Mass-spectrometry-based spatial proteomics data analysis using pRoloc and pRolocdata
(2014) Bioinformatics — Vol. 30, n° 9, p. 1322-1324 (2014)
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- Authors
- Abstract
- Motivation: Experimental spatial proteomics, i.e. the high-throughput assignment of proteins to sub-cellular compartments based on quantitative proteomics data, promises to shed new light on many biological processes given adequate computational tools. Results: Here we present pRoloc, a complete infrastructure to support and guide the sound analysis of quantitative massspectrometry- based spatial proteomics data. It provides functionality for unsupervised and supervised machine learning for data exploration and protein classification and novelty detection to identify new putative sub-cellular clusters. The software builds upon existing infrastructure for data management and data processing. Availability: pRoloc is implemented in the R language and available under an open-source license from the Bioconductor project (http://www.bioconductor.org/). A vignette with a complete tutorial describing data import/export and analysis is included in the package. Test data is available in the companion package pRolocdata.
- Affiliations
University of Cambridge
Citations
Gatto, L., Breckels, L. M., Wieczorek, S., Burger, T., & Lilley, K. S. (2014). Mass-spectrometry-based spatial proteomics data analysis using pRoloc and pRolocdata. Bioinformatics, 30(9), 1322-1324. https://doi.org/10.1093/bioinformatics/btu013 (Original work published 2014)