MloDisDB
MloDisDB curates associations between membrane-less organelles (MLOs), liquid–liquid phase separation (LLPS) components, and diseases to support research on MLO-related pathophysiology.
Key Features:
- Curated Entries: The database contains 771 curated entries derived from 607 publications, each detailing the specific MLO, the associated disease, and implicated functional factors.
- Evidence-Based Classification: Each entry is classified by level of evidence from the original research as Direct Experiment, Indirect Experiment, or Clinical Investigation.
- Functional Insights: Records include associations between MLOs and diseases, documented changes in MLOs, alterations in functional factors, and integrated predictions of components involved in LLPS.
- Comprehensive Coverage: The resource covers MLOs and LLPS-related disease associations with emphasis on MLOs such as nucleoli, nuclear speckles, and stress granules that form through LLPS by condensing proteins and RNAs.
Scientific Applications:
- Pathophysiology Research: Enables study of the roles of MLO dysfunction and LLPS in disease processes.
- Therapeutic Target Identification: Supports investigations into potential therapeutic targets and interventions linked to MLOs and LLPS components.
- Mechanistic Studies and Experimental Design: Provides detailed molecular associations to facilitate hypothesis generation and experimental planning for studies of disease mechanisms.
Methodology:
Entries were compiled through literature review and manual data curation from peer-reviewed publications.
Topics
Details
- Tool Type:
- web application
- Added:
- 1/18/2021
- Last Updated:
- 2/26/2021
Operations
Publications
Hou C, Xie H, Fu Y, Ma Y, Li T. MloDisDB: a manually curated database of the relations between membraneless organelles and diseases. Briefings in Bioinformatics. 2020;22(4). doi:10.1093/bib/bbaa271. PMID:33126250.
DOI: 10.1093/BIB/BBAA271
PMID: 33126250
Funding: - National Key Research and Development Program of China: 2018YFA0507504
- National Natural Science Foundation of China: 32070666, 61773025