CoralP

CoralP visualizes and analyzes the human phosphatome to represent protein phosphatase attributes and support interpretation of quantitative and qualitative proteomic, genomic, epidemiological, and high-throughput screening data.


Key Features:

  • Phosphatome visualization: Generates visual representations of the human phosphatome focused on protein phosphatases to display family-level relationships.
  • Visual encodings: Maps phosphatase attributes to edge color, node color, and node size for simultaneous quantitative and qualitative data representation.
  • Multiple layouts: Supports phosphatome tree, radial network, and force-directed network layouts for alternative views of phosphatase relationships.
  • Data integration: Accepts and encodes data from proteomic, genomic, epidemiological, and high-throughput screening experiments.
  • Publication-quality output: Produces customizable visual representations intended for inclusion in scientific publications.

Scientific Applications:

  • Phosphatase family contextualization: Situates individual protein phosphatases within the broader phosphatome to assess family relationships.
  • Specificity and activity analysis: Enables exploration of phosphatase specificity and activity patterns across datasets.
  • Signaling research: Facilitates analysis of phosphatase roles and interactions relevant to signaling pathways and disease mechanisms.
  • Integrative dataset interpretation: Supports interpretation and comparison of proteomic, genomic, epidemiological, and high-throughput screening data mapped onto the phosphatome.

Methodology:

Implements phosphatome tree, radial network, and force-directed network visualizations and encodes attributes via edge color, node color, and node size.

Topics

Details

License:
MIT
Tool Type:
web application
Programming Languages:
R
Added:
1/18/2021
Last Updated:
2/17/2021

Operations

Publications

Min A, Deoudes E, Bond M, Davis E, Phanstiel D. CoralP: Flexible visualization of the human phosphatome. Journal of Open Source Software. 2019;4(44):1837. doi:10.21105/joss.01837. PMID:31903446. PMCID:PMC6941781.

Links