<i>microbioTA</i>: an atlas of the microbiome in multiple disease tissues of <i>Homo sapiens</i> and <i>Mus musculus</i>
<i>microbioTA</i>: an atlas of the microbiome in multiple disease tissues of <i>Homo sapiens</i> and <i>Mus musculus</i> is a dataset published in Nucleic Acids Research (2022). On theSindex it has a DataRank of 0.980, placing it in the top 41.9% of the data-sharing corpus. It has been cited 22 times, with 19 citing works in its 1-hop citation network. Its calibrated FAIR score is 50/100.
Abstract
microbioTA (http://bio-annotation.cn/microbiota) was constructed to provide a comprehensive, user-friendly resource for the application of microbiome data from diseased tissues, helping users improve their general knowledge and deep understanding of tissue-derived microbes. Various microbes have been found to colonize cancer tissues and play important roles in cancer diagnoses and outcomes, with many studies focusing on developing better cancer-related microbiome data. However, there are currently no independent, comprehensive open resources cataloguing cancer-related microbiome data, which limits the exploration of the relationship between these microbes and cancer progression. Given this, we propose a new strategy to re-align the existing next-generation sequencing data to facilitate the mining of hidden sequence data describing the microbiome to maximize available resources. To this end, we collected 417 publicly available datasets from 25 human and 14 mouse tissues from the Gene Expression Omnibus database and use these to develop a novel pipeline to re-align microbiome sequences facilitating in-depth analyses designed to reveal the microbial profile of various cancer tissues and their healthy controls. microbioTA is a user-friendly online platform which allows users to browse, search, visualize, and download microbial abundance data from various tissues along with corresponding analysis results, aimimg at providing a reference for cancer-related microbiome research.
›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
0.457
From this paper's citation signal
Citation Network Contribution
0.524
From 17 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.
- fastp: an ultra-fast all-in-one FASTQ preprocessorBioinformatics201828,544 citationsDataRank 1.5
- Metagenomic biomarker discovery and explanationGenome Biology201116,383 citationsDataRank 1.5
- Measurement of DiversityNature194913,836 citationsDataRank 1.4
- Improved metagenomic analysis with Kraken 2Genome Biology20196,841 citationsDataRank 1.3
- Bracken: estimating species abundance in metagenomics dataPeerJ Computer Science20172,213 citationsDataRank 1.2
Why this DataRank?
DataRank blends this paper's own citation count with the influence of the papers that cite it. Here, roughly 47% comes from its base citations and 53% from the citation network (17 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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