bio-alignment-io

name	bio-alignment-io
description	Read, write, and convert multiple sequence alignment files using Biopython Bio.AlignIO. Supports Clustal, PHYLIP, Stockholm, FASTA, Nexus, and other alignment formats for phylogenetics and conservation analysis. Use when reading, writing, or converting alignment file formats.
tool_type	python
primary_tool	Bio.AlignIO

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.

Alignment File I/O

Read, write, and convert multiple sequence alignment files in various formats.

Required Import

Goal: Load modules for reading, writing, and manipulating multiple sequence alignments.

Approach: Import AlignIO for file I/O and supporting classes for programmatic alignment construction.

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

Format Coverage Map

Three Python libraries cover the alignment-format space, with overlapping but non-identical support. Pick by what is actually required.

Format	Bio.AlignIO	Bio.Align (modern)	pyhmmer.easel	Notes
Aligned FASTA	R/W	R/W	R/W	Most portable; loses annotations
Clustal	R/W	R/W	R	Clustal conservation marks NOT round-tripped
PHYLIP (interleaved/sequential/relaxed)	R/W	R/W	R	Strict 10-char names is silent footgun
Stockholm	R/W	R/W	R/W	Only format preserving GS/GR/GC/GF annotations
NEXUS	R/W	R/W	--	MrBayes / PAUP* input
MAF (Multiple Alignment Format)	R/W	R/W	--	UCSC whole-genome alignments
A2M / A3M	-- (use 'fasta' parser then post-process)	--	R/W	HMMER (a2m), HHsuite/ColabFold (a3m)
MSF (GCG)	R	--	--	GCG legacy
EMBOSS / Mauve XMFA / FASTA-m10	R	partial	--	One-way: read-only

Formats NOT in BioPython (use dedicated tools):

Format	Tool	Why
HAL	progressiveCactus, halTools	HDF5-backed multi-genome alignments at TB scale
chain / net	UCSC Kent tools (liftOver, chainNet)	Pairwise genome alignment
AXT	BLASTZ / lastz native	Pairwise alignment blocks
PSL	UCSC Kent tools (pslPretty, blat)	BLAT alignment summary
GFA / rGFA	vg, odgi, pggb, gfatools	Pangenome graph
GAF	vg surject, vg call	Graph alignment format (read-to-graph)

Recommend Bio.Align (modern API) over Bio.AlignIO (legacy) for new code; it returns Alignment objects with built-in .counts() and .substitutions properties. For multi-gigabyte Stockholm databases such as Pfam-A.full, pyhmmer.easel.MSAFile streams record-by-record where Bio.AlignIO.parse works but at higher per-record cost.

Reading Alignments

"Read an alignment file" → Parse an alignment file into an alignment object with sequences and metadata accessible.

Goal: Load alignment data from files in various formats (Clustal, PHYLIP, Stockholm, FASTA).

Approach: Use AlignIO.read() for single-alignment files or AlignIO.parse() for files containing multiple alignments.

Single Alignment File

from Bio import AlignIO

alignment = AlignIO.read('alignment.aln', 'clustal')
print(f'Alignment length: {alignment.get_alignment_length()}')
print(f'Number of sequences: {len(alignment)}')

Multiple Alignments in One File

for alignment in AlignIO.parse('multi_alignment.sto', 'stockholm'):
    print(f'Alignment with {len(alignment)} sequences, length {alignment.get_alignment_length()}')

Read as List

alignments = list(AlignIO.parse('alignments.phy', 'phylip'))
print(f'Read {len(alignments)} alignments')

Writing Alignments

Goal: Save alignment data to files in standard formats for downstream tools or archival.

Approach: Use AlignIO.write() with the target format specifier, supporting single or multiple alignments and file handles.

Write Single Alignment

AlignIO.write(alignment, 'output.fasta', 'fasta')

Write Multiple Alignments

alignments = [alignment1, alignment2, alignment3]
count = AlignIO.write(alignments, 'output.sto', 'stockholm')
print(f'Wrote {count} alignments')

Write to Handle

with open('output.aln', 'w') as handle:
    AlignIO.write(alignment, handle, 'clustal')

Format Conversion

"Convert alignment format" → Transform an alignment file from one format to another (e.g., Clustal to PHYLIP).

Goal: Convert alignment files between formats for compatibility with different analysis tools.

Approach: Use AlignIO.convert() for direct one-step conversion, or read-modify-write for cases requiring intermediate manipulation.

