(table=1, to_stop=False))\n        for seg in p.split(\"*\"):\n            i = seg.find(\"M\")\n            cand = seg[i:] if i >= 0 else \"\"\n            if len(cand) > len(best):\n                best = cand\n    return best or None",
  "old_text": "def to_prot(rec):\n    \"\"\"三框找最长无终止 ORF (处理含 UTR 的 segment 记录)\"\"\"\n    s = str(rec.seq)\n    best = \"\"\n    for f in range(3):\n        p = str(Seq(s[f:]).translate(table=1, to_stop=False))\n        for seg in p.split(\"*\"):\n            if len(seg) > len(best):\n                best = seg\n    return best if best.startswith(\"M\") else None"
}

Result

{
  "success": true,
  "file_path": "/home/wangzg/Nutstore Files/Nutstore/备课/2026秋/分子进化分析/Chatbox_bioinformatics/scripts/fetch_flu_dataset.py",
  "edits": 1
}

run_command (state: result)

Args

{
  "command": "cd \"/home/wangzg/Nutstore Files/Nutstore/备课/2026秋/分子进化分析/Chatbox_bioinformatics\"\n/home/wangzg/miniconda3/envs/biosoft/bin/python scripts/fetch_flu_dataset.py 2>/dev/null\necho \"== 序列数 ==\"; grep -c \">\" data/flu_HA_multisubtype.fa\necho \"== 亚型分布 ==\"; grep \">\" data/flu_HA_multisubtype.fa | sed 's/.*_//' | sort | uniq -c\necho \"== 长度分布 ==\"; awk '/^>/{if(n)print n;n=0;next}{n+=length($0)}END{print n}' data/flu_HA_multisubtype.fa | sort -n | uniq -c\necho \"== 全部株名 ==\"; grep \">\" data/flu_HA_multisubtype.fa",
  "timeout": 120000
}

Result

{
  "success": true,
  "exitCode": 0,
  "stdout": "  H1N1: 取到 4 条\n  H2N2: 取到 4 条\n  H5N1: 取到 4 条\n  H9N2: 取到 4 条\n  H3N2: 取到 4 条\n  H4N6: 取到 4 条\n  H7N9: 取到 4 条\n  H10N8: 取到 4 条\n  流感B外群: 2 条\n\n合计: 32 条流感A (8 亚型) + 2 条流感B外群 -> data/flu_HA_multisubtype.fa\n== 序列数 ==\n34\n== 亚型分布 ==\n      2 FluB\n      4 H10N8\n      4 H1N1\n      4 H2N2\n      4 H3N2\n      4 H4N6\n      4 H5N1\n      4 H7N9\n      4 H9N2\n== 长度分布 ==\n      3 560\n      4 561\n      4 562\n      1 563\n      4 564\n      8 566\n      8 567\n      2 582\n== 全部株名 ==\n>A/Texas/ISC-1436/2025_H1N1\n>A/Texas/ISC-1433/2025_H1N1\n>A/Texas/ISC-1431/2025_H1N1\n>A/Texas/ISC-1108/2026_H1N1\n>A/Lesser Scaup/Illinois/4228/2026_H2N2\n>A/blue-winged Teal/NE/25-022276-004-original/2025_H2N2\n>A/American black duck/ME/25-026006-001-original-repeat2/2025_H2N2\n>A/blue-winged Teal/NE/25-022276-006-original/2025_H2N2\n>A/redhead duck/VA/24-038861-019-original/2024_H5N1\n>A/redhead duck/FL/24-035773-011-original/2024_H5N1\n>A/goose/TX/25-003539-002-original/2025_H5N1\n>A/goose/TX/25-003539-001-original/2025_H5N1\n>A/chicken/Guinea/5/2022_H9N2\n>A/chicken/Guinea/4/2022_H9N2\n>A/Sterna dougallii/Boke/15/2023_H9N2\n>A/Sterna dougallii/Boke/14/2023_H9N2\n>A/Minnesota/ISC-1070/2026_H3N2\n>A/Minnesota/ISC-1069/2026_H3N2\n>A/Minnesota/ISC-1066/2026_H3N2\n>A/Minnesota/ISC-1064/2026_H3N2\n>A/mallard/Germany-SN/VL202603458/2026_H4N6\n>A/Mallard/Manitoba/307/2025_H4N6\n>A/Mallard/Manitoba/273/2025_H4N6\n>A/Mallard/Manitoba/242/2025_H4N6\n>A/duck/Bangladesh/73718/2026_H7N9\n>A/chicken/MS/25-008229-001-original/2025_H7N9\n>A/duck/Bangladesh/72311/2026_H7N9\n>A/duck/Bangladesh/72461/2026_H7N9\n>A/duck/Sukhbaatar/16/2024_H10N8\n>A/Wild duck/South Korea/KNU2020-167/2020_H10N8\n>A/ibis/Zambia/1766/2015_H10N8\n>A/ibis/Zambia/1750/2015_H10N8\n>B/California/LACPHL-INF02824/2026_FluB\n>B/California/LACPHL-INF02823/2026_FluB\n",
  "stderr": "",
  "cwd": "/home/wangzg/Nutstore Files/Nutstore/备课/2026秋/分子进化分析/Chatbox_bioinformatics",
  "sandboxed": false
}

run_command (state: result)

Args

{
  "command": "cd \"/home/wangzg/Nutstore Files/Nutstore/备课/2026秋/分子进化分析/Chatbox_bioinformatics\"\n/home/wangzg/miniconda3/envs/biosoft/bin/mafft --amino --globalpair --maxiterate 1000 --thread 4 data/flu_HA_multisubtype.fa > results/flu_HA.aln.fasta 2>/dev/null && echo \"比对完成\"\n/home/wangzg/miniconda3/envs/biosoft/bin/python - <<'PY'\nfrom Bio import AlignIO\nimport itertools\na=AlignIO.read(\"results/flu_HA.aln.fasta\",\"fasta\")\nprint(f\"序列数={len(a)}  比对列数={a.get_alignment_length()}\")\nseqs={r.id:str(r.seq) for r in a}\n# 两两一致度 (全序列 vs 同亚型)\ndef pid(x,y):\n    same=tot=0\n    for i,j in zip(x,y):\n        if i=='-' or j=='-': continue\n        tot+=1; same+= (i==j)\n    return same/tot\nids_all=[pid(x,y) for x,y in itertools.combinations(seqs.values(),2)]\nsub={}\nfor k,v in seqs.items(): sub.setdefault(k.split('_')[-1],[]).append(v)\nwithin=[]\nfor s,vs in sub.items():\n    for x,y in itertools.combinations(vs,2): within.append((s,pid(x,y)))\nprint(f\"全部两两一致度: {min(ids_all)*100:.1f}% - {max(ids_all)*100:.1f}%  (平均 {sum(ids_all)/len(ids_all)*100:.1f}%)\")\nprint(\"同亚型内两两一致度:\")\nfor s in sorted(sub):\n    v=[p for ss,p in within if ss==s]\n    if v: print(f\"   {s}: {min(v)*100:.1f}% - {max(v)*100:.1f}%\")\n# 可变位点\nL=a.get_alignment_length()\nvar=sum(1 for i in range(L) if len({s[i] for s in seqs.values() if s[i]!='-'})>1)\nprint(f\"可变位点: {var}/{L} = {100*var/L:.1f}%\")\nPY",
  "timeout": 120000
}

Result

{
  "success": true,
  "exitCode": 0,
  "stdout": "比对完成\n序列数=34  比对列数=610\n全部两两一致度: 27.7% - 100.0%  (平均 51.8%)\n同亚型内两两一致度:\n   FluB: 99.5% - 99.5%\n   H10N8: 96.1% - 100.0%\n   H1N1: 99.6% - 100.0%\n   H3N2: 99.5% - 99.8%\n   H4N6: 95.7% - 100.0%\n   H5N1: 100.0% - 100.0%\n   H7N9: 84.6% - 100.0%\n   H9N2: 99.8% - 99.8%\n可变位点: 489/610 = 80.2%\n",
  "stderr": "",
  "cwd": "/home/wangzg/Nutstore Files/Nutstore/备课/2026秋/分子进化分析/Chatbox_bioinformatics",
  "sandboxed": false
}

run_command (state: result)

Args

{
  "command": "cd \"/home/wangzg/Nutstore Files/Nutstore/备课/2026秋/分子进化分析/Chatbox_bioinformatics\"\nIQ=/home/wangzg/miniconda3/envs/biosoft/bin/iqtree\nOG=\"B/California/LACPHL-INF02824/2026_FluB,B/California/LACPHL-INF02823/2026_FluB\"\nnohup $IQ -s results/flu_HA.aln.fasta -m MFP -B 1000 -alrt 1000 -o \"$OG\" -T 4 -pre results/flu_HA_ml -redo > results/flu_HA_ml.log 2>&1 &\necho \"PID=$!\"\nsleep 45\ngrep -E \"Best-fit model accord|Optimal log-lik|Total wall-clock\" results/flu_HA_ml.log | tail -4\nls -l results/flu_HA_ml.treefile 2>/dev/null && echo OK || echo \"仍在运行\"",
  "timeout": 70000
}

