Bacterial riboproteogenomics : the era of N-terminal proteoform existence revealed

With the rapid increase in the number of sequenced prokaryotic genomes, relying on automated gene annotation became a necessity. Multiple lines of evidence, however, suggest that current bacterial genome annotations may contain inconsistencies and are incomplete, even for so-called well-annotated genomes. We here discuss underexplored sources of protein diversity and new methodologies for high-throughput genome re-annotation. The expression of multiple molecular forms of proteins (proteoforms) from a single gene, particularly driven by alternative translation initiation, is gaining interest as a prominent contributor to bacterial protein diversity. In consequence, riboproteogenomic pipelines were proposed to comprehensively capture proteoform expression in prokaryotes by the complementary use of (positional) proteomics and the direct readout of translated genomic regions using ribosome profiling. To complement these discoveries, tailored strategies are required for the functional characterization of newly discovered bacterial proteoforms

2014 Rankings: Key Findings Report

The County Health Rankings & Roadmaps program helps communities identify and implement solutions that make it easier for people to be healthy in their schools, workplaces, and neighborhoods. Ranking the health of nearly every county in the nation, the County Health Rankings illustrate what we know when it comes to what is making people sick or healthy. The Roadmaps show what we can do to create healthier places to live, learn, work, and play. The Robert Wood Johnson Foundation (RWJF) collaborates with the University of Wisconsin Population Health Institute (UWPHI) to bring this program to cities, counties, and states across the nation. Now in its fifth year, the County Health Rankings continue to bring actionable data to communities across the nation. Based on data available, the Rankings are unique in their ability to measure the overall health of each county in all 50 states on the many factors that influence health. They have been used to garner support among government agencies, healthcare providers, community organizations, business leaders, policymakers, and the public for local health improvement initiatives. We compile the Rankings using county-level measures from a variety of national data sources which can be found on page 10. These measures are standardized and combined using scientifically-informed weights. We then rank counties by state, providing two overall ranks: 1. Health outcomes: how healthy a county is now? 2. Health factors: how healthy a county will be in the future? We report these ranks at countyhealthrankings.org, along with all the underlying measures for this year and prior years. We also provide tools to help communities use their data to identify opportunities for improvement and guidance to help them act and improve their health. This document includes: A. A summary of our key national findings B. Information on key measures C. Information on new measures D. Healthiest and least healthy counties in each state E. Comparison of top 10 percent healthiest and bottom 10 percent least healthy counties F. Listing of measures and data source

Quantification Of Rainfall Forecast Uncertainty And Its Impact On Flood Forecasting

Rainfall forecast errors are considered to be the key source of uncertainty in flood forecasting. To quantify the rainfall forecast uncertainty itself and its impact on the total flood forecast uncertainty, a Monte-Carlo based statistical method has been developed. This method takes into account the dependency of the rainfall forecast error with the lead time and the rainfall amount. The forecasted rainfall errors are described by truncated normal distributions, allowing to quantify the full uncertainty distribution of the deterministic rainfall forecast. By means of Monte-Carlo sampling and taking the forecast error autocorrelation into account, the impact of the rainfall forecast uncertainty on a flood forecast was quantified. This was done for the Rivierbeek river in Belgium. In addition, comparison is made between the total flood forecast uncertainty and the uncertainty due to the forecasted rainfall. The total flood forecast uncertainty was quantified by a non-parametric data-based approach. It was concluded that the forecasted rainfall uncertainty contributes for about 30 percent to the total flood forecast uncertainty

Implications of climate change on hydrological extremes in the Blue Nile basin: A review

AbstractStudy regionThe Blue Nile river basin in East Africa.Study focusThis review paper presents the current understanding of hydrological extremes in the Blue Nile River basin under historic and future climate conditions, largely drawing on research outputs over the past decade. Characteristics of precipitation and streamflow extremes, including historic trends and future projections, are considered.New hydrological insightsThe review illustrates some discrepancy among research outputs. For the historical context, this is partially related to the period and length of data analyzed and the failure to consider the influence of multi-decadal oscillations. Consequently, we show that annual cycle of Blue Nile flow has not changed in the past five decades. For the future context, discrepancy is partially attributable to the various and differing climate and hydrological models included and the downscaling techniques applied. The need to prudently consider sources of uncertainty and potential causes of bias in historical trend and climate change impact research is highlighted

Identification of uncertainty sources in distributed hydrological modelling: Case study of the Grote Nete catchment in Belgium

