MSGD: a manually curated database of genomic, transcriptomic, proteomic and drug information for multiple sclerosis

TL;DR

MSGD is a manually curated database that compiles genomic, transcriptomic, proteomic, and drug data for multiple sclerosis to help researchers better understand and treat the disease.

Contribution

The novel contribution is the creation of a centralized, manually curated database for MS-related omics and drug data.

Findings

MSGD includes 6255 entries with data on genes, variants, RNA, proteins, and drugs related to MS.

The database provides a user-friendly interface for searching, managing, and submitting MS-related gene data.

It is freely accessible and aims to reduce barriers to data interpretation for MS research.

Abstract

Multiple sclerosis (MS) is the most common inflammatory demyelinating disease of the central nervous system. ‘Omics’ technologies (genomics, transcriptomics, proteomics) and associated drug information have begun reshaping our understanding of multiple sclerosis. However, these data are scattered across numerous references, making them challenging to fully utilize. We manually mined and compiled these data within the Multiple Sclerosis Gene Database (MSGD) database, intending to continue updating it in the future. We screened 5485 publications and constructed the current version of MSGD. MSGD comprises 6255 entries, including 3274 variant entries, 1175 RNA entries, 418 protein entries, 313 knockout entries, 612 drug entries and 463 high-throughput entries. Each entry contains detailed information, such as species, disease type, detailed gene descriptions (such as official gene symbols), and original references. MSGD is freely accessible and provides a user-friendly web interface. Users can easily search for genes of interest, view their expression patterns and detailed information, manage gene sets and submit new MS-gene associations through the platform. The primary principle behind MSGD’s design is to provide an exploratory platform, aiming to minimize filtration and interpretation barriers while ensuring highly accessible presentation of data. This initiative is expected to significantly assist researchers in deciphering gene mechanisms and improving the prevention, diagnosis and treatment of MS.

Database URL: http://bio-bigdata.hrbmu.edu.cn/MSGD

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