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Case Western Reserve University

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Tutorial 2: Computational Regulatory Genomics

Title: Regulatory and Epigenetic Landscapes of Genomes: Fundamental Concepts and In Silico Analysis Methods

Presenters:
Laura Elnitski, Head, Genomic Functional Analysis Section, Genome Technology Branch, National Human Genome Research Institute, National Institutes of Health

Lonnie Welch, Stuckey Professor, School of Elec. Eng. & Computer Science, Biomedical Engineering Program, Molecular and Cellular Biology Program, Ohio University

Frank Drews, Assistant Professor of School of Electrical Engineering and Computer Science, Ohio University

Stephen Sauchi Lee, Associate Professor of Statistics, Affiliated Professor of Bioinformatics and Computational Biology, Department of Statistics, College of Science, University of Idaho

Abstract: Regulation of any genome is a complex and multifaceted enterprise. This tutorial will discuss aspects of genome regulation including global regulatory organization, techniques for assessing chromosomal interactions, types of functional elements, pattern searching in the genome, epigenomics, genome methylation, and the landscape of regulatory mutations. As we enter into the era of research where integrative analyses are essential for the interpretation of biological data, familiarity with these and other emerging areas of genome regulation will be vital to ensure success.

This tutorial will present an overview of bioinformatics technology that is used in the field of regulatory and functional genomics. Specifically, the tutorial will (1) provide a taxonomic and experimental characterization of bioinformatics methods for discovering functional regulatory elements in genomic sequences, (2) teach attendees how to use several bioinformatics tools for identifying putative functional elements in DNA sequences, (3) highlight important outcomes of the ENCyclopedia Of DNA Elements (ENCODE) project, and (4) present research opportunities and bioinformatics challenges related to understanding the regulatory aspects of genomes.

The tutorial will present the algorithmic concepts employed in bioinformatics tools that are used to discover putative functional elements. Specifically, the tutorial will discuss state-of-the-art algorithms, data structures, that are used to efficiently enumerate the “word spaces” of genomic sequences. The tutorial will demonstrate that the analysis of large input sets (e.g., entire genomes) and the enumeration of large words pose significant computational challenges.  Algorithmic strategies for increasing the scalability of the word search will be discussed.

Pattern discovery is one of the fundamental problems in bioinformatics. The assumption is that regulatory and functional genomic patterns are better conserved and therefore occur more significantly than expected by random chance. The genomic ‘texts’ can be treated as long sequences of characters from the DNA alphabet. This tutorial will discuss statistical models of DNA sequences. Efficient methods and statistical models that are useful for finding significant genomic word patterns will be introduced. Statistical measures to assess and rank the significance of word patterns will be described.

Biographical Sketches of Presenters:
Laura Elnitski received her Ph.D. in Biochemistry and Molecular Biology at Penn State University with Dr. Ross Hardison. She was awarded an NIH Research Scholar Award (NRSA), for postdoctoral research in the field of computational biology entitled "Genomic Alignment to Detect Conserved Regulatory Regions".  Dr. Webb Miller served as the project advisor. In 2007 Dr Elnitski was the recipient of an Outstanding Research Achievement Award at the 2007 International Symposium on Bioinformatics Research and Applications. Her professional duties include serving as an Associate Editor of the journal Genome Research. She is also an ad hoc Reviewer for the NIH Scientific Grant Review Panel Study Section entitled Genomics, Computational Biology and Technology.  Dr. Elnitski's research contributions include participation as a member of the sequencing and analysis consortia for the mouse, rat, chicken and bovine genomes and an ENCODE member and participant. Her research specializes in identifying higher-level sequence patterns that define regulatory mechanisms of gene expression. Such approaches include: computing scores for the regulatory-potential of DNA to discover functional regions in vertebrate genomes, screening for elements that silence transcription, and defining classes of promoters to predict their involvement in regulatory networks.

