Whole Genome Phylogenetic Analysis

Yifeng Liu and Reihaneh Rabbanyk Khorasgani

April 8th, 2009

2

Agenda

• Introduction

• Our method proposals

• Datasets and experiments

• Results

• Discussion

• Future work

• Conclusion

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

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

Complete Composition Vector (CCV)

• The observed occurrence probability for a k-string:

• The estimated background occurrence probability based on the Markov assumption is:

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Complete Composition Vector (CCV)

• The occurrence probability due to selective pressure:

The k-th composition vector:

The Complete Composition Vector (CCV):

7

Compression Methods

• Kolmogorov Complexity

• Lempel-Ziv complexity

8

Agenda

• Introduction

• Our method proposals

• Datasets and experiments

• Results

• Discussion

• Future work

• Conclusion

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?

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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.

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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.

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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 dfi provides global information.

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Ensemble Measures

Normalizing distances to same range

Combining distance matrixes

These parameters should be adjusted

TFIDFdnCompressiodCCVdd ___

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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

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

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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

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Tree Evaluation (Example)

• Phylogenetic tree

• Taxonomy– T1;T2; A– T1;T3; B– T1;T3; C– T1;T3; D

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A B

D

C

1 2 3

1

1;2

1;2;3

1;2;3

<T1;T2,1>

<T1,1> <T3,2>

<T1,1><T3, 2;3>

<T1,1><T3, 2;3>

P1

P2

P3

P4

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Agenda

• Introduction

• Our method proposals

• Datasets and experiments

• Results

• Discussion

• Future work

• Conclusion

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

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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.

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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)

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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 K1 = 1 and only vary K2, L = K2 - K1 + 1 = K2 )– Flu: L = 7, L = 15– Bac and mito: L = 7 and L = 9

• Each run generates a pairwise distance matrix.

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Experiments

• We ran GenCompress and LZ compression programs on flu and mito and calculate pairwise distance

• We tried ensembling different measures [Reihaneh]

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Experiments

• We converted pairwise distance matrices into phylogenetic trees using the Neighbor-Joining program in PHYLIP

• We visualized resulting trees using DRAWGRAM and TreeView.

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Agenda

• Introduction

• Our method proposals

• Datasets and experiments

• Results

• Discussion

• Future work

• Conclusion

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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

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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

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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

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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

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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

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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

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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

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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.

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AA versus DNA

CCV k1=3, k2=7 protein CCV k1=1, k2=7 DNA

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CCV L=7 and L=9

CCV k1=1, k2=7 DNA CCV k1=1, k2=9 DNA

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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

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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.

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Mito: primatesTF-IDF L9 cos

CCV L9 cos

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Agenda

• Introduction

• Our method proposals

• Datasets and experiments

• Results

• Discussion

• Future work

• Conclusion

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.

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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

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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

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Agenda

• Introduction

• Our method proposals

• Datasets and experiments

• Results

• Discussion

• Future work

• Conclusion

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.

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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.

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Future works

• Perform bootstrapping analysis– We are unable to perform bootstrapping

analysis due to time and computational resource constraints

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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

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Agenda

• Introduction

• Our method proposals

• Datasets and experiments

• Results

• Discussion

• Future work

• Conclusion

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.

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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

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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.

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Special thanks to …

• Professor Guohui Lin

• Dr. Zhipeng Cai

• Proteome Analyst Research Group

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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.

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