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Develop mathematical, statistical and computational methods for the analysis of

Develop mathematical, statistical and computational methods for the analysis of biologically or technologically novel experiments in order to understand regulatory and genetic interaction networks. Microarrays and genetics.

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Develop mathematical, statistical and computational methods for the analysis of

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  1. Develop mathematical, statistical and computational methods for the analysis of biologically or technologically novel experiments in order to understand regulatory and genetic interaction networks

  2. Microarrays and genetics High-resolution mapping of meiotic recombination with tiling arrays; Genetics Richard Bourgon (collaboration with Lars Steinmetz, EMBL Heidelberg) Tiling arrays for ChiP and transcriptomics Jörn Tödling (c.w. Lars) Microarray Quality Metrics Audrey Kauffmann (c.w. Alvis Brazma, ArrayExpress) ChIP-Seq; RHS genetic screens; hyperantagonistic drug interactions Simon Anders (c.w. Lars)

  3. RNAi, imaging and signaling • cellHTS2 • Rémy Clement, Greg Pau (c.w. DKFZ-Signaling) Genetic interaction networks Elin Axelsson (c.w. DKFZ-Signaling, Robert Gentleman, FHCRC Seattle, Amy Kiger, UCSD) High-content automated microscopy;modeling of morphological and dynamic phenotypes Greg Pau (c.w. Michael) Protein-Protein Interaction Networks Tony Chiang (c.w. Robert and Denise Scholtens, U Chicago) • Bernd Fischer • Starting 1.7.08

  4. Typically, the projects involve 1:1 collaborations with experimentalists and involve all aspects experiment design,quality control & normalization statistical analysis biological interpretation bioinformatic integration / comparison with other data design of follow-up experiment required skills: solid maths, statistics, programming; perfectionism, speed, communication

  5. EMBL established in 1974, supported by 20 member states and one associate member. Five facilities: main laboratory in Heidelberg outstations in Hamburg, Grenoble, Hinxton and Monterotondo

  6. EMBL ranks as the highest non-US institute in research performance by ISI Science Indicator for 1992-2002 more than 1400 people from 60 nations currently work at EMBL; more than 3000 alumni Mission: • conduct basic research in molecular biology • provide essential services to scientists in its Member States • provide high-level training to its staff, students, and visitors • develop new instrumentation for biological research • technology transfer Outreach activities in the areas of Science and Society and training for science teachers

  7. EBI Mission • provide freely available data and bioinformatics services to the scientific community in ways that promote scientific progress • contribute to the advancement of biology through basic investigator-driven research in bioinformatics • provide advanced bioinformatics training to scientists at all levels, from PhD students to independent investigators • help disseminate cutting-edge technologies to industry

  8. EBI

  9. EBI

  10. Why? • Biology is becoming a computational science • Data analysis and mathematical modeling require computational solutions • We put a premium on code reuse: - many of the tasks have already been solved - if we use those solutions we can put effort into new research • Data complexity is dealt with using well designed, self-describing data structures • Reproducible research requires open access to computational code

  11. Bioconductor an open sourceand open development software project for the analysis of biomedical and genomic data was started in the autumn of 2001 and includes core developers in the US, Europe, and Australia R and the R package system are used to design and distribute software

  12. The S language • The S language has been developed since the late 1970s by John Chambers and his colleagues at Bell Labs. • The language has been through a number of major changes but has been relatively stable since the mid 1990s • The language combines ideas from a variety of sources (e.g. Awk, Lisp, APL...) and provides an environment for quantitative computations and visualization.

  13. Implementations • S-Plus is a commercialization of the Bell Labs code. • R is an independent open source version that was originally developed at the University of Auckland but which is now developed by a world wide group of developers. • Each version has advantages and problems.

  14. Packages • Packages are the main unit of software authoring, versioning and distribution • CRAN is the major repository for R packages. It is hosted by TU Vienna and ETH Zürich, and has many mirrors world-wide • Bioconductor is a repository for biology related packages. It is hosted at the Fred Hutchinson Cancer Research Centre.

  15. Goals of the Bioconductor project Provide access to powerful statistical and graphical methods for the analysis of genomic data. Facilitate the integration of biological metadata (e.g. Entrez, Ensembl, GO(A), PubMed) in the analysis of experimental data. Allow the rapid development of extensible, interoperable, and scalable software. Promote high-quality documentation and reproducible research. Provide training in computational and statistical methods.

  16. Userfriendliness vs Flexibility • Ideally, we want both • The development of an easy to use, robust, intuitive (perhaps graphical) user interface is appropriate for standardized and widely used workflows (production time and costs) • The real strength of Bioconductor is in rapidly creating bespoke workflows out of standardized, well-tested, technologically state of the art modules (packages)

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