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RABC: Rheumatoid Arthritis Bioinformatics Center

Advances in sequencing technologies have led to the rapid growth of multi-omics data on rheumatoid arthritis (RA). However, a comprehensive database that systematically collects and classifies the scattered data is still lacking. Here, we developed the Rheumatoid Arthritis Bioinformatics Center (RAB...

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Detalles Bibliográficos
Autores principales: Chen, Haiyan, Xu, Jing, Wei, Siyu, Jia, Zhe, Sun, Chen, Kang, Jingxuan, Guo, Xuying, Zhang, Nan, Tao, Junxian, Dong, Yu, Zhang, Chen, Ma, Yingnan, Lv, Wenhua, Tian, Hongsheng, Bi, Shuo, Lv, Hongchao, Huang, Chen, Kong, Fanwu, Tang, Guoping, Jiang, Yongshuai, Zhang, Mingming
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9825551/
https://www.ncbi.nlm.nih.gov/pubmed/36243962
http://dx.doi.org/10.1093/nar/gkac850
Descripción
Sumario:Advances in sequencing technologies have led to the rapid growth of multi-omics data on rheumatoid arthritis (RA). However, a comprehensive database that systematically collects and classifies the scattered data is still lacking. Here, we developed the Rheumatoid Arthritis Bioinformatics Center (RABC, http://www.onethird-lab.com/RABC/), the first multi-omics data resource platform (data hub) for RA. There are four categories of data in RABC: (i) 175 multi-omics sample sets covering transcriptome, epigenome, genome, and proteome; (ii) 175 209 differentially expressed genes (DEGs), 105 differentially expressed microRNAs (DEMs), 18 464 differentially DNA methylated (DNAm) genes, 1 764 KEGG pathways, 30 488 GO terms, 74 334 SNPs, 242 779 eQTLs, 105 m6A-SNPs and 18 491 669 meta-mQTLs; (iii) prior knowledge on seven types of RA molecular markers from nine public and credible databases; (iv) 127 073 literature information from PubMed (from 1972 to March 2022). RABC provides a user-friendly interface for browsing, searching and downloading these data. In addition, a visualization module also supports users to generate graphs of analysis results by inputting personalized parameters. We believe that RABC will become a valuable resource and make a significant contribution to the study of RA.