Computational prediction of gene function

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Over the next five years, tens of thousands of new organisms will have their genomes sequenced. These new data present a substantial challenge and an unprecedented opportunity. The new challenge is answering the question: How do we understand these genomes? Although we can identify the genes within them, how can we tell what the genes do? Much about the function of a gene is difficult to extract from genome sequence alone and requires further experimentation to determine. However, this type of experimentation is much more difficult and costly than genome sequencing. Traditionally, scientists have focused on a small number of representative "model" organisms to perform these more detailed experiments. However, there is not always a clear link between gene function in model organisms and that in other, closely related ones. One focus of this proposal is to attempt to define the conditions under which we can confidently predict that a gene's function is conserved between two organisms. We will accomplish this goal by training computational algorithms to predict when gene function is conserved between two model organisms. In this situation, we know the correct answer and can therefore evaluate how well our methods are doing before applying them to situations in which we do not know the correct answer. The exciting part of this opportunity is that we can use these new data to help us to analyze biological samples in terms of what cells are contained within. Such a technique has a wide variety of uses, ranging from analyzing cancerous tumours to detecting infectious agents in blood. We will pursue this opportunity by first constructing a library of cell type specific signatures from different organisms and then using that library, along with a new computational technique (that we will develop), we will analyze new biological samples. These two projects represent excellent thesis projects for two or more students; and we predict that if successful, they will have a substantial and lasting impact on the scientific community and, potentially, society in general.