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.