Statistical Characterization of MRM-MS Assays for Quantitative Proteomics
News Nov 30, 2012
Multiple reaction monitoring mass spectrometry (MRM-MS) with stable isotope dilution (SID) is increasingly becoming a widely accepted assay for the quantification of proteins and peptides. These assays have shown great promise in relatively high throughput verification of candidate biomarkers. While the use of MRM-MS assays is well established in the small molecule realm, their introduction and use in proteomics is relatively recent. As such, statistical and computational methods for the analysis of MRM-MS data from proteins and peptides are still being developed. Based on our extensive experience with analyzing a wide range of SID-MRM-MS data, we set forth a methodology for analysis that encompasses significant aspects ranging from data quality assessment, assay characterization including calibration curves, limits of detection (LOD) and quantification (LOQ), and measurement of intra- and inter-laboratory precision. We draw upon publicly available seminal datasets to illustrate our methods and algorithms.
The article is published online in the journal BMC Bioinformatics and is free to access.
Protein Discovery Could Lead to Better Diagnosis of StressNews
Researchers have found a protein that is present in people while they are in stressful situations. The discovery could lead to identifying new ways to predict, diagnose and treat stress.READ MORE
Steaming Fish Eliminates More Cyanotoxins Than BoilingNews
Utilizing UHPLC researchers have shown that steaming freshwater fish for more than two minutes reduces the presence of the cyanotoxin, cylindrospermopsin, by up to 26% compared to 18% for boiling.READ MORE
Tiny “Tornado” Boosts Performance of Electrospray Ionization Mass SpectrometryNews
Known as Dry Ion Localization and Locomotion (DRILL), the new device creates a swirling flow that can separate electrospray droplets depending on their size.READ MORE
Comments | 0 ADD COMMENT
11th Edition of International Conference on Proteomics 2018
Mar 22 - Mar 23, 2018