deepTools NGS Toolkit
davila7/claude-code-templates
Guides use of deepTools on sequencing data: BAM to bigWig conversion, QC, sample correlation, and heatmaps or profiles around TSS and peaks for ChIP-seq, RNA-seq and ATAC-seq.
Edit phylogenetic tree structure with Biopython Bio.Phylo, and treat rooting as a separate statistical inference rather than a display choice.
$ npx skills add GPTomics/bioSkills --skill bio-phylo-tree-manipulation -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install GPTomics/bioSkills bio-phylo-tree-manipulation --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/GPTomics/bioSkills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/phylogenetics/tree-manipulation .claude/skills/bio-phylo-tree-manipulation && rm -rf skills-srcUse ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.
Claude Code skills documentation · loads skills from .claude/skills/
Install the "bio-phylo-tree-manipulation" agent skill from https://github.com/GPTomics/bioSkills/tree/main/phylogenetics/tree-manipulation into .claude/skills/bio-phylo-tree-manipulation/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "bio-phylo-tree-manipulation", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/GPTomics/bioSkills/tree/main/phylogenetics/tree-manipulationType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add GPTomics/bioSkills --skill bio-phylo-tree-manipulation -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install GPTomics/bioSkills bio-phylo-tree-manipulation --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/GPTomics/bioSkills.git skills-src && mkdir -p .agents/skills && cp -r skills-src/phylogenetics/tree-manipulation .agents/skills/bio-phylo-tree-manipulation && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "bio-phylo-tree-manipulation" agent skill from https://github.com/GPTomics/bioSkills/tree/main/phylogenetics/tree-manipulation into .agents/skills/bio-phylo-tree-manipulation/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "bio-phylo-tree-manipulation", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add GPTomics/bioSkills --skill bio-phylo-tree-manipulation -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install GPTomics/bioSkills bio-phylo-tree-manipulation --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/GPTomics/bioSkills.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/phylogenetics/tree-manipulation .cursor/skills/bio-phylo-tree-manipulation && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "bio-phylo-tree-manipulation" agent skill from https://github.com/GPTomics/bioSkills/tree/main/phylogenetics/tree-manipulation into .cursor/skills/bio-phylo-tree-manipulation/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "bio-phylo-tree-manipulation", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/GPTomics/bioSkills.git --path phylogenetics/tree-manipulation--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add GPTomics/bioSkills --skill bio-phylo-tree-manipulation -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install GPTomics/bioSkills bio-phylo-tree-manipulation --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/GPTomics/bioSkills.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/phylogenetics/tree-manipulation .gemini/skills/bio-phylo-tree-manipulation && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "bio-phylo-tree-manipulation" agent skill from https://github.com/GPTomics/bioSkills/tree/main/phylogenetics/tree-manipulation into .gemini/skills/bio-phylo-tree-manipulation/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "bio-phylo-tree-manipulation", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install GPTomics/bioSkills bio-phylo-tree-manipulationInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add GPTomics/bioSkills --skill bio-phylo-tree-manipulation -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/GPTomics/bioSkills.git skills-src && mkdir -p .github/skills && cp -r skills-src/phylogenetics/tree-manipulation .github/skills/bio-phylo-tree-manipulation && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "bio-phylo-tree-manipulation" agent skill from https://github.com/GPTomics/bioSkills/tree/main/phylogenetics/tree-manipulation into .github/skills/bio-phylo-tree-manipulation/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "bio-phylo-tree-manipulation", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add GPTomics/bioSkills --skill bio-phylo-tree-manipulation -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install GPTomics/bioSkills bio-phylo-tree-manipulation --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/GPTomics/bioSkills.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/phylogenetics/tree-manipulation .opencode/skills/bio-phylo-tree-manipulation && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "bio-phylo-tree-manipulation" agent skill from https://github.com/GPTomics/bioSkills/tree/main/phylogenetics/tree-manipulation into .opencode/skills/bio-phylo-tree-manipulation/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "bio-phylo-tree-manipulation", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
bio-phylo-tree-manipulationEdit phylogenetic tree structure with Biopython Bio.Phylo, and treat rooting as a separate statistical inference rather than a display choice.