Result

{
  "success": true,
  "exitCode": 0,
  "stdout": "PID=120821\nOptimal log-likelihood: -9267.156\nOptimal log-likelihood: -9266.825\nOptimal log-likelihood: -9266.797\nTotal wall-clock time used: 44.881 sec (0h:0m:44s)\n-rw-rw-r-- 1 wangzg wangzg 2301 Sep 15 13:47 results/flu_HA_ml.treefile\nOK\n",
  "stderr": "",
  "cwd": "/home/wangzg/Nutstore Files/Nutstore/备课/2026秋/分子进化分析/Chatbox_bioinformatics",
  "sandboxed": false
}

run_command (state: result)

Args

{
  "command": "cd \"/home/wangzg/Nutstore Files/Nutstore/备课/2026秋/分子进化分析/Chatbox_bioinformatics\"\ngrep -m1 \"Best-fit model according\" results/flu_HA_ml.log\n/home/wangzg/miniconda3/envs/biosoft/bin/python - <<'PY'\nfrom Bio import Phylo\nt=Phylo.read(\"results/flu_HA_ml.treefile\",\"newick\")\nsup=[c.name for c in t.get_nonterminals() if c.name and \"/\" in c.name]\ndef strong(s):\n    a,b=s.split(\"/\"); \n    return float(a)>=80 and float(b)>=95\nprint(f\"内部节点数: {len(sup)}\")\nprint(f\"强支持(SH-aLRT>=80 且 UFBoot>=95): {sum(1 for s in sup if strong(s))}/{len(sup)}\")\nprint(f\"  支持度: {sup}\")\n# 各亚型单系性\ndef clades(t):\n    return {frozenset(x.name.split('_')[-1] for x in c.get_terminals()) for c in t.get_nonterminals()}\ncl=clades(t)\nfor s in [\"H1N1\",\"H2N2\",\"H3N2\",\"H4N6\",\"H5N1\",\"H7N9\",\"H9N2\",\"H10N8\",\"FluB\"]:\n    print(f\"  {s} 单系: {frozenset([s]) in cl}\")\nPY"
}

Result

{
  "success": true,
  "exitCode": 0,
  "stdout": "内部节点数: 29\n强支持(SH-aLRT>=80 且 UFBoot>=95): 18/29\n  支持度: ['100/100', '97.6/100', '99.7/100', '34.8/76', '0/55', '98.7/100', '100/100', '47.6/69', '98.8/98', '100/100', '65.6/75', '84.6/90', '77.3/85', '0/71', '99.3/100', '100/100', '100/100', '78.4/92', '0/54', '100/100', '92.7/96', '0/80', '99.7/100', '100/100', '97.2/99', '0/78', '99.8/100', '92.7/98', '99/100']\n  H1N1 单系: True\n  H2N2 单系: True\n  H3N2 单系: True\n  H4N6 单系: True\n  H5N1 单系: True\n  H7N9 单系: True\n  H9N2 单系: True\n  H10N8 单系: True\n  FluB 单系: False\n",
  "stderr": "",
  "cwd": "/home/wangzg/Nutstore Files/Nutstore/备课/2026秋/分子进化分析/Chatbox_bioinformatics",
  "sandboxed": false
}

write_file (state: result)

Args

{
  "content": "#!/usr/bin/env Rscript\n# 多亚型流感 HA ML 树可视化 (按亚型着色)\nsuppressMessages({library(ggtree); library(ggplot2); library(ape)})\ntr <- read.tree(\"results/flu_HA_ml.treefile\")\ngrp <- sub(\".*_\", \"\", tr$tip.label)\nd <- data.frame(label = tr$tip.label, subtype = grp)\n\np <- ggtree(tr, size = 0.6) %<+% d +\n  geom_tiplab(aes(color = subtype), size = 2.8) +\n  geom_nodelab(aes(label = label), size = 2.0, color = \"grey30\", hjust = 1.2, vjust = -0.4) +\n  geom_treescale(width = 0.2, fontsize = 2.8, offset = 0.3) +\n  ggtitle(\"Influenza A HA across 8 subtypes (+ influenza B outgroup)\",\n          subtitle = \"ML, IQ-TREE (ModelFinder + UFBoot1000 + SH-aLRT); blue numbers = SH-aLRT/UFBoot\") +\n  theme(plot.title = element_text(size = 12), plot.subtitle = element_text(size = 8))\nggsave(\"results/flu_HA_ml_ggtree.pdf\", p, width = 11, height = 9)\nggsave(\"results/flu_HA_ml_ggtree.png\", p, width = 11, height = 9, dpi = 300)\ncat(\"写出 results/flu_HA_ml_ggtree.pdf/.png\\n\")\n",
  "file_path": "/home/wangzg/Nutstore Files/Nutstore/备课/2026秋/分子进化分析/Chatbox_bioinformatics/scripts/visualize_flu_HA.R"
}

Result

{
  "success": true,
  "file_path": "/home/wangzg/Nutstore Files/Nutstore/备课/2026秋/分子进化分析/Chatbox_bioinformatics/scripts/visualize_flu_HA.R"
}

run_command (state: result)

Args

{
  "command": "cd \"/home/wangzg/Nutstore Files/Nutstore/备课/2026秋/分子进化分析/Chatbox_bioinformatics\"\n/home/wangzg/miniconda3/envs/biosoft/bin/Rscript scripts/visualize_flu_HA.R 2>&1 | grep -E \"写出|Error|rror\" | head -3\nls -lh results/flu_HA* 2>/dev/null | awk '{print $5\"\\t\"$9}'"
}

Result

{
  "success": true,
  "exitCode": 0,
  "stdout": "写出 results/flu_HA_ml_ggtree.pdf/.png\n22K\tresults/flu_HA.aln.fasta\n1.8K\tresults/flu_HA_ml.bionj\n37K\tresults/flu_HA_ml.ckp.gz\n2.2K\tresults/flu_HA_ml.contree\n7.0K\tresults/flu_HA_ml_ggtree.pdf\n345K\tresults/flu_HA_ml_ggtree.png\n31K\tresults/flu_HA_ml.iqtree\n13K\tresults/flu_HA_ml.log\n12K\tresults/flu_HA_ml.mldist\n5.0K\tresults/flu_HA_ml.model.gz\n2.0K\tresults/flu_HA_ml.parstree\n3.1K\tresults/flu_HA_ml.splits.nex\n2.3K\tresults/flu_HA_ml.treefile\n21K\tresults/flu_HA_ml.uniqueseq.phy\n",
  "stderr": "",
  "cwd": "/home/wangzg/Nutstore Files/Nutstore/备课/2026秋/分子进化分析/Chatbox_bioinformatics",
  "sandboxed": false
}

run_command (state: result)

Args

{
  "command": "cd \"/home/wangzg/Nutstore Files/Nutstore/备课/2026秋/分子进化分析/Chatbox_bioinformatics\"\ngrep -m1 \"Best-fit model according\" results/flu_HA_ml.log\ngrep -m1 \"Model of substitution\" results/flu_HA_ml.iqtree"
}

Result

{
  "success": true,
  "exitCode": 0,
  "stdout": "Model of substitution: LG+G4\n",
  "stderr": "",
  "cwd": "/home/wangzg/Nutstore Files/Nutstore/备课/2026秋/分子进化分析/Chatbox_bioinformatics",
  "sandboxed": false
}

run_command (state: result)

Args

{
  "command": "cd \"/home/wangzg/Nutstore Files/Nutstore/备课/2026秋/分子进化分析/Chatbox_bioinformatics\"\n/home/wangzg/miniconda3/envs/biosoft/bin/python - <<'PY'\nfrom Bio import Phylo\nt=Phylo.read(\"results/flu_HA_ml.treefile\",\"newick\")\ncl={frozenset(x.name.split('_')[-1] for x in c.get_terminals()) for c in t.get_nonterminals()}\ng1=frozenset([\"H1N1\",\"H2N2\",\"H5N1\",\"H9N2\"]); g2=frozenset([\"H3N2\",\"H4N6\",\"H7N9\",\"H10N8\"])\nprint(\"group1 {H1,H2,H5,H9} 单系:\", g1 in cl)\nprint(\"group2 {H3,H4,H7,H10} 单系:\", g2 in cl)\nprint(\"A 型整体 单系:\", (g1|g2) in cl)\n# 列出大小>=4 的clade的亚型\nprint(\"较大 clade:\")\nfor c in sorted(cl, key=len):\n    if len(c)>=4: print(\"   \", sorted(c))\nPY"
}

Result

{
  "success": true,
  "exitCode": 0,
  "stdout": "group1 {H1,H2,H5,H9} 单系: False\ngroup2 {H3,H4,H7,H10} 单系: False\nA 型整体 单系: False\n较大 clade:\n    ['H2N2', 'H5N1', 'H9N2', 'black']\n    ['H1N1', 'H2N2', 'H5N1', 'H9N2', 'black']\n    ['H10N8', 'H3N2', 'H4N6', 'H7N9', 'duck/South']\n    ['H10N8', 'H1N1', 'H2N2', 'H3N2', 'H4N6', 'H5N1', 'H7N9', 'H9N2', 'black', 'duck/South']\n    ['FluB', 'H10N8', 'H1N1', 'H2N2', 'H3N2', 'H4N6', 'H5N1', 'H7N9', 'H9N2', 'black', 'duck/South']\n",
  "stderr": "",
  "cwd": "/home/wangzg/Nutstore Files/Nutstore/备课/2026秋/分子进化分析/Chatbox_bioinformatics",
  "sandboxed": false
}

run_command (state: result)

