M
Murat Sincan
Researcher at National Institutes of Health
Publications - 32
Citations - 1877
Murat Sincan is an academic researcher from National Institutes of Health. The author has contributed to research in topics: Exome & Exome sequencing. The author has an hindex of 18, co-authored 31 publications receiving 1480 citations. Previous affiliations of Murat Sincan include Sanford Health & University of South Dakota.
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Journal ArticleDOI
Expansion of the Human Phenotype Ontology (HPO) knowledge base and resources
Sebastian Köhler,Leigh C. Carmody,Nicole Vasilevsky,Julius O.B. Jacobsen,Daniel Danis,Jean-Philippe F. Gourdine,Michael A. Gargano,Nomi L. Harris,Nicolas Matentzoglu,Julie A. McMurry,David Osumi-Sutherland,Valentina Cipriani,James P. Balhoff,Tom Conlin,Hannah Blau,Gareth Baynam,Richard Palmer,Dylan Gratian,Hugh Dawkins,Michael M. Segal,Anna Jansen,Ahmed Muaz,Willie H. Chang,Jenna R.E. Bergerson,Stanley J. F. Laulederkind,Zafer Yüksel,Sergi Beltran,Alexandra F. Freeman,Panagiotis I. Sergouniotis,Daniel Durkin,Andrea L. Storm,Marc Hanauer,Michael Brudno,Susan M. Bello,Murat Sincan,Kayli Rageth,Matthew T. Wheeler,Renske Oegema,Halima Lourghi,Maria G. Della Rocca,Rachel Thompson,Francisco Castellanos,James R. Priest,Charlotte Cunningham-Rundles,Ayushi Hegde,Ruth C. Lovering,Catherine Hajek,Annie Olry,Luigi D. Notarangelo,Morgan Similuk,Xingmin Aaron Zhang,David Gómez-Andrés,Hanns Lochmüller,Hélène Dollfus,Sergio Rosenzweig,Shruti Marwaha,Ana Rath,Kathleen E. Sullivan,Cynthia L. Smith,Joshua D. Milner,Dorothée Leroux,Cornelius F. Boerkoel,Amy D. Klion,Melody C. Carter,Tudor Groza,Damian Smedley,Melissa A. Haendel,Melissa A. Haendel,Christopher J. Mungall,Peter N. Robinson +69 more
TL;DR: The HPO’s interoperability with other ontologies has enabled it to be used to improve diagnostic accuracy by incorporating model organism data and plays a key role in the popular Exomiser tool, which identifies potential disease-causing variants from whole-exome or whole-genome sequencing data.
Journal ArticleDOI
The National Institutes of Health Undiagnosed Diseases Program: insights into rare diseases
William A. Gahl,Thomas C. Markello,Camilo Toro,Karin Fuentes Fajardo,Murat Sincan,Fred Gill,Hannah Carlson-Donohoe,Andrea L. Gropman,Andrea L. Gropman,Tyler Mark Pierson,Gretchen Golas,Lynne A. Wolfe,Catherine Groden,Rena A. Godfrey,Michele Nehrebecky,Colleen E. Wahl,Dennis M.D. Landis,Sandra Yang,Anne Madeo,James C. Mullikin,Cornelius F. Boerkoel,Cynthia J. Tifft,David H. Adams +22 more
TL;DR: The National Institutes of Health Undiagnosed Diseases Program addresses an unmet need and may serve as a model for the clinical application of emerging genomic technologies and is providing insights into the characteristics of diseases that remain undiagnose after extensive clinical workup.
Journal ArticleDOI
Detecting false-positive signals in exome sequencing†
Karin Fuentes Fajardo,David H. Adams,Nisc Comparative Sequencing Program,Christopher E. Mason,Murat Sincan,Cynthia J. Tifft,Camilo Toro,Cornelius F. Boerkoel,William A. Gahl,Thomas C. Markello +9 more
TL;DR: Several groups of genes are identified that are candidates for provisional exclusion during exome analysis: 23,389 positions with excess heterozygosity suggestive of alignment errors and 1,009 positions in which the hg18 human genome reference sequence appeared to contain a minor allele.
Journal ArticleDOI
Characteristics of congenital hepatic fibrosis in a large cohort of patients with autosomal recessive polycystic kidney disease.
Meral Gunay Aygun,Esperanza Font–Montgomery,Linda Lukose,Maya Gerstein,Katie Piwnica–Worms,Peter L. Choyke,Kailash T. Daryanani,Baris Turkbey,Roxanne Fischer,Isa Bernardini,Murat Sincan,Xiongce Zhao,Netanya G. Sandler,Annelys Roque,Daniel C. Douek,Jennifer Graf,Marjan Huizing,Joy Bryant,Parvathi Mohan,William A. Gahl,Theo Heller +20 more
TL;DR: Platelet count is the best predictor of the severity of portal hypertension, which has early onset but is underdiagnosed in patients with ARPKD and is not explainable by type of PKHD1 mutation.
Journal ArticleDOI
Deep Geodesic Learning for Segmentation and Anatomical Landmarking
TL;DR: A novel deep learning framework for anatomy segmentation and automatic landmarking of mandible segmentation from cone-beam computed tomography scans and identification of 9 anatomical landmarks of the mandible on the geodesic space is proposed.