Analyzes and visualizes high-throughput sequencing data for genomics research and quality control.
This skill integrates the deepTools suite into the Claude environment, providing a specialized toolkit for processing Next-Generation Sequencing (NGS) data. It enables researchers to convert BAM alignments into normalized coverage tracks, perform rigorous quality control checks like fingerprinting and correlation, and generate publication-quality heatmaps and profile plots. Designed for ChIP-seq, RNA-seq, and ATAC-seq workflows, it streamlines complex genomic analyses by providing standardized command-line patterns, effective genome size references, and visualization best practices.
Key Features
01Comprehensive QC assessment including fingerprints, PCA, and correlation
02Publication-ready heatmap and profile plot generation for genomic features
03Tailored workflows for ChIP-seq, RNA-seq, and ATAC-seq analysis
04Standardized normalization methods including RPGC, CPM, and RPKM
0516 GitHub stars
06Normalized coverage track generation (BAM to bigWig/bedGraph)
Use Cases
01Generating normalized coverage tracks and comparing ChIP-seq treatment vs control
02Performing quality control on large sequencing datasets to ensure replicate consistency
03Visualizing signal enrichment around transcription start sites (TSS) or peak regions
