Detection of simple and complex de novo mutations with multiple reference sequences.

ABSTRACT: The characterization of de novo mutations in regions of high sequence and structural diversity from whole-genome sequencing data remains highly challenging. Complex structural variants tend to arise in regions of high repetitiveness and low complexity, challenging both de novo assembly, in which short reads do not capture the long-range context required for resolution, and mapping approaches, in which improper alignment of reads to a reference genome that is highly diverged from that of the sample can lead to false or partial calls. Long-read technologies can potentially solve such problems but are currently unfeasible to use at scale. Here we present Corticall, a graph-based method that combines the advantages of multiple technologies and prior data sources to detect arbitrary classes of

SUBMITTER: Garimella KV 

PROVIDER: S-EPMC7462078 | biostudies-literature | 2020 Aug

REPOSITORIES: biostudies-literature