New Precursor Selection Algorithm for More Efficient MS/MS Acquisition


Shotgun mass spectrometry (MS) has emerged as a powerful technology for large-scale proteomic analysis recently. By measuring thousands of peptides in a prepared sample, health researchers can gain insight into the complex disease pathways, which can be used for diagnosis and treatment. A crucial problem in the current MS experiments is that not every peptide can be effectively measured by the instrument, and a lot of spectra measured are from contaminants but not peptides. We aim to develop new algorithm to optimize the so called “data-dependent” acquisition procedure, so that the instruments can utilize its duty cycles to measure the real peptides instead of contaminants. The software tool developed in this project will become an independent product and bring commercial benefits to Bioinformatics Solutions Inc. Besides, it will help to generate high quality spectra, which will enhance the vast applications of MS in biology, clinical diagnosis and medicine discovery areas.

Faculty Supervisor:

Dr. Bin Ma


Xi Han


Bioinformatics Solutions Inc.


Computer science


Information and communications technologies


University of Waterloo



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