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.

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.

Percent Identity Definitions

There are four common denominators, producing up to 11.5% difference on the same alignment. Combined with different alignment algorithms, variation reaches 22%. Always report which method was used.

sum(a != '-' or b != '-' for a, b in zip(s1, s2))

sum(a != '-' and b != '-' for a, b in zip(s1, s2))

min(len(s1.replace('-','')), len(s2.replace('-','')))

PID2 always gives the highest value; PID4 correlates best with structural similarity and is recommended for evolutionary analyses.

Calculate Identity Between Two Sequences

def pairwise_identity(seq1, seq2, method='pid1'):
    matches = sum(a == b and a != '-' for a, b in zip(seq1, seq2))
    if method == 'pid1':
        denom = sum(a != '-' or b != '-' for a, b in zip(seq1, seq2))
    elif method == 'pid2':
        denom = sum(a != '-' and b != '-' for a, b in zip(seq1, seq2))
    elif method == 'pid3':
        denom = min(len(seq1.replace('-', '')), len(seq2.replace('-', '')))
    elif method == 'pid4':
        denom = (len(seq1.replace('-', '')) + len(seq2.replace('-', ''))) / 2
    return matches / denom if denom > 0 else 0

alignment = AlignIO.read('alignment.fasta', 'fasta')
seq1, seq2 = str(alignment[0].seq), str(alignment[1].seq)
for method in ['pid1', 'pid2', 'pid3', 'pid4']:
    print(f'{method}: {pairwise_identity(seq1, seq2, method) * 100:.1f}%')

Identity Matrix for All Sequences

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)
seq_ids = [r.id for r in alignment]
print('Pairwise Identity Matrix:')
print(f'{"":>10}', ' '.join(f'{s[:8]:>8}' for s in seq_ids))
for i, row in enumerate(mat):
    print(f'{seq_ids[i][:10]:>10}', ' '.join(f'{v*100:>7.1f}%' for v in row))

Conservation Score

Goal: Quantify per-column and overall alignment conservation to identify conserved and variable regions.

Approach: Calculate the fraction of the most common residue at each column, optionally ignoring gaps, and smooth with a sliding window.

Per-Column Conservation

def column_conservation(alignment, col_idx, ignore_gaps=True):
    column = alignment[:, col_idx]
    if ignore_gaps:
        column = column.replace('-', '')
    if not column:
        return 0.0
    counts = Counter(column)
    most_common_count = counts.most_common(1)[0][1]
    return most_common_count / len(column)

alignment = AlignIO.read('alignment.fasta', 'fasta')
for i in range(min(20, alignment.get_alignment_length())):
    cons = column_conservation(alignment, i)
    print(f'Column {i}: {cons*100:.0f}% conserved')

Average Conservation Across Alignment

def average_conservation(alignment, ignore_gaps=True):
    scores = []
    for col_idx in range(alignment.get_alignment_length()):
        scores.append(column_conservation(alignment, col_idx, ignore_gaps))
    return sum(scores) / len(scores)

avg_cons = average_conservation(alignment)
print(f'Average conservation: {avg_cons*100:.1f}%')

Conservation Profile

def conservation_profile(alignment, window=10):
    profile = []
    for i in range(alignment.get_alignment_length()):
        start = max(0, i - window // 2)
        end = min(alignment.get_alignment_length(), i + window // 2)
        scores = [column_conservation(alignment, j) for j in range(start, end)]
        profile.append(sum(scores) / len(scores))
    return profile

profile = conservation_profile(alignment, window=10)

Substitution Counts

Goal: Tabulate observed substitution frequencies from the alignment for evolutionary analysis or custom scoring matrices.

Approach: Enumerate all pairwise non-gap character comparisons at each column and tally substitution pairs.

