1. Whole Genome Phylogenetic Analysis Yifeng Liu and Reihaneh Rabbanyk Khorasgani April 8th, 2009

2. 2 Agenda Introduction Our method proposals Datasets and experiments Results Discussion Future work Conclusion

3. 3 Whole Genome Phylogeny: Motivations Currently the dominant method for phylogenetic analysis is based on a single gene or protein. However different gene tells a different story Recently more genomic sequences became available We hope to resolve the above inconsistency by using the entire genome (or proteome) to reconstruct phylogenetic tree.

4. 4 Whole Genome Phylogeny: Methods Major categories of methods are based on: –Shared gene (ortholog) content –Nucleotide and amino acid (string) composition –Genome Compression –Gene order In our study, we focus on string composition and compression methods

5. 5 Complete Composition Vector (CCV) The observed occurrence probability for a k-string: The estimated background occurrence probability based on the Markov assumption is:

6. 6 Complete Composition Vector (CCV) The occurrence probability due to selective pressure: The k-th composition vector: The Complete Composition Vector (CCV):

7. 7 Compression Methods Kolmogorov Complexity Lempel-Ziv complexity

8. 8 Agenda Introduction Our method proposals Datasets and experiments Results Discussion Future work Conclusion

9. 9 A new term weighting scheme CCV uses S() to weight each k-string, which –Utilizes only local information available within a single sequence –Estimates random background based on Markov model Can we have a measure that use both local and global information without making the Markov assumption?

10. 10 Term and Document Frequency Genomes are documents written in a language of four alphabets {A,T,C,G}; similarly, proteomes are documents written in a language of twenty alphabets. Each k-string can be viewed as a word within a gnome (or proteome) document. The collection of all genomes in the dataset is therefore a corpus.

11. 11 Term and Document Frequency In statistical Natural Language Processing, a well- known term weighting scheme TF-IDF combines both term frequency and document frequency into a single weight.

12. 12 CCV meets Document Frequency We can also combine the occurrence probability due to selection S() with the inverse document frequency into a single weight called CCV-IDF. S() provides local information and df i provides global information.

13. 13 Ensemble Measures Normalizing distances to same range Combining distance matrixes These parameters should be adjusted

14. 14 Tree Evaluation We propose a new evaluation method for evaluating phylogenetic trees A numeric measure Shows how compatible the tree is with the given taxonomy

15. Tree Evaluation (Cont.) Labeling the inner nodes in the tree For each species –A path in the tree  sequence of inner node labels –A taxonomy description  taxonomy sequence –There should be a many to many alignment between these two sequences 15

16. Tree Evaluation (Cont.) Finding alignment between these sequences for all the species –Using Bayesian Network Finding the most probable alignments Measuring the Log likelihood of these alignment –How probable is this tree given this taxonomy 16

17. Tree Evaluation (Example) Phylogenetic tree Taxonomy –T1;T2; A –T1;T3; B –T1;T3; C –T1;T3; D 17 AB D C 123 1 1;2 1;2;3 P1 P2 P3 P4

18. 18 Agenda Introduction Our method proposals Datasets and experiments Results Discussion Future work Conclusion

19. 19 Dataset: influenza virus Influenza virus genomes (flu) –44 influenza A genomes (3 for H1-H13, 2 for H16) –3 influenza B genomes –1 influenza C genome (out group) –Coding gene sequences only –Collected and joined from individual gene sequences according to the following order: HA, NA, NP, M, NS, PA, PB1, PB2

20. 20 Dataset: Prokaryotes Prokaryote genomes (bac) –88 bacterial genomes –11 archaean genomes –Uses Nanoarchaeum equitans as the out group. –Collected from NCBI according to the accession number provided in the CCV paper. –Genomeic DNA sequence including intergenic regions.

21. 21 Dataset: Mammal mitochondria Mammal mitochondria (mito) –425 mammal mitochondria –1 Arabidopsis mitochondrion (out group) –Collected from the Organelle Genome Megasequencing Program website. –converted from NCBI format to fasta format. –Contains many duplicated entries for: Bos taurus (cattle) Sus scrofa (wild Boar) Mus musculus (mouse) Rattus norvegicus (rat)

22. 22 Experiments We built a multiple sequence alignment tree for flu We ran CCV, TF-IDF and CCV-IDF on all three datasets with the following k-string length: ( we fixed K 1 = 1 and only vary K 2, L = K 2 - K 1 + 1 = K 2 ) –Flu: L = 7, L = 15 –Bac and mito: L = 7 and L = 9 Each run generates a pairwise distance matrix.

