Fig. 1 | Nature Communications

Fig. 1

From: Flexible and scalable diagnostic filtering of genomic variants using G2P with Ensembl VEP

Fig. 1

Summary of LGMDET structure and application in diagnostic filtering. a summarizes the components of a LGMDET thread. Each locus-genotype-mechanism-disease-evidence thread (LGMDET) associates an allelic requirement and a mutational consequence at a defined locus with a disease entity and a confidence level and evidence links. The publicly available G2PDD and G2PCancer data can be searched or downloaded on the website (https://www.ebi.ac.uk/gene2phenotype). b gives examples for LGMDE threads from curated datasets. In addition to the publicly available G2PDD and G2PCancer data, G2PEye is actively curated and will be publicly available soon. Access to the curation system can be requested for the creation of user-defined datasets. c summarizes the workflow for diagnostic filtering. The VCF files derived from the next-generation sequence data are passed to VEP which uses Ensembl annotation data to compute and annotate the consequence of each variant. The VEP-G2P plugin runs as an additional step of the VEP analysis. It uses the results of VEP’s computations and annotations together with the knowledge from the LGMDETs to filter the variants from the patients input VCF file. The plugin results, plausible genotypes of likely deleterious variants, are returned together with the VEP output file for clinical review. d lastly, the combined analysis of running VEP and VEP-G2P is repeated for a control cohort. The comparison between results from a population unselected for disease with the results from a disease cohort yields the expected background to quantify diagnostic noise and to identify discriminating features between the two cohorts

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