Abstract

Genome-wide association studies and candidate-gene based approaches have identified multiple common variants associated with increased risk of sudden cardiac death (SCD). However, the independent contribution of these individual loci to disease risk is modest. To investigate the cumulative effects of genetic variants previously associated with SCD risk. A total of 966 SCD cases from the Oregon-Sudden Unexpected Death Study and 1926 coronary artery disease controls from the Wellcome Trust Case–Control Consortium were investigated. We generated genetic risk scores (GRSs) for each trait composed of variants previously associated with SCD or with abnormalities in specific electrocardiographic traits such as QRS duration, QTc interval and heart rate. GRSs were calculated using a weighted approach based on the number of risk alleles weighted by the beta coefficients derived from the original studies. We also compared the highest and lowest quintiles for the GRS composed of SCD SNPs. Increased cumulative risk was observed for a GRS composed of 14 SCD-SNPs (OR = 1.17 [1.05–1.29], P = 0.002). The risk for SCD was 1.5 fold greater in the highest risk quintile when compared to the lowest risk quintile (OR = 1.46 [1.11–1.92]). We did not observe significant associations with SCD for SNPs that determine electrocardiographic traits. A modest but significant effect on SCD risk was identified for a GRS composed of 14 previously associated SCD SNPs. While next generation sequencing methodology will continue to identify additional novel variants, these findings represent proof of concept for the additive effects of gene variants on SCD risk.

Abstract

A bicuspid aortic valve (BAV) is associated with accelerated aortic valve disease (AVD) and abnormalities in aortic elasticity. We investigated the intima-media thickness of the descending aorta (AoIMT) in patients with AVD with or without an ascending aortic aneurysm (AscAA), in relation to BAV versus tricuspid aortic valve (TAV) phenotype, type of valve disease, cardiovascular risk factors, and single-nucleotide polymorphisms (SNPs) with a known association with carotid IMT. 368 patients (210 with BAV, 158 with TAV, ), mean age 64 ± 13 years) were examined using transesophageal echocardiography (TEE) before valvular and/or aortic surgery. No patient had a coronary disease (CAD). The AoIMT was measured on short-axis TEE images of the descending aorta using a semi-automated edge-detection technique. AoIMT was univariately (P < 0.05) related to age, blood pressure, smoking, creatinine, highly sensitive C-reactive protein, HDL, valve hemodynamics and BAV. In the TAV subgroup it was also associated with the rs200991 SNP. Using multivariate regression analysis, age was the main determinant for AoIMT (P < 0.001), followed by male gender (P = 0.02), BAV was no longer a significant predictor of AoIMT. AoIMT was still related to the rs200991 SNP in TAV (P = 0.034), and to creatinine in BAV (P = 0.019), when other variables were accounted for. Intima-media thickness of the descending aorta is not affected by aortic valve morphology (BAV/TAV), age is the main determinant of AoIMT. Genetic markers (SNPs) known to influence IMT in the carotid artery seem to correlate to IMT in the descending aorta only in patients with TAV.

Abstract

International audience; Green mobility solutions are receiving currently an enormous attention. Indeed, during last years, electric vehicles, being part of the field of the smart-grid, entered the automobile market of the whole world. This technology requires an effective deployment of charging stations of electric refill since the main problem in this system remains over the duration of refill of the batteries. In this work, we propose an optimized algorithm to locate electric charging stations. The main task of the algorithm is to find the best site of charging stations locations so as to minimize loss on the way to the charging station, as well as minimize investment cost, we take into account several constraints to find the optimal number and placement of charging station. Our hybrid algorithm with improved K-means clustering and genetic algorithm can be used to find optimal number and place of charging stations.

Abstract

- The data-driven parallelization framework Hadoop MapReduce allows analysing large data sets in a scalable way. Since the development of MapReduce programs can be a time-intensive and challenging task, the application and usage of Hadoop in Biomedical Research is still limited. Here we present Cloudflow, a high-level framework to hide the implementation details of Hadoop and to provide a set of building blocks to create biomedical pipelines in a more intuitive way. We demonstrate the benefit of Cloudflow on three different genetic use cases. It will be shown how the framework can be combined with the Hadoop workflow system Cloudgene and the cloud orchestration platform CloudMan to provide Hadoop pipelines as a service to everyone. The framework is open source and free available at https://github.com/genepi/cloudflow. Document type: Conference object

Abstract

We present pipeComp ( https://github.com/plger/pipeComp ), a flexible R framework for pipeline comparison handling interactions between analysis steps and relying on multi-level evaluation metrics. We apply it to the benchmark of single-cell RNA-sequencing analysis pipelines using simulated and real datasets with known cell identities, covering common methods of filtering, doublet detection, normalization, feature selection, denoising, dimensionality reduction, and clustering. pipeComp can easily integrate any other step, tool, or evaluation metric, allowing extensible benchmarks and easy applications to other fields, as we demonstrate through a study of the impact of removal of unwanted variation on differential expression analysis. Document type: Article

