HAT:de novovariant calling for highly accurate short-read and long-read sequencing data

Abstract

AbstractMotivationde novovariant (DNV) calling is challenging from parent-child sequenced trio data. We developedHareAndTortoise (HAT) to work as an automated workflow to detect DNVs in highly accurate short-read and long-read sequencing data. Reliable detection of DNVs is important for human genetics studies (e.g., autism, epilepsy).ResultsHAT is a workflow to detect DNVs from short-read and long read sequencing data. This workflow begins with aligned read data (i.e., CRAM or BAM) from a parent-child sequenced trio and outputs DNVs. HAT detects high-quality DNVs from short-read whole-exome sequencing, short-read wholegenome sequencing, and highly accurate long-read sequencing data.Availabilityhttps://github.com/TNTurnerLab/HATContacttychele@wustl.eduSupplementary informationSupplementary data are available at bioRxiv.