Parse and analyze multiple sequence alignments using Biopython...
Reference examples tested with: BioPython 1.83+, numpy 1.26+
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
import numpy as np
import pandas as pd
Optional for streaming and Easel-based weighting:
import pyhmmer
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'
API note: Bio.AlignIO returns MultipleSeqAlignment objects whose [:, idx] returns a plain str; [:, start:end] returns another MultipleSeqAlignment. The newer Bio.Align.Alignment (from Align.read / Align.parse) uses numpy-backed slicing -- verify with type(alignment[:, 0]) before assuming string methods work. For numpy-array access to the full alignment, use np.array(alignment).
for col_idx in range(alignment.get_alignment_length()):
column = alignment[:, col_idx]
counts = Counter(column)
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)
Trimming controversy and tool selection (ClipKIT, trimAl, BMGE, Divvier, HMMcleaner, Noisy) is the subject of a dedicated skill. Use this short decision matrix for routing:
| Goal | First-line tool |
|---|---|
| Phylogenetic-tree input | ClipKIT kpic-smart-gap (Steenwyk et al 2020 PLOS Bio) |
| HMM profile building | trimAl -gappyout (Capella-Gutierrez et al 2009 Bioinf) |
| Selection / dN/dS input | Avoid aggressive trimming; use TCS / GUIDANCE2 column masking |
| Deep prokaryotic phylogenomics | BMGE (Criscuolo & Gribaldo 2010 BMC Evol Biol) |
| Preserve column-mapping for residue-level analysis | trimAl -colnumbering |
See alignment/alignment-trimming for full mode comparisons, decision trees, and runnable examples.
How gaps are treated in downstream phylogenetic analysis significantly affects tree topology:
| Treatment | Method | Tradeoff |
|---|---|---|
| Missing data (default) | Gaps = unknown character | Most common; can be statistically inconsistent under ML |
| Fifth state | Gap = 5th nucleotide | Biologically problematic (gaps of different lengths treated equally) |
| Simple indel coding | Each unique indel coded as binary character | Most biologically realistic; adds phylogenetic signal |
For slow- to mid-rate datasets where indels are phylogenetically informative, prefer SIC indel coding or fifth-state treatment; for rapidly-evolving datasets (intra-species, ITS regions, retroelement-rich plant genomes), default to missing-data treatment because gap homology is unreliable. Run a sensitivity analysis comparing both treatments before drawing topological conclusions.
Columns exhibiting both high gap fraction AND low conservation are the strongest indicators of alignment uncertainty. These often reflect guide tree artifacts rather than true evolutionary events. Before phylogenetic analysis:
"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. When deprecation warnings appear from AlignInfo, callers should switch to the custom implementation.
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)
return MultipleSeqAlignment([r for r in alignment if regex.search(r.id)])
def filter_by_gap_content(alignment, max_gap_fraction=0.1):
return MultipleSeqAlignment(
[r for r in alignment if str(r.seq).count('-') / len(r.seq) <= max_gap_fraction]
)
def remove_duplicates(alignment):
seen = set()
return MultipleSeqAlignment([r for r in alignment if not (str(r.seq) in seen or seen.add(str(r.seq)))])
Stockholm-derived alignments expose secondary-structure markup, per-column GC/GR annotations, and per-sequence metadata via record.annotations, record.letter_annotations, and alignment.column_annotations. See usage-guide.md "Working with Annotations" for the full access pattern.
Goal: Convert between alignment column coordinates and ungapped sequence coordinates.
Approach: For one-off lookups, walk the sequence tracking gap characters. For repeated queries on the same sequence, vectorize with numpy.cumsum over a gap-mask -- O(L) preprocessing, O(1) lookups.
import numpy as np
def coordinate_map(record):
chars = np.frombuffer(str(record.seq).encode('ascii'), dtype=np.uint8)
is_residue = chars != ord('-')
seq_to_aln = np.flatnonzero(is_residue)
aln_to_seq = np.where(is_residue, np.cumsum(is_residue) - 1, -1)
return seq_to_aln, aln_to_seq
seq_to_aln, aln_to_seq = coordinate_map(alignment[0])
column_for_residue_42 = seq_to_aln[42]
residue_at_column_100 = aln_to_seq[100]
aln_to_seq[i] == -1 indicates a gap at alignment column i. This pattern handles 1 M-site genomic alignments in milliseconds compared to the loop-based version's seconds.
For single lookups without numpy, walk the sequence tracking a counter (seq_pos += 1 for each non-- char). The vectorized form above subsumes both directions.
A column-to-PDB mapping requires THREE coordinate systems: alignment column -> SEQRES residue (ungapped FASTA) -> ATOM residue (resolved structure). The SEQRES-to-ATOM map is non-trivial because PDB structures have unmodelled loops, N-terminal tags, engineered mutations, and seleno-substitutions. Conservation scores mapped via the bare alignment-to-SEQRES path will be off-by-many residues whenever the structure has missing density. For SEQRES/ATOM extraction and the authoritative _pdbx_poly_seq_scheme mapping, see structural-biology/structure-navigation.