Count Substitutions from Alignment

def substitution_counts(alignment):
    from collections import defaultdict
    counts = defaultdict(int)
    for col_idx in range(alignment.get_alignment_length()):
        column = alignment[:, col_idx]
        chars = [c for c in column if c != '-']
        for i, c1 in enumerate(chars):
            for c2 in chars[i+1:]:
                if c1 != c2:
                    pair = tuple(sorted([c1, c2]))
                    counts[pair] += 1
    return dict(counts)

subs = substitution_counts(alignment)
print('Substitution counts:')
for pair, count in sorted(subs.items(), key=lambda x: -x[1])[:10]:
    print(f'  {pair[0]}<->{pair[1]}: {count}')

Build Substitution Matrix from MSA

def build_substitution_matrix(alignment):
    from collections import defaultdict
    matrix = defaultdict(lambda: defaultdict(int))

    for col_idx in range(alignment.get_alignment_length()):
        column = alignment[:, col_idx]
        chars = [c for c in column if c != '-']
        for c1 in chars:
            for c2 in chars:
                matrix[c1][c2] += 1

    return {k: dict(v) for k, v in matrix.items()}

sub_matrix = build_substitution_matrix(alignment)

Using Alignment.substitutions (Pairwise Alignments)

For pairwise alignments created with

PairwiseAligner

.substitutions

from Bio.Align import PairwiseAligner

aligner = PairwiseAligner(mode='global', match_score=1, mismatch_score=-1)
alignments = aligner.align(seq1, seq2)
substitutions = alignments[0].substitutions

# Returns Array with substitution counts
print(substitutions)

Information Content

Goal: Measure column variability using Shannon entropy and derive information content for identifying functionally important positions.

Approach: Compute Shannon entropy from character frequencies per column; information content is max entropy minus observed entropy.

Shannon Entropy Per Column

import math

def shannon_entropy(column, ignore_gaps=True):
    if ignore_gaps:
        column = column.replace('-', '')
    if not column:
        return 0.0
    counts = Counter(column)
    total = len(column)
    entropy = 0.0
    for count in counts.values():
        p = count / total
        if p > 0:
            entropy -= p * math.log2(p)
    return entropy

alignment = AlignIO.read('alignment.fasta', 'fasta')
for i in range(min(20, alignment.get_alignment_length())):
    column = alignment[:, i]
    ent = shannon_entropy(column)
    print(f'Column {i}: entropy = {ent:.2f} bits')

Information Content (Max Entropy - Observed Entropy)

def information_content(column, alphabet_size=4):
    max_entropy = math.log2(alphabet_size)  # 4 for DNA, 20 for protein
    observed_entropy = shannon_entropy(column)
    return max_entropy - observed_entropy

# DNA alignment
for i in range(min(20, alignment.get_alignment_length())):
    column = alignment[:, i]
    ic = information_content(column, alphabet_size=4)
    print(f'Column {i}: IC = {ic:.2f} bits')

Gap Statistics

Goal: Summarize gap distribution across the alignment to assess alignment quality and identify problematic regions.

Approach: Calculate gap fractions per column and aggregate statistics including total gaps, gap-free columns, and gappiest sequence/column.

Gap Fraction Per Column

def gap_profile(alignment):
    profile = []
    for col_idx in range(alignment.get_alignment_length()):
        column = alignment[:, col_idx]
        gap_fraction = column.count('-') / len(alignment)
        profile.append(gap_fraction)
    return profile

gaps = gap_profile(alignment)
avg_gaps = sum(gaps) / len(gaps)
print(f'Average gap fraction: {avg_gaps*100:.1f}%')

Gap Statistics Summary

def gap_statistics(alignment):
    num_seqs = len(alignment)
    num_cols = alignment.get_alignment_length()

    total_positions = num_seqs * num_cols
    total_gaps = sum(str(r.seq).count('-') for r in alignment)

    gaps_per_seq = [str(r.seq).count('-') for r in alignment]
    gaps_per_col = [alignment[:, i].count('-') for i in range(num_cols)]

    return {
        'total_gaps': total_gaps,
        'gap_fraction': total_gaps / total_positions,
        'gappiest_seq': max(range(num_seqs), key=lambda i: gaps_per_seq[i]),
        'gappiest_col': max(range(num_cols), key=lambda i: gaps_per_col[i]),
        'gap_free_cols': sum(1 for g in gaps_per_col if g == 0),
    }

stats = gap_statistics(alignment)
print(f"Total gaps: {stats['total_gaps']}")
print(f"Gap fraction: {stats['gap_fraction']*100:.1f}%")
print(f"Gap-free columns: {stats['gap_free_cols']}")

