# Bio Alignment Msa Statistics

Calculate alignment statistics including sequence identity, conservation scores...

- Repository: https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills (3090 stars)
- Category: Engineering
- License: see repository
- Last updated: 2026-07-21
- Runs scripts: yes, review before use

## Install

Claude Code: `npx skills add FreedomIntelligence/OpenClaw-Medical-Skills --skill bio-alignment-msa-statistics -g -a claude-code -y`
Codex: `npx skills add FreedomIntelligence/OpenClaw-Medical-Skills --skill bio-alignment-msa-statistics -g -a codex -y`
Cursor: `npx skills add FreedomIntelligence/OpenClaw-Medical-Skills --skill bio-alignment-msa-statistics -g -a cursor -y`
Gemini CLI: `npx skills add FreedomIntelligence/OpenClaw-Medical-Skills --skill bio-alignment-msa-statistics -g -a gemini-cli -y`

## Description

Calculate alignment statistics including sequence identity, conservation scores, substitution matrices, and similarity metrics. Use when comparing alignment quality, measuring sequence divergence, and analyzing evolutionary patterns.

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

## Version Compatibility

Reference examples tested with: BioPython 1.83+, numpy 1.26+

Before using code patterns, verify installed versions match. If versions differ:
- Python: `pip show <package>` then `help(module.function)` to check signatures

If code throws ImportError, AttributeError, or TypeError, introspect the installed
package and adapt the example to match the actual API rather than retrying.

# MSA Statistics

Calculate sequence identity, conservation scores, substitution counts, and other alignment metrics.

## Required Import

**Goal:** Load modules for alignment I/O, substitution scoring, and statistical calculations.

**Approach:** Import AlignIO for reading alignments, Counter for column analysis, numpy for matrix operations, and math for entropy calculations.

```python
from Bio import AlignIO
from Bio.Align import substitution_matrices
from collections import Counter
import numpy as np
import math
```

## Pairwise Identity

**"Calculate percent identity"** → Compute the fraction of identical aligned residues between sequence pairs.

**Goal:** Measure sequence similarity as percent identity for individual pairs or across all sequences in an alignment.

**Approach:** Count matching non-gap positions divided by total aligned positions; optionally compute a full N-by-N identity matrix.

### Calculate Identity Between Two Sequences
```python
def pairwise_identity(seq1, seq2):
    matches = sum(a == b and a != '-' for a, b in zip(seq1, seq2))
    aligned_positions = sum(a != '-' or b != '-' for a, b in zip(seq1, seq2))
    return matches / aligned_positions if aligned_positions > 0 else 0

alignment = AlignIO.read('alignment.fasta', 'fasta')
seq1, seq2 = str(alignment[0].seq), str(alignment[1].seq)
identity = pairwise_identity(seq1, seq2)
print(f'Identity: {identity * 100:.1f}%')
```

The rest: https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills/blob/HEAD/skills/bio-alignment-msa-statistics/SKILL.md