Mako: A Graph-based Pattern Growth Approach to Detect Complex Structural Variants.

ABSTRACT: Complex structural variants (CSVs) are genomic alterations that have more than two breakpoints and are considered as the simultaneous occurrence of simple structural variants. However, detecting the compounded mutational signals of CSVs is challenging through a commonly used model-match strategy. As a result, there has been limited progress for CSV discovery compared with simple structural variants. Here, we systematically analyzed the multi-breakpoint connection feature of CSVs, and proposed Mako, utilizing a bottom-up guided model-free strategy, to detect CSVs from paired-end short-read sequencing. Specifically, we implemented a graph-based pattern growth approach, where the graph depicts potential breakpoint connections, and pattern growth enables CSV detection without pre-defined model

SUBMITTER: Lin J 

PROVIDER: S-EPMC9510932 | biostudies-literature | 2022 Feb

REPOSITORIES: biostudies-literature