Table 1.
Overview of currently available deepTools
| Tool name | Type | Input files | Main output | Application |
|---|---|---|---|---|
| bamCorrelate | QC | 2 or more BAM | Clustered heatmap of similarity measures | Determine Pearson or Spearman correlations between read distributions |
| bamFingerprint | QC | 2 BAM | Diagnostic plot | Assess enrichment strength of a ChIP-seq sample versus a control |
| computeGCBias | QC | 1 BAM | Diagnostic plots | Compare expected and observed GC distribution of reads |
| correctGCBias | Normalization | 1 BAM | BAM or bigWig | Obtain GC-corrected read (coverage) file |
| bamCoverage | Normalization | 1 BAM | bedGraph or bigWig | Obtain normalized read coverage of a single BAM |
| bamCompare | Normalization | 2 BAM | bedGraph or bigWig | Normalize 2 BAM files to each other with a mathematical operation of Choice (fold change, log2 (ratio), sum, difference) |
| computeMatrix | Visualization | 1 bigWig, min. 1 BED | gzipped table | Calculate the values for heatmaps and summary plots |
| profiler | Visualization | gzipped table from computeMatrix | xy-plot (summary plot) | Average profiles of read coverage for (groups of) genome regions |
| heatmapper | Visualization | gzipped table from computeMatrix | (Un)clustered heatmap or read coverages | Identify patterns of read coverages for genome regions |
Here, we only indicate the main output files, but every data table underlying any image produced by deepTools can be downloaded and used in subsequent analyses. For a comparison of functionalities with previously published web servers, see Supplementary Table S1.