跳到正文
cccskills
免费在 GitHub 公开

bio-paired-end-fastq

Process paired FASTQ files containing reads from both ends of DNA fragments with Biopython. Keep R1/R2 records together while filtering by quality or length, check pair IDs and counts, and combine or split alternating records.

中文简介正在准备中。当前显示英语简介。

原文查看原文说明

Handle paired-end FASTQ files (R1/R2) using Biopython. Use when working with Illumina paired reads, synchronizing pairs, interleaving/deinterleaving, or filtering paired data.

实测评分 未实测

查看安装方法

适用场景

  • Checking R1/R2 record IDs and counts
  • Filtering read pairs by quality or length
  • Combining paired reads into alternating records
  • Processing large or gzip-compressed paired files

包含的文件(3)

  • SKILL.md10.0 KB
  • examples/paired_end_io.py2.2 KB
  • usage-guide.md1.5 KB

SKILL.md(原文)

安装前可以查看交给 AI 代理的指令内容。

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.

Paired-End FASTQ

"Work with my paired-end FASTQ files" → Iterate R1/R2 pairs in sync, filter both mates together, interleave/deinterleave files, and auto-detect paired file naming.

  • Python: SeqIO.parse() with zip() iteration (BioPython)

Handle paired-end sequencing data (R1/R2 files) using Biopython.

Required Import

from Bio import SeqIO

Paired File Naming Conventions

Common patterns for paired files:

  • sample_R1.fastq / sample_R2.fastq
  • sample_1.fastq / sample_2.fastq
  • sample_R1_001.fastq / sample_R2_001.fastq

Iterate Pairs Together

Basic Paired Iteration

r1_records = SeqIO.parse('reads_R1.fastq', 'fastq')
r2_records = SeqIO.parse('reads_R2.fastq', 'fastq')

for r1, r2 in zip(r1_records, r2_records):
    print(f'R1: {r1.id}, R2: {r2.id}')
    print(f'Lengths: {len(r1.seq)}, {len(r2.seq)}')

Verify Pair Matching

def iterate_pairs(r1_file, r2_file, format='fastq'):
    r1_records = SeqIO.parse(r1_file, format)
    r2_records = SeqIO.parse(r2_file, format)

    for r1, r2 in zip(r1_records, r2_records):
        # Strip /1, /2 or .1, .2 suffixes for comparison
        r1_base = r1.id.rsplit('/', 1)[0].rsplit('.', 1)[0]
        r2_base = r2.id.rsplit('/', 1)[0].rsplit('.', 1)[0]
        if r1_base != r2_base:
            raise ValueError(f'Pair mismatch: {r1.id} vs {r2.id}')
        yield r1, r2

for r1, r2 in iterate_pairs('reads_R1.fastq', 'reads_R2.fastq'):
    process_pair(r1, r2)

Filter Pairs Together

Filter by Quality (Both Must Pass)

def filter_pairs_by_quality(r1_file, r2_file, min_avg_qual=25):
    r1_records = SeqIO.parse(r1_file, 'fastq')
    r2_records = SeqIO.parse(r2_file, 'fastq')

    r1_passed, r2_passed = [], []
    for r1, r2 in zip(r1_records, r2_records):
        q1 = sum(r1.letter_annotations['phred_quality']) / len(r1.seq)
        q2 = sum(r2.letter_annotations['phred_quality']) / len(r2.seq)
        if q1 >= min_avg_qual and q2 >= min_avg_qual:
            r1_passed.append(r1)
            r2_passed.append(r2)

    return r1_passed, r2_passed

r1_good, r2_good = filter_pairs_by_quality('reads_R1.fastq', 'reads_R2.fastq')
SeqIO.write(r1_good, 'filtered_R1.fastq', 'fastq')
SeqIO.write(r2_good, 'filtered_R2.fastq', 'fastq')

Filter by Length (Both Must Pass)

def filter_pairs_by_length(r1_file, r2_file, min_length=50):
    r1_records = SeqIO.parse(r1_file, 'fastq')
    r2_records = SeqIO.parse(r2_file, 'fastq')

    r1_passed, r2_passed = [], []
    for r1, r2 in zip(r1_records, r2_records):
        if len(r1.seq) >= min_length and len(r2.seq) >= min_length:
            r1_passed.append(r1)
            r2_passed.append(r2)

    return r1_passed, r2_passed

Memory-Efficient Paired Filtering

Goal: Quality-filter paired reads while maintaining R1/R2 synchronization without loading all reads into memory.

Approach: Stream both files in lockstep with zip, evaluate both mates, and write only pairs where both pass.

