A graph clustering algorithm for detection and genotyping of structural variants from long reads.

ABSTRACT:

Background

Structural variants (SVs) are genomic polymorphisms defined by their length (>50 bp). The usual types of SVs are deletions, insertions, translocations, inversions, and copy number variants. SV detection and genotyping is fundamental given the role of SVs in phenomena such as phenotypic variation and evolutionary events. Thus, methods to identify SVs using long-read sequencing data have been recently developed.

Findings

We present an accurate and efficient algorithm to predict germline SVs from long-read sequencing data. The algorithm starts collecting evidence (signatures) of SVs from read alignments. Then, signatures are clustered based on a Euclidean graph with coordinates calculated from lengths and genomic positions. Clustering is performed by the DBSCAN algor

SUBMITTER: Gaitan N 

PROVIDER: S-EPMC10783151 | biostudies-literature | 2024 Jan

REPOSITORIES: biostudies-literature