Keynote speakers

Dr. Laurent Gatto is a Senior Research Associate in the Department of Biochemistry at the University of Cambridge.

He moved to Cambridge in January 2010 to work in the Cambridge Centre for Proteomics on various aspects of quantitative and spatial proteomics, developing methodological advances and implementing computational tools with a strong emphasis on rigorous and reproducible data analysis. He is also a visiting scientist in the PRIDE team at the European Bioinformatics Institute, affiliate teaching staff at the Cambridge Computational Biology Institute, a Software Sustainability Institute fellow and a Software/Data Carpentry instructor. Since August 2013, he heads the Computational Proteomics Unit.

The Computational Proteomics Unit's main activities center around the sound analysis of proteomics data and integration of different sources of heterogeneous data. They work in close collaboration with biologists to tackle biologically challenging questions using statistics and machine learning to understand the data and uncover biologically relevant patterns. The development and publication of scientific software (1, 2) is an integral part of their work and is reflected by their contributions to the Bioconductor project.

Prof. Dr. Lukas Käll is Associate Professor in Statistical Biotechnology at the KTH Royal Institute of Technology, School of Biotechnology, Science for Life Laboratory in Stockholm.

He has a PhD from Karolinska Institutet and completed a post-doc at the University of Washington, USA. His group develops methods for facilitating the interpretation of high-throughput experiments, i.e. massively parallel experiments that generate a large set of readouts. A common challenge for these kinds of experiments is that the interpretation of the outcomes, as the individual measurements are of varying quality. He aims at increasing the yield and facilitating the interpretation of high-throughput experiments by using different machine learning methods such as support vector machines and dynamical Bayesian networks, and he has developed a wide variety of bioinformatical tools based on machine learning techniques, such as Phobius/Philius, qvality, and Percolator.

Dr. Frédérique Lisacek is group leader of the Proteome Informatics group at the SIB Swiss Institute of Bioinformatics and the University of Geneva.

The Proteome Informatics Group is involved with software and database development for the benefit of the proteomics and glycomics communities. These resources are made available through the ExPASy server. Software tools support experimental mass spectrometry data analysis, mainly for the detection of posttranslational modifications. Databases store knowledge of carbohydrates attached to proteins as well as protein-carbohydrate interactions. Notably, in recent years, the Proteome Informatics Group has set out to collect and integrate information on glycans whose role is increasingly described as key in many normal and pathological cellular processes. With the creation of a dedicated tab on the ExPASy server, it centralizes databases and tools that are useful for glycomics and glycoproteomics, while new tools are continuously added to support scientists in shaping assumptions on glycan-binding properties and glycan attachment on proteins.

Prof. Dr. Lennart Martens is Professor of Systems Biology in the Department of Biochemistry at the Faculty of Medicine and Health Sciences at Ghent University, and Group Leader of the Computational Omics and Systems Biology group in the Department of Medical Protein Research at VIB, both in Ghent, Belgium.

Prof. Martens obtained his Ph.D. in Sciences: Biotechnology from Ghent University, and afterwards served as PRIDE Group Coordinator at EMBL-EBI before returning to Ghent University and VIB in his current position. He is head of the CompOmics research group, which has its roots in Ghent, but has active members all over Europe, and specializes in the management, analysis and integration of high-throughput data (as obtained from various Omics approaches) with an aim towards establishing solid data stores, processing methods and tools to enable downstream systems biology research.