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

SurfaceGenie: a web-based application for prioritizing cell-type specific marker candidates 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 Calculation of GenieScores and lookup of SPC scores is made freely accessible via the SurfaceGenie web-application: www.cellsurfer.net/surfacegenie. Supplementary information Supplementary data are available at Bioinformatics online. This content is only available as a PDF. Author notes Current Address: CardiOmics Program, Center for Heart and Vascular Research; Division of Cardiovascular Medicine; and Department of Cellular and Integrative Physiology, University of Nebraska Medical Center, Omaha, NE, 68198, USA. © The Author(s) (2020). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com This article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model) http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Bioinformatics Oxford University Press

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Publisher
Oxford University Press
Copyright
© The Author(s) (2020). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com
ISSN
1367-4803
eISSN
1460-2059
DOI
10.1093/bioinformatics/btaa092
Publisher site
See Article on Publisher Site

Abstract

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 Calculation of GenieScores and lookup of SPC scores is made freely accessible via the SurfaceGenie web-application: www.cellsurfer.net/surfacegenie. Supplementary information Supplementary data are available at Bioinformatics online. This content is only available as a PDF. Author notes Current Address: CardiOmics Program, Center for Heart and Vascular Research; Division of Cardiovascular Medicine; and Department of Cellular and Integrative Physiology, University of Nebraska Medical Center, Omaha, NE, 68198, USA. © The Author(s) (2020). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com This article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model)

Journal

BioinformaticsOxford University Press

Published: Aug 13, 18

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