Direct Conversion (Most Efficient)

AlignIO.convert('input.aln', 'clustal', 'output.phy', 'phylip')

With Alphabet Specification

AlignIO.convert('input.sto', 'stockholm', 'output.nex', 'nexus', molecule_type='DNA')

Manual Conversion (When Modification Needed)

alignment = AlignIO.read('input.aln', 'clustal')
# ... modify alignment ...
AlignIO.write(alignment, 'output.fasta', 'fasta')

Accessing Alignment Data

Goal: Navigate and extract data from alignment objects including sequences, columns, and slices.

Approach: Use iteration, indexing, and column slicing on the alignment object.

alignment = AlignIO.read('alignment.aln', 'clustal')

# Iterate over sequences
for record in alignment:
    print(f'{record.id}: {record.seq}')

# Access by index
first_seq = alignment[0]
last_seq = alignment[-1]

# Slice columns
column_slice = alignment[:, 10:20]  # Columns 10-19

# Get specific column
column = alignment[:, 5]  # Column 5 as string

Working with Alignment Objects

Get Alignment Properties

alignment = AlignIO.read('alignment.aln', 'clustal')

length = alignment.get_alignment_length()
num_seqs = len(alignment)
seq_ids = [record.id for record in alignment]

Slice Alignments

# Get subset of sequences
subset = alignment[0:5]  # First 5 sequences

# Get subset of columns
trimmed = alignment[:, 50:150]  # Columns 50-149

# Combine slicing
region = alignment[0:5, 50:150]  # 5 sequences, columns 50-149

Creating Alignments Programmatically

Goal: Build an alignment object from sequences defined in code rather than read from a file.

Approach: Construct SeqRecord objects with gap characters and wrap them in a MultipleSeqAlignment.

from Bio.Align import MultipleSeqAlignment
from Bio.SeqRecord import SeqRecord
from Bio.Seq import Seq

records = [
    SeqRecord(Seq('ACTGACTGACTG'), id='seq1'),
    SeqRecord(Seq('ACTGACT-ACTG'), id='seq2'),
    SeqRecord(Seq('ACTG-CTGACTG'), id='seq3'),
]
alignment = MultipleSeqAlignment(records)
AlignIO.write(alignment, 'new_alignment.fasta', 'fasta')

Format Selection for Downstream Tools

Choosing the output format depends on which downstream tool consumes the alignment:

Downstream Tool	Required Format	BioPython Format String
RAxML-NG, IQ-TREE	PHYLIP (relaxed)	'phylip-relaxed'
MrBayes	NEXUS	'nexus'
PAUP*	NEXUS or PHYLIP	'nexus' or 'phylip'
HMMER, Infernal	Stockholm	'stockholm'
Pfam/Rfam databases	Stockholm	'stockholm'
PAML/codeml	PHYLIP (sequential)	'phylip-sequential'
Most tools	FASTA	'fasta'

Annotation Preservation

Not all formats support annotations. Converting between formats can silently discard metadata:

Format	Sequence Annotations	Column Annotations	Secondary Structure
Stockholm	Yes (GS/GR lines)	Yes (GC lines)	Yes (SS_cons)
NEXUS	Partial (SETS block)	Via CHARSET	No
Clustal	No (conservation marks not parsed)	No	No
PHYLIP	No	No	No
FASTA	No	No	No

Converting Stockholm to FASTA or PHYLIP discards all annotations, secondary structure markup, and per-residue quality scores. If annotations matter, keep a Stockholm master copy.

Format-Specific Notes

PHYLIP Format Pitfalls

PHYLIP has two incompatible variants (interleaved vs sequential) and two name-length modes (strict vs relaxed). Confusing these causes silent data corruption.

Strict PHYLIP truncates sequence names to exactly 10 characters. This can silently merge distinct sequences whose names share a 10-character prefix (e.g., Homo_sapiens_chr1 and Homo_sapiens_chr2 both become Homo_sapie).

# Strict PHYLIP (10-char names, interleaved) -- only for tools requiring it
alignment = AlignIO.read('file.phy', 'phylip')

# Sequential PHYLIP (10-char names, one sequence at a time) -- PAML/codeml
alignment = AlignIO.read('file.phy', 'phylip-sequential')

# Relaxed PHYLIP (no name limit) -- RAxML-NG, IQ-TREE (recommended default)
alignment = AlignIO.read('file.phy', 'phylip-relaxed')

# Always prefer phylip-relaxed for writing unless the downstream tool
# specifically requires strict format
AlignIO.write(alignment, 'output.phy', 'phylip-relaxed')

PHYLIP-Relaxed Dialect Mismatches Between Tree Tools

Biopython's 'phylip-relaxed' writes a single space between name and sequence. RAxML-NG and IQ-TREE accept this; PhyML rejects sequence names containing colons or parentheses; PAML's codeml expects sequential format with name-truncation behaviour distinct from interleaved. Common silent failures:

Symptom	Cause	Fix
RAxML-NG: terminating with uncaught exception ... bad alphabet	Stop codons (*) in protein alignment	Replace * with X before writing
IQ-TREE: not a valid PHYLIP file	Sequence name contains : (NEXUS-tree-style refs)	Sanitize names: re.sub(r'[():,]', '_', record.id)
PhyML: silently truncated names	Names >100 chars	PhyML truncates without warning at 100 chars in current build
codeml: cannot read sequences	Used phylip-relaxed instead of phylip-sequential	codeml requires strict sequential

Always verify by running the downstream tool's "validate input only" mode (e.g. iqtree2 -s file.phy --check) before committing to a long compute.