Alignment Quality Metrics

Goal: Score alignment quality using sum-of-pairs or simple match/mismatch/gap scoring across all columns.

Approach: For each column, score all pairwise residue comparisons and sum across the alignment.

Overall Alignment Score

def alignment_score(alignment, match=1, mismatch=-1, gap=-2):
    total_score = 0
    for col_idx in range(alignment.get_alignment_length()):
        column = alignment[:, col_idx]
        for i, c1 in enumerate(column):
            for c2 in column[i+1:]:
                if c1 == '-' or c2 == '-':
                    total_score += gap
                elif c1 == c2:
                    total_score += match
                else:
                    total_score += mismatch
    return total_score

score = alignment_score(alignment)
print(f'Alignment score: {score}')

Sum of Pairs Score

def sum_of_pairs(alignment, substitution_matrix=None):
    if substitution_matrix is None:
        substitution_matrix = substitution_matrices.load('BLOSUM62')

    total = 0
    for col_idx in range(alignment.get_alignment_length()):
        column = alignment[:, col_idx]
        for i, c1 in enumerate(column):
            for c2 in column[i+1:]:
                if c1 != '-' and c2 != '-':
                    total += substitution_matrix.get((c1, c2), 0)
    return total

Position-Specific Score Matrix (PSSM)

Goal: Build a position-specific score matrix (PSSM) from the alignment for motif analysis or sequence scoring.

Approach: Count non-gap character frequencies at each column, producing a list of per-position dictionaries.

def position_specific_score_matrix(alignment):
    pssm = []
    for col_idx in range(alignment.get_alignment_length()):
        column = alignment[:, col_idx]
        counts = Counter(column)
        if '-' in counts:
            del counts['-']
        pssm.append(dict(counts))
    return pssm

alignment = AlignIO.read('alignment.fasta', 'fasta')
pssm = position_specific_score_matrix(alignment)
for i, row in enumerate(pssm[:10]):
    print(f'Position {i}: {row}')

Alignment Quality Assessment

When to Worry About Alignment Quality

Quantifying Alignment Uncertainty

For critical downstream analyses (phylogenetics, selection analysis), alignment uncertainty should be quantified rather than assumed. Two approaches:

• GUIDANCE2: Generates ~400 perturbed alignments varying guide trees, gap penalties, and co-optimal solutions. Column confidence = frequency of that column arrangement across perturbations. Default reliability threshold: 0.93.
• MUSCLE5 ensemble: Generates alignments with perturbed HMM parameters. Column confidence = fraction of ensemble members supporting that column. Faster than GUIDANCE2.

Alignment uncertainty propagates directly into evolutionary inference. Different aligners can produce phylogenies supporting fundamentally different biological conclusions. For selection analysis (dN/dS), misaligned codons create artificial nonsynonymous differences, leading to false positive selection signals.

Note on Bio.Align.AlignInfo

The

AlignInfo.SummaryInfo

• For PSSM: use

  position_specific_score_matrix()

  above
• For information content: use

  information_content()

  function earlier in this skill
• For consensus: see msa-parsing skill

Quick Reference: Metrics

Common Errors

ZeroDivisionError

KeyError

Related Skills

• multiple-alignment - Run MSA tools and quantify alignment confidence with ensembles
• msa-parsing - Parse, filter, trim, and assess alignment quality
• alignment-io - Read/write alignment files
• pairwise-alignment - Create and score pairwise alignments
• sequence-manipulation/sequence-properties - Sequence-level statistics