Targeted next generation sequencing as a diagnostic tool in epileptic disorders

Epilepsies have a highly heterogeneous background with a strong genetic contribution. The variety of unspecific and overlapping syndromic and nonsyndromic phenotypes often hampers a clear clinical diagnosis and prevents straightforward genetic testing. Knowing the genetic basis of a patient's epilepsy can be valuable not only for diagnosis but also for guiding treatment and estimating recurrence risks.

The molybdenum isotopic composition in river water: Constraints from small catchments

We report molybdenum isotope compositions and concentrations in water samples from a variety of river catchment profiles in order to investigate the influence of anthropogenic contamination, catchment geology, within-river precipitation, and seasonal river flow variations on riverine molybdenum. Our results show that the observed variations in δ98/95Mo from 0‰ to 1.9‰ are primarily controlled by catchment lithology, particularly by weathering of sulfates and sulfides. Erosion in catchments dominated by wet-based glaciers leads to very high dissolved molybdenum concentrations. In contrast, anthropogenic inputs affect neither the concentration nor the isotopic composition of dissolved molybdenum in the rivers studied here. Seasonal variations are also quite muted. The finding that catchment geology exerts the primary control on the delivery of molybdenum to seawater indicates that the flux and isotope composition of molybdenum to seawater has likely varied in the geologic past.

Identification of the bovine Arachnomelia mutation by massively parallel sequencing implicates sulfite oxidase (SUOX) in bone development

Arachnomelia is a monogenic recessive defect of skeletal development in cattle. The causative mutation was previously mapped to a approximately 7 Mb interval on chromosome 5. Here we show that array-based sequence capture and massively parallel sequencing technology, combined with the typical family structure in livestock populations, facilitates the identification of the causative mutation. We re-sequenced the entire critical interval in a healthy partially inbred cow carrying one copy of the critical chromosome segment in its ancestral state and one copy of the same segment with the arachnomelia mutation, and we detected a single heterozygous position. The genetic makeup of several partially inbred cattle provides extremely strong support for the causality of this mutation. The mutation represents a single base insertion leading to a premature stop codon in the coding sequence of the SUOX gene and is perfectly associated with the arachnomelia phenotype. Our findings suggest an important role for sulfite oxidase in bone development.

Graph-Based Multi-Surface Segmentation of OCT Data Using Trained Hard and Soft Constraints

Optical coherence tomography (OCT) is a well-established image modality in ophthalmology and used daily in the clinic. Automatic evaluation of such datasets requires an accurate segmentation of the retinal cell layers. However, due to the naturally low signal to noise ratio and the resulting bad image quality, this task remains challenging. We propose an automatic graph-based multi-surface segmentation algorithm that internally uses soft constraints to add prior information from a learned model. This improves the accuracy of the segmentation and increase the robustness to noise. Furthermore, we show that the graph size can be greatly reduced by applying a smart segmentation scheme. This allows the segmentation to be computed in seconds instead of minutes, without deteriorating the segmentation accuracy, making it ideal for a clinical setup. An extensive evaluation on 20 OCT datasets of healthy eyes was performed and showed a mean unsigned segmentation error of 3.05 ±0.54 μm over all datasets when compared to the average observer, which is lower than the inter-observer variability. Similar performance was measured for the task of drusen segmentation, demonstrating the usefulness of using soft constraints as a tool to deal with pathologies.

Generation and analysis of a mouse intestinal metatranscriptome through Illumina based RNA-sequencing

With the advent of high through-put sequencing (HTS), the emerging science of metagenomics is transforming our understanding of the relationships of microbial communities with their environments. While metagenomics aims to catalogue the genes present in a sample through assessing which genes are actively expressed, metatranscriptomics can provide a mechanistic understanding of community inter-relationships. To achieve these goals, several challenges need to be addressed from sample preparation to sequence processing, statistical analysis and functional annotation. Here we use an inbred non-obese diabetic (NOD) mouse model in which germ-free animals were colonized with a defined mixture of eight commensal bacteria, to explore methods of RNA extraction and to develop a pipeline for the generation and analysis of metatranscriptomic data. Applying the Illumina HTS platform, we sequenced 12 NOD cecal samples prepared using multiple RNA-extraction protocols. The absence of a complete set of reference genomes necessitated a peptide-based search strategy. Up to 16% of sequence reads could be matched to a known bacterial gene. Phylogenetic analysis of the mapped ORFs revealed a distribution consistent with ribosomal RNA, the majority from Bacteroides or Clostridium species. To place these HTS data within a systems context, we mapped the relative abundance of corresponding Escherichia coli homologs onto metabolic and protein-protein interaction networks. These maps identified bacterial processes with components that were well-represented in the datasets. In summary this study highlights the potential of exploiting the economy of HTS platforms for metatranscriptomics.