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

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, r...

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Bibliographic Details
Published in:BMC bioinformatics 2023-01, Vol.23 (Suppl 8), p.568-16, Article 568
Main Authors: Li, Xin, Wu, Yufeng
Format: Article
Language:English
Subjects:
Accuracy
Boundaries
Candidates
Chromosomes
Deletion
Deoxyribonucleic acid
DNA
DNA biosynthesis
DNA sequencing
Genetic diversity
Genetic research
Genome, Human
Genomes
Genomic Structural Variation
Genomic structural variations
Genomics
Genomics - methods
High-Throughput Nucleotide Sequencing - methods
High-throughput sequencing
Humans
Machine learning
Methodology
Methods
Nucleotide sequence
Nucleotide sequencing
Sequence Analysis, DNA
Software
Structure variation
Unsupervised learning
Unsupervised Machine Learning
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Summary: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. 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 unsupervised learning methods. After that, EigenDel uses a carefully designed approach for calling true deletions from each cluster. We conduct various experiments to evaluate the performance of EigenDel on low coverage sequence data. Our results show that EigenDel outperforms other major methods in terms of improving capability of balancing accuracy and sensitivity as well as reducing bias. EigenDel can be downloaded from https://github.com/lxwgcool/EigenDel .
ISSN:1471-2105
1471-2105
DOI:10.1186/s12859-023-05139-w