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Althagafi A, Zhapa-Camacho F, Hoehndorf R. Prioritizing genomic variants through neuro-symbolic, knowledge-enhanced learning. Bioinformatics 2024; 40:btae301. [PMID: 38696757 PMCID: PMC11132820 DOI: 10.1093/bioinformatics/btae301] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2023] [Revised: 04/05/2024] [Accepted: 04/30/2024] [Indexed: 05/04/2024] Open
Abstract
MOTIVATION Whole-exome and genome sequencing have become common tools in diagnosing patients with rare diseases. Despite their success, this approach leaves many patients undiagnosed. A common argument is that more disease variants still await discovery, or the novelty of disease phenotypes results from a combination of variants in multiple disease-related genes. Interpreting the phenotypic consequences of genomic variants relies on information about gene functions, gene expression, physiology, and other genomic features. Phenotype-based methods to identify variants involved in genetic diseases combine molecular features with prior knowledge about the phenotypic consequences of altering gene functions. While phenotype-based methods have been successfully applied to prioritizing variants, such methods are based on known gene-disease or gene-phenotype associations as training data and are applicable to genes that have phenotypes associated, thereby limiting their scope. In addition, phenotypes are not assigned uniformly by different clinicians, and phenotype-based methods need to account for this variability. RESULTS We developed an Embedding-based Phenotype Variant Predictor (EmbedPVP), a computational method to prioritize variants involved in genetic diseases by combining genomic information and clinical phenotypes. EmbedPVP leverages a large amount of background knowledge from human and model organisms about molecular mechanisms through which abnormal phenotypes may arise. Specifically, EmbedPVP incorporates phenotypes linked to genes, functions of gene products, and the anatomical site of gene expression, and systematically relates them to their phenotypic effects through neuro-symbolic, knowledge-enhanced machine learning. We demonstrate EmbedPVP's efficacy on a large set of synthetic genomes and genomes matched with clinical information. AVAILABILITY AND IMPLEMENTATION EmbedPVP and all evaluation experiments are freely available at https://github.com/bio-ontology-research-group/EmbedPVP.
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Affiliation(s)
- Azza Althagafi
- Computational Bioscience Research Center (CBRC), King Abdullah University of Science and Technology (KAUST), 4700 KAUST, Thuwal 23955, Saudi Arabia
- Computer Science Program, Computer, Electrical and Mathematical Sciences & Engineering Division (CEMSE), King Abdullah University of Science and Technology (KAUST), 4700 KAUST, Thuwal 23955, Saudi Arabia
- Computer Science Department, College of Computers and Information Technology, Taif University, Taif 26571, Saudi Arabia
| | - Fernando Zhapa-Camacho
- Computational Bioscience Research Center (CBRC), King Abdullah University of Science and Technology (KAUST), 4700 KAUST, Thuwal 23955, Saudi Arabia
- Computer Science Program, Computer, Electrical and Mathematical Sciences & Engineering Division (CEMSE), King Abdullah University of Science and Technology (KAUST), 4700 KAUST, Thuwal 23955, Saudi Arabia
| | - Robert Hoehndorf
- Computational Bioscience Research Center (CBRC), King Abdullah University of Science and Technology (KAUST), 4700 KAUST, Thuwal 23955, Saudi Arabia
- Computer Science Program, Computer, Electrical and Mathematical Sciences & Engineering Division (CEMSE), King Abdullah University of Science and Technology (KAUST), 4700 KAUST, Thuwal 23955, Saudi Arabia
- SDAIA-KAUST Center of Excellence in Data Science and Artificial Intelligence, King Abdullah University of Science and Technology (KAUST), 4700 KAUST, Thuwal 23955, Saudi Arabia
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2
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Alghamdi SM, Hoehndorf R. Improving the classification of cardinality phenotypes using collections. J Biomed Semantics 2023; 14:9. [PMID: 37550716 PMCID: PMC10405428 DOI: 10.1186/s13326-023-00290-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2023] [Accepted: 07/07/2023] [Indexed: 08/09/2023] Open
Abstract
MOTIVATION Phenotypes are observable characteristics of an organism and they can be highly variable. Information about phenotypes is collected in a clinical context to characterize disease, and is also collected in model organisms and stored in model organism databases where they are used to understand gene functions. Phenotype data is also used in computational data analysis and machine learning methods to provide novel insights into disease mechanisms and support personalized diagnosis of disease. For mammalian organisms and in a clinical context, ontologies such as the Human Phenotype Ontology and the Mammalian Phenotype Ontology are widely used to formally and precisely describe phenotypes. We specifically analyze axioms pertaining to phenotypes of collections of entities within a body, and we find that some of the axioms in phenotype ontologies lead to inferences that may not accurately reflect the underlying biological phenomena. RESULTS We reformulate the phenotypes of collections of entities using an ontological theory of collections. By reformulating phenotypes of collections in phenotypes ontologies, we avoid potentially incorrect inferences pertaining to the cardinality of these collections. We apply our method to two phenotype ontologies and show that the reformulation not only removes some problematic inferences but also quantitatively improves biological data analysis.
