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Gene Clustering

Gene Clustering. Haleh Ashki School of Informatics, Indiana University, Aug 2008 Advisor: Professor Sun Kim. Goal of the project. Gene cluster prediction algorithms are useful in discovering a set of gene “ conserved ” in a pair of genomes.

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Gene Clustering

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  1. Gene Clustering Haleh Ashki School of Informatics, Indiana University, Aug 2008 Advisor: Professor Sun Kim

  2. Goal of the project Gene cluster prediction algorithms are useful in discovering a set of gene “conserved” in a pair of genomes. However, the prediction result depend highly on the phylogenetic distance of two genomes. In particular, when two genomes are close, sizes of predicted gene clusters are large, containing several functional gene sets in one cluster.

  3. Ecoli - Shigella Ecoli - Salmonella

  4. Thus a new computational tool is needed to predict“functionally related gene sets” • In this study, we developed a novel computational method to predict functionally related gene sets from gene clusters, using gene-ontologybased clustering of genes and one dimensionaldynamic programmingtechniques.

  5. The input for this algorithm are the EGGS Clusters algorithm output: EGGS: Extraction of Gene clusters by iteratively using Genome context based Sequence matching techniques. Genes are matched between two genomes using two concepts, pairs of close bidirectional best hits (PCBBHs) and pairs of close homologs (PCHs), where the term close means the physical proximity, say within 300 bp.

  6. This Cluster Contain 54 genes which have different Operons, Pathways and strand information.

  7. predicted clusters are often too long and need to be dissected; BUT how? Predicting biologically meaningful gene clusters from conserved gene clusters: • A conserved gene cluster depends much on phylogenic distance between two genomes and it often contains “multiple” biologically meaning clusters. • Our method uses clustering technique using gene ontology information. • Results from our method are shown biologically meaningful in terms of operon (a set of genes in a single transcription) and biological pathways.

  8. GO : Gene Ontology The GO project has developed three structured controlled vocabularies (ontologies) that describe gene products in terms of their associated: biological processes cellular components molecular functions in a species-independent manner. The ontologies are structured as directed acyclic graphs. GO terms can be linked by different types of relationships: is_a, part_of For each gene there are more than one GO terms. in all different component and also in all different level of the hierarchal tree. Here the UniProt IDs have been used as a key to get the Go terms of each gene.

  9. Semantic Similarity Value (SS): Different methods to calculate the semantic similarity value: Resnik: is solely based on the information content of shared parents of the two terms. If there is more than one shared parent, the minimum information content is taken. Then the similarity score is derived as follows: where S(t1, t2) is the set of parent terms shared by t1 and t2. Lin and Jiang: Both methods use not only the information content of the shared parents, but also that of the query terms where p(t1), p(t2) and p(t) are information content values for t1, t2 and their parents, respectively.

  10. The semantic of a GO term is determined by it’s location in the entire GO graph and semantic relations with all of it’s ancestor term. So we are using the subgraph, starting from the specific Go term and end at root (Biological, cellular, Molecular) In this study I have worked with Molecular Go Terms. Our method : by (James Z. Wang1, Zhidian Du) DAGA=(A,TA,EA) TA :is a set of GO terms,including A and all it’s ancestors in subgraph. EA:set of edges. SV(A)=4.52

  11. Sim(ADh4,Ldb3)=.693 max .427 .427 .664 .814 .482 .664 .482 .664 .664 .814 .390 .480 max From Paper Here I have used the online tool to measure the Semantic Similarity value for each two genes based on their GO terms. I made a matrix of semantic value for each group of genes. this value is normalized between 0 and 1.

