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Metz, Thomas O

Publications and source records attributed to Metz, Thomas O.

Serum markers for type II diabetes mellitus

A method for identifying persons with increased risk of developing type 2 diabetes mellitus utilizing selected biomarkers described hereafter either alone or in combination. The present invention allows for broad based, reliable, screening of large population bases and provides other advantages, including the formulation of effective strategies for characterizing, archiving, and contrasting data from multiple sample types under varying conditions.

59 BASIC BIOLOGICAL SCIENCES↗

MultiAlign: a multiple LC-MS analysis tool for targeted omics analysis

MultiAlign is a free software tool that aligns multiple liquid chromatography-mass spectrometry datasets to one another by clustering mass and LC elution features across datasets. Applicable to both label-free proteomics and metabolomics comparative analyses, the software can be operated in several modes. Clustered features can be matched to a reference database to identify analytes, used to generate abundance profiles, linked to tandem mass spectra based on parent precursor masses, and culled for targeted liquid chromatography-tandem mass spectrometric analysis. MultiAlign is also capable of tandem mass spectral clustering to describe proteome structure and find similarity in subsequent sample runs.

Environmental Molecular Sciences Laboratory↗

A Comparative Analysis of Computational Approaches to Relative Protein Quantification Using Peptide Peak Intensities in Label-free LC-MS Proteomics Experiments

Liquid chromatography coupled with mass spectrometry (LC-MS) is widely used to identify and quantify peptides in complex biological samples. In particular, label-free shotgun proteomics is highly effective for the identification of peptides and subsequently obtaining a global protein profile of a sample. As a result, this approach is widely used for discovery studies. Typically, the objective of these discovery studies is to identify proteins that are affected by some condition of interest (e.g. disease, exposure). However, for complex biological samples, label-free LC-MS proteomics experiments measure peptides and do not directly yield protein quantities. Thus, protein quantification must be inferred from one or more measured peptides. In recent years, many computational approaches to relative protein quantification of label-free LC-MS data have been published. In this review, we examine the most commonly employed quantification approaches to relative protein abundance from peak intensity values, evaluate their individual merits, and discuss challenges in the use of the various computational approaches.

Environmental Molecular Sciences Laboratory↗