Abstract

Rare skin diseases include more than 800 diseases affecting more than 6.8 million patients worldwide. However, only 100 drugs have been developed for treating rare skin diseases in the past 38 years. To investigate potential treatments through drug repurposing for rare skin diseases, it is necessary to have a well-organized database to link all known disease causes, mechanisms, and related information to accelerate the process. Drug repurposing provides less expensive and faster potential options to develop treatments for known diseases. In this work, we designed and constructed a rare skin disease database (RSDB) as a disease-centered information depository to facilitate repurposing drug candidates for rare skin diseases. We collected and integrated associated genes, chemicals, and phenotypes into a network connected by pairwise relationships between different components for rare skin diseases. The RSDB covers 891 rare skin diseases defined by the Orphanet and GARD databases. The organized network for each rare skin disease comprises associated genes, phenotypes, and chemicals with the corresponding connections. The RSDB is available at https://rsdb.cmdm.tw.

Measurement(s)

Relationships between chemicals and genes • Relationships between diseases and genes • Relationships between diseases and phenotypes • Relationships between genes and phenotypes

Technology Type(s)

The Comparative Toxicogenomics Database (CTD) and DrugBank • DisGeNET, UniProt, The Comparative Toxicogenomics Database (CTD), Orphanet, ClinGen, Genomics England, NCBI ClinVar, The Human Phenotype Ontology (HPO), the GWAS Catalog, GWASdb28, the LHGDN and BeFree system • The Human Phenotype Ontology (HPO) and Genetic and Rare Diseases Information Center (GARD)

Sample Characteristic - Organism

Homo sapiens

Details

Title
RSDB: A rare skin disease database to link drugs with potential drug targets for rare skin diseases
Author
Kuo, Tien-Chueh 1   VIAFID ORCID Logo  ; Wang, Pei-Hua 2 ; Wang, Yu-Ke 2 ; Chang, Chia-I. 2 ; Chang, Ching-Yao 2 ; Tseng, Yufeng Jane 3 

 National Taiwan University, The Metabolomics Core Laboratory, Center of Genomic Medicine, Taipei, Taiwan (GRID:grid.19188.39) (ISNI:0000 0004 0546 0241); National Taiwan University, Graduate Institute of Biomedical Electronics and Bioinformatics, College of Electrical Engineering and Computer Science, Taipei, Taiwan (GRID:grid.19188.39) (ISNI:0000 0004 0546 0241) 
 National Taiwan University, Graduate Institute of Biomedical Electronics and Bioinformatics, College of Electrical Engineering and Computer Science, Taipei, Taiwan (GRID:grid.19188.39) (ISNI:0000 0004 0546 0241) 
 National Taiwan University, Graduate Institute of Biomedical Electronics and Bioinformatics, College of Electrical Engineering and Computer Science, Taipei, Taiwan (GRID:grid.19188.39) (ISNI:0000 0004 0546 0241); National Taiwan University, Department of Computer Science and Information Engineering, College of Electrical Engineering and Computer Science, Taipei, Taiwan (GRID:grid.19188.39) (ISNI:0000 0004 0546 0241) 
Publication year
2022
Publication date
2022
Publisher
Nature Publishing Group
e-ISSN
20524463
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
2707112324
Copyright
© The Author(s) 2022. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.