Bio Phylo Tree Manipulation is an agent skill from GPTomics/bioSkills. Edit phylogenetic tree structure with Biopython Bio.Phylo, and treat rooting as a separate statistical inference rather than a display choice. Covers why most inference returns an unrooted tree so placing the root creates every ancestor/descendant and basal claim; why a distant or lonely outgroup misroots inside the ingroup via long-branch attraction; the outgroup/midpoint/MAD/MinVar/non-reversible-likelihood rooting tradeoffs; why pruning must suppress degree-2 nodes and sum their branch lengths or all patristic…
Its SKILL.md is about 5k tokens, which your agent loads only when the skill is triggered. The skill folder holds 6 other files (for example `examples/collapse_support.py`, `examples/common_ancestor.py` and `examples/prune_taxa.py`).
It sits in Research & Science, covering Bioinformatics and Data visualization. It works with Biopython and Python. The repository describes itself as: a set of SKILLS.md for doing bioinformatics with agents like claude code. The licence is MIT.
3 steps, taken from the first numbered list in SKILL.md.
Read from SKILL.md and the folder at commit d91ed3d. It shows what the files ask for, not the result of running them.
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
Ships script files (Python), which the agent can run.
Shell commands in SKILL.md call:
pipFrom the folder's file list and the shell code blocks in SKILL.md.
No URLs in SKILL.md. Its commands use pip, which can reach the network depending on how they are called.
From URLs in SKILL.md, links to its own repository left out.
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Bio Phylo Tree Manipulation loads about 5k tokens when it runs. Until then it costs about 244 tokens; SKILL.md has 2,166 words of instructions outside code blocks.
Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.
The automated check found no risky patterns in SKILL.md.
Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.
The full file from GPTomics/bioSkills at commit d91ed3d, republished under its MIT licence (© GPTomics). 2,166 words, ~4,985 tokens.
.claude/skills/bio-phylo-tree-manipulation/SKILL.md (or your agent's skills folder). This skill also uses 5 other files; get the full folder from GitHub.Reference examples tested with: BioPython 1.83+. Alternatives: ape 5.8+ / phangorn / phytools (R), DendroPy 5+ and ete3 (Python), Newick Utilities 1.6+, MAD and RootDigger as standalone CLIs.
Before using code patterns, verify installed versions match. If versions differ:
pip show biopython then help(module.function) to check signaturespackageVersion('ape') then ?drop.tip to verify parameters<tool> --version then <tool> --help to confirm flagsIf code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying.
ape has NO midpoint function of its own (use phangorn::midpoint or phytools::midpoint.root); ete3 prune drops branch lengths unless preserve_branch_length=True; ape di2multi filters by branch LENGTH (tol), not support.
"Root and prune my tree" -> Edit an existing tree object, treating the root placement as a separate statistical inference and every structural edit as potentially distance-corrupting.
tree.root_with_outgroup(...), tree.root_at_midpoint(), tree.prune(...), tree.collapse_all(...) (Bio.Phylo)root(), phangorn::midpoint(), drop.tip(), di2multi() (ape/phangorn); CLI: nw_reroot, nw_prune (Newick Utilities); outgroup-free rooters: mad, FastRoot.py, rootdiggerScope: editing an existing tree -- rooting, pruning/subsetting, extracting clades or induced subtrees, collapsing branches into polytomies, resolving polytomies, ladderizing. Reading/writing/converting tree files -> tree-io. Plotting and mapping annotations -> tree-visualization. Inferring the tree in the first place -> modern-tree-inference. Clock-based or Bayesian rooting co-estimated with dates -> divergence-dating.
