The genus

Species in this genus

SpeciesHealthy guts ↓ 0–100%Products
Merdimonas faecis0
Across the genus 0

* Counted in the same healthy people as the species (whole-genome samples). All bars use the same 0–100% scale. A product with two Merdimonas species counts once in the genus total.

What they are

Its species' DNA

A C G T
Genome sizes · 1 Merdimonas species
Shortest we have Mycoplasmoides genitalium 0.6M
⋮ 1,280 shorter than any Merdimonas
⋮ 1,071 longer than any Merdimonas
Longest we have Bradyrhizobium altum 9.8M

Merdimonas’s one genome we have is 3.3M: mid-sized for gut microbes.

One reference genome per species; strains vary.

Their ID genes, lined up: shared stretches form solid stripes

Fewer than two of its species have a reference ID-gene sequence in NCBI yet, so there's nothing to line up Data sources

From its species' genomes and lab tests

What its species can do

A species counts when most of its strains can. Not measured in anyone's gut.

  • Can ferment fiber 0 of 1 species None of its species
  • Can use lactose 0 of 1 species None of its species
What’s typical · a description, not a comparison

In healthy guts

Everyone · 12,213 people · 129 datasets
How common

Found in 9.2% of healthy people.

How big a slice of the gut
  1. largest Bacteroides 9.8%
  2. 2nd largest Phocaeicola 9.1%
  3. 3rd largest Segatella 6.4%
  4. 4th largest Bifidobacterium 6.2%
  5. 5th largest Faecalibacterium 5.4%
  6. 6th largest Alistipes 5.2%
  7. 7th largest Blautia 3.6%
  8. 8th largest Ruminococcus 3.0%
  9. top 20% Merdimonas 0.01%
  10. 649 other microbes, each a sliver 32.3%
  11. 18.9%

On average, 0.01% of the microbes in a healthy gut: larger than 80% of genera’s slices.

By age and sex

Every group, drawn the same way. Only some samples record age or sex, so each group says how many people it comes from. Tap one to see it above.

By age
367 people · under 1% carry it under 0.01% of the microbes
198 people · 5 datasets 6.6% carry it under 0.01% of the microbes
57 people · 1.8% carry it under 0.01% of the microbes
1,283 people · 27 datasets 4.4% carry it under 0.01% of the microbes
142 people · 15 datasets 2.8% carry it 0.01% of the microbes top 45%
By sex
2,063 people · 6.1% carry it under 0.01% of the microbes
1,530 people · 29 datasets 4.6% carry it under 0.01% of the microbes

Where these numbers come from

Who
12,213 healthy people from 129 datasets in GMrepo, by its own “Health” label. Whole-genome sequencing only; one sample is one person.
Dataset
A dataset is the samples one study published. We analyse them ourselves, so a count of datasets isn’t a count of what those studies concluded.
How common
The share of them whose sample has any of it.
Slice
Its average share of the microbes in a healthy gut, across everyone, carriers or not. The slices add up to 81.1%: the rest is reads GMrepo couldn’t place at this rank, shown as “Not identified to a genus”.
Age and sex
From the samples that record them (about 17% record an age and 29% a sex). A group with fewer than 50 people isn’t shown.
Ranks
A slice’s rank is a place among the 658 genera we have numbers for, within the group shown: exact in the top 10, then rounded bands.
Analysis
Ours, of GMrepo’s per-sample data. GMrepo on Data sources
The 129 datasets behind these numbers

Largest first by people. Each is a GMrepo project.

