J Chromatogr B Analyt Technol Biomed Life Sci 2014 Sep 25;966:77-82. Epub 2014 Apr 25.
Institute for Diabetes Research and Metabolic Diseases of the Helmholtz Centre Munich at the University of Tübingen, Tübingen, Germany; Division of Clinical Chemistry and Pathobiochemistry, Department of Internal Medicine IV, University Hospital Tübingen, Tübingen, Germany; German Center for Diabetes Research (DZD), Germany. Electronic address:
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BMC Syst Biol 2013 Jul 23;7:64. Epub 2013 Jul 23.
Graduate Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, Taipei, Taiwan.
Background: Integrative and comparative analyses of multiple transcriptomics, proteomics and metabolomics datasets require an intensive knowledge of tools and background concepts. Thus, it is challenging for users to perform such analyses, highlighting the need for a single tool for such purposes. The 3Omics one-click web tool was developed to visualize and rapidly integrate multiple human inter- or intra-transcriptomic, proteomic, and metabolomic data by combining five commonly used analyses: correlation networking, coexpression, phenotyping, pathway enrichment, and GO (Gene Ontology) enrichment. Read More
Bioinformatics 2008 Dec 2;24(23):2726-32. Epub 2008 Sep 2.
International NRW Graduate School in Bioinformatics and Genome Research, Bielefeld University, Germany.
Motivation: The recent advances in metabolomics have created the potential to measure the levels of hundreds of metabolites which are the end products of cellular regulatory processes. The automation of the sample acquisition and subsequent analysis in high-throughput instruments that are capable of measuring metabolites is posing a challenge on the necessary systematic storage and computational processing of the experimental datasets. Whereas a multitude of specialized software systems for individual instruments and preprocessing methods exists, there is clearly a need for a free and platform-independent system that allows the standardized and integrated storage and analysis of data obtained from metabolomics experiments. Read More
Microbiology 2010 Feb 12;156(Pt 2):287-301. Epub 2009 Nov 12.
Center for Ecogenomics, Biodesign Institute, Arizona State University, Tempe, AZ 85287-6501, USA.
Recent advances in various 'omics' technologies enable quantitative monitoring of the abundance of various biological molecules in a high-throughput manner, and thus allow determination of their variation between different biological states on a genomic scale. Several popular 'omics' platforms that have been used in microbial systems biology include transcriptomics, which measures mRNA transcript levels; proteomics, which quantifies protein abundance; metabolomics, which determines abundance of small cellular metabolites; interactomics, which resolves the whole set of molecular interactions in cells; and fluxomics, which establishes dynamic changes of molecules within a cell over time. However, no single 'omics' analysis can fully unravel the complexities of fundamental microbial biology. Read More
BMC Bioinformatics 2012 11;13 Suppl 15:S7. Epub 2012 Sep 11.
Department of Academic and Institutional Resources and Technology, University of North Texas Health Science Center, Fort Worth, USA.
Background: Next-Generation Sequencing (NGS) technologies and Genome-Wide Association Studies (GWAS) generate millions of reads and hundreds of datasets, and there is an urgent need for a better way to accurately interpret and distill such large amounts of data. Extensive pathway and network analysis allow for the discovery of highly significant pathways from a set of disease vs. healthy samples in the NGS and GWAS. Read More