bio-filter-sequences

name	bio-filter-sequences
description	Filter and select sequences by criteria (length, ID, GC content, patterns) using Biopython. Use when subsetting sequences, removing unwanted records, or selecting by specific criteria.
tool_type	python
primary_tool	Bio.SeqIO

Version Compatibility

Reference examples tested with: BioPython 1.83+, samtools 1.19+

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.

Filter Sequences

"Filter sequences by length, quality, or content" → Apply boolean criteria to a stream of sequence records and write survivors to output.

• Python: generator expression with SeqIO.parse() + SeqIO.write() (BioPython)
• CLI: seqkit seq -m 200 (SeqKit) or awk on FASTA

Filter and select sequences based on various criteria using Biopython.

Required Imports

from Bio import SeqIO
from Bio.SeqUtils import gc_fraction

Core Pattern

Use generator expressions for memory-efficient filtering:

records = SeqIO.parse('input.fasta', 'fasta')
filtered = (rec for rec in records if len(rec.seq) >= 100)
SeqIO.write(filtered, 'output.fasta', 'fasta')

Filter by Length

Minimum Length

records = SeqIO.parse('input.fasta', 'fasta')
long_seqs = (rec for rec in records if len(rec.seq) >= 500)
SeqIO.write(long_seqs, 'long.fasta', 'fasta')

Length Range

records = SeqIO.parse('input.fasta', 'fasta')
sized = (rec for rec in records if 100 <= len(rec.seq) <= 1000)
SeqIO.write(sized, 'sized.fasta', 'fasta')

Remove Short Sequences

min_length = 200
records = SeqIO.parse('input.fasta', 'fasta')
filtered = (rec for rec in records if len(rec.seq) >= min_length)
count = SeqIO.write(filtered, 'filtered.fasta', 'fasta')

Filter by ID

Select Specific IDs

wanted_ids = {'seq1', 'seq2', 'seq3'}
records = SeqIO.parse('input.fasta', 'fasta')
selected = (rec for rec in records if rec.id in wanted_ids)
SeqIO.write(selected, 'selected.fasta', 'fasta')

Select from ID File

Goal: Extract sequences whose IDs appear in an external list file.

Approach: Load IDs into a set for O(1) lookup, then stream-filter and write matches.

Reference (BioPython 1.83+):

with open('ids.txt') as f:
    wanted_ids = {line.strip() for line in f}

records = SeqIO.parse('input.fasta', 'fasta')
selected = (rec for rec in records if rec.id in wanted_ids)
SeqIO.write(selected, 'selected.fasta', 'fasta')

Exclude Specific IDs

exclude_ids = {'bad_seq1', 'bad_seq2'}
records = SeqIO.parse('input.fasta', 'fasta')
kept = (rec for rec in records if rec.id not in exclude_ids)
SeqIO.write(kept, 'kept.fasta', 'fasta')

Filter by ID Pattern

import re

pattern = re.compile(r'^chr\d+$')  # Match chr1, chr2, etc.
records = SeqIO.parse('input.fasta', 'fasta')
chromosomes = (rec for rec in records if pattern.match(rec.id))
SeqIO.write(chromosomes, 'chromosomes.fasta', 'fasta')

Filter by GC Content

from Bio.SeqUtils import gc_fraction

records = SeqIO.parse('input.fasta', 'fasta')
moderate_gc = (rec for rec in records if 0.4 <= gc_fraction(rec.seq) <= 0.6)
SeqIO.write(moderate_gc, 'moderate_gc.fasta', 'fasta')

High GC Sequences

high_gc = (rec for rec in records if gc_fraction(rec.seq) >= 0.6)

Low GC Sequences

low_gc = (rec for rec in records if gc_fraction(rec.seq) <= 0.4)

Filter by Sequence Content

Remove Sequences with N's

records = SeqIO.parse('input.fasta', 'fasta')
clean = (rec for rec in records if 'N' not in str(rec.seq).upper())
SeqIO.write(clean, 'clean.fasta', 'fasta')