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Affiliation(s)
- Sarah M Alghamdi
- Computational Bioscience Research Center (CBRC), Computer, Electrical, and Mathematical Sciences & Engineering Division, King Abdullah University of Science and Technology, 4700 KAUST, 23955, Thuwal, Saudi Arabia.
- King Abdul-Aziz University, Faculty of Computing and Information Technology, 25732, Rabigh, Saudi Arabia.
| | - Robert Hoehndorf
- Computational Bioscience Research Center (CBRC), Computer, Electrical, and Mathematical Sciences & Engineering Division, King Abdullah University of Science and Technology, 4700 KAUST, 23955, Thuwal, Saudi Arabia.
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3
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Generation of Monogenic Candidate Genes for Human Nephrotic Syndrome Using 3 Independent Approaches. Kidney Int Rep 2020; 6:460-471. [PMID: 33615071 PMCID: PMC7879125 DOI: 10.1016/j.ekir.2020.11.013] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2020] [Revised: 10/22/2020] [Accepted: 11/10/2020] [Indexed: 12/18/2022] Open
Abstract
Introduction Steroid-resistant nephrotic syndrome (SRNS) is the second most common cause of chronic kidney disease during childhood. Identification of 63 monogenic human genes has delineated 12 distinct pathogenic pathways. Methods Here, we generated 2 independent sets of nephrotic syndrome (NS) candidate genes to augment the discovery of additional monogenic causes based on whole-exome sequencing (WES) data from 1382 families with NS. Results We first identified 63 known monogenic causes of NS in mice from public databases and scientific publications, and 12 of these genes overlapped with the 63 known human monogenic SRNS genes. Second, we used a set of 64 genes that are regulated by the transcription factor Wilms tumor 1 (WT1), which causes SRNS if mutated. Thirteen of these WT1-regulated genes overlapped with human or murine NS genes. Finally, we overlapped these lists of murine and WT1 candidate genes with our list of 120 candidate genes generated from WES in 1382 NS families, to identify novel candidate genes for monogenic human SRNS. Using this approach, we identified 7 overlapping genes, of which 3 genes were shared by all datasets, including SYNPO. We show that loss-of-function of SYNPO leads to decreased CDC42 activity and reduced podocyte migration rate, both of which are rescued by overexpression of wild-type complementary DNA (cDNA), but not by cDNA representing the patient mutation. Conclusion Thus, we identified 3 novel candidate genes for human SRNS using 3 independent, nonoverlapping hypotheses, and generated functional evidence for SYNPO as a novel potential monogenic cause of NS.
