Adding automated data analysis and biological evaluation to affyanalysisQC
Authors: Dutta Anwesha 
Published: May 2011

AbstractAlmost every cell in the body contains a full set of chromosomes and identical genes. However, only a fraction of these genes are turned on, at any given time and it is this subset of genes that are expressed, which confers unique properties to each cell type. Gene expression is the term used to describe the transcription of the information contained within the DNA into messenger RNA (mRNA) molecules that are then translated into proteins which perform most of the critical functions of cells.Gene expression is a highly complex and tightly regulated process that allows a cell to respond dynamically both to environmental stimuli and to its own changing needs. This mechanism acts as both an on/off switch to control which genes are expressed in a cell as well as a volume control that increases or decreases the level of expression of particular genes as necessary. The proper and harmonious expression of a large number of genes is a critical component of normal growth and development and the maintenance of proper health. Disruptions or changes in gene expression are responsible for many diseases. Scientists study the kinds and amounts of mRNA produced by a cell to learn which genes are expressed, which in turn provides insights into how the cell responds to its changing needs.Biomedical research evolves and advances not only through the compilation of knowledge but also through the development of new technologies. Using traditional methods to assay gene expression, researchers were able to survey a relatively small number of genes at a time. The emergence of new tools enables researchers to address previously intractable problems and to uncover novel potential targets for therapies. Microarrays allow scientists to analyze expression of many genes in a single experiment quickly and efficiently.Microarray technologies are currently used to try to understand fundamental aspects of growth and development as well as to explore the underlying genetic causes of many human diseases. DNA Microarrays are small, solid supports onto which the sequences from thousands of different genes are immobilized, or attached, at fixed locations. The supports themselves are usually glass microscope slides, silicon chips or nylon membranes. The DNA is printed, spotted, or actually synthesized directly onto the support. There are three basic types of samples that can be used to construct DNA microarrays, two are genomic and the other is transcriptomic, that is, it measures mRNA levels.In order to use the huge amount of data obtained from these platforms microarray data must be efficiently analyzed. Affymetrix microarrays of different technology versions are very often used in transcriptomics analysis. Quality control and normalization approaches do exist, especially as packages in Bioconductor/R. However they are not always easy to access as they run through command lines and it is often not clear what the meaning of the specific settings and results are.To tackle this, the BiGCaT department of bioinformatics at Maastricht University, proposed an automated, well-documented and user-friendly pipeline the affyanalysisQC workflow which is called by the www.arrayanalysis.org server. The existing workflow allows for the quality control (QC) and preprocessing (normalisation) of Affymetrix microarray gene expression data sets. The code called by the webform can alternatively be run locally in R by setting few parameters in an initialization file. However the functionality offered by the affyanalysisQC needs to be extended for statistical analysis of normalized data and visualizing genes of interest on pathways for biological evaluation of the data. The presentation illustrates the extension of the pipeline by adding automated statistical analysis and biological evaluation modules to affyanalysisQC.The affyanalysisQC workflow's functionality is to be extended by this project. The methods developed in the project presented in this presentation do not have to be developed from scratch. The statistical analysis module uses the functionality of the R/Bioconductor package LIMMA and the visualization of genes on pathways uses the basic functions provided by www.pathvisio.org extended with a pathvisioRPC package developed by using XMLRPC to allow automated calls from the www.arrayanalysis.org server for direct pathway visualization of the genes analyzed.