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RSeQC

Evaluates high throughput RNA-seq data

http://rseqc.sourceforge.net/

The module parses results generated by RSeQC, a package that provides a number of useful modules that can comprehensively evaluate high throughput RNA-seq data.

Supported scripts:

  • bam_stat
  • gene_body_coverage
  • infer_experiment
  • inner_distance
  • junction_annotation
  • junction_saturation
  • read_distribution
  • read_duplication
  • read_gc
  • tin

You can choose to hide sections of RSeQC output and customise their order. To do this, add and customise the following to your MultiQC config file:

rseqc_sections:
  - read_distribution
  - tin
  - gene_body_coverage
  - inner_distance
  - read_gc
  - read_duplication
  - junction_annotation
  - junction_saturation
  - infer_experiment
  - bam_stat

Change the order to rearrange sections or remove to hide them from the report.

Note that some scripts (for example, junction_annotation.py) write the logs to stderr. To make a file parable by MultiQC, redirect the stderr to a file using 2> mysample.log.

File search patterns​

rseqc/bam_stat:
  contents: "Proper-paired reads map to different chrom:"
  max_filesize: 500000
rseqc/gene_body_coverage:
  fn: "*.geneBodyCoverage.txt"
rseqc/infer_experiment:
  - fn: "*infer_experiment.txt"
  - contents: Fraction of reads explained by
    max_filesize: 500000
rseqc/inner_distance:
  fn: "*.inner_distance_freq.txt"
rseqc/junction_annotation:
  contents: "Partial Novel Splicing Junctions:"
  max_filesize: 500000
rseqc/junction_saturation:
  fn: "*.junctionSaturation_plot.r"
rseqc/read_distribution:
  contents: Group               Total_bases         Tag_count           Tags/Kb
  max_filesize: 500000
rseqc/read_duplication_pos:
  fn: "*.pos.DupRate.xls"
rseqc/read_gc:
  fn: "*.GC.xls"
rseqc/tin:
  contents: TIN(median)
  fn: "*.summary.txt"
  num_lines: 1