R code for Kernel Convolved Rule Based Mapping
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README.md

README.md

Introduction

R code for Kernel Convolved Rule Based Mapping (KC-RBM) as described by De Jong et al. KC-RBM exploits distance, orientation and insertion density across tumors to automatically map integration sites to target genes.

Getting started

  1. Make sure the packages biomaRt, BSgenome* of choice, and CIMPL v1.0 are installed.
  2. Execute the sample code in file 'prog.r' in directory 'r' to check if everything works.
  3. Adapt code to use your own input files and configurations.

Code documentation

Directories

  • data: sample MuLV and SB transposon data for use with the sample program
  • r: KC-RBM scripts, and sample script for running KC-RBM

Output

KC-RBM returns a data.frame with the following columns:

All fields in the original idata, and additional fields indicating for each insertion (or insertion cluster):

  • $transid: to which transcript ID(s) this insertion maps, including all possible transID with all possible mechanisms
  • $d2tss: distance to TSS
  • $d2tts: distance to TTS
  • $dfrac_trans: fractional distance w.r.t. transcript length, relative to TSS (only if insertion is within a transcript).
  • $d2css: distance to coding start
  • $d2cts: distance to coding end
  • $dfrac_cds: fractional distance w.r.t. coding region length, relative to coding start (only if insertion is within a coding region).
  • $ensid: to which gene ID(s) the transcript ID(s) belong
  • $d2gss: distance to gene start
  • $d2gts: distance to gene end
  • $dfrac_gene: fractional distance w.r.t. gene length, relative to gene start (only if insertion is within a gene).
  • $distance: the distance between the insertion and the TSS of the mapped transcripts(bp, not absolute value) (distance = insertion_locus - TSS)
  • $mechanism: by which mechanism the insertion is mapped to a transcript
    • us: upstream-sense (insertion is upstream of a target gene, in sense orientation relative to the gene)
    • ua: upstream-antisense
    • ds: downstream-sense
    • da: downstream-antisense
    • utr3_intron_s: 3'UTR-intron-sense (insertion is in a 3'UTR intron, in sense orientation relative to the gene)
    • utr3_intron_a: 3'UTR-intron-antisense
    • utr5_intron_s: 5'UTR-intron-sense
    • utr5_intron_a: 5'UTR-intron-antisense
    • utr3_exon_s: 3'UTR-exon-sense (insertion is in a 3'UTR exon, in sense orientation relative to the gene)
    • utr3_exon_a: 3'UTR-exon-antisense
    • utr5_exon_s: 5'UTR-exon-sense
    • utr5_exon_a: 5'UTR-exon-antisense
    • other_within_s: any other insertions, within genes and in sense orientation relative to the gene
    • other_within_a: any other insertions, within genes and in antisense orientation relative to the gene
  • $gene_mechanism: orientation/location relative to the target gene
    • us: upstream-sense (insertion is upstream of a target gene, in sense orientation relative to the gene)
    • ua: upstream-antisense
    • ds: downstream-sense
    • da: downstream-antisense
    • is: inside-sense (insertion within a gene, in sense orientation relative to the gene)
    • ia: inside-antisense
  • $id: numeric identifier of the insertion
  • $clusterpeak: location of the nearest KC peak
  • $clusterorientation: overall orientation of the cluster consisting of all insertions that have $clusterpeak as the nearest peak
  • $ids: $id's of all insertions in this cluster
  • $n_insertions: no. of insertions in this cluster

Reference

De Jong et al. Computational identification of insertional mutagenesis targets for cancer gene discovery. Nucleic Acids Res. 2011 Aug;39(15):e105. doi: 10.1093/nar/gkr447.