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VEGAWES: variational segmentation on whole exome sequencing for copy number detection

Anjum, Samreen; Morganella, Sandro; D’Angelo, Fulvio; Iavarone, Antonio; Ceccarelli, Michele

Background
Copy number variations are important in the detection and progression of significant tumors and diseases. Recently, Whole Exome Sequencing is gaining popularity with copy number variations detection due to low cost and better efficiency. In this work, we developed VEGAWES for accurate and robust detection of copy number variations on WES data. VEGAWES is an extension to a variational based segmentation algorithm, VEGA: Variational estimator for genomic aberrations, which has previously outperformed several algorithms on segmenting array comparative genomic hybridization data.

Results
We tested this algorithm on synthetic data and 100 Glioblastoma Multiforme primary tumor samples. The results on the real data were analyzed with segmentation obtained from Single-nucleotide polymorphism data as ground truth. We compared our results with two other segmentation algorithms and assessed the performance based on accuracy and time.

Conclusions
In terms of both accuracy and time, VEGAWES provided better results on the synthetic data and tumor samples demonstrating its potential in robust detection of aberrant regions in the genome.

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Also Published In

Title
BMC Bioinformatics
DOI
https://doi.org/10.1186/s12859-015-0748-0

More About This Work

Academic Units
Irving Comprehensive Cancer Center
Published Here
September 29, 2015
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