Detecting genomic deletions from high-throughput sequence data with unsupervised learning.

ABSTRACT:

Background

Structural variation (SV), which ranges from 50 bp to [Formula: see text] 3 Mb in size, is an important type of genetic variations. Deletion is a type of SV in which a part of a chromosome or a sequence of DNA is lost during DNA replication. Three types of signals, including discordant read-pairs, reads depth and split reads, are commonly used for SV detection from high-throughput sequence data. Many tools have been developed for detecting SVs by using one or multiple of these signals.

Results

In this paper, we develop a new method called EigenDel for detecting the germline submicroscopic genomic deletions. EigenDel first takes advantage of discordant read-pairs and clipped reads to get initial deletion candidates, and then it clusters similar candidates by using

SUBMITTER: Li X 

PROVIDER: S-EPMC9881243 | biostudies-literature | 2023 Jan

REPOSITORIES: biostudies-literature