A guide to using MapMan to visualize and compare Omics data in plants: a case study in the crop species, Maize

TLDR

MapMan is a versatile software platform that visualizes omics data using hierarchical gene ontologies and schematic maps, originally developed for Arabidopsis but now extended to multiple species and available as downloadable or web-based tools. The study presents a maize case study applying MapMan to analyze transcriptional responses to diurnal cycles and night extension. The authors describe customizing MapMan to enable visual and systematic comparison of transcriptional responses between maize and Arabidopsis. The analyses demonstrate that MapMan effectively compares global transcriptional responses across distant species, revealing that functional category–level comparisons are particularly informative.

Abstract

MapMan is a software tool that supports the visualization of profiling data sets in the context of existing knowledge. Scavenger modules generate hierarchical and essentially non-redundant gene ontologies ('mapping files'). An ImageAnnotator module visualizes the data on a gene-by-gene basis on schematic diagrams ('maps') of biological processes. The PageMan module uses the same ontologies to statistically evaluate responses at the pathway or processes level. The generic structure of MapMan also allows it to be used for transcripts, proteins, enzymes and metabolites. MapMan was developed for use with Arabidopsis, but has already been extended for use with several other species. These tools are available as downloadable and web-based versions. After providing an introduction to the scope and use of MapMan, we present a case study where MapMan is used to analyse the transcriptional response of the crop plant maize to diurnal changes and an extension of the night. We then explain how MapMan can be customized to visually and systematically compare responses in maize and Arabidopsis. These analyses illustrate how MapMan can be used to analyse and compare global transcriptional responses between phylogenetically distant species, and show that analyses at the level of functional categories are especially useful in cross-species comparisons.