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SurfaceGenie: a web-based application for prioritizing cell-type-specific marker candidates. Bioinformatics 2020 Jun 01;36(11):3447-3456

Date

02/14/2020

Pubmed ID

32053146

Pubmed Central ID

PMC7267825

DOI

10.1093/bioinformatics/btaa092

Scopus ID

2-s2.0-85085770971 (requires institutional sign-in at Scopus site)   40 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.

AVAILABILITY AND IMPLEMENTATION: Calculation of GenieScores and lookup of SPC scores is made freely accessible via the SurfaceGenie web application: www.cellsurfer.net/surfacegenie.

CONTACT: Rebekah.gundry@unmc.edu.

SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Author List

Waas M, Snarrenberg ST, Littrell J, Jones Lipinski RA, Hansen PA, Corbett JA, Gundry RL

Authors

John A. Corbett PhD Chair, Professor in the Biochemistry department at Medical College of Wisconsin
Rachel Jones Lipinski Research Scientist I in the Biochemistry department at Medical College of Wisconsin




MESH terms used to index this publication - Major topics in bold

Humans
Internet
Proteomics
Software
Transcriptome