Workflow for read alignment, sorting, indexing, mapping statistics, and downstream-ready alignment artifacts.
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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.
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설치 방법 보기설치하기 전에 에이전트에게 전달되는 지시 내용을 확인할 수 있습니다.
Reference examples tested with: BioPython 1.83+, samtools 1.19+
Before using code patterns, verify installed versions match. If versions differ:
pip show <package> then help(module.function) to check signaturesIf code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying.
"Filter sequences by length, quality, or content" → Apply boolean criteria to a stream of sequence records and write survivors to output.
SeqIO.parse() + SeqIO.write() (BioPython)seqkit seq -m 200 (SeqKit) or awk on FASTAFilter and select sequences based on various criteria using Biopython.
from Bio import SeqIO
from Bio.SeqUtils import gc_fraction
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')
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')
records = SeqIO.parse('input.fasta', 'fasta')
sized = (rec for rec in records if 100 <= len(rec.seq) <= 1000)
SeqIO.write(sized, 'sized.fasta', 'fasta')
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')
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')
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_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')
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')
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 = (rec for rec in records if gc_fraction(rec.seq) >= 0.6)
low_gc = (rec for rec in records if gc_fraction(rec.seq) <= 0.4)
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')
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)
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')
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)))
records = SeqIO.parse('input.fasta', 'fasta')
kinases = (rec for rec in records if 'kinase' in rec.description.lower())
SeqIO.write(kinases, 'kinases.fasta', 'fasta')
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))
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')
import random
records = list(SeqIO.parse('input.fasta', 'fasta'))
sample = random.sample(records, min(100, len(records)))
SeqIO.write(sample, 'sample.fasta', 'fasta')
from itertools import islice
records = SeqIO.parse('input.fasta', 'fasta')
first_100 = islice(records, 100)
SeqIO.write(first_100, 'first100.fasta', 'fasta')
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')
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')
| 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 |
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Workflow for read alignment, sorting, indexing, mapping statistics, and downstream-ready alignment artifacts.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Validate protein designs using AlphaFold2 structure prediction. Use this skill when: (1) Validating designed sequences fold correctly, (2) Predicting binder-target complex structures, (3) Calculating confidence metrics (pLDDT, pTM, ipTM), (4) Self-consistency validation of designs, (5) Multi-chain complex prediction with AlphaFold-Multimer. For faster single-chain prediction, use esm. For QC thresholds, use protein-qc.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
AlphaFold2 / AlphaFold-Multimer structure prediction for validation and confidence scoring. Use this skill when: (1) Validating designed sequences fold correctly, (2) Predicting binder-target complex structures, (3) Calculating confidence metrics (pLDDT, pTM, ipTM), (4) Self-consistency validation of designs, (5) Multi-chain complex prediction with AlphaFold-Multimer. For faster single-chain prediction, use esm2-sequence-scoring. For QC thresholds, use protein-design-qc.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Workflow for event-level and isoform-level splicing analysis with sashimi-ready outputs and splice QC.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
ATAC-seq processing with assay QC, MACS3 peak calling, consensus peak matrices, differential accessibility, and motif or footprint follow-up.
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End-to-end binder design using BindCraft hallucination. Use this skill when: (1) Designing protein binders with built-in AF2 validation, (2) Running production-quality binder campaigns, (3) Using different design protocols (fast, default, slow), (4) Need joint backbone and sequence optimization, (5) Want high experimental success rate. For backbone-only generation, use rfdiffusion. For QC thresholds, use protein-qc. For tool selection guidance, use binder-design.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.