DatasetPeopleWith ageWith sex
A dataset in the United States PRJNA695570 ↗ 1,767 0 1,759
A dataset in Japan PRJNA1077400 ↗ 1,268 0 0
A dataset in Spain PRJEB61680 ↗ 839 0 0
Zhong et al. 2019 · the Netherlands PRJEB26795 ↗ 572 0 0
A dataset in the Netherlands PRJNA942468 ↗ 517 0 0
Shang et al. 2021 · Australia PRJNA492716 ↗ 370 0 84
A dataset in the United States PRJNA398089 ↗ 363 237 363
A dataset in the United States PRJNA795985 ↗ 290 0 0
Zhang et al. 2022 · China PRJNA801051 ↗ 239 0 0
A dataset in Ireland PRJEB46398 ↗ 237 0 0
Valles-Colomer et al. 2023 · Italy PRJEB63351 ↗ 225 0 0
A dataset in the United States PRJNA633576 ↗ 225 223 0
Lee et al. 2024 · the United Kingdom PRJEB52380 ↗ 177 0 0
A dataset in the United States PRJEB37924 ↗ 164 0 0
A dataset in China PRJNA542578 ↗ 151 0 0
A dataset in the United States PRJNA993675 ↗ 142 142 142
Lee et al. 2022 · South Korea PRJEB45443 ↗ 112 0 0
A dataset in China PRJEB65297 ↗ 111 0 0
A dataset in India PRJNA397112 ↗ 110 110 0
A dataset in Japan PRJDB13214 ↗ 105 0 0
A dataset in China PRJNA686835 ↗ 104 0 0
A dataset in China PRJNA763023 ↗ 100 0 0
A dataset in China PRJNA994318 ↗ 100 0 0
A dataset in the United States PRJEB28543 ↗ 96 96 96
A dataset in Italy PRJNA716780 ↗ 94 94 94
A dataset in China PRJNA509999 ↗ 93 0 0
A dataset in the United States PRJNA878603 ↗ 92 92 92
Guo et al. 2023 · the United States PRJNA751448 ↗ 91 91 91
A dataset in China PRJNA793776 ↗ 89 89 89
A dataset in China PRJNA888860 ↗ 88 0 0
A dataset in Ireland PRJEB15388 ↗ 85 0 0
A dataset in China PRJNA731589 ↗ 85 85 0
A dataset in Israel PRJNA994433 ↗ 80 80 0
A dataset in Canada PRJNA521587 ↗ 79 78 79
La Reau et al. 2023 · the United States PRJNA917645 ↗ 76 0 0
Pomyen et al. 2023 · Thailand PRJNA932948 ↗ 76 0 76
A dataset in the United States PRJEB53403 ↗ 75 45 45
Eriksen et al. 2023 · Sweden PRJNA633381 ↗ 74 0 0
A dataset in China PRJNA356102 ↗ 72 0 0
A dataset in China PRJNA689961 ↗ 70 0 0
A dataset in China PRJNA449784 ↗ 69 0 0
Duru et al. 2024 · Finland PRJEB59350 ↗ 66 0 0
Roth-Schulze et al. 2021 · Australia PRJNA604850 ↗ 66 66 48
Wan et al. 2024 · China PRJNA1037036 ↗ 64 64 64
A dataset in China PRJNA686821 ↗ 64 0 0
Fulcher et al. 2022 · the United States PRJNA836336 ↗ 64 0 0
Hu et al. 2023 · China PRJNA893406 ↗ 64 0 0
A dataset in Italy PRJNA553191 ↗ 62 0 0
Wilson et al. 2024 · the United States PRJEB59709 ↗ 59 0 0
A dataset in the United States PRJNA822660 ↗ 58 58 58
A dataset in the United States PRJNA314903 ↗ 50 0 0
A dataset in the United States PRJNA379741 ↗ 50 0 50
A dataset in China PRJNA674716 ↗ 50 0 0
A dataset in China PRJNA762484 ↗ 49 20 21
Yan et al. 2023 · China PRJNA808806 ↗ 48 0 0
Stockdale et al. 2022 · Ireland PRJNA813736 ↗ 48 40 41
Tao et al. 2025 · China PRJNA1135717 ↗ 47 0 0
A dataset in the United States PRJNA660443 ↗ 44 0 0
A dataset in China PRJNA946183 ↗ 44 0 0
A dataset in China PRJNA530971 ↗ 43 0 0
Ye et al. 2023 · China PRJNA853814 ↗ 42 42 0
A dataset in China PRJNA943232 ↗ 42 42 42
A dataset in the United Kingdom PRJEB17978 ↗ 41 0 0
A dataset in China PRJNA588035 ↗ 39 0 0
A dataset in China PRJNA349463 ↗ 33 0 0
A dataset in India PRJNA531203 ↗ 31 0 0
A dataset in Switzerland PRJEB24557 ↗ 30 0 0
A dataset in India PRJNA792685 ↗ 30 0 0
A dataset in China PRJNA808649 ↗ 30 30 30
Pötgens et al. 2024 · Belgium PRJNA813705 ↗ 30 0 0
Grion et al. 2024 · Spain PRJNA961076 ↗ 30 0 0
A dataset in Canada PRJEB21933 ↗ 29 0 0
A dataset PRJEB32767 ↗ 29 0 0
A dataset in the United States PRJEB53401 ↗ 29 29 29
A dataset in Poland PRJNA1125836 ↗ 28 28 28
Chen et al. 2023 · China PRJEB55711 ↗ 27 0 0
A dataset in the United States PRJNA629755 ↗ 27 0 0
A dataset in China PRJNA818062 ↗ 27 0 0
Wang et al. 2023 · China PRJNA905444 ↗ 27 0 0
A dataset in Spain PRJEB62571 ↗ 26 0 0
A dataset in China PRJNA769284 ↗ 26 0 0
Lv et al. 2024 · China PRJEB52189 ↗ 25 0 0
Xiao et al. 2024 · China PRJNA938297 ↗ 25 25 25
A dataset in Germany PRJEB76677 ↗ 24 0 0
Elie et al. 2023 · France PRJNA648321 ↗ 24 0 0
A dataset in Mali PRJNA936589 ↗ 24 0 0
A dataset in the United States PRJNA1044118 ↗ 23 0 0
A dataset in Turkey PRJNA1167935 ↗ 23 0 0
A dataset in Russia PRJNA762199 ↗ 23 23 23
Ievina et al. 2024 · Latvia PRJEB80379 ↗ 22 0 0
A dataset in China PRJNA896336 ↗ 22 0 0
A dataset in China PRJNA688881 ↗ 21 0 0
Edwinson et al. 2022 · the United States PRJNA705217 ↗ 21 21 21
A dataset in France PRJEB55534 ↗ 20 0 0
A dataset in Canada PRJNA1022308 ↗ 20 0 0
A dataset in China PRJNA1138893 ↗ 20 0 0
Schaefer et al. 2022 · the United States PRJNA800662 ↗ 20 0 0
Ding et al. 2023 · China PRJNA999028 ↗ 20 0 0
A dataset in the United States PRJEB42155 ↗ 18 0 0
A dataset in Japan PRJNA665854 ↗ 18 0 0
A dataset in Canada PRJNA742740 ↗ 17 0 0
Stockdale et al. 2022 · the United States PRJNA626004 ↗ 16 0 0
A dataset in China PRJNA858062 ↗ 16 16 16
A dataset in China PRJNA429990 ↗ 15 0 0
Wan et al. 2019 · China PRJNA532538 ↗ 15 0 0
A dataset in China PRJNA624223 ↗ 15 15 15
A dataset in the United States PRJNA634145 ↗ 15 15 15
A dataset in China PRJNA734379 ↗ 15 0 15
Sadeghi et al. 2024 · France PRJEB70916 ↗ 14 14 14
A dataset in Singapore PRJNA272371 ↗ 14 14 14
A dataset in India PRJNA983946 ↗ 14 14 14
A dataset in Sweden PRJEB38997 ↗ 13 0 0
Seishima et al. 2019 · Japan PRJNA511372 ↗ 13 0 0
A dataset in China PRJEB54728 ↗ 12 0 0
A dataset in Switzerland PRJEB13835 ↗ 11 0 0
A dataset in Japan PRJNA846495 ↗ 11 0 0
Joensen et al. 2017 · Denmark PRJEB14038 ↗ 10 0 0
A dataset in the United Kingdom PRJEB53891 ↗ 10 0 0
A dataset in Iran PRJNA1050885 ↗ 10 0 0
A dataset in China PRJNA730640 ↗ 10 0 0
A dataset in Israel PRJNA939026 ↗ 10 9 0
Ye et al. 2023 · China PRJNA993632 ↗ 10 0 0
A dataset in China PRJNA1024678 ↗ 9 0 0
Wang et al. 2024 · Switzerland PRJNA363003 ↗ 9 0 0
A dataset in the United States PRJNA613834 ↗ 9 0 0
A dataset in the United States PRJNA896226 ↗ 8 0 0
A dataset in Spain PRJNA1029046 ↗ 7 0 0
A dataset in China PRJNA492189 ↗ 5 0 0
A dataset in France PRJNA287878 ↗ 2 0 0
Compared within datasets that had both

In conditions

Compared with healthy people in the same datasets, it’s less common in none and more common in 1 condition. Only datasets that had both are counted.

A difference is an association: it doesn’t mean this microbe causes, prevents or treats any of these.

Strongest first, then the biggest gap · Each figure is 10 people
  1. Chronic kidney failure 487 people · 3 datasets More common in people with it Healthy 2.0% With it 9.0%

Where these numbers come from

Datasets
A dataset is the samples one study published. We analyse them ourselves, so a count of datasets isn’t a count of what those studies concluded. Only datasets that had people with the condition and healthy people, 10 or more of each, are used. Numbers come from those datasets, never from pooling.
How common
The share of people whose sample has any of it, in the people with the condition and in the healthy people of those same datasets. Tested within datasets (Cochran–Mantel–Haenszel, corrected for testing many microbes); a difference counts when it is clear and meaningful (Cohen’s h of 0.2 or more).
Strength word
Counted by code from how many datasets point the same way: Seen again and again (3 or more, none clearly the other way), Seen more than once (2, the majority), Seen once (1, listed last). Where datasets disagree, a row isn’t listed.
Each dataset
Tap a row for every dataset’s own numbers, and for how much carriers have.
Analysis
Our analysis of GMrepo data. GMrepo on Data sources · Read the rules