New BAR tools for mining expression data and exploring Cis‐elements in Arabidopsis thaliana

Abstract

Identifying sets of genes that are specifically expressed in certain tissues or in response to an environmental stimulus is useful for designing reporter constructs, generating gene expression markers, or for understanding gene regulatory networks. We have developed an easy-to-use online tool for defining a desired expression profile (a modification of our Expression Angler program), which can then be used to identify genes exhibiting patterns of expression that match this profile as closely as possible. Further, we have developed another online tool, Cistome, for predicting or exploring cis-elements in the promoters of sets of co-expressed genes identified by such a method, or by other methods. We present two use cases for these tools, which are freely available on the Bio-Analytic Resource at http://BAR.utoronto.ca.

Predictive Models of Spatial Transcriptional Response to High Salinity

“…The greater importance of combinatorial rules aligns well with what is already known in mammals, where individual CREs are important for expression in multiple tissues but CRE combinations are more relevant in controlling tissue-specific expression (Priest et al, 2009;Austin et al, 2016). Both root rules and shoot rules incorporate pCREs from the full set of organ pCREs, but there is little overlap (0.4%-3%) in the two sets of rules.…”

“…Currently, TFs and their corresponding CREs regulating the stress response have received considerable attention (Seki et al, 2002;Haberer et al, 2011;Qin et al, 2011), but our knowledge of the spatial regulation of the stress response is limited. CREs can be identified based on coexpression (Beer and Tavazoie, 2004;Priest et al, 2009;Wang et al, 2009;Zou et al, 2011;Austin et al, 2016) and/or through in vitro and in vivo TF binding experiments (Harbison et al, 2004;FrancoZorrilla et al, 2014;Weirauch et al, 2014;O'Malley et al, 2016). The coexpression approach has been used successfully to identify putative cis-regulatory elements (pCREs) regulating stress-responsive gene expression in yeast (Saccharomyces cerevisiae; Beer and Tavazoie, 2004) and in Arabidopsis (Arabidopsis thaliana; Zou et al, 2011).…”

ePlant: Visualizing and Exploring Multiple Levels of Data for Hypothesis Generation in Plant Biology

“…For users who do not know which gene (or genes) they want to look at, the "Expression Angler" button opens a tool that helps identify genes based on a user-defined expression pattern (Austin et al, 2016), and the "Mutant Phenotype Selector" button opens a tool that helps identify genes based on Lloyd and Meinke's mutant phenotype classification system (Lloyd and Meinke, 2012 Icons appear gray when a module is unavailable, turn black once the data have loaded, and are highlighted green when the module is active (i.e., has been selected for viewing by the user).…”

“…The Expression Angler tool, an implementation of the tool described by Austin et al (2016), helps users identify and download Arabidopsis genes by their expression pattern instead of by name. It does this by calculating the correlation coefficients for expression for all gene expression vectors compared with an expression pattern that the user defines or to the expression pattern associated with a single AGI ID or gene name that a user enters (Toufighi et al, 2005).…”

SeqEnrich: A tool to predict transcription factor networks from co-expressed Arabidopsis and Brassica napus gene sets

“…Many of the currently available tools, including Grassius Regulatory Grid Explorer hosted by the Arabidopsis Gene Regulatory Information Server (AGRIS; [1]) and HRGRN [2], are ideal for individual gene lookups but are not conducive to large datasets. Additionally, several tools can effectively identify enriched promoter motifs from Arabidopsis datasets, such as the motif analysis tool hosted by The Arabidopsis Information Server (TAIR; [3]), the Cistome tool at the Toronto the Bio-Analytic Resource (BAR; [4]), and Arabidopsis Motif Scanner [5]; however, these programs are not designed to identify enriched biological processes or generate TF networks. The Arabidopsis Interactions Viewer hosted by the Toronto BAR identifies experimentally validated protein-DNA interactions and generates a transcription network from an input query list [6].…”