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Abstract
Structural ribonucleic acid (RNA) molecules play an important role in regulating gene expression in organisms throughout the tree of life. The number of different classes of structural RNA, their possible mechanisms of action, their interaction partners, etc. are poorly understood. Here we consider the challenging computational problem of ab initio detection of novel structural RNA. Ab initio approaches are especially useful for RNA that is not well conserved across species. We focus on the genome of Plasmodium falciparum, where there is evidence that structural RNA plays a dominant role in regulating gene expression. P. falciparum is an important organism to understand since every year it is responsible for 300-500 million clinical cases of Malaria and around a million deaths, of which over 75% occur in African children under 5 years of age.The genome of an organism codes for its biochemical building blocks as well as its regulatory elements. The “language” used to represent information in the genomic sequence is about as “natural” as it gets and it is not clear what are the appropriate features one should use to detect novel structural RNA. After a brief introduction to the salient biology, we will describe a pragmatic and computationally intensive approach based on methods originally developed by others for detecting structural RNAs in very short viral genomes. We describe a pilot study demonstrating the feasibility of the approach, which also highlighted computational limitations, as well as the fact that the signals are deeply buried in noise. We will describe new algorithms that have allowed us to reduce the computational complexity, and probably increase the signal-to-noise, thereby allowing us to scale up this approach to a truly genome-wide level.
Biography
Dr. Fernando Pineda is Associate Professor of Molecular Microbiology and Immunology at the Johns Hopkins Bloomberg School of Public Health. Where he collaborates with laboratory-based colleagues to model biological systems. He also directs the High Performance Scientific Computing Core. He received his PhD in Theoretical Physics from the University of Maryland, College Park. He has served on the editorial boards of several journals including Neural Computation and IEEE Transactions on Neural Networks. Prior to joining the faculty at the school of Public Health, he was on the Principal Staff at the Johns Hopkins Applied Physics Laboratory. He has also worked at the Jet Propulsion Laboratory and the Harvard-Smithsonian Center for Astrophysics.