MAF Block Coordinate Conventions

UCSC MAF (read via AlignIO.parse(file, 'maf')) returns blocks with per-row annotations:

• start (0-based; converts directly to BED but is off-by-one vs GFF)
• size (length on src strand)
• strand (+ or -)
• srcSize (length of source chromosome)

For minus-strand rows, start is measured from the END of the source contig: the corresponding plus-strand start is srcSize - start - size. Without this conversion, lifting MAF to genome coordinates places minus-strand blocks at the wrong locus. Reference: UCSC MAF spec at genome.ucsc.edu/FAQ/FAQformat.html#format5.

def maf_to_plus_strand_coords(row_anno):
    if row_anno['strand'] == '-':
        return row_anno['srcSize'] - row_anno['start'] - row_anno['size']
    return row_anno['start']

Stockholm Format Annotations

Stockholm format (used by Pfam, Rfam, HMMER) supports four annotation line types:

Line Prefix	Scope	Description	Example
#=GF	File	Alignment-level metadata (ID, accession, description)	#=GF AC PF00001
#=GC	Column	Per-column annotation (1 char per alignment column)	#=GC SS_cons ..(((...)))..
#=GS	Sequence	Per-sequence free text (organism, description)	#=GS seq1 OS Homo sapiens
#=GR	Residue	Per-residue annotation (1 char per residue)	#=GR seq1 SS ..HHH..EEE..

Common GC annotations: SS_cons (consensus secondary structure), RF (reference coordinates), seq_cons (consensus sequence).

WUSS notation in RNA #=GC SS_cons lines uses nested bracket pairs (<>, (), [], {}) for paired bases and characters like _, -, ,, :, ., ~ for unpaired regions; pseudoknots use upper/lower-case letter pairs (Aa, Bb). Consult the Infernal user guide for the full character table before writing or parsing custom SS_cons strings.

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

for record in alignment:
    print(record.id, record.annotations)
    if 'secondary_structure' in record.letter_annotations:
        print(f'  SS: {record.letter_annotations["secondary_structure"]}')

ss_cons = alignment.column_annotations.get('secondary_structure')

Round-trip caveat: AlignIO.write(alignment, 'out.fasta', 'fasta') discards every Stockholm annotation silently. Re-reading and re-writing as Stockholm preserves GC/GR but dropped/added sequences invalidate the per-residue annotations -- regenerate annotations after edits.

Pfam-style name/start-end identifier convention: Pfam, Rfam, and Dfam Stockholm IDs (e.g. Q9Y6Y0/45-198) encode a 1-based inclusive region. Biopython does not split this; before passing to RAxML or IQ-TREE, parse the suffix into record.annotations['start'] / ['end'] and strip from record.id, then restore it after.

A2M / A3M Conventions

A2M (HMMER) and A3M (HHsuite, ColabFold) encode match vs insert columns by case (uppercase / - = match column, lowercase / . = insert column). A2M pads inserts across rows so it loads as a rectangular MSA; A3M does not, so convert with HHsuite reformat.pl a3m a2m in.a3m out.a2m (or pyhmmer.easel.MSAFile(..., format='a2m')) before parsing as a normal alignment.

reformat.pl pitfall: HHsuite's reformat.pl a3m a2m uses the FIRST sequence in the A3M as the match-state reference. ColabFold MSAs typically place the query first, which is the desired reference; merged or sorted A3Ms can have a non-query first sequence, producing match-state assignments that mis-align the query. Either (a) verify the first sequence is the query before reformatting, or (b) renormalise with hhfilter -i in.a3m -o out.a3m -id 100 -qid 0 -cov 0 before running reformat.pl. A3M files emitted by hhblits always have the query first; A3M files concatenated from MSA databases do not.

alignment = AlignIO.read('hhsearch.a2m', 'fasta')
match_only_seqs = [
    ''.join(c for c in str(r.seq) if c.isupper() or c == '-')
    for r in alignment
]

Streaming Large Stockholm Databases

Bio.AlignIO.read() is in-memory; for Pfam-A.full (multi-gigabyte; ~22,000 family alignments in Pfam 37) or BFD (>2 TB), use pyhmmer.easel.MSAFile for streaming Stockholm or A3M.

import pyhmmer

with pyhmmer.easel.MSAFile('Pfam-A.full', digital=True) as msa_file:
    for msa in msa_file:
        if msa.nseq < 50:
            continue
        weights = msa.compute_weights(method='pb')
        print(msa.name.decode(), msa.nseq, msa.alen, f'sum_w={sum(weights):.1f}')

msa.compute_weights(method='pb') computes Henikoff PB weights via the same Easel routine HMMER uses; the weights sum to the number of sequences (not Neff). For an Henikoff-style Neff estimate, see msa-parsing/examples/neff.py.

