pRoloc
is a Bioconductor package for the analysis of experimental spatial
proteomics data. It is available from Bioconductor >= 2.12 and
requires R (>= 3.0.0).
Current build status:
releasedevel
The pRoloc suite set of software are distributed as part of the
R/Bioconductor project and are developed
at the Computational Proteomics Unit
and
Cambridge Centre for Proteomics
labs, at the University of Cambridge.
Getting started
The pRoloc software comes with ample documentation. The main
tutorial
(release
and
devel)
provides a broad overview of the package and its functionality. See
the package page
(release
and
devel)
for additional manuals.
Two associated packages, pRolocdata
(devel
and
release)
and pRolocGUI
(release
and
devel)
offer spatial proteomics data and a graphical user interface to
interactively explore the data.
Here are a set of
video tutorial
that illustrate the pRoloc framework.
Help
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.
Installation
The preferred installation procedure uses the Bioconductor infrastructure:
source("http://bioconductor.org/biocLite.R")
biocLite("pRoloc")
biocLite("pRolocdata")
biocLite("pRolocGUI")Pre-release/development version
The pre-release/development code on github can be installed using
biocLite. 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.
## install from github
biocLite("lgatto/pRoloc")
biocLite("lgatto/pRolocdata")
biocLite("ComputationalProteomicsUnit/pRolocGUI")References:
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.
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.
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
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