Trappe Kathrin, Marschall Tobias, Renard Bernhard Y
Robert Koch Institute, Research Group Bioinformatics (NG4), 13353 Berlin, Germany.
Center for Bioinformatics, Saarland University, and Max Planck Institute for Informatics, 66123 Saarbrücken, Germany.
Bioinformatics. 2016 Sep 1;32(17):i595-i604. doi: 10.1093/bioinformatics/btw423.
Horizontal gene transfer (HGT) is a fundamental mechanism that enables organisms such as bacteria to directly transfer genetic material between distant species. This way, bacteria can acquire new traits such as antibiotic resistance or pathogenic toxins. Current bioinformatics approaches focus on the detection of past HGT events by exploring phylogenetic trees or genome composition inconsistencies. However, these techniques normally require the availability of finished and fully annotated genomes and of sufficiently large deviations that allow detection and are thus not widely applicable. Especially in outbreak scenarios with HGT-mediated emergence of new pathogens, like the enterohemorrhagic Escherichia coli outbreak in Germany 2011, there is need for fast and precise HGT detection. Next-generation sequencing (NGS) technologies facilitate rapid analysis of unknown pathogens but, to the best of our knowledge, so far no approach detects HGTs directly from NGS reads.
We present Daisy, a novel mapping-based tool for HGT detection. Daisy determines HGT boundaries with split-read mapping and evaluates candidate regions relying on read pair and coverage information. Daisy successfully detects HGT regions with base pair resolution in both simulated and real data, and outperforms alternative approaches using a genome assembly of the reads. We see our approach as a powerful complement for a comprehensive analysis of HGT in the context of NGS data.
Daisy is freely available from http://github.com/ktrappe/daisy
Supplementary data are available at Bioinformatics online.
水平基因转移（HGT）是一种基本机制，它使细菌等生物体能够在远缘物种之间直接转移遗传物质。通过这种方式，细菌可以获得新的特性，如抗生素抗性或致病毒素。当前的生物信息学方法侧重于通过探索系统发育树或基因组组成不一致性来检测过去的HGT事件。然而，这些技术通常需要完整且充分注释的基因组，以及足够大的偏差以便进行检测，因此应用并不广泛。特别是在由HGT介导的新病原体出现的爆发场景中，如2011年德国的肠出血性大肠杆菌爆发，需要快速且精确地检测HGT。下一代测序（NGS）技术有助于对未知病原体进行快速分析，但据我们所知，到目前为止还没有方法能直接从NGS读数中检测HGT。
我们展示了Daisy，一种用于HGT检测的基于映射的新型工具。Daisy通过分裂读数映射确定HGT边界，并依靠读数对和覆盖信息评估候选区域。Daisy在模拟数据和真实数据中均成功检测到具有碱基对分辨率的HGT区域，并且在使用读数的基因组组装时优于其他替代方法。我们认为我们的方法是在NGS数据背景下对HGT进行全面分析的有力补充。
Daisy可从http://github.com/ktrappe/daisy免费获取。
补充数据可在《生物信息学》在线获取。
