scikit-bio
A Python bioinformatics toolkit for sequence, phylogeny, and microbiome/community-ecology analysis; use it when you need to compute diversity/ordination/statistics from biological data and standard formats (FASTA/FASTQ/Newick/BIOM).
Best use case
scikit-bio is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
A Python bioinformatics toolkit for sequence, phylogeny, and microbiome/community-ecology analysis; use it when you need to compute diversity/ordination/statistics from biological data and standard formats (FASTA/FASTQ/Newick/BIOM).
Teams using scikit-bio should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/scikit-bio/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How scikit-bio Compares
| Feature / Agent | scikit-bio | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
A Python bioinformatics toolkit for sequence, phylogeny, and microbiome/community-ecology analysis; use it when you need to compute diversity/ordination/statistics from biological data and standard formats (FASTA/FASTQ/Newick/BIOM).
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
> **Source**: [https://github.com/aipoch/medical-research-skills](https://github.com/aipoch/medical-research-skills)
## When to Use
- You need to parse, validate, and manipulate biological sequences (DNA/RNA/protein) and their metadata.
- You are running microbiome/community-ecology workflows (alpha/beta diversity, UniFrac, ordination, PERMANOVA).
- You need to build, transform, or compare phylogenetic trees (Newick I/O, pruning/rerooting, patristic distances).
- You want to compute and work with distance matrices and downstream multivariate analyses (PCoA, Mantel, ANOSIM).
- You need to read/write common bioinformatics formats (FASTA/FASTQ, Newick, BIOM) and convert between them.
## Key Features
- **Sequence objects**: `DNA`, `RNA`, `Protein`, and generic `Sequence` with validation, slicing, motif search, reverse complement, transcription/translation, and metadata handling.
- **Alignment utilities**: pairwise local alignment (SSW-based) and multiple sequence alignment containers (`TabularMSA`) with consensus support.
- **Phylogenetics**: `TreeNode` manipulation, tree construction from distance matrices (e.g., Neighbor Joining), and tree distance/metrics.
- **Diversity**: alpha diversity (e.g., Shannon, Faith’s PD) and beta diversity (e.g., Bray-Curtis, UniFrac) returning `Series`/`DistanceMatrix`.
- **Ordination & stats**: PCoA and ecological hypothesis tests (PERMANOVA, ANOSIM, Mantel) operating on distance matrices.
- **I/O ecosystem**: FASTA/FASTQ and Newick reading/writing; BIOM table support via `Table`.
## Dependencies
- `scikit-bio>=0.6.0`
- `numpy>=1.23`
- `pandas>=1.5`
## Example Usage
```python
# pip install scikit-bio numpy pandas
import numpy as np
import pandas as pd
import skbio
from skbio import DNA, TreeNode
from skbio.diversity import alpha_diversity, beta_diversity
from skbio.stats.ordination import pcoa
from skbio.stats.distance import permanova
# ----------------------------
# 1) Sequence manipulation
# ----------------------------
seq = DNA("ACGTACGTNN--ACGT", metadata={"id": "seq1"})
seq_clean = seq.degap()
rc = seq_clean.reverse_complement()
motif_hits = seq_clean.find_with_regex("ACG[TA]")
print("Original:", str(seq))
print("Degapped:", str(seq_clean))
print("Reverse complement:", str(rc))
print("Motif hits:", list(motif_hits))
# ----------------------------
# 2) Microbiome-style counts
# ----------------------------
# rows = samples, cols = features/OTUs/ASVs
counts = np.array([
[10, 0, 3, 1],
[ 0, 8, 2, 0],
[ 5, 1, 0, 4],
], dtype=int)
sample_ids = ["S1", "S2", "S3"]
feature_ids = ["F1", "F2", "F3", "F4"]
# Alpha diversity (Shannon)
shannon = alpha_diversity("shannon", counts, ids=sample_ids)
print("\nAlpha diversity (Shannon):")
print(shannon)
# Beta diversity (Bray-Curtis) -> DistanceMatrix
dm = beta_diversity("braycurtis", counts, ids=sample_ids)
print("\nBeta diversity (Bray-Curtis) distance matrix:")
print(dm)
# ----------------------------
# 3) Ordination (PCoA)
# ----------------------------
ord_res = pcoa(dm)
print("\nPCoA sample coordinates (first 2 axes):")
print(ord_res.samples[["PC1", "PC2"]])
# ----------------------------
# 4) PERMANOVA on the distance matrix
# ----------------------------
grouping = pd.Series(["A", "A", "B"], index=sample_ids)
perma = permanova(dm, grouping=grouping, permutations=99)
print("\nPERMANOVA result:")
print(perma)
# ----------------------------
# 5) Tree I/O (Newick) + basic manipulation
# ----------------------------
newick = "((F1:0.1,F2:0.2):0.3,(F3:0.2,F4:0.4):0.1);"
tree = TreeNode.read([newick])
subtree = tree.shear(["F1", "F2", "F3"])
print("\nSheared tree (tips F1,F2,F3):")
print(subtree.ascii_art())
```
## Implementation Details
- **Sequence model**
- Use `DNA`/`RNA`/`Protein` for alphabet-aware validation and biological operations (e.g., `reverse_complement`, `transcribe`, `translate`).
- Use `Sequence` when you need a generic container without strict alphabet constraints.
- FASTQ quality scores (when read via scikit-bio I/O) are stored as positional metadata.
- **Diversity computations**
- `alpha_diversity(metric, counts, ids=...)` returns a per-sample vector (typically a pandas `Series`).
- `beta_diversity(metric, counts, ids=...)` returns a `DistanceMatrix` suitable for ordination and hypothesis tests.
- Count inputs should be **non-negative integers** representing abundances (not relative frequencies). Phylogenetic metrics (e.g., Faith’s PD, UniFrac) additionally require a tree and feature/OTU IDs.
- **Distance matrices**
- `DistanceMatrix` enforces symmetry and a zero diagonal; IDs are used for consistent alignment with metadata and group labels.
- Many downstream methods (PCoA, PERMANOVA, ANOSIM, Mantel) operate directly on `DistanceMatrix`.
- **Ordination**
- `pcoa(dm)` performs eigen-decomposition on a transformed distance matrix and returns `OrdinationResults` containing eigenvalues and sample coordinates.
- **Permutation-based statistics**
- `permanova(dm, grouping, permutations=N)` estimates significance by permuting group labels; increase `permutations` (e.g., 999+) for more stable p-values in real analyses.Related Skills
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