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

As a result of recent advances in high-throughput technologies, 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. We believe that the project results will contribute major advances to the research field of MS-based proteomics and substantially improve the performance of the partner’s software products.

Faculty Supervisor:

Mark Giesbrecht

Student:

Ngoc Hieu Tran

Partner:

Bioinformatics Solutions Inc.

Discipline:

Computer science

Sector:

Information and communications technologies

University:

Program: