GMrepo v2: a curated human gut microbiome database with special focus on disease markers and cross-dataset comparison
GMrepo v2: a curated human gut microbiome database with special focus on disease markers and cross-dataset comparison is a dataset published in Nucleic Acids Research (2021). On theSindex it has a DataRank of 4.7, placing it in the top 29% of the data-sharing corpus. It has been cited 181 times, with 174 citing works in its 1-hop citation network. Its calibrated FAIR score is 56/100.
Abstract
GMrepo (data repository for Gut Microbiota) is a database of curated and consistently annotated human gut metagenomes. Its main purposes are to increase the reusability and accessibility of human gut metagenomic data, and enable cross-project and phenotype comparisons. To achieve these goals, we performed manual curation on the meta-data and organized the datasets in a phenotype-centric manner. GMrepo v2 contains 353 projects and 71,642 runs/samples, which are significantly increased from the previous version. Among these runs/samples, 45,111 and 26,531 were obtained by 16S rRNA amplicon and whole-genome metagenomics sequencing, respectively. We also increased the number of phenotypes from 92 to 133. In addition, we introduced disease-marker identification and cross-project/phenotype comparison. We first identified disease markers between two phenotypes (e.g. health versus diseases) on a per-project basis for selected projects. We then compared the identified markers for each phenotype pair across datasets to facilitate the identification of consistent microbial markers across datasets. Finally, we provided a marker-centric view to allow users to check if a marker has different trends in different diseases. So far, GMrepo includes 592 marker taxa (350 species and 242 genera) for 47 phenotype pairs, identified from 83 selected projects. GMrepo v2 is freely available at: https://gmrepo.humangut.info.
›Data sources & pipeline
FAIR Checklist
Context only (not used in score)- Has DOI
- Open Access
- Dataset classification
FAIR checklist signals are shown for context only and do not affect DataRank scoring.
Calibrated FAIR score — a parallel quality metric, independent of the DataRank citation score. See the full evaluation →
DataRank Breakdown
Base Score Contribution
0.767
From this paper's citation signal
Citation Network Contribution
3.9
From 128 citing papers with measurable signal
Top 5 citers driving the network score
Ranked by citation count — the same ordering the engine uses when summing log1p(Cq) over citers.
- DADA2: High-resolution sample inference from Illumina amplicon dataNature Methods201635,281 citationsDataRank 1.6
- Metagenomic biomarker discovery and explanationGenome Biology201116,383 citationsDataRank 1.5
- Enterotypes of the human gut microbiomeNature20117,570 citationsDataRank 1.3
- Linking Long-Term Dietary Patterns with Gut Microbial EnterotypesScience20116,514 citationsDataRank 1.3
- A new genomic blueprint of the human gut microbiotaNature20191,363 citationsDataRank 1.1
Why this DataRank?
DataRank blends this paper's own citation count with the influence of the papers that cite it. Here, roughly 16% comes from its base citations and 84% from the citation network (128 citing papers contributed measurable signal).
- Base score B(p)
- log1p(citation_count) — grows sub-linearly, so a paper with 1,000 citations is not 10× a paper with 100.
- Network N(p)
- Σ over citers of log1p(Cq) ÷ max(outdegreeq, 1). Being cited by a highly-cited paper with few references counts most.
- Damping factor d = 0.85
- DataRank = (1−d)·B(p) + d·N(p) — the two cards above are each already multiplied by their share.
- Self-citations excluded
- Citers sharing any OpenAlex author ID with this paper are filtered out before the network sum.
Citers are pulled from OpenAlex sorted by cited_by_count:descand capped per paper, so when the cap binds we keep the highest-signal references and the score is reproducible across reruns.
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