VirtualPlant

Our long term goal is to understand how internal and external perturbations affect processes and networks controlling plant growth and development. In this project, we start with data integration of the known relationships among genes, proteins and molecules (extracted from public databases and/or generated with predictive algorithms) as well as experimental measurements under many different treatments. We go beyond data integration to conceptual integration by using novel visualization techniques to render the multivariate information in visual formats that facilitate extraction of biological concepts. We also use mathematical and statistical methods to help summarize the data. We implement and combine these approaches in a system we term "VirtualPlant". Whereas our project relates specifically to Arabidopsis, the data structures, algorithms, and visualization tools are designed in a species-independent way. Thus the informatic, math, statistic and visualization tools that we develop can be used to model the cellular and physiological responses of any organism for which genomic data is available.

We have implemented a proto-type that is already being actively and effectively used (http://www.virtualplant.org). This tool is being used by biologists and computer scientist alike for the purpose it was designed for - to support the analysis of original genomic data generated by the researchers themselves. We have found that working with experimental biologists, even from very early stages of software development, to be the most effective way to generate real solutions to the problems encountered by researchers in the laboratory.

Sungear

Sungear is a generalized Venn Diagram. You can use this tool to compare an arbitrary number of lists or gene sets. Sungear runs as an applet and can be started from the GeneCart. Sungear can be used to learn the biological significance of gene lists or intersections between gene lists. We have integrated Gene Names and Gene Ontology information to rapidly evaluate the significance of any intersection or selection made within Sungear. You can also hypothesize major trends in your data by using Sungear. The software can suggest biased GO terms (suggest over-representation) as well as identify the most "peculiar" intersections or gene sets based on the distribution of all GO terms associated with it.

For a demo version of Sungear follow this link.
Please note: Sungear requires Java 1.4.2. You can download either the J2SE SDK (full install) or the J2SE JRE (runtime only), both of which can be found here. Be sure not to get the J2EE (Java 2 Enterprise Edition, as opposed to the Java 2 Standard Edition).
If the data set doesn't load automatically: Choose "File->Load->development" and click open to look at an example dataset generated from the AtGenExpress developmental time courses. This example shows genes enriched at the indicated developmental stages.

BioMaps

Find biological themes (Based on MIPS funcats or GO terms) in gene lists. This program analyses the distribution of functional assignments (Gene Ontology or MIPS) for one or more lists of genes. It reports back those terms that are found over-represented in the list(s) provided, as compared to the frequency of the term in a background population (e.g. the whole genome). A graphical and tabular output is given to facilitate analysis and interpretation of the results.

PathExplore

PathExplore is a "K" based system that takes individual biochemical reactions and from them builds metabolic pathways. However, PathExplore is more than just a metabolic database it is designed to help users analyze gene expression (microarray), metabolomic and/or proteome data with respect to biochemical pathways and metabolism. The objective is to help users understand at the molecular level what is happening. PathExplore has been designed specifically for the Affymetrix gene chips for Arabidopsis. However, it has functionality that might be useful to researchers not using the Affymetrix gene chips and even to researchers that that do not study Arabidopsis (More Info). This site also contains other non-pathway related tools that researchers are likely to find useful, including the ability to find promoters that contain motifs and the InterAct Class system. If there is functionality you'd like to see added, please let us know.

InterAct Class

InterAct Class system allows you to find similarly regulated items (e.g. genes) based on -omic profile data.

Vicogenta

ViCoGenTA is a data-mining tool we have developed which allows us to simultaneously search sequence databases for multiple taxa to find closest matches to the Arabidopsis genome and proteome based on sequence similarity. ViCoGenTA is a BLAST-based web tool that provides a rapid method to identify putative orthologs across several species based on their top matches in the Arabidopsis genome and proteome. Currently we have aligned Brassica oleracea shotgun sequences and the Oryza sativa rough draft sequences against the Arabidopsis genome sequence and 24 Plant Gene Indices from TIGR (http://www.tigr.org/tdb/tgi/plant.shtml) plus our Cycad, Ginkgo, and Gnetales unigene set against the Arabidopsis proteins. The ability to show ESTs from several species at the same time makes ViCoGenTA a powerful tool for identifying putative orthologs. ViCoGenTA is also useful in identifying the reads and/or ESTs that align to regions of interest in the Arabidopsis genome. The database and viewer used were developed by the Generic Model Organism Project (GMOD, http://www.gmod.org). The ViCoGenTA interface is a CGI script written in Perl.

Orthologid

This web-based tool is initiated by the Plant Genomics Consortium, and is designed to facilitate the identification of orthologous gene regions within a character-based phylogenetic framework. OrthologID will use a submitted sequence to query a local database to find all putative orthologs within the complete genomes of Oryza and Arabidopsis. Secondly, OrthologID will generate a gene tree (here referred to as a "guide tree") of all putative orthologous gene regions from the complete genomes (as additional complete genomes become available, they will be added to the local database). It will assemble the matrix, remove redundant sequences, align sequences, perform tree searches using the parsimony ratchet and compute a strict consensus when multiple equally parsimonious trees are recovered. This guide tree will be probed for the presence of characters diagnostic of orthologous groups by passing the tree to the program P-Gnome (Sarkar et al., 2002). The submitted query sequence then will be screened for the presence of shared diagnostic characters using the program P-Elf (Sarkar et al., 2002). Finally, OrthologID will compare the output from P-Elf to the guide tree diagnostics and display the results as a tree with the query sequence appended to its ortholog.