Grant: Discovery Grants Program - Individual

The field of bioinformatics has borrowed from other fields of computer science and mathematics--such as statistics, machine learning, probabilistic modelling, and optimization--to develop sound, general algorithms for analyzing high-throughput genetic and molecular data. However, almost without exception, those algorithms treat every "entity" under consideration the same. For example, to identify which genes are differentially expressed between two conditions, the same statistical model is applied individually to every gene. When we want to identify regions of the genomic DNA bound by a certain protein, the same statistical model is applied to every genomic locus. Of course the observed data for each gene or each locus, provided by the high-throughput assay, is different. But the test applied is the same, and for a simple reason: traditionally, bioinformatics has dealt with situations where the number of observations per entity (e.g. two conditions, a handful of time points, or a few tens of patients) is vastly outnumbered by the number of entities (e.g. tens of thousands of genes or millions of genomic loci). Anything but simple statistical models would be in danger of overfitting the sparse data available. However, the accumulation of massive public databases of personal genomes, epigenomes, and cell-, tissue-, and disease-specific expression profiles, means that we now have at our disposal high-throughput data from tens or hundreds of thousands of "conditions". Moreover, statistical analyses of such data reveals a startling fact: all genes and all genomic loci are not alike. For example, the expression of some genes is inherently more variable than others. Furthermore, our measurements of some genes are noisier and/or more systematically biased than for other genes. Similarly for genomic loci, where we have varying signal-to-noise ratios in different assays, and different sources and amounts of measurement bias. The central idea behind this proposal is to use that mass of already-collected data to build and test more sophisticated, machine learning-based models of every single gene or locus in the genome. Further, we can use those models not just for the sake of analyzing that same data, but rather for creating tools to analyze new datasets, whatever their size. By modelling the particular biases and variability of each gene or locus, we can get a more accurate measure of the novelty of new measurements, and more successfully identify truly significant alterations in gene and genome behaviour.