23. 23 Experiments We ran GenCompress and LZ compression programs on flu and mito and calculate pairwise distance We tried ensembling different measures [Reihaneh]

24. 24 Experiments We converted pairwise distance matrices into phylogenetic trees using the Neighbor-Joining program in PHYLIP We visualized resulting trees using DRAWGRAM and TreeView.

25. 25 Agenda Introduction Our method proposals Datasets and experiments Results Discussion Future work Conclusion

26. 26 MSA trees versus HA tree HA tree by Suzuki et.al. MSA tree H1, 2, 3 H5, 6, 9 H4, 15, 16, 13 B H7, 10, 12, 8

27. 27 MSA versus Compression MSA tree 1, 2, 3 5, 6, 9 4, 15, 16, 13 B 7, 10, 12, 8 GenCompress 1, 2, 3 15, 4, 5, 6, 9 10, 12, 8 13, 16 B 7

28. 28 MSA versus CCV MSA tree H1, 2, 3 H5, 6, 9 H4, 15, 16, 13 B 7, 8, 10, 12 CCV L15 cos 1, 2, 3 7, 8, 10, 12 B 4, 5, 6, 9 15 13, 16

29. 29 MSA vs TF-IDF MSA tree H1, 2, 3 H5, 6, 9 H4, 15, 16, 13 B H7, 10, 12, 8 TF-IDF L15 cos H1, 2, 3 B 13, 16 4, 5, 6, 9 15 7, 8, 10, 12, 11

30. 30 MSA vs CCV-IDF MSA tree H1, 2, 3 H5, 6, 9 H4, 15, 16, 13 B 7, 8, 10, 12 CCV-IDF L15 cos H1, 2, 3 13, 16 8, 10, 12 B 7 4, 5, 6, 9 15

31. 31 CCV vs TF-IDF TF-IDF L15 cos H1, 2, 3 B 13, 16 4, 5, 6, 9 15 7, 8, 10, 12, 11 CCV L15 cos 1, 2, 3 7, 8, 10, 12 B 4, 5, 6, 9 15 13, 16

32. 32 CCV vs CCV-IDF CCV-IDF L15 cos 1, 2, 3 13, 16 8, 10, 12 B 7 4, 5, 6, 9 15 CCV L15 cos 1, 2, 3 7, 8, 10, 12 B 4, 5, 6, 9 15 13, 16

33. 33 Observations All methods (MSA, CCV, GenCompress, TF- IDF, CCV-IDF) generate similar results. Our results are significantly different from previous studies. Most clades are intact while some are scattered around. Most clades are pure while some are mixed with species from nearby clades. CCV and CCV-IDF results are highly similar.

34. 34 AA versus DNA CCV k1=3, k2=7 proteinCCV k1=1, k2=7 DNA

35. 35 CCV L=7 and L=9 CCV k1=1, k2=7 DNACCV k1=1, k2=9 DNA

36. 36 Observations Most clades are intact. For similar CCV length, the DNA tree is worse than the protein tree and unable to recognize Archaea as a distinctive clade. CCV trees are similar for length 7 and length 9. –Similarly the L7, L15 and L21 tree for flu are almost identical

37. 37 Mito results For the mito dataset, we have similar observations. All methods failed to resolve fine branches of the tree by mixing in distant species.

38. 38 Mito: primates TF-IDF L9 cos CCV L9 cos

39. 39 Agenda Introduction Our method proposals Datasets and experiments Results Discussion Future work Conclusion

40. 40 DNA versus AA Sequence There are more k-strings for protein sequence than DNA sequence for the same length. –We need longer k-strings for DNA to achieve the same resolution as amino acid (AA) sequence. Due to the redundant nature of the genetic code, different DNA k-strings may correspond to the same AA k-string. –AA k-strings can share information even though their DNA sequence might be different DNA sequence may contain intergenic regions which do not response to selection pressure –Intergenic region may not contribute much to the resolution of the tree; they might even reduce such resolution.

