The Human Genome Variation Society (HGVS) variant nomenclature is widely used to describe sequence variants in scientific publications, clinical reports, and databases. However, the HGVS recommendations are complex and this often results in inaccurate variant descriptions being reported. The open‐source hgvs Python package (https://github.com/biocommons/hgvs) provides a programmatic interface for parsing, manipulating, formatting, and validating of variants according to the HGVS recommendations, but does not provide a user‐friendly Web interface. We have developed a Web‐based variant validation tool, VariantValidator (https://variantvalidator.org/), which utilizes the hgvs Python package and provides additional functionality to assist users who wish to accurately describe and report sequence‐level variations that are compliant with the HGVS recommendations. VariantValidator was designed to ensure that users are guided through the intricacies of the HGVS nomenclature, for example, if the user makes a mistake, VariantValidator automatically corrects the mistake if it can, or provides helpful guidance if it cannot. In addition, VariantValidator has the facility to interconvert genomic variant descriptions in HGVS and Variant Call Format with a degree of accuracy that surpasses most competing solutions.

BackgroundAbdominal aortic aneurysm (AAA) is a deadly cardiovascular disease characterised by the gradual, irreversible dilation of the abdominal aorta. AAA is a complex genetic disease but little is known about the role of epigenetics. Our objective was to determine if global DNA methylation and CpG-specific methylation at known AAA risk loci is associated with AAA, and the functional effects of methylation changes.ResultsWe assessed global methylation in peripheral blood mononuclear cell DNA from 92 individuals with AAA and 93 controls using enzyme-linked immunosorbent assays, identifying hyper-methylation in those with large AAA and a positive linear association with AAA diameter (P < 0.0001, R2 = 0.3175).We then determined CpG methylation status of regulatory regions in genes located at AAA risk loci identified in genome-wide association studies, using bisulphite next-generation sequencing (NGS) in vascular smooth muscle cells (VSMCs) taken from aortic tissues of 44 individuals (24 AAAs and 20 controls). In IL6R, 2 CpGs were hyper-methylated (P = 0.0145); in ERG, 13 CpGs were hyper-methylated (P = 0.0005); in SERPINB9, 6 CpGs were hypo-methylated (P = 0.0037) and 1 CpG was hyper-methylated (P = 0.0098); and in SMYD2, 4 CpGs were hypo-methylated (P = 0.0012).RT-qPCR was performed for each differentially methylated gene on mRNA from the same VSMCs and compared with methylation. This analysis revealed downregulation of SMYD2 and SERPINB9 in AAA, and a direct linear relationship between SMYD2 promoter methylation and SMYD2 expression (P = 0.038). Furthermore, downregulation of SMYD2 at the site of aneurysm in the aortic wall was further corroborated in 6 of the same samples used for methylation and gene expression analysis with immunohistochemistry.ConclusionsThis study is the first to assess DNA methylation in VSMCs from individuals with AAA using NGS, and provides further evidence there is an epigenetic basis to AAA. Our study shows that methylation status of the SMYD2 promoter may be linked with decreased SMYD2 expression in disease pathobiology. In support of our work, downregulated SMYD2 has previously been associated with adverse cardiovascular physiology and inflammation, which are both hallmarks of AAA. The identification of such adverse epigenetic modifications could potentially contribute towards the development of epigenetic treatment strategies in the future.Electronic supplementary materialThe online version of this article (10.1186/s13148-018-0460-9) contains supplementary material, which is available to authorized users.

BackgroundFor many analytical methods the efficiency of DNA amplification varies across the genome and between samples. The most affected genome regions tend to correlate with high C + G content, however this relationship is complex and does not explain why the direction and magnitude of effects varies considerably between samples.ResultsHere, we provide evidence that sequence elements that are particularly high in C + G content can remain annealed even when aggressive melting conditions are applied. In turn, this behavior creates broader ‘Thermodynamically Ultra-Fastened’ (TUF) regions characterized by incomplete denaturation of the two DNA strands, so reducing amplification efficiency throughout these domains.ConclusionsThis model provides a mechanistic explanation for why some genome regions are particularly difficult to amplify and assay in many procedures, and importantly it also explains inter-sample variability of this behavior. That is, DNA samples of varying quality will carry more or fewer nicks and breaks, and hence their intact TUF regions will have different lengths and so be differentially affected by this amplification suppression mechanism – with ‘higher’ quality DNAs being the most vulnerable. A major practical consequence of this is that inter-region and inter-sample variability can be largely overcome by employing routine fragmentation methods (e.g. sonication or restriction enzyme digestion) prior to sample amplification.

This paper discusses integration of production surveillance techniques, focusing on the use of 4D seismic data to identify reservoir compartmentalization. We present two examples of recently drilled compartments that were successfully identified following integration of surveillance data with detailed reservoir modelling work. Our examples are from the faulted, Paleocene channelized turbidite reservoirs of the Schiehallion oil field, offshore West of Shetlands, U.K. The first example provides a good case history of a 4D Integrated Reservoir Modelling (4D IRM) approach -which involved integration of dynamic well data with 4D seismic, and iterative revision of geological and reservoir simulation models. Two newly identified targets were drilled and completed successfully using these techniques. The second example illustrates a situation where 4D seismic interpretation was key in identifying a new infill target.Production in the Schiehallion Field started in 1998, and the current development scheme totals 46 wells (22 producers and 24 water injectors). During the early years of production it became apparent that geological connectivity, fluid flow and pressure communication between wells was not as inter-connected as expected. As a result the number of wells required to maximize the recovery has more than doubled to that specified in the original development plan, and the number is expected to increase further as the field matures. Continuous collection of bottom hole pressure data from permanently installed gauges, well testing and production logging (PLT) supported by a regular time-lapse (4D) seismic programme are used to update conceptual thinking and thus constrain geological and flow simulation modelling. This data integration results in improved understanding of the static and dynamic reservoir compartmentalization and well connectivity.

The Human Genome Variation Society (HGVS) nomenclature guidelines encourage the accurate and standard description of DNA, RNA, and protein sequence variants in public variant databases and the scientific literature. Inconsistent application of the HGVS guidelines can lead to misinterpretation of variants in clinical settings. Reliable software tools are essential to ensure consistent application of the HGVS guidelines when reporting and interpreting variants. We present the hgvs Python package, a comprehensive tool for manipulating sequence variants according to the HGVS nomenclature guidelines. Distinguishing features of the hgvs package include: (1) parsing, formatting, validating, and normalizing variants on genome, transcript, and protein sequences; (2) projecting variants between aligned sequences, including those with gapped alignments; (3) flexible installation using remote or local data (fully local installations eliminate network dependencies); (4) extensive automated tests; and (5) open source development by a community from eight organizations worldwide. This report summarizes recent and significant updates to the hgvs package since its original release in 2014, and presents results of extensive validation using clinical relevant variants from ClinVar and HGMD.