Applications of deep learning to large-scale data analysis in mass spectrometry-based proteomics

Rapidly increasing amounts of mass spectrometry (MS) data pose new opportunities as well as challenges to existing analysis methods. Novel computational approaches are needed to take advantage of latest breakthroughs in high-performance computing for the large-scale analysis of big data from MS-based proteomics. In this project, we aim to develop new applications of deep learning and neural networks for the analysis of MS data. In particular, we focus on three fundamental problems in a typical MS analysis workflow: peptide feature detection and quantification, de novo peptide sequencing, and protein identification and quantification. Once successfully evaluated, the proposed techniques will be implemented and integrated to PEAKS Studio, the current MS analysis platform of the partner.

Faculty Supervisor:

Mark Giesbrecht

Student:

Partner:

Bioinformatics Solutions Inc;University of Waterloo

Discipline:

Computer science

Sector:

Information and cultural industries; Professional, scientific and technical services

University:

University of Waterloo

Program: