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Article 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.
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| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4408095 | PMC |
| http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0123569 | PLOS |
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