DROMPAplus: a pipeline tool for ChIP-seq analysis

DROMPA (DRaw and Observe Multiple enrichment Profiles and Annotation) is a ChIP-seq pipeline tool that meets various needs, including quality check, analysis, and visualization of multiple ChIP samples.

DROMPAplus is an update of DROMPA3. It is written in C++ and runs from a single launch command on conventional Linux systems.

• The main features of DROMPAplus are summarized below. DROMPAplus:

  • Accepts multiple map file formats (SAM, BAM, CRAM, Bowtie, TagAlign(.gz)) and read distribution formats (WIG(.gz), bigWig, bedGraph).
  • Supports spike-in normalization and total read normalization.
  • Outputs various quality metrics for ChIP-seq analysis.
  • Visualizes read distributions in conventional PDF format; therefore, no additional programs are required which is preferable for many users, especially when sharing results (e.g., on cloud storage) with collaborators who do not have a strong bioinformatics background.
  • Automatically estimates the fragment length from single-end reads using SSP.
  • Can visualize two samples in a single line, which delineates the co-occurrence (e.g., H3K4me3 and H3K27ac) and exclusivity (e.g., H3K27me3 and H3K36me3) of read enrichment. Transparency (alpha) of read color can be specified.
  • Supports chromatin loops from ChIA-PET (Mango format) and Hi-C (HICCUPS format) with colors corresponding to the p-values.
  • Bases the HEATMAP command on Python3, which enables flexible customization.

Fig. 1 Schematic representation of DROMPA+ features and functionality.

Contents:

• 1. Installation
  • 1.1. Docker image
  • 1.2. Building from source
• 2. Parse2wig
  • 2.1. Example
  • 2.2. Quality check
  • 2.3. Total read normalization
  • 2.4. High resolution with central regions of fragments
  • 2.5. Mappability information
  • 2.6. GC content estimation
• 3. DROMPAplus: read distritbution
  • 3.1. PC_SHARP: Read distribution visualization
  • 3.2. PC_ENRICH: Enrichment visualization
  • 3.3. Peak-calling
  • 3.4. GV: chromosome-wide overview
  • 3.5. PROFILE: Aggregation plot (using R)
  • 3.6. HEATMAP (using Python3)
  • 3.7. MULTICI: Generate matrix of averaged read density
  • 3.8. GENWIG: Generate wig data
• 4. Appendix
  • 4.1. Data
  • 4.2. Mappability files
  • 4.3. Gene-density files

Citation:

• Nakato R., Sakata T., Methods for ChIP-seq analysis: A practical workflow and advanced applications, Methods, 2020.

Contact:

Mail:rnakato AT iqb.u-tokyo.ac.jp Twitter:@RyuichiroNakato