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Chromatin Potential Identified by Shared Single-Cell Profiling of RNA and Chromatin

Cell(2020)10.1016/j.cell.2020.09.056Source: DataRank Database

Chromatin Potential Identified by Shared Single-Cell Profiling of RNA and Chromatin is a research paper published in Cell (2020). On theSindex it has a DataRank of 1.1. It has been cited 1,182 times. Its calibrated FAIR score is 74/100.

N/A
1.1DataRank · unranked
1.1
Open Access1182 citations · base score 7.1
Cite:
datarank_citation_only_1hop_v6· scope data_onlyMethodology

Abstract

Cell differentiation and function are regulated across multiple layers of gene regulation, including modulation of gene expression by changes in chromatin accessibility. However, differentiation is an asynchronous process precluding a temporal understanding of regulatory events leading to cell fate commitment. Here we developed simultaneous high-throughput ATAC and RNA expression with sequencing (SHARE-seq), a highly scalable approach for measurement of chromatin accessibility and gene expression in the same single cell, applicable to different tissues. Using 34,774 joint profiles from mouse skin, we develop a computational strategy to identify cis-regulatory interactions and define domains of regulatory chromatin (DORCs) that significantly overlap with super-enhancers. During lineage commitment, chromatin accessibility at DORCs precedes gene expression, suggesting that changes in chromatin accessibility may prime cells for lineage commitment. We computationally infer chromatin potential as a quantitative measure of chromatin lineage-priming and use it to predict cell fate outcomes. SHARE-seq is an extensible platform to study regulatory circuitry across diverse cells in tissues.

Data sources & pipeline
Pipeline:MetadataData-paper checkEnrichmentCitation networkScoring
Enrichment:Pending

FAIR Checklist

Context only (not used in score)
Findable (1/2)
  • Has DOI
Accessible (1/2)
  • Open Access
Interoperable (0/2)
    Reusable (0/3)

      FAIR checklist signals are shown for context only and do not affect DataRank scoring.

      74FAIR score
      F Findable
      100
      A Accessible
      70
      I Interoperable
      100
      R Reusable
      25
      Top 1% by FAIRdeterministic⚠ abstract only
      Estimated from the abstract only. The agent couldn't read this paper's full text, so body-dependent criteria (data-availability statement, formats, license) are inferred. For a confident score, upload the PDF or supply full text →

      Calibrated FAIR score — a parallel quality metric, independent of the DataRank citation score. See the full evaluation →

      DataRank Breakdown

      Base Score 100%Citation Network 0%

      Base Score Contribution

      1.1

      From this paper's citation signal

      Citation Network Contribution

      0

      Citation network not refreshed for this result

      This paper's DataRank is currently driven only by its base citation score. Citation network data was not refreshed for this result.

      Learn more about DataRank methodology →
      Why this DataRank?

      DataRank blends this paper's own citation count with the influence of the papers that cite it. Here, roughly 100% comes from its base citations and 0% from the citation network.

      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.

      Read the full methodology →

      Authors (16)

      Bing ZhangORCID,Lindsay M. LaFaveORCID,Andrew S. Earl,Zachary ChiangORCID,Yan HuORCID