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Biological data toolkit. Sequence analysis, alignments, phylogenetic trees, diversity metrics (alpha/beta, UniFrac), ordination (PCoA), PERMANOVA, FASTA/Newick I/O, for microbiome analysis.
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scikit-bio.SKILL.md
---name: scikit-bio
description: Biological data toolkit. Sequence analysis, alignments, phylogenetic trees, diversity metrics (alpha/beta, UniFrac), ordination (PCoA), PERMANOVA, FASTA/Newick I/O, for microbiome analysis.
license: BSD-3-Clause license
allowed-tools: Read Write Edit Bash
compatibility: Requires Python 3.10+ and scikit-bio 0.7+ (uv pip install scikit-bio). NumPy 2.0+ is required. Optional matplotlib/seaborn/plotly for plotting; biom-format for BIOM tables; polars/anndata for table interoperability.
metadata: {"version": "1.1", "skill-author": "K-Dense Inc."}
---# scikit-bio
## Overview
scikit-bio is a comprehensive Python library for working with biological data. Apply this skill for bioinformatics analyses spanning sequence manipulation, alignment, phylogenetics, microbial ecology, and multivariate statistics.
## When to Use This Skill
This skill should be used when the user:
- Works with biological sequences (DNA, RNA, protein)
- Needs to read/write biological file formats (FASTA, FASTQ, GenBank, Newick, BIOM, etc.)
- Performs sequence alignments or searches for motifs
- Constructs or analyzes phylogenetic trees
- Calculates diversity metrics (alpha/beta diversity, UniFrac distances)
- Performs ordination analysis (PCoA, CCA, RDA)
- Runs statistical tests on biological/ecological data (PERMANOVA, ANOSIM, Mantel)
Perform pairwise and multiple sequence alignments using the `pair_align` engine (introduced in scikit-bio 0.7.0), a versatile and efficient dynamic-programming aligner.
**Key capabilities:**
- Global, local, and semi-global alignment (free ends configurable) in one function
- Convenience wrappers `pair_align_nucl` (BLASTN-like) and `pair_align_prot` (BLASTP-like)
- Configurable scoring: match/mismatch tuple or named substitution matrix; linear or affine gap penalties
- `PairAlignPath` results carry CIGAR strings and convert to aligned sequences
- Multiple sequence alignment storage and manipulation with `TabularMSA`
**Common patterns:**
```python
from skbio import DNA, Protein
from skbio.alignment import pair_align_nucl, pair_align_prot, pair_align, TabularMSA
- `pair_align` replaces the removed SSW wrapper (`local_pairwise_align_ssw`, `StripedSmithWaterman`) and the deprecated pure-Python aligners (`global_pairwise_align`, `local_pairwise_align_nucleotide`, etc.)
- The result is a `PairAlignResult` that also unpacks as `score, paths, matrices` (use `keep_matrices=True` to retain the DP matrix)
- `sub_score` accepts a `(match, mismatch)` tuple or a matrix name (e.g., `'NUC.4.4'`, `'BLOSUM62'`); `gap_cost` accepts a single number (linear) or `(open, extend)` tuple (affine)
- Parse external CIGAR strings with `PairAlignPath.from_cigar('1I8M2D5M2I')`; score an existing alignment with `align_score(...)` and build a distance matrix from an MSA with `align_dists(...)`
### 3. Phylogenetic Trees
Construct, manipulate, and analyze phylogenetic trees representing evolutionary relationships.
