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.

Batch Processing

"Process all my sequence files in a directory" → Iterate, merge, split, convert, and generate summary statistics across multiple sequence files.

• Python: SeqIO.parse(), Path.glob() (BioPython, pathlib)

Process multiple sequence files efficiently using Biopython.

Required Imports

from pathlib import Path
from Bio import SeqIO

Process Multiple Files

Iterate Over Files in Directory

from pathlib import Path

for fasta_file in Path('data/').glob('*.fasta'):
    records = list(SeqIO.parse(fasta_file, 'fasta'))
    print(f'{fasta_file.name}: {len(records)} sequences')

Process All FASTQ Files

for fq_file in Path('.').glob('*.fastq'):
    count = sum(1 for _ in SeqIO.parse(fq_file, 'fastq'))
    print(f'{fq_file.name}: {count} reads')

Recursive File Search

for gb_file in Path('data/').rglob('*.gb'):
    print(f'Found: {gb_file}')

Merge Files

Merge All FASTA Files

from pathlib import Path

def all_records(directory, pattern, format):
    for filepath in Path(directory).glob(pattern):
        yield from SeqIO.parse(filepath, format)

records = all_records('data/', '*.fasta', 'fasta')
count = SeqIO.write(records, 'merged.fasta', 'fasta')
print(f'Merged {count} records')

Merge with Source Tracking

Goal: Combine sequences from multiple files into one, tagging each record with its source filename.

Approach: Stream records from each file through a generator that appends source metadata to the description.

Reference (BioPython 1.83+):

def records_with_source(directory, pattern, format):
    for filepath in Path(directory).glob(pattern):
        for record in SeqIO.parse(filepath, format):
            record.description = f'{record.description} [source={filepath.name}]'
            yield record

records = records_with_source('data/', '*.fasta', 'fasta')
SeqIO.write(records, 'merged_tracked.fasta', 'fasta')

Merge Specific Files

files = ['sample1.fasta', 'sample2.fasta', 'sample3.fasta']

def merge_files(file_list, format):
    for filepath in file_list:
        yield from SeqIO.parse(filepath, format)

SeqIO.write(merge_files(files, 'fasta'), 'combined.fasta', 'fasta')

Split Files

Split by Number of Records

Goal: Divide a large sequence file into smaller chunks of N records each.

Approach: Consume the iterator in fixed-size batches using islice, writing each batch to a numbered output file.

Reference (BioPython 1.83+):

from itertools import islice

def split_file(input_file, format, records_per_file, output_prefix):
    records = SeqIO.parse(input_file, format)
    file_num = 1
    while True:
        batch = list(islice(records, records_per_file))
        if not batch:
            break
        output_file = f'{output_prefix}_{file_num}.{format}'
        SeqIO.write(batch, output_file, format)
        print(f'Wrote {len(batch)} records to {output_file}')
        file_num += 1

split_file('large.fasta', 'fasta', 1000, 'split')

Split by Sequence ID Prefix

Goal: Group sequences into separate files based on a shared ID prefix (e.g., sample or chromosome).

Approach: Parse all records into a prefix-keyed dictionary, then write each group to its own file.

Reference (BioPython 1.83+):

from collections import defaultdict

records_by_prefix = defaultdict(list)
for record in SeqIO.parse('input.fasta', 'fasta'):
    prefix = record.id.split('_')[0]
    records_by_prefix[prefix].append(record)

for prefix, records in records_by_prefix.items():
    SeqIO.write(records, f'{prefix}.fasta', 'fasta')

One Sequence Per File

for record in SeqIO.parse('multi.fasta', 'fasta'):
    SeqIO.write(record, f'{record.id}.fasta', 'fasta')

Batch Convert

Convert All Files in Directory

from pathlib import Path

for gb_file in Path('genbank/').glob('*.gb'):
    fasta_file = Path('fasta/') / gb_file.with_suffix('.fasta').name
    count = SeqIO.convert(str(gb_file), 'genbank', str(fasta_file), 'fasta')
    print(f'{gb_file.name} -> {fasta_file.name}: {count} records')

