Phevor™ (Phenotype Driven Variant Ontological Re-ranking tool) integrates a patient’s or a cohort’s phenotypic information into a comprehensive clinical bioinformatics genome analysis.
Phevor, published in 2014, uses a novel algorithmic approach to directly integrate clinical phenotype information with gene function and disease information – bridging the gap between clinicians and computational biologists3. Phevor starts by mapping phenotype terms to the Human Phenotype Ontology4, Gene Ontology and other ontologies then uses a unique network propagation approach to identify additional gene candidates. This process creates a ranked list of genes ordered by the specific phenotype provided. Phevor then combines this prioritized list of genes with the VAAST analysis to produce a combined ranking of candidate genes based on deleteriousness and the specific phenotype or phenotypes in question.
The 2014 Phevor paper outlines three cases where Phevor was used to ascertain the genetic cause of disease in three undiagnosed children, including identification of a novel disease gene in a 6-month old infant with idiopathic liver disease. The integration of VAAST and Phevor within the Fabric Enterprise platform provides intuitive access to these advanced algorithms, and usage within Fabric Enterprise’s clinical interpretation workflows. Fabric Enterprise also allows users to combine these advanced algorithms with traditional filtering techniques, accelerating and improving the accuracy of interpretation while providing flexibility.
This combined algorithmic variant interpretation approach significantly increases the power and likelihood for diagnosis in individual patients or patients with two or three other family members, the most commonly occurring clinical scenarios.
Fabric’s latest gene-ranking algorithm, GEM, builds on VAAST and PHEVOR and incorporates clinical information from OMIM and ClinVar. GEM uses novel Artificial Intelligence (AI) methods to dramatically improve the speed, efficiency, and effectiveness of clinical genome interpretation. GEM takes as inputs the clinical features of the patient along with their genome sequence and produces a concise ranked list of clinically relevant variants. Benchmarking data across several cohorts of previously diagnosed rare genetic disease patients has shown the algorithm to be highly sensitive for finding known disease genes, with the causal gene ranking in the top 2 over 90% of the time. GEM consistently outranks other algorithms in its ability to detect causal variants while reviewing the smallest number of genes. GEM is positioned to dramatically increase the speed and efficiency of clinical genome analysis, with applications in the analysis of undiagnosed rare disease, rapid analysis of NICU/PICU cases and routine re-analysis of negative genomes.
 Adzhubei et al, Nat Methods 2010.
 Rope et al, Am J Hum Genet. 2011, Yandell et al, Genome Res. 2011
 Singleton et al, Am. J. Hum. Gen. 2014
 Köhler, S., et al. The Human Phenotype Ontology project: linking molecular biology and disease through phenotype data. Nucleic Acids Res. 42, D966-74 (2014).