Is it suitable for ATAC-seq data?

Absolutely. The skill includes instructions for ATAC-seq specific tasks such as Tn5 offset correction using alignmentSieve and fragment size distribution analysis.

Does it handle normalization for different organisms?

Yes, it provides pre-set effective genome sizes for major builds like GRCh38 (Human), mm10 (Mouse), and dm6 (Drosophila) to facilitate accurate RPGC normalization.

What file formats does the deepTools skill support?

The skill primarily processes BAM, bigWig, and BED files, allowing for conversion, normalization, and genomic visualization.

Can I use this for ChIP-seq quality control?

Yes, it includes specific commands for plotFingerprint, PCA, and correlation analysis to assess ChIP-seq enrichment strength and replicate consistency.

How does this help with RNA-seq visualization?

It provides workflows for generating strand-specific coverage tracks and gene-level normalization (RPKM) while ensuring reads are not incorrectly extended over splice junctions.

deepTools NGS Analysis

Name: deepTools NGS Analysis
Author: Activer007

byActiver007

Data Science & ML

Processes, analyzes, and visualizes high-throughput sequencing data including ChIP-seq, RNA-seq, and ATAC-seq workflows.

About

The deepTools skill is a specialized suite for bioinformaticians and researchers working with Next-Generation Sequencing (NGS) data. It facilitates complex genomic analysis tasks directly within Claude, including converting BAM alignments to normalized coverage tracks, performing multi-sample quality control via PCA and correlation plots, and generating publication-ready heatmaps. Whether you are normalizing ChIP-seq signals, adjusting for Tn5 offsets in ATAC-seq, or assessing RNA-seq coverage, this skill provides the standardized parameters and best practices needed for reproducible genomic research.

Key Features

Seamless BAM to bigWig conversion with diverse normalization options (RPGC, CPM, RPKM, BPM).
Comprehensive QC suite including fingerprint plots, PCA analysis, and sample correlation heatmaps.
Automated workflow generation for ChIP-seq, RNA-seq, and ATAC-seq analysis pipelines.
Built-in reference for effective genome sizes across major organisms like Human, Mouse, and Drosophila.
0 GitHub stars
Publication-quality visualization tools for generating heatmaps and profiles around genomic features.

Use Cases

Normalizing and comparing ChIP-seq enrichment between treatment and control samples.
Visualizing signal intensity and distribution around Transcription Start Sites (TSS) or custom BED regions.
Performing quality control on NGS replicates to ensure high correlation and adequate sequencing depth.

About

Key Features

Seamless BAM to bigWig conversion with diverse normalization options (RPGC, CPM, RPKM, BPM).
Comprehensive QC suite including fingerprint plots, PCA analysis, and sample correlation heatmaps.
Automated workflow generation for ChIP-seq, RNA-seq, and ATAC-seq analysis pipelines.
Built-in reference for effective genome sizes across major organisms like Human, Mouse, and Drosophila.
0 GitHub stars
Publication-quality visualization tools for generating heatmaps and profiles around genomic features.

Use Cases

Normalizing and comparing ChIP-seq enrichment between treatment and control samples.
Visualizing signal intensity and distribution around Transcription Start Sites (TSS) or custom BED regions.
Performing quality control on NGS replicates to ensure high correlation and adequate sequencing depth.