Rooting is a separate statistical inference layered on top of the topology, and it is the highest-error decision in the entire tree. Nearly every inference method (ML under a time-reversible model, neighbor-joining, most Bayesian runs) returns an UNROOTED tree: a reversible model is blind to the direction of time, so the likelihood is identical wherever the root sits. The unrooted tree states which taxa are neighbors; it is silent about who is ancestor and who is descendant. Placing the root is what converts a neighbor-graph into an evolutionary narrative, and it CREATES every "X is basal", every "the common ancestor had trait T", every character-polarity, every divergence ordering. Three load-bearing facts:
| Method | Core assumption | Needs | Robust to rate variation | Gives root uncertainty | Best when | Avoid when |
|---|---|---|---|---|---|---|
| Outgroup (multiple, close) | outgroup truly outside ingroup, not LBA-misplaced | a priori outgroup taxa + sequences | moderate (depends on outgroup branch) | weakly (via ingroup-monophyly check) | closely related, balanced, monophyletic outgroups exist | only a distant/lonely outgroup available |
| Outgroup (single) | same, but one long branch | one outgroup taxon | poor (max LBA exposure) | no | a close single sister exists, nothing better | only a distant/lonely outgroup (one long branch, max LBA exposure) |
| Midpoint | strict molecular clock | tree with branch lengths | no | no | shallow, clock-like, intraspecific/viral data | deep trees, heterotachy, any long branch |
| MinVar | clock deviations are random/unbiased | tree with branch lengths | better than midpoint | no | outgroup-free, noise looks like unbiased clock scatter | strong lineage-specific rate shifts |
| MAD | minimal relative ancestor deviation | tree with branch lengths | yes (tolerates heterotachy) | yes (root ambiguity index) | outgroup-free deep/prokaryotic trees; default no-outgroup choice | strongly structured rate variation |
| Non-reversible likelihood (RootDigger / IQ-TREE) | non-reversible model carries directional signal | tree + ALIGNMENT; enough data | yes (model-based) | yes (per-branch likelihood confidence) | the alignment is available and a root confidence is needed | little data; weak signal; compute-limited |
| Relaxed clock (BEAST) -> divergence-dating | explicit clock + tree prior | alignment + calibrations/tip dates; MCMC | yes | yes (posterior over roots) | time-scaled / dated / phylodynamic analyses | a quick structural edit, no dating intended |
Rule: never report a root from a single method without a sanity check. If a close, monophyletic outgroup is available, use it. If not, run MAD and MinVar and prefer agreement; disagreement means the root is poorly determined and all "basal" claims must be hedged. With an alignment and a need for a confidence value, use RootDigger (Bettisworth and Stamatakis 2021) or non-reversible IQ-TREE. MAD = Tria et al. 2017; MinVar = Mai et al. 2017; outgroup-free CLIs and Newick Utilities = Junier and Zdobnov 2010.
| Tool (lang) | Rooting | Pruning / clade | Collapse / resolve | When |
|---|---|---|---|---|
| Bio.Phylo (Py) | root_with_outgroup, root_at_midpoint | prune, common_ancestor | collapse_all | general Python pipelines; the default here |
| ape / phangorn / phytools (R) | root, phangorn::midpoint, phytools::midpoint.root | drop.tip, extract.clade | di2multi, multi2di | R workflows; drop.tip sums suppressed branch lengths correctly |
| DendroPy (Py) | reroot_at_edge, reroot_at_midpoint | retain_taxa_with_labels(suppress_unifurcations=True) | edge collapse, resolve_polytomies | metadata-aware edits, posterior tree sets |
| ete3 (Py) | set_outgroup, set_outgroup(get_midpoint_outgroup()) | prune([...], preserve_branch_length=True), detach | delete, resolve_polytomy | NHX features; MUST pass preserve_branch_length |
| Newick Utilities (CLI) | nw_reroot | nw_prune, nw_clade | nw_ed, nw_condense | streaming/Unix-pipeline edits on many trees |
| MAD / MinVar / RootDigger (CLI) | mad, FastRoot.py, rootdigger | -- | -- | outgroup-free or likelihood rooting when no trustworthy outgroup exists |
Goal: Root the tree using known sister-group taxa, preferring multiple close outgroups and verifying ingroup monophyly first.
