RABC: Rheumatoid Arthritis Bioinformatics Center

作者：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^*

来源：Nucleic Acids Research, 2023, 51(D1): D1381-D1387.

DOI：10.1093/nar/gkac850

• AUTOIMMUNE-DISEASES
• PTPN22
• METAANALYSIS
• MECHANISMS
• DATABASE
• ARCHIVE
• PACKAGE

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.

• 单位
  浙江大学; 哈尔滨医科大学