Abstract
Motivation: Comparative transcriptomics is a common approach in functional gene discovery efforts. It allows for finding conserved co-expression patterns between orthologous genes in closely related plant species, suggesting that these genes potentially share similar function and regulation. Several efficient co-expression-based tools have been commonly used in plant research but most of these pipelines are limited to data from model systems, which greatly limit their utility. Moreover, in addition, none of the existing pipelines allow plant researchers to make use of their own unpublished gene expression data for performing a comparative co-expression analysis and generate multi-species co-expression networks.Results: We introduce CoExpNetViz, a computational tool that uses a set of query or “bait” genes as an input (chosen by the user) and a minimum of one pre-processed gene expression dataset. The CoExpNetViz algorithm proceeds in three main steps; (i) for every bait gene submitted, co-expression values are calculated using mutual information and Pearson correlation coefficients, (ii) non-bait (or target) genes are grouped based on cross-species orthology, and (iii) output files are generated and results can be visualized as network graphs in Cytoscape.Availability: The CoExpNetViz tool is freely available both as a PHP web server (link: http://bioinformatics.psb.ugent.be/webtools/coexpr/) (implemented in C++) and as a Cytoscape plugin (implemented in Java). Both versions of the CoExpNetViz tool support LINUX and Windows platforms.
Highlights
A biological pathway is represented by a set of molecular entities that are involved in a given biological process and often interact with each other
Co-expression analysis allows the transfer of information from model (e.g., Arabidopsis, tomato, rice, maize, etc.) to non-model plant species (Stuart et al, 2003; Usadel et al, 2009; Heyndrickx and Vandepoele, 2012; Tzfadia et al, 2012; Movahedi et al, 2012; Itkin et al, 2013; Amar et al, 2014; Rhee and Mutwil, 2014)
A recent publication by Itkin et al (2013), presented comparative co-expression analysis to discover new genes that participate in the steroidal glycoalkaloids (SGAs) biosynthesis pathway in species of the Solanaceae family
Summary
A biological pathway is represented by a set of molecular entities (e.g., genes) that are involved in a given biological process and often interact with each other. Current insight into some biological pathways is substantial and useful for systems-level analyses, not all genes that participate in these pathways and affect their function are known and even in extensively studied model plants such as Arabidopsis and rice, many genes are still lacking experimental. Considering the premise that genes participating in the same biological process might posses a more similar expression pattern than expected by chance, co-expression is one of the most widely used functional gene discovery methods to fill gaps in metabolic pathways. Co-expression analysis allows the transfer of information from model (e.g., Arabidopsis, tomato, rice, maize, etc.) to non-model plant species (Stuart et al, 2003; Usadel et al, 2009; Heyndrickx and Vandepoele, 2012; Tzfadia et al, 2012; Movahedi et al, 2012; Itkin et al, 2013; Amar et al, 2014; Rhee and Mutwil, 2014)
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