October 15 – 20 , 2017, Dagstuhl Seminar 17421

Computational Proteomics


Bernhard Küster (TU München, DE)
Kathryn Lilley (University of Cambridge, GB)
Lennart Martens (Ghent University, BE)

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Proteomics has become a data science. Driven by continuous improvements in instrumentation and increasing sophistication of experimental approaches, the sensitivity of the analyses has grown rapidly while the complexity of the experimental designs has expanded. The result is the generation of ever more data, and often higher complexity of these data to boot. A side effect of this recent transition to a data science, is that much of the acquired proteomics data remains incompletely interpreted due to a lack of truly exhaustive analysis approaches.

Fortunately, there has been a very strong drive to publicly share data in biology, and proteomics is no exception. The result is that a very large amount of essentially uninterpreted yet undoubtedly valuable data is currently available (over 100TB already today, and doubling yearly).

Other omics fields have meanwhile experienced a similar evolution, in the process transforming much of molecular biology into an overall data science. This is evident in the ever more central role of bioinformatics as the main gateway to knowledge in biology.

All these advances create great opportunities for new discoveries, yet in order to make maximal use of these opportunities it will be paramount to bring experimental and computational experts from across different domains together to intensify collaborations.

Indeed, it has become quite clear that a more complete understanding of biological systems will require the integration of data across the four traditional omics domains: genomics, transcriptomics, proteomics and metabolomics. At the same time, it has also become evident that the importance of integration extends to the macroscopic level as well, through meta-(proteo-)omics studies that analyze entire communities of (microbial) cells, often in relation to a host organism.

In this Dagstuhl Seminar on Computational Proteomics, we will therefore bring together the three key communities involved in proteomics: life scientists relying on mass spectrometry; analytical chemists and engineers developing the instruments; and computer scientists, bioinformaticians and statisticians developing algorithms and software. In addition, we will also reach out beyond proteomics, and start up collaborations aimed at ultimately extensive integration with scientists from the genomics, transcriptomics, and metabolomics domains.

Our key topics for discussion and investigation at this Dagstuhl Seminar will follow the outlines of the challenges identified above, and will center on:

  1. Integration of proteomics and transcriptomics data to model the dynamics of gene expression
    The processes that drive gene transcription and translation remain poorly understood, as is evident from long non-coding RNAs (lncRNAs), small open reading frames (sORFs), and complex correlations between protein and mRNA abundances. We will therefore endeavor to develop integrative strategies to explore these issues in more detail.
  2. Analysis and interpretation of public proteomics data in orthogonal contexts
    The mass of proteomics data in the public domain are uniquely suited to orthogonal reanalysis. Notable examples could be the integration with transcriptomics data to better understand translation, or the exploration of the large proportion (70%) of as-yet unidentified spectra.
  3. Assessing and addressing the specific computational challenges of metaproteomics
    The field of metaproteomics is a reasonably young discipline, but is rapidly becoming more popular. Yet data analysis is quite specific, and very few tools or algorithms exist. We will therefore chart the greatest needs in the field, and plan to address these.
  4. Exploration of the key computational interfaces between omics domains
    Interfaces between omics domains are very exciting places, and we should chart obvious overlaps and opportunities for across-omics integration as seed cores for collaboration.
  5. Training of integrative bioinformatics experts
    The future of bioinformatics will undoubtedly involve a lot of integrative data analysis, and we should consider carefully how we can ensure that we train future researchers appropriately.

  Creative Commons BY 3.0 DE
  Bernhard Küster, Kathryn Lilley, and Lennart Martens

Dagstuhl Seminar Series


  • Bioinformatics


  • Computational Mass Spectrometry
  • Proteomics
  • Computational Biology
  • Integrative Bioinformatics
  • Large Scale Public Data

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