Databases & Software

Website: http://www.virtualplant.org

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.

Website: http://www.yeastrc.org/pdr

Currently large fractions of proteins in all genomes have unknown function, and the vast majority do not have any annotation of protein structure (i.e., no homology to a sequence with known structure). Structure-based methods provide an alternate route to function-annotation, yet the automatic interpretation of structure-function relationships is non-trivial and requires addressing several data integration challenges.

Website: http://www.rnai.org

RNAiDB provides comprehensive access to publicly available RNAi phenotypic data from C. elegans. RNAiDB provides raw data (in the form of images and movies), annotated phenotypes, graphical gene maps, analysis of potential off-target gene inhibition, and tools for searching and mining phenotypic data.

As of v3.0, RNAiDB contains all published RNAi experiments in C. elegans that have been deposited in WormBase, including data from the literature and published large-scale RNAi studies. RNAi to gene mappings for all experiments have been re-analyzed using ePCR and/or a sliding n-mer window method to identify all genes in different genomic locations that may potentially be inhibited by each experiment. Gene maps showing canonical and putative alternate mappings are displayed graphically on RNAi Experiment and Gene/ORF card pages.

A manuscript describing the database was published in the 2004 NAR Database issue: RNAiDB and PhenoBlast: Web tools for genome-wide phenotypic mapping projects. Gunsalus KC, Yueh WC, MacMenamin P, Piano F. Nucl Acids Res (2004) 32, Database issue: D406-D410.

RNAiDB is currently being developed and maintained by Philip MacMenamin and Kris Gunsalus at NYU's Center for Genomics and Systems Biology. Development of RNAiDB has been supported by a grant from the National Science Foundation and is currently supported by a grant from the National Institutes of Health.