bio-alignment-msa-parsing

Parse and analyze multiple sequence alignments using Biopython. Extract sequences, identify conserved regions, analyze gaps, work with annotations, and manipulate alignment data for downstream analysis. Use when parsing or manipulating multiple sequence alignments.

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Repository: mdbabumiamssm/LLMs-Universal-Life-Science-and-Clinical-Skills-
Letzte Quellaktivität: 4. Februar 2026 um 19:55
Erkannte Sprache von SKILL.md: Englisch
Sterne: 31
Forks: 8

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seq_ids = [record.id for record in alignment]

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

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')

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

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

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

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

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

gap_profile = gaps_per_column(alignment)

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)

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)

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)

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

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

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)

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_')

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)

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)

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}')

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

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

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

Task	Code
Get column	`alignment[:, col_idx]`
Get sequence	`alignment[seq_idx]`
Column count	`alignment.get_alignment_length()`
Sequence count	`len(alignment)`
Find gaps	`str(record.seq).count('-')`
Consensus	Use custom `consensus_sequence()` function

Error	Cause	Solution
`IndexError`	Column index out of range	Check `get_alignment_length()`
Unequal sequence lengths	Invalid MSA	Ensure all sequences same length
Empty Counter	All gaps in column	Handle gap-only columns

name	bio-alignment-msa-parsing
description	Parse and analyze multiple sequence alignments using Biopython. Extract sequences, identify conserved regions, analyze gaps, work with annotations, and manipulate alignment data for downstream analysis. Use when parsing or manipulating multiple sequence alignments.
tool_type	python
primary_tool	Bio.AlignIO

bio-alignment-msa-parsing

Quellinformationen

Installationsoptionen

Quelldateien prüfen

Mehr aus diesem Repository

MSA Parsing and Analysis

Required Import

Loading Alignments

Mehr aus diesem Repository

MSA Parsing and Analysis

Required Import

Loading Alignments

Extracting Sequence Information

Get All Sequence IDs

Get Sequences as Strings

Get Sequence by ID

Access Descriptions and Annotations

Column-wise Analysis

Get Single Column

Iterate Over Columns

Count Characters in Column

Find Conserved Positions

Gap Analysis

Count Gaps Per Sequence

Count Gaps Per Column

Find Gappy Columns

Remove Gappy Columns

Consensus Sequence

Simple Majority Consensus

Note on Bio.Align.AlignInfo

Extracting Regions

Slice by Column Range

Slice by Sequence Range

Extract Ungapped Regions from Reference

Sequence Filtering

Filter by Sequence ID Pattern

Filter by Gap Content

Remove Duplicate Sequences

Working with Annotations

Stockholm Format Annotations

Add Annotations to Records

Position Mapping

Map Alignment Position to Sequence Position

Map Sequence Position to Alignment Position

Quick Reference: Common Operations

Common Errors

Related Skills