Proteomics Data Analysis

Lennart Martens studied biotechnology at Ghent University in Belgium, where he did his Master's thesis on the computational interpretation of peptide mass spectra. He then worked as a software developer and framework architect for a software company for a few years, before returning to Ghent University to pursue a Ph.D. in Biotechnology. During this time, he focussed on the development of high-throughput peptide centric proteomics techniques and on bioinformatics tools to support these new approaches. In 2003 he started the PRIDE proteomics database at the EBI as a Marie Curie fellow of the European Commission. After obtaining his Ph.D., Lennart Martens rejoined the PRIDE group at EBI. He is Bioinformatics Section Editor for the Springer journal Amino Acids, is an author on more than 50 peer-reviewed papers, and has written a Wiley textbook on Computational Methods for Mass Spectrometry. Presently he is a Professor in Systems Biology at the University of Ghent in Belgium.

Lukas Käll has a PhD in Bioinformatics from the Karolinska Institutet, Sweden, and completed a post-doc in William S. Noble's lab at the University of Washington, USA. His group develops methods for facilitating the interpretation of high-throughput experiments. He developed the software package Percolator, which uses semi-supervised machine learning techniques to improve the yield of shotgun proteomics assays. The algorithm is currently being incorporated into Matrix Science's search engine Mascot. Lucas Käll has co-authored several tutorials and methodological papers on statistical methods that assess confidence in identifications from mass spectrometry assays.

This course is aimed at teaching you five aspects of proteomics data:
- the origins of your data, including instrumentation and methodologies;
- the outlined workings and idiosyncrasies of search engines and peptide identification strategies;
- the importance of post-validation, either through automated or semi-automated algorithms as well as the issue with protein inference;
- the various pitfalls and challenges of analyzing quantitative proteomics data; and
- the downstream functional analysis of your proteomics results.

All topics will be illustrated through extensive hands-on practicals, employing freely available software that you can take home to your lab afterwards.

Target audience: Scientists, researchers, students aiming at bridging the gap between Proteomics experimental results and integrated data resources