401
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Yüksel Z, Yazol M, Gümüş E. Pathogenic homozygous variations in ACTL6B cause DECAM syndrome: Developmental delay, Epileptic encephalopathy, Cerebral Atrophy, and abnormal Myelination. Am J Med Genet A 2019; 179:1603-1608. [PMID: 31134736 DOI: 10.1002/ajmg.a.61210] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/01/2019] [Revised: 04/17/2019] [Accepted: 05/12/2019] [Indexed: 11/12/2022]
Abstract
The extensive usage of next generation sequencing, particularly for the patients affected with neurodevelopmental disorders, has increased our understanding and enabled identifying novel disorder genes. Here, we report an extended consanguineous family having at least three affected children with ACTL6B-related neurodevelopmental disorder and expand the known phenotypic spectrum by characterizing the clinical findings using a standardized vocabulary, Human Phenotype Ontology Terms.
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Affiliation(s)
- Zafer Yüksel
- Bioscientia Center for Human Genetics, Ingelheim, Germany
| | - Merve Yazol
- Department of Radiology, Sanliurfa Education and Research Hospital, Sanliurfa, Turkey
| | - Evren Gümüş
- Department of Medical Genetics, Faculty of Medicine, University of Harran, Sanliurfa, Turkey.,Department of Medical Genetics, Faculty of Medicine, Mugla Sitki Kocman University, Mugla, Turkey
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402
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New models for human disease from the International Mouse Phenotyping Consortium. Mamm Genome 2019; 30:143-150. [PMID: 31127358 PMCID: PMC6606664 DOI: 10.1007/s00335-019-09804-5] [Citation(s) in RCA: 47] [Impact Index Per Article: 9.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2019] [Accepted: 05/15/2019] [Indexed: 12/21/2022]
Abstract
The International Mouse Phenotyping Consortium (IMPC) continues to expand the catalogue of mammalian gene function by conducting genome and phenome-wide phenotyping on knockout mouse lines. The extensive and standardized phenotype screens allow the identification of new potential models for human disease through cross-species comparison by computing the similarity between the phenotypes observed in the mutant mice and the human phenotypes associated to their orthologous loci in Mendelian disease. Here, we present an update on the novel disease models available from the most recent data release (DR10.0), with 5861 mouse genes fully or partially phenotyped and a total number of 69,982 phenotype calls reported. With approximately one-third of human Mendelian genes with orthologous null mouse phenotypes described, the range of available models relevant for human diseases keeps increasing. Among the breadth of new data, we identify previously uncharacterized disease genes in the mouse and additional phenotypes for genes with existing mutant lines mimicking the associated disorder. The automated and unbiased discovery of relevant models for all types of rare diseases implemented by the IMPC constitutes a powerful tool for human genetics and precision medicine.
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403
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Tomar S, Sethi R, Lai PS. Specific phenotype semantics facilitate gene prioritization in clinical exome sequencing. Eur J Hum Genet 2019; 27:1389-1397. [PMID: 31053788 DOI: 10.1038/s41431-019-0412-7] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2018] [Revised: 02/21/2019] [Accepted: 04/15/2019] [Indexed: 12/13/2022] Open
Abstract
Selection and prioritization of phenotype-centric variants remains a challenging part of variant analysis and interpretation in clinical exome sequencing. Phenotype-driven shortlisting of patient-specific gene lists can avoid missed diagnosis. Here, we analyzed the relevance of using primary Human Phenotype Ontology identifiers (HPO IDs) in prioritizing Mendelian disease genes across 30 in-house, 10 previously reported, and 10 recently published cases using three popular web-based gene prioritization tools (OMIMExplorer, VarElect & Phenolyzer). We assessed partial HPO-based gene prioritization using randomly chosen and top 10%, 30%, and 50% HPO IDs based on information content and found high variance within rank ratios across the former vs the latter. This signified that randomly selected less-specific HPO IDs for a given disease phenotype performed poorly by ranking probe gene farther away from the top rank. In contrast, the use of top 10%, 30%, and 50% HPO IDs individually could rank the probe gene among the top 1% in the ranked list of genes that was equivalent to the results when the full list of HPO IDs were used. Hence, we conclude that use of just the top 10% of HPO IDs chosen based on information content is sufficient for ranking the probe gene at top position. Our findings provide practical guidance for utilizing structured phenotype semantics and web-based gene-ranking tools to aid in identifying known as well unknown candidate gene associations in Mendelian disorders.
