CNV-P: a machine-learning framework for predicting high confident copy number variations.

ABSTRACT:

Background

Copy-number variants (CNVs) have been recognized as one of the major causes of genetic disorders. Reliable detection of CNVs from genome sequencing data has been a strong demand for disease research. However, current software for detecting CNVs has high false-positive rates, which needs further improvement.

Methods

Here, we proposed a novel and post-processing approach for CNVs prediction (CNV-P), a machine-learning framework that could efficiently remove false-positive fragments from results of CNVs detecting tools. A series of CNVs signals such as read depth (RD), split reads (SR) and read pair (RP) around the putative CNV fragments were defined as features to train a classifier.

Results

The prediction results on several real biological datasets showed th

SUBMITTER: Wang T 

PROVIDER: S-EPMC8645205 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature