An integrated metagenomics pipeline for strain profiling reveals novel patterns of bacterial transmission and biogeography
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Abstract
We present the M etagenomic I ntra-species D iversity A nalysis S ystem (MIDAS), which is an integrated computational pipeline for quantifying bacterial species abundance and strain-level genomic variation, including gene content and single-nucleotide polymorphisms (SNPs), from shotgun metagenomes. Our method leverages a database of more than 30,000 bacterial reference genomes that we clustered into species groups. These cover the majority of abundant species in the human microbiome but only a small proportion of microbes in other environments, including soil and seawater. We applied MIDAS to stool metagenomes from 98 Swedish mothers and their infants over one year and used rare SNPs to track strains between hosts. Using this approach, we found that although species compositions of mothers and infants converged over time, strain-level similarity diverged. Specifically, early colonizing bacteria were often transmitted from an infant’s mother, while late colonizing bacteria were often transmitted from other sources in the environment and were enriched for spore-formation genes. We also applied MIDAS to 198 globally distributed marine metagenomes and used gene content to show that many prevalent bacterial species have population structure that correlates with geographic location. Strain-level genetic variants present in metagenomes clearly reveal extensive structure and dynamics that are obscured when data are analyzed at a coarser taxonomic resolution.
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Whole genomes of organisms, in addition to strain tracking, can be extracted from shotgun metagenomic data, allowing for evolutionary analysis of specific organisms associated with a particular disease or environment.
“Whole genomes of organisms, in addition to strain tracking, can be extracted from the data, allowing for evolutionary analysis of specific organisms associated with a particular disease or environment.79, 80, 81”
From The oral microbiome: Role of key organisms and complex networks in oral health and disease (2021) · cites this paper as [81]
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- Authors
- Nayfach, S., Rodriguez-Mueller, B., Garud, N., & Pollard, K. S.
- Affiliations
- Gladstone Institutes (US)
- Venue
- Genome Research
- Publisher
- Cold Spring Harbor Laboratory
- Published
- 2016-10-18
- Volume
- 26
- Issue
- 11
- Pages
- 1612-1625
- ISSN
- 1088-9051 (ISSN-L)
- 1549-5469
- PMC
- PMC5088602
- OpenAlex
- W2533132894
- Cited by
- 470
- References
- 66
- Citations by year
- 2026: 19 · 2025: 42 · 2024: 33 · 2023: 36 · 2022: 74 · 2021: 74 · 2020: 72 · 2019: 43 · 2018: 40 · 2017: 36 · 2016: 1
- Type
- posted content
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Genome Research (publishedVersion)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- eScholarship (California Digital Library) (submittedVersion, cc-by)
- HighWire Press Open Archive (submittedVersion)
- Funders
- National Science Foundation (DMS-1069303)
- Gordon & Betty Moore Foundation (3300)
- Gladstone Institutes
- 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
- MIDAS, bacterial transmission, biogeography, single nucleotide polymorphisms, gene content, human microbiome, mother-to-child transmission, marine metagenomes, bacterial population structure, metagenomics pipeline
- MeSH terms
- Adult, Bacteria, Bacterial Infections, Feces, Female, Humans, Infant, Phylogeny, Reference Standards, Soil Microbiology, Water Microbiology, DNA Fingerprinting, Genome, Bacterial, Sequence Analysis, DNA, Infectious Disease Transmission, Vertical, Polymorphism, Single Nucleotide, Metagenome, Metagenomics, Phylogeography
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 ›
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