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A Bioconductor workflow for the Bayesian analysis of spatial proteomics.

ABSTRACT: Knowledge of the subcellular location of a protein gives valuable insight into its function. The field of spatial proteomics has become increasingly popular due to improved multiplexing capabilities in high-throughput mass spectrometry, which have made it possible to systematically localise thousands of proteins per experiment. In parallel with these experimental advances, improved methods for analysing spatial proteomics data have also been developed. In this workflow, we demonstrate using `pRoloc` for the Bayesian analysis of spatial proteomics data. We detail the software infrastructure and then provide step-by-step guidance of the analysis, including setting up a pipeline, assessing convergence, and interpreting downstream results. In several places we provide additional details on Bayesian analysis to provide users with a holistic view of Bayesian analysis for spatial proteomics data.

SUBMITTER: Crook OM

PROVIDER: S-EPMC6509962 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

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A Bioconductor workflow for the Bayesian analysis of spatial proteomics.

Crook Oliver M OM Breckels Lisa M LM Lilley Kathryn S KS Kirk Paul D W PDW Gatto Laurent L

F1000Research 20190411

Knowledge of the subcellular location of a protein gives valuable insight into its function. The field of spatial proteomics has become increasingly popular due to improved multiplexing capabilities in high-throughput mass spectrometry, which have made it possible to systematically localise thousands of proteins per experiment. In parallel with these experimental advances, improved methods for analysing spatial proteomics data have also been developed. In this workflow, we demonstrate using `pRo ...[more]

PMID: 31119032

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