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ZENODO
Dataset . 2024
License: CC BY
Data sources: ZENODO
image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
ZENODO
Dataset . 2024
License: CC BY
Data sources: ZENODO
image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
ZENODO
Dataset . 2024
License: CC BY
Data sources: ZENODO
ZENODO
Dataset . 2024
License: CC BY
Data sources: Datacite
ZENODO
Dataset . 2024
License: CC BY
Data sources: Datacite
ZENODO
Dataset . 2024
License: CC BY
Data sources: Datacite
ZENODO
Dataset . 2024
License: CC BY
Data sources: Datacite
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Processed scRNA and scATAC data in DRCTdb

Authors: Yunhui, kong;

Processed scRNA and scATAC data in DRCTdb

Abstract

Understanding the molecular mechanisms underlying genetic diseases is challenging due to the involvement of both environmental and genetic factors. Genome-wide association studies (GWAS) have identified numerous genetic loci, but their functional implications remain largely unknown. Single-cell multiomics sequencing has emerged as a powerful tool to study disease-specific cell types and their relationship with genetic variants. However, there is a lack of comprehensive databases for exploring genetic disease-related cell types and their mechanisms across different human tissues. In this study, we present the disease-related cell type database (DRCTdb), a database that integrates GWAS data and single-cell multiomics data to identify disease-related cell types and elucidate their regulatory mechanisms. DRCTdb contains well-processed single-cell multiomics data in 16 studies, encompassing transcriptome and epigenetic information overall 4 million cells within 28 tissues. Through DRCTdb, user can easily browse relationships and regulatory mechanisms between SNPs of 42 genetic disease and cell type in different human tissue based on GWAS and single cell multiomics data. Moreover, DRCTdb also provides data download, which allowing users to download well-processed single-cell multiomics data and analysis result from DRCTdb

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average