Objective: To assess the relationship between short sleep duration and obesity-related variables in children involved in the 'Québec en Forme' Project. Design: Cross-sectional study. Subjects: A total of 422 children (211 boys and 211 girls) aged between 5 and 10 years from primary schools in the City of Trois-Rivières (Québec) were selected to participate in this study. Measurements: Body weight, height and waist circumference were measured. The children were classified as normal, underweight, overweight or obese, according to body mass index (BMI) per age. An exhaustive questionnaire was administered by telephone to the parents of children. Results: The percentage of overweight/obesity was 20.0% in boys and 24.0% in girls. When compared to children reporting 12-13 h of sleep per day, the adjusted odds ratio for childhood overweight/obesity was 1.42 (95% confidence interval 1.09-1.98) for those with 10.5-11.5 h of sleep and 3.45 (2.61-4.67) for those with 8-10 h of sleep after adjustment for age, sex, and other risk factors. Parental obesity, low parental educational level, low total family income, long hours of TV watching, playing videogames or computer utilization, absence of breastfeeding and physical inactivity were also significantly associated with childhood overweight/obesity. In addition, we observed a significant negative association adjusted for age between sleep duration and body weight (À0.33, Po0.01), BMI (À0.12, Po0.01) and waist circumference (À0.24, Po0.01) in boys. Conclusion: An inverse association was observed between sleep duration and the risk to develop childhood overweight/obesity. Longitudinal research will be required to confirm a potential link of causality between these variables.
Advances in proteomics and sequencing have highlighted many non-annotated open reading frames (ORFs) in eukaryotic genomes. Genome annotations, cornerstones of today's research, mostly rely on protein prior knowledge and on ab initio prediction algorithms. Such algorithms notably enforce an arbitrary criterion of one coding sequence (CDS) per transcript, leading to a substantial underestimation of the coding potential of eukaryotes. Here, we present OpenProt, the first database fully endorsing a polycistronic model of eukaryotic genomes to date. OpenProt contains all possible ORFs longer than 30 codons across 10 species, and cumulates supporting evidence such as protein conservation, translation and expression. OpenProt annotates all known proteins (RefProts), novel predicted isoforms (Isoforms) and novel predicted proteins from alternative ORFs (AltProts). It incorporates cutting-edge algorithms to evaluate protein orthology and re-interrogate publicly available ribosome profiling and mass spectrometry datasets, supporting the annotation of thousands of predicted ORFs. The constantly growing database currently cumulates evidence from 87 ribosome profiling and 114 mass spectrometry studies from several species, tissues and cell lines. All data is freely available and downloadable from a web platform (www.openprot.org) supporting a genome browser and advanced queries for each species. Thus, OpenProt enables a more comprehensive landscape of eukaryotic genomes’ coding potential.
OpenProt (www.openprot.org) is the first proteogenomic resource supporting a polycistronic annotation model for eukaryotic genomes. It provides a deeper annotation of open reading frames (ORFs) while mining experimental data for supporting evidence using cutting-edge algorithms. This update presents the major improvements since the initial release of OpenProt. All species support recent NCBI RefSeq and Ensembl annotations, with changes in annotations being reported in OpenProt. Using the 131 ribosome profiling datasets re-analysed by OpenProt to date, non-AUG initiation starts are reported alongside a confidence score of the initiating codon. From the 177 mass spectrometry datasets re-analysed by OpenProt to date, the unicity of the detected peptides is controlled at each implementation. Furthermore, to guide the users, detectability statistics and protein relationships (isoforms) are now reported for each protein. Finally, to foster access to deeper ORF annotation independently of one’s bioinformatics skills or computational resources, OpenProt now offers a data analysis platform. Users can submit their dataset for analysis and receive the results from the analysis by OpenProt. All data on OpenProt are freely available and downloadable for each species, the release-based format ensuring a continuous access to the data. Thus, OpenProt enables a more comprehensive annotation of eukaryotic genomes and fosters functional proteomic discoveries.
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