Approach: Confirm the outgroup taxa form a monophyletic group, root on the branch separating them from the ingroup, then check the ingroup is recovered as monophyletic -- if not, the rooting is suspect.
from Bio import Phylo
tree = Phylo.read('tree.nwk', 'newick')
outgroup = [{'name': 'OutA'}, {'name': 'OutB'}] # multiple close outgroups beat a single long branch
if tree.is_monophyletic([tree.find_any(name='OutA'), tree.find_any(name='OutB')]):
tree.root_with_outgroup(*outgroup) # root_with_outgroup, NOT root_with_midpoint
else:
print('outgroup not monophyletic: root placement is unreliable, re-check taxon choice')Goal: Root an outgroup-free tree where evolution is approximately clock-like (shallow/viral data).
Approach: Place the root at the midpoint of the longest tip-to-tip path; trust it only when no single long branch can hijack that path. For deep trees with rate variation prefer MAD/MinVar.
tree = Phylo.read('tree.nwk', 'newick')
tree.root_at_midpoint() # assumes a clock; a long branch slides the root onto the fast lineage
# Outgroup-free and clock-relaxed (deep trees): standalone CLIs run on the Newick file
# MAD: mad tree.nwk -> tree.nwk.rooted (per-branch root ambiguity index)
# MinVar: FastRoot.py -i tree.nwk -m MV -o rooted.nwkGoal: Remove tips and keep every surviving patristic distance unchanged.
Approach: Drop the tip and SUPPRESS the resulting degree-2 ("knee") node, ADDING its branch length to the child so path lengths are conserved. Bio.Phylo prune and ape drop.tip do this by default; ete3 prune needs preserve_branch_length=True.
tree = Phylo.read('tree.nwk', 'newick')
keep = {'Human', 'Chimp', 'Mouse'}
for term in list(tree.get_terminals()):
if term.name not in keep:
tree.prune(term) # collapses the degree-2 parent and sums branch lengths
# ape (R): drop.tip(phy, c('X','Y')) # sums suppressed branch lengths by default
# ete3 (Py): tree.prune(list(keep), preserve_branch_length=True) # the flag is mandatory, else distances shrinkNon-monophyletic targets cannot be "extracted as a clade" -- there is no node whose descendants are exactly those taxa. Prune to the taxon set to get the induced subtree instead; common_ancestor of non-monophyletic taxa returns an MRCA whose clade contains EXTRA taxa.
Goal: Replace poorly-supported resolved nodes with multifurcations that honestly say "unresolved".
Approach: Collapse any internal branch whose support is below a stated cutoff. The result is a SOFT (uncertainty) polytomy, never a HARD (simultaneous-radiation) one -- label it as such.
tree = Phylo.read('tree.nwk', 'newick') # support parsed into clade.confidence
tree.collapse_all(lambda c: c.confidence is not None and c.confidence < 70) # 70 for std bootstrap; use 95 for UFBoot2
tree.collapse_all(lambda c: c.branch_length is not None and c.branch_length < 1e-8) # collapse genuinely-zero branches
# ape (R): di2multi filters by LENGTH (tol), not support -> zero out low-support branch lengths first, THEN di2multi(phy)Resolving the inverse direction (multi2di / resolve_polytomy) invents an arbitrary order with zero-length branches; analyzing one random resolution treats an arbitrary choice as fact. If a binary tree is required, integrate over many random resolutions and treat the inserted zero-length branches as "no information", not instantaneous divergence.
Trigger: A single distant outgroup, or many outgroups all far from the ingroup, used to root. Mechanism: The long outgroup branch attracts the fastest ingroup taxon (LBA), pulling the root into the ingroup; far outgroups attach at essentially random positions (DeSalle et al. 2023). Symptom: Ingroup not recovered as monophyletic; a fast taxon appears "basal"; the root jumps as the outgroup set changes. Fix: Use multiple CLOSELY-related, monophyletic, roughly-equidistant outgroups; verify ingroup monophyly; treat a lonely distant outgroup as a red flag.