Reference (BioPython 1.83+):

def filter_pairs_streaming(r1_in, r2_in, r1_out, r2_out, min_qual=25):
    r1_records = SeqIO.parse(r1_in, 'fastq')
    r2_records = SeqIO.parse(r2_in, 'fastq')

    with open(r1_out, 'w') as r1_handle, open(r2_out, 'w') as r2_handle:
        passed = 0
        for r1, r2 in zip(r1_records, r2_records):
            q1 = sum(r1.letter_annotations['phred_quality']) / len(r1.seq)
            q2 = sum(r2.letter_annotations['phred_quality']) / len(r2.seq)
            if q1 >= min_qual and q2 >= min_qual:
                SeqIO.write(r1, r1_handle, 'fastq')
                SeqIO.write(r2, r2_handle, 'fastq')
                passed += 1
    return passed

count = filter_pairs_streaming('R1.fastq', 'R2.fastq', 'R1_filt.fastq', 'R2_filt.fastq')
print(f'{count} pairs passed filtering')

Interleave Pairs

Create Interleaved File

Goal: Merge separate R1/R2 files into a single interleaved file (R1, R2, R1, R2, ...).

Approach: Zip both iterators together and yield alternating records through a generator.

Reference (BioPython 1.83+):

def interleave_pairs(r1_file, r2_file, output_file, format='fastq'):
    r1_records = SeqIO.parse(r1_file, format)
    r2_records = SeqIO.parse(r2_file, format)

    def interleaved():
        for r1, r2 in zip(r1_records, r2_records):
            yield r1
            yield r2

    count = SeqIO.write(interleaved(), output_file, format)
    return count // 2  # Return number of pairs

pairs = interleave_pairs('reads_R1.fastq', 'reads_R2.fastq', 'reads_interleaved.fastq')
print(f'Interleaved {pairs} pairs')

Interleave with Modified IDs

def interleave_with_suffix(r1_file, r2_file, output_file):
    r1_records = SeqIO.parse(r1_file, 'fastq')
    r2_records = SeqIO.parse(r2_file, 'fastq')

    def interleaved():
        for r1, r2 in zip(r1_records, r2_records):
            r1.id = f'{r1.id}/1'
            r1.description = ''
            r2.id = f'{r2.id}/2'
            r2.description = ''
            yield r1
            yield r2

    SeqIO.write(interleaved(), output_file, 'fastq')

Deinterleave

Split Interleaved to Paired Files

def deinterleave(interleaved_file, r1_file, r2_file, format='fastq'):
    records = SeqIO.parse(interleaved_file, format)

    r1_records = []
    r2_records = []
    for i, record in enumerate(records):
        if i % 2 == 0:
            r1_records.append(record)
        else:
            r2_records.append(record)

    SeqIO.write(r1_records, r1_file, format)
    SeqIO.write(r2_records, r2_file, format)
    return len(r1_records)

pairs = deinterleave('interleaved.fastq', 'R1.fastq', 'R2.fastq')
print(f'Deinterleaved {pairs} pairs')

Memory-Efficient Deinterleave

def deinterleave_streaming(interleaved_file, r1_file, r2_file, format='fastq'):
    records = SeqIO.parse(interleaved_file, format)

    with open(r1_file, 'w') as r1_h, open(r2_file, 'w') as r2_h:
        pairs = 0
        for i, record in enumerate(records):
            if i % 2 == 0:
                SeqIO.write(record, r1_h, format)
            else:
                SeqIO.write(record, r2_h, format)
                pairs += 1
    return pairs

Paired Statistics

Count and Verify Pairs

def paired_stats(r1_file, r2_file):
    r1_count = sum(1 for _ in SeqIO.parse(r1_file, 'fastq'))
    r2_count = sum(1 for _ in SeqIO.parse(r2_file, 'fastq'))

    if r1_count != r2_count:
        print(f'WARNING: Unequal counts! R1={r1_count}, R2={r2_count}')
    else:
        print(f'Pairs: {r1_count}')
        print(f'Total reads: {r1_count * 2}')

    return r1_count, r2_count

paired_stats('reads_R1.fastq', 'reads_R2.fastq')

Paired Quality Summary

def paired_quality_summary(r1_file, r2_file):
    r1_quals, r2_quals = [], []

    r1_records = SeqIO.parse(r1_file, 'fastq')
    r2_records = SeqIO.parse(r2_file, 'fastq')

    for r1, r2 in zip(r1_records, r2_records):
        r1_quals.append(sum(r1.letter_annotations['phred_quality']) / len(r1.seq))
        r2_quals.append(sum(r2.letter_annotations['phred_quality']) / len(r2.seq))

    print(f'R1 mean quality: {sum(r1_quals)/len(r1_quals):.1f}')
    print(f'R2 mean quality: {sum(r2_quals)/len(r2_quals):.1f}')

paired_quality_summary('reads_R1.fastq', 'reads_R2.fastq')

Find Paired Files

Auto-Detect Pair from R1

from pathlib import Path

def find_r2(r1_path):
    r1_path = Path(r1_path)
    name = r1_path.name

    # Try common patterns
    patterns = [
        ('_R1', '_R2'),
        ('_1', '_2'),
        ('_R1_', '_R2_'),
        ('.R1.', '.R2.'),
    ]

    for p1, p2 in patterns:
        if p1 in name:
            r2_name = name.replace(p1, p2, 1)
            r2_path = r1_path.parent / r2_name
            if r2_path.exists():
                return r2_path

    return None

r2_file = find_r2('sample_R1.fastq')
if r2_file:
    print(f'Found pair: {r2_file}')

