pRoloc suite set of software offers a complete software pipeline to analyse, visualise and interpret mass spectrometry-based spatial proteomics data such, for example, as LOPIT (Localization of Organelle Proteins by Isotope Tagging), PCP (Protein Correlation Profiling) or hyperLOPIT (hyperplexed LOPIT). The suite includes
pRoloc, the mail software that focuses on data analysis using state-of-the-art machine learning,
pRolocdata, that distributes numerous datasets, and
pRolocGUI, that offers interactive visualisations dedicated to spatial proteomics. The software are distributed as part of the R/Bioconductor project.
pRoloc software comes with ample documentation. The main tutorial provides a broad overview of the package and its functionality. See the Articles tab for additional manuals.
pRolocGUI also offer several documentation files.
Here are a set of video tutorial that illustrate the
Post your questions on the Bioconductor support site, tagging it with the package name
pRoloc (the maintainer will automatically be notified by email). If you identify a bug or have a feature request, please open an issue on the github development page.
The preferred installation procedure uses the Bioconductor infrastructure:
The pre-release/development code on github can also be installed using
BiocManager::install, as shown below. Note that this requires a working R build environment (i.e
Rtools on Windows - see here). New pre-release features might not be documented not thoroughly tested and could substantially change prior to release. Use at your own risks.
For refences about the software, how to use it and spatial proteomics data analysis:
Crook OM, Breckels LM, Lilley KS, Kirk PWD, Gatto L. A Bioconductor workflow for the Bayesian analysis of spatial proteomics [version 1; peer review: awaiting peer review]. F1000Research 2019, 8:446 (https://doi.org/10.12688/f1000research.18636.1)
Breckels LM, Mulvey CM, Lilley KS and Gatto L. A Bioconductor workflow for processing and analysing spatial proteomics data [version 2; peer review: 2 approved]. F1000Research 2018, 5:2926 (https://doi.org/10.12688/f1000research.10411.2)
Gatto L, Breckels LM, Burger T, Nightingale DJ, Groen AJ, Campbell C, Nikolovski N, Mulvey CM, Christoforou A, Ferro M, Lilley KS. A foundation for reliable spatial proteomics data analysis Mol Cell Proteomics. 2014 Aug;13(8):1937-52. doi: 10.1074/mcp.M113.036350. Epub 2014 May 20. PubMed PMID: 24846987
Gatto L, Breckels LM, Wieczorek S, Burger T, Lilley KS. Mass-spectrometry-based spatial proteomics data analysis using pRoloc and pRolocdata Bioinformatics. 2014 May 1;30(9):1322-4. doi: 10.1093/bioinformatics/btu013. Epub 2014 Jan 11. PubMed PMID: 24413670.
Specific algorithms available in the software:
Breckels LM, Gatto L, Christoforou A, Groen AJ, Lilley KS, Trotter MW. The effect of organelle discovery upon sub-cellular protein localisation J Proteomics. 2013 Aug 2;88:129-40. doi: 10.1016/j.jprot.2013.02.019. Epub 2013 Mar 21. PubMed PMID: 23523639.
Breckels LM, Holden S, Wojnar D, Mulvey CMM, Christoforou A, Groen AJ, Kohlbacher O, Lilley KS and Gatto L. Learning from heterogeneous data sources: an application in spatial proteomics 2015 biorXiv, doi: http://dx.doi.org/10.1101/022152
Oliver M Crook, Claire M Mulvey, Paul D. W. Kirk, Kathryn S Lilley, Laurent Gatto A Bayesian Mixture Modelling Approach For Spatial Proteomics PLOS Computational Biology doi:[10.1371/journal.pcbi.1006516](https://doi.org/10.1371/journal.pcbi.1006516)
Contributions to the package are more than welcome. If you want to contribute to this package, you should follow the same conventions as the rest of the functions whenever it makes sense to do so. Feel free to get in touch (preferable opening a github issue) to discuss any suggestions.
Please note that this project is released with a Contributor Code of Conduct. By participating in this project you agree to abide by its terms.