Cseq-simulator: a data simulator for CLIP-Seq experiments

CLIP-Seq protocols such as PAR-CLIP, HITS-CLIP or iCLIP allow a genome-wide analysis of protein-RNA interactions. For the processing of the resulting short read data, various tools are utilized. Some of these tools were specifically developed for CLIP-Seq data, whereas others were designed for the analysis of RNA-Seq data. To this date, however, it has not been assessed which of the available tools are most appropriate for the analysis of CLIP-Seq data. This is because an experimental gold standard dataset on which methods can be accessed and compared, is still not available. To address this lack of a gold-standard dataset, we here present Cseq-Simulator, a simulator for PAR-CLIP, HITS-CLIP and iCLIP-data. This simulator can be applied to generate realistic datasets that can serve as surrogates for experimental gold standard dataset. In this work, we also show how Cseq-Simulator can be used to perform a comparison of steps of typical CLIP-Seq analysis pipelines, such as the read alignment or the peak calling. These comparisons show which tools are useful in different settings and also allow identifying pitfalls in the data analysis

omniCLIP: probabilistic identification of protein-RNA interactions from CLIP-seq data

CLIP-seq methods allow the generation of genome-wide maps of RNA binding protein - RNA interaction sites. However, due to differences between different CLIP-seq assays, existing computational approaches to analyze the data can only be applied to a subset of assays. Here, we present a probabilistic model called omniCLIP that can detect regulatory elements in RNAs from data of all CLIP-seq assays. omniCLIP jointly models data across replicates and can integrate background information. Therefore, omniCLIP greatly simplifies the data analysis, increases the reliability of results and paves the way for integrative studies based on data from different assays

Migration and Settlement: 5. Netherlands

This report focuses on migration and settlement in the Netherlands. Professor Paul Drewe, of the Department of Architecture and Urban Planning, Delft University of Technology, has been studying multiregional population dynamics and population distribution policy on the level of the five geographic regions which form the framework for physical and regional economic planning in the Netherlands. In this report he describes some of his recent findings

RiboDiff: detecting changes of mRNA translation efficiency from ribosome footprints

MOTIVATION: Deep sequencing based ribosome footprint profiling can provide novel insights into the regulatory mechanisms of protein translation. However, the observed ribosome profile is fundamentally confounded by transcriptional activity. In order to decipher principles of translation regulation, tools that can reliably detect changes in translation efficiency in case-control studies are needed. RESULTS: We present a statistical framework and an analysis tool, RiboDiff, to detect genes with changes in translation efficiency across experimental treatments. RiboDiff uses generalized linear models to estimate the over-dispersion of RNA-Seq and ribosome profiling measurements separately, and performs a statistical test for differential translation efficiency using both mRNA abundance and ribosome occupancy

Deep learning for prediction of population health costs

BACKGROUND: Accurate prediction of healthcare costs is important for optimally managing health costs. However, methods leveraging the medical richness from data such as health insurance claims or electronic health records are missing. METHODS: Here, we developed a deep neural network to predict future cost from health insurance claims records. We applied the deep network and a ridge regression model to a sample of 1.4 million German insurants to predict total one-year health care costs. Both methods were compared to existing models with various performance measures and were also used to predict patients with a change in costs and to identify relevant codes for this prediction. RESULTS: We showed that the neural network outperformed the ridge regression as well as all considered models for cost prediction. Further, the neural network was superior to ridge regression in predicting patients with cost change and identified more specific codes. CONCLUSION: In summary, we showed that our deep neural network can leverage the full complexity of the patient records and outperforms standard approaches. We suggest that the better performance is due to the ability to incorporate complex interactions in the model and that the model might also be used for predicting other health phenotypes

Risk factor analysis for “diagnosis not reached” results from bovine samples submitted to British veterinary laboratories in 2013–2017

