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Full-Text Articles in Genetics and Genomics
Metagomics: A Web-Based Tool For Peptide-Centric Functional And Taxonomic Analysis Of Metaproteomics Data, Michael Riffle, Damon H. May, Emma Timmins-Schiffman, Molly P. Mikan, Daniel Jaschob, William S. Noble, Brook L. Nunn
Metagomics: A Web-Based Tool For Peptide-Centric Functional And Taxonomic Analysis Of Metaproteomics Data, Michael Riffle, Damon H. May, Emma Timmins-Schiffman, Molly P. Mikan, Daniel Jaschob, William S. Noble, Brook L. Nunn
OES Faculty Publications
Metaproteomics is the characterization of all proteins being expressed by a community of organisms in a complex biological sample at a single point in time. Applications of metaproteomics range from the comparative analysis of environmental samples (such as ocean water and soil) to microbiome data from multicellular organisms (such as the human gut). Metaproteomics research is often focused on the quantitative functional makeup of the metaproteome and which organisms are making those proteins. That is: What are the functions of the currently expressed proteins? How much of the metaproteome is associated with those functions? And, which microorganisms are expressing the …
Selecting Differentially Expressed Genes From Microarray Experiments, Margaret S. Pepe, Gary M. Longton, Garnet L. Anderson, Michel Schummer
Selecting Differentially Expressed Genes From Microarray Experiments, Margaret S. Pepe, Gary M. Longton, Garnet L. Anderson, Michel Schummer
UW Biostatistics Working Paper Series
High throughput technologies, such as gene expression arrays and protein mass spectrometry, allow one to simultaneously evaluate thousands of potential biomarkers that distinguish different tissue types. Of particular interest here is cancer versus normal organ tissues. We consider statistical methods to rank genes (or proteins) in regards to differential expression between tissues. Various statistical measures are considered and we argue that two measures related to the Receiver Operating Characteristic Curve are particularly suitable for this purpose. We also propose that sampling variability in the gene rankings be quantified and suggest using the “selection probability function”, the probability distribution of rankings …