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Alghamdi SM, Schofield PN, Hoehndorf R. How much do model organism phenotypes contribute to the computational identification of human disease genes? Dis Model Mech 2022; 15:275986. [PMID: 35758016 PMCID: PMC9366895 DOI: 10.1242/dmm.049441] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/24/2021] [Accepted: 06/13/2022] [Indexed: 12/04/2022] Open
Abstract
Computing phenotypic similarity helps identify new disease genes and diagnose rare diseases. Genotype–phenotype data from orthologous genes in model organisms can compensate for lack of human data and increase genome coverage. In the past decade, cross-species phenotype comparisons have proven valuble, and several ontologies have been developed for this purpose. The relative contribution of different model organisms to computational identification of disease-associated genes is not fully explored. We used phenotype ontologies to semantically relate phenotypes resulting from loss-of-function mutations in model organisms to disease-associated phenotypes in humans. Semantic machine learning methods were used to measure the contribution of different model organisms to the identification of known human gene–disease associations. We found that mouse genotype–phenotype data provided the most important dataset in the identification of human disease genes by semantic similarity and machine learning over phenotype ontologies. Other model organisms' data did not improve identification over that obtained using the mouse alone, and therefore did not contribute significantly to this task. Our work impacts on the development of integrated phenotype ontologies, as well as for the use of model organism phenotypes in human genetic variant interpretation. This article has an associated First Person interview with the first author of the paper. Editor's choice: We investigated the use of model organism phenotypes in the computational identification of disease genes, identifying several data biases and concluding that mouse model phenotypes contribute most to computational disease gene identification.
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Affiliation(s)
- Sarah M Alghamdi
- Computational Bioscience Research Center (CBRC), King Abdullah University of Science and Technology, 4700 KAUST, 23955 Thuwal, Saudi Arabia
| | - Paul N Schofield
- Department of Physiology, Development & Neuroscience, University of Cambridge, Downing Street, CB2 3EG, Cambridge, UK
| | - Robert Hoehndorf
- Computational Bioscience Research Center (CBRC), King Abdullah University of Science and Technology, 4700 KAUST, 23955 Thuwal, Saudi Arabia
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2
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Gourdine JPF, Brush MH, Vasilevsky NA, Shefchek K, Köhler S, Matentzoglu N, Munoz-Torres MC, McMurry JA, Zhang XA, Robinson PN, Haendel MA. Representing glycophenotypes: semantic unification of glycobiology resources for disease discovery. Database (Oxford) 2019; 2019:baz114. [PMID: 31735951 PMCID: PMC6859258 DOI: 10.1093/database/baz114] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2019] [Revised: 08/27/2019] [Accepted: 08/28/2019] [Indexed: 12/11/2022]
Abstract
While abnormalities related to carbohydrates (glycans) are frequent for patients with rare and undiagnosed diseases as well as in many common diseases, these glycan-related phenotypes (glycophenotypes) are not well represented in knowledge bases (KBs). If glycan-related diseases were more robustly represented and curated with glycophenotypes, these could be used for molecular phenotyping to help to realize the goals of precision medicine. Diagnosis of rare diseases by computational cross-species comparison of genotype-phenotype data has been facilitated by leveraging ontological representations of clinical phenotypes, using Human Phenotype Ontology (HPO), and model organism ontologies such as Mammalian Phenotype Ontology (MP) in the context of the Monarch Initiative. In this article, we discuss the importance and complexity of glycobiology and review the structure of glycan-related content from existing KBs and biological ontologies. We show how semantically structuring knowledge about the annotation of glycophenotypes could enhance disease diagnosis, and propose a solution to integrate glycophenotypes and related diseases into the Unified Phenotype Ontology (uPheno), HPO, Monarch and other KBs. We encourage the community to practice good identifier hygiene for glycans in support of semantic analysis, and clinicians to add glycomics to their diagnostic analyses of rare diseases.
