Mobile element insertions in rare diseases: a comparative benchmark and reanalysis of 60,000 exome samples.

ABSTRACT: Mobile element insertions (MEIs) are a known cause of genetic disease but have been underexplored due to technical limitations of genetic testing methods. Various bioinformatic tools have been developed to identify MEIs in Next Generation Sequencing data. However, most tools have been developed specifically for genome sequencing (GS) data rather than exome sequencing (ES) data, which remains more widely used for routine diagnostic testing. In this study, we benchmarked six MEI detection tools (ERVcaller, MELT, Mobster, SCRAMble, TEMP2 and xTea) on ES data and on GS data from publicly available genomic samples (HG002, NA12878). For all the tools we evaluated sensitivity and precision of different filtering strategies. Results show that there were substantial differences in tool performance

SUBMITTER: Wijngaard R 

PROVIDER: S-EPMC10853235 | biostudies-literature | 2024 Feb

REPOSITORIES: biostudies-literature