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Zhu S, Li Y, Bennett S, Chen J, Weng IZ, Huang L, Xu H, Xu J. The role of glial cell line-derived neurotrophic factor family member artemin in neurological disorders and cancers. Cell Prolif 2020; 53:e12860. [PMID: 32573073 PMCID: PMC7377943 DOI: 10.1111/cpr.12860] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2019] [Revised: 05/17/2020] [Accepted: 06/02/2020] [Indexed: 12/12/2022] Open
Abstract
Artemin (ARTN) is a member of the glial cell line‐derived neurotrophic factor (GDNF) family ligands (GFLs), which encompasses family members, GDNF, neurturin (NRTN) and persephin (PSPN). ARTN is also referred to as Enovin or Neublastin, and bears structural characteristics of the TGF‐β superfamily. ARTN contains a dibasic cleavage site (RXXR) that is predicted to be cleaved by furin to yield a carboxy‐terminal 113 amino acid mature form. ARTN binds preferentially to receptor GFRα3, coupled to a receptor tyrosine kinase RET, forming a signalling complex for the regulation of intracellular pathways that affect diverse outcomes of nervous system development and homoeostasis. Standard signalling cascades activated by GFLs via RET include the phosphorylation of mitogen‐activated protein kinase or MAPK (p‐ERK, p‐p38 and p‐JNK), PI3K‐AKT and Src. Neural cell adhesion molecule (NCAM) is an alternative signalling receptor for ARTN in the presence of GFRα1, leading to activation of Fyn and FAK. Further, ARTN also interacts with heparan sulphate proteoglycan syndecan‐3 and mediates non‐RET signalling via activation of Src kinases. This review discusses the role of ARTN in spinal cord injury, neuropathic pain and other neurological disorders. Additionally, ARTN plays a role in non‐neuron tissues, such as the formation of Peyer's patch‐like structures in the lymphoid tissue of the gut. The emerging role of ARTN in cancers and therapeutic resistance to cancers is also explored. Further research is necessary to determine the function of ARTN in a tissue‐specific manner, including its signalling mechanisms, in order to improve the therapeutic potential of ARTN in human diseases.
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Affiliation(s)
- Sipin Zhu
- Department of Orthopaedics, The Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University, Wenzhou, China.,School of Biomedical Sciences, University of Western Australia, Perth, WA, Australia
| | - Yihe Li
- Department of Orthopaedics, The Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University, Wenzhou, China.,School of Biomedical Sciences, University of Western Australia, Perth, WA, Australia
| | - Samuel Bennett
- School of Biomedical Sciences, University of Western Australia, Perth, WA, Australia
| | - Junhao Chen
- School of Biomedical Sciences, University of Western Australia, Perth, WA, Australia
| | - Isabel Ziwai Weng
- School of Biomedical Sciences, University of Western Australia, Perth, WA, Australia
| | - Lin Huang
- School of Biomedical Sciences, University of Western Australia, Perth, WA, Australia.,Department of Spine Surgery, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China.,Department of Orthopaedics, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China
| | - Huazi Xu
- Department of Orthopaedics, The Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University, Wenzhou, China
| | - Jiake Xu
- Department of Orthopaedics, The Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University, Wenzhou, China.,School of Biomedical Sciences, University of Western Australia, Perth, WA, Australia
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An atlas of evidence-based phenotypic associations across the mouse phenome. Sci Rep 2020; 10:3957. [PMID: 32127602 PMCID: PMC7054260 DOI: 10.1038/s41598-020-60891-w] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2019] [Accepted: 02/17/2020] [Indexed: 01/18/2023] Open
Abstract
To date, reliable relationships between mammalian phenotypes, based on diagnostic test measurements, have not been reported on a large scale. The purpose of this study was to present a large mouse phenotype-phenotype relationships dataset as a reference resource, alongside detailed evaluation of the resource. We used bias-minimized comprehensive mouse phenotype data and applied association rule mining to a dataset consisting of only binary (normal and abnormal phenotypes) data to determine relationships among phenotypes. We present 3,686 evidence-based significant associations, comprising 345 phenotypes covering 60 biological systems (functions), and evaluate their characteristics in detail. To evaluate the relationships, we defined a set of phenotype-phenotype association pairs (PPAPs) as a module of phenotypic expression for each of the 345 phenotypes. By analyzing each PPAP, we identified phenotype sub-networks consisting of the largest numbers of phenotypes and distinct biological systems. Furthermore, using hierarchical clustering based on phenotype similarities among the 345 PPAPs, we identified seven community types within a putative phenome-wide association network. Moreover, to promote leverage of these data, we developed and published web-application tools. These mouse phenome-wide phenotype-phenotype association data reveal general principles of relationships among mammalian phenotypes and provide a reference resource for biomedical analyses.