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Affiliation(s)
- Swati Tomar
- Department of Paediatrics, Yong Loo Lin School of Medicine, National University of Singapore, National University Health System (NUHS), 1E Kent Ridge Road, 119228, Singapore
| | - Raman Sethi
- Department of Paediatrics, Yong Loo Lin School of Medicine, National University of Singapore, National University Health System (NUHS), 1E Kent Ridge Road, 119228, Singapore
| | - Poh San Lai
- Department of Paediatrics, Yong Loo Lin School of Medicine, National University of Singapore, National University Health System (NUHS), 1E Kent Ridge Road, 119228, Singapore.
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404
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Zhang XA, Yates A, Vasilevsky N, Gourdine JP, Callahan TJ, Carmody LC, Danis D, Joachimiak MP, Ravanmehr V, Pfaff ER, Champion J, Robasky K, Xu H, Fecho K, Walton NA, Zhu RL, Ramsdill J, Mungall CJ, Köhler S, Haendel MA, McDonald CJ, Vreeman DJ, Peden DB, Bennett TD, Feinstein JA, Martin B, Stefanski AL, Hunter LE, Chute CG, Robinson PN. Semantic integration of clinical laboratory tests from electronic health records for deep phenotyping and biomarker discovery. NPJ Digit Med 2019; 2:32. [PMID: 31119199 PMCID: PMC6527418 DOI: 10.1038/s41746-019-0110-4] [Citation(s) in RCA: 31] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2018] [Accepted: 04/18/2019] [Indexed: 12/22/2022] Open
Abstract
Electronic Health Record (EHR) systems typically define laboratory test results using the Laboratory Observation Identifier Names and Codes (LOINC) and can transmit them using Fast Healthcare Interoperability Resource (FHIR) standards. LOINC has not yet been semantically integrated with computational resources for phenotype analysis. Here, we provide a method for mapping LOINC-encoded laboratory test results transmitted in FHIR standards to Human Phenotype Ontology (HPO) terms. We annotated the medical implications of 2923 commonly used laboratory tests with HPO terms. Using these annotations, our software assesses laboratory test results and converts each result into an HPO term. We validated our approach with EHR data from 15,681 patients with respiratory complaints and identified known biomarkers for asthma. Finally, we provide a freely available SMART on FHIR application that can be used within EHR systems. Our approach allows readily available laboratory tests in EHR to be reused for deep phenotyping and exploits the hierarchical structure of HPO to integrate distinct tests that have comparable medical interpretations for association studies.
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Affiliation(s)
| | - Amy Yates
- Oregon Clinical and Translational Research Institute, Oregon Health and Science University, Portland, OR 97239 USA
| | - Nicole Vasilevsky
- Oregon Clinical and Translational Research Institute, Oregon Health and Science University, Portland, OR 97239 USA
- Department of Medical Informatics and Clinical Epidemiology, Oregon Health and Science University, Portland, OR 97239 USA
| | - J. P. Gourdine
- Oregon Clinical and Translational Research Institute, Oregon Health and Science University, Portland, OR 97239 USA
- Library, Oregon Health and Science University, Portland, OR 97239 USA
| | - Tiffany J. Callahan
- Computational Bioscience Program, Department of Pharmacology, University of Colorado Anschutz School of Medicine, Aurora, CO 80045 USA
| | - Leigh C. Carmody
- The Jackson Laboratory for Genomic Medicine, Farmington CT, 06032 USA
| | - Daniel Danis
- The Jackson Laboratory for Genomic Medicine, Farmington CT, 06032 USA
| | - Marcin P. Joachimiak
- Environmental Genomics and Systems Biology Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720 USA
| | - Vida Ravanmehr
- The Jackson Laboratory for Genomic Medicine, Farmington CT, 06032 USA
| | - Emily R. Pfaff
- North Carolina Translational and Clinical Sciences Institute (NC TraCS), University of North Carolina at Chapel Hill, Chapel Hill, NC 27599 USA
| | - James Champion
- North Carolina Translational and Clinical Sciences Institute (NC TraCS), University of North Carolina at Chapel Hill, Chapel Hill, NC 27599 USA
| | - Kimberly Robasky
- North Carolina Translational and Clinical Sciences Institute (NC TraCS), University of North Carolina at Chapel Hill, Chapel Hill, NC 27599 USA
- Genetics Department, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599 USA
- School of Information and Library Sciences, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599 USA
| | - Hao Xu
- Renaissance Computing Institute, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599 USA
| | - Karamarie Fecho
- Renaissance Computing Institute, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599 USA
| | - Nephi A. Walton
- Genomic Medicine Institute, Geisinger Health System, Danville, PA 17822 USA
