Applications of Combinatorial Pattern Discovery in Computational Genomics Nikos Darzentas Wolfson College A dissertation submitted to the University of Cambridge for the degree of Doctor of Philosophy European Molecular Biology Laboratory European Bioinformatics Institute Wellcome Trust Genome Campus Hinxton, Cambridge, CB10 1SD United Kingdom Email: [email protected] 26 November 2005 To my mother and father and sister, for reasons which would easily cover this Thesis, and another, and another... To all those who have put up with me, especially Melanie, and to all those who have shown me respect, my life’s fuel. ii This Thesis is the result of my own work and includes nothing which is the outcome of work done in collaboration except where specifically indicated in the text. This Thesis does not exceed the specified length limit of 300 pages as defined by the Biology Degree Committee. This Thesis has been typeset in 12pt font according to the specifications defined by the Board of Graduate Studies and the Biology Degree Committee. iii Applications of Combinatorial Pattern Discovery in Computational Genomics: Summary This Thesis describes various applications of combinatorial pattern discovery in protein sequence analysis and computational comparative genomics. The work is the result of more than three years of personal and collaborative research towards the completion of my doctoral research in computational biology and bioinformatics. In this Thesis, Chapters 1 to 4 correspond to an introduction that describes aspects of computational genomics, resources used and specific components developed by the candidate. Chapter 1 forms an introduction to Bioinformatics, and discusses key issues of data availability and integration. Chapter 2 introduces joint work involving the monitoring of genome sequences and their re-distribution, as well as the availability of scientific literature for the corresponding species. Chapter 3 describes key resources jointly developed and further used in the following Chapters, including the COGENT genome sequence database and its various extensions, the OFAM database of reciprocal best hits corresponding to putative orthologs, and the integration of genome information into metabolic pathways using the BioCyc collection. Chapter 4 discusses other methods that were used in the analyses presented in this Thesis, including TRIBE-MCL, GeneTRACE and Cluster Annotator. Chapter 5 is an interlude, describing the representation of the tree of life as a network and the reconstruction of the last universal common ancestor. The phylogeny of the microbial world with vertically and horizontally inherited gene flows results in the net of life, a new representation of phylogenetic relationships. The subsequent analysis uses ancestral reconstruction for gene content to infer a minimal estimate for the genome of the last universal common ancestor. The remaining Chapters, namely Chapters 6 to 9, address issues of sequence analysis and in particular the detection of distantly related protein families. Chapter 6 addresses sequence comparison, sequence alignment, and methods for the detection of remote sequence similarity. Chapter 7 advocates combinatorial pattern discovery as an attractive alternative to widely used methods, in terms of computational efficiency, biological accuracy and general applicability, where a number of other collaborative projects are also described. Chapter 8 presents a well-defined control experiment for the detection of the blue-copper binding domain across distantly related protein families. Finally, Chapter 9 extends the previous work across the entire range of the protein fold hierarchy and is reported as work in progress. iv Preface This Thesis is the end result of an adventurous and challenging but also immensely rewarding Ph.D. career at the European Bioinformatics Institute (EBI), an outstation of the European Molecular Biology Laboratory (EMBL). It started in October 2001 with an excellent course in Heidelberg (the EMBL headquarters), and ends late 2005. I was awarded an EMBL predoctoral fellowship to work with the Computational Genomics Group under the supervision of Dr. Christos Ouzounis. Firstly, I would like to thank Dr. Christos Ouzounis, for his supervision in general, and in particular for those long late night meetings at the EBI that have taught me a lot about science and life in equal measure. Then, a very loud thank you to every member of the Computational Genomics Group, past and present, long-term and visiting, everyone, for making this worthwhile and being there in the good times and the bad. Also, I would like to acknowledge familiar faces at the EBI and EMBL who shared science, sports, lunches and dinners and coffee breaks with me. To my fellow PhD students, I would like to say that it has been a privilege to be around so talented individuals, and of course wish everyone best of luck! Finally, to my Cambridge University Automobile Club (CUAC) friends, I would like to send my appreciation for not throwing me in tyre walls too often during kart races, and for making it possible for me to have so much fun racing with and against them. v Table of Contents Applications of Combinatorial Pattern Discovery: Summary ......................................iv Preface............................................................................................................................v Table of Contents..........................................................................................................vi List of Figures................................................................................................................x List of Tables ................................................................................................................xi Chapter 1 Introduction................................................................................................1 1.1 Bioinformatics................................................................................................1 1.2 Comparative Genomics and Phylogenetic Studies ........................................3 1.3 Structural and Functional Genomics..............................................................4 1.4 Protein Domains and Sequence Patterns........................................................5 1.5 Data Availability, Integration and Knowledge Discovery.............................5 1.6 Conclusion .....................................................................................................6 1.7 Disclaimer......................................................................................................6 Chapter 2 Genome explosion .....................................................................................7 2.1 Beyond 100 genomes.....................................................................................7 2.1.1 Disclaimer............................................................................................11 2.2 Genome coverage, literally speaking - The challenge of annotating 200 genomes with 4 million publications .......................................................................11 2.2.1 Disclaimer............................................................................................17 Chapter 3 Developed Databases...............................................................................18 3.1 Complete Genome Tracking (CoGenT): a flexible data environment for computational genomics ..........................................................................................18 3.1.1 Introduction..........................................................................................18 3.1.2 Implementation ....................................................................................20 3.1.3 Design ..................................................................................................20 3.1.4 Usage and future plans.........................................................................22 3.1.5 Disclaimer............................................................................................22 3.2 CoGenT++: An extensive and extensible data environment for computational genomics ..........................................................................................23 3.2.1 Abstract................................................................................................23 3.2.2 Introduction..........................................................................................23 3.2.3 The CoGenT++ environment...............................................................24 3.2.4 System backbone.................................................................................27 3.2.5 ProXSim: a similarity database............................................................28 3.2.6 Incremental update mechanism............................................................29 3.2.7 TRIBEs and OFAM: Protein families and putative orthologs.............30 3.2.8 AllFuse: protein fusions.......................................................................30 3.2.9 ProfUse: phylogenetic profiles ............................................................31 3.2.10 CoGenT++ extensions.........................................................................31 3.2.11 Comparison to other systems...............................................................32 3.2.12 Data access and platform requirements ...............................................33 3.2.13 Disclaimer............................................................................................33