Accounting for the Multiple Natures of Missing Values in Label-Free Quantitative Proteomics Data Sets to Compare Imputation Strategies.

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.his work was supported by the following funding: ANR-2010-GENOM-BTV-002-01 (Chloro-Types), ANR-10-INBS-08 (ProFI project, “Infrastructures Nationales en Biologie et Santé”, “Investissements d’Avenir”), EU FP7 program (Prime-XS project, Contract no. 262067), the Prospectom project (Mastodons 2012 CNRS challenge), and the BBSRC Strategic Longer and Larger grant (Award BB/L002817/1).This is the final version of the article. It first appeared from the American Chemical Society via https://dx.doi.org/10.1021/acs.jproteome.5b0098

Prediction of subplastidial localization of chloroplast proteins from spectral count data - Comparison of machine learning algorithms

To study chloroplast metabolism and functions, subplastidial localization is a prerequisite to achieve protein functional characterization. As the accurate localization of many chloroplast proteins often remains hypothetical, we set up a proteomics strategy in order to assign the accurate subplastidial localization. A comprehensive study of Arabidopsis thaliana chloroplast proteome has been carried out in our group [1], involving high performance mass spectrometry analyses of highly fractionated chloroplasts. In particular, spectral count data were acquired for the three major chloroplast sub-fractions (stroma, thylakoids and envelope) obtained by sucrose gradient purification. As the distribution of spectral counts over compartments is a fair predicator of relative abundance of proteins [2], it was justified to propose a prime statistical model [1] relating spectral counts to subplastidial localization. This predictive model was based on a logistic regression, and demonstrated an accuracy rate of 84% for chloroplast proteins. In the present work, we conducted a comparative study of various machine learning techniques to generate a predictive model of subplastidial localization of chloroplast proteins based on spectral count data

Pericentric heterochromatin reprogramming by new histone variants during mouse spermiogenesis

During male germ cell postmeiotic maturation, dramatic chromatin reorganization occurs, which is driven by completely unknown mechanisms. For the first time, we describe a specific reprogramming of mouse pericentric heterochromatin. Initiated when histones undergo global acetylation in early elongating spermatids, this process leads to the establishment of new DNA packaging structures organizing the pericentric regions in condensing spermatids. Five new histone variants were discovered, which are expressed in late spermiogenic cells. Two of them, which we named H2AL1 and H2AL2, specifically mark the pericentric regions in condensing spermatids and participate in the formation of new nucleoprotein structures. Moreover, our investigations also suggest that TH2B, an already identified testis-specific H2B variant of unknown function, could provide a platform for the structural transitions accompanying the incorporation of these new histone variants

DAPAR & ProStaR: software to perform statistical analyses in quantitative discovery proteomics.

UNLABELLED: 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. CONTACT: [email protected], [email protected], [email protected] (ChloroTypes), ANR-10-INBS-08 (ProFI project, ‘Infrastructures Nationales en Biologie et Sante´’, ‘Investissements d’Avenir’), European Union FP7 program (Prime-XS Project, Contract no. 262067), Prospectom project (Mastodons 2012 CNRS Challenge), Biotechnology and Biological Sciences Research Council (Strategic Longer and Larger Grant ID: BB/L002817/1)This is the final version of the article. It first appeared from Oxford University Press via https://doi.org/10.1093/bioinformatics/btw58

A foundation for reliable spatial proteomics data analysis.

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..G., C.M.M., and M.F. were supported by the European Union 7th Framework Program (PRIME-XS Project, Grant No. 262067). L.M.B. was supported by a BBSRC Tools and Resources Development Fund (Award No. BB/K00137X/1). T.B. was supported by the Proteomics French Infrastructure (ProFI, ANR-10-INBS-08). A.C. was supported by BBSRC Grant No. BB/D526088/1. A.J.G. was supported by BBSRC Grant No. BB/E024777/ and a generous gift from King Abdullah University for Science and Technology, Saudi Arabia. D.J.N.H. was supported by a BBSRC CASE studentship (BB/I016147/1)

In vivo spectroscopy and NMR metabolite fingerprinting approaches to connect the dynamics of photosynthetic and metabolic phenotypes in resurrection plant Haberlea rhodopensis during desiccation and recovery.