Abstract

International audience; Targeted metagenomics, also known as metagenetics, is a high-throughput sequencing application focusing on a nucleotide target in a microbiome to describe its taxonomic content. A wide range of bioinformatics pipelines are available to analyze sequencing outputs, and the choice of an appropriate tool is crucial and not trivial. No standard evaluation method exists for estimating the accuracy of a pipeline for targeted metagenomics analyses. This article proposes an evaluation protocol containing real and simulated targeted metage-nomics datasets, and adequate metrics allowing us to study the impact of different variables on the biological interpretation of results. This protocol was used to compare six different bioinformatics pipelines in the basic user context: Three common ones (mothur, QIIME and BMP) based on a clustering-first approach and three emerging ones (Kraken, CLARK and One Codex) using an assignment-first approach. This study surprisingly reveals that the effect of sequencing errors has a bigger impact on the results that choosing different amplified regions. Moreover, increasing sequencing throughput increases richness overestima-tion, even more so for microbiota of high complexity. Finally, the choice of the reference database has a bigger impact on richness estimation for clustering-first pipelines, and on correct taxa identification for assignment-first pipelines. Using emerging assignment-first pipelines is a valid approach for targeted metagenomics analyses, with a quality of results comparable to popular clustering-first pipelines, even with an error-prone sequencing technology like Ion Torrent. However, those pipelines are highly sensitive to the quality of databases and their annotations, which makes clustering-first pipelines still the only reliable approach for studying microbiomes that are not well described. Document type: Article

Abstract

The aim of this study was to investigate the effects of long-distance road transport (19 h, from Poland to Italy) during 2 seasons (summer vs. win- ter) on clinical and hematological variables in calves. The environmental temperature range that could com- promise the thermoregulation system (thermal stress) of the calves was tested. For the 7 Holstein calves in each transport, the BW and rectal temperature (RT) were measured, and blood samples were collected at the farm of origin, before loading at the transit center (T2), after unloading at the farm of destination (T3), and 1, 2, 3, and 4 d after arrival. The body temperature (BT) and heart rate (HR) were continuously monitored from T2 to T3. The data were statistically analyzed according to a mixed model that considered the fixed effects of transport (repeated measurements), season of journey, and their interaction. Within the observed temperature-humidity index (THI) range (30 to 80), effective thermoregulation allowed the calves to main- tain their BT with small physiologic changes to pre- vent thermal stress, particularly in the summer. With no seasonal differences, the HR was greater at load- ing than unloading (120 vs. 115 beats per min; P = 0.012). As for the transport effect, the BW was less (P " 0.001) after unloading, and the RT was greater (P = 0.004). This effect was more marked in summer. The hematological variables indicated a moderate ef- fect of transport on the hydration condition, reactive and muscular systems, and metabolism, although he- matocrit (P = 0.004), erythrocytes, cortisol, NEFA, β-hydroxybutyrate, lactate, creatine kinase, lactate de- hydrogenase, and aspartate aminotransferase activity (P " 0.001), and total protein (P = 0.007) were greater after unloading. This was confirmed by a moderate decrease in total leukocytes (P = 0.031) and glucose concentration (P = 0.002). The changes in the clinical variables were similar for both seasons even though in the summer, hematocrit (P " 0.001), urea (P = 0.008), and total protein (P = 0.010) increased and glucose concentration (P = 0.038) decreased. In conclusion, the data did not show a pronounced effect attributable to the season of the journey. Long-distance road transport leads to notable changes in clinical and hematological variables at the end of the journey. However, these vari- ables remained within their physiological ranges and returned to basal values within a few days after the journey.

Abstract

Background Chromatin accessibility profiling assays such as ATAC-seq and DNase1-seq offer the opportunity to rapidly characterize the regulatory state of the genome at a single nucleotide resolution. Optimization of molecular protocols has enabled the molecular biologist to produce next-generation sequencing libraries in several hours, leaving the analysis of sequencing data as the primary obstacle to wide-scale deployment of accessibility profiling assays. To address this obstacle we have developed an optimized and efficient pipeline for the analysis of ATAC-seq and DNase1-seq data. Results We executed a multi-dimensional grid-search on the NIH Biowulf supercomputing cluster to assess the impact of parameter selection on biological reproducibility and ChIP-seq recovery by analyzing 4560 pipeline configurations. Our analysis improved ChIP-seq recovery by 15% for ATAC-seq and 3% for DNase1-seq and determined that PCR duplicate removal improves biological reproducibility by 36% without significant costs in footprinting transcription factors. Our analyses of down sampled reads identified a point of diminishing returns for increased library sequencing depth, with 95% of the ChIP-seq data of a 200 million read footprinting library recovered by 160 million reads. Conclusions We present optimized ATAC-seq and DNase-seq pipelines in both Snakemake and bash formats as well as optimal sequencing depths for ATAC-seq and DNase-seq projects. The optimized ATAC-seq and DNase1-seq analysis pipelines, parameters, and ground-truth ChIP-seq datasets have been made available for deployment and future algorithmic profiling. Electronic supplementary material The online version of this article (10.1186/s12864-018-4943-z) contains supplementary material, which is available to authorized users. Document type: Article

Abstract

Background: The current challenges of Clinical Genetics include the integration into medical practice of the fundamental tools that constitute the diagnostic process, with the basic knowledge of the specialty. However, in training professionals, there is a tendency to prioritize the application of technology. Aim: To increase the knowledge that supports the application of the clinical method in residents of Clinical Genetics.Methods: A quasi-experimental study of longitudinal strategy, educational intervention type (before and after design) was carried out with 25 resident physicians of Clinical Genetics, who gave their consent to participate. Two equivalent instruments were applied, validated by expert criteria, which consisted of five forced choice questions, true and false type, where knowledge necessary for the diagnosis related to Medelian inheritance, congenital defects, molecular genetics, dysmorphic signs and diagnostic tools were explored. Mac Nemar's Chi-square was applied for dichotomous nominal qualitative variables in related or paired samples with a significance level of 0.05, in order to demonstrate the impact of the intervention on each of the evaluated topics. The educational intervention consisted of an interactive talk. Results: It was found that with the educational intervention the knowledge related to dysmorphic signs and diagnostic tools increased statistically significantly. Conclusion: The educational intervention raised the level of knowledge on the subject of dysmorphic signs, as well as on diagnostic tools.