1. Liming Cai’s Research Department of Computer Science The University of Georgia

2. Summary of Research Algorithms and computation research that responds to needs and challenges in biology and other sciences

4. Telomerase RNA Complete replication of chromosome 3’ end Important to aging Related to cancer diseases A non-coding RNA Protein-coding genes (DNA) --> mRNA --> proteins Non-coding RNA genes (DNA) --> RNAs

5. Classes of non-coding RNAs (Bompfunewerer, et al, 2005) ClassSizeFunctionPhylogenetic distribution tRNA70-80Translationubiquitous rRNA 16S/18S 28S+5.8S/23S 5S 1.5K 3K 130 translationubiquitous RNase P MRP 220-440 250-350 tRNA - maturation ubiquitous eukarya snoRNA telomerase 130 400-550 pseudouridinyl ation addition of repeats snRNA U1 ~ U6 100-600 130-140 Spliceosome mRNA maturation Eukarya Eukarya, archaea U7 7SK ~65 ~300 Histone mRNA Maturation Translational regulation Eukayotes vertebrata tmRNA300-400Tags protein For proteolysis bacteria miRNA~22Post-tran. Reg.Multi-cellular orgs

6. Identification of ncRNAs is challenging Human genome (DNA): 3,300,000,000 80% of human genome are transcribed (Nature, Aug 2008) Average size of RNAs: 5,000 Total 528,000 RNAs Estimated 28,000 protein coding RNAs 500,000 possible non-coding RNAs Biological experiments are very expensive and time consuming!

7. Computational prediction: also a challenge ncRNA genes do not have significant statistical signals large in number diverse, 20 nts to 22,000 nts Non-coding RNA fold, but each has its unique structure. - Computationally intensive, - Simply no good method, or - Methods compromising accuracy

9. 1. RNA structural homology search

10. Saccharomyces telomerase RNA

11. Tobacco Mosaic Virus 3’ UTR (Leathers et al. 1993 MCB 13 Zeenko et al. 2002 JVI 76)

12. 1: software for biological applications 2: algorithms to support 1 3: computation theory for such algorithms Three types of research projects

13. Investigate two classes of graph optimization problems 1.Subgraph optimization: Find some subgraph from a given graph to optimize certain objective function A desirable subgraph

14. Investigate two classes of graph optimization problems (cont.) 2. Graph mapping: Find a mappingbetween two graphs to optimize certain objective function A desirable mapping

15. Why these two classes of graph optimization problems? 1. Subgraph optimization corresponds to ab initio approaches used in biology or other sciences e.g., predict a pathway from a gene network based on biological principles and data. 2.Graph mapping corresponds to comparative analysis approaches e.g., predict genes for one species based on known genes in (an)other related species.

16. Many graph problems in these two classes have been studied for a long time. Many of them do not admit fast algorithms. Why study them again? Examine these problems from modern, topological graph theory. Computational biology/bioinformatics is a new interdisciplinary area.

17. 1 234 56 7 8 910 Find a largest subset of vertices, in which vertices are not connected with edges Max Independent Set Problem Actually for general graphs, you may have to try all 2 n, or a little less, 2 cn combinations for some c<1.

18. 1 234 56 7 8 910 1,2,3 2,3,6 2,5,6 3,6,7 3,4,7 6,7,9 6,8,9 7,9,10 Tree decomposition of tree width 2 Max Independet Set can be solved by trying 2 3 n combinations View from a different perspective

19. Where is tree decomposition useful ? Biological molecules have small tree width in their structures; Many biological networks, pathways have small tree width; IT networks, ontology graphs may have small tree width too!

20. My research group: http://www.uga.edu/RNA-Informatics