  12. Make the Cluster based on Semantic Similarity Matrix: 0 1 2 3 4 5 6 7 8 9 10 1 1.000 0.250 0.000 0.000 0.000 0.000 0.313 0.571 0.433 0.250 2 0.250 1.000 0.000 0.000 0.000 0.000 0.000 0.250 0.278 0.188 3 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 4 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 5 0.000 0.000 0.000 0.000 1.000 0.500 0.000 0.000 0.000 0.000 6 0.000 0.000 0.000 0.000 0.500 1.000 0.000 0.000 0.000 0.000 7 0.313 0.000 0.000 0.000 0.000 0.000 1.000 0.313 0.222 0.438 8 0.571 0.250 0.000 0.000 0.000 0.000 0.313 1.000 0.900 0.286 9 0.433 0.278 0.000 0.000 0.000 0.000 0.222 0.900 1.000 0.233 10 0.250 0.188 0.000 0.000 0.000 0.000 0.438 0.286 0.233 1.000 Clustering Result: Value Genes 0.9 8 9 0.2 1 2 0.4 7 10 0.5 6 5 this method group the genes based on their SS value. Descending (0.9 – 0.1) So each gene is grouped based on it’s highest SS value. The genes with SS value of 0 are omitted on this step. HCluster • Is one of the features of R which make the cluster based on the Dissimilarity value of group of elements. I have used that for visualization of clustering based on my Semantic Similarity Matrix.

  13. Hcluster visualization:

  14. Now each Eggs cluster is grouped based on the Semantic similarity value. I made a key like as: FirstGenome.SecondGenome.EggClusterNumber.SSvalue ESC12S0.8 EcoliSalmonellaCluster12Subcluster0.8 In this study I used clusters from four pairs of genomes: Ecoli Salmonella Ecoli Yersinia Ecoli Shigella Ecoli Shewanella I gathered all existence keys for each gene in Ecoli genome. For sure more conserved genes have more keys in all four groups: Break point • 16131330 ESGc102s0.8 ESc125s0.8 EYc25s0.8 • 16131335 ESGc102s0.8 ESc125s0.8 EShc106s0.6 EYc25s0.8    • 16131350 ESGc102s0.8 ESc126s0.8 EShc107s0.8 EYc99s0.3  • 16131351 ESGc102s0.9 EYc99s0.3  • 16131352 ESGc102s0.9 EYc99s0.5

  15. Break Point and Cluster Score Break points are defined in target genome (Ecoli). break points are the genes which the keys are changed. Based on both “cluster number” or “sub cluster value”. All breakpoints are collected and been removed of redundancies. Formula for “gene set score”: ((# of same keys inside the cluster)/(# of same keys outside the cluster) ) ^ 2 _______________________________________________________________ Size of cluster (number of genes)

  16. Breakpoint1-breakpoint2 genes #inner gene # outer gene Size gene set Score Break point interval score= Sum of gene set score / number of genes4.36 /5 =0.872 ***************************************** 16127996-16127997 0.583 16127996-16127998 0.830 16127996-16128000 0.901 16127996-16128002 0.872 16127996-16128008 0.815 16127996-16128014 0.782 16127996-16128019 0.840 16127996-16128020 0.889 16127996-16128021 0.939 16127996-16128025 0.94 16127996-16128026 0.920 16127996-16128029 0.870 16127996-16128030 0.846 16127996-16128035 0.760 16127996-16128042 0.709 ***************************************** • Each group is defined as genes between each breakpoint and the 5th ,10th ,15th break point ahead. • Here: 15 break points in group

  17. Problem definition any pair of breakpoints can define a functionally related gene set, but there are too many candidates: O(n^2) for n break points. We formulate a problem of functional gene set prediction as generating maximal cover of genes based on the Break point interval score. This problem is similar to exon chaining problem that predict exons from a number of intron-exon boundaries. Thus we used one dimensional dynamic programming technique to solve the functional gene set prediction problem: Select non overlapping break points’ intervals that maximize sum of break point interval scores.

  18. One dimensional dynamic programming 16127996 On each group ( each breakpoint with the next 5th,.. Breakpoint ) the four highest score have been chosen as blocks for dynamic programming. This dynamic programming get the block as potential clusters, the start and stop position and the weight of that block (“Break point interval score”). and finally generate the clusters with highest score. This algorithm is modified based on our data such as overlapping on end points etc.