Limit N Content

def n_fraction(seq):
    return str(seq).upper().count('N') / len(seq)

records = SeqIO.parse('input.fasta', 'fasta')
low_n = (rec for rec in records if n_fraction(rec.seq) < 0.05)

Contains Specific Motif

motif = 'GAATTC'  # EcoRI site
records = SeqIO.parse('input.fasta', 'fasta')
with_motif = (rec for rec in records if motif in str(rec.seq).upper())
SeqIO.write(with_motif, 'with_ecori.fasta', 'fasta')

Regex Pattern in Sequence

import re

pattern = re.compile(r'ATG.{30,100}T(AA|AG|GA)')  # ORF-like pattern
records = SeqIO.parse('input.fasta', 'fasta')
matches = (rec for rec in records if pattern.search(str(rec.seq)))

Filter by Description

Description Contains Keyword

records = SeqIO.parse('input.fasta', 'fasta')
kinases = (rec for rec in records if 'kinase' in rec.description.lower())
SeqIO.write(kinases, 'kinases.fasta', 'fasta')

Multiple Keywords (OR)

keywords = ['kinase', 'phosphatase', 'transferase']
records = SeqIO.parse('input.fasta', 'fasta')
enzymes = (rec for rec in records if any(k in rec.description.lower() for k in keywords))

Combine Multiple Filters

Goal: Remove sequences that fail any of several quality/content thresholds.

Approach: Define a predicate function that checks all criteria, apply it as a generator filter, and write survivors.

Reference (BioPython 1.83+):

from Bio.SeqUtils import gc_fraction

def passes_filters(record):
    if len(record.seq) < 100:
        return False
    if gc_fraction(record.seq) < 0.3 or gc_fraction(record.seq) > 0.7:
        return False
    if 'N' in str(record.seq).upper():
        return False
    return True

records = SeqIO.parse('input.fasta', 'fasta')
filtered = (rec for rec in records if passes_filters(rec))
SeqIO.write(filtered, 'filtered.fasta', 'fasta')

Sample Sequences

Random Sample (requires loading all)

import random

records = list(SeqIO.parse('input.fasta', 'fasta'))
sample = random.sample(records, min(100, len(records)))
SeqIO.write(sample, 'sample.fasta', 'fasta')

First N Sequences

from itertools import islice

records = SeqIO.parse('input.fasta', 'fasta')
first_100 = islice(records, 100)
SeqIO.write(first_100, 'first100.fasta', 'fasta')

Every Nth Sequence

records = SeqIO.parse('input.fasta', 'fasta')
every_10th = (rec for i, rec in enumerate(records) if i % 10 == 0)
SeqIO.write(every_10th, 'sampled.fasta', 'fasta')

Split by Criteria

Split by Length

Goal: Partition sequences into separate files based on a length threshold.

Approach: Load all records, apply list comprehension split, and write each partition.

Reference (BioPython 1.83+):

records = list(SeqIO.parse('input.fasta', 'fasta'))
short = [r for r in records if len(r.seq) < 500]
long = [r for r in records if len(r.seq) >= 500]
SeqIO.write(short, 'short.fasta', 'fasta')
SeqIO.write(long, 'long.fasta', 'fasta')

Common Errors

Error	Cause	Solution
Generator exhausted	Used generator twice	Re-create generator or use list()
Empty output	Filter too strict	Check filter conditions
Memory error	List too large	Use generator expressions

Related Skills

• read-sequences - Parse sequences before filtering
• write-sequences - Write filtered sequences to output
• fastq-quality - Filter FASTQ by quality scores
• paired-end-fastq - Synchronized filtering of paired reads
• sequence-manipulation/motif-search - Filter by complex motif patterns
• alignment-files - Filter aligned reads with samtools view -f/-F