SurfaceGenie

SurfaceGenie prioritizes cell-type-specific marker candidates by computing the GenieScore that integrates consensus-based cell surface localization predictions with proteomic and transcriptomic data.


Key Features:

  • GenieScore metric: Integrates consensus-based predictions of cell surface localization with user-provided proteomic and transcriptomic data to rank-order marker candidates.
  • SPC scores: Uses consensus-based surface prediction (SPC) values as inputs or lookup values for surface localization evidence.
  • IsoGenieScore: A modified GenieScore variant tailored to prioritize co-expressed surface marker combinations.
  • OmniGenieScore: A modified GenieScore variant tailored to prioritize intracellular cell-type-specific markers.
  • Cross-species data support: Applicable to both human and rodent datasets derived from proteomic and transcriptomic experiments.

Scientific Applications:

  • Immunophenotyping: Prioritizes surface proteins suitable as markers for immunophenotyping live cells.
  • Targeted drug delivery: Identifies candidate surface proteins for targeted drug delivery strategies.
  • In vivo imaging: Ranks surface markers for potential use in in vivo imaging applications.
  • Cancer research: Supports selection of cell-type-specific surface markers relevant to cancer biology.
  • Stem cell research: Aids identification of markers for stem cell characterization and sorting.
  • Islet biology: Facilitates prioritization of surface markers pertinent to islet biology studies.

Methodology:

Combines consensus-based cell surface localization predictions (SPC) with user-provided proteomic and transcriptomic data to compute GenieScore and its variants (IsoGenieScore and OmniGenieScore) and provides SPC score lookup.

Topics

Details

Added:
1/18/2021
Last Updated:
2/24/2021

Operations

Publications

Waas M, Snarrenberg ST, Littrell J, Jones Lipinski RA, Hansen PA, Corbett JA, Gundry RL. SurfaceGenie: a web-based application for prioritizing cell-type-specific marker candidates. Bioinformatics. 2020;36(11):3447-3456. doi:10.1093/bioinformatics/btaa092. PMID:32053146. PMCID:PMC7267825.

PMID: 32053146
PMCID: PMC7267825
Funding: - National Institutes of Health: AI-44458, DK-052194, F31-HL140914, R01-HL126785, R01-HL134010 - Juvenile Diabetes Research Foundation: 2-SRA-2019-829-S-B - National Institute of General Medical Sciences: GM080202