Find All Paired Files in Directory

from pathlib import Path

def find_all_pairs(directory, r1_pattern='*_R1*.fastq*'):
    pairs = []
    for r1_file in Path(directory).glob(r1_pattern):
        r2_file = find_r2(r1_file)
        if r2_file:
            pairs.append((r1_file, r2_file))
    return pairs

pairs = find_all_pairs('data/')
for r1, r2 in pairs:
    print(f'{r1.name} <-> {r2.name}')

Compressed Paired Files

Handle Gzipped Pairs

import gzip

def iterate_gzipped_pairs(r1_gz, r2_gz):
    with gzip.open(r1_gz, 'rt') as r1_h, gzip.open(r2_gz, 'rt') as r2_h:
        r1_records = SeqIO.parse(r1_h, 'fastq')
        r2_records = SeqIO.parse(r2_h, 'fastq')
        for r1, r2 in zip(r1_records, r2_records):
            yield r1, r2

for r1, r2 in iterate_gzipped_pairs('reads_R1.fastq.gz', 'reads_R2.fastq.gz'):
    print(r1.id, r2.id)

Common Errors

ErrorCauseSolution
Pair count mismatchFiles out of syncRe-download or repair files
ID mismatchWrong file pairingCheck file naming conventions
Memory errorLarge files loaded to listUse streaming/generator approach
Missing R2Wrong naming patternCheck find_r2() patterns

Related Skills

  • read-sequences - Parse individual FASTQ files
  • fastq-quality - Quality filtering before paired processing
  • filter-sequences - Additional filtering criteria
  • compressed-files - Handle gzipped paired files
  • alignment-files - After filtering, align paired reads with bwa mem; proper pairs in BAM

评价

还没有评价。欢迎分享你的使用感受。

评论

无需登录即可发表。欢迎分享使用感受、提问,或说明在哪些环境下无法运行。

还没有评论。来写第一条吧。

提交后会立即公开。请勿填写邮箱、电话等个人信息。URL 最多 2 个。

0 / 1000 字

此表单受 reCAPTCHA 保护,适用 Google 的隐私权政策和服务条款。

同一仓库的技能

adaptyv

免费

Support protein design with cloud laboratory testing for binding, expression, thermal stability, and enzyme activity. Covers sequence optimization, submitting experiments through the Adaptyv API, checking progress, and downloading experimental results.

中文简介正在准备中。当前显示英语简介。

  • Validate designed proteins with laboratory assays
  • Screen and redesign sequences for better solubility
  • Submit protein testing experiments through an API
FreedomIntelligence/OpenClaw-Medical-Skills30802026年7月21日 更新

Build flexible daily plans that account for ADHD-related difficulties with time estimation and getting started. Narrow priorities to three tasks, add transition buffers, and choose small next actions that match the energy available that day.

中文简介正在准备中。当前显示英语简介。

  • Choosing three priorities for today
  • Planning a schedule with transition buffers
  • Finding a small first step when starting feels difficult
FreedomIntelligence/OpenClaw-Medical-Skills30802026年7月21日 更新

aeon

免费

Build Python workflows for data recorded over time, including classification, forecasting, anomaly detection, and pattern search. Using aeon, the skill guides preprocessing, algorithm selection, feature extraction, and evaluation through interfaces compatible with scikit-learn.

中文简介正在准备中。当前显示英语简介。

  • Classify time series by their patterns
  • Forecast future values from historical data
  • Detect anomalies and change points
FreedomIntelligence/OpenClaw-Medical-Skills30802026年7月21日 更新

Use a browser to research topics, extract page information, fill forms, and test websites. Guides agents through identifying page controls, interacting with web apps, saving screenshots or PDFs, and reusing saved login sessions.

中文简介正在准备中。当前显示英语简介。

  • Researching topics and reading web articles
  • Filling and submitting online forms
  • Extracting page text and links
FreedomIntelligence/OpenClaw-Medical-Skills30802026年7月21日 更新

Combine health measurements, exercise, sleep, nutrition, and mental health records to examine trends, unusual changes, and estimated risks. The skill produces personalized suggestions and text or interactive HTML reports using local data without external API calls.

中文简介正在准备中。当前显示英语简介。

  • Review health records together in a comprehensive report
  • Explore links between sleep, mood, exercise, and weight
  • Summarize estimated disease and nutrition risks
FreedomIntelligence/OpenClaw-Medical-Skills30802026年7月21日 更新

alphafold

免费

Predict structures for designed protein sequences and protein complexes with AlphaFold2 or ColabFold. Inspect structure files and confidence scores to assess folding and chain interfaces, with guidance for local runs, Modal execution, and common prediction errors.

中文简介正在准备中。当前显示英语简介。

  • Check predicted folding of a designed protein sequence
  • Predict a binder–target protein complex
  • Review structure and interface confidence scores
FreedomIntelligence/OpenClaw-Medical-Skills30802026年7月21日 更新

查看 FreedomIntelligence 的全部技能

报告此技能的问题