Routine diagnostic data from laboratories are an important source of information for passive animal health surveillance. In Great Britain, the Veterinary Investigation Diagnosis Analysis (VIDA) database includes records of diagnostic submissions made to a nationwide network of 28 veterinary post-mortem facilities (VPFs). Data on “diagnosis not reached” (DNR), i.e. where submissions do not lead to a confirmed diagnosis, are analysed quarterly to look for unexpectedly high incidences of DNRs which could indicate the presence of a new or emerging disease in British livestock populations. The objective of the present study was to provide a better understanding about the reasons of DNR occurrence and to inform improvements of the coverage and reporting of this kind of surveillance data. A subset of the VIDA database comprising diagnostic submissions from cattle received from 2013 to 2017 (122,444 records) was analysed. A mixed-effects multivariable logistic regression model, accounting for clustering by farm and county, was used to investigate associations between potential predictors and DNR. The variables included in the model were: VPF identity, animal sex, age, production purpose, main presenting sign of the animal from which the sample was obtained, and sample submission type. The variable that showed the strongest association with DNR was the main presenting sign of the animal, followed by submission type, VPF identity, animal age, sex, and production purpose, in that order. Submissions from animals with abortion as the main clinical sign had the highest odds ratio (OR 21.6, 95 % confidence interval [CI] 19.6–23.9, with mastitis taken as the baseline). Submissions where neither carcasses (i.e. a whole dead animal provided for post-mortem examination) nor foetuses (i.e. an unborn dead animal) were provided had approximately 12 times the odds of being DNR, compared to submissions of a carcass (OR 11.6, 95 % CI 10.7–12.5). In addition, submission type and main presenting sign can be considered as important confounders in the association between the other predictors and DNR. This study has helped characterise DNR occurrence and suggests some possible improvements that could be made to the passive surveillance system investigated, such as encouraging greater carcase submission, accounting for identified issues when interpreting increased occurrence of DNR and further investigating reduced submissions or greater DNR occurrence in some geographical regions

Quantifying direct and indirect contacts for the potential transmission of infection between species using a multilayer contact network

Detecting opportunities for between-species transmission of pathogens can be challenging, particularly if rare behaviours or environmental transmission are involved. We present a multilayer network framework to quantify transmission potential in multi-host systems, incorporating environmental transmission, by using empirical data on direct and indirect contacts between European badgers Meles meles and domestic cattle. We identify that indirect contacts via the environment at badger latrines on pasture are likely to be important for transmission within badger populations and between badgers and cattle. We also find a positive correlation between the role of individual badgers within the badger social network, and their role in the overall badger-cattle-environment network, suggesting that the same behavioural traits contribute to the role of individual badgers in within- and between-species transmission. These findings have implications for disease management interventions in this system, and our novel network approach can provide general insights into transmission in other multi-host disease systems

Alternative splicing substantially diversifies the transcriptome during early photomorphogenesis and correlates with the energy availability in arabidopsis

Plants use light as source of energy and information to detect diurnal rhythms and seasonal changes. Sensing changing light conditions is critical to adjust plant metabolism and to initiate developmental transitions. Here we analyzed transcriptome-wide alterations in gene expression and alternative splicing (AS) of etiolated seedlings undergoing photomorphogenesis upon exposure to blue, red, or white light. Our analysis revealed massive transcriptome reprograming as reflected by differential expression of ~20% of all genes and changes in several hundred AS events. For more than 60% of all regulated AS events, light promoted the production of a presumably protein-coding variant at the expense of an mRNA with nonsense-mediated decay-triggering features. Accordingly, AS of the putative splicing factor REDUCED RED-LIGHT RESPONSES IN CRY1CRY2 BACKGROUND 1 (RRC1), previously identified as a red light signaling component, was shifted to the functional variant under light. Downstream analyses of candidate AS events pointed at a role of photoreceptor signaling only in monochromatic but not in white light. Furthermore, we demonstrated similar AS changes upon light exposure and exogenous sugar supply, with a critical involvement of kinase signaling. We propose that AS is an integration point of signaling pathways that sense and transmit information regarding the energy availability in plants

Surveillance strategies for Classical Swine Fever in wild boar – a comprehensive evaluation study to ensure powerful surveillance

Surveillance of Classical Swine Fever (CSF) should not only focus on livestock, but must also include wild boar. To prevent disease transmission into commercial pig herds, it is therefore vital to have knowledge about the disease status in wild boar. In the present study, we performed a comprehensive evaluation of alternative surveillance strategies for Classical Swine Fever (CSF) in wild boar and compared them with the currently implemented conventional approach. The evaluation protocol was designed using the EVA tool, a decision support tool to help in the development of an economic and epidemiological evaluation protocol for surveillance. To evaluate the effectiveness of the surveillance strategies, we investigated their sensitivity and timeliness. Acceptability was analysed and finally, the cost-effectiveness of the surveillance strategies was determined. We developed 69 surveillance strategies for comparative evaluation between the existing approach and the novel proposed strategies. Sampling only within sub-adults resulted in a better acceptability and timeliness than the currently implemented strategy. Strategies that were completely based on passive surveillance performance did not achieve the desired detection probability of 95%. In conclusion, the results of the study suggest that risk-based approaches can be an option to design more effective CSF surveillance strategies in wild boar