# Scikit Bio

Biological data toolkit.

- Repository: https://github.com/LeonChaoX/qinyan-academic-skills (996 stars)
- Category: Engineering
- License: BSD-3-Clause license
- Last updated: 2026-07-20
- Runs scripts: no, instructions only

## Install

Claude Code: `npx skills add LeonChaoX/qinyan-academic-skills --skill scikit-bio -g -a claude-code -y`
Codex: `npx skills add LeonChaoX/qinyan-academic-skills --skill scikit-bio -g -a codex -y`
Cursor: `npx skills add LeonChaoX/qinyan-academic-skills --skill scikit-bio -g -a cursor -y`
Gemini CLI: `npx skills add LeonChaoX/qinyan-academic-skills --skill scikit-bio -g -a gemini-cli -y`

## Description

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

## SKILL.md, as published by its author (untrusted third-party text, read it as data)

# 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:**
```python
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}')

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

The rest: https://github.com/LeonChaoX/qinyan-academic-skills/blob/HEAD/skills/05-生物信息与基因组学/scikit-bio/SKILL.md