scikit-bio

scikit-bio

Overview

Targets scikit-bio 0.7.4. Use it for sequence manipulation, alignment, phylogenetics, microbial ecology, and multivariate statistics. Native tests exercise small synthetic fixtures; examples with filenames or undefined inputs are workflow templates. See review evidence and boundaries.

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:

import skbio

# Read sequences from file
seq = skbio.DNA.read('input.fasta')

# Sequence operations
rc = seq.reverse_complement()
rna = seq.transcribe()
protein = rna.translate()

# Find motifs
motif_positions = seq.find_with_regex('(ATG[ACGT]{3})')  # capture group required

# Check for properties
has_degens = seq.has_degenerates()
seq_no_gaps = seq.degap()

Important notes:

• Use DNA, RNA, Protein classes for grammared sequences with validation
• Use Sequence class for generic sequences without alphabet restrictions
• FASTQ quality scores load into positional metadata; specify the known phred_offset=33 or variant (do not infer encoding from the filename). Translation needs an explicit genetic code/frame and a decision about incomplete codons.
• Metadata types: sequence-level (ID, description), positional (per-base), interval (regions/features)