title: VEGAWES: variational segmentation on whole exome sequencing for copy number detection.
abstract: 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.
authors.affiliation: Computational Sciences and Engineering, Qatar Computing Research Institute, Doha, P. O. Box 5825, Qatar. sshaikh2@qf.org.qa.
authors.forename: Samreen
authors.initials: S
authors.lastname: Anjum
authors.affiliation: European Molecular Biology Laboratory, European Bioinformatics Institute, (EMBL -EBI), Wellcome Trust Genome Campus, Cambridge, CB10 1SD, UK. sandro@ebi.ac.uk.
authors.forename: Sandro
authors.initials: S
authors.lastname: Morganella
authors.affiliation: BIOGEM, Ariano Irpino, 83031, Italy. fulvio.dan13@gmail.com.
authors.forename: Fulvio
authors.initials: F
authors.lastname: D'Angelo
authors.affiliation: Institute for Cancer Genetics, Columbia University, New York, 10027, USA. ai2102@columbia.edu.
authors.forename: Antonio
authors.initials: A
authors.lastname: Iavarone
authors.affiliation: Computational Sciences and Engineering, Qatar Computing Research Institute, Doha, P. O. Box 5825, Qatar. m.ceccarelli@gmail.com.|Department of Science and Technology, University of Sannio, Benevento, 82100, Italy. m.ceccarelli@gmail.com.
authors.forename: Michele
authors.initials: M
authors.lastname: Ceccarelli
mesh_terms: D000465:Algorithms; D019540:Area Under Curve; D001932:Brain Neoplasms / Q000235:genetics / Q000378:metabolism / Q000473:pathology; D004247:DNA / Q000032:analysis; D056915:DNA Copy Number Variations / Q000235:genetics*; D005909:Glioblastoma / Q000235:genetics / Q000378:metabolism / Q000473:pathology; D059014:High-Throughput Nucleotide Sequencing; D006801:Humans; D020641:Polymorphism, Single Nucleotide; D012372:ROC Curve; D017422:Sequence Analysis, DNA
chemical_list: D004247:DNA