Purpose Next-generation sequencing (NGS) has transformed genetic research and is poised to revolutionize clinical diagnosis. However, the vast amount of data and inevitable discovery of incidental findings require novel analytic approaches. We therefore implemented for the first time a strategy that utilizes an a priori structured framework and a conservative threshold for selecting clinically relevant incidental findings. Methods We categorized 2016 genes linked with Mendelian diseases into “bins” based on clinical utility and validity, and used a computational algorithm to analyze 80 whole genome sequences in order to explore the use of such an approach in a simulated real-world setting. Results The algorithm effectively reduced the number of variants requiring human review and identified incidental variants with likely clinical relevance. Incorporation of the Human Gene Mutation Database (HGMD) improved the yield for missense mutations, but also revealed that a substantial proportion of purported disease-causing mutations were misleading. Conclusions This approach is adaptable to any clinically relevant bin structure, scalable to the demands of a clinical laboratory workflow, and flexible with respect to advances in genomics. We anticipate that application of this strategy will facilitate pre-test informed consent, laboratory analysis, and post-test return of results in a clinical context.

MAESTRO is a prototype runtime designed to provide simple, very light threads and synchronization between those threads on modern commodity (x86) hardware. The MAE-STRO threading library is designed to be a target for a highlevel language compiler or source-to-source translator, not for user-level programming. It provides parallel programming environments with a straight forward hardware model which can be mapped to available hardware dynamically. MAESTRO separates the size of the hardware system being used from the amount of parallelism available in an application. By separating the problem of locating parallelism from the problem of effectively using parallelism, both problems can be made easier. To the extent possible, the programming environment should be responsible for finding parallelism and the runtime should manage resource allocation and assignment.Parallel regions and parallel loops are implemented. Several simple benchmarks have been ported from OpenMP to use the MAESTRO threading interface. Two synchronization mechanisms have been implemented, one for general synchronization and one for producer-consumer relationships. We have started building a level of 'virtualization' between the programming environment and the actual hardware, which will allow better hardware utilization and support new parallel programming languages.

The problems that scientists face in creating well designed databases intersect with the concerns of data curation. Entity-relationship modeling and its variants have been the basis of most relational data modeling for decades. However, these abstractions and the relational model itself are intricate and have proved not to be very accessible among scientists with limited resources for data management. This paper explores one aspect of relational data models, the meaning of foreign key relationships. We observe that a foreign key produces a table relationship that generally references either an entity or repeating attributes. This paper proposes constructing foreign keys based on these two cases, and suggests that the method promotes intuitive data modeling and normalization.

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