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6
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Vos RA, Katayama T, Mishima H, Kawano S, Kawashima S, Kim JD, Moriya Y, Tokimatsu T, Yamaguchi A, Yamamoto Y, Wu H, Amstutz P, Antezana E, Aoki NP, Arakawa K, Bolleman JT, Bolton E, Bonnal RJP, Bono H, Burger K, Chiba H, Cohen KB, Deutsch EW, Fernández-Breis JT, Fu G, Fujisawa T, Fukushima A, García A, Goto N, Groza T, Hercus C, Hoehndorf R, Itaya K, Juty N, Kawashima T, Kim JH, Kinjo AR, Kotera M, Kozaki K, Kumagai S, Kushida T, Lütteke T, Matsubara M, Miyamoto J, Mohsen A, Mori H, Naito Y, Nakazato T, Nguyen-Xuan J, Nishida K, Nishida N, Nishide H, Ogishima S, Ohta T, Okuda S, Paten B, Perret JL, Prathipati P, Prins P, Queralt-Rosinach N, Shinmachi D, Suzuki S, Tabata T, Takatsuki T, Taylor K, Thompson M, Uchiyama I, Vieira B, Wei CH, Wilkinson M, Yamada I, Yamanaka R, Yoshitake K, Yoshizawa AC, Dumontier M, Kosaki K, Takagi T. BioHackathon 2015: Semantics of data for life sciences and reproducible research. F1000Res 2020; 9:136. [PMID: 32308977 PMCID: PMC7141167 DOI: 10.12688/f1000research.18236.1] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 02/05/2020] [Indexed: 01/08/2023] Open
Abstract
We report on the activities of the 2015 edition of the BioHackathon, an annual event that brings together researchers and developers from around the world to develop tools and technologies that promote the reusability of biological data. We discuss issues surrounding the representation, publication, integration, mining and reuse of biological data and metadata across a wide range of biomedical data types of relevance for the life sciences, including chemistry, genotypes and phenotypes, orthology and phylogeny, proteomics, genomics, glycomics, and metabolomics. We describe our progress to address ongoing challenges to the reusability and reproducibility of research results, and identify outstanding issues that continue to impede the progress of bioinformatics research. We share our perspective on the state of the art, continued challenges, and goals for future research and development for the life sciences Semantic Web.
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Affiliation(s)
- Rutger A. Vos
- Institute of Biology Leiden, Leiden University, Leiden, The Netherlands
- Naturalis Biodiversity Center, Leiden, The Netherlands
| | | | - Hiroyuki Mishima
- Department of Human Genetics, Nagasaki University Graduate School of Biomedical Sciences, Nagasaki, Japan
| | - Shin Kawano
- Database Center for Life Science, Tokyo, Japan
| | | | | | - Yuki Moriya
- Database Center for Life Science, Tokyo, Japan
| | | | | | | | - Hongyan Wu
- Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | | | - Erick Antezana
- Department of Biology, Norwegian University of Science and Technology, Trondheim, Norway
| | - Nobuyuki P. Aoki
- Faculty of Science and Engineering, SOKA University, Tokyo, Japan
| | - Kazuharu Arakawa
- Institute for Advanced Biosciences, Keio University, Tokyo, Japan
| | - Jerven T. Bolleman
- SIB Swiss Institute of Bioinformatics, Centre Medical Universitaire, Lausanne, Switzerland
| | - Evan Bolton
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, USA
| | - Raoul J. P. Bonnal
- Istituto Nazionale Genetica Molecolare, Romeo ed Enrica Invernizzi, Milan, Italy
| | | | - Kees Burger
- Dutch Techcentre for Life Sciences, Utrecht, The Netherlands
| | - Hirokazu Chiba
- National Institute for Basic Biology, National Institutes of Natural Sciences, Okazaki, Japan
| | - Kevin B. Cohen
- Computational Bioscience Program, University of Colorado School of Medicine, Denver, USA
- Université Paris-Saclay, LIMSI, CNRS, Paris, France
| | | | | | - Gang Fu
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, USA
| | | | | | | | - Naohisa Goto
- Research Institute for Microbial Diseases, Osaka University, Osaka, Japan
| | - Tudor Groza
- St Vincent's Clinical School, Faculty of Medicine, University of New South Wales, Darlinghurst, Australia
