Copy number variation detection workflow using next generation sequencing data

• Published in: 2016 International Conference on Bioinformatics and Systems Biology (BSB) pp. 1 - 5
• Main Authors: Dharanipragada, Prashanthi, Parekh, Nita
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.03.2016
• Subjects: Bioconductor; Bioinformatics; Copy number variations; Genomics; Hidden Markov models; Indexes; Libraries; Next generation networking; NGS; Sequential analysis
• DOI: 10.1109/BSB.2016.7552117

In the last decade, discovery of copy number variations (CNVs) have dramatically changed our understanding of differences between individuals. CNVs include both additional copies of sequence (duplications) and loss of genetic material (deletions) and provide an alternate paradigm for the genetic basis of human diseases. Genome-wide CNV detection is now possible using high-throughput, low-cost next generation sequencing (NGS) methods. Nature of NGS data demands various preprocessing and pretreatment steps before extracting any meaningful information. Among the plethora of variant calling methods available, R-based methods offer flexible environment, facilitating choice of various methods depending on the type of data or type of analysis to be performed. Here we give a pipeline for various steps involved in CNV detection in NGS data using R-based algorithms and packages.