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Enriched atlas of lncRNA and protein-coding genes for the GRCg7b chicken assembly and its functional annotation across 47 tissues

Scientific Reports(2024)10.1038/s41598-024-56705-ySource: DataRank Database

Enriched atlas of lncRNA and protein-coding genes for the GRCg7b chicken assembly and its functional annotation across 47 tissues is a dataset published in Scientific Reports (2024). On theSindex it has a DataRank of 0.626, placing it in the top 47.3% of the data-sharing corpus. It has been cited 23 times, with 14 citing works in its 1-hop citation network. Its calibrated FAIR score is 56/100.

Top 47%percentile
0.626DataRank
0.626Top 47%
Dataset23 citations · base score 3.2
Cite:
datarank_citation_only_1hop_v6· scope data_onlyMethodology

Abstract

Abstract Gene atlases for livestock are steadily improving thanks to new genome assemblies and new expression data improving the gene annotation. However, gene content varies across databases due to differences in RNA sequencing data and bioinformatics pipelines, especially for long non-coding RNAs (lncRNAs) which have higher tissue and developmental specificity and are harder to consistently identify compared to protein coding genes (PCGs). As done previously in 2020 for chicken assemblies galgal5 and GRCg6a, we provide a new gene atlas, lncRNA-enriched, for the latest GRCg7b chicken assembly, integrating "NCBI RefSeq", "EMBL-EBI Ensembl/GENCODE" reference annotations and other resources such as FAANG and NONCODE. As a result, the number of PCGs increases from 18,022 (RefSeq) and 17,007 (Ensembl) to 24,102, and that of lncRNAs from 5789 (RefSeq) and 11,944 (Ensembl) to 44,428. Using 1400 public RNA-seq transcriptome representing 47 tissues, we provided expression evidence for 35,257 (79%) lncRNAs and 22,468 (93%) PCGs, supporting the relevance of this atlas. Further characterization including tissue-specificity, sex-differential expression and gene configurations are provided. We also identified conserved miRNA-hosting genes with human counterparts, suggesting common function. The annotated atlas is available at gega.sigenae.org

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

FAIR Checklist

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

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

      56FAIR score
      F Findable
      65
      A Accessible
      73
      I Interoperable
      38
      R Reusable
      50
      Top 8% by FAIRLLM-assessed✓ full text read

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

      DataRank Breakdown

      Base Score 76%Citation Network 24%

      Base Score Contribution

      0.477

      From this paper's citation signal

      Citation Network Contribution

      0.149

      From 8 citing papers with measurable signal

      Learn more about DataRank methodology →

      Top 5 citers driving the network score

      Ranked by citation count — the same ordering the engine uses when summing log1p(Cq) over citers.

      1. Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing
        Journal of the Royal Statistical Society Series B: Statistical Methodology1995106,426 citationsDataRank 1.7
      2. <tt>edgeR</tt> : a Bioconductor package for differential expression analysis of digital gene expression data
        Bioinformatics200944,025 citationsDataRank 1.6
      3. BEDTools: a flexible suite of utilities for comparing genomic features
        Bioinformatics201030,023 citationsDataRank 1.5
      4. <b>FactoMineR</b> : An <i>R</i> Package for Multivariate Analysis
        Journal of Statistical Software20089,693 citationsDataRank 1.4
      5. The GENCODE v7 catalog of human long noncoding RNAs: Analysis of their gene structure, evolution, and expression
        Genome Research20125,208 citationsDataRank 14.8Top 13%
      Why this DataRank?

      DataRank blends this paper's own citation count with the influence of the papers that cite it. Here, roughly 76% comes from its base citations and 24% from the citation network (8 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.

      Read the full methodology →

      Click a node to highlight its connections. Use scroll to zoom. Drag to pan.

      Node colors:CenterData PaperData + Open AccessNon-dataSelected & links| Node size = percentile rank

      Authors (14)

      Mathieu CharlesORCID,Sylvain FoissacORCID,Haijuan Zhou,Dailu GuanORCID,Lingzhao FangORCID