EAGER: Creating Gene Network Prediction Tools Applicable To Plants and Animals

Harvard University, Cambridge MA

Investigators

Abstract

This EAGER is a high risk-high payoff project that offers immense potential benefit to the scientific community by pioneering tools to allow functional analysis of genomic data from non-model animal systems. This work will bridge researchers from the animal and plant communities, building synergistic research interactions and collaborations. Advances in all areas of biological research rely on our ability to understand the functions of genes, how genes work together in genetic networks, and how these genes and genetic networks have evolved. To gain this understanding, researchers have traditionally relied on direct tests of gene function and interaction using the techniques of genetics, biochemistry and molecular biology. However, we are now able to obtain sequence data at a rate that outpaces our ability to perform these functional tests. In other words, we can now get sequence data faster than we can figure out what it means! Moreover, an increasing amount of sequence data is being generated for organisms where sophisticated genetic tools have not been developed, which means that we must find a way to predict the functions of these gene sequences without relying on prior knowledge of other sequences, and without making assumptions that similar genes work the same way in different organisms. To address this problem, this project will develop and validate novel computational tools that will allow predictions about gene functions using new sequence data from organisms that lack fully sequenced genomes, without relying on previously generated data on the biochemical or genetic functions of their genes. This project will build and test these tools using gene transcript sequence data from insect, crustacean and spider model laboratory organisms. These predictive tool will change how existing and forthcoming sequence data are used in research across multiple NSF-supported disciplines. This work will contribute to the development of emerging model organisms by increasing the utility of existing data, rather than generating new sequence data, and by extending existing functional molecular genetic tools for new model organisms. Finally, this project will make an important contribution to the training of early career scientists in national and international cross-disciplinary collaboration.