- Kinghorn Centre for Clinical Genomics, Garvan Institute of Medical Research, Darlinghurst, Australia
| | - Colin Hercus
- Novocraft Technologies Sdn. Bhd., Selangor, Malaysia
| | - Robert Hoehndorf
- Computational Bioscience Research Center, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia
| | - Kotone Itaya
- Institute for Advanced Biosciences, Keio University, Tokyo, Japan
| | - Nick Juty
- European Molecular Biology Laboratory, European Bioinformatics Institute, Hinxton, UK
| | | | - Jee-Hyub Kim
- European Molecular Biology Laboratory, European Bioinformatics Institute, Hinxton, UK
| | - Akira R. Kinjo
- Institute for Protein Research, Osaka University, Osaka, Japan
| | - Masaaki Kotera
- School of Life Science and Technology, Tokyo Institute of Technology, Tokyo, Japan
| | - Kouji Kozaki
- The Institute of Scientific and Industrial Research, Osaka University, Osaka, Japan
| | | | - Tatsuya Kushida
- National Bioscience Database Center, Japan Science and Technology Agency, Tokyo, Japan
| | - Thomas Lütteke
- Institute of Veterinary Physiology and Biochemistry, Justus-Liebig University Giessen, Giessen, Germany
- Gesellschaft für innovative Personalwirtschaftssysteme mbH (GIP GmbH), Offenbach, Germany
| | | | | | - Attayeb Mohsen
- National Institutes of Biomedical Innovation, Health and Nutrition, Osaka, Japan
| | - Hiroshi Mori
- Center for Information Biology, National Institute of Genetics, Mishima, Japan
| | - Yuki Naito
- Database Center for Life Science, Tokyo, Japan
| | | | | | | | - Naoki Nishida
- Department of Systems Science, Osaka University, Osaka, Japan
| | - Hiroyo Nishide
- National Institute for Basic Biology, National Institutes of Natural Sciences, Okazaki, Japan
| | - Soichi Ogishima
- Tohoku Medical Megabank Organization, Tohoku University, Sendai, Japan
| | - Tazro Ohta
- Database Center for Life Science, Tokyo, Japan
| | - Shujiro Okuda
- Niigata University Graduate School of Medical and Dental Sciences, Niigata, Japan
| | - Benedict Paten
- UC Santa Cruz Genomics Institute, University of California, Santa Cruz, USA
| | | | - Philip Prathipati
- National Institutes of Biomedical Innovation, Health and Nutrition, Osaka, Japan
| | - Pjotr Prins
- University Medical Center Utrecht, Utrecht, The Netherlands
- University of Tennessee Health Science Center, Memphis, USA
| | - Núria Queralt-Rosinach
- Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, USA
| | | | - Shinya Suzuki
- School of Life Science and Technology, Tokyo Institute of Technology, Tokyo, Japan
| | - Tsuyosi Tabata
- Graduate School of Pharmaceutical Sciences, Kyoto University, Kyoto, Japan
| | | | - Kieron Taylor
- European Molecular Biology Laboratory, European Bioinformatics Institute, Hinxton, UK
| | - Mark Thompson
- Leiden University Medical Center, Leiden, The Netherlands
| | - Ikuo Uchiyama
- National Institute for Basic Biology, National Institutes of Natural Sciences, Okazaki, Japan
| | - Bruno Vieira
- WurmLab, School of Biological & Chemical Sciences, Queen Mary University of London, London, UK
| | - Chih-Hsuan Wei
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, USA
| | - Mark Wilkinson
- Escuela Técnica Superior de Ingeniería Agronómica, Alimentaria y de Biosistemas, Universidad Politécnica de Madrid, Madrid, Spain
| | | | | | - Kazutoshi Yoshitake
- Graduate School of Agricultural and Life Sciences, The University of Tokyo, Tokyo, Japan
| | | | - Michel Dumontier
- Institute of Data Science, Maastricht University, Maastricht, The Netherlands
| | - Kenjiro Kosaki
- Center for Medical Genetics, Keio University School of Medicine, Tokyo, Japan
| | - Toshihisa Takagi
- National Bioscience Database Center, Japan Science and Technology Agency, Tokyo, Japan
- Department of Biological Sciences, Graduate School of Science, The University of Tokyo, Tokyo, Japan
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Hyung D, Mallon AM, Kyung DS, Cho SY, Seong JK. TarGo: network based target gene selection system for human disease related mouse models. Lab Anim Res 2019; 35:23. [PMID: 32257911 PMCID: PMC7081697 DOI: 10.1186/s42826-019-0023-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2019] [Accepted: 10/21/2019] [Indexed: 11/25/2022] Open
