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Bioinformatics. 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.

Author information

1
Computational Proteomics Unit and Cambridge Centre for Proteomics, Department of Biochemistry, University of Cambridge, Tennis Court Road, CB2 1QR, Cambridge, UK and Université Grenoble-Alpes, CEA (iRSTV/BGE), INSERM (U1038), CNRS (FR3425), 38054 Grenoble, France.

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.

PMID:
24413670
PMCID:
PMC3998135
DOI:
10.1093/bioinformatics/btu013
[Indexed for MEDLINE]
Free PMC Article
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