Clustal Format

# Clustal preserves conservation symbols in file but not when parsed
alignment = AlignIO.read('clustal.aln', 'clustal')

Batch Processing Multiple Files

Goal: Convert a directory of alignment files from one format to another in bulk.

Approach: Glob for input files and iterate, reading each alignment and writing to the target format.

from pathlib import Path

input_dir = Path('alignments/')
output_dir = Path('converted/')

for input_file in input_dir.glob('*.aln'):
    alignment = AlignIO.read(input_file, 'clustal')
    output_file = output_dir / f'{input_file.stem}.fasta'
    AlignIO.write(alignment, output_file, 'fasta')

Alternative: Bio.Align Module I/O

Goal: Use the modern Bio.Align module for alignment I/O with access to newer features like counts and substitutions.

Approach: Use Align.read(), Align.parse(), and Align.write() which return Alignment objects instead of MultipleSeqAlignment.

The newer Bio.Align module provides its own I/O functions that return Alignment objects (instead of MultipleSeqAlignment). These support additional formats and provide access to modern alignment features.

from Bio import Align

# Read single alignment (returns Alignment object)
alignment = Align.read('alignment.aln', 'clustal')

# Parse multiple alignments
for alignment in Align.parse('multi.sto', 'stockholm'):
    print(f'Alignment with {len(alignment)} sequences')

# Write alignment
Align.write(alignment, 'output.fasta', 'fasta')

When to Use Which

Use Case	Module
Legacy code, MultipleSeqAlignment needed	Bio.AlignIO
Modern features (counts, substitutions)	Bio.Align
Format conversion	Either works
Working with pairwise alignments	Bio.Align

Quick Reference: Common Operations

Task	Code
Read single alignment	AlignIO.read(file, format)
Read multiple alignments	AlignIO.parse(file, format)
Write alignment(s)	AlignIO.write(align, file, format)
Convert format	AlignIO.convert(in_file, in_fmt, out_file, out_fmt)
Get length	alignment.get_alignment_length()
Get sequence count	len(alignment)
Slice columns	alignment[:, start:end]

Common Errors

Error	Cause	Solution
ValueError: No records	Empty file	Check file path and format
ValueError: More than one record	Multiple alignments with read()	Use parse() instead
ValueError: Sequences different lengths	Invalid alignment	Ensure all sequences same length
ValueError: unknown format	Unsupported format string	Check supported formats list

Related Skills

• alignment/multiple-alignment - Run MSA tools (MAFFT, MUSCLE5, ClustalOmega) to generate alignments
• alignment/pairwise-alignment - Create pairwise alignments with PairwiseAligner
• alignment/msa-parsing - Analyze alignment content and annotations
• alignment/msa-statistics - Calculate conservation and identity
• alignment/structural-alignment - Foldseek/TM-align outputs and Foldmason result_aa.fa / result_3di.fa MSAs (FASTA-loadable; the per-column LDDT report is HTML, not BioPython-parseable)
• alignment/alignment-trimming - Pre-format trimming with column-mapping retention
• sequence-io/format-conversion - Convert sequence (non-alignment) formats

References

• Nawrocki EP, Eddy SR. 2013. Infernal 1.1: 100-fold faster RNA homology searches. Bioinf 29:2933-2935 (WUSS notation reference; see also the Infernal user guide).
• Larralde M et al. 2023. PyHMMER: a Python library binding to HMMER for efficient sequence analysis. Bioinf 39:btad214.
• Cock PJA et al. 2009. Biopython: freely available Python tools for computational molecular biology and bioinformatics. Bioinf 25:1422-1423.
• Mistry J et al. 2021. Pfam: the protein families database in 2021. NAR 49:D412-D419.
• Steinegger M et al. 2019. HH-suite3 for fast remote homology detection and deep protein annotation. BMC Bioinf 20:473.
• Mirdita M et al. 2022. ColabFold: making protein folding accessible to all. Nat Methods 19:679-682.
• Blanchette M et al. 2004. Aligning multiple genomic sequences with the threaded blockset aligner. Genome Res 14:708-715.