1. Introduction to BioConductor Friday 23th nov 2007 Ståle Nygård (staaln@math.uio.no) Statistical methods and bioinformatics for the analysis of microarray data Course in Statistical methods and bioinformatics for the analysis of microarray data

2. What is BioConductor? An open source and open development software project for the analysis and comprehension of genomic data. Started in 2001. The is based primarily at the Fred Hutchinson Cancer Research Center. Started in 2001. The core team is based primarily at the Fred Hutchinson Cancer Research Center. Is primarily based on the R programming language. There are two releases of Bioconductor every year. In addition there are a large number of meta-data packages available, mainly, but not solely oriented towards different types of microarrays.

3. Goals of the Bioconductor Project Provide access to a wide range of powerful statistical and graphical methods for the analysis of genomic data. Facilitate the integration of biological metadata in the analysis of experimental data: e.g. literature data from PubMed, annotation data from LocusLink. Allow the rapid development of extensible, scalable, and interoperable software. Promote high-quality documentaion and reproducible research Provide training in computational and statistical methods for the analysis of genomic data.

4. Main features of the Bioconductor Project Use of R Documentation and reproducible research Statistical and graphical methods Annotation Bioconductor short courses Open source Open development

5. Use of R R and the R package system are the main vehicles for designing and releasing software.

6. Documentation and reproducible research Each package contains at least one vignette, which is a document that provides a textual, task-oriented description of the package's functionality and that can be used interactively. packagevignettepackagevignette In the future: looking towards vignettes not specifically tied to a package, but rather demonstrating more complex concepts.

7. Bioconductor FAQ: http://www.bioconductor.org/docs/faq/index.html#Open%20source Book:

8. Statistical and graphical methods Bioconductor analysis packages –Preprosessing Affymetrix and cDNA array data –Identifying differentially expressed genes –Graph theoretical analyses –Plotting genomic data In addition, R itself provides implementations for a broad range of state-of-the-art statistical and graphical techniques including –Linear and non-linear modeling –Cluster analysis –Prediction –Resampling –Survival analysis –Time series analysis (Screenshots: http://www.bioconductor.org/whatisit/screenshots/)

9. Annotation Bioconductor project provides software for associating genomic data in real time to biological metadata from web databases such as GenBank, Locus Link, and Pubmed (annotate package). Provides functions for incorporating the results in HTML reports with links to annotation www resources Provides software tools for assembling and processing genomic annotation from databases such as GenBank, the Gene Ontology Consortium, LocusLink, UniGene, the UCSC Human Genome Project (AnnBuilder package). AnnBuilder Data packagesData packages are distributed to provide mappings between different probe identifiers (e.g. Affy IDs, LocusLink, PubMed). Customized annotation libraries can also be assembled. Data packages

10. Bioconductor short courses The Bioconductor project has developed a program of short courses on software and statistical methods for the analysis of genomic data. (course materials etc at: http://www.bioconductor.org/services/workshops)

11. Open source There are many different reasons why open-source software is beneficial to the analysis of microarray data and to computational biology in general, because it –facilitates full access to algorithms and their impementation –enables to fix bugs and extend and improve the supplied software –encourages good scientific computing and statistical practice by providing appropriate tools and instruction –provides a workbench of tools that allow researchers to explore and expand the methods used to analyze biological data –ensures that the international scientific community is the owner of the software tools needed to carry out research –leads and encourages commercial support and development of those tools that are successful –promotes reproducible research by providing open and accessible tools with which to carry out that research

12. Open development Users are encouraged to become developers, either by contributing bioconductor compliant packages or documentation.

13. Installation of bioconductor Install R Install bioconductor packages: http://www.bioconductor.org/docs/install-howto.html Installation tailored for this course: http://sfi.nr.no/sfi/index.php/Click_here http://sfi.nr.no/sfi/index.php/Click_herehttp://sfi.nr.no/sfi/index.php/Click_here To check if your packages really is installed type library().