M Kroon1, E W Lameijer1, N Lakenberg1, J Y Hehir-Kwa2, D T Thung3, P E Slagboom1, J N Kok1, K Ye4. 1. Department of Molecular Epidemiology, Leiden University Medical Center, Leiden. 2. Department of Human Genetics, Nijmegen Center for Molecular Life Sciences, Institute for Genetic and Metabolic Disease, Radboud University Nijmegen Medical Center, Nijmegen, Donders Centre for Neuroscience, Nijmegen, The Netherlands and. 3. Department of Human Genetics, Nijmegen Center for Molecular Life Sciences, Institute for Genetic and Metabolic Disease, Radboud University Nijmegen Medical Center, Nijmegen. 4. Department of Molecular Epidemiology, Leiden University Medical Center, Leiden, The Genome Institute, Washington University, St Louis, MO 63108, USA.
Abstract
MOTIVATION: Dispersed duplications (DDs) such as transposon element insertions and copy number variations are ubiquitous in the human genome. They have attracted the interest of biologists as well as medical researchers due to their role in both evolution and disease. The efforts of discovering DDs in high-throughput sequencing data are currently dominated by database-oriented approaches that require pre-existing knowledge of the DD elements to be detected. RESULTS: We present DD_DETECTION, a database-free approach to finding DD events in high-throughput sequencing data. DD_DETECTION is able to detect DDs purely from paired-end read alignments. We show in a comparative study that this method is able to compete with database-oriented approaches in recovering validated transposon insertion events. We also experimentally validate the predictions of DD_DETECTION on a human DNA sample, showing that it can find not only duplicated elements present in common databases but also DDs of novel type. AVAILABILITY AND IMPLEMENTATION: The software presented in this article is open source and available from https://bitbucket.org/mkroon/dd_detection.
MOTIVATION: Dispersed duplications (DDs) such as transposon element insertions and copy number variations are ubiquitous in the human genome. They have attracted the interest of biologists as well as medical researchers due to their role in both evolution and disease. The efforts of discovering DDs in high-throughput sequencing data are currently dominated by database-oriented approaches that require pre-existing knowledge of the DD elements to be detected. RESULTS: We present DD_DETECTION, a database-free approach to finding DD events in high-throughput sequencing data. DD_DETECTION is able to detect DDs purely from paired-end read alignments. We show in a comparative study that this method is able to compete with database-oriented approaches in recovering validated transposon insertion events. We also experimentally validate the predictions of DD_DETECTION on a human DNA sample, showing that it can find not only duplicated elements present in common databases but also DDs of novel type. AVAILABILITY AND IMPLEMENTATION: The software presented in this article is open source and available from https://bitbucket.org/mkroon/dd_detection.