2016
DOI: 10.1038/gim.2015.137
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Computational evaluation of exome sequence data using human and model organism phenotypes improves diagnostic efficiency

Abstract: Purpose:Medical diagnosis and molecular or biochemical confirmation typically rely on the knowledge of the clinician. Although this is very difficult in extremely rare diseases, we hypothesized that the recording of patient phenotypes in Human Phenotype Ontology (HPO) terms and computationally ranking putative disease-associated sequence variants improves diagnosis, particularly for patients with atypical clinical profiles.Genet Med 18 6, 608–617.Methods:Using simulated exomes and the National Institutes of He… Show more

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Cited by 93 publications
(97 citation statements)
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References 30 publications
(36 reference statements)
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“…Exomiser (62), which has been shown to improve the prioritization of disease genes from WES variant calls through cross-species phenotype comparisons (www.sanger.ac.uk/science/tools/exomiser). …”
Section: Knowledge-driven Variant Prioritizationmentioning
confidence: 99%
See 1 more Smart Citation
“…Exomiser (62), which has been shown to improve the prioritization of disease genes from WES variant calls through cross-species phenotype comparisons (www.sanger.ac.uk/science/tools/exomiser). …”
Section: Knowledge-driven Variant Prioritizationmentioning
confidence: 99%
“…HPO plays a key role in the Monarch Initiative (61), which provides tools for genotypephenotype analysis across broad areas of disease (https://monarchinitiative.org). Toolkits that have been built on top of these and other annotations primarily for the discovery of Mendelian genes include: Exomiser (62), which has been shown to improve the prioritization of disease genes from WES variant calls through cross-species phenotype comparisons (www.sanger.ac.uk/science/tools/exomiser). …”
Section: Knowledge-driven Variant Prioritizationmentioning
confidence: 99%
“…Monarch supports researchers and clinicians using this data with visualization tools, application programming interfaces, and a rich web site (https://monarchinitiative.org). These approaches make it possible to overcome limitations in the data for many applications; including disease diagnostics (Bone et al 2016), drug repurposing, and improved phenotyping; both clinically and in model organisms (e.g., helping identify candidate phenotyping assays based on preliminary phenotyping). Indeed, Monarch's unified data corpus and tools have been applied to diagnosing real patients and plans are underway to scale up their use with larger efforts, including the Undiagnosed Diseases Network (Brownstein et al 2015) and the 100,000 Genomes Project (http://www.genomicsengland.co.uk/the-100000-genomes-project/).…”
Section: A Common Conceptual Frameworkmentioning
confidence: 99%
“…These searches may miss certain gene to phenotype associations due to the lack of frequent updates, synonymous word challenges and the lack of comprehensive search engines. However, new computational algorithms are necessary to be developed to address these challenges [32] . Recently, a number of algorithms that associate a specific phenotype to genes have started to emerge, namely, PHIVE, PhenIX, hiPHIVE, ENDEAVOUR, Phenolyzer, Ingenuity Variant Analysis (Ingenuity) and phenomizer.…”
Section: Introductionmentioning
confidence: 99%