Batch Convert with Summary

from pathlib import Path

results = []
for input_file in Path('input/').glob('*.gb'):
    output_file = Path('output/') / input_file.with_suffix('.fasta').name
    count = SeqIO.convert(str(input_file), 'genbank', str(output_file), 'fasta')
    results.append({'file': input_file.name, 'records': count})

print(f'Converted {len(results)} files, {sum(r["records"] for r in results)} total records')

Parallel Processing

Using multiprocessing

from multiprocessing import Pool
from pathlib import Path

def process_file(filepath):
    records = list(SeqIO.parse(filepath, 'fasta'))
    return {'file': filepath.name, 'count': len(records), 'total_bp': sum(len(r.seq) for r in records)}

files = list(Path('data/').glob('*.fasta'))
with Pool(4) as pool:
    results = pool.map(process_file, files)

for r in results:
    print(f'{r["file"]}: {r["count"]} seqs, {r["total_bp"]} bp')

Using concurrent.futures

from concurrent.futures import ThreadPoolExecutor
from pathlib import Path

def count_records(filepath):
    return filepath.name, sum(1 for _ in SeqIO.parse(filepath, 'fasta'))

files = list(Path('data/').glob('*.fasta'))
with ThreadPoolExecutor(max_workers=4) as executor:
    results = executor.map(count_records, files)

for name, count in results:
    print(f'{name}: {count}')

Summary Statistics

Aggregate Stats Across Files

from pathlib import Path

total_seqs = 0
total_bp = 0
file_count = 0

for fasta_file in Path('data/').glob('*.fasta'):
    for record in SeqIO.parse(fasta_file, 'fasta'):
        total_seqs += 1
        total_bp += len(record.seq)
    file_count += 1

print(f'Files: {file_count}')
print(f'Sequences: {total_seqs}')
print(f'Total bp: {total_bp}')
print(f'Average length: {total_bp / total_seqs:.0f}')

Per-File Summary Report

Goal: Generate a CSV summary of sequence counts and length statistics for every file in a directory.

Approach: Iterate files, compute per-file stats, collect into a list of dicts, and write as CSV.

Reference (BioPython 1.83+):

from pathlib import Path
import csv

summaries = []
for fasta_file in Path('data/').glob('*.fasta'):
    records = list(SeqIO.parse(fasta_file, 'fasta'))
    lengths = [len(r.seq) for r in records]
    summaries.append({
        'file': fasta_file.name,
        'sequences': len(records),
        'total_bp': sum(lengths),
        'min_len': min(lengths) if lengths else 0,
        'max_len': max(lengths) if lengths else 0,
        'avg_len': sum(lengths) / len(lengths) if lengths else 0
    })

with open('summary.csv', 'w', newline='') as f:
    writer = csv.DictWriter(f, fieldnames=summaries[0].keys())
    writer.writeheader()
    writer.writerows(summaries)

File Organization

Organize by Criteria

from pathlib import Path
from Bio.SeqUtils import gc_fraction

Path('high_gc').mkdir(exist_ok=True)
Path('low_gc').mkdir(exist_ok=True)

for fasta_file in Path('input/').glob('*.fasta'):
    records = list(SeqIO.parse(fasta_file, 'fasta'))
    avg_gc = sum(gc_fraction(r.seq) for r in records) / len(records)

    if avg_gc >= 0.5:
        dest = Path('high_gc') / fasta_file.name
    else:
        dest = Path('low_gc') / fasta_file.name

    SeqIO.write(records, dest, 'fasta')

Common Patterns

Task	Approach
Merge files	Generator yielding from each file
Split file	islice with batch size
Convert all	Loop with SeqIO.convert
Parallel processing	multiprocessing.Pool or ThreadPoolExecutor
Summary stats	Accumulate while iterating

Related Skills

• read-sequences - Core parsing functions for each file
• write-sequences - Write processed outputs
• sequence-statistics - Generate per-file statistics
• format-conversion - Batch format conversion
• compressed-files - Handle compressed files in batch
• database-access - Batch download sequences from NCBI