Biological data toolkit. Sequence analysis, alignments, phylogenetic trees, diversity metrics (alpha/beta, UniFrac), ordination (PCoA), PERMANOVA, FASTA/Newick I/O, for microbiome analysis.

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) Analyzes microbiome or community ecology data Works with protein embeddings from language models Needs to manipulate biological data tables Core Capabilities 1. Sequence Manipulation Work with biological sequences using specialized classes for DNA, RNA, and protein data. Key operations: Read/write sequences from FASTA, FASTQ, GenBank, EMBL formats Sequence slicing, concatenation, and searching Reverse complement, transcription (DNA→RNA), and translation (RNA→protein) Find motifs and patterns using regex Calculate distances (Hamming, k mer based) Handle sequence quality scores and metadata Common patterns: Important notes: Use DNA , RNA , Protein classes for grammared sequences with validation Use Sequence class for generic sequences without alphabet restrictions Quality scores automatically loaded from FASTQ files into positional metadata Metadata types: sequence level (ID, description), positional (per base), interval (regions/features) 2. Sequence Alignment 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: Important notes: 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: 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. Key capabilities: Alpha diversity: richness ( sobs , observed features , chao1 , ace ), Shannon, Simpson, Hill numbers ( hill ), Faith's PD ( faith pd ), generalized PD ( phydiv ), Pielou's evenness Beta diversity: Bray Curtis, Jaccard, weighted/unweighted UniFrac, Euclidean distances Phylogenetic diversity metrics (require tree input) Rarefaction and subsampling Integration with ordination and statistical tests Common patterns: 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: Important notes: 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) OrdinationResults exposes pandas based attributes: samples , features , eigvals , proportion explained , biplot scores , sample constraints CCA reveals environmental drivers of community composition OrdinationResults.plot() produces a matplotlib figure; results also integrate with seaborn/plotly 6. Statistical Testing Perform hypothesis tests specific to ecological and biological data. Key capabilities: PERMANOVA: test group differences using distance matrices ANOSIM: alternative test for group differences PERMDISP: test homogeneity of group dispersions Mantel test: correlation between distance matrices Bioenv: find environmental variables correlated with distances Differential abundance: ancom , dirmult ttest , and dirmult lme (longitudinal mixed effects) in skbio.stats.composition Common patterns: Important notes: Permutation tests provide non parametric significance testing Use 999+ permutations for robust p values PERMANOVA sensitive to dispersion differences; pair with PERMDISP Mantel tests assess matrix correlation (e.g., geographic vs genetic distance) Supply differential abundance tests with raw counts, not pre normalized proportions, to preserve magnitude information 7. File I/O and Format Conversion Read and write 19+ biological file formats with automatic format detection. Supported formats: Sequences: FASTA, FASTQ, GenBank, EMBL, QSeq Alignments: Clustal, PHYLIP, Stockholm Trees: Newick Tables: BIOM (HDF5 and JSON) Distances: delimited square matrices Analysis: BLAST+6/7, GFF3, Ordination results Metadata: TSV/CSV with validation Common patterns: Important notes: Use generators for large files to avoid memory issues Format can be auto detected when into parameter specified Some objects can be written to multiple formats Support for stdin/stdout piping with verify=False 8. Distance Matrices Create and manipulate distance/dissimilarity matrices with statistical methods. Key capabilities: Store symmetric ( DistanceMatrix , hollow diagonal) or general pairwise ( PairwiseMatrix ) data ID based indexing and slicing Integration with diversity, ordination, and statistical tests Read/write delimited text format Common patterns: Important notes: DistanceMatrix enforces symmetry and a zero (hollow) diagonal; it is a subclass of SymmetricMatrix PairwiseMatrix (renamed from DissimilarityMatrix , which is kept as a deprecated alias) allows general/asymmetric values IDs enable integration with metadata and biological knowledge Compatible with pandas, numpy, and scikit learn 9. Biological Tables Work with feature tables (OTU/ASV tables) common in microbiome research. Key capabilities: BIOM format I/O (HDF5 and JSON) via the native Table class Table dispatch system (0.7.0+): functions accept any table like input — BIOM Table , pandas/polars DataFrame, NumPy array, or AnnData — without explicit conversion Data augmentation techniques ( phylomix , mixup , aitchison mixup , compos cutmix ) Sample/feature filtering and normalization Metadata integration Common patterns: Important notes: BIOM tables are standard in QIIME 2 workflows Rows typically represent samples, columns represent features (OTUs/ASVs) Supports sparse and dense representations With the dispatch system, functions return the same format as their input, or a user specified output format 10. Protein Embeddings Work with protein language model embeddings for downstream analysis. Key capabilities: Store embeddings from protein language models (ESM, ProtTrans, etc.) Convert embeddings to distance matrices Generate ordination objects for visualization Export to numpy/pandas for ML workflows Common patterns: Important notes: 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 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) Common Workflows 1. Microbiome diversity analysis : Read BIOM table → Calculate alpha/beta diversity → Ordination (PCoA) → Statistical testing (PERMANOVA) 2. Phylogenetic analysis : Read sequences → Align → Build distance matrix → Construct tree → Calculate phylogenetic distances 3. Sequence processing : Read FASTQ → Quality filter → Trim/clean → Find motifs → Translate → Write FASTA 4. Comparative genomics : Read sequences → Pairwise alignment → Calculate distances → Build tree → Analyze clades Reference Documentation 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 co

scikit-bio