Monday, 4 October
10:00 – 13:30
Making Sense of Next-Gen Sequencing DataOrganized by
With the widespread deployment of second-generation sequencing and the emergence of new third-generation sequencing platforms, the extraordinary throughput of next-generation sequencing (NGS) technology is outpacing our ability to analyze and interpret the data. Researchers need productive strategies to cope and handle this deluge of data. This workshop will focus on practical informatics methods, strategies and software tools for transforming NGS data into usable information.
What you will learn:
NGS assembly and annotation methods
Tools for data analysis
An appraisal of commercial software and freeware
Case studies of analysis to support research
Short Course Instructors:
Michele Clamp, Senior Consultant, BioTeam, Inc.
Daniel Blankenberg, Ph.D., Postdoctoral Research Associate, Biochemistry & Molecular Biology, Pennsylvania State University
Additional instructors to be announced.
14:00 – 17:00
Creating Synergy – Introduction to Biomedical Data Fusion
Systems biology and personalized medicine increasingly require a synergistic consideration of different molecular or clinical data sets. Making such heterogeneous data available is only the first step for obtaining the big picture through a coherent analysis, i.e. data fusion. This introductory tutorial will provide a broad overview of the different options and methodologies for making the most of your data through data fusion.
A principled approach to data fusion
Powerful methods from machine learning, multivariate statistics and pattern recognition
How to deal with any kind of data
QTL mapping of omics data
Application examples in cancer and diabetes
Agenda:
13:30 - 14:00 Short Course Registration
14:00 Opening Remarks
14:05 Principles of Data Fusion
Juergen von Frese, Ph.D., Managing Director, Data Analysis Solutions DA-SOL GmbH - Biography
The fusion of complementary biomedical data can be used to obtain a comprehensive characterization for each sample – the “big picture” in terms of systems biology. The data could comprise clinical and patient data, microarray, proteomics and metabolomics data or even histological images. This talk will offer a comprehensive overview on data fusion, ranging from the principles and an overall workflow to a discussion of the analytical options and approaches. It will provide a conceptual understanding of some of the major issues, pitfalls and chances. Powerful approaches from various disciplines such as bioinformatics, chemometrics and pattern recognition will be introduced.
14:40 Data Fusion and Network Biology of Metabolic Profiles
Marc-Emmanuel Dumas, Ph.D., Lecturer in Systems Biomedicine, Imperial College - Biography
Integration of metabolic phenotyping with other –Omics provides a systems biology approach to identify biomarkers and susceptibility genes related to the cardio-metabolic syndrome as well as other diseases. In particular, approaches such as metabolomic Quantitative Trait Locus (mQTL) mapping, or Metabolomic Genome-Wide Association Studies consist of the robust and accurate statistical integration of genome-wide genotyping (single nucleotide polymorphisms, microsatellites) and metabolome-wide profiling by NMR spectroscopy and mass spectrometry. New signal processing and statistical developments for enhancing signal recovery, locus detection and biomarker identification will be shown. Mechanistic insights derived from this systems biology approach clarify the influence of gene variants on metabolic profiles and results in a better understanding of disease phenotypes and identification of potential drug targets.
15:15 – 15:45 Refreshment Break
15:45 Kernel Methods for Fusing Diverse Biomedical Data
Gunnar Rätsch, Ph.D., Friedrich Miescher Laboratory, Max Planck Society
Kernel methods, in particular support vector machines, have established themselves as a very powerful and versatile paradigm for learning from high-dimensional data. Kernels have been developed not only to deal with numerical data but also sequence information or even graphs representing e.g. protein-protein interaction data. Their widespread use for developing molecular signatures as well as the large number and diversity of bioinformatics applications testify the power of this approach. Adding to that the ability to combine various kernels irrespective of their underlying data type and to learn optimal combinations from the data itself provides therefore a unique tool for achieving optimal prediction performance and data understanding through data fusion.
16:20 Interactive Panel Discussion
Who Should Attend:
Researchers with a basic understanding of omics data analysis who want to combine data from different sources for extracting maximal information.
14:00 – 17:00
Cloud Computing for Life SciencesOrganized by
Cycle Computing is leading the efforts for many life science organizations in using the cloud, helping research labs and companies leverage internal and external clouds for collaboration, calculations, and storage. We’ll cover real world use cases across drug discovery & design, collaboration, next generation sequencing, proteomics, software as a service, and bioinformatics, to explore how life sciences are using cloud computing, its challenges and effectiveness, how money can be saved by an organization, and regulatory compliance. Join thought leaders in this day long workshop to examine how cloud computing can be used effectively as an external IT service and an internal computing model.
Short Course Instructors:
Instructors to be announced.
14:00 – 17:00
Visualization of Large-Scale Biological Data
Data visualization has become increasingly important for life scientists as the amount of data generated in biomedical studies continues to grow rapidly. Visual representations are powerful tools in exploring large quantities of data quickly, helping to detect patterns and generate hypotheses, which guide further analyses. This practical course will provide a comprehensive view on utilizing visualization to support the analysis of large biological data sets, and will cover interaction networks and biochemical pathways, as well as transcriptomics, proteomics and metabolomics data.
Visualization principles and pitfalls
The roles of visualization in data analysis
Key methods and software tools
Integration of visualization with automated methods: Visual Analytics
Future technologies
Short Course Instructor:
Nils Gehlenborg, European Bioinformatics Institute, Wellcome Trust Genome Campus
Kay Nieselt, Ph.D., Group Leader, Center for Bioinformatics Tuebingen, University of Tuebingen
*Separate Registration Required.