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Affiliation(s)
- Jean-Philippe F Gourdine
- Oregon Clinical & Translational Research Institute, Oregon Health & Science University, Portland, OR 97239, USA
- OHSU Library, Oregon Health & Science University Library, Portland, OR 97239, USA
- Monarch Initiative, monarchinitiative.org
| | - Matthew H Brush
- Oregon Clinical & Translational Research Institute, Oregon Health & Science University, Portland, OR 97239, USA
- Monarch Initiative, monarchinitiative.org
| | - Nicole A Vasilevsky
- Oregon Clinical & Translational Research Institute, Oregon Health & Science University, Portland, OR 97239, USA
- Monarch Initiative, monarchinitiative.org
| | - Kent Shefchek
- Monarch Initiative, monarchinitiative.org
- Linus Pauling Institute, Oregon State University, Corvallis, OR 97331, USA
| | - Sebastian Köhler
- Monarch Initiative, monarchinitiative.org
- Charité Centrum für Therapieforschung, Charité-Universitätsmedizin Berlin Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin and Berlin Institute of Health, Berlin 10117, Germany
| | - Nicolas Matentzoglu
- Monarch Initiative, monarchinitiative.org
- European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Cambridge, UK
| | - Monica C Munoz-Torres
- Monarch Initiative, monarchinitiative.org
- Linus Pauling Institute, Oregon State University, Corvallis, OR 97331, USA
| | - Julie A McMurry
- Monarch Initiative, monarchinitiative.org
- Linus Pauling Institute, Oregon State University, Corvallis, OR 97331, USA
| | - Xingmin Aaron Zhang
- Monarch Initiative, monarchinitiative.org
- The Jackson Laboratory for Genomic Medicine, Farmington, CT 06032, USA
| | - Peter N Robinson
- Monarch Initiative, monarchinitiative.org
- The Jackson Laboratory for Genomic Medicine, Farmington, CT 06032, USA
| | - Melissa A Haendel
- Oregon Clinical & Translational Research Institute, Oregon Health & Science University, Portland, OR 97239, USA
- Monarch Initiative, monarchinitiative.org
- Linus Pauling Institute, Oregon State University, Corvallis, OR 97331, USA
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Smedley D, Jacobsen JOB, Jäger M, Köhler S, Holtgrewe M, Schubach M, Siragusa E, Zemojtel T, Buske OJ, Washington NL, Bone WP, Haendel MA, Robinson PN. Next-generation diagnostics and disease-gene discovery with the Exomiser. Nat Protoc 2015; 10:2004-15. [PMID: 26562621 DOI: 10.1038/nprot.2015.124] [Citation(s) in RCA: 229] [Impact Index Per Article: 25.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
Exomiser is an application that prioritizes genes and variants in next-generation sequencing (NGS) projects for novel disease-gene discovery or differential diagnostics of Mendelian disease. Exomiser comprises a suite of algorithms for prioritizing exome sequences using random-walk analysis of protein interaction networks, clinical relevance and cross-species phenotype comparisons, as well as a wide range of other computational filters for variant frequency, predicted pathogenicity and pedigree analysis. In this protocol, we provide a detailed explanation of how to install Exomiser and use it to prioritize exome sequences in a number of scenarios. Exomiser requires ∼3 GB of RAM and roughly 15-90 s of computing time on a standard desktop computer to analyze a variant call format (VCF) file. Exomiser is freely available for academic use from http://www.sanger.ac.uk/science/tools/exomiser.
