What are Proteome Discoverer Community Nodes?
Starting from version 2.0 Proteome Discoverer functionality can be extended by external plugins. Several OpenMS workflows are now made available as Proteome Discoverer Community Nodes. If you are interested in metabolite or small molecule analysis you might want to check out our Compound Discoverer Community Node.
Label-free quantification (LFQ) of peptides and proteins has become a very popular analytical technique in particular in clinical proteomics. Large-scale studies comprising hundreds or even thousands of LC-MS experiments require efficient computational processing tools. Here, we present the integration of an OpenMS-based LFQ workflow into the Proteome Discoverer platform.
In addition, we provide the intended processing and consensus workflow. Please note that, at the moment, the combination with Sequest HT and Percolator is mandatory, since our community nodes require some of the information produced by these nodes. Combinations with other peptide identification and validation nodes might work, but have not been tested yet.
UV-induced cross-linking combined with LC-MS/MS analysis has been successful in the elucidation of protein-DNA and protein-RNA interactions. We recently presented RNPxl, a computational pipeline, implemented in the OpenMS framework, for the analysis of LC-MS/MS cross-link data. The RNPxl workflow is now made available to a larger audience by integration into the Proteome Discoverer platform. In addition, we were able to increase processing speed by a factor of ten compared to the previous version.
If you have any questions, bug reports, or feature requests, please take a look at the support page.
Please note that Thermo only provides the framework for the integration of third-party plugins. They do neither provide any support nor take any legal responsibility for external contributions. If you have any questions, bug reports, or feature requests regarding the OpenMS Community Nodes for Proteome Discoverer, please contact the OpenMS mailing list or open an issue on GitHub instead.
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Weisser et al. An automated pipeline for high-throughput label-free quantitative proteomics. J. Proteome Res., 12(4):1628–1644, 2013.
Kramer et al. Photo-cross-linking and high-resolution mass spectrometry for assignment of RNA-binding sites in RNA-binding proteins. Nat Methods., 11(10):1064-70, 2014.
Berthold et al. KNIME: The Konstanz Information Miner. In Studies in Classification, Data Analysis, and Knowledge Organization (GfKL 2007). Springer, 2007.
Serang et al. Efficient marginalization to compute protein posterior probabilities from shotgun mass spectrometry data. J Proteome Res., 9(10): 5346–5357, 2010.