The verbal, vocal, and gestural expression of (in)dependency in two types of subordinate constructions

Based on a video recording of conversational British English, this paper tests within the framework of Multimodal Discourse Analysis whether two different subordinate structures are evenly integrated to their environment. Subordinate constructions have been described in linguistics as dependent forms elaborating on primary elements of discourse. Although their verbal characteristics have been deeply analysed, few studies have focused on the articulation of the different communicative modalities in their production or provided a qualified picture of their integration. The main hypothesis is based on the capacity of subordinate constructions to show distinct forms of autonomy depending on their syntactic type, thus expressing different degrees of break. Beyond showing that subordinate constructions are not evenly dependent on their environment depending on how speakers use the prosodic and kinetic modalities to express greater (in)dependency, the results suggest that the creation of a break mainly relies on prosodic cues. Changes in the modal configuration throughout the sequence suggest modalities are dynamic and flexible resources for integrating or demarcating subordinate constructions in function of their syntactic type

MiCoViTo: a tool for gene-centric comparison and visualization of yeast transcriptome states

BACKGROUND: Information obtained by DNA microarray technology gives a rough snapshot of the transcriptome state, i.e., the expression level of all the genes expressed in a cell population at any given time. One of the challenging questions raised by the tremendous amount of microarray data is to identify groups of co-regulated genes and to understand their role in cell functions. RESULTS: MiCoViTo (Microarray Comparison Visualization Tool) is a set of biologists' tools for exploring, comparing and visualizing changes in the yeast transcriptome by a gene-centric approach. A relational database includes data linked to genome expression and graphical output makes it easy to visualize clusters of co-expressed genes in the context of available biological information. To this aim, upload of personal data is possible and microarray data from fifty publications dedicated to S. cerevisiae are provided on-line. A web interface guides the biologist during the usage of this tool and is freely accessible at . CONCLUSIONS: MiCoViTo offers an easy-to-read picture of local transcriptional changes connected to current biological knowledge. This should help biologists to mine yeast microarray data and better understand the underlying biology. We plan to add functional annotations from other organisms. That would allow inter-species comparison of transcriptomes via orthology tables

Comparative analysis of missing value imputation methods to improve clustering and interpretation of microarray experiments

<p>Abstract</p> <p>Background</p> <p>Microarray technologies produced large amount of data. In a previous study, we have shown the interest of <it>k-Nearest Neighbour </it>approach for restoring the missing gene expression values, and its positive impact of the gene clustering by hierarchical algorithm. Since, numerous replacement methods have been proposed to impute missing values (MVs) for microarray data. In this study, we have evaluated twelve different usable methods, and their influence on the quality of gene clustering. Interestingly we have used several datasets, both kinetic and non kinetic experiments from yeast and human.</p> <p>Results</p> <p>We underline the excellent efficiency of approaches proposed and implemented by Bo and co-workers and especially one based on expected maximization (<it>EM_array</it>). These improvements have been observed also on the imputation of extreme values, the most difficult predictable values. We showed that the imputed MVs have still important effects on the stability of the gene clusters. The improvement on the clustering obtained by hierarchical clustering remains limited and, not sufficient to restore completely the correct gene associations. However, a common tendency can be found between the quality of the imputation method and the gene cluster stability. Even if the comparison between clustering algorithms is a complex task, we observed that <it>k-means </it>approach is more efficient to conserve gene associations.</p> <p>Conclusions</p> <p>More than 6.000.000 independent simulations have assessed the quality of 12 imputation methods on five very different biological datasets. Important improvements have so been done since our last study. The <it>EM_array </it>approach constitutes one efficient method for restoring the missing expression gene values, with a lower estimation error level. Nonetheless, the presence of MVs even at a low rate is a major factor of gene cluster instability. Our study highlights the need for a systematic assessment of imputation methods and so of dedicated benchmarks. A noticeable point is the specific influence of some biological dataset.</p

Statistical inference of the time-varying structure of gene-regulation networks

<p>Abstract</p> <p>Background</p> <p>Biological networks are highly dynamic in response to environmental and physiological cues. This variability is in contrast to conventional analyses of biological networks, which have overwhelmingly employed static graph models which stay constant over time to describe biological systems and their underlying molecular interactions.</p> <p>Methods</p> <p>To overcome these limitations, we propose here a new statistical modelling framework, the ARTIVA formalism (Auto Regressive TIme VArying models), and an associated inferential procedure that allows us to learn temporally varying gene-regulation networks from biological time-course expression data. ARTIVA simultaneously infers the topology of a regulatory network and how it changes over time. It allows us to recover the chronology of regulatory associations for individual genes involved in a specific biological process (development, stress response, etc.).</p> <p>Results</p> <p>We demonstrate that the ARTIVA approach generates detailed insights into the function and dynamics of complex biological systems and exploits efficiently time-course data in systems biology. In particular, two biological scenarios are analyzed: the developmental stages of <it>Drosophila melanogaster </it>and the response of <it>Saccharomyces cerevisiae </it>to benomyl poisoning.</p> <p>Conclusions</p> <p>ARTIVA does recover essential temporal dependencies in biological systems from transcriptional data, and provide a natural starting point to learn and investigate their dynamics in greater detail.</p