Compute sequence weights before any column-wise statistic on phylogenetically structured datasets (lots of closely-related sequences plus a few outliers); without weighting, every per-column metric is biased toward the over-represented clades.
Goal: Give each sequence a weight that reflects its non-redundant contribution.
Approach: Each column c contributes 1 / (k_c * n_{s,c}) to sequence s, where k_c is the number of distinct residues at column c and n_{s,c} is the count of s's residue at that column (Henikoff & Henikoff 1994 JMB).
def henikoff_weights(alignment):
seq_array = np.array([list(str(r.seq)) for r in alignment])
weights = np.zeros(len(alignment))
for col_idx in range(seq_array.shape[1]):
residues, inverse, counts = np.unique(seq_array[:, col_idx], return_inverse=True, return_counts=True)
if '-' in residues:
continue
weights += 1.0 / (len(residues) * counts[inverse])
return weights / weights.sum()
Full implementation: examples/henikoff_weights.py.
Edge case: The implementation skips columns containing any gap, so sequences whose non-gap residues fall ONLY in gap-rich columns receive weight zero. This typically affects fragmentary or terminal-truncated sequences. HMMER's pb weighting handles this by including gaps as a residue type. For Pfam-style alignments where many columns are gappy, switch to pyhmmer.easel.MSAFile + compute_weights(method='pb') rather than this implementation, or augment the pure-numpy version with an ignore_gaps=False branch.
Goal: Estimate effective non-redundant sequence count after similarity-based clustering.
Approach: Cluster sequences at an identity threshold (HMMER and AlphaFold use 0.62 for protein, 0.80 for nucleotide), assign each sequence weight 1 / cluster_size, and sum. Neff/L > 0.5 is the rule-of-thumb threshold for direct-coupling-analysis contact prediction. Full implementation: examples/neff.py.
The thresholds reflect convention: 0.62 traces back to BLOSUM62 derivation; 0.80 is HHsuite's default for cd-hit-style clustering of the BFD database.
Neff is estimator-dependent. Different tools report different "Neff" values for the same MSA, often differing by 2-3x:
| Tool | Method | Typical relationship |
|---|---|---|
HMMER, pyhmmer (method='pb') |
Henikoff position-based | Baseline |
HMMER (method='gsc') |
Gerstein-Sonnhammer-Chothia | ~0.5-1.5x of pb |
HMMER (method='blosum') |
BLOSUM62-similarity clusters | ~0.7-1.2x of pb |
| AlphaFold2 / ColabFold | MMseqs2 cluster count at id=0.62 | ~1.5-3x of pb (counts unique sequences, not weights) |
| EVcouplings | HHfilter clusters at id=0.80 | ~0.5-1x of pb |
HHsuite hhmake |
inverse-purity count | Closer to pb |
Always report which estimator was used. Threshold rules ("Neff/L > 0.5 for DCA") are calibrated against a specific estimator -- Hopf et al 2017 use HHfilter-80 cluster count; Marks et al 2011 use 70%-identity cluster reweighting. AlphaFold2's MSA-depth gating uses unweighted cluster count at 62% identity.
Goal: Identify columns whose residue identities co-vary, indicating direct or indirect physical/functional coupling.
Approach: Compute pairwise mutual information across column pairs, then subtract the average-product correction (APC) from Dunn, Wahl & Gloor (2008 Bioinf) to remove per-column-entropy and phylogenetic background. This is the foundation of plmDCA and EVcouplings; full DCA needs Potts-model inference but APC-corrected MI runs in pure numpy and is informative on its own.
Skeleton (full implementation: examples/mi_apc.py):
def mi_matrix_apc(alignment):
# Compute pairwise MI across columns -> column_means -> APC = outer(means) / overall_mean
# Return mi - apc
...
For production-grade contact prediction, switch to plmDCA (Ekeberg et al 2013 Phys Rev E) or EVcouplings (Hopf et al 2017 Nat Biotechnol). APC-corrected MI scales to a few hundred columns; deeper analyses need approximate likelihood methods.
APC over-correction on short alignments: APC subtracts each column-pair's product of column-average MIs. For alignments with <100 columns, the column-averages are noisy estimates dominated by their constituent column-pairs, and APC removes signal proportional to noise. Empirically on Pfam alignments <100 columns, APC-corrected MI underperforms raw MI for contact prediction (Cocco et al 2018 Rep Prog Phys review). Apply APC only when L > 100 and Neff/L > 1; below this, raw MI plus a phylogenetic-distance threshold is more reliable. plmDCA and EVcouplings auto-skip APC when input depth is insufficient.
A2M (HMMER) and A3M (HHsuite, ColabFold) encode insert vs match columns via case (uppercase = match column residue, lowercase = insert). A3M does not pad inserts across sequences and must be reformatted to A2M before loading as a rectangular MSA. See alignment/alignment-io A2M / A3M Conventions section for the full character table, BioPython load pattern, and the reformat.pl reference-sequence pitfall.
For Pfam-scale streaming (multi-gigabyte Stockholm or A3M databases that exceed RAM), use pyhmmer.easel.MSAFile with compute_weights(method='pb') for in-flight Henikoff weighting. See alignment/alignment-io Streaming Large Stockholm Databases section for the full code pattern.
| 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 |
