Computational proteomics: management and analysis of proteomics data

Proteomics is about the study of the proteins expressed in a cell, organism, or tissue. This includes protein identification and quantification (or quantitation), protein–protein interactions, protein complexes prediction, protein modifications and protein localization in the cell. Mass Spectrometry (MS) is one of the main technologies in proteomics and is more and more used for its increasing precision and for the possibility to automate the proteomics analysis pipeline, yielding to large-scale high-throughput experiments. Since proteins play a central role in the life of an organism, proteomics is instrumental in many biomedical applications, such as biomarker discovery and drug treatment evaluation, as well as for investigating the dynamics of cells in Systems Biology. Computational Proteomics is about the computational methods, algorithms, databases and methodologies used to process, manage, analyze and interpret the data produced in proteomics experiments. The broad application of proteomics in different biological and medical fields, as well as the diffusion of high-throughput platforms, leads to increasing volumes of available proteomics data requiring efficient algorithms, new data management capabilities and novel analysis, inference and visualization techniques. Moreover, high-throughput production and collection of data pose new challenges in data handling and reusability as well as in tools interoperability and interconnection. On the other hand, the increasing availability of data and tools opens new research directions and opportunities (e.g. annotated spectral libraries) that can be exploited only through the rigorous application of computer science, machine learning, knowledge discovery, statistics and signal processing techniques.