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

Erica A K DePasquale1,2,3, Khaled Alganem4, Eduard Bentea5

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A new R package, Kinome Random Sampling Analyzer (KRSA), simplifies the analysis of functional proteomics data from peptide arrays. KRSA helps researchers identify differential phosphorylation patterns and upstream kinase activity in complex biological systems.

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Area of Science:

  • Proteomics
  • Bioinformatics
  • Systems Biology

Background:

  • Protein phosphorylation by kinases regulates cellular functions.
  • High-throughput peptide array technologies generate complex functional proteomics data.
  • Analyzing this data requires efficient software tools.

Purpose of the Study:

  • Introduce Kinome Random Sampling Analyzer (KRSA), an R package and Shiny web application.
  • Provide an all-in-one solution for analyzing PamStation12 kinome array data.
  • Facilitate understanding of functional proteomics patterns and kinase activity.

Main Methods:

  • KRSA package and R Shiny web application development.
  • Application of KRSA to analyze dorsolateral prefrontal cortex (DLPFC) kinome data from male and female subjects.
  • Comparison of identified kinase activity differences with a previously published dataset.

Main Results:

  • KRSA streamlines reading, formatting, modeling, analysis, and visualization of kinome array data.
  • Differential phosphorylation signatures and upstream kinase activity were identified in DLPFC samples.
  • Global phosphorylation signal patterns showed similarities between male and female subjects across datasets.

Conclusions:

  • KRSA is a validated, comprehensive tool for analyzing kinome array data.
  • The software enables deeper insights into functional proteomics in complex biological systems.
  • The study demonstrates KRSA's utility in identifying sex-based differences in kinase activity.