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

Abstract

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.

Developmental pyrethroid exposure disrupts molecular pathways for MAP kinase and circadian rhythms in mouse brain

“…To maximize our confidence in the prediction of which kinases were upstream of differences in substrate phosphorylation, we used four bioinformatics pipelines to analyze the raw data, each of which relies on its own database and its own mechanism of prediction to identify candidate kinases. The Kinome Random Sampling Analyzer (KRSA) (40) was used as the primary analysis and basis for comparison. The PamGene Upstream Kinase Analysis (UKA), Post-Translational Modification Signature Enrichment Analysis (PTM-SEA)(41), and Kinase Enrichment Analysis Version 3 (KEA3)(38) were used to analyze the same processed data.…”

Alterations in protein kinase networks in astrocytes and neurons derived from patients with familial Alzheimer’s Disease

“…UKA reports the final score as a metric for ranking implicated kinases, which is calculated based on the specificity of the peptides mapped to the kinases and the significance of phosphorylation changes of the peptides between the compared groups. To analyze the kinome array profiles, we further deployed the Kinome Random Sampling Analyzer (KRSA) R package to pre-process, apply quality control checks, and select differentially phosphorylated peptides (33). KRSA was used to analyze the kinome profiles of our cell lines.…”

Molecular Neurobiology of Loss

“…Phosphorylation levels were obtained for the 144 peptides on the STK chip. Kinome Random Sampling Analyzer 20 (KRSA) was used to infer kinase activity from this data. This approach uses a permutation method to generate z-scores for each kinase.…”

“…The kinomics yielded a pattern similar to the gene expression data, with upregulation in EE followed by downregulation in ER, and implicated the immune system, cell cycle, regulation, and signaling (Extended Data 6B; Table S4). Upstream kinase analysis was also performed using KRSA, pointing to several kinases that may be implicated in the present ER changes (Extended Data 6C; Table S3) 20 .…”

“…Upstream kinase analysis was also performed using KRSA, pointing to several kinases that may be implicated in ER (Extended Data 6C; Table S3) 20 .…”