Gene Slider helps visualize the conservation and entropy of orthologous DNA and protein sequences by presenting them as one long sequence logo that can be zoomed in and out of, from an overview of the entire sequence down to just a few residues at a time. A search function enables users to find motifs such as cis-elements in promoter regions by simply 'drawing' a sequence logo representation of the desired motif as a query. In addition to displaying user-supplied FASTA files, our demonstration version of Gene Slider loads and displays a rich database of 90 000+ conserved non-coding regions across the Brassicaceae indexed to the TAIR10 Col-0 Arabidopsis thaliana sequence. It also displays transcription factor binding sites, enabling easy identification of regions that are both conserved across multiple species and may contain transcription factor binding sites. AVAILABILITY AND IMPLEMENTATION: Freely available on the web at: http://www.bar.utoronto.ca/GeneSlider and also as an app on http://araport.org Website implemented in JavaScript and Processing.js with all major browsers supported. Source code available under GNU GPLv2 at SourceForge: https://sourceforge.net/projects/geneslider/ CONTACT: [email protected].

SUMMARY: With increasing numbers of eukaryotic genome sequences, phylogenetic profiles of eukaryotic genes are becoming increasingly informative. Here, we introduce a new web-tool Phylopro (http://compsysbio.org/phylopro/), which uses the 120 available eukaryotic genome sequences to visualize the evolutionary trajectories of user-defined subsets of model organism genes. Applied to pathways or complexes, PhyloPro allows the user to rapidly identify core conserved elements of biological processes together with those that may represent lineage-specific innovations. PhyloPro thus provides a valuable resource for the evolutionary and comparative studies of biological systems.

BACKGROUND: Arabidopsis thaliana is the model species of current plant genomic research with a genome size of 125 Mb and approximately 28,000 genes. The function of half of these genes is currently unknown. The purpose of this study is to infer gene function in Arabidopsis using machine-learning algorithms applied to large-scale gene expression data sets, with the goal of identifying genes that are potentially involved in plant response to abiotic stress. RESULTS: Using in house and publicly available data, we assembled a large set of gene expression measurements for A. thaliana. Using those genes of known function, we first evaluated and compared the ability of basic machine-learning algorithms to predict which genes respond to stress. Predictive accuracy was measured using ROC50 and precision curves derived through cross validation. To improve accuracy, we developed a method for combining these classifiers using a weighted-voting scheme. The combined classifier was then trained on genes of known function and applied to genes of unknown function, identifying genes that potentially respond to stress. Visual evidence corroborating the predictions was obtained using electronic Northern analysis. Three of the predicted genes were chosen for biological validation. Gene knockout experiments confirmed that all three are involved in a variety of stress responses. The biological analysis of one of these genes (At1g16850) is presented here, where it is shown to be necessary for the normal response to temperature and NaCl. CONCLUSION: Supervised learning methods applied to large-scale gene expression measurements can be used to predict gene function. However, the ability of basic learning methods to predict stress response varies widely and depends heavily on how much dimensionality reduction is used. Our method of combining classifiers can improve the accuracy of such predictions - in this case, predictions of genes involved in stress response in plants - and it effectively chooses the appropriate amount of dimensionality reduction automatically. The method provides a useful means of identifying genes in A. thaliana that potentially respond to stress, and we expect it would be useful in other organisms and for other gene functions.