AUTHOR=Zheng Tian 

TITLE=DETexT: An SNV detection enhancement for low read depth by integrating mutational signatures into TextCNN

JOURNAL=Frontiers in Genetics

VOLUME=Volume 13 - 2022

YEAR=2022

URL=https://www.frontiersin.org/journals/genetics/articles/10.3389/fgene.2022.943972

DOI=10.3389/fgene.2022.943972

ISSN=1664-8021

ABSTRACT=Detecting of SNV at very low read depths helps to reduce sequencing requirements, lower sequencing costs and aids in the early screening, diagnosis and treatment of cancer. However, the accuracy of SNV detection is significantly reduced at read depths below 34× due to the lack of a sufficient number of read pairs to help filter out false positives. Many recent studies have revealed the potential of mutational signature (MS) in detecting true SNV, understanding the mutational processes that lead to the development of human cancers, and analyzing the endogenous and exogenous causes. Here we present DETexT, an SNV detection method better suited to low read depths, which classifies false positive variants by combining MS with deep learning algorithms to mine correlation information around bases in individual reads without relying on the support of duplicate read pairs. We have validated the effectiveness of DETexT on simulated and real datasets and conducted comparative experiments. The source code has been uploaded to https://github.com/TrinaZ/extra-lowRD for academic use only.