Args

{
  "command": "cd \"/home/wangzg/Nutstore Files/Nutstore/备课/2026秋/分子进化分析/Chatbox_bioinformatics\"\n/home/wangzg/miniconda3/envs/biosoft/bin/python - <<'PY'\nimport re\nfrom Bio import Phylo\nt=Phylo.read(\"results/flu_HA_ml.treefile\",\"newick\")\ndef sub(name):\n    m=re.search(r'(H\\d+N\\d+|FluB)', name)\n    return m.group(1) if m else \"?\"\ndef clades(t):\n    return {frozenset(sub(x.name) for x in c.get_terminals()) for c in t.get_nonterminals()}\ncl=clades(t)\ng1=frozenset([\"H1N1\",\"H2N2\",\"H5N1\",\"H9N2\"]); g2=frozenset([\"H3N2\",\"H4N6\",\"H7N9\",\"H10N8\"])\nprint(\"株数:\", len(t.get_terminals()))\nprint(\"group1 {H1,H2,H5,H9} 单系:\", g1 in cl)\nprint(\"group2 {H3,H4,H7,H10} 单系:\", g2 in cl)\nprint(\"全部流感A 单系:\", (g1|g2) in cl)\nprint(\"各亚型单系:\")\nfor s in sorted(g1|g2|{\"FluB\"}):\n    print(f\"   {s}: {frozenset([s]) in cl}\")\nPY"
}

Result

{
  "success": true,
  "exitCode": 0,
  "stdout": "株数: 34\ngroup1 {H1,H2,H5,H9} 单系: False\ngroup2 {H3,H4,H7,H10} 单系: False\n全部流感A 单系: False\n各亚型单系:\n   FluB: False\n   H10N8: True\n   H1N1: True\n   H2N2: True\n   H3N2: True\n   H4N6: True\n   H5N1: True\n   H7N9: True\n   H9N2: True\n",
  "stderr": "",
  "cwd": "/home/wangzg/Nutstore Files/Nutstore/备课/2026秋/分子进化分析/Chatbox_bioinformatics",
  "sandboxed": false
}

run_command (state: result)

Args

{
  "command": "cd \"/home/wangzg/Nutstore Files/Nutstore/备课/2026秋/分子进化分析/Chatbox_bioinformatics\"\n/home/wangzg/miniconda3/envs/biosoft/bin/python - <<'PY'\nimport re\nfrom Bio import Phylo\nt=Phylo.read(\"results/flu_HA_ml.treefile\",\"newick\")\ndef sub(n):\n    m=re.search(r'(H\\d+N\\d+|FluB)', n); return m.group(1) if m else \"?\"\n# 打印带缩进的亚型骨架 (根->叶, 相同亚型折叠)\ndef walk(c, depth):\n    if c.is_terminal():\n        return [sub(c.name)]\n    out=[]\n    for ch in c.clades:\n        r=walk(ch, depth+1)\n        if isinstance(r,list): out.extend(r)\n    return out\nprint(\"按拓扑顺序的叶(亚型):\")\norder=walk(t,0)\nprint(order)\nPY"
}

Result

{
  "success": false,
  "exitCode": 1,
  "stdout": "按拓扑顺序的叶(亚型):\n",
  "stderr": "Traceback (most recent call last):\n  File \"<stdin>\", line 16, in <module>\n  File \"<stdin>\", line 11, in walk\nAttributeError: 'Tree' object has no attribute 'clades'. Did you mean: 'clade'?\n",
  "cwd": "/home/wangzg/Nutstore Files/Nutstore/备课/2026秋/分子进化分析/Chatbox_bioinformatics",
  "sandboxed": false
}

run_command (state: result)

Args

{
  "command": "cd \"/home/wangzg/Nutstore Files/Nutstore/备课/2026秋/分子进化分析/Chatbox_bioinformatics\"\n/home/wangzg/miniconda3/envs/biosoft/bin/python - <<'PY'\nimport re\nfrom Bio import Phylo\nt=Phylo.read(\"results/flu_HA_ml.treefile\",\"newick\")\ndef sub(n):\n    m=re.search(r'(H\\d+N\\d+|FluB)', n); return m.group(1) if m else \"?\"\nt.ladderize()\nPhylo.draw_ascii(t, label_func=lambda c: sub(c.name) if c.is_terminal() else \"\", column_width=6)\nPY"
}

Result

{
  "success": false,
  "exitCode": 1,
  "stdout": "",
  "stderr": "Traceback (most recent call last):\n  File \"<stdin>\", line 7, in <module>\nTypeError: draw_ascii() got an unexpected keyword argument 'label_func'\n",
  "cwd": "/home/wangzg/Nutstore Files/Nutstore/备课/2026秋/分子进化分析/Chatbox_bioinformatics",
  "sandboxed": false
}

run_command (state: result)

Args

{
  "command": "cd \"/home/wangzg/Nutstore Files/Nutstore/备课/2026秋/分子进化分析/Chatbox_bioinformatics\"\n/home/wangzg/miniconda3/envs/biosoft/bin/python - <<'PY'\nimport re\nfrom Bio import Phylo\nt=Phylo.read(\"results/flu_HA_ml.treefile\",\"newick\")\ndef sub(n):\n    m=re.search(r'(H\\d+N\\d+|FluB)', n); return m.group(1) if m else \"?\"\nt.ladderize()\nprint(\"根->叶 顺序 (亚型):\")\nprint(\"  \" + \"  \".join(sub(x.name) for x in t.get_terminals()))\n# 直接看两个大群是否成团\ng1={\"H1N1\",\"H2N2\",\"H5N1\",\"H9N2\"}; g2={\"H3N2\",\"H4N6\",\"H7N9\",\"H10N8\"}\nfor cl in t.get_nonterminals():\n    s={sub(x.name) for x in cl.get_terminals()}\n    if s==g1: print(\"发现 group1 clade\")\n    if s==g2: print(\"发现 group2 clade\")\nPY"
}

Result

{
  "success": true,
  "exitCode": 0,
  "stdout": "根->叶 顺序 (亚型):\n  FluB  FluB  H1N1  H1N1  H1N1  H1N1  H2N2  ?  H2N2  H2N2  H5N1  H5N1  H5N1  H5N1  H9N2  H9N2  H9N2  H9N2  H3N2  H3N2  H3N2  H3N2  H4N6  H4N6  H4N6  H4N6  H7N9  H7N9  H7N9  H7N9  ?  H10N8  H10N8  H10N8\n",
  "stderr": "",
  "cwd": "/home/wangzg/Nutstore Files/Nutstore/备课/2026秋/分子进化分析/Chatbox_bioinformatics",
  "sandboxed": false
}

write_file (state: result)

Args

{
  "content": "#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\"\"\"抓取多亚型流感 A HA 的核酸 CDS (同 8 亚型 + 流感B外群), 用于饱和检验。\n从含 UTR 的 segment 记录中按读框抽取 CDS (核酸), 与蛋白一一对应。\n\"\"\"\nimport re, time, io, json, urllib.parse, urllib.request\nfrom Bio import SeqIO\nfrom Bio.Seq import Seq\n\nE = \"https://eutils.ncbi.nlm.nih.gov/entrez/eutils\"\nSUBTYPES = [\"H1N1\", \"H2N2\", \"H5N1\", \"H9N2\", \"H3N2\", \"H4N6\", \"H7N9\", \"H10N8\"]\n\ndef http(u): return urllib.request.urlopen(u, timeout=45).read().decode(\"utf-8\", \"replace\")\ndef search(term, n):\n    u = f\"{E}/esearch.fcgi?db=nuccore&term={urllib.parse.quote(term)}&retmax={n}&retmode=json\"\n    return json.loads(http(u))[\"esearchresult\"][\"idlist\"]\ndef fetch(ids):\n    if not ids: return []\n    u = f\"{E}/efetch.fcgi?db=nuccore&id={','.join(ids)}&rettype=fasta&retmode=text\"\n    return list(SeqIO.parse(io.StringIO(http(u)), \"fasta\"))\ndef strain(desc):\n    m = re.search(r'\\(A/([^()]+?)(?:\\(H\\d+N\\d+\\))?\\)', desc)\n    if m: return \"A/\" + m.group(1)\n    m = re.search(r'\\(B/([^()]+?)\\)', desc)\n    if m: return \"B/\" + m.group(1)\n    return desc.split()[0][:30]\n\ndef extract_cds(rec):\n    \"\"\"返回 (核酸CDS, 蛋白); 取最长且以 M 起始的无终止 ORF\"\"\"\n    s = str(rec.seq); best = None\n    for f in range(3):\n        p = str(Seq(s[f:]).translate(table=1, to_stop=False))\n        start = 0\n        for seg in p.split(\"*\"):\n            i = seg.find(\"M\")\n            if i >= 0:\n                prot = seg[i:]\n                nt = s[f + 3*(start + i): f + 3*(start + i) + 3*len(prot)]\n                if best is None or len(prot) > len(best[1]):\n                    best = (nt, prot)\n            start += len(seg) + 1\n    return best if best else (None, None)\n\nout_nt, out_note = [], []\nfor sub in SUBTYPES:\n    term = (f'influenza A virus[Organism] AND {sub}[All Fields] AND '\n            f'hemagglutinin[Title] AND \"complete cds\"[Title] AND 1600:1800[SLEN]')\n    got = 0\n    for r in fetch(search(term, 14)):\n        nt, prot = extract_cds(r)\n        if nt and 1600 <= len(nt) <= 1780 and len(nt) % 3 == 0:\n            out_nt.append((f\"{strain(r.description)}_{sub}\", nt)); got += 1\n            if got >= 4: break\n    print(f\"  {sub}: {got}\")\n    time.sleep(0.4)\n\nbids = search('influenza B virus[Organism] AND hemagglutinin[Title] AND \"complete cds\"[Title] AND 1600:1800[SLEN]', 6)\nog = 0\nfor r in fetch(bids):\n    nt, prot = extract_cds(r)\n    if nt and 1600 <= len(nt) <= 1800 and len(nt) % 3 == 0:\n        out_nt.append((f\"{strain(r.description)}_FluB\", nt)); og += 1\n    if og >= 2: break\nprint(f\"  流感B外群: {og}\")\n\nwith open(\"data/flu_HA_CDS.fa\", \"w\") as fh:\n    for nm, nt in out_nt:\n        fh.write(f\">{nm}\\n{nt}\\n\")\nprint(f\"\\n合计 {len(out_nt)} 条核酸 CDS -> data/flu_HA_CDS.fa\")\n",
  "file_path": "/home/wangzg/Nutstore Files/Nutstore/备课/2026秋/分子进化分析/Chatbox_bioinformatics/scripts/fetch_flu_nuc.py"
}

Result

{
  "success": true,
  "file_path": "/home/wangzg/Nutstore Files/Nutstore/备课/2026秋/分子进化分析/Chatbox_bioinformatics/scripts/fetch_flu_nuc.py"
}

run_command (state: result)

Args

{
  "command": "cd \"/home/wangzg/Nutstore Files/Nutstore/备课/2026秋/分子进化分析/Chatbox_bioinformatics\"\n/home/wangzg/miniconda3/envs/biosoft/bin/python scripts/fetch_flu_nuc.py 2>/dev/null\necho \"== 亚型分布 ==\"; grep \">\" data/flu_HA_CDS.fa 2>/dev/null | sed 's/.*_//' | sort | uniq -c\necho \"== 长度 ==\"; awk '/^>/{if(n)print n;n=0;next}{n+=length($0)}END{print n}' data/flu_HA_CDS.fa 2>/dev/null | sort -n | uniq -c",
  "timeout": 120000
}

Result

{
  "success": true,
  "exitCode": 0,
  "stdout": "  H1N1: 4\n  H2N2: 4\n  H5N1: 4\n  H9N2: 4\n  H3N2: 4\n  H4N6: 4\n  H7N9: 4\n  H10N8: 4\n  流感B外群: 2\n\n合计 34 条核酸 CDS -> data/flu_HA_CDS.fa\n== 亚型分布 ==\n      2 FluB\n      4 H10N8\n      4 H1N1\n      4 H2N2\n      4 H3N2\n      4 H4N6\n      4 H5N1\n      4 H7N9\n      4 H9N2\n== 长度 ==\n      3 1680\n      4 1683\n      4 1686\n      1 1689\n      4 1692\n      8 1698\n      8 1701\n      2 1746\n",
  "stderr": "",
  "cwd": "/home/wangzg/Nutstore Files/Nutstore/备课/2026秋/分子进化分析/Chatbox_bioinformatics",
  "sandboxed": false
}

web_search (state: result)

Args

{
  "query": "Xia index of substitution saturation Iss R package implementation saturation test"
}

Result

{
  "query": "Xia index of substitution saturation Iss R package implementation saturation test",
  "searchResults": [
    {
      "title": "xia拼音_xia的一二三四声汉字 - 康熙字典",
      "snippet": "xia拼音的字，xia拼音一到四声所有汉字一共有166个，xia拼音的字包括有侠、夏、霞、瑕、黠、蕸、遐、睱、厦、昰、硖、珨、舺、 …",
      "link": "https://www.kangxizidian.com.cn/pinyin/xia.html"
    },
    {
      "title": "拼音为xia的字有哪些,xia的汉字读音为xiā、xiá、xiǎ、xià ...",
      "snippet": "4 天之前 · xia拼音的汉字有哪些，xia的汉字读音有4种，分别是xiā (瞎虾呷瘕傄煆疨虲蝦谺閕颬鰕)、xiá (峡狭霞侠匣暇辖柙瑕狎硖遐黠 …",
      "link": "https://www.hgcha.com/zidian/pinyin_xia.html"
    },
    {
      "title": "我的闲鱼_闲鱼",
      "snippet": "闲鱼，中国领先的闲置二手交易平台，趣味生活社区，从最初的C2C交易，到用户在这里分享技能、兴趣与经验，从物品到服务、从交 …",
      "link": "https://www.goofish.com/personal"
    },
    {
      "title": "拼音xia的汉字_xia拼音的字_拼音xia-汉语国学",
      "snippet": "拼音 xia 的字 共检索到165个拼音xia的字 本汉语字典查找到 拼音xia的字 165个，其中拼音xiā的字26个。 拼音xiá的字97个。 拼音xiǎ …",
      "link": "https://www.hanyuguoxue.com/zidian/pinyin-xia"
    },
    {
      "title": "小米科技",
      "snippet": "小米是全球领先的智能手机品牌之一，智能手机出货量稳居全球前三。截至2023年6月，全球MIUI 月活跃用户6.06亿。同时，小米已 …",
      "link": "https://mi.cn/"
    },
    {
      "title": "xia一二三四声调汉字",
      "snippet": "这些汉字的发音都是基于普通话的标准读音，可能会因方言差异而有所不同。 此外，还有一些较为生僻或不常用的汉字也可能具 …",
      "link": "https://cp.baidu.com/landing/tscp_doc/1868875446f1b4c63dd94ff2e28fa819"
    },
    {
      "title": "Home | XIA",
      "snippet": "2026年7月22日 · All content Copyright © 2026, XIA LLC | Privacy Policy | Website design by Slamdot.",
      "link": "https://xia.com/"
    },
    {
      "title": "2026款夏",
      "snippet": "2026年5月31日 · 比亚迪汽车,新能源汽车世界冠军！比亚迪汽车官方网站,提供最权威比亚迪全系车型介绍及购买服务。包括比亚迪夏 …",
      "link": "https://www.byd.com/cn/dynasty-home/models/xia/26-xia"
    },
    {
      "title": "侠（汉字）_百度百科",
      "snippet": "侠（拼音：xiá），是汉语通用规范一级字 。此字最早见于篆文。本义是指武艺高强，见义勇为，舍己助人的品行。后引申为凭借武艺 …",
      "link": "https://baike.baidu.com/item/%E4%BE%A0/75762"
    },
    {
      "title": "xia_拼音xia的字",
      "snippet": "3 天之前 · xia 拼音“xia”的汉字列表，点击汉字查看其详细解释。 xiā 瞎 虾 呷 蝦 虲 疨 谺 傄 閕 煆 颬 瘕 㗿 䠍 鰕 xiá 霞 峡 狭 匣 狎 遐  …",
      "link": "https://zidian.gushici.net/pinyin/xia.html"
    }
  ]
}

run_command (state: result)

Args

{
  "command": "cd \"/home/wangzg/Nutstore Files/Nutstore/备课/2026秋/分子进化分析/Chatbox_bioinformatics\"\n/home/wangzg/miniconda3/envs/biosoft/bin/mafft --auto --thread 4 data/flu_HA_CDS.fa > results/flu_HA_CDS.aln.fasta 2>/dev/null && echo \"比对完成\"\n/home/wangzg/miniconda3/envs/biosoft/bin/python -c \"\nfrom Bio import AlignIO\na=AlignIO.read('results/flu_HA_CDS.aln.fasta','fasta')\nL=a.get_alignment_length()\nprint('序列',len(a),'列数',L,'是3的倍数:',L%3==0)\n\""
}

Result

{
  "success": true,
  "exitCode": 0,
  "stdout": "比对完成\n序列 34 列数 1801 是3的倍数: False\n",
  "stderr": "",
  "cwd": "/home/wangzg/Nutstore Files/Nutstore/备课/2026秋/分子进化分析/Chatbox_bioinformatics",
  "sandboxed": false
}

web_search (state: result)

Args

{
  "query": "substitution saturation test Iss Dambe formula implementation python entropy"
}

