Transcriptomic signatures across human tissues identify functional rare genetic variation
Transcriptomic signatures across human tissues identify functional rare genetic variation is a research paper published in Science (2020). On theSindex it has a DataRank of 5.0. It has been cited 166 times, with 125 citing works in its 1-hop citation network. Its calibrated FAIR score is 74/100.
Abstract
Rare genetic variants are abundant across the human genome, and identifying their function and phenotypic impact is a major challenge. Measuring aberrant gene expression has aided in identifying functional, large-effect rare variants (RVs). Here, we expanded detection of genetically driven transcriptome abnormalities by analyzing gene expression, allele-specific expression, and alternative splicing from multitissue RNA-sequencing data, and demonstrate that each signal informs unique classes of RVs. We developed Watershed, a probabilistic model that integrates multiple genomic and transcriptomic signals to predict variant function, validated these predictions in additional cohorts and through experimental assays, and used them to assess RVs in the UK Biobank, the Million Veterans Program, and the Jackson Heart Study. Our results link thousands of RVs to diverse molecular effects and provide evidence to associate RVs affecting the transcriptome with human traits.
›Data sources & pipeline
FAIR Checklist
Context only (not used in score)- Has DOI
- Open Access
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
0.768
From this paper's citation signal
Citation Network Contribution
4.3
From 103 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.
- Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2Genome Biology201497,097 citationsDataRank 1.7
- The Sequence Alignment/Map format and SAMtoolsBioinformatics200966,179 citationsDataRank 1.7
- STAR: ultrafast universal RNA-seq alignerBioinformatics201355,202 citationsDataRank 1.6
- FLASH: fast length adjustment of short reads to improve genome assembliesBioinformatics201115,526 citationsDataRank 1.4
- Enzymatic assembly of DNA molecules up to several hundred kilobasesNature Methods200910,766 citationsDataRank 1.4
Why this DataRank?
DataRank blends this paper's own citation count with the influence of the papers that cite it. Here, roughly 15% comes from its base citations and 85% from the citation network (103 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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