The quest for good practice in modelling merits thorough and sustained attention since good practice increases the credibility and impact of the information, and insight that modelling seeks to generate. This paper presents the findings of an evaluation whose goal was to understand the uncertainty in applying a distributed hydrological model to the Grote Nete catchment in Flanders, Belgium. Uncertainties were selected for investigation depending on how significantly they affected the model’s decision variables. A Fault Tree was used to determine various combinations of inputs, mathematical code, and human error failures that could result in a specified risk. A combination of forward and backward approaches was used in developing the Fault Tree. Eleven events were identified as contributing to the top event. A total of 7 gates were used to describe the Fault Tree. A critical path analysis was carried out for the events and established their rank or order of significance. Three measures of importance were applied, namely the F-Vesely, the Birnbaum, and the B-Proschan importance measures. Model development of distributed models involves considerable uncertainty. Many of these dependencies arise naturally and their correct evaluation is crucial to the accurate analysis of the modelling system reliability.Keywords: distributed hydrological models, Grote Nete, MIKE SHE, uncertaint

Lost and found : re-searching and re-scoring proteomics data aids genome annotation and improves proteome coverage

Prokaryotic genome annotation is heavily dependent on automated gene annotation pipelines that are prone to propagate errors and underestimate genome complexity. We describe an optimized proteogenomic workflow that uses ribosome profiling (ribo-seq) and proteomic data for Salmonella enterica serovar Typhimurium to identify unannotated proteins or alternative protein forms. This data analysis encompasses the searching of cofragmenting peptides and postprocessing with extended peptide-to-spectrum quality features, including comparison to predicted fragment ion intensities. When this strategy is applied, an enhanced proteome depth is achieved, as well as greater confidence for unannotated peptide hits. We demonstrate the general applicability of our pipeline by reanalyzing public Deinococcus radiodurans data sets. Taken together, our results show that systematic reanalysis using available prokaryotic (proteome) data sets holds great promise to assist in experimentally based genome annotation

The ROS wheel: refining ROS transcriptional footprints

In the last decade, microarray studies have delivered extensive inventories of transcriptome-wide changes in messenger RNA levels provoked by various types of oxidative stress in Arabidopsis (Arabidopsis thaliana). Previous cross-study comparisons indicated how different types of reactive oxygen species (ROS) and their subcellular accumulation sites are able to reshape the transcriptome in specific manners. However, these analyses often employed simplistic statistical frameworks that are not compatible with large-scale analyses. Here, we reanalyzed a total of 79 Affymetrix ATH1 microarray studies of redox homeostasis perturbation experiments. To create hierarchy in such a high number of transcriptomic data sets, all transcriptional profiles were clustered on the overlap extent of their differentially expressed transcripts. Subsequently, meta-analysis determined a single magnitude of differential expression across studies and identified common transcriptional footprints per cluster. The resulting transcriptional footprints revealed the regulation of various metabolic pathways and gene families. The RESPIRATORY BURST OXIDASE HOMOLOG F-mediated respiratory burst had a major impact and was a converging point among several studies. Conversely, the timing of the oxidative stress response was a determining factor in shaping different transcriptome footprints. Our study emphasizes the need to interpret transcriptomic data sets in a systematic context, where initial, specific stress triggers can converge to common, aspecific transcriptional changes. We believe that these refined transcriptional footprints provide a valuable resource for assessing the involvement of ROS in biological processes in plants

N-terminal proteomics assisted profiling of the unexplored translation initiation landscape in Arabidopsis thaliana

Proteogenomics is an emerging research field yet lacking a uniform method of analysis. Proteogenomic studies in which N-terminal proteomics and ribosome profiling are combined, suggest that a high number of protein start sites are currently missing in genome annotations. We constructed a proteogenomic pipeline specific for the analysis of N-terminal proteomics data, with the aim of discovering novel translational start sites outside annotated protein coding regions. In summary, unidentified MS/MS spectra were matched to a specific N-terminal peptide library encompassing protein N termini encoded in the Arabidopsis thaliana genome. After a stringent false discovery rate filtering, 117 protein N termini compliant with N-terminal methionine excision specificity and indicative of translation initiation were found. These include N-terminal protein extensions and translation from transposable elements and pseudogenes. Gene prediction provided supporting protein-coding models for approximately half of the protein N termini. Besides the prediction of functional domains (partially) contained within the newly predicted ORFs, further supporting evidence of translation was found in the recently released Araport11 genome re-annotation of Arabidopsis and computational translations of sequences stored in public repositories. Most interestingly, complementary evidence by ribosome profiling was found for 23 protein N termini. Finally, by analyzing protein N-terminal peptides, an in silico analysis demonstrates the applicability of our N-terminal proteogenomics strategy in revealing protein-coding potential in species with well-and poorly-annotated genomes