Automatic Discovery of Hidden Associations Using Vector Similarity

213 Pages·2017·17.49 MB·English

by  Seyed Ziaeddin Alborzi

Automatic Discovery of Hidden Associations Using
Vector Similarity: Application to Biological Annotation
Prediction
Seyed Ziaeddin Alborzi
To cite this version:
Seyed Ziaeddin Alborzi. Automatic Discovery of Hidden Associations Using Vector Similarity: Appli-
cation to Biological Annotation Prediction. Bioinformatics [q-bio.QM]. Université de Lorraine, 2018.
English. ￿NNT: 2018LORR0035￿. ￿tel-01792299￿
HAL Id: tel-01792299
https://theses.hal.science/tel-01792299
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E´cole doctorale IAEM Lorraine
Automatic Discovery of Hidden
Associations Using Vector Similarity:
Application to Biological Annotation
Prediction
`
THESE
pr´esent´ee et soutenue publiquement le 23 F´evrier 2018
pour l’obtention du
Doctorat de l’Universit´e de Lorraine
(mention informatique)
par
Seyed Ziaeddin ALBORZI
Composition du jury
Rapporteurs : Wim Vranken PR Vrije Universiteit Brussel, Brussels
Graham Kemp PR Chalmers University of Technology, Gothenburg
Examinateurs : Olivier Poch DR CNRS, Strasbourg
Alessandra Carbone PR Sorbonne Universit´e, Paris
Anne Boyer PR Universit´e de Lorraine, Nancy
Malika Sma¨ıl-Tabbone MC Universit´e de Lorraine, Nancy
Encadrants : Marie-Dominique Devignes CR CNRS, Nancy
David W. Ritchie DR INRIA, Nancy
E´quipe CAPSID – INRIA Nancy Grand Est
Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA) — UMR 7503
Mis en page avec la classe thesul.
Remerciements
I would like to thank all the people who contributed in some way to the work described in this thesis.
Firstandforemost,Ithankmyacademicadvisors,Dr. DaveRitchieandDr. Marie-DominiqueDevignes,
for accepting me into CAPSID team at Institut national de recherche en informatique et en automatique
(INRIA). During my tenure, they contributed to a rewarding doctoral school experience by giving me
intellectual freedom in my work, supporting my attendance at various conferences, engaging me in new
ideas, and demanding a high quality of work in all my endeavors. Additionally, I would like to thank
my committee members Dr. Carbone, Dr. Poch, Dr. Vranken, Dr. Kemp, Dr. Boyer, and Dr. Sma(cid:239)l-
Tabbone for their interest in my work. I was fortunate to have the chance to work in UniProt team
at European Bioinformatics Institute (EBI). I worked (nonstop) as a predoctoral visitor in the team
of Dr. Maria Martin of EBI for three months starting from October 2016. I am very grateful to Dr.
Rabie Saidi and Mr. Alex Reneaux who helped me in creating rules for protein functional annotation
I present in this thesis. I am grateful for the funding sources that allowed me to pursue my doctoral
studies: ANRFellowship,RegionofLorraineandINRIA.IwouldliketoacknowledgetheDoctoralSchool
at University of Lorraine. My graduate experience bene(cid:28)ted greatly from the courses I took. I would
like to acknowledge my friends, Farhad, Saeid, Soheib, Mojdeh, Valia, Daishi, Iordan, Ehsan, Mohanna,
Meysam,Younesandfamilymembers,Baba,Maman,Emad,Hesam,Carol,BabaMapar,MamanMapar,
Mahsa, Mahna, Payam, Samira, Parinaz who supported me during my time here. Finally, I would like
to thank my lovely wife, Mahta for her constant love and support.
i
ii
This thesis is dedicated to
My lovely wife who always believed in me,
My wonderful parents who have raised me to be the person I am today.
iii
iv
Contents
List of Tables xi
Introduction en fran(cid:231)ais 1
Introduction
Chapter 1
Background
1.1 Data Science Context - Data Preparation, Mining, and Interpretation . . . . . . . . . . . 13
1.1.1 Knowledge Discovery from Data and Data Mining . . . . . . . . . . . . . . . . . . 13
1.1.2 Machine Learning and Data Mining . . . . . . . . . . . . . . . . . . . . . . . . . . 14
1.1.3 Information Filtering and Recommendation Systems . . . . . . . . . . . . . . . . . 18
1.1.4 Data Structure and Representation . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
1.1.5 Statistical Validation of Extracted Pattern . . . . . . . . . . . . . . . . . . . . . . 21
1.2 Biological Context - Protein Function, Domain, and Interaction . . . . . . . . . . . . . . . 22
1.2.1 Protein Sequence and Structure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22
