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Article Dans Une Revue Bioinformatics Année : 2014

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

Résumé

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. 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. CONTACT: lg390@cam.ac.uk.

Dates et versions

hal-00931902 , version 1 (16-01-2014)

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Laurent Gatto, Lisa M Breckels, Samuel Wieczorek, Thomas Burger, Kathryn S Lilley. Mass-spectrometry based spatial proteomics data analysis using pRoloc and pRolocdata.. Bioinformatics, 2014, epub ahead of print. ⟨10.1093/bioinformatics/btu013⟩. ⟨hal-00931902⟩
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