CoChat
Sign Up
Cited by 496Gut microbiota and healthGenomics and Phylogenetic StudiesMicrobial Community Ecology and Physiology

The DOI matches its Crossref record; 179 of the 179 references it cites were found in a registry or an index.

Chapter 12: Human Microbiome Analysis

Open access
Morgan, Xochitl C., Huttenhower, Curtis·2012·PLoS Computational Biology
DOI 10.1371/journal.pcbi.1002808PMC PMC3531975
Open this paper in CoChat — free accountOpen PDF

Abstract

Humans are essentially sterile during gestation, but during and after birth, every body surface, including the skin, mouth, and gut, becomes host to an enormous variety of microbes, bacterial, archaeal, fungal, and viral. Under normal circumstances, these microbes help us to digest our food and to maintain our immune systems, but dysfunction of the human microbiota has been linked to conditions ranging from inflammatory bowel disease to antibiotic-resistant infections. Modern high-throughput sequencing and bioinformatic tools provide a powerful means of understanding the contribution of the human microbiome to health and its potential as a target for therapeutic interventions. This chapter will first discuss the historical origins of microbiome studies and methods for determining the ecological diversity of a microbial community. Next, it will introduce shotgun sequencing technologies such as metagenomics and metatranscriptomics, the computational challenges and methods associated with these data, and how they enable microbiome analysis. Finally, it will conclude with examples of the functional genomics of the human microbiome and its influences upon health and disease.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1371/journal.pcbi.1002808

Cites

179 of 179 references verified0 citation errors · 0 not checkable

Statistics

Figures

Figure 1 Bioinformatic methods for functional metagenomics. Studies that aim to define the composition and function of uncultured microbial communities are often referred to collectively as “metagenomic,” although this refers more specifically to particular sequencing-based assays. First, community DNA is extracted from a sample, typically uncultured, containing multiple microbial members. The bacterial taxa present in the community are most frequently defined by amplifying the 16S rRNA gene and sequencing it. Highly similar sequences are grouped into Operational Taxonomic Units (OTUs), which can be compared to 16S databases such as Silva [16], Green Genes [14], and RDP [15] to identify them as precisely as possible. The community can be described in terms of which OTUs are present, their relative abundance, and/or their phylogenetic relationships. An alternate method of identifying community taxa is to directly metagenomically sequence community DNA and compare it to reference genomes or gene catalogs. This is more expensive but provides improved taxonomic resolution and allows observation of single nucleotide polymorphisms (SNPs) and other variant sequences. The functional capabilities of the community can also be determined by comparing the sequences to functional databases (e.g. KEGG [170] or SEED [171]). This allows the community to be described as relative abundances of its genes and pathways. Figure adapted from [172].
Figure 1 Bioinformatic methods for functional metagenomics. Studies that aim to define the composition and function of uncultured microbial communities are often referred to collectively as “metagenomic,” although this refers more specifically to particular sequencing-based assays. First, community DNA is extracted from a sample, typically uncultured, containing multiple microbial members. The bacterial taxa present in the community are most frequently defined by amplifying the 16S rRNA gene and sequencing it. Highly similar sequences are grouped into Operational Taxonomic Units (OTUs), which can be compared to 16S databases such as Silva [16], Green Genes [14], and RDP [15] to identify them as precisely as possible. The community can be described in terms of which OTUs are present, their relative abundance, and/or their phylogenetic relationships. An alternate method of identifying community taxa is to directly metagenomically sequence community DNA and compare it to reference genomes or gene catalogs. This is more expensive but provides improved taxonomic resolution and allows observation of single nucleotide polymorphisms (SNPs) and other variant sequences. The functional capabilities of the community can also be determined by comparing the sequences to functional databases (e.g. KEGG [170] or SEED [171]). This allows the community to be described as relative abundances of its genes and pathways. Figure adapted from [172].CC BY · PubMed Central
Figure 2 Ecological representations of microbial communities: collector's curves, alpha, and beta diversity. These examples describe the A) sequence counts and B) relative abundances of six taxa (A, B, C, D, E, and F) detected in three samples. C) A collector's curve, typically generated using a richness estimator such as Chao1 [28] or ACE [29], approximates the relationship between the number of sequences drawn from each sample and the number of taxa expected to be present based on detected abundances. D) Alpha diversity captures both the organismal richness of a sample and the evenness of the organisms' abundance distribution. Here, alpha diversity is defined by the Shannon index [32], , where pi is the relative abundance of taxon i, although many other alpha diversity indices may be employed. E) Beta diversity represents the similarity (or difference) in organismal composition between samples. In this example, it can be simplistically defined by the equation , where n1 and n2 are the number of taxa in samples 1 and 2, respectively, and c is the number of shared taxa, but again many metrics such as Bray-Curtis [34] or UniFrac [24] are commonly employed.
Figure 2 Ecological representations of microbial communities: collector's curves, alpha, and beta diversity. These examples describe the A) sequence counts and B) relative abundances of six taxa (A, B, C, D, E, and F) detected in three samples. C) A collector's curve, typically generated using a richness estimator such as Chao1 [28] or ACE [29], approximates the relationship between the number of sequences drawn from each sample and the number of taxa expected to be present based on detected abundances. D) Alpha diversity captures both the organismal richness of a sample and the evenness of the organisms' abundance distribution. Here, alpha diversity is defined by the Shannon index [32], , where pi is the relative abundance of taxon i, although many other alpha diversity indices may be employed. E) Beta diversity represents the similarity (or difference) in organismal composition between samples. In this example, it can be simplistically defined by the equation , where n1 and n2 are the number of taxa in samples 1 and 2, respectively, and c is the number of shared taxa, but again many metrics such as Bray-Curtis [34] or UniFrac [24] are commonly employed.CC BY · PubMed Central

