What file formats does the deepTools skill support?

It primarily processes BAM, bigWig, bedGraph, and BED files, which are the standard formats for high-throughput sequencing data and genomic intervals.

What normalization methods are available?

The skill supports various methods including RPGC (1x genome coverage), CPM (Counts Per Million), RPKM, BPM, and raw count normalization.

Can I use it to compare treatment vs. control samples?

Absolutely. It features tools like bamCompare specifically designed to calculate log2 ratios or differences between two samples to highlight areas of differential enrichment.

How does it assist with Quality Control (QC)?

It provides commands for generating PCA plots, correlation heatmaps, fingerprint plots for ChIP enrichment, and fragment size distributions to ensure data reliability before downstream analysis.

Can this skill help with RNA-seq specific workflows?

Yes, it includes specialized guidance for RNA-seq, such as generating strand-specific coverage tracks and using appropriate normalization methods like RPKM while avoiding read extension over splice junctions.

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 (NGS) through automated quality control, normalization, and publication-quality visualization.

关于

deepTools is a specialized toolkit designed for processing BAM, bigWig, and BED files to extract biological insights from high-throughput sequencing experiments. This skill provides Claude with domain-specific knowledge to perform sophisticated normalization, sample comparisons, and generate visualizations like heatmaps and profile plots. It is particularly useful for bioinformatics researchers and computational biologists working on ChIP-seq, RNA-seq, and ATAC-seq workflows who need to transform raw alignment data into interpretable genomic signals.

主要功能

Performs multi-sample quality control including PCA and correlation analysis
Calculates ChIP-seq enrichment strength using fingerprint plots
Provides specialized workflows for RNA-seq strand-specific coverage and ATAC-seq Tn5 correction
Converts BAM alignments to normalized bigWig/bedGraph coverage tracks
Generates publication-quality heatmaps and profile plots around genomic features
89 GitHub stars

使用场景

Generating normalized genomic coverage tracks for visualization in genome browsers
Assessing replicate consistency and sequencing depth for ChIP-seq or ATAC-seq experiments
Comparing signal enrichment between treatment and control samples using log2 ratio analysis

关于

主要功能

Performs multi-sample quality control including PCA and correlation analysis
Calculates ChIP-seq enrichment strength using fingerprint plots
Provides specialized workflows for RNA-seq strand-specific coverage and ATAC-seq Tn5 correction
Converts BAM alignments to normalized bigWig/bedGraph coverage tracks
Generates publication-quality heatmaps and profile plots around genomic features
89 GitHub stars

使用场景

Generating normalized genomic coverage tracks for visualization in genome browsers
Assessing replicate consistency and sequencing depth for ChIP-seq or ATAC-seq experiments
Comparing signal enrichment between treatment and control samples using log2 ratio analysis