How does it assist with ATAC-seq specifically?

It provides workflows for Tn5 offset correction using alignmentSieve and specialized fragment size analysis common in ATAC-seq pipelines.

Does it handle genomic normalization automatically?

Yes, it provides guidance and command generation for various normalization methods including RPGC (1x coverage), CPM, RPKM, and BPM.

What input file formats does the skill work with?

The skill primarily processes BAM files (alignments), bigWig files (coverage), and BED files (genomic regions).

What sequencing types are supported by this skill?

The deepTools skill is optimized for ChIP-seq, RNA-seq, ATAC-seq, and MNase-seq data analysis and visualization.

Can I use this for quality control of sequencing replicates?

Absolutely. It includes specific tools for assessing sample correlation, PCA analysis, and sequencing depth to ensure data quality before downstream analysis.

deepTools for NGS Analysis

Name: deepTools for NGS Analysis
Author: Microck

byMicrock

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Data Science & ML

Automates high-throughput sequencing data analysis, from BAM file processing and quality control to publication-ready visualizations like heatmaps and profile plots.

About

deepTools for Claude provides a comprehensive suite of specialized genomic analysis capabilities designed for processing high-throughput sequencing data. This skill enables users to seamlessly execute complex workflows for ChIP-seq, RNA-seq, and ATAC-seq, covering everything from BAM to bigWig conversion with advanced normalization to sophisticated quality control assessments like PCA and fingerprint plots. It is particularly useful for bioinformaticians and researchers who need to generate publication-quality heatmaps and profile plots around genomic features while ensuring data integrity through built-in file validation and standardized analysis patterns.

Key Features

Comprehensive QC workflows including sample correlation, PCA, and fingerprint plots
81 GitHub stars
Automated BAM to bigWig conversion with normalization methods like RPGC, CPM, and RPKM
Specialized processing for ATAC-seq (Tn5 offset correction) and RNA-seq (strand-specific coverage)
Workflow generator for rapid setup of standardized NGS analysis pipelines
Publication-quality visualization generation for heatmaps and profile plots

Use Cases

Generating enrichment heatmaps at Transcription Start Sites (TSS) for ChIP-seq experiments
Creating normalized coverage tracks for visualization in genome browsers
Performing multi-sample correlation and PCA to assess replicate consistency and experimental quality

About

Key Features

Comprehensive QC workflows including sample correlation, PCA, and fingerprint plots
81 GitHub stars
Automated BAM to bigWig conversion with normalization methods like RPGC, CPM, and RPKM
Specialized processing for ATAC-seq (Tn5 offset correction) and RNA-seq (strand-specific coverage)
Workflow generator for rapid setup of standardized NGS analysis pipelines
Publication-quality visualization generation for heatmaps and profile plots

Use Cases

Generating enrichment heatmaps at Transcription Start Sites (TSS) for ChIP-seq experiments
Creating normalized coverage tracks for visualization in genome browsers
Performing multi-sample correlation and PCA to assess replicate consistency and experimental quality