Computational identification of micro-structural variations and their proteogenomic consequences in cancer

Rapid advancement in high throughput genome and transcriptome sequencing (HTS) and mass spectrometry (MS) technologies has enabled the acquisition of the genomic, transcriptomic and proteomic data from the same tissue sample. We introduce a computational framework, ProTIE, to integratively analyze all three types of omics data for a complete molecular profile of a tissue sample. Our framework features MiStrVar, a novel algorithmic method to identify micro structural variants (microSVs) on genomic HTS data. Coupled with deFuse, a popular gene fusion detection method we developed earlier, MiStrVar can accurately profile structurally aberrant transcripts in tumors. Given the breakpoints obtained by MiStrVar and deFuse, our framework can then identify all relevant peptides that span the breakpoint junctions and match them with unique proteomic signatures. Observing structural aberrations in all three types of omics data validates their presence in the tumor samples.


Publication Date:
Dec 18 2017
Date Submitted:
Nov 21 2018
ISSN:
1367-4811
Citation:
Bioinformatics
Note:
A freely accessible, full text version is available using the link(s) in External Resources.
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 Record created 2018-11-21, last modified 2019-04-03


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