deeptools

Multi-sample QC for NGS data involves calculating coverage metrics like plotFingerprint, plotCorrelation, and plotPCA to assess signal consistency across BAM and bigWig files. This toolkit provides CLI workflows to validate inputs and generate these correlation profiles.

Generating heatmaps from BAM files requires computing coverage matrices around reference regions and plotting the aggregated signals. You can use computeMatrix to calculate the signal scores and plotHeatmap or plotProfile to visualize the enrichment across genomic features.

Yes, normalizing bigWig coverage using RPGC or CPM is supported for RNA-seq and ChIP-seq workflows. The bamCoverage function processes BAM files and applies normalization choices to output bigWig files, ensuring accurate cross-sample signal comparison.

Yes, the toolkit supports ATAC-seq data analysis alongside RNA-seq and ChIP-seq workflows. It processes high-throughput sequencing data to perform QC, coverage calculation, and visualization across BAM, BED, and bigWig files for these specific assay types.

Validating input files before running a sequencing coverage workflow prevents processing errors and ensures accurate normalization and heatmap generation. The CLI workflows include file validation steps to check BAM, BED, and bigWig formats before executing analytical scripts.