Understanding of biological processes and aberrations in disease conditions has over the years moved away from the study of single molecules to a more holistic and all-encompassing view to investigate the entire spectrum of proteins. This method, termed proteomics, has been enabled principally by mass spectrometry techniques. The power of mass spectrometry-based proteomics lays in its ability to investigate an entire proteome and associated expression or modification states of a huge amount of proteins in one single experiment. This massive amount of data requires a high level of automation in data processing to render it into a reduced set of information that can be used to answer the initial hypotheses, explore the biology or contextualize molecular changes associated with a physiological attribute. This chapter gives an overview of the most common proteomic approaches, biological sample considerations and data acquisition methods, data processing, software solutions for the various steps and further functional analyses of biological data. This enables the comparison of various datasets as a summation of individual experiments, to cross-compare sample types and other metadata. There are many approach pipelines in existence that cover specialist disciplines and data analytics steps, and it is a certainty that many more data analysis methodologies will be generated over the coming years, but it also emphasizes the inherent place of proteomic technologies in research in elucidating the nature of biological processes and understanding of disease etiology.
|Title of host publication||Computational Biology|
|Place of Publication||Brisbane, Australia|
|Number of pages||24|
|Publication status||Published - 31 Oct 2019|
- data analysis
- mass spectrometry
Cervantes-Gracia, K., & Husi, H. (2019). Computational Approaches in Proteomics. In Computational Biology (pp. 119-142). Brisbane, Australia: Codon Publications. https://doi.org/10.15586/computationalbiology.2019.ch8