Tools details

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deepTools consists of a set of modules that can be used independently to work with mapped reads. We have subdivided such tasks into quality controls, normalizations and visualizations.

This table gives an overview of the tools that are currently available within the current deepTools release. We have more modules in the pipeline that will be added in the future once the code is stable.

For more detailed information, follow the links in the table or this way:

tool	type	input files	main output file(s)	application
[bamCorrelate][]	QC	2 or more BAM	clustered heatmap	Pearson or Spearman correlation between read distributions
[bamFingerprint][]	QC	2 BAM	1 diagnostic plot	assess enrichment strength of a ChIP sample
[computeGCBias][]	QC	1 BAM	2 diagnostic plots	calculate the exp. and obs. GC distribution of reads
[bamCoverage][]	normalization	BAM	bedGraph or bigWig	obtain the normalized read coverage of a single BAM file
[bamCompare][]	normalization	2 BAM	bedGraph or bigWig	normalize 2 BAM files to each other using a mathematical operation of your choice (e.g. log2ratio, difference)
[computeMatrix][]	visualization	1 bigWig, 1 BED	zipped file, to be used with heatmapper or profiler	compute the values needed for heatmaps and summary plots
[heatmapper][]	visualization	computeMatrix output	heatmap of read coverages	visualize the read coverages for genomic regions
[profiler][]	visualization	computeMatrix output	summary plot ("meta-profile")	visualize the average read coverages over a group of genomic regions