variant assessment
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2020 ◽  
Vol 48 (W1) ◽  
pp. W162-W169 ◽  
Author(s):  
Manuel Holtgrewe ◽  
Oliver Stolpe ◽  
Mikko Nieminen ◽  
Stefan Mundlos ◽  
Alexej Knaus ◽  
...  

Abstract VarFish is a user-friendly web application for the quality control, filtering, prioritization, analysis, and user-based annotation of DNA variant data with a focus on 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. Variants passing the filter 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 users 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.


2020 ◽  
Author(s):  
Manuel Holtgrewe ◽  
Oliver Stolpe ◽  
Mikko Nieminen ◽  
Stefan Mundlos ◽  
Alexej Knaus ◽  
...  

ABSTRACTVarFish 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.


Andrology ◽  
2019 ◽  
Vol 8 (2) ◽  
pp. 434-441 ◽  
Author(s):  
T. F. Araujo ◽  
C. Friedrich ◽  
C. H. P. Grangeiro ◽  
L. R. Martelli ◽  
J. D. Grzesiuk ◽  
...  

2019 ◽  
Author(s):  
Tiffeney Mann ◽  
Amy Smith ◽  
Sarah Spencer ◽  
Alasdair Russell ◽  
James Thaventhiran

ABSTRACTThe functional validation of genetic variants of uncertain significance (VUS) found in PID patients by next-generation sequencing has traditionally been carried out in model systems that are susceptible to artefact. We use CRISPR correction of primary human T lymphocytes to demonstrate that a specific variant in an IL-6R deficient patient is causative for their condition. This methodology can be adapted and used for variant assessment of the heterogeneous genetic defects that affect T lymphocytes in PID.


2018 ◽  
Vol 20 (9) ◽  
pp. 936-941 ◽  
Author(s):  
Christopher A Cassa ◽  
Daniel M Jordan ◽  
Ivan Adzhubei ◽  
Shamil Sunyaev

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