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Hum Mutat. 2017 Jul;38(7):778-787. doi: 10.1002/humu.23227. Epub 2017 May 30.

DaMold: A data-mining platform for variant annotation and visualization in molecular diagnostics research.

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Health and Environment Department, Molecular Diagnostics, Austrian Institute of Technology, Vienna, Austria.
Institut für Populationsgenetik, Vetmeduni Vienna, Veterinärplatz 1, A-1210 Vienna, Austria.


Next-generation sequencing (NGS) has become a powerful and efficient tool for routine mutation screening in clinical research. As each NGS test yields hundreds of variants, the current challenge is to meaningfully interpret the data and select potential candidates. Analyzing each variant while manually investigating several relevant databases to collect specific information is a cumbersome and time-consuming process, and it requires expertise and familiarity with these databases. Thus, a tool that can seamlessly annotate variants with clinically relevant databases under one common interface would be of great help for variant annotation, cross-referencing, and visualization. This tool would allow variants to be processed in an automated and high-throughput manner and facilitate the investigation of variants in several genome browsers. Several analysis tools are available for raw sequencing-read processing and variant identification, but an automated variant filtering, annotation, cross-referencing, and visualization tool is still lacking. To fulfill these requirements, we developed DaMold, a Web-based, user-friendly tool that can filter and annotate variants and can access and compile information from 37 resources. It is easy to use, provides flexible input options, and accepts variants from NGS and Sanger sequencing as well as hotspots in VCF and BED formats. DaMold is available as an online application at, and as a Docker container and virtual machine at


Sanger sequencing; database cross-reference; diagnostic sequencing; genetic testing; hotspot mutation; mutation testing; next-generation sequencing; variant annotation; variant effect prediction

[Indexed for MEDLINE]

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