| URL: | http://www.onethird-lab.com/RABC |
| Full name: | Rheumatoid Arthritis Bioinformatics Center |
| Description: | Rheumatoid Arthritis Bioinformatics/Big data Center (RABC) is the first big data resource platform that provides data storage, processing, and analysis for RA research. It not only solves the current problems in the use of RA data, but also brings more well-categorized and uniformly processed data, and multiple data analysis results. The practical and user-friendly platform provides researchers to explore biomarkers relevant to the pathogenesis, diagnosis, and treatment of RA. |
| Year founded: | 2023 |
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| Accessibility: |
Accessible
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| Country/Region: | China |
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| University/Institution: | Harbin Medical University |
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| Country/Region: | China |
| Contact name (PI/Team): | Mingming Zhang |
| Contact email (PI/Helpdesk): | zhangmingming.@hrbmu.edu.cn |
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RABC: Rheumatoid Arthritis Bioinformatics Center. [PMID: 36243962]
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. |