Current challenges and best-practice protocols for microbiome analysis

• Published in: Briefings in bioinformatics Vol. 22; no. 1; pp. 178 - 193
• Main Authors: Bharti, Richa, Grimm, Dominik G
• Format: Journal Article
• Language: English
• Published: England Oxford University Press 18.01.2021; Oxford Publishing Limited (England)
• Subjects: Animals; Annotations; Assembly; Computer applications; Design of experiments; DNA Barcoding, Taxonomic - methods; DNA Barcoding, Taxonomic - standards; Experimental design; Humans; Metagenomics; Metagenomics - methods; Metagenomics - standards; Microbiomes; Microbiota - genetics; Microorganisms; Next-generation sequencing; Practice Guidelines as Topic; Quality control; Review; RNA, Ribosomal, 16S - genetics; rRNA 16S; Sequence Analysis, DNA - methods; Sequence Analysis, DNA - standards; Species diversity; Statistical analysis; assembly; amplicon sequencing; microbiome; 16S rRNA sequencing; metagenomics; functional and taxonomic classification
• ISSN: 1477-4054; 1467-5463; 1477-4054
• DOI: 10.1093/bib/bbz155

Abstract Analyzing the microbiome of diverse species and environments using next-generation sequencing techniques has significantly enhanced our understanding on metabolic, physiological and ecological roles of environmental microorganisms. However, the analysis of the microbiome is affected by experimental conditions (e.g. sequencing errors and genomic repeats) and computationally intensive and cumbersome downstream analysis (e.g. quality control, assembly, binning and statistical analyses). Moreover, the introduction of new sequencing technologies and protocols led to a flood of new methodologies, which also have an immediate effect on the results of the analyses. The aim of this work is to review the most important workflows for 16S rRNA sequencing and shotgun and long-read metagenomics, as well as to provide best-practice protocols on experimental design, sample processing, sequencing, assembly, binning, annotation and visualization. To simplify and standardize the computational analysis, we provide a set of best-practice workflows for 16S rRNA and metagenomic sequencing data (available at https://github.com/grimmlab/MicrobiomeBestPracticeReview).