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Show all 179 references

Research works printed but not linked: 1

  1. HC Gram, 1884, Über die isolierte Färbung der Schizomyceten in Schnitt- und Trockenpräparaten, Fortschritte der Medizin, 2, 185

Cited by

Sorted by citations

Items 101–200 of 491

Related

See all

Metadata

Authors
Morgan, X. C., & Huttenhower, C.
Affiliations
  • Harvard University (US)
  • Broad Institute (US)
Corresponding author
Huttenhower, Curtis
ORCID
Venue
PLoS Computational Biology
Publisher
Public Library of Science (PLoS)
Published
2012-12-27
Volume
8
Issue
12
Pages
e1002808
ISSN
  • 1553-734X (ISSN-L)
  • 1553-7358
DOI
10.1371/journal.pcbi.1002808
PMC
PMC3531975
OpenAlex
W2102813808
Cited by
496
References
179
Citations by year
2026: 9 · 2025: 25 · 2024: 25 · 2023: 41 · 2022: 38 · 2021: 59 · 2020: 64 · 2019: 48 · 2018: 50 · 2017: 48 · 2016: 34 · 2015: 29 · 2014: 18 · 2013: 8
FWCI
7.06
Type
journal article
Language
en
Open access
Licence
Locations
  • PLoS Computational Biology (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • Digital Access to Scholarship at Harvard (DASH) (Harvard University) (submittedVersion, cc-by)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
  • National Institutes of Health
  • National Human Genome Research Institute (R01HG005969)
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gut microbiota and health
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
  • Physical Sciences › Environmental Science › Ecology › Microbial Community Ecology and Physiology
Keywords
human microbiome, microbiota, metagenomics, metatranscriptomics, shotgun sequencing, microbial community diversity, functional genomics, inflammatory bowel disease, antibiotic resistance, microbial ecology
MeSH terms
Bacteria, Genes, Bacterial, Humans, RNA, Ribosomal, 16S, Host-Pathogen Interactions, Metagenome, Metagenomics

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review