Modular Algorithms for Biomolecular Network Alignment

163 Pages·2016·1.85 MB·English

by  Fadi George Towfic

Iowa State University Capstones, Theses and
Graduate Theses and Dissertations
Dissertations
2011
Modular Algorithms for Biomolecular Network
Alignment
Fadi George Towfic
Iowa State University
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Recommended Citation
Towfic, Fadi George, "Modular Algorithms for Biomolecular Network Alignment" (2011).Graduate Theses and Dissertations. 12031.
https://lib.dr.iastate.edu/etd/12031
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Modular algorithms for biomolecular network alignment
by
Fadi George Towfic
A dissertation submitted to the graduate faculty
in partial fulfillment of the requirements for the degree of
DOCTOR OF PHILOSOPHY
Major: Bioinformatics and Computational Biology
Program of Study Committee:
Vasant Honavar, Co-major professor
M. Heather West Greenlee, Co-major professor
Drena Dobbs
Robert Jernigan
Christopher Tuggle
Iowa State University
Ames, Iowa
2011
Copyright (cid:13)c Fadi George Towfic, 2011. All rights reserved.
ii
DEDICATION
This dissertation is dedicated to my parents, for their endless support, constant encourage-
ment and sage advice.
iii
TABLE OF CONTENTS
LIST OF TABLES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vii
LIST OF FIGURES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . x
ACKNOWLEDGEMENTS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xvii
ABSTRACT . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .xviii
CHAPTER 1. INTRODUCTION . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.1 Overview of network models and systems biology . . . . . . . . . . . . . . . . . 1
1.2 The network alignment problem . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
1.3 Formal mathematical definition of network alignment . . . . . . . . . . . . . . . 5
1.4 Brief overview of state-of-the-art methods . . . . . . . . . . . . . . . . . . . . . 5
1.4.1 MaWISh . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
1.4.2 NetworkBLAST-M . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
1.4.3 Graemlin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
1.4.4 GRAAL . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8
1.5 Limitations of current methods . . . . . . . . . . . . . . . . . . . . . . . . . . . 8
1.6 Significant contributions of dissertation . . . . . . . . . . . . . . . . . . . . . . 9
1.6.1 First highly modular algorithm in the field . . . . . . . . . . . . . . . . 9
1.6.2 Highly scalable algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . 9
1.6.3 First highly flexible algorithm . . . . . . . . . . . . . . . . . . . . . . . . 9
1.6.4 Highly portable . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
1.6.5 High accuracy in terms of biological performance . . . . . . . . . . . . . 10
1.6.6 Applied to important biological problems . . . . . . . . . . . . . . . . . 10
iv
CHAPTER 2. BIOMOLECULAR NETWORK ALIGNMENT (BiNA)
TOOLKIT . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12
2.1 Background and Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
2.2 Problem Formulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
2.3 Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
2.3.1 Divide: Partitioning methods . . . . . . . . . . . . . . . . . . . . . . . . 16
2.3.2 Conquer: Scoring Functions . . . . . . . . . . . . . . . . . . . . . . . . . 19
2.4 Summary and Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23
CHAPTER3. COMPARATIVEANALYSISOFTOPOLOGICALVS.NODE
LABEL-BASED NETWORK ALIGNMENT . . . . . . . . . . . . . . . . . . 26
3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26
3.2 Materials and methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29
3.2.1 Network Alignment Algorithm . . . . . . . . . . . . . . . . . . . . . . . 29
3.2.2 Scoring Functions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32
3.2.3 Datasets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36
3.2.4 Evaluation of Alignment . . . . . . . . . . . . . . . . . . . . . . . . . . . 37
3.3 Results. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38
3.3.1 Performance as Measured by GO Enrichment . . . . . . . . . . . . . . . 38
3.3.2 Reconstruction of Phylogenetic Relationships . . . . . . . . . . . . . . . 40
3.4 Discussion and conclusions. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43
CHAPTER 4. DETECTION OF GENE ORTHOLOGY FROM GENE CO-
EXPRESSION AND PROTEIN INTERACTION NETWORKS . . . . . . 47
4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48
4.2 Materials and methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50
4.2.1 Dataset . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50
4.2.2 Graph representation of BLAST orthologs . . . . . . . . . . . . . . . . . 51
4.2.3 Random walk graph kernel score . . . . . . . . . . . . . . . . . . . . . . 52
4.2.4 BaryCenter score . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53
v
4.2.5 Betweenness score . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54
4.2.6 Degree distribution score . . . . . . . . . . . . . . . . . . . . . . . . . . 54
4.2.7 HITS score . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54
4.2.8 Scoring candidate orthologs based on sequence and network similarity . 55
4.2.9 Ortholog detection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55
4.2.10 Performance evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . . 56
4.3 Analysis and results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57
4.3.1 Reconstructing KEGG orthologs using BLAST . . . . . . . . . . . . . . 57
4.3.2 Reconstructing KEGG orthologs using sequence, protein-protein interac-
