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. 2014 May 1;30(9):1322-4.
doi: 10.1093/bioinformatics/btu013. Epub 2014 Jan 11.

Mass-spectrometry-based spatial proteomics data analysis using pRoloc and pRolocdata

Laurent Gatto  1 , Lisa M Breckels, Samuel Wieczorek, Thomas Burger, Kathryn S Lilley
Affiliations

Mass-spectrometry-based spatial proteomics data analysis using pRoloc and pRolocdata

Laurent Gatto et al. Bioinformatics. .

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 mass-spectrometry-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.

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Figures

Fig. 1.
Fig. 1.
Current state-of-the-art experimental organelle proteomics data analysis with pRoloc. On the left, we replicated the original findings from Tan et al. (2009) on Drosophila embryos. On the right, we present results of the same data set obtained with pRoloc, utilizing the novelty discovery functionality (new color-coded organelles) and a class-weighted support vector machine (SVM) algorithm with classifier posterior probabilities (point sizes)
See this image and copyright information in PMC

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