Exploring the Non-coding RNA Genes and the Modern RNA World

Dr. Shaojie Zhang
Wednesday, October 10th, 2007
2:00 PM
Harris Engineering Center 101

Abstract:

One of the most surprising discoveries made by analyzing the human genome was the relatively small number of genes that were found. Many explanations have been given for this, but the one that most intrigues me is the notion these studies looked primarily for protein coding genes, and may have missed another class of genes -- non-coding RNA (ncRNA) genes, which are transcribed into functional RNAs, but not translated into proteins. In this talk, I will present a set of new algorithms for finding and analyzing ncRNA genes. The first topic is how to design structure-based filters and sequence-based filters to speed up the search for homologs in the genomes. State-of-the-art methods for the problem, like covariance models, suffer from high computational cost, underscoring the need for efficient filtering approaches that can identify promising sequence segments and speed up the detection process. Our approach, based on structural and sequence filters that eliminate a large portion of the database while retaining the true homologs, allows us to search a typical bacterial database in minutes on a standard PC with high sensitivity and specificity. The second topic is a novel framework to predict the common secondary structure for unaligned RNA sequences. By matching putative stems in RNA sequences, we make use of both primary sequence information and thermodynamic stability for prediction at the same time. I will also describe some of our findings from bacterial genomes, metagenomic data from the ocean, and mammal genomes by using these methods.

Bio:

Shaojie Zhang got his Ph.D. in computer science at the University of California, San Diego in 2007. His PhD study was with Dr. Vineet Bafna and Dr. Pavel Pevzner. His research is focused on bioinformatics and computational biology, which includes ncRNA gene finding, ncRNA analysis, comparative genomics, molecular evolution, population genetics and other genome annotation problems. Shaojie Zhang received the Bachelor of Science degree in computer science from Peking University, Beijing, China, and the Master of Engineering degree from Nanyang Technological University, Singapore.