Precursor charge prediction for improved peptide identification with mass spectrometry

The research project aims to develop an effective method that utilizes multiple features to improve mass spectrometry based peptide identification with database search approach. The project is a continuation to the student’s previous research on precursor charge state prediction, since predicted charge state is a novel feature and has a great potential to discriminate the correct and incorrect peptide identifications. With the assistance of the company’s onsite developers, the student is supposed to design and implement a method that integrates the additional information of multiple features into PEAKS database search, which will potentially improve the PEAKS database search engine in both accuracy and sensitivity.

Lian Yang
Faculty Supervisor: 
Dr. Bin Ma