Result

{
  "query": "substitution saturation test Iss Dambe formula implementation python entropy",
  "searchResults": [
    {
      "title": "substitution - 搜索 词典",
      "snippet": "6. A substitution character is always converted into another substitution character in any other code page. 在 任何 其他 代码 页 中，  …",
      "link": "https://global.bing.com/dict/search?q=substitution&FORM=BDVSP6&cc=cn"
    },
    {
      "title": "substitution是什么意思_substitution的翻译_音标_读音_用法 ...",
      "snippet": "爱词霸权威在线词典,为您提供substitution的中文意思,substitution的用法讲解,substitution的读音,substitution的同义词,substitution的反 …",
      "link": "https://www.iciba.com/word?w=substitution"
    },
    {
      "title": "SUBSTITUTION中文 (简体)翻译：剑桥词典",
      "snippet": "We can use many different words and phrases in substitution, including words such as both, either, some (indefinite quantifying …",
      "link": "https://dictionary.cambridge.org/zhs/%E8%AF%8D%E5%85%B8/%E8%8B%B1%E8%AF%AD-%E6%B1%89%E8%AF%AD-%E7%AE%80%E4%BD%93/substitution"
    },
    {
      "title": "replacement和substitution的区别",
      "snippet": "“substitution”则更侧重于抽象概念的替代，强调替代的逻辑关系和规则。 在实际使用中，应根据具体语境选择合适的词语来表达意思。 …",
      "link": "https://cp.baidu.com/landing/tscp_doc/9db550300896697edb43bcc7c9356130"
    },
    {
      "title": "substitution是什么意思_substitution在线翻译_英语_读音 ...",
      "snippet": "海词词典,最权威的学习词典,为您提供substitution的在线翻译,substitution是什么意思,substitution的真人发音,权威用法和精选例句等。",
      "link": "https://corp.dict.cn/substitution"
    },
    {
      "title": "SUBSTITUTION在剑桥英语词典中的解释及翻译",
      "snippet": "We can use many different words and phrases in substitution, including words such as both, either, some (indefinite quantifying …",
      "link": "https://dictionary.cambridge.org/zhs/%E8%AF%8D%E5%85%B8/%E8%8B%B1%E8%AF%AD/substitution"
    },
    {
      "title": "substitute（英语单词）_百度百科",
      "snippet": "化学领域的 取代反应 （substitution reaction）指一个原子或基团被另一个替代，遵循IUPAC规则生成的化合物可称为取代化合 …",
      "link": "https://baike.baidu.com/item/substitute/3930812"
    },
    {
      "title": "substitution是什么意思_substitution怎么读_substitution翻译 ...",
      "snippet": "substitution是什么意思？ substitution怎么读？ 新东方在线字典为用户提供单词substitution的释义、substitution的音标和发音 …",
      "link": "https://www.koolearn.com/dict/wd_144443.html"
    },
    {
      "title": "subsitude alternative replace这几个词，有什么区别? - 知乎",
      "snippet": "2018年10月13日 · 这几个单词 你想问的应是这几个单词作为“替代”意思时有什么区别吧？第一个单词拼写错误了， Substitute才是， 这 …",
      "link": "https://www.zhihu.com/question/298270331"
    },
    {
      "title": "Integration by Substitution - Math is Fun",
      "snippet": "2024年12月26日 · Integration by Substitution (also called u-Substitution or The Reverse Chain Rule) is a method to find an integral, …",
      "link": "https://www.mathsisfun.com/calculus/integration-by-substitution.html"
    }
  ]
}

write_file (state: result)

Args

{
  "content": "#!/usr/bin/env Rscript\n# 饱和检验 (核酸 CDS) —— 多亚型流感 A HA\n# 依据 distance-calculations skill: 饱和图(转换/颠换 vs 校正距离) + p vs 校正距离\nsuppressMessages(library(ape))\naln <- read.dna(\"results/flu_HA_CDS.aln.fasta\", format = \"fasta\")\ncat(sprintf(\"序列: %d  位点: %d\\n\", nrow(aln), ncol(aln)))\n\npd <- as.vector(dist.dna(aln, model=\"raw\",  pairwise.deletion=TRUE))\njc <- as.vector(dist.dna(aln, model=\"JC69\", pairwise.deletion=TRUE))\ntn <- as.vector(dist.dna(aln, model=\"TN93\", pairwise.deletion=TRUE))\nts <- as.vector(dist.dna(aln, model=\"TS\",   pairwise.deletion=TRUE))  # 转换\ntv <- as.vector(dist.dna(aln, model=\"TV\",   pairwise.deletion=TRUE))  # 颠换\n\ncat(sprintf(\"p-distance : 均值 %.3f  最大 %.3f\\n\", mean(pd), max(pd)))\ncat(sprintf(\"JC69 校正  : 均值 %.3f  最大 %.3f  (放大 %.2f 倍)\\n\", mean(jc), max(jc), mean(jc)/mean(pd)))\ncat(sprintf(\"TN93 校正  : 均值 %.3f  最大 %.3f\\n\", mean(tn), max(tn)))\ncat(sprintf(\"p>0.4 的对: %.1f%%   p>0.5 的对: %.1f%%   p>0.6 的对: %.1f%%\\n\",\n            100*mean(pd>0.4), 100*mean(pd>0.5), 100*mean(pd>0.6)))\ncat(sprintf(\"转换均值 %.3f  颠换均值 %.3f  TS/TV=%.2f\\n\", mean(ts,na.rm=TRUE), mean(tv,na.rm=TRUE), mean(ts,na.rm=TRUE)/mean(tv,na.rm=TRUE)))\n\nm_ts <- lm(ts ~ tn); m_tv <- lm(tv ~ tn)\ncat(sprintf(\"转换~距离 斜率 %.3f ; 颠换~距离 斜率 %.3f (饱和时转换斜率趋近0)\\n\", coef(m_ts)[2], coef(m_tv)[2]))\n\npng(\"results/saturation_plot.png\", width=1500, height=700, res=130)\npar(mfrow=c(1,2), mar=c(5,5,3,1))\nplot(pd, jc, pch=19, cex=.5, col=rgb(0.1,0.3,0.8,.35),\n     xlab=\"p-distance (observed)\", ylab=\"JC69 corrected distance\",\n     main=\"(a) 校正距离 vs 观察距离\")\nabline(0,1,col=\"red\",lty=2)\nlegend(\"topleft\", c(\"y = x (no saturation)\", \"pairwise\"), col=c(\"red\",\"blue\"), lty=c(2,NA), pch=c(NA,19), bty=\"n\", cex=.9)\n\nplot(tn, ts, pch=19, cex=.5, col=rgb(1,0,0,.35), xlab=\"TN93 corrected distance\",\n     ylab=\"substitutions per site\", main=\"(b) 转换(红)/颠换(蓝) vs 距离\",\n     ylim=c(0, max(c(ts,tv),na.rm=TRUE)*1.05))\npoints(tn, tv, pch=19, cex=.5, col=rgb(0,0,1,.35))\nabline(m_ts, col=\"red\", lwd=2); abline(m_tv, col=\"blue\", lwd=2)\nlegend(\"topleft\", c(\"transitions\",\"transversions\"), col=c(\"red\",\"blue\"), lwd=2, bty=\"n\", cex=.9)\ndev.off()\ncat(\"图: results/saturation_plot.png\\n\")\n",
  "file_path": "/home/wangzg/Nutstore Files/Nutstore/备课/2026秋/分子进化分析/Chatbox_bioinformatics/scripts/saturation.R"
}

Result

{
  "success": true,
  "file_path": "/home/wangzg/Nutstore Files/Nutstore/备课/2026秋/分子进化分析/Chatbox_bioinformatics/scripts/saturation.R"
}

run_command (state: result)

Args

{
  "command": "cd \"/home/wangzg/Nutstore Files/Nutstore/备课/2026秋/分子进化分析/Chatbox_bioinformatics\"\n/home/wangzg/miniconda3/envs/biosoft/bin/Rscript scripts/saturation.R 2>&1 | grep -vE \"^Warning|mbcsToSbcs|^In addition\" | tail -20\nls -lh results/saturation_plot.png 2>/dev/null | awk '{print $5\"\\t\"$9}'",
  "timeout": 90000
}

Result

{
  "success": true,
  "exitCode": 0,
  "stdout": "序列: 34  位点: 1801\np-distance : 均值 0.416  最大 0.580\nJC69 校正  : 均值 0.667  最大 1.115  (放大 1.60 倍)\nTN93 校正  : 均值 0.685  最大 1.168\np>0.4 的对: 68.4%   p>0.5 的对: 22.8%   p>0.6 的对: 0.0%\n转换均值 293.064  颠换均值 401.913  TS/TV=0.73\n转换~距离 斜率 300.660 ; 颠换~距离 斜率 546.785 (饱和时转换斜率趋近0)\nnull device \n          1 \n图: results/saturation_plot.png\n107K\tresults/saturation_plot.png\n",
  "stderr": "",
  "cwd": "/home/wangzg/Nutstore Files/Nutstore/备课/2026秋/分子进化分析/Chatbox_bioinformatics",
  "sandboxed": false
}

run_command (state: result)

Args

{
  "command": "cd \"/home/wangzg/Nutstore Files/Nutstore/备课/2026秋/分子进化分析/Chatbox_bioinformatics\"\n/home/wangzg/miniconda3/envs/biosoft/bin/python - <<'PY'\nfrom Bio import AlignIO\nimport itertools\na=AlignIO.read(\"results/flu_HA_CDS.aln.fasta\",\"fasta\")\nseqs={r.id:str(r.seq).upper() for r in a}\ntrans={('A','G'),('G','A'),('C','T'),('T','C')}\nts_tot=tv_tot=same_tot=diff_tot=0\nper=[]\nfor x,y in itertools.combinations(seqs.values(),2):\n    ts=tv=0\n    for i,j in zip(x,y):\n        if i not in \"ACGT\" or j not in \"ACGT\": continue\n        if i==j: continue\n        if (i,j) in trans: ts+=1\n        else: tv+=1\n    ts_tot+=ts; tv_tot+=tv\n    if ts+tv>0: per.append(ts/(ts+tv))\nprint(f\"总转换={ts_tot}  总颠换={tv_tot}  TS/TV={ts_tot/tv_tot:.3f}\")\nprint(f\"各序列对中 转换占比: 均值={sum(per)/len(per):.3f} 范围={min(per):.3f}-{max(per):.3f}\")\nprint(f\"转换占多数的序列对: {sum(1 for p in per if p>0.5)}/{len(per)}\")\nPY"
}

