RNA : algorithms, evolution and design

Abstract

Modern biology is being remade by a dizzying array of new technologies, a deluge of data, and an increasingly strong reliance on computation to guide and interpret experiments. In two areas of biology, computational methods have become central: predicting and designing the structure of biological molecules and inferring function from molecular evolution. In this thesis, I develop a number of algorithms for problems in these areas and combine them with experiment to provide biological insight. First, I study the problem of designing RNA sequences that fold into specific structures. To do so I introduce a novel computational problem on Hidden Markov Models (HMMs) and Stochastic Context Free Grammars (SCFGs). I show that the problem is NP-hard, resolving an open question for RNA secondary structure design, and go on to develop a number of approximation approaches. I then turn to the problem of inferring function from evolution. I develop an algorithm to identify regions in the genome that are serving two simultaneous functions: encoding a protein and encoding regulatory information. I first use this algorithm to find microRNA targets in both Drosophila and mammalian genes and show that conserved microRNA targeting in coding regions is widespread. Next, I identify a novel phenomenon where an accumulation of sequence repeats leads to surprisingly strong microRNA targeting, demonstrating a previously unknown role for such repeats. Finally, I address the problem of detecting more general conserved regulatory elements in coding DNA. I show that such elements are widespread in Drosophila and can be identified with high confidence, a result with important implications for understanding both biological regulation and the evolution of protein coding sequences.