XomAnnotate: Analysis of Heterogeneous and Complex Exome- A Step towards Translational Medicine

Abstract

In translational cancer medicine, implicated pathways and the relevant master genes are of focus. Exome's specificity, processing-time, and cost advantage makes it a compelling tool for this purpose. However, analysis of exome lacks reliable combinatory analysis tools and techniques. In this paper we present XomAnnotate – a meta- and functional-analysis software for exome. We compared UnifiedGenotyper, Freebayes, Delly, and Lumpy algorithms that were designed for whole-genome and combined their strengths in XomAnnotate for exome data through meta-analysis to identify comprehensive mutation profile (SNPs/SNVs, short inserts/deletes, and SVs) of patients. The mutation profile is annotated followed by functional analysis through pathway enrichment and network analysis to identify most critical genes and pathways implicated in the disease genesis. The efficacy of the software is verified through MDS and clustering and tested with available 11 familial non-BRCA1/BRCA2 breast cancer exome data. The results showed that the most significantly affected pathways across all samples are cell communication and antigen processing and presentation. ESCO1, HYAL1, RAF1 and PRKCA emerged as the key genes. Network analysis further showed the purine and propanotate metabolism pathways along with RAF1 and PRKCA genes to be master regulators in these patients. Therefore, XomAnnotate is able to use exome data to identify entire mutation landscape, pathways, and the master genes accurately with wide concordance from earlier microarray and whole-genome studies -- making it a suitable biomedical software for using exome in next-generation translational medicine.Availability http://www.iomics.in/research/XomAnnotate

Tracking Cancer Genetic Evolution using OncoTrack

“…Out of these 1106 genes, 146 genes are part of the 168 KEGG cancer and metabolism related pathways 33 . The pathway enrichment analysis results from XomPathways 34 are in Supplementary Table 3 . Seven pathways with adjusted p-values (Benjamini-Hochberg) less than 0.05 were obtained.…”

“…This was followed by duplicate marking and indel-realignment, using Picard ( http://picard.sourceforge.net ) and GATK 25 respectively. A collapsed geneset with exon coordinates was created from UCSC gene table for hg19 using technique described in XomAnnotate 34 to obtain a single entry for a gene at a specific locus with all splice variations for that gene. This procedure collapsed the 44292 RefSeq rows of hg19 into 19358 genes.…”

“…We identified genes that were highly significant, and analysed them to identify their roles in cancer. We took 168 KEGG cancer related pathways from Broad Institute 33 and used the XomPathways R script 34 , followed by network analysis, for the functional analysis of these genes. We also used DAVID, for further annotation.…”

A Survey of Computational Tools to Analyze and Interpret Whole Exome Sequencing Data

“…The method utilizes haplotype-based variant detection under a Bayesian statistics framework [27]. This method has been used in several studies in combination with other approaches for the identifying of unique INDELs [28, 29]. …”