VarFish - Collaborative and comprehensive variant analysis for diagnosis and research
| Item Type: | Preprint |
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| Title: | VarFish - Collaborative and comprehensive variant analysis for diagnosis and research |
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| Creators Name: | Holtgrewe, M. and Stolpe, O. and Nieminen, M. and Mundlos, S. and Knaus, A. and Kornak, U. and Seelow, D. and Segebrecht, L. and Spielmann, M. and Fischer-Zirnsak, B. and Boschann, F. and Scholl, U. and Ehmke, N. and Beule, D. |
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| Abstract: | VarFish is a user-friendly web application for the quality control, filtering, prioritization, analysis, and user-based annotation of panel and exome variant data for rare disease genetics. It is capable of processing variant call files with single or multiple samples. The variants are automatically annotated with population frequencies, molecular impact, and presence in databases such as ClinVar. Further, it provides support for pathogenicity scores including CADD, MutationTaster, and phenotypic similarity scores. Users can filter variants based on these annotations and presumed inheritance pattern and sort the results by these scores. Filtered variants are listed with their annotations and many useful link-outs to genome browsers, other gene/variant data portals, and external tools for variant assessment. VarFish allows user to create their own annotations including support for variant assessment following ACMG-AMP guidelines. In close collaboration with medical practitioners, VarFish was designed for variant analysis and prioritization in diagnostic and research settings as described in the software’s extensive manual. The user interface has been optimized for supporting these protocols. Users can install VarFish on their own in-house servers where it provides additional lab notebook features for collaborative analysis and allows re-analysis of cases, e.g., after update of genotype or phenotype databases. |
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| Source: | bioRxiv |
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| Publisher: | Cold Spring Harbor Laboratory Press |
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| Article Number: | 2020.01.27.921965 |
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| Date: | 31 January 2020 |
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| Official Publication: | https://doi.org/10.1101/2020.01.27.921965 |
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