RABC: Rheumatoid Arthritis Bioinformatics Center

Author:

Chen Haiyan12,Xu Jing12,Wei Siyu12,Jia Zhe12,Sun Chen12,Kang Jingxuan12,Guo Xuying1,Zhang Nan1,Tao Junxian12,Dong Yu1,Zhang Chen12,Ma Yingnan1,Lv Wenhua1,Tian Hongsheng1,Bi Shuo1,Lv Hongchao1,Huang Chen34,Kong Fanwu5,Tang Guoping6,Jiang Yongshuai12ORCID,Zhang Mingming12

Affiliation:

1. College of Bioinformatics Science and Technology, Harbin Medical University , Harbin , China

2. The ABC Project , Harbin , China

3. Dr. Neher's Biophysics Laboratory for Innovative Drug Discovery, Macau University of Science and Technology , Macau, SAR , China

4. Stat Key laboratory of Quality Research in Chinese Medicine, Macau Institute For Applied Research in Medicine and Health, Macau University of Science and Technology , Macau, SAR , China

5. Department of Nephrology, The Second Affiliated Hospital, Harbin Medical University , Harbin , China

6. The Fourth Affiliated Hospital, Zhejiang University School of Medicine , Yiwu , China

Abstract

Abstract 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.

Funder

National Natural Science Foundation of China

Mathematical Tianyuan Fund of the National Natural Science Foundation of China

Basic Public Welfare Research Project of Zhejiang Province

Innovative Scientific Research-Special Scientific Research Project for Basic Medical Youth

Science and Technology Development Fund

Macau University of Science and Technology

Publisher

Oxford University Press (OUP)

Subject

Genetics

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