**Key capabilities:**
- Tree construction from distance matrices (UPGMA/WPGMA, Neighbor Joining, GME, BME)
- Tree rearrangement with nearest neighbor interchange (`nni`)
- Tree manipulation (pruning, rerooting, traversal)
- Distance calculations (patristic via `cophenet`, Robinson-Foulds via `compare_rfd`)
- ASCII visualization
- Newick format I/O
**Common patterns:**
```python
from skbio import TreeNode
from skbio.tree import nj, upgma, gme, bme, rf_dists
cophenetic_dm = tree.cophenet() # patristic distance matrix among tips
# Compare two trees (Robinson-Foulds)
rf_distance = tree.compare_rfd(other_tree)
# Pairwise RF distances among many trees -> DistanceMatrix
rf_dm = rf_dists([tree, other_tree, third_tree])
```
**Important notes:**
- Use `nj()` for neighbor joining (classic phylogenetic method)
- Use `upgma()` for UPGMA/WPGMA (assumes molecular clock)
- GME and BME are highly scalable for large trees; refine topology with `nni()`
- `cophenet()` (formerly `tip_tip_distances`) returns the patristic distance matrix; `compare_rfd()` is the Robinson-Foulds method (`compare_wrfd`/`compare_cophenet` for weighted/cophenetic variants)
- `lca()` is the lowest common ancestor; `lowest_common_ancestor` remains as an alias
- Trees can be rooted or unrooted; some metrics require specific rooting
### 4. Diversity Analysis
Calculate alpha and beta diversity metrics for microbial ecology and community analysis.
from skbio.diversity import get_alpha_diversity_metrics
print(get_alpha_diversity_metrics())
```
**Important notes:**
- Counts must be integers representing abundances, not relative frequencies
- The phylogenetic-metric argument is `taxa=` (renamed from `otu_ids` in 0.6.0; the old name is a deprecated alias); `observed_otus` is now `observed_features` (or `sobs`)
- `counts_matrix` may be any table-like input (NumPy array, pandas/polars DataFrame, BIOM `Table`, or AnnData) via the dispatch system
- Phylogenetic metrics (Faith's PD, UniFrac) require tree and taxa-to-tip mapping
- Use `partial_beta_diversity()` for specific sample pairs, or `block_beta_diversity()` for large block-decomposed calculations
- Alpha diversity returns a `pandas.Series`, beta diversity returns a `DistanceMatrix`
### 5. Ordination Methods
Reduce high-dimensional biological data to visualizable lower-dimensional spaces.
**Key capabilities:**
- PCoA (Principal Coordinate Analysis) from distance matrices
- CA (Correspondence Analysis) for contingency tables
- CCA (Canonical Correspondence Analysis) with environmental constraints
- RDA (Redundancy Analysis) for linear relationships
- Biplot projection for feature interpretation
**Common patterns:**
```python
from skbio.stats.ordination import pcoa, cca
import skbio
# PCoA from distance matrix (limit dimensions for large matrices)
- PCoA works with any distance/dissimilarity matrix; pass `dimensions` as an int (count) or a float in (0, 1] (fraction of cumulative variance to retain)
- Embeddings bridge protein language models with traditional bioinformatics
- Compatible with scikit-bio's distance/ordination/statistics ecosystem
- SequenceEmbedding and ProteinEmbedding provide specialized functionality
- Useful for sequence clustering, classification, and visualization
## Best Practices
### Installation
```bash
uv pip install scikit-bio
```
Requires Python 3.10+ and NumPy 2.0+. Pre-compiled wheels are published for each release since 0.7.0, so most platforms install without a compiler. Conda users can instead run `conda install -c conda-forge scikit-bio`.
### Performance Considerations
- Use generators for large sequence files to minimize memory usage
- For massive phylogenetic trees, prefer GME or BME over NJ
- Beta diversity calculations can be parallelized with `partial_beta_diversity()`
- BIOM format (HDF5) more efficient than JSON for large tables
### Integration with Ecosystem
- Sequences interoperate with Biopython via standard formats
- Tables integrate with pandas, polars, and AnnData
- Distance matrices compatible with scikit-learn
- Ordination results visualizable with matplotlib/seaborn/plotly
- Works seamlessly with QIIME 2 artifacts (BIOM, trees, distance matrices)
For detailed API information, parameter specifications, and advanced usage examples, refer to `references/api_reference.md` which contains comprehensive documentation on:
- Complete method signatures and parameters for all capabilities
- Extended code examples for complex workflows
- Troubleshooting common issues
- Performance optimization tips
- Integration patterns with other libraries
## Additional Resources
- Official documentation: https://scikit.bio/docs/latest/