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Affiliation(s)
- Damian Smedley
- Skarnes Faculty Group, Wellcome Trust Sanger Institute, Hinxton, UK
| | | | - Marten Jäger
- Institute for Medical and Human Genetics, Charité-Universitätsmedizin Berlin, Berlin, Germany.,Berlin Brandenburg Center for Regenerative Therapies (BCRT), Charité-Universitätsmedizin Berlin, Berlin, Germany
| | - Sebastian Köhler
- Institute for Medical and Human Genetics, Charité-Universitätsmedizin Berlin, Berlin, Germany
| | - Manuel Holtgrewe
- Institute for Medical and Human Genetics, Charité-Universitätsmedizin Berlin, Berlin, Germany.,Berlin Institute for Health, Berlin, Germany
| | - Max Schubach
- Institute for Medical and Human Genetics, Charité-Universitätsmedizin Berlin, Berlin, Germany
| | - Enrico Siragusa
- Institute for Medical and Human Genetics, Charité-Universitätsmedizin Berlin, Berlin, Germany.,Berlin Institute for Health, Berlin, Germany.,Max Planck Institute for Molecular Genetics, Berlin, Germany
| | - Tomasz Zemojtel
- Institute for Medical and Human Genetics, Charité-Universitätsmedizin Berlin, Berlin, Germany.,Institute of Bioorganic Chemistry, Polish Academy of Sciences, Poznan, Poland.,Labor Berlin - Charité Vivantes, Humangenetik, Berlin, Germany
| | - Orion J Buske
- Department of Computer Science, University of Toronto, Toronto, Ontario, Canada.,Genetics and Genome Biology, The Hospital for Sick Children, Toronto, Ontario, Canada
| | - Nicole L Washington
- Division of Environmental Genomics and Systems Biology, Lawrence Berkeley National Laboratory, Berkeley, California, USA
| | - William P Bone
- The National Institutes of Health (NIH) Undiagnosed Diseases Program, Common Fund, Office of the Director, NIH, Bethesda, Maryland, USA
| | - Melissa A Haendel
- Department of Medical Informatics and Clinical Epidemiology, Oregon Health &Science University, Portland, Oregon, USA
| | - Peter N Robinson
- Institute for Medical and Human Genetics, Charité-Universitätsmedizin Berlin, Berlin, Germany.,Berlin Brandenburg Center for Regenerative Therapies (BCRT), Charité-Universitätsmedizin Berlin, Berlin, Germany.,Max Planck Institute for Molecular Genetics, Berlin, Germany.,Department of Mathematics and Computer Science, Institute for Bioinformatics, Freie Universität Berlin, Berlin, Germany
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Smedley D, Robinson PN. Phenotype-driven strategies for exome prioritization of human Mendelian disease genes. Genome Med 2015; 7:81. [PMID: 26229552 PMCID: PMC4520011 DOI: 10.1186/s13073-015-0199-2] [Citation(s) in RCA: 77] [Impact Index Per Article: 8.6] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/16/2023] Open
Abstract
Whole exome sequencing has altered the way in which rare diseases are diagnosed and disease genes identified. Hundreds of novel disease-associated genes have been characterized by whole exome sequencing in the past five years, yet the identification of disease-causing mutations is often challenging because of the large number of rare variants that are being revealed. Gene prioritization aims to rank the most probable candidate genes towards the top of a list of potentially pathogenic variants. A promising new approach involves the computational comparison of the phenotypic abnormalities of the individual being investigated with those previously associated with human diseases or genetically modified model organisms. In this review, we compare and contrast the strengths and weaknesses of current phenotype-driven computational algorithms, including Phevor, Phen-Gen, eXtasy and two algorithms developed by our groups called PhenIX and Exomiser. Computational phenotype analysis can substantially improve the performance of exome analysis pipelines.