| | - Richard L. Zhu
- Institute for Clinical and Translational Research, Johns Hopkins University, Baltimore, MD 21202 USA
| | - Justin Ramsdill
- Oregon Clinical and Translational Research Institute, Oregon Health and Science University, Portland, OR 97239 USA
| | - Christopher J. Mungall
- Environmental Genomics and Systems Biology Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720 USA
| | - Sebastian Köhler
- 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
- Einstein Center Digital Future, Berlin, 10117 Germany
| | - Melissa A. Haendel
- Oregon Clinical and Translational Research Institute, Oregon Health and Science University, Portland, OR 97239 USA
- Department of Medical Informatics and Clinical Epidemiology, Oregon Health and Science University, Portland, OR 97239 USA
- Linus Pauling Institute and Center for Genome Research and Biocomputing, Oregon State University, Corvallis, OR 97331 USA
| | - Clement J. McDonald
- Lister Hill National Center for Biomedical Communications, National Library of Medicine, National Institutes of Health, Bethesda, MD 20894 USA
| | - Daniel J. Vreeman
- Department of Medicine, Indiana University School of Medicine, Indianapolis, IN 46202 USA
- Center for Biomedical Informatics, Regenstrief Institute, Inc., Indianapolis, IN 46202 USA
| | - David B. Peden
- North Carolina Translational and Clinical Sciences Institute (NC TraCS), University of North Carolina at Chapel Hill, Chapel Hill, NC 27599 USA
- Division of Allergy, Immunology and Rheumatology, Department of Pediatrics, University of North Carolina, Chapel Hill, NC 27599 USA
- University of North Carolina Center for Environmental Medicine, Asthma and Lung Biology, University of North Carolina, Chapel Hill, NC 27599 USA
| | - Tellen D. Bennett
- Department of Pediatrics, Section of Pediatric Critical Care, University of Colorado School of Medicine, Aurora, CO 80045 USA
| | - James A. Feinstein
- Adult and Child Consortium for Health Outcomes Research and Delivery Science (ACCORDS), University of Colorado School of Medicine, Aurora, CO 80045 USA
| | - Blake Martin
- Department of Pediatrics, Section of Pediatric Critical Care, University of Colorado School of Medicine, Aurora, CO 80045 USA
| | - Adrianne L. Stefanski
- Computational Bioscience Program, Department of Pharmacology, University of Colorado Anschutz School of Medicine, Aurora, CO 80045 USA
| | - Lawrence E. Hunter
- Computational Bioscience Program, Department of Pharmacology, University of Colorado Anschutz School of Medicine, Aurora, CO 80045 USA
| | - Christopher G. Chute
- Institute for Clinical and Translational Research, Johns Hopkins University, Baltimore, MD 21202 USA
| | - Peter N. Robinson
- The Jackson Laboratory for Genomic Medicine, Farmington CT, 06032 USA
- Institute for Systems Genomics, University of Connecticut, Farmington, CT 06032 USA
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405
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Maroilley T, Tarailo-Graovac M. Uncovering Missing Heritability in Rare Diseases. Genes (Basel) 2019; 10:E275. [PMID: 30987386 PMCID: PMC6523881 DOI: 10.3390/genes10040275] [Citation(s) in RCA: 30] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2019] [Revised: 03/29/2019] [Accepted: 04/01/2019] [Indexed: 12/14/2022] Open
Abstract
The problem of 'missing heritability' affects both common and rare diseases hindering: discovery, diagnosis, and patient care. The 'missing heritability' concept has been mainly associated with common and complex diseases where promising modern technological advances, like genome-wide association studies (GWAS), were unable to uncover the complete genetic mechanism of the disease/trait. Although rare diseases (RDs) have low prevalence individually, collectively they are common. Furthermore, multi-level genetic and phenotypic complexity when combined with the individual rarity of these conditions poses an important challenge in the quest to identify causative genetic changes in RD patients. In recent years, high throughput sequencing has accelerated discovery and diagnosis in RDs. However, despite the several-fold increase (from ~10% using traditional to ~40% using genome-wide genetic testing) in finding genetic causes of these diseases in RD patients, as is the case in common diseases-the majority of RDs are also facing the 'missing heritability' problem. This review outlines the key role of high throughput sequencing in uncovering genetics behind RDs, with a particular focus on genome sequencing. We review current advances and challenges of sequencing technologies, bioinformatics approaches, and resources.
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Affiliation(s)
- Tatiana Maroilley
- Departments of Biochemistry, Molecular Biology and Medical Genetics, Cumming School of Medicine, University of Calgary, Calgary, AB T2N 4N1, Canada.
