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.

SummaryDAPAR 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 implementationDAPAR 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.

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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 , 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: is implemented in the 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 .Contact: lg390@cam.ac.uk

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Quantitative mass-spectrometry-based spatial proteomics involves elaborate, expensive, and time-consuming experimental procedures, and considerable effort is invested in the generation of such data. Multiple research groups have described a variety of approaches for establishing high-quality proteome-wide datasets. However, data analysis is as critical as data production for reliable and insightful biological interpretation, and no consistent and robust solutions have been offered to the community so far. Here, we introduce the requirements for rigorous spatial proteomics data analysis, as well as the statistical machine learning methodologies needed to address them, including supervised and semi-supervised machine learning, clustering, and novelty detection. We present freely available software solutions that implement innovative state-of-the-art analysis pipelines and illustrate the use of these tools through several case studies involving multiple organisms, experimental designs, mass spectrometry platforms, and quantitation techniques. We also propose sound analysis strategies for identifying dynamic changes in subcellular localization by comparing and contrasting data describing different biological conditions. We conclude by discussing future needs and developments in spatial proteomics data analysis. Molecular &

Recently, the problem of measuring the conflict between two bodies of evidence represented by belief functions has known a regain of interest. In most works related to this issue, Dempster's rule plays a central role. In this paper, we propose to study the notion of conflict from a different perspective. We start by examining consistency and conflict on sets and extract from this settings basic properties that measures of consistency and conflict should have. We then extend this basic scheme to belief functions in different ways. In particular, we do not make any a priori assumption about sources (in)dependence and only consider such assumptions as possible additional information.

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. Molecular & Cellular Proteomics 13: 10.1074/mcp.M114.040923, 2147-2167, 2014.As primary producers, plants are at the basis of the food chain for most ecosystems and control the planet atmosphere via photosynthesis. In eukaryotes, this process, responsible for carbon fixation and for the release of gaseous oxygen into the atmosphere, takes place in a specialized compartment, the chloroplast. This semiautonomous organelle plays a number of essential functions, including photosynthesis, nitrogen assimilation, sulfur reduction and assimilation, synthesis of amino acids, fatty acids, and many secondary metabolites (1-6). The chloroplast is not the only type of plastids. Plastids are indeed found in every plant tissue (with a very few exceptions such as angiosperm pollen grains) and in...