Result

{
  "success": true,
  "exitCode": 0,
  "stdout": "总转换=138243  总颠换=191521  TS/TV=0.722\n各序列对中 转换占比: 均值=0.452 范围=0.000-1.000\n转换占多数的序列对: 57/459\n",
  "stderr": "",
  "cwd": "/home/wangzg/Nutstore Files/Nutstore/备课/2026秋/分子进化分析/Chatbox_bioinformatics",
  "sandboxed": false
}

run_command (state: result)

Args

{
  "command": "cd \"/home/wangzg/Nutstore Files/Nutstore/备课/2026秋/分子进化分析/Chatbox_bioinformatics\"\n/home/wangzg/miniconda3/envs/biosoft/bin/python - <<'PY'\nfrom Bio import AlignIO\nimport itertools, re\na=AlignIO.read(\"results/flu_HA_CDS.aln.fasta\",\"fasta\")\ndef sub(n):\n    m=re.search(r'(H\\d+N\\d+|FluB)',n); return m.group(1) if m else \"?\"\nseqs={r.id:str(r.seq).upper() for r in a}\ntrans={('A','G'),('G','A'),('C','T'),('T','C')}\ncats={'亚型内':[], '同大群内(亚型间)':[], '跨大群':[], '含流感B(跨属)':[]}\ng1={\"H1N1\",\"H2N2\",\"H5N1\",\"H9N2\"}; g2={\"H3N2\",\"H4N6\",\"H7N9\",\"H10N8\"}\nfor (n1,s1),(n2,s2) in itertools.combinations(seqs.items(),2):\n    a1,b1=sub(n1),sub(n2)\n    ts=tv=diff=tot=0\n    for i,j in zip(s1,s2):\n        if i not in \"ACGT\" or j not in \"ACGT\": continue\n        tot+=1\n        if i!=j:\n            diff+=1\n            if (i,j) in trans: ts+=1\n            else: tv+=1\n    p=diff/tot if tot else 0\n    cat = ('含流感B(跨属)' if 'FluB' in (a1,b1) else\n           '亚型内' if a1==b1 else\n           '同大群内(亚型间)' if ({a1,b1}<=g1 or {a1,b1}<=g2) else '跨大群')\n    cats[cat].append((p, ts/(ts+tv) if ts+tv else float('nan')))\nprint(f\"{'类别':<18}{'对数':>5}{'平均p':>8}{'平均TS占比(TS/TV)':>18}\")\nfor k,v in cats.items():\n    if not v: continue\n    mp=sum(p for p,_ in v)/len(v)\n    mt=sum(t for _,t in v if t==t)/len([t for _,t in v if t==t])\n    print(f\"{k:<18}{len(v):>5}{mp:>8.3f}{mt:>18.3f}\")\nPY"
}

Result

{
  "success": true,
  "exitCode": 0,
  "stdout": "类别                   对数     平均p     平均TS占比(TS/TV)\n亚型内                  44   0.133             0.678\n同大群内(亚型间)           104   0.403             0.446\n跨大群                 258   0.452             0.434\n含流感B(跨属)             59   0.559             0.389\n",
  "stderr": "",
  "cwd": "/home/wangzg/Nutstore Files/Nutstore/备课/2026秋/分子进化分析/Chatbox_bioinformatics",
  "sandboxed": false
}

run_command (state: result)

Args

{
  "command": "cd \"/home/wangzg/Nutstore Files/Nutstore/备课/2026秋/分子进化分析/Chatbox_bioinformatics\"\nls -lh results/flu_HA_ml_ggtree.* results/flu_HA_ml.treefile 2>/dev/null\necho \"---\"\necho \"绝对路径:\"\nls -d \"$PWD\"/results/flu_HA_ml_ggtree.* 2>/dev/null"
}

Result

{
  "success": true,
  "exitCode": 0,
  "stdout": "-rw-rw-r-- 1 wangzg wangzg 7.0K Sep 15 13:48 results/flu_HA_ml_ggtree.pdf\n-rw-rw-r-- 1 wangzg wangzg 345K Sep 15 13:48 results/flu_HA_ml_ggtree.png\n-rw-rw-r-- 1 wangzg wangzg 2.3K Sep 15 13:47 results/flu_HA_ml.treefile\n---\n绝对路径:\n/home/wangzg/Nutstore Files/Nutstore/备课/2026秋/分子进化分析/Chatbox_bioinformatics/results/flu_HA_ml_ggtree.pdf\n/home/wangzg/Nutstore Files/Nutstore/备课/2026秋/分子进化分析/Chatbox_bioinformatics/results/flu_HA_ml_ggtree.png\n",
  "stderr": "",
  "cwd": "/home/wangzg/Nutstore Files/Nutstore/备课/2026秋/分子进化分析/Chatbox_bioinformatics",
  "sandboxed": false
}

run_command (state: result)

Args

{
  "command": "cd \"/home/wangzg/Nutstore Files/Nutstore/备课/2026秋/分子进化分析/Chatbox_bioinformatics\"\necho \"== treefile 各叶标签 ==\"\n/home/wangzg/miniconda3/envs/biosoft/bin/python - <<'PY'\nfrom Bio import Phylo\nt=Phylo.read(\"results/flu_HA_ml.treefile\",\"newick\")\nfor x in t.get_terminals():\n    print(repr(x.name))\nPY"
}

Result

{
  "success": true,
  "exitCode": 0,
  "stdout": "== treefile 各叶标签 ==\n'A/Texas/ISC-1436/2025_H1N1'\n'A/Texas/ISC-1431/2025_H1N1'\n'A/Texas/ISC-1433/2025_H1N1'\n'A/Texas/ISC-1108/2026_H1N1'\n'A/Lesser_Scaup/Illinois/4228/2026_H2N2'\n'A/blue-winged_Teal/NE/25-022276-004-original/2025_H2N2'\n'A/blue-winged_Teal/NE/25-022276-006-original/2025_H2N2'\n'A/American_black'\n'A/redhead_duck/VA/24-038861-019-original/2024_H5N1'\n'A/goose/TX/25-003539-002-original/2025_H5N1'\n'A/goose/TX/25-003539-001-original/2025_H5N1'\n'A/redhead_duck/FL/24-035773-011-original/2024_H5N1'\n'A/chicken/Guinea/5/2022_H9N2'\n'A/Sterna_dougallii/Boke/15/2023_H9N2'\n'A/chicken/Guinea/4/2022_H9N2'\n'A/Sterna_dougallii/Boke/14/2023_H9N2'\n'A/Minnesota/ISC-1070/2026_H3N2'\n'A/Minnesota/ISC-1069/2026_H3N2'\n'A/Minnesota/ISC-1066/2026_H3N2'\n'A/Minnesota/ISC-1064/2026_H3N2'\n'A/mallard/Germany-SN/VL202603458/2026_H4N6'\n'A/Mallard/Manitoba/307/2025_H4N6'\n'A/Mallard/Manitoba/242/2025_H4N6'\n'A/Mallard/Manitoba/273/2025_H4N6'\n'A/duck/Bangladesh/73718/2026_H7N9'\n'A/duck/Bangladesh/72461/2026_H7N9'\n'A/duck/Bangladesh/72311/2026_H7N9'\n'A/chicken/MS/25-008229-001-original/2025_H7N9'\n'A/duck/Sukhbaatar/16/2024_H10N8'\n'A/ibis/Zambia/1766/2015_H10N8'\n'A/ibis/Zambia/1750/2015_H10N8'\n'A/Wild_duck/South'\n'B/California/LACPHL-INF02824/2026_FluB'\n'B/California/LACPHL-INF02823/2026_FluB'\n",
  "stderr": "",
  "cwd": "/home/wangzg/Nutstore Files/Nutstore/备课/2026秋/分子进化分析/Chatbox_bioinformatics",
  "sandboxed": false
}

run_command (state: result)

Args

{
  "command": "cd \"/home/wangzg/Nutstore Files/Nutstore/备课/2026秋/分子进化分析/Chatbox_bioinformatics\"\necho \"== 原始 FASTA ==\"; grep \">\" data/flu_HA_multisubtype.fa | grep -E \"American|Wild|Lesser\"\necho \"== MAFFT 比对输出 ==\"; grep \">\" results/flu_HA.aln.fasta | grep -E \"American|Wild|Lesser\"\necho \"== 原始文件里带空格的株名 ==\"; grep \">\" data/flu_HA_multisubtype.fa | grep \" \" | head"
}