Trigger: Rooting on one distant outgroup taxon. Mechanism: One unbroken long branch maximizes LBA exposure, with no way to subdivide it or check ingroup monophyly, so the root is drawn toward other long branches. Symptom: The root lands inside the ingroup or on a spurious deep branch; deep nodes near the root are unstable across analyses. Fix: Add multiple closer, monophyletic outgroups to subdivide the long branch and enable a monophyly check, or use an outgroup-free method (MAD/MinVar).
Trigger: Midpoint rooting a deep tree with heterotachy or any long branch. Mechanism: Midpoint assumes a clock; a long branch hijacks the longest path and slides the root onto the fast lineage. Symptom: A rate-elevated taxon appears earliest-diverging; the root sits on a suspiciously long branch. Fix: Use MAD or MinVar (clock-relaxed) or a close outgroup; report root uncertainty and hedge "basal".
Trigger: Collapsing branches below a support threshold, then describing the multifurcation as a radiation. Mechanism: Threshold-collapse encodes UNCERTAINTY (soft polytomy = "we cannot resolve the order"); a hard polytomy is a biological claim of simultaneous divergence -- different meanings. Symptom: A paper claims simultaneous divergence from what is just unresolved data. Fix: Label threshold-collapsed nodes as unresolved/soft; never read them as a biological radiation.
Trigger: A tool that leaves the degree-2 node in place, or removes it without summing branch lengths (notably ete3 prune without preserve_branch_length=True).
Mechanism: The suppressed knee's branch length is not added to its child, so every path through that lineage shortens.
Symptom: Patristic distances shrink; subsequent midpoint/MAD/MinVar rooting on the pruned tree is now wrong.
Fix: Use Bio.Phylo prune / ape drop.tip (correct by default) or pass ete3 preserve_branch_length=True; verify a known pairwise distance is unchanged.
Trigger: multi2di / resolve_polytomy to satisfy a binary-tree requirement, then analyzing the single tree.
Mechanism: An arbitrary order with zero-length branches is invented; the downstream result depends on a topology the data never supported.
Symptom: Results that change under a different random resolution.
Fix: Integrate over many resolutions and summarize; treat zero-length branches as "no info", not instantaneous divergence.
Trigger: Ladderizing a figure so one lineage sits visually at the top/bottom. Mechanism: Rotation about internal nodes changes no topology, branch lengths, or bipartitions, but readers unconsciously read the staircase as an early-to-late sequence. Symptom: Reviewers infer a basal-to-derived trend that does not exist. Fix: Ladderize only for legibility; state that rotation changes no biology. -> tree-visualization.