- Alberta Children's Hospital Research Institute, University of Calgary, Calgary, AB T2N 4N1, Canada.
| | - Maja Tarailo-Graovac
- Departments of Biochemistry, Molecular Biology and Medical Genetics, Cumming School of Medicine, University of Calgary, Calgary, AB T2N 4N1, Canada.
- Alberta Children's Hospital Research Institute, University of Calgary, Calgary, AB T2N 4N1, Canada.
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406
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Luo P, Xiao Q, Wei PJ, Liao B, Wu FX. Identifying Disease-Gene Associations With Graph-Regularized Manifold Learning. Front Genet 2019; 10:270. [PMID: 31001321 PMCID: PMC6454152 DOI: 10.3389/fgene.2019.00270] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2018] [Accepted: 03/12/2019] [Indexed: 12/18/2022] Open
Abstract
Complex diseases are known to be associated with disease genes. Uncovering disease-gene associations is critical for diagnosis, treatment, and prevention of diseases. Computational algorithms which effectively predict candidate disease-gene associations prior to experimental proof can greatly reduce the associated cost and time. Most existing methods are disease-specific which can only predict genes associated with a specific disease at a time. Similarities among diseases are not used during the prediction. Meanwhile, most methods predict new disease genes based on known associations, making them unable to predict disease genes for diseases without known associated genes.In this study, a manifold learning-based method is proposed for predicting disease-gene associations by assuming that the geodesic distance between any disease and its associated genes should be shorter than that of other non-associated disease-gene pairs. The model maps the diseases and genes into a lower dimensional manifold based on the known disease-gene associations, disease similarity and gene similarity to predict new associations in terms of the geodesic distance between disease-gene pairs. In the 3-fold cross-validation experiments, our method achieves scores of 0.882 and 0.854 in terms of the area under of the receiver operating characteristic (ROC) curve (AUC) for diseases with more than one known associated genes and diseases with only one known associated gene, respectively. Further de novo studies on Lung Cancer and Bladder Cancer also show that our model is capable of identifying new disease-gene associations.
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Affiliation(s)
- Ping Luo
- Division of Biomedical Engineering, University of SaskatchewanSaskatoon, SKCanada
| | - Qianghua Xiao
- School of Mathematics and Physics, University of South China, Hengyang, China
| | - Pi-Jing Wei
- Division of Biomedical Engineering, University of SaskatchewanSaskatoon, SKCanada
- College of Computer Science and Technology, Anhui University, Hefei, China
| | - Bo Liao
- School of Mathematics and Statistics, Hainan Normal University, Haikou, China
| | - Fang-Xiang Wu
- Division of Biomedical Engineering, University of SaskatchewanSaskatoon, SKCanada
- School of Mathematics and Statistics, Hainan Normal University, Haikou, China
- Department of Mechanical Engineering, University of Saskatchewan, Saskatoon, SK, Canada
- Department of Computer Science, University of Saskatchewan, Saskatoon, SK, Canada
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407
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Almasi SM, Hu T. Measuring the importance of vertices in the weighted human disease network. PLoS One 2019; 14:e0205936. [PMID: 30901770 PMCID: PMC6430629 DOI: 10.1371/journal.pone.0205936] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2018] [Accepted: 02/26/2019] [Indexed: 12/11/2022] Open
Abstract
Many human genetic disorders and diseases are known to be related to each other through frequently observed co-occurrences. Studying the correlations among multiple diseases provides an important avenue to better understand the common genetic background of diseases and to help develop new drugs that can treat multiple diseases. Meanwhile, network science has seen increasing applications on modeling complex biological systems, and can be a powerful tool to elucidate the correlations of multiple human diseases. In this article, known disease-gene associations were represented using a weighted bipartite network. We extracted a weighted human diseases network from such a bipartite network to show the correlations of diseases. Subsequently, we proposed a new centrality measurement for the weighted human disease network (WHDN) in order to quantify the importance of diseases. Using our centrality measurement to quantify the importance of vertices in WHDN, we were able to find a set of most central diseases. By investigating the 30 top diseases and their most correlated neighbors in the network, we identified disease linkages including known disease pairs and novel findings. Our research helps better understand the common genetic origin of human diseases and suggests top diseases that likely induce other related diseases.