Result

{
  "success": true,
  "exitCode": 0,
  "stdout": "== 原始 FASTA ==\n>A/Lesser Scaup/Illinois/4228/2026_H2N2\n>A/American black duck/ME/25-026006-001-original-repeat2/2025_H2N2\n>A/Wild duck/South Korea/KNU2020-167/2020_H10N8\n== MAFFT 比对输出 ==\n>A/Lesser Scaup/Illinois/4228/2026_H2N2\n>A/American black duck/ME/25-026006-001-original-repeat2/2025_H2N2\n>A/Wild duck/South Korea/KNU2020-167/2020_H10N8\n== 原始文件里带空格的株名 ==\n>A/Lesser Scaup/Illinois/4228/2026_H2N2\n>A/blue-winged Teal/NE/25-022276-004-original/2025_H2N2\n>A/American black duck/ME/25-026006-001-original-repeat2/2025_H2N2\n>A/blue-winged Teal/NE/25-022276-006-original/2025_H2N2\n>A/redhead duck/VA/24-038861-019-original/2024_H5N1\n>A/redhead duck/FL/24-035773-011-original/2024_H5N1\n>A/Sterna dougallii/Boke/15/2023_H9N2\n>A/Sterna dougallii/Boke/14/2023_H9N2\n>A/Wild duck/South Korea/KNU2020-167/2020_H10N8\n",
  "stderr": "",
  "cwd": "/home/wangzg/Nutstore Files/Nutstore/备课/2026秋/分子进化分析/Chatbox_bioinformatics",
  "sandboxed": false
}

run_command (state: result)

Args

{
  "command": "cd \"/home/wangzg/Nutstore Files/Nutstore/备课/2026秋/分子进化分析/Chatbox_bioinformatics\"\necho \"== treefile 原始文本(截取含 American/blue-winged/Wild 的部分) ==\"\ntr ',' '\\n' < results/flu_HA_ml.treefile | grep -iE \"american|blue-winged|wild|lesser|redhead|mallard\" | head"
}

Result

{
  "success": true,
  "exitCode": 0,
  "stdout": "== treefile 原始文本(截取含 American/blue-winged/Wild 的部分) ==\n((A/Lesser_Scaup/Illinois/4228/2026_H2N2:0.0127999701\n((A/blue-winged_Teal/NE/25-022276-004-original/2025_H2N2:0.0000010000\nA/blue-winged_Teal/NE/25-022276-006-original/2025_H2N2:0.0000010000)98.8/98:0.0171175163\nA/American_black:0.0073217863)47.6/69:0.0083031035)100/100:0.1888263068\n((((A/redhead_duck/VA/24-038861-019-original/2024_H5N1:0.0000000000\nA/redhead_duck/FL/24-035773-011-original/2024_H5N1:0.0000010000)65.6/75:0.0037608465\n(A/mallard/Germany-SN/VL202603458/2026_H4N6:0.0215061723\n((A/Mallard/Manitoba/307/2025_H4N6:0.0000010000\nA/Mallard/Manitoba/242/2025_H4N6:0.0000010000)0/80:0.0000010000\nA/Mallard/Manitoba/273/2025_H4N6:0.0018233301)92.7/96:0.0238620820)100/100:0.2603468106)100/100:0.3993432674\n",
  "stderr": "",
  "cwd": "/home/wangzg/Nutstore Files/Nutstore/备课/2026秋/分子进化分析/Chatbox_bioinformatics",
  "sandboxed": false
}

run_command (state: result)

Args

{
  "command": "cd \"/home/wangzg/Nutstore Files/Nutstore/备课/2026秋/分子进化分析/Chatbox_bioinformatics\"\nP=/home/wangzg/miniconda3/envs/biosoft/bin\n# 1) 去空格\n$P/python - <<'PY'\nwith open(\"data/flu_HA_multisubtype.fa\") as f, open(\"data/flu_HA_clean.fa\",\"w\") as o:\n    for line in f:\n        o.write(\">\"+line[1:].strip().replace(\" \",\"_\")+\"\\n\" if line.startswith(\">\") else line)\nprint(\"去空格完成\")\nPY\n# 2) 比对\n$P/mafft --amino --globalpair --maxiterate 1000 --thread 4 data/flu_HA_clean.fa > results/flu_HA_clean.aln.fasta 2>/dev/null && echo \"比对完成\"\n# 3) 建树\n$P/iqtree -s results/flu_HA_clean.aln.fasta -m MFP -B 1000 -alrt 1000 \\\n  -o \"B/California/LACPHL-INF02824/2026_FluB,B/California/LACPHL-INF02823/2026_FluB\" \\\n  -T 4 -pre results/flu_HA_clean_ml -redo > results/flu_HA_clean_ml.log 2>&1\ngrep -m1 \"Best-fit model accord\" results/flu_HA_clean_ml.log\necho \"== 重建树后的叶标签 ==\"\n$P/python -c \"from Bio import Phylo;[print(x.name) for x in Phylo.read('results/flu_HA_clean_ml.treefile','newick').get_terminals()]\"",
  "timeout": 115000
}

Result

{
  "success": true,
  "exitCode": 0,
  "stdout": "去空格完成\n比对完成\n== 重建树后的叶标签 ==\nA/Texas/ISC-1436/2025_H1N1\nA/Texas/ISC-1431/2025_H1N1\nA/Texas/ISC-1433/2025_H1N1\nA/Texas/ISC-1108/2026_H1N1\nA/Lesser_Scaup/Illinois/4228/2026_H2N2\nA/blue-winged_Teal/NE/25-022276-004-original/2025_H2N2\nA/blue-winged_Teal/NE/25-022276-006-original/2025_H2N2\nA/American_black_duck/ME/25-026006-001-original-repeat2/2025_H2N2\nA/redhead_duck/VA/24-038861-019-original/2024_H5N1\nA/goose/TX/25-003539-002-original/2025_H5N1\nA/goose/TX/25-003539-001-original/2025_H5N1\nA/redhead_duck/FL/24-035773-011-original/2024_H5N1\nA/chicken/Guinea/5/2022_H9N2\nA/Sterna_dougallii/Boke/15/2023_H9N2\nA/chicken/Guinea/4/2022_H9N2\nA/Sterna_dougallii/Boke/14/2023_H9N2\nA/Minnesota/ISC-1070/2026_H3N2\nA/Minnesota/ISC-1069/2026_H3N2\nA/Minnesota/ISC-1066/2026_H3N2\nA/Minnesota/ISC-1064/2026_H3N2\nA/mallard/Germany-SN/VL202603458/2026_H4N6\nA/Mallard/Manitoba/307/2025_H4N6\nA/Mallard/Manitoba/242/2025_H4N6\nA/Mallard/Manitoba/273/2025_H4N6\nA/duck/Bangladesh/73718/2026_H7N9\nA/duck/Bangladesh/72461/2026_H7N9\nA/duck/Bangladesh/72311/2026_H7N9\nA/chicken/MS/25-008229-001-original/2025_H7N9\nA/duck/Sukhbaatar/16/2024_H10N8\nA/ibis/Zambia/1766/2015_H10N8\nA/ibis/Zambia/1750/2015_H10N8\nA/Wild_duck/South_Korea/KNU2020-167/2020_H10N8\nB/California/LACPHL-INF02824/2026_FluB\nB/California/LACPHL-INF02823/2026_FluB\n",
  "stderr": "",
  "cwd": "/home/wangzg/Nutstore Files/Nutstore/备课/2026秋/分子进化分析/Chatbox_bioinformatics",
  "sandboxed": false
}

write_file (state: result)

Args

{
  "content": "#!/usr/bin/env Rscript\n# 多亚型流感 HA ML 树 —— 完整株名版 (去空格后重建的树)\nsuppressMessages({library(ggtree); library(ggplot2); library(ape)})\ntr <- read.tree(\"results/flu_HA_clean_ml.treefile\")\ngrp <- sub(\".*_\", \"\", tr$tip.label)\nd <- data.frame(label = tr$tip.label, subtype = grp)\n\np <- ggtree(tr, size = 0.6, right = TRUE) %<+% d +\n  geom_tiplab(aes(color = subtype), size = 2.3, align = TRUE, linesize = 0.3) +\n  geom_nodelab(aes(label = label), size = 1.9, color = \"grey35\", hjust = 1.3, vjust = -0.5) +\n  geom_treescale(width = 0.2, fontsize = 2.6, offset = 0.2) +\n  ggtitle(\"Influenza A HA across 8 subtypes (+ influenza B outgroup)\",\n          subtitle = \"ML, IQ-TREE (LG+G4; UFBoot1000 + SH-aLRT); grey numbers = SH-aLRT/UFBoot\") +\n  theme(plot.title = element_text(size = 12),\n        plot.subtitle = element_text(size = 8),\n        plot.margin = margin(6, 120, 6, 6)) +      # 右侧留白防截断\n  scale_x_continuous(expand = expansion(mult = c(0.02, 0.35)))\n\nggsave(\"results/flu_HA_ml_ggtree.pdf\", p, width = 13, height = 9)\nggsave(\"results/flu_HA_ml_ggtree.png\", p, width = 13, height = 9, dpi = 300)\ncat(\"写出 results/flu_HA_ml_ggtree.pdf/.png (完整标签)\\n\")\n",
  "file_path": "/home/wangzg/Nutstore Files/Nutstore/备课/2026秋/分子进化分析/Chatbox_bioinformatics/scripts/visualize_flu_HA.R"
}

Result

{
  "success": true,
  "file_path": "/home/wangzg/Nutstore Files/Nutstore/备课/2026秋/分子进化分析/Chatbox_bioinformatics/scripts/visualize_flu_HA.R"
}

run_command (state: result)

