How does this skill assist with bioinformatics Quality Control?

It provides automated command generation for PCA, correlation heatmaps, fingerprint plots, and coverage assessments to validate the reliability of your sequencing data.

Can I use this skill to normalize my genomic data?

Yes, the skill provides guidance on using multiple normalization methods such as RPGC (1x coverage), CPM, RPKM, and BPM to ensure samples are statistically comparable.

What sequencing types does this deepTools skill support?

It supports a wide range of Next-Generation Sequencing (NGS) experiments including ChIP-seq, RNA-seq, ATAC-seq, and MNase-seq.

How does it handle large genomic datasets?

It includes best practices for performance optimization, such as utilizing multi-processor support, adjusting bin sizes, and processing specific genomic regions for faster testing.

Does this skill require deepTools to be installed locally?

Yes, the skill facilitates the use of the deepTools Python package, which should be installed via 'uv pip install deeptools' or your preferred package manager.

deepTools NGS Analysis

Name: deepTools NGS Analysis
Author: Microck

byMicrock

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数据科学与机器学习

Analyzes high-throughput sequencing data to perform quality control, normalization, and publication-quality visualization for NGS experiments.

关于

deepTools is a powerful Python-based suite designed for the processing and visualization of high-throughput sequencing data such as ChIP-seq, RNA-seq, and ATAC-seq. This skill enables Claude to guide researchers through complex bioinformatic workflows including BAM to bigWig conversion, sample correlation analysis, PCA, and the generation of heatmaps around genomic features. It provides standardized templates for quality control and normalization, ensuring reproducible and statistically sound genomic analysis for scientific research.

主要功能

Built-in file validation for BAM, bigWig, and BED formats to prevent pipeline errors
Automated BAM to bigWig conversion with multiple normalization methods like RPGC and CPM
81 GitHub stars
Genomic feature visualization through high-resolution heatmaps and profile plots
Tailored analysis workflows for ChIP-seq, RNA-seq, and ATAC-seq data types
Comprehensive QC reporting including sample correlation, PCA plots, and fingerprint analysis

使用场景

Assessing sequencing depth and correlation between biological replicates for quality control
Generating publication-ready heatmaps and profile plots for ChIP-seq signals around TSS or peaks
Creating normalized coverage tracks for comparative visualization in genome browsers

关于

主要功能

Built-in file validation for BAM, bigWig, and BED formats to prevent pipeline errors
Automated BAM to bigWig conversion with multiple normalization methods like RPGC and CPM
81 GitHub stars
Genomic feature visualization through high-resolution heatmaps and profile plots
Tailored analysis workflows for ChIP-seq, RNA-seq, and ATAC-seq data types
Comprehensive QC reporting including sample correlation, PCA plots, and fingerprint analysis

使用场景

Assessing sequencing depth and correlation between biological replicates for quality control
Generating publication-ready heatmaps and profile plots for ChIP-seq signals around TSS or peaks
Creating normalized coverage tracks for comparative visualization in genome browsers