tion network, and gene-coexpression data . . . . . . . . . . . . . . . . . 57
4.4 Discussion and future work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58
CHAPTER 5. B CELL LIGAND GENE COEXPRESSION NETWORKS
REVEAL REGULATORY PATHWAYS FOR LIGAND PROCESSING . 67
CHAPTER 6. TOOLS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 96
6.1 BiNA webserver. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 96
6.2 BiNA program . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 98
CHAPTER 7. CONCLUSIONS . . . . . . . . . . . . . . . . . . . . . . . . . . . 101
7.1 Significant contributions of dissertation . . . . . . . . . . . . . . . . . . . . . . 102
7.1.1 First highly modular algorithm in the field . . . . . . . . . . . . . . . . 102
7.1.2 Highly scalable algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . 102
7.1.3 First highly flexible algorithm . . . . . . . . . . . . . . . . . . . . . . . . 102
7.1.4 Highly portable . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 103
7.1.5 High accuracy in terms of biological performance . . . . . . . . . . . . . 103
7.1.6 Applied to important biological problems . . . . . . . . . . . . . . . . . 103
7.2 Open problems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 104
7.2.1 Evaluation methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 104
7.2.2 Applicability to more network models . . . . . . . . . . . . . . . . . . . 104
7.2.3 More detailed network models. . . . . . . . . . . . . . . . . . . . . . . . 105
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7.2.4 Rapid comparisons . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 105
7.2.5 Integrated pipeline for analysis and visualization . . . . . . . . . . . . . 106
APPENDIX. ADDITIONAL MATERIAL FOR CHAPTER 6 . . . . . . . . . 107
BIBLIOGRAPHY . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 127
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LIST OF TABLES
Table 3.1 ComparisonofGraphKernelPerformanceusingBLASTtomatchinitial
node centers in K-Hop alignment between human (Hs), mouse (Mm),
yeast (Sc) and fly (Dm). Bold entries are adapted from our previ-
ous results on K-hop alignments (152). The methods are denoted as
SP (Shortest Path), RW (Random Walk), PR (Page Rank), KL (Kull-
back–Leiblerdivergence),Pearson(Pearsoncorrelation),Spearman(Spear-
man rank correlation) and Chi (Chi-squared test statistic). . . . . . . . 41
Table 3.2 Comparison of Graph Kernel Performance using pure topological align-
ment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42
Table 3.3 Comparison of the bootstrap performance in reconstructing phyloge-
netic relationships between mouse-human and fly-yeast branches. . . . 44
Table 4.1 Performance of the Reciprocal BLAST hit method on the fly, yeast,
human and mouse protein-protein interaction datasets from DIP as well
as the gene coexpression networks for mouse and human from GEO . . 60
Table 4.2 Performance of the Reciprocal BLAST hit score as a feature to the
decision tree (j48), Naive Bayes (NB), Support Vector Machine (SVM)
and Ensemble classifiers on the fly, yeast, human and mouse protein-
protein interaction datasets from DIP as well as the gene coexpression
networks for mouse and human from GEO. Values in parenthesis are
the ranks for the classifiers on the specified dataset . . . . . . . . . . . 65
viii
Table 4.3 Performance of all the combined features (Reciprocal BLAST hit score,
1 and 2 hop shortest path graph kernel score, 1 and 2 hop random walk
graph kernel score, BaryCenter, betweenness, degree distribution and
HITS) as input to the decision tree (j48), Naive Bayes (NB), Support
VectorMachine(SVM)andEnsembleclassifiersonthefly,yeast,human
and mouse protein-protein interaction datasets from DIP as well as the
gene coexpression networks for mouse and human from GEO. Values in
parenthesis are the ranks for the classifiers on the specified dataset . . 65
Table 4.4 KEGG orthologs detected using the Ensemble classifier utilizing all net-
work features. The orthologs shown in the above table were missed by
the BLAST logistic regression classifier . . . . . . . . . . . . . . . . . . 66
Table 5.1 Full list of the ligands and their abbreviations used in the experiments
analyzed in this paper. This list was adapted from Lee et al. (104) . . 91
Table 5.2 List of pathways detected based on high-intensity probes from the mi-
croarray data. Please see Table 1 in supplementary material for a more
detailed version of this table with pathway names and relative number
of genes enriched in the pathway based on the data . . . . . . . . . . . 92
Table 5.3 Top matched ligands based on expression patterns in the consensus tree
shown in Figure 5. The KEGG pathway categories correspond to the
pathway categories highlighted in Table 2. Please see Table 3 in the
supplementary material for an expanded version of this table . . . . . 95
Table A.1 Full list of networks and the number of neighborhoods utilized for com-
paring the networks for figure 2 in supplementary material. . . . . . . 110
Table A.2 List of pathways detected based on high-intensity probes from the mi-
croarray data. As can be seen from the table, many of the pathways
enriched in high-intensity genes are known to be implicated in the de-
velopment of the immune system and processing of antigens . . . . . . 118
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Table A.3 Top matched ligands based on expression patterns in the consensus tree
shown in figure 5 in the paper. The KEGG pathway categories corre-
spond to the pathway categories highlighted in table 2 in the supple-
mentary material and table 2 in the paper . . . . . . . . . . . . . . . . 126