Use Biopython to inspect multiple sequence alignments, where sequences are arranged to compare corresponding positions. Find conserved positions, measure gaps, extract regions, build a representative consensus sequence, and filter alignment data for further analysis.
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.
Run score Not tested
See how to installReview the instructions the agent will receive before you install.
Reference examples tested with: BioPython 1.83+
Before using code patterns, verify installed versions match. If versions differ:
pip show <package> then help(module.function) to check signaturesIf code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying.
Parse multiple sequence alignments to extract information, analyze content, and prepare for downstream analysis.
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
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')
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}')
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.
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)
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.
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)
"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.
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)
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.
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)
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.
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'
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.
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 |
No reviews yet. Tell others what you thought.
No login needed. Share how it worked for you, ask a question, or note where it did not run.
No comments yet. Be the first to write one.
Support protein design with cloud laboratory testing for binding, expression, thermal stability, and enzyme activity. Covers sequence optimization, submitting experiments through the Adaptyv API, checking progress, and downloading experimental results.
Build flexible daily plans that account for ADHD-related difficulties with time estimation and getting started. Narrow priorities to three tasks, add transition buffers, and choose small next actions that match the energy available that day.
Build Python workflows for data recorded over time, including classification, forecasting, anomaly detection, and pattern search. Using aeon, the skill guides preprocessing, algorithm selection, feature extraction, and evaluation through interfaces compatible with scikit-learn.
Use a browser to research topics, extract page information, fill forms, and test websites. Guides agents through identifying page controls, interacting with web apps, saving screenshots or PDFs, and reusing saved login sessions.
Combine health measurements, exercise, sleep, nutrition, and mental health records to examine trends, unusual changes, and estimated risks. The skill produces personalized suggestions and text or interactive HTML reports using local data without external API calls.
Predict structures for designed protein sequences and protein complexes with AlphaFold2 or ColabFold. Inspect structure files and confidence scores to assess folding and chain interfaces, with guidance for local runs, Modal execution, and common prediction errors.