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scikit-bio

scikit-bio

Use scikit-bio for bioinformatics sequences, alignments, phylogenetic trees, diversity metrics, biological tables, IO, metadata, distance statistics, ordination, compositional analysis, and embeddings.

Independent GitHub snapshot · Automated classification · Original documentation governs

Skill-Dokumentation

GitHub

name: scikit-bio description: "Use scikit-bio for bioinformatics sequences, alignments, phylogenetic trees, diversity metrics, biological tables, IO, metadata, distance statistics, ordination, compositional analysis, and embeddings." disable-model-invocation: true metadata: disco-role: operating license: BSD 3-Clause

scikit-bio

Use this repo skill when a task involves scikit-bio Python APIs for biological sequence data, phylogenetic trees, community ecology diversity, BIOM-style tables, bioinformatics file formats, sample metadata, distance-matrix statistics, ordination, compositional data analysis, or protein/sequence embeddings.

Install and Import Check

scikit-bio is a Python package with public APIs rather than a command-line application. Prefer conda when compiled scientific dependencies are difficult to resolve:

conda install -c conda-forge scikit-bio
# or
python -m pip install scikit-bio

Minimal import check:

python - <<'PY'
import skbio
from skbio import DNA, TreeNode, DistanceMatrix
print(skbio.__version__, DNA("ACGT").gc_content(), TreeNode.read(["(a,b);"]).count(tips=True))
PY

Run scripts/check_skbio_environment.py when diagnosing installation, imports, or expected public API availability.

Route by Task

  • sub-skills/io-metadata/SKILL.md — read/write FASTA, FASTQ, Newick, BIOM, distance matrices, ordination, sample metadata, GFF3, GenBank/EMBL, BLAST tables, taxonomy dumps, and diagnose IO registry routes or metadata validation.
  • sub-skills/sequences-alignment/SKILL.md — create and validate Sequence, DNA, RNA, Protein, GeneticCode, SubstitutionMatrix, TabularMSA, pairwise alignment, alignment scoring, and sequence-distance workflows.
  • sub-skills/trees-phylogeny/SKILL.md — parse, navigate, write, build, compare, and repair TreeNode phylogenies, Newick trees, NJ/UPGMA/GME/BME construction, NNI, consensus, and tree constraints for diversity metrics.
  • sub-skills/diversity-tables/SKILL.md — compute alpha/beta diversity, Faith PD, UniFrac, block beta diversity, metric discovery, table-like count inputs, BIOM-backed Table, and table augmentation.
  • sub-skills/statistics-ordination/SKILL.md — use DistanceMatrix, PERMANOVA, ANOSIM, PERMDISP, Mantel, BioEnv, PCoA/CA/CCA/RDA, compositional transforms, ANCOM/ANCOM-BC, and embedding conversions.

Common Workflows

  • Parse data then analyze: start with io-metadata, then route parsed sequence/tree/table/distance objects to the owning sub-skill.
  • Sequence or alignment task: start with sequences-alignment; use io-metadata only for file routes and statistics-ordination for downstream distance-matrix statistics.
  • Phylogenetic diversity task: use trees-phylogeny to validate rooted trees, branch lengths, duplicate tips, and taxa order, then use diversity-tables for Faith PD or UniFrac.
  • Community ecology task: use diversity-tables to create Series or DistanceMatrix outputs, then use statistics-ordination for PERMANOVA, ordination, or compositional follow-up.
  • Metadata-heavy task: use io-metadata to load/filter/merge SampleMetadata, then pass aligned IDs to statistics-ordination or diversity-tables.

Package Facts and Guardrails

  • Requires Python 3.10 or newer and compiled scientific dependencies such as NumPy, pandas, SciPy, h5py, BIOM format, statsmodels, patsy, requests, decorator, natsort, and array-api-compat.
  • Public top-level imports include Sequence, DNA, RNA, Protein, GeneticCode, SubstitutionMatrix, DistanceMatrix, TabularMSA, TreeNode, nj, read, write, OrdinationResults, Table, SampleMetadata, get_config, and set_config.
  • Most workflows are pure Python API calls. Do not invent a scikit-bio CLI; use import/API smoke checks and focused pytest candidates when working in a repository checkout.
  • Preserve ID alignment carefully. Many failures come from mismatched sample IDs, feature/taxon IDs, distance-matrix IDs, tree tip names, or metadata indexes.
  • Keep validation enabled unless inputs have already been checked. validate=False may improve performance but can produce confusing downstream errors or invalid results.

Cross-Cutting References

  • references/troubleshooting.md for install/import issues, dependency conflicts, ID alignment, validation, optional backend, and no-CLI expectations.
  • references/repo-provenance.md before deciding whether this generated skill matches a current scikit-bio checkout or should be refreshed.
  • references/repo-routing-metadata.json contains structured repo-skills-router metadata used during managed import.
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