R/Bioconductor Workshop Session II - Bioconductor-ing

What is Bioconductor Remember this guy?? Fall 2001 Robert Gentleman, Program Head Computational Biology FHCR VJ CareyWolfgang Huber Rafael Irizarry James W Macdonald Herve Pages Martin Morgan Gordon Symth

A growing community

Citations  Google Scholar(May 2008) reports 970 scientific documents

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Mailing Lists (Bioc-help,Bioc-dev) Subject: Re: [BioC] analysis ChIP-seq dataSent By: On:June 10, :40 AM On Behalf Of:"Martin Morgan" To:"Bogdan Tanasa" bioconductor Hi Bodan -- "Bogdan Tanasa" writes: > Hi everyone, > > I am in the process of analyzing a lot of ChIP-seq data and I am writing to ask > if an analysis module becomes available for R/Bioconductor anytime soon. Visit the bioc-sig-sequencing news group (Cc'd in the reply) Current packages include Biostrings (for lots of string matching / manipulation facilities) and ShortRead (for IO from ELAND or MAQ, for instance, plus additional sorting, counting, and qa-related functionality. There are posts in the bioc-sig-sequencing news group about package use. Maybe others will reply (to bioc-sig-sequencing) with additional developments; I know people are working with and developing packages for this data in R. Martin

goCluster goCluster is a tool for the analysis of expression data in conjunction with annotation data The package provides an object- oriented “framework” that allows to flexibly combine plugins to a specialized analysis method. goCluster provides modules for annotation data as well as clustering, statistical, and visualization methods. goCluster Structure: 6 abstract classes define the general structure of the analysis task. Each abstract class can be filled with a specific module fullfilling the requested function in a special way. goCluster clusterData clusterAnnotation clusterAlgorithm clusterSignif clusterStatistic clusterVisual Biological process Example analysis: The plot shows a section of a hierarchical clustering that has been combined with gene ontology data using goCluster. The package has been able to automatically detect regions in the clustering that are enriched for specific GO-terms.

affylmGUI

GLAD: Gain and Loss Analysis of DNA GLAD is devoted to the analysis of array CGH data (Comparative Genomic Hybridization) ✔ It allows the identification of breakpoints ✔ It detects outliers ✔ Each chromosomal regions are given a status (Gain, Normal or Loss) ✔ Plot functions are available to draw genomic profiles package required: AWS Reference: Hupé et al., Bioinformatics (2004) contact: web site:

Why I am falling in love with bioconductor  Versatile and powerful– Room for development, customized analysis, hundreds of software packages  Runs only Everything – Laptops, Servers, Windows, Linux, Unix, Ultrasparc  Support – Hundreds of bioinformaticians, biologists, Statisticians, IT dudes, mailing list, etc  Free!!!!

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