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
Highlights
Knowledge of the spatial distribution of proteins is of critical importance to elucidate their role and refine our understanding of cellular processes
Spatial or organelle proteomics is the systematic study of the proteins and their subcellular localization; these compartments can be organelles, i.e. structures defined by lipid bi-layers, macro-molecular assemblies of proteins and nucleic acids or large protein complexes
Any organelles without any suitable markers will be completely omitted from subsequent classification. pRoloc provides the implementation for the phenoDisco novelty detection algorithm (Breckels et al, 2013) that, based on a minimal set of markers and unlabeled data, can be used to effectively detect new putative clusters in the data, beyond those that were initially manually described (Fig. 1)
Summary
Knowledge of the spatial distribution of proteins is of critical importance to elucidate their role and refine our understanding of cellular processes. Mis-localization of proteins have been associated with cellular dysfunction and disease states (Kau et al, 2004; Laurila et al, 2009; Park et al, 2011), highlighting the importance of localization studies. Spatial or organelle proteomics is the systematic study of the proteins and their subcellular localization; these compartments can be organelles, i.e. structures defined by lipid bi-layers, macro-molecular assemblies of proteins and nucleic acids or large protein complexes. Despite technological advances in spatial proteomics experimental designs and progress in mass-spectrometry (Gatto et al, 2010), software support is lacking. We developed the pRoloc package that provides a wide range of thoroughly documented analysis methodologies. The software includes stateof-the-art statistical machine-learning algorithms and bundles
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