KRSA: An R package and R Shiny web application for an end-to-end upstream kinase analysis of kinome array data.

Autor: DePasquale EAK; Division of Hematology, Brigham and Women's Hospital, Boston, Massachusetts, United States of America.; Harvard Medical School, Boston, Massachusetts, United States of America.; Broad Institute of MIT and Harvard, Boston, Massachusetts, United States of America., Alganem K; Department of Neurosciences, University of Toledo College of Medicine, Toledo, Ohio, United States of America., Bentea E; Neuro-Aging & Viro-Immunotherapy, Center for Neurosciences, Vrije Universiteit Brussel, Brussels, Belgium., Nawreen N; Department of Neuroscience, University of Cincinnati, Cincinnati, Ohio, United States of America., McGuire JL; Department of Neurosurgery, University of Cincinnati, Cincinnati, Ohio, United States of America., Tomar T; PamGene International B.V., s'-Hertogenbosch, The Netherlands., Naji F; Tercen Data Analytics Ltd, Co Waterford, Ireland., Hilhorst R; PamGene International B.V., s'-Hertogenbosch, The Netherlands., Meller J; Division of Biomedical Informatics, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio, United States of America.; Department of Cancer Biology, University of Cincinnati College of Medicine, Cincinnati, Ohio, United States of America.; Department of Environmental Health, University of Cincinnati College of Medicine, Cincinnati, Ohio, United States of America.; Department of Electrical Engineering and Computing Systems, University of Cincinnati College of Medicine, Cincinnati, Ohio, United States of America.; Department of Informatics, Nicolaus Copernicus University, Torun, Poland., McCullumsmith RE; Department of Neurosciences, University of Toledo College of Medicine, Toledo, Ohio, United States of America.; Neurosciences institute, ProMedica, Toledo, Ohio, United States of America.
Jazyk: angličtina
Zdroj: PloS one [PLoS One] 2021 Dec 17; Vol. 16 (12), pp. e0260440. Date of Electronic Publication: 2021 Dec 17 (Print Publication: 2021).
DOI: 10.1371/journal.pone.0260440
Abstrakt: Phosphorylation by serine-threonine and tyrosine kinases is critical for determining protein function. Array-based platforms for measuring reporter peptide signal levels allow for differential phosphorylation analysis between conditions for distinct active kinases. Peptide array technologies like the PamStation12 from PamGene allow for generating high-throughput, multi-dimensional, and complex functional proteomics data. As the adoption rate of such technologies increases, there is an imperative need for software tools that streamline the process of analyzing such data. We present Kinome Random Sampling Analyzer (KRSA), an R package and R Shiny web-application for analyzing kinome array data to help users better understand the patterns of functional proteomics in complex biological systems. KRSA is an All-In-One tool that reads, formats, fits models, analyzes, and visualizes PamStation12 kinome data. While the underlying algorithm has been experimentally validated in previous publications, we demonstrate KRSA workflow on dorsolateral prefrontal cortex (DLPFC) in male (n = 3) and female (n = 3) subjects to identify differential phosphorylation signatures and upstream kinase activity. Kinase activity differences between males and females were compared to a previously published kinome dataset (11 female and 7 male subjects) which showed similar global phosphorylation signals patterns.
Competing Interests: R.H. and T.T. are employed by PamGene International B.V., and F.N. was employed by PamGene International B.V. during the manuscript development. The remaining authors have declared that no conflicts of interests exist. We do not receive any direct funding from PamGene International - they are collaborators. This does not alter our adherence to PLOS ONE policies on sharing data and materials.
Databáze: MEDLINE
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