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. University of Houston - Clear Lake 2 Motivation 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 HumanYeast Drug Target

3. University of Houston - Clear Lake 3Motivation To compute the similarity between two genes g 1 and g 2, 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.

4. University of Houston - Clear Lake 4Motivation Given two genes G p and G q such that gene G p is annotated with a set of n different GO terms, we call it the set GO p : GO p = {t p 1, t p 2, …., t p n } Similarly, the annotation set for gene G q is: GO q = {t q 1, t q 2, …., t q n } that is, gene G q is annotated with m different GO terms The terms t p i or t q j are nodes in the GO If both genes G p and G q are annotated with only one term (n=m=1) and the same GO term ( t p 1 = t q 1 ) then the similarity between them is maximum.

5. University of Houston - Clear Lake 5Motivation In general, if both genes G p and G q are annotated with the same set of GO terms (n=m≥1) (that is, t p i = t q j ) then the similarity between them is maximum.

6. 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

7. 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) Common Ancestor (LCA) Based on Ontology Structure

8. 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.

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

10. 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(t 1,t 2 ) of the shortest path between two concepts Scale the measure by the overall depth D of the taxonomy

11. 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 in Gene Ontology (GO) –Lord et al., 2003: Applied Resnik’s to GO –Validated the correlation between sequence and semantic similarity

12. 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

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

14. 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.

15. 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

16. 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

17. University of Houston - Clear Lake 17 Gene Ontology

18. 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)

19. University of Houston - Clear Lake 19 3 Proposed Measures 1.Plain Path Length (PL) –Number of edges between the two terms 2.Path Length with Variation (PL m ) –Number of common terms –Number of minimum paths 3.Path Length with Depth (Sim PLD ) –Path Length between two terms –Depth of LCA of the two terms

20. University of Houston - Clear Lake 20 Plain Path Length 1125847126 Parents of 11 Parents of 12 Parent of 4 Parents of 8 Considers the first level ancestor of each node in the list Parent of 5

21. University of Houston - Clear Lake 21 PL between two Genes gene p is annotated with terms {t 1,..., t n } gene q is annotated with terms {t 1,..., t m } Facl6 Annotated with 3 MF d ij : Shortest PL between t i of gene 1 and t j of gene 2

22. 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

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

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

25. University of Houston - Clear Lake 25 3 Proposed Measures 1.Plain Path Length (PL) –Number of edges between the two terms 2.Path Length with Variation (PL m ) –Number of common terms –Number of minimum paths 3.Path Length with Depth (Sim PLD ) –Path Length between two terms –Depth of LCA of the two terms

26. 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

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

28. University of Houston - Clear Lake 28 Path Length with Variation gene p is annotated with terms {t 1,..., t n } gene q is annotated with terms {t 1,..., t m } Max go_pl = 15 nct = number of common GO terms between Gp, Gq.

29. University of Houston - Clear Lake 29 Validate PL m 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

30. University of Houston - Clear Lake 30 Validate PL m : Compare with Resnik Pathway 5: allantoin degradation –4 genes, 6 pairs gene1gene2ResOurs DAL1DAL22.411 DAL1DAL32.411 DAL1DUR1,21.79.5 DAL2DAL35.213 DAL2DUR1,21.79.5 DAL3DUR1,21.79.5 They Correlate well with each other Minimum Maximum

31. University of Houston - Clear Lake 31 Validate PL m : Compare with Resnik Pathway 6: 7 genes, 21 pairs gene1gene2ResOurs ARG1ARG30.288 ARG1ARG40.288 ARG2ARG31.387.5 ARG3ARG5,61.018.5 ARG4ARG80.287 ARG1ARG80.288 PL(ARG2, ARG3) > PL(ARG3, ARG5,6) PL(ARG4, ARG8) > PL(ARG1, ARG8)

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

33. University of Houston - Clear Lake 33 3 Proposed Measures 1.Plain Path Length (PL) –Number of edges between the two terms 2.Path Length with Variation (PL m ) –Number of common terms –Number of minimum paths 3.Path Length with Depth (Sim PLD ) –Path Length between two terms –Depth of LCA of the two terms

34. University of Houston - Clear Lake 34 Similarity between GO terms PL(go x, go y ) = minimum path length between the two GO terms go x and go y

35. University of Houston - Clear Lake 35 Sim PLD between two Genes g p is annotated with terms {go 1,..., go n } g q is annotated with terms {go 1,..., go m }

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

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

38. 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

39. 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)

40. 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

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