Lonnie R. Welch received a Ph.D. in Computer and Information Science from the Ohio State University. Currently, he is the Stuckey Professor of Electrical Engineering and Computer Science at Ohio University, and he is a member of the Graduate Faculties of the Biomedical Engineering Program and of the Molecular and Cellular Biology Program.  Dr. Welch performs research in the areas of bioinformatics, functional and regulatory genomics, and high performance computing.  His research has been sponsored by the Defense Advanced Research Projects Agency, the Navy, NASA, the National Science Foundation, the Army, and the Ohio Board of Regents.  Dr. Welch has more than twenty years of research experience in the area of high performance computing.  In his graduate work at Ohio State University, he developed high performance 3-D graphics rendering algorithms, and he invented a parallel virtual machine for object-oriented software.  For 15 years, his research focused on middleware and optimization algorithms for high performance computing; this work produced three successive generations of adaptive resource management middleware for high performance real-time systems, and resulted in two patents and more than 150 publications. Currently, Professor Welch directs the Bioinformatics Laboratory at Ohio University, where he performs research in the areas of computational regulatory and functional genomics.  Dr. Welch is founder and Co-Editor-in-Chief of The International Journal of Computational Biosciences, and is a member of the editorial boards of The International Journal of Computational Science, and The Journal of Scalable Computing: Practice and Experience.   He is the founder and Chair of The Ohio Bioinformatics Consortium and The Ohio Collaborative Conference on Bioinformatics.  He is also the Principal Investigator of the $9M Bioinformatics Program which is funded by the Ohio Board of Regents and eleven academic institutions from Ohio.  Dr. Welch has served on the organizing committees of the Bioinformatics Open Source Conference, the International Symposium on Bioinformatics Research and Applications, and the IEEE International Symposium on Bioinformatics and Bioengineering. 

Frank Drews is an Assistant Professor of Computer Science and Electrical Engineering at Ohio University. Dr. Drews received his PhD in Computer Science from Clausthal University of Technology in Germany. His main research interests are high-performance computing, real-time systems, and bioinformatics. He has served as General Chair and Program Chair for the IEEE International Workshop on Parallel and Distributed Real-Time Systems, as Program Chair for the Second IEEE International Symposium on Applied Computing and Computational Sciences (ACCS 2009), and as a member of the Organization Committee of the Bioinformatics Open Source Conference (BOSC 2009). He is a Member of the Editorial Board of the International Journal of Computational Bioscience, and was Guest Editor for the Journal of Systems and Software Special Issue on Resource Management for Real-Time and Distributed Systems.

Stephen Lee is an Associate Professor of Statistics at the University of Idaho. Dr. Lee received his Ph.D. in Statistics from Florida State University. He joined the Department of Statistics at the University of Idaho in 1993. He is a tenured Associate Professor of Statistics and an affiliated Professor of Bioinformatics and Computational Biology at the University of Idaho. He is a researcher and consultant in the field of Statistics and Bioinformatics. His research projects are motivated by clients from the Bioinformatics and Computational Biology areas. He teaches a wide range of undergraduate and graduate statistics courses. His main research interests are multivariate and computational statistics, and pattern recognition methods from statistics and computer science. His grant-funded work includes development of a graduate Bayesian data analysis course (funded by the Maximum Entropy and Bayes Center at Boise ID), delivery of  statistical evidence in an Idaho court (funded by the Idaho Potato Commission), statistical consultation in a contract with Schroeter, Goldmark, & Bender Company, and demonstration of the Enterprise Miner for data mining (SAS). His professional service includes membership on the Editorial Board of the International Journal of Computational Bioscience, and referee for the Journal of American Statistical Association, Annals of Statistics, and Journal of Nonparametric Statistics. His professional memberships include the American Statistical Association, Mathematics Association of America, Biometric Society, Institute of Mathematical Statistics, Royal Statistical Society, International Association for Statistical Computing, and Interface of Computing Science and Statistics. His publications are in the International Journal of Computational Science, Journal of Statistical Computation and Simulation, Journal of Computational Statistics & Data Analysis, Journal of Computational Statistics, Journal of Applied Stochastic Models in Business and Industry, Journal of Applied Artificial Intelligence, Journal of Forest Science, Journal of Vaccine,  Journal of  Microbiology, Proceedings of the International Conference on Parallel and Distributed Computing and Systems, Proceedings of the International Conference on Communications, Internet and Information Technology, and Proceedings of the IEEE LCN Workshop on Network Security.

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