Adding automated Statistical Analysis and Biological Evaluation modules to www.arrayanalysis.org
Creators: name: Bohler, Anwesha
Publisher: name: figshare
Year: 2014
Subjects: subject: 60102 Bioinformatics
subject: FOS: Computer and information sciences
subject: FOS: Computer and information sciences

description: Genomics refers to the comprehensive study of genes and their function. Recent advances in<br>bioinformatics and high-throughput technologies such as microarray analysis are bringing about<br>a revolution in our understanding of the molecular mechanisms underlying normal and<br>dysfunctional biological processes. In accordance with the word genome, used for the entire<br>DNA in the cell, the collection of all RNAs present in the cell is called the transcriptome. To<br>detect expression levels of tens of thousands of RNA molecules or even the whole known<br>transcriptome of the organism at hand, gene expression arrays have been developed. Gene<br>expression microarrays provide a snapshot of all the transcriptional activity in a biological<br>sample at a particular time.<br>The task of analysing microarray data is often at least as much an art as a science and it typically<br>consumes considerably more time than the laboratory protocols required to generate the data.<br>Part of the challenge is assessing the quality of the data and ensuring t hat all samples are<br>comparable for further analysis. Normalisation of the raw data, which controls for technical<br>variation between arrays within a study, is essential. The challenge of normalisation is to remove<br>as much of the technical variation as possible while leaving the biological variation untouched.<br>The fundamental goal of most microarray experiments is to identify biological processes or<br>pathways that consistently display differential expression between groups of samples. Statistical<br>analysis of genes can show statistically significant differences in expression between samples<br>with different phenotypes or characteristics. The set of genes thus identified is then examined for<br>over-representation on specific pathways.<br>Affymetrix microarrays of different technology versions are very often used in transcriptomic<br>analyses. Quality control and normalisation approaches do exist, especially as packages in<br>Bioconductor/R. However they are not always easy to access as they run through command lines<br>and it is often not clear what the meaning of the specific settings and results are. To tackle this,<br>the BiGCaT department of bioinformatics at Maastricht University, proposed an automated,<br>well-documented and user-friendly pipeline the affyanalysisQC workflow which is called by the<br>61<br>www.arrayanalysis.org server. The existing workflow allows for the quality control (QC) and<br>pre-processing (normalisation) of Affymetrix microarray gene expression data sets.<br>However, the functionality offered by the www.arrayanalysis.org needs to be extended for a<br>more complete analysis of microarray data. This can achieved by adding statistical analysis and<br>biological evaluation modules in the pipeline essentially creating a custom data analysis pipeline<br>for Affymetrix GeneChips.<br>Thus main goal of this thesis is as follows:<br>Goal: Develop user friendly packages for statistical analysis and pathway visualisation of gene<br>expression microarray data<br>Statistical analysis usually involves parametric or nonparametric statistics. It provides statistical<br>significance to the discovered gene expression values. This type of analysis is used for research<br>designs involving hypothesis formulation and is suited for finding differentially expressed genes.<br>The statistical analysis module developed uses the functionality of the R/Bioconductor package<br>LIMMA as it allows great flexibility, customised analysis, and access to many specialised<br>packages designed for analysing gene expression data. Not only is R freely available, but it also<br>allows the use of BioConductor, a collection of R tools including many powerful current gene<br>expression analysis methods written and tested by experts from the growing microarray<br>community.<br>To investigate the biological effect of the genes of interest obtained from the stat istical<br>comparisons performed, the genes can be visualised on pathways of interest. The Biological<br>Evaluation module developed in this project uses the basic functions provided by<br>www.pathvisio.org extended with a new pathvisioRPC package developed using XMLRPC to<br>allow automated calls from the www.arrayanalysis.org server for direct pathway over representation analysis and visualization of the genes analysed.