Genome adaptation to chemical stress: clues from comparative transcriptomics in Saccharomyces cerevisiae and Candida glabrata

Comparative transcriptomics of Saccharomyces cerevisiae and Candida glabrata revealed a remarkable conservation of response to drug-induced stress, despite underlying differences in the regulatory networks

Label-free quantitative proteomics in Candida yeast species: technical and biological replicates to assess data reproducibility

International audienceObjective: Label-free quantitative proteomics has emerged as a powerful strategy to obtain high quality quantitative measures of the proteome with only a very small quantity of total protein extract. Because our research projects were requiring the application of bottom-up shotgun mass spectrometry proteomics in the pathogenic yeasts Candida glabrata and Candida albicans, we performed preliminary experiments to (i) obtain a precise list of all the proteins for which measures of abundance could be obtained and (ii) assess the reproducibility of the results arising respectively from biological and technical replicates. Data description: Three time-courses were performed in each Candida species, and an alkaline pH stress was induced for two of them. Cells were collected 10 and 60 min after stress induction and proteins were extracted. Samples were analysed two times by mass spectrometry. Our final dataset thus comprises label-free quantitative prot-eomics results for 24 samples (two species, three time-courses, two time points and two runs of mass spectrometry). Statistical procedures were applied to identify proteins with differential abundances between stressed and unstressed situations. Considering that C. glabrata and C. albicans are human pathogens, which face important pH fluctuations during a human host infection, this dataset has a potential value to other researchers in the field. which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creat iveco mmons .org/ publi cdoma in/zero/1.0/) applies to the data made available in this article, unless otherwise stated. Objective Studying proteome dynamics is a key step in systems biology projects. In this context, label-free bottom-up shotgun MS-based proteomics produces quantitative analyses of proteomes. This technique has emerged from significant improvements achieved by mass spectrom-etry (MS) instrumentation, chromatographic separation systems and a stronger correlation between the relative measured ion intensity and the original molecule abundance in the electrospray ionization process [1-3]. Members of our research team were involved in functional genomics studies in pathogenic yeasts Candida glabrata and Candida albicans [4-8]. We observed how the experimental design is a critical step to empower the statistics used to assess the robustness of the results. "How many replicates is enough?" is certainly one of the most frequently asked questions in wet laboratories. This question is especially critical in situations where the experiments are expensive, and/or the preparation of the biological samples is challenging. Here, our objective was to assess the robustness of the results arising from label-free bottom-up shotgun MS-based proteom-ics performed in C. glabrata and C. albicans, in case of technical and biological replicates. If the importance of biological replicates was indisputable when we starte

Transcriptomic Analyses during the Transition from Biomass Production to Lipid Accumulation in the Oleaginous Yeast Yarrowia lipolytica

We previously developed a fermentation protocol for lipid accumulation in the oleaginous yeast Y. lipolytica. This process was used to perform transcriptomic time-course analyses to explore gene expression in Y. lipolytica during the transition from biomass production to lipid accumulation. In this experiment, a biomass concentration of 54.6 gCDW/l, with 0.18 g/gCDW lipid was obtained in ca. 32 h, with low citric acid production. A transcriptomic profiling was performed on 11 samples throughout the fermentation. Through statistical analyses, 569 genes were highlighted as differentially expressed at one point during the time course of the experiment. These genes were classified into 9 clusters, according to their expression profiles. The combination of macroscopic and transcriptomic profiles highlighted 4 major steps in the culture: (i) a growth phase, (ii) a transition phase, (iii) an early lipid accumulation phase, characterized by an increase in nitrogen metabolism, together with strong repression of protein production and activity; (iv) a late lipid accumulation phase, characterized by the rerouting of carbon fluxes within cells. This study explores the potential of Y. lipolytica as an alternative oil producer, by identifying, at the transcriptomic level, the genes potentially involved in the metabolism of oleaginous species

Spatio-Temporal Dynamics of Yeast Mitochondrial Biogenesis: Transcriptional and Post-Transcriptional mRNA Oscillatory Modules

Examples of metabolic rhythms have recently emerged from studies of budding yeast. High density microarray analyses have produced a remarkably detailed picture of cycling gene expression that could be clustered according to metabolic functions. We developed a model-based approach for the decomposition of expression to analyze these data and to identify functional modules which, expressed sequentially and periodically, contribute to the complex and intricate mitochondrial architecture. This approach revealed that mitochondrial spatio-temporal modules are expressed during periodic spikes and specific cellular localizations, which cover the entire oscillatory period. For instance, assembly factors (32 genes) and translation regulators (47 genes) are expressed earlier than the components of the amino-acid synthesis pathways (31 genes). In addition, we could correlate the expression modules identified with particular post-transcriptional properties. Thus, mRNAs of modules expressed “early” are mostly translated in the vicinity of mitochondria under the control of the Puf3p mRNA-binding protein. This last spatio-temporal module concerns mostly mRNAs coding for basic elements of mitochondrial construction: assembly and regulatory factors. Prediction that unknown genes from this module code for important elements of mitochondrial biogenesis is supported by experimental evidence. More generally, these observations underscore the importance of post-transcriptional processes in mitochondrial biogenesis, highlighting close connections between nuclear transcription and cytoplasmic site-specific translation