Can I normalize my data with this skill?

Yes, it supports multiple normalization methods such as RPGC (1x depth), CPM (counts per million), and RPKM (reads per kb per million).

Does it help with quality control?

Absolutely. It includes tools for assessing sample correlation, PCA analysis, sequencing depth, and ChIP enrichment strength via fingerprint plots.

How do I start a new analysis?

You can use the built-in workflow generator to create customized bash scripts for common pipelines like ChIP-seq QC or RNA-seq coverage.

What sequencing data types does deepTools support?

deepTools is optimized for various High-Throughput Sequencing (NGS) data, including ChIP-seq, RNA-seq, ATAC-seq, and MNase-seq.

deepTools Bioinformatics Suite

Name: deepTools Bioinformatics Suite
Author: Activer007

byActiver007

Ciencia de Datos y ML

Processes and visualizes high-throughput sequencing data for genomics research and quality control.

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deepTools is a comprehensive Python-based toolkit integrated into Claude Code to streamline the analysis of Next-Generation Sequencing (NGS) data. It enables researchers and bioinformaticians to convert BAM alignments into normalized coverage tracks, perform rigorous quality control checks like fingerprinting and PCA, and generate publication-quality heatmaps or profiles. Whether working with ChIP-seq, RNA-seq, or ATAC-seq, this skill provides standardized workflows and automated scripts to ensure reproducible genomic data visualization and sample comparison.

Características Principales

BAM to bigWig/bedGraph conversion with normalization (RPGC, CPM, RPKM)
Specialized workflows for ChIP-seq, RNA-seq, and ATAC-seq data
File validation and workflow template generation
Automated Quality Control (Fingerprint plots, PCA, and correlation matrices)
Genomic feature visualization (TSS heatmaps and signal profiles)
0 GitHub stars

Casos de Uso

Generating publication-ready heatmaps and profile plots for genomic regions
Visualizing ChIP-seq signal enrichment around Transcription Start Sites (TSS)
Normalizing and comparing treatment vs. control sequencing samples

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Características Principales

BAM to bigWig/bedGraph conversion with normalization (RPGC, CPM, RPKM)
Specialized workflows for ChIP-seq, RNA-seq, and ATAC-seq data
File validation and workflow template generation
Automated Quality Control (Fingerprint plots, PCA, and correlation matrices)
Genomic feature visualization (TSS heatmaps and signal profiles)
0 GitHub stars

Casos de Uso

Generating publication-ready heatmaps and profile plots for genomic regions
Visualizing ChIP-seq signal enrichment around Transcription Start Sites (TSS)
Normalizing and comparing treatment vs. control sequencing samples