Process multiple sequence files in batch using Biopython. Use when working with many files, merging/splitting sequences, or automating file operations across directories.
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Process multiple sequence files in batch using Biopython. Use when working with many files, merging/splitting sequences, or automating file operations across directories.
tool_type
python
primary_tool
Bio.SeqIO
Batch Processing
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(1for _ 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
defall_records(directory, pattern, format):
for filepath in Path(directory).glob(pattern):
yieldfrom 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
defrecords_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']
defmerge_files(file_list, format):
for filepath in file_list:
yieldfrom SeqIO.parse(filepath, format)
SeqIO.write(merge_files(files, 'fasta'), 'combined.fasta', 'fasta')
Split Files
Split by Number of Records
from itertools import islice
defsplit_file(input_file, format, records_per_file, output_prefix):
records = SeqIO.parse(input_file, format)
file_num = 1whileTrue:
batch = list(islice(records, records_per_file))
ifnot 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
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
defprocess_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
defcount_records(filepath):
return filepath.name, sum(1for _ 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 = 0for 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 += 1print(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
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 else0,
'max_len': max(lengths) if lengths else0,
'avg_len': sum(lengths) / len(lengths) if lengths else0
})
withopen('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
