STEAK: A specific tool for transposable elements and retrovirus detection in high-throughput sequencing data

Abstract

The advancements of high-throughput genomics have unveiled much about the human genome highlighting the impor-tance of variations between individuals and their contribution to disease. Even though numerous software have been devel-oped to make sense of large genomics datasets, a major short falling of these has been the inability to cope with repetitive regions, specifically to validate structural variants and accordingly assess their role in disease. Here we describe our pro-gram STEAK, a massively parallel software designed to detect chimeric reads in high-throughput sequencing data for a broad number of applications such as identifying presence/absence, as well as discovery of transposable elements (TEs), and retroviral integrations. We highlight the capabilities of STEAK by comparing its efficacy in locating HERV-K HML-2 in clinical whole genome projects, target enrichment sequences, and in the 1000 Genomes CEU Trio to the performance of other TE and virus detecting tools. We show that STEAK outperforms other software in terms of computational efficiency, sensitivity, and specificity. We demonstrate that STEAK is a robust tool, which allows analysts to flexibly detect and evalu-ate TE and retroviral integrations in a diverse range of sequencing projects for both research and clinical purposes.