  19. One more step to refine predicted clusters Strand Information: Connected gene neighborhoods in prokaryotic genomes Nucleic Acids Research, 2002, Vol. 30, No. 10 2212-2223: the genes which have the same function are in the same direction. So the strand information of Ecoli genome as target is used to dissect each cluster. in this step the clusters are dissected based on the strand information. The new clusters with one gene are removed.

  20. Gene Id StartPosition End Position Strand Operon ID Pathway

  21. Predicted gene clusters verify in terms of: Definitionof each gene: NCBI Operon information Detecting uber-operons in prokaryotic genomes, Dongsheng Che2, Guojun Li, Nucleic Acids Research, 2006 Database: http://csbl.bmb.uga.edu/uber/ This DB has grouped genes based on the operons they belongs too.Each Uber_Operon gropu represent a rich set of footprints of operon evolution. KEGG Pathway: a metabolic pathway is a series of chemical reactions occurring within a cell. In each pathway, a principal chemical is modified by chemical reaction. Enzymes catalyze these reactions. Database: http://www.genome.jp/kegg/ absence of information for non enzyme genes make that not very useful.

  22. Summary Our Method: EGGS: (Ecoli-Salmonella) Cluster Numbers:167 Gene range:2-130 (2-50) Operon Id Range:0-42 Cluster Numbers: 483 Gene range:2-25 (2-10) Operon Id Range: 0-6

  23. Conclusion By dissecting big conserved clusters we will have functionally meaningful related genes clusters without worry about phylogenetic distance of genes.

  24. Literature • Resnik P: Semantic similarity in a taxonomy: an information-based measure and its application to problems of ambiguity in natural language. J Artif Intell Res, 1999, 11:95-130. • Lin D: An information-theoretic definition of similarity. In: International Conference on Machine Learning: 1998; San Fransisco: Morgan Kaufmann; 1998: 296-304. • Jiang JaC, DW: Semantic similarity based on corpus statistics and lexical taxonomy. In: Proceedings of 10th International Conference on Research In Computational Linguistics. Taiwan; 1997: 19-33. • Wang JZ, Du Z, Payattakool R, Yu PS, Chen C-F: A new method to measure the semantic similarity of GO terms. Bioinformatics 2007, 23(10):1274-1281. • EGGS: Extraction of Gene clusters using Genome context based Sequence matching techniques. Kwangmin Choi, Bharath Kumar Maryada,SunKim • Kanehisa M, Goto S, Kawashima S, Okuno Y, Hattori M: The KEGG resource for deciphering the genome. Nucl Acids Res 2004, 32(90001):D277-280. • Database:http://www.genome.jp/kegg/ • Connected gene neighborhoods in prokaryotic genomes Nucleic Acids Research, 2002, Vol. 30, No. 10 2212-2223: • Genome Alignment, Evolution of Prokaryotic Genome Organization, and Prediction of Gene Function Using Genomic ContextYuri I. Wolf, Igor B. Rogozin, Alexey S. Kondrashov, and Eugene V. Koonin Research 11:3 356-372 (2001) • Detecting uber-operons in prokaryotic genomes, Dongsheng Che2, Guojun Li, Nucleic Acids Research, 2006

  25. Online resources: • http://bioinformatics.clemson.edu/G-SESAME • http://csbl.bmb.uga.edu/uber/ • http://www.geneontology.org/ • http://bioconductor.org • http://www.r-project.org • http://platcom.org/EGGS • http://www.genome.jp/kegg/ • http://www.ncbi.nlm.nih.gov/

  26. Thanks • Professor.Sun Kim • Professor.Dalkilic • Kwangmin choi , youngik yang • Professor.Tang,Professor.Radivojac and all other Informatics faculties. • Informatics Staffs. Mis.Linda Hostetter • All Graduate Students (my Friends) • Profesoor.Kehoe • School of informatics.

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