Intelligent workflows for automated analysis of mass spectrometry-based proteomics data

Abstract

Mass spectrometry is a powerful technique that provides the high sensitivity and throughput needed for analyzing the complex and dynamic proteome. However, this power comes with a price; the data generated by mass spectrometers are quite complex and require advanced multi-step analysis. Using scientific workflow systems for the analysis of mass spectrometry data is not entirely new. However, innovative solutions are needed to make these workflows autonomous, intelligent, flexible, and adaptable in the era of big (and complex) data. This thesis focuses on building intelligent workflows and modular tools for analyzing, integrating, and contextualizing mass spectrometry-based proteomics data. These workflows and tools could be easily adjusted for additional functionalities and be reused in different data analysis workflows. The scope of this thesis is not limited to the downstream analysis of mass spectrometry data; methods for automated literature search that would be useful for designing experiments and interpreting experimental results are covered in detail.