How does this help with quality control (QC)?

The skill includes specialized tools for PCA, Pearson/Spearman correlation, and fingerprint plots to assess ChIP enrichment strength and replicate consistency.

What sequencing types does this deepTools skill support?

It supports common Next-Generation Sequencing (NGS) data types, including ChIP-seq, RNA-seq, ATAC-seq, and MNase-seq, providing specific workflows for each.

Does it handle ATAC-seq specific requirements?

Yes, it includes workflows for Tn5 offset correction using alignmentSieve with the ATACshift flag and fragment size analysis to check for nucleosome patterns.

Can I use this skill for data normalization?

Yes, it supports various normalization methods like RPGC (1x genome coverage), CPM, RPKM, and BPM to ensure valid comparisons between different sequencing samples.

Can I generate heatmaps for specific genomic regions?

Absolutely. Using the computeMatrix and plotHeatmap tools, you can visualize signal enrichment around Transcription Start Sites (TSS), gene bodies, or custom BED regions.

deepTools NGS Analysis

Name: deepTools NGS Analysis
Author: Activer007

byActiver007

数据科学与机器学习

Analyzes and visualizes high-throughput sequencing data including ChIP-seq, RNA-seq, and ATAC-seq using the deepTools suite.

关于

deepTools is a comprehensive Python-based toolkit designed for the end-to-end processing of high-throughput sequencing data. It enables researchers to perform critical tasks such as normalizing coverage tracks, executing quality control assessments like PCA and sample correlation, and generating publication-ready visualizations like heatmaps and profile plots. Whether working with BAM, bigWig, or BED files, this skill provides specialized workflows for ChIP-seq enrichment, RNA-seq strand-specific coverage, and ATAC-seq Tn5 offset correction, ensuring robust and reproducible genomic data analysis.

主要功能

Comprehensive QC suite including PCA, sample correlation, and fingerprint plots
0 GitHub stars
Automated workflow generation for ChIP-seq, RNA-seq, and ATAC-seq analysis
Pre-configured effective genome sizes for Human, Mouse, and other model organisms
BAM to bigWig conversion with multiple normalization methods (RPGC, CPM, RPKM)
Advanced visualization for TSS heatmaps and genomic feature profile plots

使用场景

Generating normalized bigWig coverage tracks from raw BAM alignments for genome browsers
Creating publication-quality heatmaps and profile plots around gene TSS or peak regions
Assessing ChIP-seq enrichment quality using fingerprint plots and replicate correlation analysis

关于

主要功能

Comprehensive QC suite including PCA, sample correlation, and fingerprint plots
0 GitHub stars
Automated workflow generation for ChIP-seq, RNA-seq, and ATAC-seq analysis
Pre-configured effective genome sizes for Human, Mouse, and other model organisms
BAM to bigWig conversion with multiple normalization methods (RPGC, CPM, RPKM)
Advanced visualization for TSS heatmaps and genomic feature profile plots

使用场景

Generating normalized bigWig coverage tracks from raw BAM alignments for genome browsers
Creating publication-quality heatmaps and profile plots around gene TSS or peak regions
Assessing ChIP-seq enrichment quality using fingerprint plots and replicate correlation analysis