/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(