Bio Alignment Msa Parsing

MSA Parsing and Analysis

Parse multiple sequence alignments to extract information, analyze content, and prepare for downstream analysis.

Required Import

from Bio import AlignIO
from Bio.Align import MultipleSeqAlignment
from Bio.SeqRecord import SeqRecord
from Bio.Seq import Seq
from collections import Counter

Loading Alignments

from Bio import AlignIO

alignment = AlignIO.read('alignment.fasta', 'fasta')
print(f'{len(alignment)} sequences, {alignment.get_alignment_length()} columns')

Extracting Sequence Information

Get All Sequence IDs

seq_ids = [record.id for record in alignment]

Get Sequences as Strings

sequences = [str(record.seq) for record in alignment]

Get Sequence by ID

def get_sequence_by_id(alignment, seq_id):
    for record in alignment:
        if record.id == seq_id:
            return record
    return None

target = get_sequence_by_id(alignment, 'species_A')

Access Descriptions and Annotations

for record in alignment:
    print(f'ID: {record.id}')
    print(f'Description: {record.description}')
    print(f'Annotations: {record.annotations}')

Column-wise Analysis

Get Single Column

column_5 = alignment[:, 5]  # Returns string of characters at position 5
print(column_5)  # e.g., 'AAAGA'

Iterate Over Columns

for col_idx in range(alignment.get_alignment_length()):
    column = alignment[:, col_idx]
    print(f'Column {col_idx}: {column}')

Count Characters in Column

from collections import Counter

def column_composition(alignment, col_idx):
    column = alignment[:, col_idx]
    return Counter(column)

counts = column_composition(alignment, 0)
print(counts)  # Counter({'A': 3, 'G': 1, '-': 1})

Find Conserved Positions

def find_conserved_positions(alignment, threshold=1.0):
    conserved = []
    for col_idx in range(alignment.get_alignment_length()):
        column = alignment[:, col_idx]
        counts = Counter(column)
        most_common_char, most_common_count = counts.most_common(1)[0]
        if most_common_char != '-':
            conservation = most_common_count / len(alignment)
            if conservation >= threshold:
                conserved.append((col_idx, most_common_char))
    return conserved

fully_conserved = find_conserved_positions(alignment, threshold=1.0)
mostly_conserved = find_conserved_positions(alignment, threshold=0.8)

Gap Analysis

Count Gaps Per Sequence

gap_counts = [(record.id, str(record.seq).count('-')) for record in alignment]
for seq_id, gaps in gap_counts:
    print(f'{seq_id}: {gaps} gaps')

Count Gaps Per Column

def gaps_per_column(alignment):
    return [alignment[:, i].count('-') for i in range(alignment.get_alignment_length())]

gap_profile = gaps_per_column(alignment)

Find Gappy Columns

def find_gappy_columns(alignment, threshold=0.5):
    gappy = []
    num_seqs = len(alignment)
    for col_idx in range(alignment.get_alignment_length()):
        column = alignment[:, col_idx]
        gap_fraction = column.count('-') / num_seqs
        if gap_fraction >= threshold:
            gappy.append(col_idx)
    return gappy

columns_to_remove = find_gappy_columns(alignment, threshold=0.5)

Remove Gappy Columns

def remove_gappy_columns(alignment, threshold=0.5):
    num_seqs = len(alignment)
    keep_columns = []
    for col_idx in range(alignment.get_alignment_length()):
        column = alignment[:, col_idx]
        gap_fraction = column.count('-') / num_seqs
        if gap_fraction < threshold:
            keep_columns.append(col_idx)

    new_records = []
    for record in alignment:
        new_seq = ''.join(str(record.seq)[i] for i in keep_columns)
        new_records.append(SeqRecord(Seq(new_seq), id=record.id, description=record.description))
    return MultipleSeqAlignment(new_records)

cleaned = remove_gappy_columns(alignment, threshold=0.5)

