Reading Between the Genes: Computational Models to Discover Function from Noncoding DNA

Yves A. Lussier¹, Joanne Berghout¹, Francesca Vitali¹, Kenneth S. Ramos¹, Maricel Kann², Jason H. Moore³

¹Center for Biomedical Informatics and Biostatistics, The Center for Applied Genetic and Genomic Medicine, BIO5 Institute, UA Cancer Center, and Dept of Medicine, University of Arizona
²Dept of Biological Sciences, University of Maryland
³Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania
Email: yves@email.arizona.edu, jberghout@email.arizona.edu, francescavitali@email.arizona.edu, ksramos@email.arizona.edu, mkann@umbc.edu, jhmoore@exchange.upenn.edu

Pacific Symposium on Biocomputing 23:507-511(2018)

© 2018 World Scientific
Open Access chapter published by World Scientific Publishing Company and distributed under the terms of the Creative Commons Attribution (CC BY) 4.0 License.

Abstract

Noncoding DNA - once called "junk" has revealed itself to be full of function. Technology development has allowed researchers to gather genome-scale data pointing towards complex regulatory regions, expression and function of noncoding RNA genes, and conserved elements. Variation in these regions has been tied to variation in biological function and human disease. This PSB session tackles the problem of handling, analyzing and interpreting the data relating to variation in and interactions between noncoding regions through computational biology. We feature an invited speaker to how variation in transcription factor coding sequences impacts on sequence preference, along with submitted papers that span graph based methods, integrative analyses, machine learning, and dimension reduction to explore questions of basic biology, cancer, diabetes, and clinical relevance.