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The paper presents a study on data-driven diagnostic rules, which are easy to interpret by human experts. To this end, the Dempster-Shafer theory extended for fuzzy focal elements is used. Premises of the rules (fuzzy focal elements) are provided by membership functions which shapes are changing according to input symptoms. The main aim of the present study is to evaluate common membership function shapes and to introduce a rule elimination algorithm. Proposed methods are first illustrated with the popular Iris data set. Next experiments with five medical benchmark databases are performed. Results of the experiments show that various membership function shapes provide different inference efficiency but the extracted rule sets are close to each other. Thus indications for determining rules with possible heuristic interpretation can be formulated.

Accurate identification of genetic variants to a large extent is based on the type of experimental technology, quality of the material and coverage of sequencing data obtained. The latter, coverage quality, highly influences variant calling accuracy and final diagnosis. Our motivation was to create a tool that will evaluate genome coverage and accelerate the introduction of long-read sequencing to medical diagnostics and clinical practice. The implementation was guided by the ease of use of the tool by users who are not proficient in using complex software. A Docker container is perfect for this purpose. Using Docker’s advantages (flexibility, mobility and ease of use of the proposed tools), we created eXNVerify. This is a tool for inspection of clinical data in the context of pathogenic variants search. The tool calculates clinical depth coverage (CDC) – a measure of coverage which we introduce to evaluate loci with pathogenic germline and somatic variants reported in ClinVar. The tool additionally provides visualization options for user-defined genes of interest. Finally, we present examples of BRCA1, TP53, CFTR application and results of a test conducted in the Extensive Sequence Dataset of Gold-Standard Samples for Benchmarking and Development. eXNVerify improves the diagnostic process of patients related to important genetic diseases and facilitates the assessment of genetic samples by diagnosticians. The use of Docker allows to run an analysis package and does not require any special technical preparation. Detailed examples are included in the GitHub project documentation  and the package can be downloaded directly from DockerHub using the command: docker pull porebskis/exnverify:1.0.

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Accurate identification of genetic variants to a large extent is based on type of experimental technology, quality of the material and coverage of obtained sequencing data. Our motivation was to create a tool that will evaluate genome coverage and accelerate the introduction of long-read sequencing to medical diagnostics and clinical practice. Here we present eXNVerify: a tool for inspection of clinical data in the context of pathogenic variants. The tool calculates Clinical Depth Coverage – a measure of coverage which we introduce to evaluate loci with pathogenic germline and somatic variants reported in ClinVar. The tool additionally provides visualization options for user-defined genes of interest. Finally, we present an examples of BRCA1, TP53, CFTR application and results of a test conducted in the Extensive Sequence Dataset of Gold-Standard Samples for Benchmarking and Development. eXNVerify is available at https://github.com/porebskis/eXNVerify and can be directly pulled from the DockerHub repository: docker pull porebskis/exnverify:1.0.