How does it help with NGS Quality Control?

It facilitates automated QC steps such as calculating sample correlations, performing PCA to check replicate clustering, and generating fingerprint plots to assess ChIP enrichment strength.

Can it handle strand-specific RNA-seq data?

Yes, it includes specific guidance and workflows for generating strand-specific coverage tracks by filtering for forward and reverse strands.

Which normalization methods are available?

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

What file formats does this deepTools skill support?

It primarily works with BAM, bigWig, BED, and bedGraph files, which are the standard formats used in high-throughput sequencing and genomic analysis.

Is this skill suitable for ATAC-seq data?

Absolutely. It includes procedures for Tn5 offset correction (shifting reads) and fragment size analysis, which are essential for ATAC-seq workflows.

deepTools NGS Analysis

Name: deepTools NGS Analysis
Author: pur3v4d3r

bypur3v4d3r

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Data Science & ML

Processes and analyzes high-throughput sequencing data (NGS) to generate publication-quality visualizations and quality control metrics.

About

deepTools is a powerful suite of Python command-line tools designed for the end-to-end processing and visualization of high-throughput sequencing data like ChIP-seq, RNA-seq, and ATAC-seq. It enables users to convert BAM alignments into normalized coverage tracks, perform rigorous quality control through PCA and correlation analysis, and generate highly customizable heatmaps and profile plots around genomic features. This skill provides domain-specific guidance for bioinformatics workflows, ensuring proper normalization and best practices for genomic data exploration.

Key Features

1 GitHub stars
Comprehensive QC metrics including PCA, correlation, and fingerprint plots
Advanced normalization methods including RPGC, CPM, RPKM, and BPM
Customizable heatmap and profile plot generation for genomic features
BAM to normalized bigWig and bedGraph coverage track conversion
Tailored workflows for ChIP-seq, RNA-seq, and ATAC-seq analysis

Use Cases

Generating publication-quality heatmaps and profile plots from ChIP-seq data
Performing quality control and sample correlation analysis on NGS replicates
Creating normalized genome-wide coverage tracks for visualization in genome browsers

About

Key Features

1 GitHub stars
Comprehensive QC metrics including PCA, correlation, and fingerprint plots
Advanced normalization methods including RPGC, CPM, RPKM, and BPM
Customizable heatmap and profile plot generation for genomic features
BAM to normalized bigWig and bedGraph coverage track conversion
Tailored workflows for ChIP-seq, RNA-seq, and ATAC-seq analysis

Use Cases

Generating publication-quality heatmaps and profile plots from ChIP-seq data
Performing quality control and sample correlation analysis on NGS replicates
Creating normalized genome-wide coverage tracks for visualization in genome browsers