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

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submitted on 2024-09-12, 08:57 and posted on 2024-09-12, 08:58 authored by Samreen Anjum, Sandro Morganella, Fulvio D’Angelo, Antonio Iavarone, Michele Ceccarelli

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. 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. 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.

Other Information

Published in: BMC Bioinformatics
License: http://creativecommons.org/licenses/by/4.0/
See article on publisher's website: https://dx.doi.org/10.1186/s12859-015-0748-0

History

Language

  • English

Publisher

Springer Nature

Publication Year

  • 2015

License statement

This Item is licensed under the Creative Commons Attribution 4.0 International License.

Institution affiliated with

  • Hamad Bin Khalifa University
  • Qatar Computing Research Institute - HBKU

Related Datasets

Samreen Anjum. (2015). VEGAWES. Last modified 2015. GitHub Repository. https://github.com/samreenanjum/VEGAWES

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