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Genome-resolved metagenomics using environmental and clinical samples

期刊

BRIEFINGS IN BIOINFORMATICS
卷 22, 期 5, 页码 -

出版社

OXFORD UNIV PRESS
DOI: 10.1093/bib/bbab030

关键词

read preprocessing; de novo assembly; metagenome assembly validation; genome binning; metagenome-assembled genomes; MAG refinement; MAG taxonomic classification; MAG annotation

资金

  1. Shanghai Jiao Tong University, School of Medicine New PI Startup Fund [17X100040046]

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Genome-resolved metagenomics is a powerful approach for recovering microbial genomes, classifying them, and analyzing their functions from metagenomic samples. It involves four main steps: preprocessing of sequencing reads, de novo metagenome assembly, genome binning, and taxonomic and functional analysis of the recovered genomes. The use of this technology has led to the discovery of previously unknown microbial species and provides valuable insights into the microbial communities in various environments.
Recent advances in high-throughput sequencing technologies and computational methods have added a new dimension to metagenomic data analysis i.e. genome-resolved metagenomics. In general terms, it refers to the recovery of draft or high-quality microbial genomes and their taxonomic classification and functional annotation. In recent years, several studies have utilized the genome-resolved metagenome analysis approach and identified previously unknown microbial species from human and environmental metagenomes. In this review, we describe genome-resolved metagenome analysis as a series of four necessary steps: (i) preprocessing of the sequencing reads, (ii) de novo metagenome assembly, (iii) genome binning and (iv) taxonomic and functional analysis of the recovered genomes. For each of these four steps, we discuss the most commonly used tools and the currently available pipelines to guide the scientific community in the recovery and subsequent analyses of genomes from any metagenome sample. Furthermore, we also discuss the tools required for validation of assembly quality as well as for improving quality of the recovered genomes. We also highlight the currently available pipelines that can be used to automate the whole analysis without having advanced bioinformatics knowledge. Finally, we will highlight the most widely adapted and actively maintained tools and pipelines that can be helpful to the scientific community in decision making before they commence the analysis.

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