1.2.2 Protein Function . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29
1.2.3 Protein Domains and Families . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33
1.2.4 Protein Interaction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41
Chapter 2
Discovering Hidden Associations between Enzyme Commission Numbers and Pfam
Domains
2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48
2.2 Methods and Materials. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50
2.2.1 Data Preparation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50
2.2.2 Inferring EC-Pfam Domain Associations . . . . . . . . . . . . . . . . . . . . . . . . 52
2.2.3 De(cid:28)ning a Con(cid:28)dence Score Threshold . . . . . . . . . . . . . . . . . . . . . . . . . 52
2.2.4 Exploiting the EC Number Hierarchy . . . . . . . . . . . . . . . . . . . . . . . . . 53
2.2.5 Hypergeometric Distribution p-Value Analysis . . . . . . . . . . . . . . . . . . . . 53
2.3 Results and Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54
2.3.1 Data Source Weights and Score Threshold . . . . . . . . . . . . . . . . . . . . . . . 54
v
Contents
2.3.2 Global Analysis of Inferred EC-Pfam Associations . . . . . . . . . . . . . . . . . . 54
2.3.3 Comparison with dcGO . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56
2.3.4 Selecting plausible associations in multi-domain proteins . . . . . . . . . . . . . . . 57
2.3.5 Single and Multiple EC-Pfam Associations . . . . . . . . . . . . . . . . . . . . . . 57
2.3.6 Annotating PDB Chains with EC Numbers . . . . . . . . . . . . . . . . . . . . . . 60
2.3.7 The ECDomainMiner web server . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60
2.4 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60
Chapter 3
Computational Discovery of Direct Associations between Annotations using Common
Content - CODAC
3.1 CODAC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64
3.1.1 Tripartite Graph Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64
3.1.2 Biadjacency Representation of bigraphs . . . . . . . . . . . . . . . . . . . . . . . . 65
3.1.3 Gold Standard of Positive and Negative Examples . . . . . . . . . . . . . . . . . . 65
3.1.4 Determining the Score Threshold . . . . . . . . . . . . . . . . . . . . . . . . . . . . 68
3.1.5 Combining Multiple Datasets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 68
3.1.6 Bipartite Graph Extension with Hierarchy of Classes . . . . . . . . . . . . . . . . . 68
3.1.7 Clustering Graph Edges . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 70
3.1.8 Calculating Statistically Signi(cid:28)cant Edges in E . . . . . . . . . . . . . . . . . . . 70
3∗
3.1.9 Classi(cid:28)cation into Gold, Silver, and Bronze Associations . . . . . . . . . . . . . . . 71
3.2 GODomainMiner: ComputationalDiscoveryofDirectAssociationsbetweenGOtermsand
Protein Domains . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 72
3.2.1 GODomainMiner Data Preparation . . . . . . . . . . . . . . . . . . . . . . . . . . 73
3.2.2 Dataset Weights and Threshold Scores . . . . . . . . . . . . . . . . . . . . . . . . . 74
3.2.3 Analysis of Calculated GO-Pfam Associations . . . . . . . . . . . . . . . . . . . . . 74
3.2.4 Distribution of GO-Domain Associations per GO term or per domain . . . . . . . 75
3.2.5 Comparison with GO-Domain Associations from dcGO . . . . . . . . . . . . . . . 80
3.2.6 Biological Assessment of New Discovered GO-Pfam Associations . . . . . . . . . . 81
3.3 Implementation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 84
3.4 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 85
Chapter 4
Functional Annotation of Protein Sequences and Structures
4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 88
4.2 Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 89
4.2.1 Method Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 89
4.2.2 Using CODAC to Infer Function-Domain Associations . . . . . . . . . . . . . . . . 91
4.2.3 Combinatorial Generation of Association Rules . . . . . . . . . . . . . . . . . . . . 92
4.2.4 Knowledge-based Filtering of Association Rules . . . . . . . . . . . . . . . . . . . . 92
4.2.5 Aggregating and Applying Function Annotation Models . . . . . . . . . . . . . . . 95
4.2.6 Extension to Other Protein Annotations . . . . . . . . . . . . . . . . . . . . . . . . 96
vi