Literature-based priors for gene regulatory networks

Motivation: The use of prior knowledge to improve gene regulatory network modelling has often been proposed. In this paper we present the first research on the massive incorporation of prior knowledge from literature for Bayesian network learning of gene networks. As the publication rate of scientific papers grows, updating online databases, which have been proposed as potential prior knowledge in past rese-arch, becomes increasingly challenging. The novelty of our approach lies in the use of gene-pair association scores that describe the over-lap in the contexts in which the genes are mentioned, generated from a large database of scientific literature, harnessing the information contained in a huge number of documents into a simple, clear format. Results: We present a method to transform such literature-based gene association scores to network prior probabilities, and apply it to learn gene sub-networks for yeast, E. coli and Human organisms. We also investigate the effect of weighting the influence of the prior know-ledge. Our findings show that literature-based priors can improve both the number of true regulatory interactions present in the network and the accuracy of expression value prediction on genes, in comparison to a network learnt solely from expression data. Networks learnt with priors also show an improved biological interpretation, with identified subnetworks that coincide with known biological pathways. Contact

Exploiting the full power of temporal gene expression profiling through a new statistical test: Application to the analysis of muscular dystrophy data

Background: The identification of biologically interesting genes in a temporal expression profiling dataset is challenging and complicated by high levels of experimental noise. Most statistical methods used in the literature do not fully exploit the temporal ordering in the dataset and are not suited to the case where temporal profiles are measured for a number of different biological conditions. We present a statistical test that makes explicit use of the temporal order in the data by fitting polynomial functions to the temporal profile of each gene and for each biological condition. A Hotelling T2-statistic is derived to detect the genes for which the parameters of these polynomials are significantly different from each other. Results: We validate the temporal Hotelling T2-test on muscular gene expression data from four mouse strains which were profiled at different ages: dystrophin-, beta-sarcoglycan and gammasarcoglycan deficient mice, and wild-type mice. The first three are animal models for different muscular dystrophies. Extensive biological validation shows that the method is capable of finding genes with temporal profiles significantly different across the four strains, as well as identifying potential biomarkers for each form of the disease. The added value of the temporal test compared to an identical test which does not make use of temporal ordering is demonstrated via a simulation study, and through confirmation of the expression profiles from selected genes by quantitative PCR experiments. The proposed method maximises the detection of the biologically interesting genes, whilst minimising false detections. Conclusion: The temporal Hotelling T2-test is capable of finding relatively small and robust sets of genes that display different temporal profiles between the conditions of interest. The test is simple, it can be used on gene expression data generated from any experimental design and for any number of conditions, and it allows fast interpretation of the temporal behaviour of genes. The R code is available from V.V. The microarray data have been submitted to GEO under series GSE1574 and GSE3523

25 years of the WHO essential medicines lists: progress and challenges.

The first WHO essential drugs list, published in 1977, was described as a peaceful revolution in international public health. The list helped to establish the principle that some medicines were more useful than others and that essential medicines were often inaccessible to many populations. Since then, the essential medicines list (EML) has increased in size; defining an essential medicine has moved from an experience to an evidence-based process, including criteria such as public-health relevance, efficacy, safety, and cost-effectiveness. High priced medicines such as antiretrovirals are now included. Differences exist between the WHO model EML and national EMLs since countries face varying challenges relating to costs, drug effectiveness, morbidity patterns, and rationality of prescribing. Ensuring equitable access to and rational use of essential medicines has been promoted through WHO's revised drug strategy. This approach has required an engagement by WHO on issues such as the effect of international trade agreements on access to essential medicines and research and development to ensure availability of new essential medicines

A continuously updated, geospatially rectified database of utility-scale wind turbines in the United States.

Over 60,000 utility-scale wind turbines are installed in the United States as of October, 2019, representing over 97 gigawatts of electric power capacity; US wind turbine installations continue to grow at a rapid pace. Yet, until April 2018, no publicly-available, regularly updated data source existed to describe those turbines and their locations. Under a cooperative research and development agreement, analysts from three organizations collaborated to develop and release the United States Wind Turbine Database (USWTDB) - a publicly available, continuously updated, spatially rectified data source of locations and attributes of utility-scale wind turbines in the United States. Technical specifications and wind facility data, incorporated from five sources, undergo rigorous quality control. The location of each turbine is visually verified using high-resolution aerial imagery. The quarterly-updated data are available in a variety of formats, including an interactive web application, comma-separated values (CSV), shapefile, and application programming interface (API). The data are used widely by academic researchers, engineers and developers from wind energy companies, government agencies, planners, educators, and the general public

Deuterium retention in tungsten and tungsten: tantalum alloys exposed to high-flux deuterium plasmas

A direct comparison of deuterium retention in samples of tungsten and two grades of tungsten-tantalum alloys-W-1% Ta and W-5% Ta, exposed to deuterium plasmas (ion flux similar to 10(24) m(-2) s(-1), ion energy at the biased target similar to 50 eV) at the plasma generator Pilot-PSI was performed using thermal desorption spectroscopy (TDS). No systematic difference in terms of total retention in tungsten and tungsten-tantalum was identified. The measured retention value for each grade did not deviate by more than 24% from the value averaged over the three grades exposed to the same conditions. No additional desorption peaks appeared in the TDS spectra of the W-Ta samples as compared with the W target, indicating that no additional kinds of traps are introduced by the alloying of W with Ta. In the course of the experiment the same samples were exposed to the same plasma conditions several times, and it is demonstrated that samples with the history of prior exposures yield an increase in deuterium retention of up to 130% under the investigated conditions compared with the samples that were not exposed before. We consider this as evidence that exposure of the considered materials to ions with energy below the displacement threshold generates additional traps for deuterium. The positions of the release peaks caused by these traps are similar for W and W-Ta, which indicates that the corresponding traps are of the same kind

Joint modeling of ChIP-seq data via a Markov random field model

Chromatin ImmunoPrecipitation-sequencing (ChIP-seq) experiments have now become routine in biology for the detection of protein-binding sites. In this paper, we present a Markov random field model for the joint analysis of multiple ChIP-seq experiments. The proposed model naturally accounts for spatial dependencies in the data, by assuming first-order Markov dependence and, for the large proportion of zero counts, by using zero-inflated mixture distributions. In contrast to all other available implementations, the model allows for the joint modeling of multiple experiments, by incorporating key aspects of the experimental design. In particular, the model uses the information about replicates and about the different antibodies used in the experiments. An extensive simulation study shows a lower false non-discovery rate for the proposed method, compared with existing methods, at the same false discovery rate. Finally, we present an analysis on real data for the detection of histone modifications of two chromatin modifiers from eight ChIP-seq experiments, including technical replicates with different IP efficiencies