Comprehensively benchmarking applications for detecting copy number variation

Abstract

Motivation: Recently, copy number variation (CNV) has gained considerable interest as a type of genomic variation that plays an important role in complex phenotypes and disease susceptibility. Since a number of CNV detection methods have recently been developed, it is necessary to help investigators choose suitable methods for CNV detection depending on their objectives. For this reason, this study compared ten commonly used CNV detection applications, including CNVnator, ReadDepth, RDXplorer, LUMPY and Control-FREEC, benchmarking the applications by sensitivity, specificity and computational demands. Taking the DGV gold standard variants as a standard dataset, we evaluated the ten applications with real sequencing data at sequencing depths from 5X to 50X. Among the ten methods benchmarked, LUMPY performs the best for both high sensitivity and specificity at each sequencing depth. For the purpose of high specificity, Canvas is also a good choice. If high sensitivity is preferred, CNVnator and RDXplorer are better choices. Additionally, CNVnator and GROM-RD perform well for low-depth sequencing data. Our results provide a comprehensive performance evaluation for these selected CNV detection methods and facilitate future development and improvement in CNV prediction methods.

Development of an Early Prediction Model for Subarachnoid Hemorrhage With Genetic and Signaling Pathway Analysis

“…For example, the SAH intervention experiment sample size was too small for us to demonstrate high predictive accuracy for the model. In future work, we will integrate more recent bioinformatics research algorithms (Zhang et al, 2016(Zhang et al, , 2017a(Zhang et al, , 2018(Zhang et al, , 2019aGao et al, 2017; and data into the system to overcome the problems.…”

“…Second, we use E-Bayes (Carlin and Louis, 2010), SVM-RFE (Duan et al, 2005), SPCA (Zou et al, 2006), and statistical tests (Zhang et al, 2016(Zhang et al, , 2018(Zhang et al, , 2019b(Zhang et al, ,d, 2020Xiao et al, 2019) to investigate key genes from experimental data by considering both SAH and LCN2 as factors. Third, we integrate the logistic regression (LR), support vector machine (SVM), and Naive-Bayes algorithms (Xia et al, 2017;Zhang et al, 2017aZhang et al, , 2019a into an ensemble learning model (Gao et al, 2017;Zhang et al, 2019b) to build a model for early SAH prediction.…”

Medium-coverage DNA sequencing in the design of the genetic association study

“…Gilly et al found that genotype accuracy is substantially more dependent on sequencing depth for indels than for SNPs [ 13 ]. In a recent study, the performance of several CNV detection tools varied with the sequencing depth, with high-coverage resulted in high sensitivity and specificity [ 20 ]. We expected the sequencing coverage would play a more important role than that in the present study of SNP.…”

A Practical Guide for Structural Variation Detection in the Human Genome

“…Many algorithms have been developed to detect CNVs in short-read WGS data based on read depth such as BIC-seq (Xi et al, 2011), CNVnator (Abyzov, Urban, Snyder, & Gerstein, 2011), and Control-FREEC (Boeva et al, 2012 (Trost et al, 2018;Zhang, Bai, Yuan, & Du, 2019). Evaluation of CNV detection tools in exome sequencing and targeted sequencing data are also available (Kadalayil et al, 2014;Yao, Yu, Qing, Wang, & Shen, 2019;Zare, Dow, Monteleone, Hosny, & Nabavi, 2017).…”