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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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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
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author 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
author_facet 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
author_sort Chen, Haiyan
collection PubMed
description 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.
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spelling pubmed-98255512023-01-10 RABC: Rheumatoid Arthritis Bioinformatics Center 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 Nucleic Acids Res Database Issue 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. Oxford University Press 2022-10-16 /pmc/articles/PMC9825551/ /pubmed/36243962 http://dx.doi.org/10.1093/nar/gkac850 Text en © The Author(s) 2022. Published by Oxford University Press on behalf of Nucleic Acids Research. https://creativecommons.org/licenses/by-nc/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial License (https://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com
spellingShingle Database Issue
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
RABC: Rheumatoid Arthritis Bioinformatics Center
title RABC: Rheumatoid Arthritis Bioinformatics Center
title_full RABC: Rheumatoid Arthritis Bioinformatics Center
title_fullStr RABC: Rheumatoid Arthritis Bioinformatics Center
title_full_unstemmed RABC: Rheumatoid Arthritis Bioinformatics Center
title_short RABC: Rheumatoid Arthritis Bioinformatics Center
title_sort rabc: rheumatoid arthritis bioinformatics center
topic Database Issue
url 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
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