Computational evaluation of exome sequence data using human and model organism phenotypes improves diagnostic efficiency.

doi:10.1038/gim.2015.137

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Article

Computational evaluation of exome sequence data using human and model organism phenotypes improves diagnostic efficiency.

Genetics in medicine : official journal of the American College of Medical Genetics - 1 Jun 2016

Bone William P, Washington Nicole L, Buske Orion J, Adams David R, Davis Joie, Draper David, Flynn Elise D, Girdea Marta, Godfrey Rena, Golas Gretchen, Groden Catherine, Jacobsen Julius, Köhler Sebastian, Lee Elizabeth M J, Links Amanda E, Markello Thomas C, Mungall Christopher J, Nehrebecky Michele, Robinson Peter N, Sincan Murat, Soldatos Ariane G, Tifft Cynthia J, Toro Camilo, Trang Heather, Valkanas Elise, Vasilevsky Nicole, Wahl Colleen, Wolfe Lynne A, Boerkoel Cornelius F, Brudno Michael, Haendel Melissa A, Gahl William A, Smedley Damian

Abstract excerpt

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...

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PMID: 26562225
DOI: 10.1038/gim.2015.137
PMCID: PMC4916229

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PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 7 Sept 2026. Manifest publication-graph-cohort-2026-09-08.1.

Source receipt: pubmed, retrieved 7 Sept 2026.

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