Bioinformatics for the Human Microbiome Project
Open accessAbstract
Microbes inhabit virtually all sites of the human body, yet we know very little about the role they play in our health. In recent years, there has been increasing interest in studying human-associated microbial communities, particularly since microbial dysbioses have now been implicated in a number of human diseases [1]–[3]. Dysbiosis, the disruption of the normal microbial community structure, however, is impossible to define without first establishing what “normal microbial community structure” means within the healthy human microbiome. Recent advances in sequencing technologies have made it feasible to perform large-scale studies of microbial communities, providing the tools necessary to begin to address this question [4], [5]. This led to the implementation of the Human Microbiome Project (HMP) in 2007, an initiative funded by the National Institutes of Health Roadmap for Biomedical Research and constructed as a large, genome-scale community research project [6]. Any such project must plan for data analysis, computational methods development, and the public availability of tools and data; here, we provide an overview of the corresponding bioinformatics organization, history, and results from the HMP (Figure 1).
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HMP1 yielded community resources including nucleotide sequences of microorganisms and communities, protocols for reproducible body-wide microbiome sampling and data generation, and computational methods for microbiome analysis and epidemiology.
“This first phase of the HMP (HMP1) thus yielded a wealth of community resources: nucleotide sequences of microorganisms and communities from a large number of isolates, individuals, and populations (http://hmpdacc.org)34–37; protocols to support reproducible body-wide microbiome sampling and data generation38–40; and computational methods for microbiome analysis and epidemiology41–47.”
From The Integrative Human Microbiome Project (2019) · cites this paper as [41]
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- Authors
- Gevers, D., Pop, M., Schloss, P. D., & Huttenhower, C.
- Affiliations
- Broad Institute (US)
- University of Maryland, College Park (US)
- University of Michigan (US)
- Corresponding author
- Gevers, Dirk
- Pop, Mihai
- Schloss, Patrick D.
- Huttenhower, Curtis
- Venue
- PLoS Computational Biology
- Publisher
- Public Library of Science (PLoS)
- Published
- 2012-11-29
- Volume
- 8
- Issue
- 11
- Pages
- e1002779
- ISSN
- 1553-734X (ISSN-L)
- 1553-7358
- PMC
- PMC3510052
- OpenAlex
- W2103725779
- Cited by
- 91
- References
- 87
- Citations by year
- 2026: 1 · 2025: 3 · 2024: 9 · 2023: 4 · 2022: 5 · 2021: 6 · 2020: 7 · 2019: 8 · 2018: 7 · 2017: 9 · 2016: 10 · 2015: 8 · 2014: 10 · 2013: 4
- FWCI
- 2.83
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- 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)
- DSpace@MIT (Massachusetts Institute of Technology) (submittedVersion, cc-by)
- PLoS eBooks
- Funders
- National Institutes of Health
- National Human Genome Research Institute (R01HG004885, R01HG005975, U54HG004969, R01HG005969)
- National Institute of Diabetes and Digestive and Kidney Diseases (P30DK043351)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gut microbiota and health
- Physical Sciences › Environmental Science › Ecology › Microbial Community Ecology and Physiology
- Keywords
- Human Microbiome Project, human microbiome, microbial communities, dysbiosis, bioinformatics infrastructure, microbial community structure, sequencing data analysis, public data availability, microbial genomics
- MeSH terms
- Database Management Systems, Humans, Databases, Factual, Computational Biology, Metagenome, Metagenomics
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