KNIME-MSBayesPro is a KNIME node implementation to perform protein inference analysis on Mass Spectrometry data. MSBayesPro implements a Bayesian protein inference algorithm for LC-MS/MS proteomics experiment and it includes peptide detectability in the probabilistic model. It use a non-parametric model for estimating protein probabilities, putting particular emphasis on the peptide degeneracy problem.
knime-msbayespro is a JOmics licensed under Apache License 2.0.
Li YF, Arnold RJ, Tang H, Radivojac P. The importance of peptide detectability for protein identification, quantification, and experiment design in MS/MS proteomics. Journal of proteome research. 2010;9:6288-97.
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Fast and efficient processing of large dataset (tabular format like input).
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Simple structure (one port input/one port output).
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Requires peptide's Posterior Error Probabilities (PEP) and peptide's detectability like input parameters.
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Compute posterior error probabilities for proteins.
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Possibility of easy integration with OpenMS workflow.
Basically, the MSBayesPro node handle the data in tabular format. Input: Peptide list with proteins group, peptide probability and peptide detectability linked. Output: report Protein list with probability associated.
Note: The node is still evolving, we are committed to expand the node and add more features such as additional settings in the configuration dialog to controlling the inference process.
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Java: KNIME SDK 9.2 (or above) and Java JRE 1.6 (or above), which you can download for free here. (Note: most computers should have Java installed already).
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Operating System: The current version has been tested on Linux and Max OS X, it may require additional adjustment for other platform. If you come across any problems on your platform, please contact us (enriquea@cim.sld.cu).
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Memory: MS dataset can be very large sometimes, in order to get good performance from this KNIME node, we recommend the following settings for VM arguments: -ea -Xmx1G -XX:MaxPermSize=512M. For additional information see https://tech.knime.org/test-your-node.
Click [here] (https://github.com/KNIME-OMICS/MSBayesPro) to launch directly the latest MSBayesPro (KNIME node) implementation.
This node is ready to use. However, taking into account you current platform, it may requiere additional adjustment. You can build the standalone node in few step (see https://tech.knime.org/developer/documentation/export).
You can get the latest MSBayesPro KNIME node implementation from our Download Section. Unzipping the file and load via Eclipse project.
##Maven Dependency
If you have questions or need additional help, please contact us via Enrique Audain Martinez (enriquea@cim.sld.cu) and Yasset Perez-Riverol (yperez@ebi.ac.uk).
Please send us your feedback, including error reports, improvement suggestions, new feature requests and any other things you might want to suggest.
Peptide/Protein Identification workflow (OpenMS-KNIME). (1) Mass spectrometry data pre-processing. (2) Database searching/Peptide identification. (3) idXML file processing. (4) Peptide detectability prediction based on SVM approach. (5) Protein inference analysis.