Abstract
Genetically engineered mouse models are used in high-throughput phenotyping screens to understand genotype-phenotype associations and their relevance to human diseases. However, not all mutant mouse lines with detectable phenotypes are associated with human diseases. Here, we propose the “Target gene selection system for Genetically engineered mouse models” (TarGo). Using a combination of human disease descriptions, network topology, and genotype-phenotype correlations, novel genes that are potentially related to human diseases are suggested. We constructed a gene interaction network using protein-protein interactions, molecular pathways, and co-expression data. Several repositories for human disease signatures were used to obtain information on human disease-related genes. We calculated disease- or phenotype-specific gene ranks using network topology and disease signatures. In conclusion, TarGo provides many novel features for gene function prediction.
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Affiliation(s)
- Daejin Hyung
- 1National Cancer Center, 323 Ilsan-ro, Goyang-si, Kyeonggi-do 10408 Republic of Korea
| | - Ann-Marie Mallon
- 2MRC Harwell Institute, Mammalian Genetics Unit, Oxfordshire, OX11 0RD UK
| | - Dong Soo Kyung
- 3Laboratory of Developmental Biology and Genomics, Research Institute for Veterinary Science, and BK21 Plus Program for Creative Veterinary Science, College of Veterinary Medicine, Seoul National University, Seoul, 08826 Republic of Korea.,4Korea Mouse Phenotyping Center (KMPC), Seoul National University, Seoul, 08826 Republic of Korea.,5Interdisciplinary Program for Bioinformatics, Program for Cancer Biology and BIO-MAX institute, Seoul National University, Seoul, 08826 Republic of Korea
| | - Soo Young Cho
- 1National Cancer Center, 323 Ilsan-ro, Goyang-si, Kyeonggi-do 10408 Republic of Korea.,4Korea Mouse Phenotyping Center (KMPC), Seoul National University, Seoul, 08826 Republic of Korea
| | - Je Kyung Seong
- 3Laboratory of Developmental Biology and Genomics, Research Institute for Veterinary Science, and BK21 Plus Program for Creative Veterinary Science, College of Veterinary Medicine, Seoul National University, Seoul, 08826 Republic of Korea.,4Korea Mouse Phenotyping Center (KMPC), Seoul National University, Seoul, 08826 Republic of Korea.,5Interdisciplinary Program for Bioinformatics, Program for Cancer Biology and BIO-MAX institute, Seoul National University, Seoul, 08826 Republic of Korea
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8
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Alshahrani M, Hoehndorf R. Semantic Disease Gene Embeddings (SmuDGE): phenotype-based disease gene prioritization without phenotypes. Bioinformatics 2019; 34:i901-i907. [PMID: 30423077 PMCID: PMC6129260 DOI: 10.1093/bioinformatics/bty559] [Citation(s) in RCA: 32] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2023] Open
Abstract
Motivation In the past years, several methods have been developed to incorporate information about phenotypes into computational disease gene prioritization methods. These methods commonly compute the similarity between a disease’s (or patient’s) phenotypes and a database of gene-to-phenotype associations to find the phenotypically most similar match. A key limitation of these methods is their reliance on knowledge about phenotypes associated with particular genes which is highly incomplete in humans as well as in many model organisms such as the mouse. Results We developed SmuDGE, a method that uses feature learning to generate vector-based representations of phenotypes associated with an entity. SmuDGE can be used as a trainable semantic similarity measure to compare two sets of phenotypes (such as between a disease and gene, or a disease and patient). More importantly, SmuDGE can generate phenotype representations for entities that are only indirectly associated with phenotypes through an interaction network; for this purpose, SmuDGE exploits background knowledge in interaction networks comprised of multiple types of interactions. We demonstrate that SmuDGE can match or outperform semantic similarity in phenotype-based disease gene prioritization, and furthermore significantly extends the coverage of phenotype-based methods to all genes in a connected interaction network. Availability and implementation https://github.com/bio-ontology-research-group/SmuDGE
