Computational Analysis Workflows for Omics Data Interpretation

• DOI: 10.1007/978-1-61779-027-0_17
• Publication Date: 2011-02-24T17:23:59Z
• Authors: Irmgard Mühlberger; Julia Wilflingseder; Andreas Bernthaler; Raul Fechete; Arno Lukas; Paul Perco
• Keywords: Hyperlipoproteinemia Type II; Internet; Data Interpretation, Statistical; Gene Expression Profiling; Databases, Genetic; Computational Biology; Humans; Molecular Sequence Annotation; Monocytes

Progress in experimental procedures has led to rapid availability of Omics profiles. Various open-access as well as commercial tools have been developed for storage, analysis, and interpretation of transcriptomics, proteomics, and metabolomics data. Generally, major analysis steps include data storage, retrieval, preprocessing, and normalization, followed by identification of differentially expressed features, functional annotation on the level of biological processes and molecular pathways, as well as interpretation of gene lists in the context of protein-protein interaction networks. In this chapter, we discuss a sequential transcriptomics data analysis workflow utilizing open-source tools, specifically exemplified on a gene expression dataset on familial hypercholesterolemia.