KEGGexpressionMapper allows for analysis of pathways over multiple conditions by integrating transcriptomics and proteomics measurements

TL;DR

KEGGexpressionMapper is an R tool that visualizes and analyzes pathway activity across multiple conditions using transcriptomics and proteomics data, supporting time series and enrichment analyses.

Contribution

It introduces a new R package that enables comprehensive pathway analysis over multiple conditions, including time series, which was limited in previous tools.

Findings

Supports multi-condition pathway visualization

Enables gene enrichment and clustering analyses

Handles time series data from multiple individuals

Abstract

Motivation: In transcriptomic and proteomics-based studies, the abundance of genes is often compared to functional pathways such as the Kyoto Encyclopaedia at Genes and Genomes (KEGG) to identify active metabolic processes. Even though a plethora of tools allow to analyze and to compare omics data in respect to KEGG pathways, the analysis of multiple conditions is often limited to only a defined set of conditions. Furthermore, for transcriptomic datasets, it is crucial to compare the entire set of pathways in order to obtain a global overview of the species' metabolic functions. Results: Here, we present the tool KEGGexpressionMapper, a module, that is implemented in the programming language R. The module allows to highlight the expression of transcriptomic or proteomic measurements in various conditions on pathways and incorporates methods to analyze gene enrichment analyses and expression clustering in time series data. KEGGexpressionMapper supports time series data from transcriptomic or proteomics measurements from different individuals. As the tool is implemented in the scripting language R, it can be integrated into existing analysis pipelines to obtain a global overview of the dataset. The R package can be downloaded from https://github.com/nthomasCUBE/KEGGexpressionmapper.