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Network-based strategies for discovering functional associations of uncharacterized gene sets

High-throughput technology is changing the face of research biology, generating an ever growing amount of large-scale data sets. With experiments utilizing next-generation gene sequencing, mass spectrometry, and various other global surveys of proteins, translating the plethora of data into biology has become a daunting task. In response, functional networks have been developed as a means for integrating the data into models of proteomic organization. In these networks, proteins are linked if they are evidenced to operate together in the same function, facilitating predictions about the functions, phenotypes, and disease associations of uncharacterized genes. We explore various ways this so-called “guilt-by-association” concept may be used to predict loss-of-function phenotypes and disease associated genes. We also address another common biological challenge: the functional characterization of whole sets of genes. We present RIDDLE, a machine learning-based method that provides a measure of network distance, and thus functional association, between two sets of genes. RIDDLE may be applied to a wide range of potential applications, as we demonstrate with several biological examples, including linking microRNA-450a to ocular development and disease. In the last decade, functional networks have proven to be a useful strategy for interpreting large-scale proteomic and genomic data sets. With the continued growth of genome coverage in networks and the creation of innovative predictive methods, we will surely advance towards our ultimate goal of understanding the genetic changes that underlie disease.

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