By : Elham Khabiri Adviser : Dr. Hisham Al-Mubaid

1. Using Semantic Similarity Measures in the Biomedical Domain for Computing Similarity between Genes based on Gene Ontology By : Elham Khabiri Adviser : Dr. Hisham Al-Mubaid

2. Motivation Drug Target Human Yeast • Goal : • Measure functional similarity between genes and Proteins • Reason: • It is useful to measure the functional difference between genes in different organisms • Find the genes with unknown functions University of Houston - Clear Lake

3. Motivation • To compute the similarity between two genes g1 and g2, we can use one of the following information sources: • gene sequence information • gene functional annotations (GO terms) • biomedical literature and texts • gene expression profiles. • In this work, we use Gene functional annotations and the gene ontology GO to measure the similarity between genes. University of Houston - Clear Lake

4. Motivation • Given two genes Gp and Gq such that gene Gp is annotated with a set of n different GO terms, we call it the set GOp: GOp = {tp1, tp2, …., tpn} • Similarly, the annotation set for gene Gq is: GOq = {tq1, tq2, …., tqn} that is, gene Gq is annotated with m different GO terms • The terms tpi ortqj are nodes in the GO • If both genes Gp and Gq are annotated with only one term (n=m=1) and the same GO term ( tp1 =tq1) then the similarity between them is maximum. University of Houston - Clear Lake

5. Motivation • In general, if both genes Gp and Gq are annotated with the same set of GO terms (n=m≥1) (that is, tpi =tqj) then the similarity between them is maximum. University of Houston - Clear Lake

6. Motivation • Many data resources in bioinformatics not only hold data in the form of sequences, but also as annotation • Scientific natural language • Suitable for human but not easy for machine processing University of Houston - Clear Lake

7. Related Work:Semantic Measures in NLP Resnik, 1995 Lin, 1998 Jiang and Conrath, 1997 Wu & Palmer, 1994 Leacock and Chodorow, 1998 Based on Information Content (IC) of Least Common Ancestor(LCA) Based on Ontology Structure University of Houston - Clear Lake

8. Related Work • WordNet [Miller 1995] • Information Content Based Measures • Resnik, 1995 freq(t): Frequency of concept c in database. N: the number of all the concepts in database. University of Houston - Clear Lake

9. Related Work • Jiang and Conrath, 1997 • Lin, 1998 University of Houston - Clear Lake

10. Related Work • Ontology Structure Based Measures: • Wu & Palmer, 1994 • Based on the depths of the two concepts in the taxonomies, and the depth of the LCS • Leacock and Chodorow, 1998: PL • Based on the PL(t1,t2) of the shortest path between two concepts • Scale the measure by the overall depth D of the taxonomy University of Houston - Clear Lake

11. Related Work:Measures in Biomedical Domain • First semantic similarity measure in biomedical domain: • Rada et al., 1989 : Path Length between biomedical terms in the MeSH ontology • Measure of semantic similarity inGene Ontology (GO) • Lord et al., 2003: Applied Resnik’s to GO • Validated the correlation between sequence and semantic similarity University of Houston - Clear Lake

12. Related Work:Recent Works in Biomedical Domain • Al-Mubaid and Nguyen, 2007 • Investigated the effectiveness of using Medline corpus as the information source for measuring the semantic similarity in the biomedical domain • Al-Mubaid and Nguyen, 2007 • A technique for computing the semantic similarity between biomedical terms across multiple ontologies within a unified framework like UMLS • Wang et. al , 2007 • Functional similarity measure of GO terms based on contributions of the term’s ancestors in GO Evaluation: Compare it with Resnik’s measure • Found it was closer to human perception University of Houston - Clear Lake

13. Sequence Similarity • E-value • Bit-score Output • Sequence Similarity • BLAST [Altschul 1990] :Finds regions of local similarity between sequences of genes • WU-BLAST2 University of Houston - Clear Lake

14. Drawbacks of Sequence Similarity • Sequence similarity holds for most genes with the same functionality • Devos 2000: 30% of the functional similarity found by sequence similarity might be erroneous • Reason: Genes that are not evolved from a common ancestors do not have a considerable sequence similarity • One drawback for the sequence notation is that, it is not readable and understandable by human. University of Houston - Clear Lake

15. New approach • Ontology structure based • Path Length (PL) between the two terms • Number of minimum paths between terms • Depth of LCA of two terms • Ontology used: Gene Ontology • A comprehensive resource for gene functional information • Validation • Correlation with sequence similarity • Correlation with two other semantic measures University of Houston - Clear Lake

16. Gene Ontology • One of the greatest project in bioinformatics • Created in 2000 by GO Consortium [Ashburner et. al] • Consists of a set of controlled vocabularies for • Biological Process • Molecular Functions • Cellular Components • Shows the functional and biological terms related to genes in a hierarchical and structured way University of Houston - Clear Lake

17. Gene Ontology University of Houston - Clear Lake

18. Gene Ontology • Directed Acyclic Graph • Each term may have more than one parent • There may be more than one path between two nodes (terms) • Each two node have at least one LCA (Least Common Ancestor) University of Houston - Clear Lake

19. 3 Proposed Measures • Plain Path Length (PL) • Number of edges between the two terms • Path Length with Variation (PLm) • Number of common terms • Number of minimum paths • Path Length with Depth (SimPLD) • Path Length between two terms • Depth of LCA of the two terms University of Houston - Clear Lake

20. Plain Path Length Parent of 5 Parents of 12 Parent of 4 11 12 7 4 8 5 2 6 Parents of 11 Parents of 8 Considers the first level ancestor of each node in the list University of Houston - Clear Lake

