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

Full text: Download

Publisher: Oxford University Press (OUP) (Policy B - Oxford Open Option B)

Preprint: archiving allowed. Upload

Postprint: archiving restricted: Upload

  • 12 months embargo

Published version: archiving forbidden. Upload

Policy details (opens in a new window). Data provided by SHERPA/RoMEO
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. 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.