Publication, funding, and experimental data in support of Human Reference Atlas construction and usage.
Journal
Scientific data
ISSN: 2052-4463
Titre abrégé: Sci Data
Pays: England
ID NLM: 101640192
Informations de publication
Date de publication:
04 Jun 2024
04 Jun 2024
Historique:
received:
31
01
2024
accepted:
24
05
2024
medline:
5
6
2024
pubmed:
5
6
2024
entrez:
4
6
2024
Statut:
epublish
Résumé
Experts from 18 consortia are collaborating on the Human Reference Atlas (HRA) which aims to map the 37 trillion cells in the healthy human body. Information relevant for HRA construction and usage is held by experts, published in scholarly papers, and captured in experimental data. However, these data sources use different metadata schemas and cannot be cross-searched efficiently. This paper documents the compilation of a dataset, named HRAlit, that links the 136 HRA v1.4 digital objects (31 organs with 4,279 anatomical structures, 1,210 cell types, 2,089 biomarkers) to 583,117 experts; 7,103,180 publications; 896,680 funded projects, and 1,816 experimental datasets. The resulting HRAlit has 22 tables with 20,939,937 records including 6 junction tables with 13,170,651 relationships. The HRAlit can be mined to identify leading experts, major papers, funding trends, or alignment with existing ontologies in support of systematic HRA construction and usage.
Identifiants
pubmed: 38834597
doi: 10.1038/s41597-024-03416-8
pii: 10.1038/s41597-024-03416-8
doi:
Types de publication
Dataset
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
574Informations de copyright
© 2024. The Author(s).
Références
Börner, K. et al. Anatomical structures, cell types and biomarkers of the Human Reference Atlas. Nat. Cell Biol. 23, 1117–1128 (2021).
doi: 10.1038/s41556-021-00788-6
pubmed: 34750582
pmcid: 10079270
Release v1.4 DOI Landing Pages. https://hubmapconsortium.github.io/ccf-releases/v1.4/docs/index.html (2023).
Snyder, M. P. et al. The human body at cellular resolution: the NIH Human Biomolecular Atlas Program. Nature 574, 187–192 (2019).
doi: 10.1038/s41586-019-1629-x
Lee, P. J. et al. NIH SenNet Consortium to map senescent cells throughout the human lifespan to understand physiological health. Nat. Aging 2, 1090–1100 (2022).
doi: 10.1038/s43587-022-00326-5
Himmelstein, D. S. et al. Systematic integration of biomedical knowledge prioritizes drugs for repurposing. eLife 6, e26726 (2017).
doi: 10.7554/eLife.26726
pubmed: 28936969
pmcid: 5640425
El-Achkar, T. M. et al. A multimodal and integrated approach to interrogate human kidney biopsies with rigor and reproducibility: guidelines from the Kidney Precision Medicine Project. Physiol. Genomics 53, 1–11 (2021).
doi: 10.1152/physiolgenomics.00104.2020
pubmed: 33197228
McMahon, A. P. et al. GUDMAP: The Genitourinary Developmental Molecular Anatomy Project. J. Am. Soc. Nephrol. 19, 667 (2008).
doi: 10.1681/ASN.2007101078
pubmed: 18287559
Lonsdale, J. et al. The Genotype-Tissue Expression (GTEx) project. Nat. Genet. 45, 580–585 (2013).
doi: 10.1038/ng.2653
Chan Zuckerberg Initiative. Chan Zuckerberg CELLxGENE Discover. Cellxgene Data Portal https://cellxgene.cziscience.com/ (2022).
Herr, B. W. et al. Specimen, biological structure, and spatial ontologies in support of a Human Reference Atlas. Sci. Data 10, 171 (2023).
doi: 10.1038/s41597-023-01993-8
pubmed: 36973309
pmcid: 10043028
Tan, S. Z. K. et al. Brain Data Standards - A method for building data-driven cell-type ontologies. Sci. Data 10, 50 (2023).
doi: 10.1038/s41597-022-01886-2
pubmed: 36693887
pmcid: 9873614
Ono, H., Ogasawara, O., Okubo, K. & Bono, H. RefEx, a reference gene expression dataset as a web tool for the functional analysis of genes. Sci. Data 4, 170105 (2017).
doi: 10.1038/sdata.2017.105
pubmed: 28850115
pmcid: 5574374
Bezdvornykh, I., Cherkasov, N., Kanapin, A. & Samsonova, A. A collection of read depth profiles at structural variant breakpoints. Sci. Data 10, 186 (2023).
doi: 10.1038/s41597-023-02076-4
pubmed: 37024526
pmcid: 10079824
Chervova, O. et al. The Personal Genome Project-UK, an open access resource of human multi-omics data. Sci. Data 6, 257 (2019).
doi: 10.1038/s41597-019-0205-4
pubmed: 31672996
pmcid: 6823446
Zook, J. M. et al. Extensive sequencing of seven human genomes to characterize benchmark reference materials. Sci. Data 3, 160025 (2016).
doi: 10.1038/sdata.2016.25
pubmed: 27271295
pmcid: 4896128
Tedersoo, L. et al. Data sharing practices and data availability upon request differ across scientific disciplines. Sci. Data 8, 192 (2021).
doi: 10.1038/s41597-021-00981-0
pubmed: 34315906
pmcid: 8381906
Färber, M., Lamprecht, D., Krause, J., Aung, L. & Haase, P. SemOpenAlex: The Scientific Landscape in 26 Billion RDF Triples. in The Semantic Web – ISWC 2023 (eds. Payne, T. R. et al.) vol. 14266 94–112 (Springer Nature Switzerland, Cham, 2023).