| Quantity | Value | Source / rationale |
|---|---|---|
| Midpoint root recovery, single-outgroup source data | ~54% (barely a coin flip) | Hess and Russo 2007 |
| Midpoint root recovery, multiple-outgroup source data | ~82% (inconsistent) to ~94% (consistent) | Hess and Russo 2007 |
| MAD accuracy on benchmarks | >~70%, beating midpoint | Tria et al. 2017 |
| MinVar vs midpoint | matched or beat midpoint in all simulated conditions | Mai et al. 2017 |
| Bootstrap collapse cutoff | <50% (near-uninformative floor) or <70% (reliability boundary); state which | common practice |
| UFBoot2 collapse cutoff | <95% (a different scale from bootstrap -- not 70) | Hoang 2018 |
| Bayesian posterior collapse cutoff | <0.95 | common practice |
| Zero-length collapse tolerance | ~1e-8 (or machine epsilon) | removes genuinely-zero branches without deleting real short ones |
| Outgroup distance | accuracy degrades monotonically with distance | prefer the closest credible outgroup |
| Error / symptom | Cause | Solution |
|---|---|---|
ape::midpoint function-not-found | midpoint is not in base ape | use phangorn::midpoint or phytools::midpoint.root |
| Pruned tree has wrong distances | degree-2 node not suppressed / not summed | Bio.Phylo prune / ape drop.tip default, or ete3 preserve_branch_length=True |
| "Extract the clade of X,Y,Z" returns extra taxa or None | X,Y,Z are not monophyletic | prune to the taxon set (induced subtree) instead of extracting a clade |
di2multi(tol) did not drop low-support nodes | tol filters branch LENGTH, not support | zero out low-support branch lengths first, then di2multi |
| Bootstrap re-read as support for a pruned subtree | support was computed on the full taxon set | re-run inference on the subset for valid support |
| MAD and MinVar disagree on the root | the root is genuinely poorly determined | treat as uncertain; seek a close outgroup or RootDigger confidence; hedge all ancestral claims |
root_with_midpoint AttributeError | no such method | the methods are root_at_midpoint() and root_with_outgroup() |
Tria FDK, Landan G, Dagan T. 2017. Phylogenetic rooting using minimal ancestor deviation. Nature Ecology & Evolution 1:0193. Mai U, Sayyari E, Mirarab S. 2017. Minimum variance rooting of phylogenetic trees and implications for species tree reconstruction. PLOS ONE 12(8):e0182238. Bettisworth B, Stamatakis A. 2021. Root Digger: a root placement program for phylogenetic trees. BMC Bioinformatics 22:225. Hess PN, De Moraes Russo CA. 2007. An empirical test of the midpoint rooting method. Biological Journal of the Linnean Society 92(4):669-674. DeSalle R, Narechania A, Tessler M. 2023. Multiple outgroups can cause random rooting in phylogenomics. Molecular Phylogenetics and Evolution 184:107806. Junier T, Zdobnov EM. 2010. The Newick utilities: high-throughput phylogenetic tree processing in the Unix shell. Bioinformatics 26(13):1669-1670. Huerta-Cepas J, Serra F, Bork P. 2016. ETE 3: reconstruction, analysis, and visualization of phylogenomic data. Molecular Biology and Evolution 33(6):1635-1638. Paradis E, Schliep K. 2019. ape 5.0: an environment for modern phylogenetics and evolutionary analyses in R. Bioinformatics 35(3):526-528.
© GPTomics, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
SKILL.md and 5 other files in phylogenetics/tree-manipulation of GPTomics/bioSkills.
Open the folder on GitHubat commit d91ed3d
We found 1 copy of this SKILL.md (exact, near-identical or edited) in other folders, from 1 other GitHub owner. This page covers the copy in GPTomics/bioSkills, which our catalogue first saw on October 7, 2026.
Bio Phylo Tree Manipulation next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Bio Phylo Tree Manipulation this skillGPTomics/bioSkills | 1.2k | 1 repos | ~5k | Automated safety check: Pass | MIT | |
| deepTools NGS Toolkitdavila7/claude-code-templates | 33k | 12 repos | ~4.5k | Automated safety check: Pass | MIT | |
| FBA Flux Analyzeraiming-lab/AutoResearchClaw | 15k | — | ~2.3k | Automated safety check: Pass | MIT | |
| Biopythondavila7/claude-code-templates | 33k | 12 repos | ~3.4k | Automated safety check: Pass | MIT | |
| Ggetdavila7/claude-code-templates | 33k | 10 repos | ~6.3k | Automated safety check: Pass | MIT | |
| GgetK-Dense-AI/scientific-agent-skills | 48k | 1 repos | ~2.8k | Automated safety check: Notes | BSD-2-Clause |
davila7/claude-code-templates
Guides use of deepTools on sequencing data: BAM to bigWig conversion, QC, sample correlation, and heatmaps or profiles around TSS and peaks for ChIP-seq, RNA-seq and ATAC-seq.
aiming-lab/AutoResearchClaw
Turns raw flux balance analysis output and a COBRApy model into gene essentiality maps, phenotypic phase planes, flux sampling results, pathway summaries and secretion predictions.
davila7/claude-code-templates
Primary Python toolkit for molecular biology. An agent skill from davila7/claude-code-templates.
davila7/claude-code-templates
CLI/Python toolkit for rapid bioinformatics queries. An agent skill from davila7/claude-code-templates.