Consensus Sequence

Simple Majority Consensus

def consensus_sequence(alignment, threshold=0.5, gap_char='-', ambiguous='N'):
    consensus = []
    for col_idx in range(alignment.get_alignment_length()):
        column = alignment[:, col_idx]
        counts = Counter(column)
        most_common_char, most_common_count = counts.most_common(1)[0]
        if most_common_char == gap_char:
            counts.pop(gap_char, None)
            if counts:
                most_common_char, most_common_count = counts.most_common(1)[0]
            else:
                most_common_char = gap_char

        if most_common_count / len(alignment) >= threshold:
            consensus.append(most_common_char)
        else:
            consensus.append(ambiguous)
    return ''.join(consensus)

consensus = consensus_sequence(alignment, threshold=0.5)

Note on Bio.Align.AlignInfo

The AlignInfo.SummaryInfo class is deprecated in recent Biopython versions. The custom consensus_sequence() function above is the recommended approach. If you see deprecation warnings when using AlignInfo, use the custom implementation instead.

Extracting Regions

Slice by Column Range

region = alignment[:, 100:200]  # Columns 100-199

Slice by Sequence Range

subset = alignment[0:10]  # First 10 sequences

Extract Ungapped Regions from Reference

def extract_ungapped_regions(alignment, ref_idx=0):
    ref_seq = str(alignment[ref_idx].seq)
    ungapped_cols = [i for i, char in enumerate(ref_seq) if char != '-']

    new_records = []
    for record in alignment:
        new_seq = ''.join(str(record.seq)[i] for i in ungapped_cols)
        new_records.append(SeqRecord(Seq(new_seq), id=record.id, description=record.description))
    return MultipleSeqAlignment(new_records)

ungapped = extract_ungapped_regions(alignment, ref_idx=0)

Sequence Filtering

Filter by Sequence ID Pattern

import re

def filter_by_id(alignment, pattern):
    regex = re.compile(pattern)
    matching = [record for record in alignment if regex.search(record.id)]
    return MultipleSeqAlignment(matching)

bacteria_only = filter_by_id(alignment, r'^Bac_')

Filter by Gap Content

def filter_by_gap_content(alignment, max_gap_fraction=0.1):
    filtered = []
    for record in alignment:
        gap_fraction = str(record.seq).count('-') / len(record.seq)
        if gap_fraction <= max_gap_fraction:
            filtered.append(record)
    return MultipleSeqAlignment(filtered)

low_gap_seqs = filter_by_gap_content(alignment, max_gap_fraction=0.1)

Remove Duplicate Sequences

def remove_duplicates(alignment):
    seen_seqs = {}
    unique_records = []
    for record in alignment:
        seq_str = str(record.seq)
        if seq_str not in seen_seqs:
            seen_seqs[seq_str] = record.id
            unique_records.append(record)
    return MultipleSeqAlignment(unique_records)

unique_alignment = remove_duplicates(alignment)

Working with Annotations

Stockholm Format Annotations

alignment = AlignIO.read('pfam.sto', 'stockholm')

for record in alignment:
    if 'secondary_structure' in record.letter_annotations:
        ss = record.letter_annotations['secondary_structure']
        print(f'{record.id}: {ss}')

Add Annotations to Records

for record in alignment:
    record.annotations['source'] = 'my_analysis'
    record.annotations['quality'] = 'high'

Position Mapping

Map Alignment Position to Sequence Position

def alignment_to_sequence_position(record, align_pos):
    seq_pos = 0
    for i, char in enumerate(str(record.seq)):
        if i == align_pos:
            return seq_pos if char != '-' else None
        if char != '-':
            seq_pos += 1
    return None

Map Sequence Position to Alignment Position

def sequence_to_alignment_position(record, seq_pos):
    current_seq_pos = 0
    for i, char in enumerate(str(record.seq)):
        if char != '-':
            if current_seq_pos == seq_pos:
                return i
            current_seq_pos += 1
    return None

Quick Reference: Common Operations

Common Errors

Related Skills

• alignment-io - Read/write alignment files in various formats
• pairwise-alignment - Create pairwise alignments
• msa-statistics - Calculate conservation metrics
• sequence-manipulation/motif-search - Search for patterns