Cited by 602Genomics and Phylogenetic StudiesMicrobial Community Ecology and PhysiologyGut microbiota and health

Current challenges and best-practice protocols for microbiome analysis

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Bharti, Richa, Grimm, Dominik G·2021·Briefings in Bioinformatics

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).

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  • Whole-genome shotgun metagenomic sequencing provides more comprehensive taxonomic and functional characterization than 16S rRNA sequencing.

    “In contrast to 16S rRNA sequencing, whole-genome shotgun metagenomic sequencing provides more comprehensive taxonomic and functional characterization, encompassing broader genomic content and enabling higher-resolution bacterial community profiling.”

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  1. [75]Bowtie LBA s s r w, Bioinformatics CP Chapter 11:Unit 2010; 11: 17.
  2. [81]Haider B, Ahn TH, Bushnell B, et al. Omega: an overlap-graph de novo assembler for metagenomics. Bioinformatics 2014; 30: 2717– 22. 24947750
  3. [175]al BF e Computational and statistical considerations in the analysis of metagenomic data In: Nagarajan M (ed). Metagenomics. Cambridge, MA: Academic Press, 2018.

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Authors
Bharti, R., & Grimm, D. G.
Affiliations
Weihenstephan-Triesdorf University of Applied Sciences (DE)
Corresponding author
Grimm, Dominik G
Venue
Briefings in Bioinformatics
Published
2019-12-18
Volume
22
Issue
1
Pages
178-193
ISSN
  • 1467-5463 (ISSN-L)
  • 1477-4054
OpenAlex
W2995649573
Cited by
592
References
184
Citations by year
2026: 91 · 2025: 129 · 2024: 101 · 2023: 107 · 2022: 80 · 2021: 74 · 2020: 10
FWCI
3.61
Type
journal article
Language
en
Open access
Locations
  • Briefings in Bioinformatics (publishedVersion, cc-by)
  • Europe PMC (PubMed Central) (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • mediaTUM – the media and publications repository of the Technical University Munich (Technical University Munich) (submittedVersion)
  • mediaTUM – the media and publications repository of the Technical University Munich (Technical University Munich) (submittedVersion, other-oa)
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
  • Physical Sciences › Environmental Science › Ecology › Microbial Community Ecology and Physiology
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gut microbiota and health
Keywords
microbiome analysis, 16S rRNA gene sequencing, shotgun metagenomics, long-read metagenomics, best practice protocol, sample processing, sequencing quality control, metagenome assembly, metagenome binning, functional annotation, data visualization, experimental design
MeSH terms
Animals, Humans, RNA, Ribosomal, 16S, Practice Guidelines as Topic, Sequence Analysis, DNA, Metagenomics, DNA Barcoding, Taxonomic, Microbiota

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