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.
Calculate Identity Between Two Sequences
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
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, use the built-in .substitutions property:
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}')
Note on Bio.Align.AlignInfo
The AlignInfo.SummaryInfo class is deprecated in recent Biopython versions. Use the custom functions in this skill instead:
- 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
| Metric | Description | Range |
|---|
| Identity | Fraction of identical residues | 0-1 |
| Conservation | Most common residue frequency | 0-1 |
| Shannon Entropy | Variability measure | 0 to log2(alphabet) |
| Information Content | Max entropy - observed entropy | 0 to log2(alphabet) |
| Gap Fraction | Proportion of gaps | 0-1 |
Common Errors
| Error | Cause | Solution |
|---|
ZeroDivisionError | Empty column after gap removal | Check for gap-only columns |
KeyError | Character not in substitution matrix | Handle gaps separately |
| Negative IC | Wrong alphabet size | Use 4 for DNA, 20 for protein |
Related Skills
- msa-parsing - Parse and manipulate alignments
- alignment-io - Read/write alignment files
- pairwise-alignment - Create and score pairwise alignments
- sequence-manipulation/sequence-properties - Sequence-level statistics