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Affiliation(s)
- Damian Smedley
- />Skarnes Faculty Group, Wellcome Trust Sanger Institute, Hinxton, UK
| | - Peter N. Robinson
- />Institute for Medical Genetics and Human Genetics, Charité-Universitätsmedizin Berlin, Berlin, Germany
- />Max Planck Institute for Molecular Genetics, Ihnestrasse, 14195 Berlin, Germany
- />Berlin Brandenburg Center for Regenerative Therapies (BCRT), Charité-Universitätsmedizin Berlin, Augustenburger Platz, 13353 Berlin, Germany
- />Institute for Bioinformatics, Department of Mathematics and Computer Science, Freie Universität Berlin, Takustrasse, 14195 Berlin, Germany
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5
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Lloyd KCK, Meehan T, Beaudet A, Murray S, Svenson K, McKerlie C, West D, Morse I, Parkinson H, Brown S, Mallon AM, Moore M. Precision medicine: Look to the mice. Science 2015. [PMID: 26206923 DOI: 10.1126/science.349.6246.390-a] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/22/2023]
Affiliation(s)
| | - Terry Meehan
- European Bioinformatics Institute, Hinxton, Cambridge, CB10 1SD, UK
| | | | - Steve Murray
- The Jackson Laboratory, Bar Harbor, ME 04609, USA
| | | | - Colin McKerlie
- Toronto Centre for Phenogenomics, Toronto, ON, M5T 3H7, Canada
| | - David West
- Children's Hospital Oakland Research Institute, Oakland, CA 94609, USA
| | - Iva Morse
- Charles River Laboratories, Wilmington, MA 01887, USA
| | - Helen Parkinson
- European Bioinformatics Institute, Hinxton, Cambridge, CB10 1SD, UK
| | - Steve Brown
- Medical Research Council Harwell, Oxfordshire, OX11 0RD, UK
| | | | - Mark Moore
- International Mouse Phenotyping Consortium, Hinxton, Cambridge, CB10 1SD, UK
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Haendel MA, Vasilevsky N, Brush M, Hochheiser HS, Jacobsen J, Oellrich A, Mungall CJ, Washington N, Köhler S, Lewis SE, Robinson PN, Smedley D. Disease insights through cross-species phenotype comparisons. Mamm Genome 2015; 26:548-55. [PMID: 26092691 PMCID: PMC4602072 DOI: 10.1007/s00335-015-9577-8] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2015] [Accepted: 05/20/2015] [Indexed: 11/30/2022]
Abstract
New sequencing technologies have ushered in a new era for diagnosis and discovery of new causative mutations for rare diseases. However, the sheer numbers of candidate variants that require interpretation in an exome or genomic analysis are still a challenging prospect. A powerful approach is the comparison of the patient’s set of phenotypes (phenotypic profile) to known phenotypic profiles caused by mutations in orthologous genes associated with these variants. The most abundant source of relevant data for this task is available through the efforts of the Mouse Genome Informatics group and the International Mouse Phenotyping Consortium. In this review, we highlight the challenges in comparing human clinical phenotypes with mouse phenotypes and some of the solutions that have been developed by members of the Monarch Initiative. These tools allow the identification of mouse models for known disease-gene associations that may otherwise have been overlooked as well as candidate genes may be prioritized for novel associations. The culmination of these efforts is the Exomiser software package that allows clinical researchers to analyse patient exomes in the context of variant frequency and predicted pathogenicity as well the phenotypic similarity of the patient to any given candidate orthologous gene.
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Affiliation(s)
- Melissa A Haendel
- University Library and Department of Medical Informatics and Epidemiology, Oregon Health & Science University, Portland, OR, USA
| | - Nicole Vasilevsky
- University Library and Department of Medical Informatics and Epidemiology, Oregon Health & Science University, Portland, OR, USA
| | - Matthew Brush
- University Library and Department of Medical Informatics and Epidemiology, Oregon Health & Science University, Portland, OR, USA
| | - Harry S Hochheiser
- Department of Biomedical Informatics and Intelligent Systems Program, University of Pittsburgh, Pittsburgh, PA, 15206, USA
| | - Julius Jacobsen
- Skarnes Faculty Group, Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SA, UK
| | - Anika Oellrich
- Skarnes Faculty Group, Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SA, UK
| | - Christopher J Mungall
- Genomics Division, Lawrence Berkeley National Laboratory, 1 Cyclotron Road, Berkeley, CA, 94720, USA
| | - Nicole Washington
- Genomics Division, Lawrence Berkeley National Laboratory, 1 Cyclotron Road, Berkeley, CA, 94720, USA
| | - Sebastian Köhler
- Computational Biology Group, Institute for Medical Genetics and Human Genetics, Universitatsklinikum Charité, Augustenburger Platz 1, 13353, Berlin, Germany
| | - Suzanna E Lewis
- Genomics Division, Lawrence Berkeley National Laboratory, 1 Cyclotron Road, Berkeley, CA, 94720, USA
| | - Peter N Robinson
- Computational Biology Group, Institute for Medical Genetics and Human Genetics, Universitatsklinikum Charité, Augustenburger Platz 1, 13353, Berlin, Germany
| | - Damian Smedley
- Skarnes Faculty Group, Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SA, UK.
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