International audienceThe resurrection plant Haberlea rhodopensis was used to study dynamics of drought response of photosynthetic machinery parallel with changes in primary metabolism. A relation between leaf water content and photosynthetic performance was established, enabling us to perform a non-destructive evaluation of the plant water status during stress. Spectroscopic analysis of photosynthesis indicated that, at variance with linear electron flow (LEF) involving photosystem (PS) I and II, cyclic electron flow around PSI remains active till almost full dry state at the expense of the LEF, due to the changed protein organization of photosynthetic apparatus. We suggest that, this activity could have a photoprotective role and prevent a complete drop in adenosine triphosphate (ATP), in the absence of LEF, to fuel specific energy-dependent processes necessary for the survival of the plant, during the late states of desiccation. The NMR fingerprint shows the significant metabolic changes in several pathways. Due to the declining of LEF accompanied by biosynthetic reactions during desiccation, a reduction of the ATP pool during drought was observed, which was fully and quickly recovered after plants rehydration. We found a decline of valine accompanied by lipid degradation during stress, likely to provide alternative carbon sources for sucrose accumulation at late stages of desiccation. This accumulation, as well as the increased levels of glycerophosphodiesters during drought stress could provide osmoprotection to the cells

Identification of a novel BET bromodomain inhibitor-sensitive, gene regulatory circuit that controls Rituximab response and tumour growth in aggressive lymphoid cancers.: CYCLON-induced Rituximab resistance

International audienceImmuno-chemotherapy elicit high response rates in B-cell non-Hodgkin lymphoma but heterogeneity in response duration is observed, with some patients achieving cure and others showing refractory disease or relapse. Using a transcriptome-powered targeted proteomics screen, we discovered a gene regulatory circuit involving the nuclear factor CYCLON which characterizes aggressive disease and resistance to the anti-CD20 monoclonal antibody, Rituximab, in high-risk B-cell lymphoma. CYCLON knockdown was found to inhibit the aggressivity of MYC-overexpressing tumours in mice and to modulate gene expression programs of biological relevance to lymphoma. Furthermore, CYCLON knockdown increased the sensitivity of human lymphoma B cells to Rituximab in vitro and in vivo. Strikingly, this effect could be mimicked by in vitro treatment of lymphoma B cells with a small molecule inhibitor for BET bromodomain proteins (JQ1). In summary, this work has identified CYCLON as a new MYC cooperating factor that autonomously drives aggressive tumour growth and Rituximab resistance in lymphoma. This resistance mechanism is amenable to next-generation epigenetic therapy by BET bromodomain inhibition, thereby providing a new combination therapy rationale for high-risk lymphoma

Seasonal Variation in TP53 R249S-Mutated Serum DNA with Aflatoxin Exposure and Hepatitis B Virus Infection

Background: Chronic hepatitis B virus (HBV) infection and dietary aflatoxin B1 (AFB1) exposure are etiological factors for hepatocellular carcinoma (HCC) in countries with hot, humid climates. HCC often harbors a TP53 (tumor protein p53) mutation at codon 249 (R249S). In chronic carriers, 1762T/1764A mutations in the HBV X gene are associated with increased HCC risk. Both mutations have been detected in circulating cell-free DNA (CFDNA) from asymptomatic HBV carriers

A community proposal to integrate proteomics activities in ELIXIR

Computational approaches have been major drivers behind the progress of proteomics in recent years. The aim of this white paper is to provide a framework for integrating computational proteomics into ELIXIR in the near future, and thus to broaden the portfolio of omics technologies supported by this European distributed infrastructure. This white paper is the direct result of a strategy meeting on ‘The Future of Proteomics in ELIXIR’ that took place in March 2017 in Tübingen (Germany), and involved representatives of eleven ELIXIR nodes.   These discussions led to a list of priority areas in computational proteomics that would complement existing activities and close gaps in the portfolio of tools and services offered by ELIXIR so far. We provide some suggestions on how these activities could be integrated into ELIXIR’s existing platforms, and how it could lead to a new ELIXIR use case in proteomics. We also highlight connections to the related field of metabolomics, where similar activities are ongoing. This white paper could thus serve as a starting point for the integration of computational proteomics into ELIXIR. Over the next few months we will be working closely with all stakeholders involved, and in particular with other representatives of the proteomics community, to further refine this paper

Optimasi Portofolio Resiko Menggunakan Model Markowitz MVO Dikaitkan dengan Keterbatasan Manusia dalam Memprediksi Masa Depan dalam Perspektif Al-Qur`an

Risk portfolio on modern finance has become increasingly technical, requiring the use of sophisticated mathematical tools in both research and practice. Since companies cannot insure themselves completely against risk, as human incompetence in predicting the future precisely that written in Al-Quran surah Luqman verse 34, they have to manage it to yield an optimal portfolio. The objective here is to minimize the variance among all portfolios, or alternatively, to maximize expected return among all portfolios that has at least a certain expected return. Furthermore, this study focuses on optimizing risk portfolio so called Markowitz MVO (Mean-Variance Optimization). Some theoretical frameworks for analysis are arithmetic mean, geometric mean, variance, covariance, linear programming, and quadratic programming. Moreover, finding a minimum variance portfolio produces a convex quadratic programming, that is minimizing the objective function ðð¥with constraintsð ð 𥠥 ðandð´ð¥ = ð. The outcome of this research is the solution of optimal risk portofolio in some investments that could be finished smoothly using MATLAB R2007b software together with its graphic analysis