Summer School - Gel-based Proteomics and Data Analysis. A Practical Course.
Summer School - Gel-based Proteomics and Data Analysis. A Practical Course.Gel-based Proteomics and Data Analysis. A Practical Course
(11-23 July 2016, St-Petersburg)
This intensive laboratory and lecture course is intended for undergraduate and graduate students who want to get familiar with methods to study the differences in the profiles of protein expression of cells, tissues or organisms under varying conditions. During two weeks, the students will receive hands-on training in differential protein expression analysis using two-dimensional fluorescence difference gel electrophoresis (2D-DIGE) and LC-MS/MS "bottom up" protein identification. The course aims to create a complete picture of proteomic research and its application in different areas of biology and medicine.
In the lectures we will cover a broad range of methods. We will discuss theoretical basis, potential for application of different types of electrophoresis, methods of protein visualization, variety of LC and MS analyses. We will consider the advantages and disadvantages of each method for answering the specific scientific questions.
In the lab students will analyse the protein expression pattern in two animal lineages (mutant vs wild type). They will perform protein separation using 2D-DIGE, MS/MS identification of protein gel spots, followed by qualitative and quantitative 2D-gels analysis in PDQuest BioRad Software.
In the computational part of the course we will discuss design of proteomic experiments, and practice different techniques of visualisation and quantitative analysis. We will cover data preprocessing, imputation of missing data, outlier detection techniques, cluster analysis, differential expression analysis, correction for multiple testing, multivariate methods of analysis of proteomic data. Although we will use R statistical programming language for all computations, the course is designed for students without prior programming experience, but with basic knowledge of statistics.
We also offer a brief Russian language course (6 hour intensive, basic level).
Finally, students will prepare the report of the experimental work they have done. Students who successfully passed the exam will be awarded a certificate.
Maximum enrolment: 10
Fees:EUR 825 (fee includes reagents, lunch during the workdays, accommodation in the hostel of the Saint Petersburg State University)
Application dates: 01 February - 15 April 2016
Application forms: curriculum vitae, motivation letter, personal data for a Visa invitation, enrollment certification for students from German universities
Accommodation: in double or triple rooms at the hostel of the Saint Petersburg University (St Petersburg, 199155, Vasilievsky Island, Kapitanskaya Street, 3
Location: St. Petersburg, Russia
Duration: 11-23 July 2016, with one day-off on 17 July
Activities: Lectures, practical classes, self-study
Requirements: No prior programming experience is required. Students are required to bring their own laptops, preferably with WiFi. During the course we will use the software for data analysis (R with additional packages and RStudio)
The course aims to create a complete picture of proteomic research and its application in different areas of biology and medicine. We will apply modern techniques for studying the proteome as a system and learn to analyse the results of 2D-DIGE as a basic proteomic method.
Characterize the spectrum of basic and applied problems, which can be solved using proteomic methods;
To familiarize with a pipeline of the modern proteomics: sample preparation, 2D-DIGE, MS/MS identification of protein gel spots, qualitative and quantitative 2D-gels analysis in PDQuest BioRad Software;
To show how to design a proteomic study;
Develop practical skills for experimental proteomic studies, data analysis and interpretation of results.
Upon the course completion the student should:
know about the spectrum of methods of electrophoresis, of protein visualization, of LC and MS analyses;
be familiar with the proteomic research pipeline;
be able to explain why technical and biological replicates are necessary in the proteomic experiments;
be able to visualize the proteomic data;
be able to perform differential expression analysis of quantitative proteomic data and interpret its results