A statistical procedure to selectively detect metabolite signals in LC-MS data based on using variable isotope ratios.

The tracing of metabolite signals in LC-MS data using stable isotope-labeled compounds has been described in the literature. However, the filtering efficiency and confidence when mining metabolite signals in complex LC-MS datasets can be improved. Here, we propose an additional statistical procedure to increase the compound-derived signal mining efficiency.

Combining alkaline phosphatase treatment and hybrid linear ion trap/Orbitrap high mass accuracy liquid chromatography-mass spectrometry data for the efficient and confident identification of protein phosphorylation.

Protein phosphorylation is a vital post-translational modification that is involved in a variety of biological processes. Several mass spectrometry-based methods have been developed for phosphoprotein characterization. In our previous work, we demonstrated the capability of a computational algorithm in mining phosphopeptide signals in large LC-MS data sets by measuring the mass shifts due to phosphatase treatment (Wu, H.

Mining phosphopeptide signals in liquid chromatography-mass spectrometry data for protein phosphorylation analysis.

Protein phosphorylation is a key post-translational modification that governs biological processes. Despite the fact that a number of analytical strategies have been exploited for the characterization of protein phosphorylation, the identification of protein phosphorylation sites is still challenging. We proposed here an alternative approach to mine phosphopeptide signals generated from a mixture of proteins when liquid chromatography-tandem mass spectrometry (LC-MS/MS) analysis is involved.