eyeVarP: A computational framework for the identification of pathogenic variants specific to eye disease |
| |
| Institution: | 1. Department of Bioinformatics, Aravind Medical Research Foundation, Madurai, Tamil Nadu, India;2. School of Chemical and Biotechnology, SASTRA (Deemed to be a university), Thanjavur, Tamil Nadu, India;1. Department of General Pediatrics, Neonatology and Pediatric Cardiology, Medical Faculty, University Hospital Düsseldorf, Heinrich-Heine-University, Düsseldorf, Germany;2. Max Planck Research Group Mechanisms of DNA Repair, Max Planck Institute for Biology of Ageing, Cologne, Germany;3. Department I of Internal Medicine, Center for Integrated Oncology Aachen Bonn Cologne and Duesseldorf, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany;4. Cologne Excellence Cluster on Cellular Stress Response in Aging-Associated Diseases, University of Cologne, Cologne, Germany;5. Center for Molecular Medicine Cologne, University of Cologne, Cologne, Germany;6. Institute of Biochemistry and Molecular Cell Biology, Medical School, RWTH Aachen University, Aachen, Germany;7. Institute for Human Genetics, Biocenter, University of Würzburg, Würzburg, Germany;8. Northern Ireland Regional Genetics Service, Belfast City Hospital, Belfast HSC Trust, Belfast, United Kingdom;9. Institute of Genomic Statistics and Bioinformatics, University of Bonn, Bonn, Germany;10. Department of Molecular Genetics, Baylor College of Medicine, Houston, TX;11. Division of Medical Genetics, Children’s Specialized Hospital, King Fahad Medical City, Riyadh, Saudi Arabia;12. Technical University of Munich, School of Medicine, Institute of Human Genetics, Munich, Germany;13. Institute of Neurogenomics, Helmholtz Zentrum München, Munich, Germany;14. Institute of Pathology and Electron Microscopy Facility, Medical Faculty, RWTH Aachen University, Aachen, Germany;15. Department of Human Genetics, Radboud University Medical Center, Nijmegen, The Netherlands;16. Institute of Human Genetics, University Hospital Düsseldorf, Heinrich-Heine-Universität, Düsseldorf, Germany;17. Division of Hematology/Oncology, Department of Pediatrics, Texas Children''s Cancer Center, Baylor College of Medicine, Houston, TX;18. Center of Rare Diseases, Medical Faculty, Heinrich-Heine-University, Düsseldorf, Germany;19. Department of General Pediatrics, Children’s Specialized Hospital, King Fahad Medical City, Riyadh, Saudi Arabia;20. Centre de Génétique Humaine, Institut de Pathologie et Génétique, Gosselies, Belgium;21. FDNA Inc., Boston, MA;22. Department of Dermatology and Allergology, Medical Faculty, RWTH Aachen University, Aachen, Germany;1. Natera Inc., Austin, TX;2. Department of Obstetrics and Gynecology and Women''s Health, Montefiore Medical Center, Albert Einstein College of Medicine, Bronx, NY;3. The Biostatistics Center, George Washington University, Washington, DC;4. Ouma Health, Park City, UT;5. Rotunda Hospital, Royal College of Surgeons in Ireland, Department of Obstetrics and Gynecology, Dublin, Ireland;6. Columbia Presbyterian Medical Center, Department of Obstetrics and Gynecology, New York, NY;7. New York University Langone, Department of Obstetrics and Gynecology, New York, NY;8. St. George’s Hospital, University of London, Department of Obstetrics and Gynecology, London, United Kingdom;9. St. Peter’s University Hospital, Department of Obstetrics and Gynecology, New Brunswick, NJ;10. Long Island Jewish Medical Center, Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Department of Obstetrics and Gynecology, New Hyde Park, NY;11. Icahn School of Medicine at Mount Sinai, Department of Obstetrics and Gynecology, New York, NY;12. University of Utah, Department of Obstetrics and Gynecology, Salt Lake City, UT;13. North Shore University Hospital, Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Department of Obstetrics and Gynecology, Manhasset, NY;14. Western Sydney University, Department of Obstetrics and Gynaecology, Liverpool, NSW, Australia;15. Center for Applied Genomics, Children’s Hospital of Philadelphia, Philadelphia, PA;16. Department of Obstetrics and Gynecology, Sahlgrenska University Hospital, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden;17. Department of Obstetrics and Gynecology, Sahlgrenska University Hospital, Gothenburg, Sweden;18. Department of Obstetrics, Gynecology and Reproductive Sciences, University of California, San Francisco, San Francisco, CA;1. Department of Medicine, NewYork-Presbyterian Brooklyn Methodist Hospital, Brooklyn, NY;2. Department of Pathology and Laboratory Medicine, University of California Los Angeles, Los Angeles, CA;1. Institute for Advanced Materials and Manufacturing, Department of Materials Science and Engineering, The University of Tennessee Knoxville, Knoxville, TN 37996, USA;1. Endocrine Division, Hospital de Clínicas de Porto Alegre, Porto Alegre, Rio Grande do Sul, Brazil;2. Postgraduate Program in Medical Sciences: Endocrinology, Faculdade de Medicina, Universidade Federal do Rio Grande do Sul, Porto Alegre, Rio Grande do Sul, Brazil;3. Department of Nutrition, Food Sciences and Physiology, Center for Nutrition Research, University of Navarra, Pamplona, Navarra, Spain;4. Centro de Investigación Biomédica en Red de la Fisiopatología de la Obesidad y Nutrición, Instituto de Salud Carlos III, Madrid, Spain;5. IMDEA Food, Madrid, Spain |
| |
| Abstract: | PurposeDisease-specific pathogenic variant prediction tools that differentiate pathogenic variants from benign have been improved through disease specificity recently. However, they have not been evaluated on disease-specific pathogenic variants compared with other diseases, which would help to prioritize disease-specific variants from several genes or novel genes. Thus, we hypothesize that features of pathogenic variants alone would provide a better model.MethodsWe developed an eye disease–specific variant prioritization tool (eyeVarP), which applied the random forest algorithm to the data set of pathogenic variants of eye diseases and other diseases. We also developed the VarP tool and generalized pipeline to filter missense and insertion-deletion variants and predict their pathogenicity from exome or genome sequencing data, thus we provide a complete computational procedure.ResultseyeVarP outperformed pan disease–specific tools in identifying eye disease–specific pathogenic variants under the top 10. VarP outperformed 12 pathogenicity prediction tools with an accuracy of 95% in correctly identifying the pathogenicity of missense and insertion-deletion variants. The complete pipeline would help to develop disease-specific tools for other genetic disorders.ConclusioneyeVarP performs better in identifying eye disease–specific pathogenic variants using pathogenic variant features and gene features. Implementing such complete computational procedure would significantly improve the clinical variant interpretation for specific diseases. |
| |
| Keywords: | Eye disease Machine learning Pathogenic variants Variant filtering Variant prioritization |
| 本文献已被 ScienceDirect 等数据库收录！ |
|
