Please note: This master’s thesis presentation will be given online.
Xiangyuan Zeng, Master’s candidate
David R. Cheriton School of Computer Science
Supervisor: Professor Bin Ma
Liquid chromatography with tandem mass spectrometry (LC-MS/MS) has been widely used in proteomics. An LC-MS/MS experiment produces both MS and MS/MS data. The MS data contains signal peaks corresponding to the intact peptides in the samples being analyzed. However, research on MS data has focused more on extracting information from MS/MS data than on MS data. To effectively utilize MS information, we introduce a software tool, MSTracer, to detect peptide features from MS which incorporate two machine-learning-combined scoring functions: one for detecting the peptide features, and the other for assigning a quality score for each detected feature. The software was compared with a few other existing tools and demonstrated significantly improved performance.
To join this master’s thesis presentation on Zoom, please go to https://us02web.zoom.us/j/85747893419?pwd=aVFNajJiUUFOZmc5RmRFaWJBaXh3dz09.
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