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Shaojie Zhang

Computational methods for genome-wide non-coding RNA finding and Analysis
UCF Computational Biology and Bioinformatics (CBB) Research Group

Thursday, March 20th, 2008
Location: Troy 2018 - 4:00 p.m. to 5:00 p.m.
Refreshments at 3:30 p.m.


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.

Hosted by: Li Ding and Mohammed Zaki
Administrative support: Jacqueline Carley (x4384)

For more information:

Dr. Shaojie Zhang's webpage

Last updated: March 11, 2008