A Machine-Learning-Based Algorithm for Peptide Feature Detection from Protein Mass Spectrometry Data
Year: 2021
Type: dissertation
Abstract: Liquid chromatography with tandem mass spectrometry (LC-MS/MS) has been widely used in proteomics. Two types of data, MS and MS/MS data, are produced in an LC- MS/MS experiment. The MS data contains signal peaks corresponding to the intact pep- tides in the samples being analyzed. However, research on protein mass spectrometry data has focused more on extracting information from MS/MS data than on MS data. To effectively utilize MS information, we propose a novel software tool, MSTracer, to detect peptide features from MS data. Two machine-learning-combined scoring functions were incorporated in the implementation: one for detecting the peptide features and another for assigning a quality score to each detected peptide feature. The software was compared with several other tools and demonstrated significantly better performance.
Source: UWSpace (University of Waterloo)
Author: Xiangyuan Zeng
Language: English
Subfield: Spectroscopy
Field: Chemistry
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W3162399094