INSPIRE METADATA EDITORS

INSPIRE is the European Spatial Data Infrastructure (ESDI) standard based on the ISO 19100 geodata standards. INSPIRE was published 2007 and expected to be implemented till 2019. An important part of the standard is related to the metadata record which contains the characteristic parameters of a geodata set in standardized XML form. Several editor programs were developed to support metadata editing. We investigated three software packages representing different category of metadata editors, namely the stand alone CatMDEdit, MetaD and the server based GeoNetwork programs. The programs were qualified according to their versatility, the ease of their setup process, configuration and use, the knowledge what they demanded from the user, the help what they provide to the user. The editors were applied to the same hydrologic data set to prepare the metadata file corresponding to the INSPIRE profile in the investigation.  The CatMDEdit was found the best almost from every respect except the versatility in which the GeoNetwork package was the first with its multi-user, Internet related searching and disseminating capabilities. Therefore the CatMDEdit is proposed for small or medium size institutions and the GeoNetwork for large organizations.  

HEIGHT REFERENCE OF TERRAIN RELIEFS

The accuracy and resolution of real topographic heights in the terrain reliefs has a special importance in meteorology, hydrology and other Earth’s disciplines dealing with dynamical processes on regional scale. The accuracy of topographic height referencing of a region composed from several areas with independent national geodetic networks depends on the homogeneity of the applied measuring processes and geodetic datum on these areas.                  The territories falling out of homogeneity can be revealed by comparing their leveled height data to topographic heights produced from GPS measurements. The difference indicate either local gravity anomalies or height datum problem.  Two territories were selected (one in Hungary and one in Greece) to demonstrate this method what revealed a height datum problem on the Greek area.

Retroviral activation of the mir-106a microRNA cistron in T lymphoma

Retroviral insertion into a host genome is a powerful tool not only for the discovery of cancer genes, but also for the discovery of potential oncogenic noncoding RNAs. In a large-scale mouse T lymphocyte tumor screen we found a high density of integrations upstream of the mir-106a microRNA cistron. In tumors containing an integration, the primary transcript encoding the mir-106a cistron was overexpressed five to 20-fold compared with that of control tumors; concomitantly, the mature mir-106a and mir-363 microRNAs were highly overexpressed as well. These findings suggest the mir-106a cistron plays an important role in T cell tumorigenesis

Model consent clauses for rare disease research

Background: Rare Disease research has seen tremendous advancements over the last decades, with the development of new technologies, various global collaborative efforts and improved data sharing. To maximize the impact of and to further build on these developments, there is a need for model consent clauses for rare diseases research, in order to improve data interoperability, to meet the informational needs of participants, and to ensure proper ethical and legal use of data sources and participants' overall protection. Methods: A global Task Force was set up to develop model consent clauses specific to rare diseases research, that are comprehensive, harmonized, readily accessible, and internationally applicable, facilitating the recruitment and consent of rare disease research participants around the world. Existing consent forms and notices of consent were analyzed and classified under different consent themes, which were used as background to develop the model consent clauses. Results: The IRDiRC-GA4GH MCC Task Force met in September 2018, to discuss and design model consent clauses. Based on analyzed consent forms, they listed generic core elements and designed the following rare disease research specific core elements; Rare Disease Research Introductory Clause, Familial Participation, Audio/Visual Imaging, Collecting, storing, sharing of rare disease data, Recontact for matching, Data Linkage, Return of Results to Family Members, Incapacity/Death, and Benefits. Conclusion: The model consent clauses presented in this article have been drafted to highlight consent elements that bear in mind the trends in rare disease research, while providing a tool to help foster harmonization and collaborative efforts

Achieving high-sensitivity for clinical applications using augmented exome sequencing

daSNVs in ACMG genes where inadequate coverage was observed among at least one platform, using WES/ACE data normalized to both 12 Gb and 100× mean coverage. (XLSX 312 kb

svclassify: a method to establish benchmark structural variant calls

The human genome contains variants ranging in size from small single nucleotide polymorphisms (SNPs) to large structural variants (SVs). High-quality benchmark small variant calls for the pilot National Institute of Standards and Technology (NIST) Reference Material (NA12878) have been developed by the Genome in a Bottle Consortium, but no similar high-quality benchmark SV calls exist for this genome. Since SV callers output highly discordant results, we developed methods to combine multiple forms of evidence from multiple sequencing technologies to classify candidate SVs into likely true or false positives. Our method (svclassify) calculates annotations from one or more aligned bam files from many high-throughput sequencing technologies, and then builds a one-class model using these annotations to classify candidate SVs as likely true or false positives. We first used pedigree analysis to develop a set of high-confidence breakpoint-resolved large deletions. We then used svclassify to cluster and classify these deletions as well as a set of high-confidence deletions from the 1000 Genomes Project and a set of breakpoint-resolved complex insertions from Spiral Genetics. We find that likely SVs cluster separately from likely non-SVs based on our annotations, and that the SVs cluster into different types of deletions. We then developed a supervised one-class classification method that uses a training set of random non-SV regions to determine whether candidate SVs have abnormal annotations different from most of the genome. To test this classification method, we use our pedigree-based breakpoint-resolved SVs, SVs validated by the 1000 Genomes Project, and assembly-based breakpoint-resolved insertions, along with semi-automated visualization using svviz. We find that candidate SVs with high scores from multiple technologies have high concordance with PCR validation and an orthogonal consensus method MetaSV (99.7 % concordant), and candidate SVs with low scores are questionable. We distribute a set of 2676 high-confidence deletions and 68 high-confidence insertions with high svclassify scores from these call sets for benchmarking SV callers. We expect these methods to be particularly useful for establishing high-confidence SV calls for benchmark samples that have been characterized by multiple technologies.https://doi.org/10.1186/s12864-016-2366-