Missing values are a genuine issue in label-free quantitative proteomics. Recent works have surveyed the different statistical methods to conduct imputation and have compared them on real or simulated data sets and recommended a list of missing value imputation methods for proteomics application. Although insightful, these comparisons do not account for two important facts: (i) depending on the proteomics data set, the missingness mechanism may be of different natures and (ii) each imputation method is devoted to a specific type of missingness mechanism. As a result, we believe that the question at stake is not to find the most accurate imputation method in general but instead the most appropriate one. We describe a series of comparisons that support our views: For instance, we show that a supposedly “under-performing” method (i.e., giving baseline average results), if applied at the “appropriate” time in the data-processing pipeline (before or after peptide aggregation) on a data set with the “appropriate” nature of missing values, can outperform a blindly applied, supposedly “better-performing” method (i.e., the reference method from the state-of-the-art). This leads us to formulate few practical guidelines regarding the choice and the application of an imputation method in a proteomics context.

Summary: DAPAR and ProStaR are software tools to perform the statistical analysis of label-free XIC-based quantitative discovery proteomics experiments. DAPAR contains procedures to filter, normalize, impute missing value, aggregate peptide intensities, perform null hypothesis significance tests and select the most likely differentially abundant proteins with a corresponding false discovery rate. ProStaR is a graphical user interface that allows friendly access to the DAPAR functionalities through a web browser. Availability and implementation: DAPAR and ProStaR are implemented in the R language and are available on the website of the Bioconductor project (http://www.bioconductor.org/). A complete tutorial and a toy dataset are accompanying the packages.

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

Photosynthesis has shaped atmospheric and ocean chemistries and probably changed the climate as well, as oxygen is released from water as part of the photosynthetic process. In photosynthetic eukaryotes, this process occurs in the chloroplast, an organelle containing the most abundant biological membrane, the thylakoids. The thylakoids of plants and some green algae are structurally inhomogeneous, consisting of two main domains: the grana, which are piles of membranes gathered by stacking forces, and the stroma-lamellae, which are unstacked thylakoids connecting the grana. The major photosynthetic complexes are unevenly distributed within these compartments because of steric and electrostatic constraints. Although proteomic analysis of thylakoids has been instrumental to define its protein components, no extensive proteomic study of subthylakoid localization of proteins in the BBY (grana) and the stroma-lamellae fractions has been achieved so far. To fill this gap, we performed a complete survey of the protein composition of these thylakoid subcompartments using thylakoid membrane fractionations. We employed semiquantitative proteomics coupled with a data analysis pipeline and manual annotation to differentiate genuine BBY and stroma-lamellae proteins from possible contaminants. About 300 thylakoid (or potentially thylakoid) proteins were shown to be enriched in either the BBY or the stroma-lamellae fractions. Overall, present findings corroborate previous observations obtained for photosynthetic proteins that used nonproteomic approaches. The originality of the present proteomic relies in the identification of photosynthetic proteins whose differential distribution in the thylakoid subcompartments might explain already observed phenomenon such as LHCII docking. Besides, from the present localization results we can suggest new molecular actors for photosynthesis-linked activities. For instance, most PsbP-like subunits being differently localized in stroma-lamellae, these proteins could be linked to the PSI-NDH complex in the context of cyclic electron flow around PSI. In addition, we could identify about a hundred new likely minor thylakoid (or chloroplast) proteins, some of them being potential regulators of the chloroplast physiology.