bio-alignment-statistics

npx skills add majiayu000/claude-skill-registry --skill alignment-statistics --agent claude-code

Same command for any agent — swap --agent for codex, cursor, copilot.

# Alignment Statistics Calculate sequence identity, conservation scores, substitution counts, and other alignment metrics. ## Required Import ```python from Bio import AlignIO from Bio.Align import substitution_matrices from collections import Counter import numpy as np import math ``` ## Pairwise Identity ### 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}%') ``` ### Identity Matrix for All Sequences ```python def identity_matrix(alignment): n = len(alignment) matrix = np.zeros((n, n)) for i in range(n): for j in range(i, n): seq_i = str(alignment[i].seq) seq_j = str(alignment[j].seq) ident = pairwise_identity(seq_i, seq_j) matrix[i, j] = matrix[j, i] = ident return matrix alignment = AlignIO.read('alignment.fasta', 'fasta') mat = identity_matrix(alignment