Args

{
  "command": "cd \"/home/wangzg/Nutstore Files/Nutstore/备课/2026秋/分子进化分析/Chatbox_bioinformatics\"\n/home/wangzg/miniconda3/envs/biosoft/bin/Rscript scripts/visualize_flu_HA.R 2>&1 | grep -viE \"warning|mbcsToSbcs|In addition|deprecated|report the issue|^[0-9]+:\" | tail -5\nls -lh results/flu_HA_ml_ggtree.* | awk '{print $5\"\\t\"$9}'",
  "timeout": 90000
}

Result

{
  "success": true,
  "exitCode": 0,
  "stdout": "ℹ Please use `linewidth` instead.\n写出 results/flu_HA_ml_ggtree.pdf/.png (完整标签)\n7.4K\tresults/flu_HA_ml_ggtree.pdf\n423K\tresults/flu_HA_ml_ggtree.png\n",
  "stderr": "",
  "cwd": "/home/wangzg/Nutstore Files/Nutstore/备课/2026秋/分子进化分析/Chatbox_bioinformatics",
  "sandboxed": false
}

write_file (state: result)

Args

{
  "content": "#!/usr/bin/env Rscript\n# 多亚型流感 HA ML 树 —— 调整版\n# 调整: 1) 株名去掉末尾 _HxNx 后缀, 改由颜色图例表示亚型\n#       2) 加宿主标注(点形状+图例)  3) 株名本身保持完整  4) 右侧留白防截断\nsuppressMessages({library(ggtree); library(ggplot2); library(ape)})\ntr <- read.tree(\"results/flu_HA_clean_ml.treefile\")\ntip     <- tr$tip.label\nsubtype <- sub(\".*_\", \"\", tip)          # H1N1 ... FluB\nstrain  <- sub(\"_[^_]*$\", \"\", tip)       # 去掉 _HxNx 后缀\nhost <- ifelse(grepl(\"Texas|Minnesota|California\", tip), \"human\",\n        ifelse(grepl(\"chicken|goose\", tip), \"poultry\", \"wild bird\"))\nd <- data.frame(tip = tip, strain = strain, subtype = subtype, host = host)\n\np <- ggtree(tr, size = 0.6) %<+% d +\n  geom_tippoint(aes(shape = host), size = 1.9, color = \"grey25\") +\n  geom_tiplab(aes(label = strain, color = subtype), size = 2.3, align = TRUE, offset = 0.02) +\n  geom_nodelab(aes(label = label), size = 1.8, color = \"grey40\", hjust = 1.3, vjust = -0.5) +\n  geom_treescale(width = 0.2, fontsize = 2.6, offset = 0.2) +\n  scale_x_continuous(expand = expansion(mult = c(0.02, 0.35))) +\n  labs(title = \"Influenza A HA across 8 subtypes (+ influenza B outgroup)\",\n       subtitle = \"ML, IQ-TREE (LG+G4; UFBoot1000 + SH-aLRT); grey numbers = SH-aLRT/UFBoot\",\n       color = \"subtype\", shape = \"host\") +\n  theme(plot.title = element_text(size = 12),\n        plot.subtitle = element_text(size = 8),\n        plot.margin = margin(6, 150, 6, 6))\n\nggsave(\"results/flu_HA_ml_ggtree.pdf\", p, width = 13, height = 9)\nggsave(\"results/flu_HA_ml_ggtree.png\", p, width = 13, height = 9, dpi = 300)\nwrite.csv(d[, c(\"tip\", \"subtype\", \"host\")], \"results/flu_HA_tipinfo.csv\", row.names = FALSE)\ncat(\"写出 results/flu_HA_ml_ggtree.pdf/.png + results/flu_HA_tipinfo.csv\\n\")\n",
  "file_path": "/home/wangzg/Nutstore Files/Nutstore/备课/2026秋/分子进化分析/Chatbox_bioinformatics/scripts/visualize_flu_HA.R"
}

Result

{
  "success": true,
  "file_path": "/home/wangzg/Nutstore Files/Nutstore/备课/2026秋/分子进化分析/Chatbox_bioinformatics/scripts/visualize_flu_HA.R"
}

run_command (state: result)

Args

{
  "command": "cd \"/home/wangzg/Nutstore Files/Nutstore/备课/2026秋/分子进化分析/Chatbox_bioinformatics\"\n/home/wangzg/miniconda3/envs/biosoft/bin/Rscript scripts/visualize_flu_HA.R 2>&1 | grep -viE \"warning|mbcsToSbcs|In addition|deprecated|report the issue|linewidth|^[0-9]+:|^ℹ\" | tail -4\necho \"== 宿主分类核对 ==\"\ncolumn -s, -t results/flu_HA_tipinfo.csv 2>/dev/null | sed 's/_H7N9//' | head -40\nls -lh results/flu_HA_ml_ggtree.pdf results/flu_HA_ml_ggtree.png | awk '{print $5\"\\t\"$9}'",
  "timeout": 90000
}

Result

{
  "success": true,
  "exitCode": 0,
  "stdout": "写出 results/flu_HA_ml_ggtree.pdf/.png + results/flu_HA_tipinfo.csv\n== 宿主分类核对 ==\n\"tip\"                                                                \"subtype\"  \"host\"\n\"A/Texas/ISC-1436/2025_H1N1\"                                         \"H1N1\"     \"human\"\n\"A/Texas/ISC-1431/2025_H1N1\"                                         \"H1N1\"     \"human\"\n\"A/Texas/ISC-1433/2025_H1N1\"                                         \"H1N1\"     \"human\"\n\"A/Texas/ISC-1108/2026_H1N1\"                                         \"H1N1\"     \"human\"\n\"A/Lesser_Scaup/Illinois/4228/2026_H2N2\"                             \"H2N2\"     \"wild bird\"\n\"A/blue-winged_Teal/NE/25-022276-004-original/2025_H2N2\"             \"H2N2\"     \"wild bird\"\n\"A/blue-winged_Teal/NE/25-022276-006-original/2025_H2N2\"             \"H2N2\"     \"wild bird\"\n\"A/American_black_duck/ME/25-026006-001-original-repeat2/2025_H2N2\"  \"H2N2\"     \"wild bird\"\n\"A/redhead_duck/VA/24-038861-019-original/2024_H5N1\"                 \"H5N1\"     \"wild bird\"\n\"A/goose/TX/25-003539-002-original/2025_H5N1\"                        \"H5N1\"     \"poultry\"\n\"A/goose/TX/25-003539-001-original/2025_H5N1\"                        \"H5N1\"     \"poultry\"\n\"A/redhead_duck/FL/24-035773-011-original/2024_H5N1\"                 \"H5N1\"     \"wild bird\"\n\"A/chicken/Guinea/5/2022_H9N2\"                                       \"H9N2\"     \"poultry\"\n\"A/Sterna_dougallii/Boke/15/2023_H9N2\"                               \"H9N2\"     \"wild bird\"\n\"A/chicken/Guinea/4/2022_H9N2\"                                       \"H9N2\"     \"poultry\"\n\"A/Sterna_dougallii/Boke/14/2023_H9N2\"                               \"H9N2\"     \"wild bird\"\n\"A/Minnesota/ISC-1070/2026_H3N2\"                                     \"H3N2\"     \"human\"\n\"A/Minnesota/ISC-1069/2026_H3N2\"                                     \"H3N2\"     \"human\"\n\"A/Minnesota/ISC-1066/2026_H3N2\"                                     \"H3N2\"     \"human\"\n\"A/Minnesota/ISC-1064/2026_H3N2\"                                     \"H3N2\"     \"human\"\n\"A/mallard/Germany-SN/VL202603458/2026_H4N6\"                         \"H4N6\"     \"wild bird\"\n\"A/Mallard/Manitoba/307/2025_H4N6\"                                   \"H4N6\"     \"wild bird\"\n\"A/Mallard/Manitoba/242/2025_H4N6\"                                   \"H4N6\"     \"wild bird\"\n\"A/Mallard/Manitoba/273/2025_H4N6\"                                   \"H4N6\"     \"wild bird\"\n\"A/duck/Bangladesh/73718/2026\"                                  \"H7N9\"     \"wild bird\"\n\"A/duck/Bangladesh/72461/2026\"                                  \"H7N9\"     \"wild bird\"\n\"A/duck/Bangladesh/72311/2026\"                                  \"H7N9\"     \"wild bird\"\n\"A/chicken/MS/25-008229-001-original/2025\"                      \"H7N9\"     \"poultry\"\n\"A/duck/Sukhbaatar/16/2024_H10N8\"                                    \"H10N8\"    \"wild bird\"\n\"A/ibis/Zambia/1766/2015_H10N8\"                                      \"H10N8\"    \"wild bird\"\n\"A/ibis/Zambia/1750/2015_H10N8\"                                      \"H10N8\"    \"wild bird\"\n\"A/Wild_duck/South_Korea/KNU2020-167/2020_H10N8\"                     \"H10N8\"    \"wild bird\"\n\"B/California/LACPHL-INF02824/2026_FluB\"                             \"FluB\"     \"human\"\n\"B/California/LACPHL-INF02823/2026_FluB\"                             \"FluB\"     \"human\"\n8.7K\tresults/flu_HA_ml_ggtree.pdf\n416K\tresults/flu_HA_ml_ggtree.png\n",
  "stderr": "",
  "cwd": "/home/wangzg/Nutstore Files/Nutstore/备课/2026秋/分子进化分析/Chatbox_bioinformatics",
  "sandboxed": false
}