Copy number variations (CNVs) are the gains or losses of genomic regions which range from 500 bases upwards in size. Whole-genome studies have revealed the presence of large numbers of CNV regions in human and a broad range of genetic diversity among the general population. CNVs have been the focus of many recent studies because of their roles in human genetic disorders.
CNV detection can be applied on variety of data types such as WES, RNA-Seq, and Targeted panel. While there are a lot of methods out there, I've found and used Hidden Markov Model-based methods more interesting.
HMM is applied to the segmentation section to calculate CNV events.
CNVs are usually detected by techniques like chromosomal microarray and MLPA. But nowadays, advanced NGS technologies enable also CNV detection along with SNV detection. The Read depth and Pair end mapping/End-pair mapping are usual algorithms used for CNV detection using several statistical models or clustering approach. Markov model is also used. CNVs can affect 15% of the human genome and lengths can vary from 50 bp to 1 Mbp. They can also be intragenic. Decrease in copy at particular locus is deletion and increase in copy can be duplication or triplication..
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