Baranzini, S. E. et al. A biomedical open knowledge network harnesses the power of AI to understand deep human biology. AI Mag. 43, 46–58 (2022).
pubmed: 36093122
pmcid: 9456356
Zhang, X. et al. CellMarker: a manually curated resource of cell markers in human and mouse. Nucleic Acids Res. 47, D721–D728 (2019).
doi: 10.1093/nar/gky900
pubmed: 30289549
Eraslan, G. et al. Single-nucleus cross-tissue molecular reference maps toward understanding disease gene function. Science 376, eabl4290 (2022).
doi: 10.1126/science.abl4290
pubmed: 35549429
pmcid: 9383269
Kong, Y. X. & Börner, K. Human Reference Atlas Literature (HRAlit) Database. Figshare https://doi.org/10.6084/m9.figshare.24580669.v2 (2023).
Baran, J., Gerner, M., Haeussler, M., Nenadic, G. & Bergman, C. M. pubmed2ensembl: A Resource for Mining the Biological Literature on Genes. PLoS ONE 6, e24716 (2011).
doi: 10.1371/journal.pone.0024716
pubmed: 21980353
pmcid: 3183000
Sequeira, E., McEntyre, J. & Lipman, D. PubMed Central decentralized. Nature 410, 740–740 (2001).
doi: 10.1038/35071270
pubmed: 11298409
Kersey, P. & Apweiler, R. Linking publication, gene and protein data. Nat. Cell Biol. 8, 1183–1189 (2006).
doi: 10.1038/ncb1495
pubmed: 17060904
Lake, B. B. et al. An atlas of healthy and injured cell states and niches in the human kidney. Nature 619, 585–594 (2023).
doi: 10.1038/s41586-023-05769-3
pubmed: 37468583
pmcid: 10356613
Sikkema, L. et al. An integrated cell atlas of the lung in health and disease. Nat. Med. 29, 1563–1577 (2023).
doi: 10.1038/s41591-023-02327-2
pubmed: 37291214
pmcid: 10287567
Guo, M. et al. Guided construction of single cell reference for human and mouse lung. Nat. Commun. 14, 4566 (2023).
doi: 10.1038/s41467-023-40173-5
pubmed: 37516747
pmcid: 10387117
Jain, S. et al. Advances and prospects for the Human BioMolecular Atlas Program (HuBMAP). Nat. Cell Biol. 25, 1089–1100 (2023).
doi: 10.1038/s41556-023-01194-w
pubmed: 37468756
pmcid: 10681365
Osumi-Sutherland, D. et al. Cell type ontologies of the Human Cell Atlas. Nat. Cell Biol. 23, 1129–1135 (2021).
doi: 10.1038/s41556-021-00787-7
pubmed: 34750578
Börner, K. et al. Tissue registration and exploration user interfaces in support of a human reference atlas. Commun. Biol. 5, 1–9 (2022).
doi: 10.1038/s42003-022-03644-x
Jiao, C., Li, K. & Fang, Z. How are exclusively data journals indexed in major scholarly databases? An examination of four databases. Sci. Data 10, 737 (2023).
doi: 10.1038/s41597-023-02625-x
pubmed: 37880300
pmcid: 10600123
Lin, Z., Yin, Y., Liu, L. & Wang, D. SciSciNet: A large-scale open data lake for the science of science research. Sci. Data 10, 315 (2023).
doi: 10.1038/s41597-023-02198-9
pubmed: 37264014
pmcid: 10235093
Wang, K. et al. A Review of Microsoft Academic Services for Science of Science Studies. Frontiers in Big Data 2, 45 (2019).
doi: 10.3389/fdata.2019.00045
pubmed: 33693368
pmcid: 7931949
Hendricks, G., Tkaczyk, D., Lin, J. & Feeney, P. Crossref: The sustainable source of community-owned scholarly metadata. Quantitative Science Studies 1, 414–427 (2020).
doi: 10.1162/qss_a_00022
Liu, L., Jones, B. F., Uzzi, B. & Wang, D. Data, measurement and empirical methods in the science of science. Nat. Hum. Behav. 7, 1046–1058 (2023).
doi: 10.1038/s41562-023-01562-4
pubmed: 37264084
Wittenberg, J. Cadre. https://doi.org/10.26313/RDY8-4W58 .
Fortunato, S. et al. Science of science. Science 359, eaao0185 (2018).
doi: 10.1126/science.aao0185
pubmed: 29496846
pmcid: 5949209
Bornmann, L., Haunschild, R. & Mutz, R. Growth rates of modern science: A latent piecewise growth curve approach to model publication numbers from established and new literature databases. Humanit. Soc. Sci. Commun. 8, 1–15 (2021).
doi: 10.1057/s41599-021-00903-w
Jorgenson, L. A. et al. The BRAIN Initiative: developing technology to catalyse neuroscience discovery. Philos. Trans. R. Soc. B Biol. Sci. 370, 20140164 (2015).
doi: 10.1098/rstb.2014.0164
Maroso, M. A quest into the human brain. Science 382, 166–167 (2023).
doi: 10.1126/science.adl0913
pubmed: 37824675
Miao, L. et al. The latent structure of global scientific development. Nat. Hum. Behav. 6, 1206–1217 (2022).
doi: 10.1038/s41562-022-01367-x
pubmed: 35654964