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. 2022 Jul 5;50(W1):W551-W559.
doi: 10.1093/nar/gkac352.

PaintOmics 4: new tools for the integrative analysis of multi-omics datasets supported by multiple pathway databases

Tianyuan Liu  1 , Pedro Salguero  2 , Marko Petek  3 , Carlos Martinez-Mira  4 , Leandro Balzano-Nogueira  5 , Živa Ramšak  3 , Lauren McIntyre  6 , Kristina Gruden  3 , Sonia Tarazona  2 , Ana Conesa  7   8
Affiliations

PaintOmics 4: new tools for the integrative analysis of multi-omics datasets supported by multiple pathway databases

Tianyuan Liu et al. Nucleic Acids Res. .

Abstract

PaintOmics is a web server for the integrative analysis and visualisation of multi-omics datasets using biological pathway maps. PaintOmics 4 has several notable updates that improve and extend analyses. Three pathway databases are now supported: KEGG, Reactome and MapMan, providing more comprehensive pathway knowledge for animals and plants. New metabolite analysis methods fill gaps in traditional pathway-based enrichment methods. The metabolite hub analysis selects compounds with a high number of significant genes in their neighbouring network, suggesting regulation by gene expression changes. The metabolite class activity analysis tests the hypothesis that a metabolic class has a higher-than-expected proportion of significant elements, indicating that these compounds are regulated in the experiment. Finally, PaintOmics 4 includes a regulatory omics module to analyse the contribution of trans-regulatory layers (microRNA and transcription factors, RNA-binding proteins) to regulate pathways. We show the performance of PaintOmics 4 on both mouse and plant data to highlight how these new analysis features provide novel insights into regulatory biology. PaintOmics 4 is available at https://paintomics.org/.

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Figures

Graphical Abstract
Graphical Abstract
Overview of Paintomics 4 functionalities.
Figure 1.
Figure 1.
New metabolomics analysis in PaintOmics 4. (A) Neighbouring genes for each metabolite at 1 to 4 network steps are identified. (B) The percentile and binomial tests are used to identify metabolites with a high density of DEGs in their proximal network. (C) Metabolites and Genes identified in the analysis are shown as heatmaps with links to associated pathways. DEM: differentially expressed metabolite; DEG: differentially expressed gene.
Figure 2.
Figure 2.
PaintOmics multi-omics analysis of RET signalling pathway. (A) RET signalling pathway. (B) Multi-omics data for metagene 1 containing receptor proteins. (C) Multi-omics data for DOK gene family.
Figure 3.
Figure 3.
Metabolite class and hub analyses of STATegra data. (A) PaintOmics 4 panel with Metabolite Class Activity analysis results. (B) Heatmap of amino-acid values during B3 cell differentiation. (C) PaintOmics 4 panel with Metabolite Hub Analysis results. (D) Neighbouring network for spermine and spermidine in KEGG. (E) Expression values for spermine neighbouring differentially expressed genes.
Figure 4.
Figure 4.
PaintOmics 4 analysis of Arabidopsis BRL3ox data. (A) List of enriched pathways. (B) MapMan synthesis chorismate pathway. The pathway shows a downregulation trend at 5 days. (C) MapMan synthesis lignin pathway. The pathway contains a large number of BINs with many associated genes. The figure shows both metagene representations and the heatmaps of the corresponding genes linked by green connectors.
See this image and copyright information in PMC

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