Integrative Method Significantly Increases The Accuracy (Isa) Of CNV Detection

Whole-genome sequencing(WGS)has already been a common method for detecting genetic variations in human diseases.Although the data of genome-wide sequencing is primarily used to identify single nucleotide variations and insertions,it also makes it possible to detect copy number variation(CNV)and large structural variations(SVs)at high resolution.Somatic copy number alterations(SCNAs)play an important role in the pathogenesis and progression of cancer,and confer susceptibility to a variety of human disorders.However,current available algorithms or software for detecting CNV generate various results with low overlap between each other,which result from the low accuracy of CNV detection for those algorithms or software.In view of the above problems,those five tools related to CNV detection,such as BIC-seq2,cn.MOPS,Control-FREEC,LUMPY,and SAAS-CNV,have certain advantages in the detection of CNV and have individual accuracy,but still exist internal biases.We combined the results of each method with statistical methods and generated new software(Isa)for CNV testing.First,we will introduce the various models in the developed process,and how to evaluate the model.Then we use the simulator to simulate a batch of data to train our model and parameter optimization.Next,we apply this new procedure to paired whole-genome sequencing(WGS)data for hepatocellular carcinoma(HCC).Finally,we verified the accuracy of our results from a biological perspective.Using our method,we determined the number of CNVs that are more accurate than each individual software.Our approach provides an alternative solution for detecting CNV,which may provide a new perspective on biological processes and new causes of diseases.