21. PL between two Genes Facl6 Annotated with 3 MF • genep is annotated with terms {t1,..., tn} • geneq is annotated with terms {t1,..., tm } dij: Shortest PL between ti ofgene1 and tj of gene2 University of Houston - Clear Lake

22. PL Evaluation • Based on Correlation with Sequence Similarity • Genome Used: • SGD (Saccharomyces cerevisiae): 3 datasets • FlyBase (Drosophila Melanogaster): 1 dataset • Divide datasets Based On E-Value: • High Sequence Similarity (HSS): E-value ≤ 10-5 • Low Sequence Similarity (LSS): 10-5 < E-value <1 • No Sequence Similarity (NSS): E-value = 1 University of Houston - Clear Lake

23. Evaluation: Compare PL with Sequence Similarity University of Houston - Clear Lake

24. Evaluation: Compare PL with Sequence Similarity • 80% of HSS have PL<=2 • 4% of HSS have PL>7 • 17% of NSS have PL<=2 • 70% of HSS have PL<=2 • 7% of HSS have PL>7 • 7% of NSS have PL<=2 University of Houston - Clear Lake

25. 3 Proposed Measures • Plain Path Length (PL) • Number of edges between the two terms • Path Length with Variation (PLm) • Number of common terms • Number of minimum paths • Path Length with Depth (SimPLD) • Path Length between two terms • Depth of LCA of the two terms University of Houston - Clear Lake

26. Path Length with Variation • More than one LCA • Two minimum Paths • “6-10-7-5-1” • “6-10-11-5-1” • More functional similarity that those who have only one minimum path between them University of Houston - Clear Lake

27. PL with Variation PL(gox, goy) if nmp = 1 PL(gox, goy)/w1.nmp, otherwise PLm (gox, goy) PL(gox, goy) = the minimum path length in the GO graph between the two GO terms gox and goy University of Houston - Clear Lake

28. Path Length with Variation • genep is annotated with terms {t1,..., tn} • geneq is annotated with terms {t1,..., tm } Max go_pl = 15 nct = number of common GO terms between Gp, Gq. University of Houston - Clear Lake

29. Validate PLm • We measured the similarity of gene pairs in SGD pathways • Pathway is a series of chemical reactions occurring within a cell • Pathway #5 (allantoin degradation): 4 genes • pathway #6 (arginine biosynthesis): 7 genes • pathway #141 (tryptophan degradation): 12 genes • Compare with • Resnik measure • Wang et. al measure University of Houston - Clear Lake

30. Validate PLm: Compare with Resnik • Pathway 5: allantoin degradation • 4 genes, 6 pairs         They Correlate well with each other Minimum Maximum University of Houston - Clear Lake

31. Validate PLm : Compare with Resnik PL(ARG2, ARG3) > PL(ARG3, ARG5,6) PL(ARG4, ARG8) > PL(ARG1, ARG8) Pathway 6: 7 genes, 21 pairs         University of Houston - Clear Lake

32. Evaluation: Clusters of Genes Wang et. al vs. Our Method University of Houston - Clear Lake

33. 3 Proposed Measures • Plain Path Length (PL) • Number of edges between the two terms • Path Length with Variation (PLm) • Number of common terms • Number of minimum paths • Path Length with Depth (SimPLD) • Path Length between two terms • Depth of LCA of the two terms University of Houston - Clear Lake

34. Similarity between GO terms • PL(gox, goy) = minimum path length between the two GO terms gox and goy University of Houston - Clear Lake

35. SimPLD between two Genes • gp is annotated with terms {go1,..., gon} • gq is annotated with terms {go1,..., gom } University of Houston - Clear Lake

36. Evaluation: SimPLD • Correlation between SimPLD and sequence similarity • Dataset: • SGD • FlyBase • Human-Yeast • Ontology Used: • Molecular function (MF) University of Houston - Clear Lake

37. Compare SimPLD with Sequence Similarity Based On BLAST E-Value: • High Sequence Similarity • Low Sequence Similarity • No Sequence Similarity University of Houston - Clear Lake

38. Conclusion • Gene Ontology is a reliable source to be used for functional similarity • Our semantic measures • Can be used as an automated tool to determine the genes with the similar functionalities • Has a fairly well agreement with Blast sequence similarity and results of other famous semantic measures University of Houston - Clear Lake

39. Resulted Publications • Khabiri E., Al-Mubaid H. (2007) “A path length method for gene functional similarity using GO annotations.” 16th International Conference on Software Engineering and Data Engineering SEDE 2007. Las Vegas, Nevada USA, 2007 • Khabiri E. (2007) “A Preliminary study of Correlation between depth and Path Length of GO nodes with Gene Sequence Similarity.” IEEE 7 International Conference on BioInformatics and BioEngineering BIBE07, Boston, Massachusetts USA, 2007 • Al-Mubaid H., Khabiri E., “A New Path Length Based Measure for Functional Similarity of Genes with Evaluation Using SGD Pathways.” Computational Structural Bioinformatics Workshop (CSBW), San Jose, CA (Accepted, not finalized) University of Houston - Clear Lake

40. Future Work • Apply path length-based measures to more datasets from different model organisms • More accurate evaluation • Biomedical literature • Microarray data analysis • Consider the number of distinct paths • Prediction of functionally unknown genes University of Houston - Clear Lake

41. THANK YOU! University of Houston - Clear Lake

42. THANK YOU! University of Houston - Clear Lake