TY - JOUR
T1 - SurfaceGenie
T2 - A web-based application for prioritizing cell-type-specific marker candidates
AU - Waas, Matthew
AU - Snarrenberg, Shana T.
AU - Littrell, Jack
AU - Jones Lipinski, Rachel A.
AU - Hansen, Polly A.
AU - Corbett, John A.
AU - Gundry, Rebekah L.
N1 - Funding Information:
This work was supported by the National Institutes of Health [R01-HL126785 and R01-HL134010 to R.L.G., F31-HL140914 to M.W., DK-052194 and AI-44458 to J.A.C.] and Juvenile Diabetes Research Foundation [2-SRA-2019-829-S-B to R.L.G. and J.A.C.]. S.S. is a member of the MCW-MSTP, which was partially supported by a T32 grant from National Institute of General Medical Sciences [GM080202].
Publisher Copyright:
© 2020 The Author(s) 2020. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: [email protected].
PY - 2020/6/1
Y1 - 2020/6/1
N2 - 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: [email protected]
AB - 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: [email protected]
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U2 - 10.1093/bioinformatics/btaa092
DO - 10.1093/bioinformatics/btaa092
M3 - Article
C2 - 32053146
AN - SCOPUS:85085770971
SN - 1367-4803
VL - 36
SP - 3447
EP - 3456
JO - Bioinformatics
JF - Bioinformatics
IS - 11
ER -