Improving data analysis methods for shotgun proteomics
Proteomics is the systematic study of all proteins. State-of-the-art methods based on mass spectrometry can detect, identify and quantify thousands of proteins simultaneously in one experiment. A critical component of this technology is the computational methods to deduce peptide or protein sequences from fragmentation patterns of these molecules collected in the mass spectrometer. This project focuses on improving the existing data analysis methods in various aspects such as speed, sensitivity, range of applicability and ease of use.
LAM Henry Hei Ning
Applicants are expected to learn the details of methods and algorithms used in computational proteomics, and propose reasonable ways to improve them. Support will be given to help the applicants implement their ideas. Computer programming will be needed, but is not a prerequisite for the project.
Applicant's Learning Objectives
Applicants are expected to gain exposure in scientific research, in particular in computational method development, in this project. Through the project applicants will improve their programming skills, learn to handle and reason with a large amount of data, and make a positive impact in methods which are being used in the scientific community.
Complexity of the project