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We address biological questions by computational and bioinformatics methods. The main research areas include gene regulation, in particular non-coding RNA, and emerging viruses.

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OLS Bioinformatics for Biologists seminar series

The "virtuous cycle" of bioinformatics is revolutionizing the biomedical sciences. Computational biologists produce analytical and predictive bioinformatics tools that are increasingly used to guide wet-lab research. In turn, the wet-lab biologist has valuable experience, knowledge and data that can be used to refine those bioinformatics tools.

The OLS Bioinformatics for Biologists seminar series will bring together wet-lab and computational biologists, in an informal setting, to promote collaborations and exchange of ideas. Selected NUS computational biologists whose research has practical application to wet-lab biology will present their work. Active exchange of information during the seminar is encouraged.


TITLE: Rapid Protein Structure Database Searching: A Filter-and-refine Approach

Abstract:

As protein databases continue to grow in size, exhaustive search methods that compare a query structure against every database structure can no longer provide satisfactory performance. Instead, the filter-and-refine paradigm offers an efficient alternative to database search without compromising the accuracy of the answers. In this paradigm, protein structures are represented in an abstract form. During querying, based on the abstract representations, the filtering phase prunes away dissimilar structures quickly so that only a small collection of promising structures are examined using a detailed structure alignment technique in the refinement phase. In this talk, we will cover an overview of the existing methods developed for the filtering phase, and discuss the techniques that we have proposed for both the filtering and the refinement phases.

Speaker: Dr. Zeyar Aung
Date: September 25, 2007, 2007
Venue: Seminar Room S2-04-1, Dept. of Biological Sciences

ABOUT THE SPEAKER:

Zeyar Aung received his Ph.D. in Computer Science from the National University of Singapore in 2006. He is currently a Research Fellow at the Institute for Infocomm Research, A*-Star, Singapore. His research interests include bioinformatics, cheminformatics, data mining, and database indexing and retrieval. He is a member of the Life Sciences Society.



Motif finding in Protein and DNA sequences

Abstract: Although a lot of works have been done on de novo motif finding (e.g. MEME, Glam, MotifSampler, Mitra, Weeder, and YMF), the problem of extracting motif is still difficult. For DNA motif, a recent comparison done by Tompa et al (2005) in Nature biotechnology showed that the current motif finders have sensitivity < 0.1 and specificity < 0.3. For protein linear motif, the motif is even weaker (e.g. PxxP --- a motif with only two residues) and standard methods usually fail to find the motif. In this presentation, we would like to discuss some directions to improve motif finding. For DNA motif,  Berger et al (nature biotechology 2006) showed that almost 25% of known motifs have one or more gaps. This indicates that spaced motif may be a better motif model. Our study confirms the conjecture. We showed that spaced motif can fit binding sites better in both synthetic and real datasets. When we apply our solution to Tompa’s benchmark dataset, we improve both the sensitivity and specificity. For protein motif, since the motif is weak, existing methods can discover protein motif only when biologists manually curate some high quality dataset. We would like to utilize protein interaction data to automate the protein motif discovery process. Note that protein interaction data can be generated economically by high-throughput methods like yeast two-hybrid or tandem affinity purification. Our observation is that a protein motif actually interacts directly or indirectly with another linear motif. Hence, we expected such a linear motif pair will co-occur in a pair of interacting proteins. Hence, we propose to mine protein motif pairs which are over-represented in the dataset of interacting protein pairs.

Ken pic
Speaker: Dr. Ken Sun Wing Kin, Assistant Professor School of Computing and Senior Group Leader at Genome Institute of Singapore.

Date: May 29, 2007

Venue: Seminar Room S2-04-1, Dept. Of Biological Sciences








David Hsu poster
Speaker: Dr. David Hsu, Sung Kah Kay Assistant Professor, Department of Computer Science, NUS.

Date: March 27, 2007

Venue: Block S2, Level 4, Seminar Room 1, Dept. Of Biological Sciences













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