Improving data analysis methods in proteomics
Project Description

Mass spectrometry is one of the most powerful analytical techniques used in chemistry and biology, and is the foundation of much of the "omics" revolution that dominates 21st-century life science. Data analysis, in particular the assignment of mass spectra to their originating biomolecule, is a major bottleneck and key challenge in these experiments. This aim of this project is to improve existing methods in terms of efficiency, accuracy, interoperability, and user-friendliness. In the process the student will gain exposure to an exciting research field at the intersection of biology and computer science, and contribute meaningfully to improve methods used by many researchers worldwide.

LAM Henry Hei Ning
Course type
Applicant's Roles

Students in all majors, particularly those in CSE, ECE, CPEG, and DSCT, are encouraged to apply. The applicant is expected to be adept at programming. Knowledge in biology is not necessary. Knowledge in applied statistics and basic machine learning is helpful but not a must.

The role of the applicant is to write and modify existing code in various programming languages to improve the data analysis pipeline in computational proteomics.

Applicant's Learning Objectives

At the end of the project, the student is expected to obtain:
- A familiarity with computational proteomics, a branch of bioinformatics
- A real-life experience of writing and improving software used in scientific research
- Practice in computer programming, algorithm and software design and statistics

Complexity of the project