New York University Langonne Medical Center, 333 E 38th St, New York, NY, 10016, USA.
Department of Biological Sciences and Center for Translational and Basic Research, Belfer Research Building, Hunter College of The City University of New York, 333 E 38th St, New York, NY, 10016, US.
Gigascience. 2019 Apr 1;8(4). doi: 10.1093/gigascience/giz020.
Current methods used for annotating metagenomics shotgun sequencing (MGS) data rely on a computationally intensive and low-stringency approach of mapping each read to a generic database of proteins or reference microbial genomes.
We developed MGS-Fast, an analysis approach for shotgun whole-genome metagenomic data utilizing Bowtie2 DNA-DNA alignment of reads that is an alternative to using the integrated catalog of reference genes database of well-annotated genes compiled from human microbiome data. This method is rapid and provides high-stringency matches (>90% DNA sequence identity) of the metagenomics reads to genes with annotated functions. We demonstrate the use of this method with data from a study of liver disease and synthetic reads, and Human Microbiome Project shotgun data, to detect differentially abundant Kyoto Encyclopedia of Genes and Genomes gene functions in these experiments. This rapid annotation method is freely available as a Galaxy workflow within a Docker image.
MGS-Fast can confidently transfer functional annotations from gene databases to metagenomic reads, with speed and accuracy.
目前用于注释宏基因组测序（MGS）数据的方法依赖于一种计算密集型且低严格性的方法，即将每个读取映射到蛋白质通用数据库或参考微生物基因组。
我们开发了 MGS-Fast，这是一种用于 shotgun 全基因组宏基因组数据分析的方法，它利用 Bowtie2 DNA-DNA 比对读取，这是替代使用集成参考基因目录数据库的方法，该数据库是从人类微生物组数据中编译的注释良好的基因的综合目录。该方法快速且提供了具有注释功能的基因与宏基因组读取之间的高严格性匹配（>90% DNA 序列同一性）。我们使用来自肝病和合成读取研究以及人类微生物组计划 shotgun 数据的这些方法来检测这些实验中京都基因和基因组百科全书基因功能的差异丰度。这种快速注释方法可作为 Galaxy 工作流程在 Docker 映像中免费获得。
MGS-Fast 可以快速准确地将功能注释从基因数据库转移到宏基因组读取。