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Affiliation(s)
| | - Ting Hu
- Department of Computer Science, Memorial University, St. John’s, NL, Canada
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408
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ClinTAD: a tool for copy number variant interpretation in the context of topologically associated domains. J Hum Genet 2019; 64:437-443. [PMID: 30765865 DOI: 10.1038/s10038-019-0573-9] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2018] [Revised: 01/08/2019] [Accepted: 01/22/2019] [Indexed: 12/22/2022]
Abstract
Standard clinical interpretation of DNA copy number variants (CNVs) identified by cytogenomic microarray involves examining protein-coding genes within the region and comparison to other CNVs. Emerging basic research suggests that CNVs can also exert a pathogenic effect through disruption of DNA structural elements such as topologically associated domains (TADs). To begin to integrate these discoveries with current practice, we developed ClinTAD, a free browser-based tool to assist with interpretation of CNVs in the context of TADs ( www.clintad.com ). We used ClinTAD to examine 209 randomly selected single-nucleotide polymorphism microarray cases with a total of 236 CNVs. We compared 118 CNVs classified as variants of uncertain clinical significance (VUS), where additional insight into pathogenicity of these CNVs would be of greatest utility, to 118 CNVs classified as benign. We found that a higher proportion of VUS had at least two genes in a nearby TAD related to a phenotype seen in the patient based on Human Phenotype Ontology (HPO) annotation. We present example cases demonstrating scenarios where ClinTAD may either increase or decrease clinical suspicion of pathogenicity for VUS, depending on disruption of TAD boundaries and HPO phenotype match. ClinTAD is an easy-to-use tool, based on emerging research in chromatin architecture, that can help inform CNV interpretation.
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409
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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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410
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Hoyt CT, Domingo-Fernández D, Aldisi R, Xu L, Kolpeja K, Spalek S, Wollert E, Bachman J, Gyori BM, Greene P, Hofmann-Apitius M. Re-curation and rational enrichment of knowledge graphs in Biological Expression Language. Database (Oxford) 2019; 2019:baz068. [PMID: 31225582 PMCID: PMC6587072 DOI: 10.1093/database/baz068] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2019] [Revised: 04/03/2019] [Accepted: 04/29/2019] [Indexed: 12/23/2022]
Abstract
The rapid accumulation of new biomedical literature not only causes curated knowledge graphs (KGs) to become outdated and incomplete, but also makes manual curation an impractical and unsustainable solution. Automated or semi-automated workflows are necessary to assist in prioritizing and curating the literature to update and enrich KGs. We have developed two workflows: one for re-curating a given KG to assure its syntactic and semantic quality and another for rationally enriching it by manually revising automatically extracted relations for nodes with low information density. We applied these workflows to the KGs encoded in Biological Expression Language from the NeuroMMSig database using content that was pre-extracted from MEDLINE abstracts and PubMed Central full-text articles using text mining output integrated by INDRA. We have made this workflow freely available at https://github.com/bel-enrichment/bel-enrichment.
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Affiliation(s)
- Charles Tapley Hoyt
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Sankt Augustin, Germany
- Bonn-Aachen International Center for Information Technology, Rheinische Friedrich-Wilhelms-Universität Bonn, Bonn, Germany
| | - Daniel Domingo-Fernández
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Sankt Augustin, Germany
- Bonn-Aachen International Center for Information Technology, Rheinische Friedrich-Wilhelms-Universität Bonn, Bonn, Germany
| | - Rana Aldisi
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Sankt Augustin, Germany
- Bonn-Aachen International Center for Information Technology, Rheinische Friedrich-Wilhelms-Universität Bonn, Bonn, Germany
| | - Lingling Xu
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Sankt Augustin, Germany
- Bonn-Aachen International Center for Information Technology, Rheinische Friedrich-Wilhelms-Universität Bonn, Bonn, Germany
| | - Kristian Kolpeja
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Sankt Augustin, Germany
| | - Sandra Spalek
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Sankt Augustin, Germany
| | - Esther Wollert
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Sankt Augustin, Germany
| | - John Bachman
- Laboratory of Systems Pharmacology, Harvard Medical School, 200 Longwood Ave, Boston, MA, USA
| | - Benjamin M Gyori
- Laboratory of Systems Pharmacology, Harvard Medical School, 200 Longwood Ave, Boston, MA, USA
| | - Patrick Greene
- Laboratory of Systems Pharmacology, Harvard Medical School, 200 Longwood Ave, Boston, MA, USA
| | - Martin Hofmann-Apitius
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Sankt Augustin, Germany
- Bonn-Aachen International Center for Information Technology, Rheinische Friedrich-Wilhelms-Universität Bonn, Bonn, Germany
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411
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Yip RK, Chan D, Cheah KS. Mechanistic insights into skeletal development gained from genetic disorders. Curr Top Dev Biol 2019; 133:343-385. [DOI: 10.1016/bs.ctdb.2019.02.002] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
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