Workflow for read alignment, sorting, indexing, mapping statistics, and downstream-ready alignment artifacts.
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BioTender-max/awesome-bio-agent-skills204Updated Jul 2, 2026
FreePublished on GitHub
bio-reporting-automated-qc-reports
Generates standardized quality control reports by aggregating metrics from FastQC, alignment, and other tools using MultiQC. Use when summarizing QC metrics across samples, creating shareable quality reports, or building automated QC pipelines.
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Run score Not tested
See how to installFiles included (3)
- SKILL.md2.6 KB
- examples/multiqc_pipeline.sh3.6 KB
- usage-guide.md1.5 KB
SKILL.md (original)
Review the instructions the agent will receive before you install.
Version Compatibility
Reference examples tested with: Cell Ranger 8.0+, FastQC 0.12+, GATK 4.5+, HISAT2 2.2.1+, MultiQC 1.21+, STAR 2.7.11+, Subread 2.0+, bcftools 1.19+, fastp 0.23+, kallisto 0.50+
Before using code patterns, verify installed versions match. If versions differ:
- Python:
pip show <package>thenhelp(module.function)to check signatures - CLI:
<tool> --versionthen<tool> --helpto confirm flags
If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying.
Automated QC Reports with MultiQC
"Aggregate QC results into one report" → Combine outputs from FastQC, samtools, Picard, and other tools into a single interactive HTML report.
- CLI:
multiqc .(scans current directory for recognized tool outputs)
Basic Usage
# Aggregate all QC outputs in directory
multiqc results/ -o qc_report/
# Specify output name
multiqc results/ -n my_project_qc
# Include specific tools only
multiqc results/ --module fastqc --module star
Supported Tools
MultiQC recognizes outputs from 100+ bioinformatics tools:
| Category | Tools |
|---|---|
| Read QC | FastQC, fastp, Cutadapt |
| Alignment | STAR, HISAT2, BWA, Bowtie2 |
| Quantification | featureCounts, Salmon, kallisto |
| Variant Calling | bcftools, GATK |
| Single-cell | CellRanger, STARsolo |
Configuration
Create multiqc_config.yaml:
title: "RNA-seq QC Report"
subtitle: "Project XYZ"
intro_text: "QC metrics for all samples"
# Custom sample name cleaning
extra_fn_clean_exts:
- '.sorted'
- '.dedup'
# Report sections to include
module_order:
- fastqc
- star
- featurecounts
# Highlight samples
table_cond_formatting_rules:
pct_mapped:
fail: [{lt: 50}]
warn: [{lt: 70}]
Custom Data
# Add custom data file
# File format: sample\tmetric1\tmetric2
multiqc results/ --data-format tsv --custom-data-file custom_metrics.tsv
Python API
from multiqc import run as multiqc_run
# Run programmatically
multiqc_run(analysis_dir='results/', outdir='qc_report/')
Related Skills
- read-qc/quality-reports - Generate input FastQC reports
- read-qc/fastp-workflow - Preprocessing QC
- workflows/rnaseq-to-de - Full workflow with QC
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