23.08.15 - 28.08.15, Seminar 15351

Computational Mass Spectrometry

Diese Seminarbeschreibung wurde vor dem Seminar auf unseren Webseiten veröffentlicht und bei der Einladung zum Seminar verwendet.

Motivation

Following in the steps of high-throughput sequencing, mass spectrometry (MS) has become a key analytical technique for large-scale studies of complex biological mixtures. MS-based experiments generate datasets of increasing complexity and size. Computational and statistical analysis of these datasets becomes a major bottleneck, which requires both methodological and practical solutions. We also hope to encourage discussions of emerging areas of research such as design and analysis of large, complex and heterogeneous datasets, which combine mass spectrometric and other experimental components for systems biology investigations. The participants will be able to suggest the details of these and other relevant topics, and each participant will be able to discuss multiple topics. While regular venues typically adhere to strict schedules and prepared remarks, Dagstuhl seminars devote a lot of time to spontaneous participant-driven interactions. The unique atmosphere of the castle fosters both technical discussions around a blackboard during the day, and more informal interactions over a glass of wine at night.

Dagstuhl seminars on computational mass spectrometry already have a rich history. Three previous seminars in 2008, 2010 and 2013 attracted many leading scientists of the field. The seminars resulted in joint papers, grant applications, and several community efforts with significant impact for the whole field. In particular, the stated goal of the 2013 seminar was to identify the grand challenges of the field. In contrast, in 2015 the organizers intend to explore more specific sub-areas and issues. We plan to include a series of overview talks by experts in experimental and computational mass spectrometry. We will expand the activities of the group beyond mass spectrometry-based proteomics, and devote more time to under-represented areas such as computational metabolomics.