Overview
Cancer genomes exhibit multiple genomic and epigenomic alterations. To investigate the combined effects of single nucleotide variations (SNVs), structural rearrnagements (SVs) and DNA methylation changes on the prostate transcriptional machinery, we integrated whole-genome sequencing (WGS), RNA-Seq and DNA methylation data from primary prostate tumor samples with functional genomics data from the ENCODE and Roadmap Epigenomics projects. Our three step computational model involves: (a) construction of prostate regulatory network using DNase I hypersensitivity data, (b) identification of significantly mutated, rearranged and differentially methylated coding and non-coding regulatory regions in prostate cancer and, (c) interpretation of the effects of these alterations on prostate regulatory network. We have provided scripts to construct tissue-specific regulatory network.
Downloads
Prostate Regulatory Network
✤ DHS based prostate regulatory network. This file contains transcription factor and target gene interactions Prostate regulatory network .
✤ Transcription Factor hubs (TF hubs) in prostate regulatory network Prostate TF hubs .
✤ List of genes altered by SNVs,SVs and DNA methylation in prostate tumor Altered_genes .
Source Codes
✤ Programs to create tissue-specific regulatory network tissue-specific regulatory network .
✤ FSig-SNV (Functionally significant single nucleotide variant) method analyzes the somatic mutations in both coding and non-coding regulatory regions to identify elements that show more recurrent (present in multiple samples) and more functional mutations than expected randomly. For functional annotation, the method uses FunSeq2 to annotate and calculate functional score for each variant. Output of FSig-SNV is a list of significantly mutated coding and non-coding elements that show higher than expected frequency of functional mutations across multiple tumor samples.
Source code to find genes with significanlty mutated coding and non-coding (promoter and enhancer) regions FSig-SNV .
Inputs for running FSig-SNV for analyzing prostate tumor:
Single Nucleotide Variants ICGC SNVs, Baca et al and Berger et al
FunSeq2 annotated prostate tumor variants in VCF format Download FunSeq2
✤ FSig-SV (Functionally significant structural variants) method identifies coding and non-coding elements significantly affected by deletion, insertion, duplication, inversion and translocation events. Output of the method is a list of coding and non-coding elements that are rearranged in more samples than expected randomly. Source code to find genes with significantly rearranged coding and non-coding (promoter and enhancer) regions FSig-SV .
Inputs for running FSig-SV for analyzing prostate tumor:
Somatic Structural Variants ICGC SVs, Baca et al and Berger et al
✤ ELMER method to find significantly differentially methylated promoters and enhancers ELMER .
✤ README of computational pipeline for identifying genetic and epigenetic alterations in the coding and non-coding regulatory regions of the tumor genome and studying their effects on tissue-specific regulatory network README .
✤ For any queries or comments contact ekk2003@med.cornell.edu or prd2007@med.cornell.edu
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.