Bioinformatics tools for proteomics, also called proteome informatics tools, span today a large panel of very diverse applications ranging from simple tools to compare protein amino acid compositions to sophisticated software for large-scale protein structure determination. This course will give an introduction of proteomics and mass spectrometry workflows, experimental design, and data analysis with a focus on algorithms for extracting information from experimental data.
Proteomics is the study of the function of all expressed proteins. The term proteome was first coined to describe the set of proteins encoded by the genome1. The study of the proteome, called proteomics, now evokes not only all the proteins in any given cell, but also the set of all protein isoforms and modifications, the interactions between them, the structural description of proteins and their higher-order complexes, and for that matter almost everything ‘post-genomic’. In this overview we will use proteomics in an overall sense to mean protein biochemistry on an unprecedented, high-throughput scale.
Proteomics complements other functional genomics approaches, including microarray-based expression profiles, systematic phenotypic profiles at the cell and organism level, systematic genetics and small-molecule-based arrays. Integration of these data sets through bioinformatics will yield a comprehensive database of gene function that will serve as a powerful reference of protein properties and functions, and a useful tool for the individual researcher to both build and test hypotheses. Moreover, this large-scale data sets will be of utmost importance for the emerging field of systems biology.
Advances in proteomics technology offer great promise in the understanding and treatment of the molecular basis of disease. The past decade of proteomics research, the study of dynamic protein expression, post-translational modifications, cellular and sub-cellular protein distribution, and protein-protein interactions, has culminated in the identification of many disease-related biomarkers and potential new drug targets. While proteomics remains the tool of choice for discovery research, new innovations in proteomic technology now offer the potential for proteomic profiling to become standard practice in the clinical laboratory. Indeed, protein profiles can serve as powerful diagnostic markers, and can predict treatment outcome in many diseases, in particular cancer. A number of technical obstacles remain before routine proteomic analysis can be achieved in the clinic; however the standardisation of methodologies and dissemination of proteomic data into publicly available databases is starting to overcome these hurdles. At present the most promising application for proteomics is in the screening of specific subsets of protein biomarkers for certain diseases, rather than large scale full protein profiling. Armed with these technologies the impending era of individualised patient-tailored therapy is imminent. This review summarises the advances in proteomics that has propelled us to this exciting age of clinical proteomics, and highlights the future work that is required for this to become a reality.
We provide Proteomics Informatics course in two levels as Professional Designation for undergraduate students and Advance PG Program for Graduated Students. The details of the courses are as below:
Increasingly in the last decade, proteomics has become a widely used technique in the field of cell biology. This is due not only to improvements in technology and analytical procedures, but also because of the innovative and diverse ways in which the methodologies of protein and peptide analysis have been applied. This has resulted in the broadening of applications and potential uses for proteomics, including analyses of cellular substructures and mechanisms to imaging of protein distributions in cells and tissues. The growing scale and scope of modern proteomics projects has brought to the fore computing challenges involved in managing and mining the resulting very large and complex data sets. Modern proteomics is therefore inherently multidisciplinary, requiring the expertise of software engineers and statisticians as well as cell biologists, protein chemists and mass spectrometrists. This review article will describe some of the elegant, quirky, and novel applications of mass spectrometry based proteomics to medical research, diagnostics, quality control and drug discovery.