Workflow for read alignment, sorting, indexing, mapping statistics, and downstream-ready alignment artifacts.
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All-in-one read preprocessing with fastp including adapter trimming, quality filtering, deduplication, base correction, and HTML report generation. Use when preprocessing Illumina data and wanting a single fast tool instead of separate Cutadapt, Trimmomatic, and FastQC steps.
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Run score Not tested
See how to installReview the instructions the agent will receive before you install.
Reference examples tested with: FastQC 0.12+, fastp 0.23+
Before using code patterns, verify installed versions match. If versions differ:
pip show <package> then help(module.function) to check signatures<tool> --version then <tool> --help to confirm flagsIf code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying.
All-in-one preprocessing tool that handles adapter trimming, quality filtering, deduplication, and report generation in a single pass.
"Preprocess FASTQ reads with fastp" → Run adapter trimming, quality filtering, and QC reporting in a single pass.
fastp -i R1.fq -I R2.fq -o clean_R1.fq -O clean_R2.fq --html report.htmlfastp -i input.fastq.gz -o output.fastq.gz
fastp -i R1.fastq.gz -I R2.fastq.gz -o R1_clean.fastq.gz -O R2_clean.fastq.gz
fastp -i R1.fq.gz -I R2.fq.gz \
-o R1_clean.fq.gz -O R2_clean.fq.gz \
-h sample_report.html \
-j sample_report.json
fastp auto-detects Illumina adapters by default.
# Auto-detect (default)
fastp -i in.fq -o out.fq
# Specify adapters manually
fastp -i in.fq -o out.fq \
--adapter_sequence AGATCGGAAGAGCACACGTCTGAACTCCAGTCA
# Paired-end with manual adapters
fastp -i R1.fq -I R2.fq -o R1.out.fq -O R2.out.fq \
--adapter_sequence AGATCGGAAGAGCACACGTCTGAACTCCAGTCA \
--adapter_sequence_r2 AGATCGGAAGAGCGTCGTGTAGGGAAAGAGTGT
# Disable adapter trimming
fastp -i in.fq -o out.fq --disable_adapter_trimming
# Adapter FASTA file
fastp -i in.fq -o out.fq --adapter_fasta adapters.fa
# Per-base quality threshold (default Q15)
fastp -i in.fq -o out.fq -q 20
# Mean read quality threshold
fastp -i in.fq -o out.fq -e 25
# Max unqualified bases percent (default 40)
fastp -i in.fq -o out.fq -q 20 --unqualified_percent_limit 30
# Disable quality filtering
fastp -i in.fq -o out.fq --disable_quality_filtering
# Sliding window from 3' end (recommended)
fastp -i in.fq -o out.fq \
--cut_right \
--cut_right_window_size 4 \
--cut_right_mean_quality 20
# Sliding window from 5' end
fastp -i in.fq -o out.fq \
--cut_front \
--cut_front_window_size 4 \
--cut_front_mean_quality 20
# Both ends
fastp -i in.fq -o out.fq \
--cut_front --cut_tail \
--cut_front_window_size 4 \
--cut_front_mean_quality 20 \
--cut_tail_window_size 4 \
--cut_tail_mean_quality 20
# Minimum length (default 15)
fastp -i in.fq -o out.fq -l 36
# Maximum length
fastp -i in.fq -o out.fq --length_limit 150
# Required length (discard shorter AND longer)
fastp -i in.fq -o out.fq -l 100 --length_limit 100
# Trim poly-G (NovaSeq/NextSeq artifacts) - auto-enabled for these platforms
fastp -i in.fq -o out.fq --trim_poly_g
# Disable poly-G trimming
fastp -i in.fq -o out.fq --disable_trim_poly_g
# Trim poly-X (any homopolymer)
fastp -i in.fq -o out.fq --trim_poly_x
# Custom poly-G minimum length (default 10)
fastp -i in.fq -o out.fq --trim_poly_g --poly_g_min_len 5
# Max N bases (default 5)
fastp -i in.fq -o out.fq -n 3
# Disable N filtering
fastp -i in.fq -o out.fq --n_base_limit 50
# Enable deduplication
fastp -i in.fq -o out.fq --dedup
# Accuracy level (1-6, higher = more memory, default 3)
fastp -i in.fq -o out.fq --dedup --dup_calc_accuracy 4
# Enable overlap-based correction
fastp -i R1.fq -I R2.fq -o R1.out.fq -O R2.out.fq --correction
# Required overlap length (default 30)
fastp -i R1.fq -I R2.fq -o R1.out.fq -O R2.out.fq \
--correction --overlap_len_require 20
# Merge overlapping paired reads
fastp -i R1.fq -I R2.fq \
--merge --merged_out merged.fq \
-o R1_unmerged.fq -O R2_unmerged.fq
# UMI in read (extract to header)
fastp -i in.fq -o out.fq \
--umi --umi_loc read1 --umi_len 8
# UMI in separate read
fastp -i R1.fq -I R2.fq -o R1.out.fq -O R2.out.fq \
--umi --umi_loc index1
# UMI locations: index1, index2, read1, read2, per_index, per_read
fastp \
-i raw_R1.fastq.gz -I raw_R2.fastq.gz \
-o clean_R1.fastq.gz -O clean_R2.fastq.gz \
--detect_adapter_for_pe \
--cut_right --cut_right_window_size 4 --cut_right_mean_quality 20 \
-q 20 -l 36 \
--thread 8 \
-h sample_fastp.html -j sample_fastp.json
fastp \
-i raw_R1.fastq.gz -I raw_R2.fastq.gz \
-o clean_R1.fastq.gz -O clean_R2.fastq.gz \
--detect_adapter_for_pe \
--trim_poly_g \
--cut_right --cut_right_window_size 4 --cut_right_mean_quality 20 \
-q 20 -l 36 \
--thread 8 \
-h sample_fastp.html -j sample_fastp.json
fastp \
-i raw_R1.fastq.gz -I raw_R2.fastq.gz \
-o clean_R1.fastq.gz -O clean_R2.fastq.gz \
--detect_adapter_for_pe \
--cut_right --cut_right_window_size 4 --cut_right_mean_quality 20 \
-q 20 -l 50 \
--thread 8 \
-h sample_fastp.html -j sample_fastp.json
| File | Description |
|---|---|
*.html | Interactive HTML report |
*.json | Machine-readable statistics |
| Output FASTQ | Processed reads |
import json
with open('sample_fastp.json') as f:
report = json.load(f)
summary = report['summary']
print(f"Total reads: {summary['before_filtering']['total_reads']}")
print(f"Passed reads: {summary['after_filtering']['total_reads']}")
print(f"Q20 rate: {summary['after_filtering']['q20_rate']:.2%}")
print(f"Q30 rate: {summary['after_filtering']['q30_rate']:.2%}")
# Set threads (default 3)
fastp -i in.fq -o out.fq --thread 8
# Disable HTML report (faster)
fastp -i in.fq -o out.fq --html /dev/null
# Process from stdin
zcat in.fq.gz | fastp --stdin -o out.fq
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Workflow for read alignment, sorting, indexing, mapping statistics, and downstream-ready alignment artifacts.
The English summary is being prepared. Showing the original description.
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.
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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.
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Workflow for event-level and isoform-level splicing analysis with sashimi-ready outputs and splice QC.
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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.
The English summary is being prepared. Showing the original description.