MDDOmics: multi-omics resource of major depressive disorder.
Yichao ZhaoJu XiangXingyuan ShiPengzhen JiaYan ZhangMin LiPublished in: Database : the journal of biological databases and curation (2024)
Major depressive disorder (MDD) is a pressing global health issue. Its pathogenesis remains elusive, but numerous studies have revealed its intricate associations with various biological factors. Consequently, there is an urgent need for a comprehensive multi-omics resource to help researchers in conducting multi-omics data analysis for MDD. To address this issue, we constructed the MDDOmics database (Major Depressive Disorder Omics, (https://www.csuligroup.com/MDDOmics/), which integrates an extensive collection of published multi-omics data related to MDD. The database contains 41 222 entries of MDD research results and several original datasets, including Single Nucleotide Polymorphisms, genes, non-coding RNAs, DNA methylations, metabolites and proteins, and offers various interfaces for searching and visualization. We also provide extensive downstream analyses of the collected MDD data, including differential analysis, enrichment analysis and disease-gene prediction. Moreover, the database also incorporates multi-omics data for bipolar disorder, schizophrenia and anxiety disorder, due to the challenge in differentiating MDD from similar psychiatric disorders. In conclusion, by leveraging the rich content and online interfaces from MDDOmics, researchers can conduct more comprehensive analyses of MDD and its similar disorders from various perspectives, thereby gaining a deeper understanding of potential MDD biomarkers and intricate disease pathogenesis. Database URL: https://www.csuligroup.com/MDDOmics/.
Keyphrases
- major depressive disorder
- bipolar disorder
- single cell
- data analysis
- electronic health record
- global health
- rna seq
- adverse drug
- genome wide
- gene expression
- emergency department
- public health
- randomized controlled trial
- healthcare
- social media
- magnetic resonance
- cell free
- machine learning
- genome wide identification
- single molecule
- human health
- nucleic acid