Comparison of bioinformatics tools to detect the copy number variation (CNV) based on the whole-exome sequencing

Copy number variation (CNV) is a kind of structural variation, which leads to an increase and decrease in DNA sequence length of ۱kb to several pairs of megabase. This type of variation can lead to changes in gene dose, coding sequences, gene expression regulation, and biomarkers that are important predictors of cancer. Comparative Genomic Hybridization methods, SNP arrays, and various forms of next-generation sequencing (NGS), such as whole-genome sequencing (WGS) and whole-exome sequencing (WES), have been used to identify CNA in the laboratory. The use of WES data has been found to be useful in clinical trials because it includes only protein-coding regions in the genome, due to high coverage, and relatively low cost. Unwanted signals associated with WES data that result from the detection of exons and contaminants generated by the target tissues complicate CNA estimation. Using bioinformatics tools to accurately estimate CNA data related to WES can be helpful. In this study, the seven bioinformatics tools (ExomeCNV, CoNIFER, VarScan۲, CODEX, ngCGH, saasCNV, and falcon) were used to identify CNV using data from the total eczema sequence of ۴۱۹ pairs of breast cancer tumor samples obtained from the Cancer Genome Atlas. SaasCNV showed the highest increase and decrease (۰.۶۵%), sensitivity (۴۹.۶%) and specificity (۸۹.۱%) to estimate the increase or decrease in the number of copies. Finally, to improve the identification of CNV, it is recommended to create software to identify CNV in higher plants and bacteria, software that has a high level of sensitivity, accuracy, and precision. Creating an algorithm for predicting and identifying CNV without the need for a control sample, using combination machine learning methods to produce stronger software for accurate CNV identification can also be helpful, also it is recommended to create software that combines both the sequences of the whole-genome sequencing (WGS) and the whole-exome sequencing (WES).

کلیدواژه ها:

Chromosomal structure variation ، whole-genome sequencing ، whole-exome sequencing ، Bioinformatics tools