DisPhaseDB

DisPhaseDB provides an integrated database of disease-related variations in proteins involved in liquid-liquid phase separation (LLPS) and membraneless organelles (MLOs) to support analysis of LLPS-associated pathologies.


Key Features:

  • Data integration: Consolidates information from ten distinct databases into a unified resource.
  • Protein coverage: Contains 5,741 proteins involved in LLPS and MLOs.
  • Variant collection: Aggregates over 1.6 million variants associated with these proteins.
  • Disease annotations: Includes more than 4,000 disease terms linked to protein variants.
  • Protein metadata: Integrates protein location and biological roles within MLOs for each protein-disease entry.

Scientific Applications:

  • Variant mapping in LLPS proteins: Enables analysis of disease-associated sequence variations in LLPS and MLO proteins.
  • Genotype–phenotype linking: Supports linking variants to disease terms across integrated datasets.
  • Contextual functional analysis: Facilitates examination of protein location and biological roles within MLOs in relation to disease.
  • Resource for computational analyses and tool development: Provides a centralized dataset for further computational analyses and development of new tools in LLPS research.

Methodology:

Consolidation of records from ten distinct databases and compilation of entries including 5,741 proteins, over 1.6 million variants, and more than 4,000 disease terms, with integration of associated metadata such as protein location and biological roles within MLOs.

Topics

Details

License:
CC-BY-4.0
Cost:
Free of charge
Tool Type:
web application
Operating Systems:
Mac, Linux, Windows
Programming Languages:
Python
Added:
9/12/2022
Last Updated:
11/24/2024

Operations

Publications

Navarro AM, Orti F, Martínez-Pérez E, Alonso M, Simonetti FL, Iserte JA, Marino-Buslje C. DisPhaseDB: An integrative database of diseases related variations in liquid–liquid phase separation proteins. Computational and Structural Biotechnology Journal. 2022;20:2551-2557. doi:10.1016/j.csbj.2022.05.004. PMID:35685370. PMCID:PMC9156858.