K-Dense-AI/scientific-agent-skills
Queries 20+ bioinformatics resources through CLI/Python. An agent skill from K-Dense-AI/scientific-agent-skills.
K-Dense-AI/scientific-agent-skills
Provides Biopython workflows for sequence manipulation, file parsing (FASTA/GenBank/PDB), phylogenetics, and programmatic NCBI/PubMed access (Bio.Entrez).
GPTomics/bioSkills
Read, write, and convert multiple sequence alignment files using Biopython Bio.AlignIO.
GPTomics/bioSkills
Installs the bioSkills collection of 425 bioinformatics skills in one step, or only chosen categories, so sequencing, RNA-seq, single-cell and variant tasks get specialized help.
GPTomics/bioSkills
Write biological sequences to files (FASTA, FASTQ, GenBank, EMBL) using Biopython Bio.SeqIO.
GPTomics/bioSkills
Soft- or hard-clips PCR primer footprints from aligned amplicon BAMs so primer bases stop masquerading as confirmed reference sequence.
GPTomics/bioSkills
Filters BAM alignments by FLAG bits, mapping quality and regions with samtools view or pysam, with recipes for common keep and drop cases.
GPTomics/bioSkills
Create and use BAI/CSI indices for BAM/CRAM files using samtools and pysam.
Categories
Edit phylogenetic tree structure with Biopython Bio.Phylo, and treat rooting as a separate statistical inference rather than a display choice. Bio Phylo Tree Manipulation is an agent skill from GPTomics/bioSkills.Phylo, and treat rooting as a separate statistical inference rather than a display choice.
Bio Phylo Tree Manipulation fits situations like: subsetting taxa; extracting a clade; induced subtree; collapsing low-support branches.
Run `npx skills add GPTomics/bioSkills --skill bio-phylo-tree-manipulation -a claude-code`. Or copy the skill folder (phylogenetics/tree-manipulation in GPTomics/bioSkills) into .claude/skills/bio-phylo-tree-manipulation in your project. Claude Code loads it when a task matches its description.
Run `npx skills add GPTomics/bioSkills --skill bio-phylo-tree-manipulation -a codex`. Or copy the skill folder (phylogenetics/tree-manipulation in GPTomics/bioSkills) into .agents/skills/bio-phylo-tree-manipulation in your project. Codex loads it when a task matches its description.
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add GPTomics/bioSkills --skill bio-phylo-tree-manipulation -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/bio-phylo-tree-manipulation, .gemini/skills/bio-phylo-tree-manipulation, .github/skills/bio-phylo-tree-manipulation and .opencode/skills/bio-phylo-tree-manipulation in your project.
Going by SKILL.md and its folder, Bio Phylo Tree Manipulation needs Python for the scripts in its folder and the command-line tools its instructions call (pip). Our summary lists: Python 3.
SKILL.md contains no URLs. Its commands use pip, which can reach the network depending on how they are called. This is read from the text; nothing was executed.
Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.
Bio Phylo Tree Manipulation is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.
About 5k tokens (SKILL.md is roughly 20k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.
Skills that share tags, products or a category with Bio Phylo Tree Manipulation: deepTools NGS Toolkit (davila7/claude-code-templates, 33k stars), FBA Flux Analyzer (aiming-lab/AutoResearchClaw, 15k stars), Biopython (davila7/claude-code-templates, 33k stars) and Gget (davila7/claude-code-templates, 33k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
GPTomics (a GitHub organization) maintains it in GPTomics/bioSkills, which has 1,218 GitHub stars. The repository holds 559 skills in this directory. The repository was last updated on August 15, 2026.
Source: GPTomics/bioSkills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.