41. 41 Thoughts on Document Frequency We did not observe significant performance difference by adding in document frequency information. For longer genome (e.g. bac), we need longer k-strings to see the effect of DF. –All bac genomes share 87.9% 9-strings and only 0.8% 11-strings

42. 42 Compression programs Current compression programs are problematic –LZ could not handle large datasets –Kolmogorov is not applicable for large sequences These method should be reimplemented

43. 43 Agenda Introduction Our method proposals Datasets and experiments Results Discussion Future work Conclusion

44. 44 Future works Run the same experiments on protein sequence –To investigate the effect of using AA versus DNA sequences. –We expect to see better results with protein sequences –New result may reveal subtle difference between different methods.

45. 45 Future works Speed up the implementation for TF-IDF and run them on longer k-strings –Computational complexity is the bottle neck for achieving high resolution in a reasonable amount of time. –Initially the calculations for TF and IDF are separated: slow –We achieved significant speedup by integrating the calculation of TF and IDF into a two-pass algorithm –We may drop k-string with low TF-IDF values to further speed up the program.

46. 46 Future works Perform bootstrapping analysis –We are unable to perform bootstrapping analysis due to time and computational resource constraints

47. 47 Future works In our proposed evaluation method, we need a Many to many alignment which is not a trivial task –It is well studied in Machine translation and Natural Language Processing and those techniques could help here This measure could also be used as a measure of similarity between trees

48. 48 Agenda Introduction Our method proposals Datasets and experiments Results Discussion Future work Conclusion

49. 49 Conclusion All string composition methods (CCV, TF-IDF, CCV-IDF) somewhat group most similar species together and produce consistent results. –However they all failed to resolve big branches as well as fine branches. We did not observe significant improvement by adding document frequency. –But we will need further experiments (with longer k-strings on AA sequences) to fully understand the effect.

50. 50 Major Contributions We proposed a novel term weighting scheme which achieves similar performance as CCV in our experiments We proposed the notion of adding in global information in the form of document frequency We discovered that using protein sequence may significantly improve performance for all methods We proposed a novel evaluation method for phylogenetic trees

51. 51 Author Contributions Yifeng –Collected all three data sets –Performed CCV experiments –Implemented TF-IDF and CCV-IDF Reihaneh –Built MSA tree for flu –Performed Compression experiments –Implemented ensemble and evaluation methods.

52. 52 Special thanks to … Professor Guohui Lin Dr. Zhipeng Cai Proteome Analyst Research Group

53. 53 References [1] Xin Chen, Sam Kwong, and Ming Li. A compression algorithm for dna sequences and its applications in genome comparison. In in Genome Informatics, pages 52-61, 1999. [2] Joseph Felsenstein. Phylip - phylogeny inference package (version 3.2). Cladistics, 5:164-166, 1989. [3] M. Li, J. H. Badger, X. Chen, S. Kwong, P. Kearney, and H. Zhang. An information-based sequence distance and its application to whole mitochondrial genome phylogeny. Bioinformatics, 17(2):149-154, 2001. [4] Yifeng Liu and Reihaneh Rabbanyk Khorasgani. A survey on whole genome phylogenetic analysis. CM- PUT 606 course survey, Feburary 2009. [5] Christopher D. Manning and Hinrich Schtze. Foundations of Statistical Natural Language Processing. The MIT Press, June 1999. [6] Hasan H. Otu and Khalid Sayood. A new sequence distance measure for phylogenetic tree construction. Bioinformatics, 19(16):2122-2130, 2003. [7] N. Saitou and M. Nei. The neighbor-joining method: a new method for reconstructing phylogenetic trees. Mol Biol Evol, 4(4):406-425, July 1987. [8] Yoshiyuki Suzuki and Masatoshi Nei. Origin and evolution of influenza virus hemagglutinin genes. Mol Biol Evol, 19(4):501-509, April 2002. [9] Xiaomeng Wu, Xiufeng Wan, Gang Wu, Dong Xu, and Guohui Lin. Phylogenetic analysis using complete signature information of whole genomes and clustered neighbor-joining method. International Journal on Bioinformatics Research and Application, 2(3):219-248, 2006.