SurfaceGenie: A web-based application for prioritizing cell-type-specific marker candidates

Matthew Waas, Shana T. Snarrenberg, Jack Littrell, Rachel A. Jones Lipinski, Polly A. Hansen, John A. Corbett, Rebekah L. Gundry

Research output: Contribution to journalArticlepeer-review

4 Scopus citations

Abstract

Motivation: Cell-type-specific surface proteins can be exploited as valuable markers for a range of applications including immunophenotyping live cells, targeted drug delivery and in vivo imaging. Despite their utility and relevance, the unique combination of molecules present at the cell surface are not yet described for most cell types. A significant challenge in analyzing 'omic' discovery datasets is the selection of candidate markers that are most applicable for downstream applications. Results: Here, we developed GenieScore, a prioritization metric that integrates a consensus-based prediction of cell surface localization with user-input data to rank-order candidate cell-type-specific surface markers. In this report, we demonstrate the utility of GenieScore for analyzing human and rodent data from proteomic and transcriptomic experiments in the areas of cancer, stem cell and islet biology. We also demonstrate that permutations of GenieScore, termed IsoGenieScore and OmniGenieScore, can efficiently prioritize co-expressed and intracellular cell-type-specific markers, respectively. Contact: Rebekah.gundry@unmc.edu

Original languageEnglish (US)
Pages (from-to)3447-3456
Number of pages10
JournalBioinformatics
Volume36
Issue number11
DOIs
StatePublished - Jun 1 2020

ASJC Scopus subject areas

  • Statistics and Probability
  • Biochemistry
  • Molecular Biology
  • Computer Science Applications
  • Computational Theory and Mathematics
  • Computational Mathematics

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