Plyusnin Ilya, Kant Ravi, Jääskeläinen Anne J, Sironen Tarja, Holm Liisa, Vapalahti Olli, Smura Teemu
Institute of Biotechnology, University of Helsinki, Helsinki 00014, Finland.
Department of Veterinary Bioscience, University of Helsinki, Helsinki 00014, Finland.
Virus Evol. 2020 Dec 2;6(2):veaa091. doi: 10.1093/ve/veaa091. eCollection 2020 Jul.
The study of the microbiome data holds great potential for elucidating the biological and metabolic functioning of living organisms and their role in the environment. Metagenomic analyses have shown that humans, along with for example, domestic animals, wildlife and arthropods, are colonized by an immense community of viruses. The current Coronavirus pandemic (COVID-19) heightens the need to rapidly detect previously unknown viruses in an unbiased way. The increasing availability of metagenomic data in this era of next-generation sequencing (NGS), along with increasingly affordable sequencing technologies, highlight the need for reliable and comprehensive methods to manage such data. In this article, we present a novel bioinformatics pipeline called LAZYPIPE for identifying both previously known and novel viruses in host associated or environmental samples and give examples of virus discovery based on it. LAZYPIPE is a Unix-based pipeline for automated assembling and taxonomic profiling of NGS libraries implemented as a collection of C++, Perl, and R scripts.
微生物组数据的研究在阐明生物体的生物学和代谢功能及其在环境中的作用方面具有巨大潜力。宏基因组分析表明,人类以及例如家畜、野生动物和节肢动物都被庞大的病毒群落所定植。当前的冠状病毒大流行(COVID-19)凸显了以无偏见方式快速检测以前未知病毒的必要性。在这个下一代测序(NGS)时代,宏基因组数据的可用性不断增加,以及测序技术越来越实惠,凸显了需要可靠且全面的方法来管理此类数据。在本文中,我们提出了一种名为LAZYPIPE的新型生物信息学流程,用于在宿主相关或环境样本中识别已知和新型病毒,并给出基于该流程的病毒发现示例。LAZYPIPE是一个基于Unix的流程,用于对NGS文库进行自动组装和分类分析,它由C++、Perl和R脚本集合实现。
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