Version Compatibility
Reference examples tested with: BioPython 1.83+
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 Parsing and Analysis
Parse multiple sequence alignments to extract information, analyze content, and prepare for downstream analysis.
Required Import
Goal: Load modules for parsing, analyzing, and manipulating multiple sequence alignments.
Approach: Import AlignIO for reading, Counter for column analysis, and alignment classes for constructing modified alignments.
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
Goal: Read an MSA file and inspect its dimensions.
Approach: Use AlignIO.read() specifying the file and format.
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
Goal: Analyze alignment content column by column to assess composition, conservation, and variability.
Approach: Use column indexing (alignment[:, idx]) and Counter to examine character frequencies at each position.
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
Goal: Quantify gap distribution across sequences and columns to identify problematic regions or sequences.
Approach: Count gap characters per sequence and per column, then identify positions exceeding a gap fraction threshold.
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
"Get consensus sequence" → Derive a single representative sequence from an MSA based on majority-rule voting at each column.
Goal: Generate a consensus sequence from the alignment using a frequency threshold.
Approach: At each column, select the most common non-gap character if it exceeds the threshold; otherwise mark as ambiguous.
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
Goal: Subset an alignment to retain only sequences matching specific criteria (ID pattern, gap content, uniqueness).
Approach: Iterate over alignment records, apply filter conditions, and reconstruct a new MultipleSeqAlignment from matching records.
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
Goal: Convert between alignment column coordinates and ungapped sequence coordinates.
Approach: Walk through the sequence tracking gap characters to map between the two coordinate systems.
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
| 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 |
Common Errors
| 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 |
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