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.

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 length, ID, GC content, N content, motifs, regex patterns, and description.

The governing principle

Two traps cause silent, downstream-corrupting errors. Neither raises an exception, so the agent must guard against both up front.

1. Never filter one mate of a paired-end set independently. Aligners (bwa, bowtie2) read R1 and R2 as two parallel streams and pair the i-th record of each, assuming SAME ORDER and SAME COUNT. Dropping a read from R1 alone DESYNCS the files: best case the aligner crashes on a name mismatch, worst case it silently pairs the wrong R1 with the wrong R2, producing mismapped reads and garbage insert sizes with no error. If the input is paired, route to the paired-end-fastq skill (synchronized filtering that writes matched output plus separate orphan/singleton files). Do NOT apply the single-file patterns below to one mate.
2. Filter by STREAMING, not by loading. SeqIO.parse() yields one record at a time; a generator expression into SeqIO.write() holds a single record in RAM regardless of file size. list(SeqIO.parse(...)) materializes every record and OOMs on large FASTQ. Only the patterns that genuinely need all records at once (random sampling, splitting into multiple files) load the file; they say so explicitly.

Required Imports

from Bio import SeqIO
from Bio.SeqUtils import gc_fraction

Core Pattern

Stream records through a generator expression so memory stays flat:

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

SeqIO.write() consumes the generator lazily and returns the count written.

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')

len(rec.seq) counts every base, including soft-masked lowercase (see Case Sensitivity below).

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')

rec.id is the first whitespace-delimited token of the header. For CASAVA 1.8+ paired FASTQ, R1 and R2 share this id (the mate number lives after the space), so an id set matches both mates equally - another reason mate-aware filtering belongs in paired-end-fastq.

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+$')  # matches 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

Goal: Keep records whose GC fraction falls in a target band.

Approach: Use gc_fraction(), which returns a FRACTION (0-1), NOT a percentage - thresholds must be 0.4, not 40. The ambiguous= mode decides how N and other IUPAC ambiguity codes are counted, and the same sequence yields a different GC value per mode, so set it explicitly rather than relying on the default.

Reference (BioPython 1.83+):

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, ambiguous='ignore') <= 0.6)
SeqIO.write(moderate_gc, 'moderate_gc.fasta', 'fasta')

Choosing the ambiguous= mode

gc_fraction(seq, ambiguous='remove') is the default. For the same sequence the three modes give different answers - an N-containing read can pass or fail purely because of the mode:

A naive (G+C)/len silently equals 'ignore' mode and under-reports GC whenever N's are present. The default 'remove' ignores N's entirely, so a heavily-N read can post a misleadingly extreme GC. Pick the mode that matches the intent and pass it explicitly.

High / Low GC bands

records = SeqIO.parse('input.fasta', 'fasta')
high_gc = (rec for rec in records if gc_fraction(rec.seq, ambiguous='ignore') >= 0.6)
SeqIO.write(high_gc, 'high_gc.fasta', 'fasta')

Case Sensitivity (soft-masking)

Seq is CASE-PRESERVING: lowercase soft-masked bases (from RepeatMasker, Ensembl, dustmasker) survive parse and round-trip unchanged. Length, GC, motif, and regex filters are CASE-SENSITIVE - a naive uppercase test silently misses masked bases. Always .upper() the sequence before content matching when the masking should not affect the decision:

seq_upper = str(rec.seq).upper()
has_site = 'GAATTC' in seq_upper            # matches gaattc and GAATTC

gc_fraction() itself is case-insensitive, but a hand-rolled .count('G') is not - count on the uppercased string.

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):
    upper = str(seq).upper()
    return 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)  # under 5% ambiguous bases
SeqIO.write(low_n, 'low_n.fasta', 'fasta')

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).upper()))
SeqIO.write(matches, 'orf_like.fasta', 'fasta')

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))
SeqIO.write(enzymes, 'enzymes.fasta', 'fasta')

Combine Multiple Filters

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

Approach: Define a predicate that checks all criteria against the uppercased sequence once, set the GC ambiguous= mode explicitly, apply the predicate as a generator filter, and stream survivors to output.

Reference (BioPython 1.83+):

from Bio.SeqUtils import gc_fraction

def passes_filters(record):
    if len(record.seq) < 100:
        return False
    gc = gc_fraction(record.seq, ambiguous='ignore')
    if gc < 0.3 or gc > 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'))  # loads file - needs all records up front
sample = random.sample(records, min(100, len(records)))
SeqIO.write(sample, 'sample.fasta', 'fasta')

First N Sequences (streams)

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 (streams)

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 once, partition with list comprehensions, and write each partition. Loading is acceptable here because both partitions are needed in a single pass; for very large files, run two streaming passes instead.

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

Related Skills

• read-sequences - Parse sequences before filtering
• write-sequences - Write filtered sequences to output
• fastq-quality - Filter FASTQ by per-base quality scores and encoding
• paired-end-fastq - Synchronized filtering of R1/R2 with orphan handling
• sequence-manipulation/sequence-properties - Per-sequence GC, length, and composition
• sequence-manipulation/motif-search - Filter by complex motif patterns
• alignment-files/alignment-filtering - Filter aligned reads with samtools view -f/-F