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Affiliation(s)
- Mona Alshahrani
- Computer, Electrical and Mathematical Sciences and Engineering Division, Computational Bioscience Research Center, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia
| | - Robert Hoehndorf
- Computer, Electrical and Mathematical Sciences and Engineering Division, Computational Bioscience Research Center, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia
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9
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Cornish AJ, David A, Sternberg MJE. PhenoRank: reducing study bias in gene prioritization through simulation. Bioinformatics 2019; 34:2087-2095. [PMID: 29360927 PMCID: PMC5949213 DOI: 10.1093/bioinformatics/bty028] [Citation(s) in RCA: 29] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2017] [Accepted: 01/16/2018] [Indexed: 02/07/2023] Open
Abstract
Motivation Genome-wide association studies have identified thousands of loci associated with human disease, but identifying the causal genes at these loci is often difficult. Several methods prioritize genes most likely to be disease causing through the integration of biological data, including protein-protein interaction and phenotypic data. Data availability is not the same for all genes however, potentially influencing the performance of these methods. Results We demonstrate that whilst disease genes tend to be associated with greater numbers of data, this may be at least partially a result of them being better studied. With this observation we develop PhenoRank, which prioritizes disease genes whilst avoiding being biased towards genes with more available data. Bias is avoided by comparing gene scores generated for the query disease against gene scores generated using simulated sets of phenotype terms, which ensures that differences in data availability do not affect the ranking of genes. We demonstrate that whilst existing prioritization methods are biased by data availability, PhenoRank is not similarly biased. Avoiding this bias allows PhenoRank to effectively prioritize genes with fewer available data and improves its overall performance. PhenoRank outperforms three available prioritization methods in cross-validation (PhenoRank area under receiver operating characteristic curve [AUC]=0.89, DADA AUC = 0.87, EXOMISER AUC = 0.71, PRINCE AUC = 0.83, P < 2.2 × 10-16). Availability and implementation PhenoRank is freely available for download at https://github.com/alexjcornish/PhenoRank. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Alex J Cornish
- Department of Life Sciences, Center of Bioinformatics and Systems Biology, Imperial College London, London, UK
| | - Alessia David
- Department of Life Sciences, Center of Bioinformatics and Systems Biology, Imperial College London, London, UK
| | - Michael J E Sternberg
- Department of Life Sciences, Center of Bioinformatics and Systems Biology, Imperial College London, London, UK
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10
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Köhler S, Vasilevsky NA, Engelstad M, Foster E, McMurry J, Aymé S, Baynam G, Bello SM, Boerkoel CF, Boycott KM, Brudno M, Buske OJ, Chinnery PF, Cipriani V, Connell LE, Dawkins HJS, DeMare LE, Devereau AD, de Vries BBA, Firth HV, Freson K, Greene D, Hamosh A, Helbig I, Hum C, Jähn JA, James R, Krause R, F Laulederkind SJ, Lochmüller H, Lyon GJ, Ogishima S, Olry A, Ouwehand WH, Pontikos N, Rath A, Schaefer F, Scott RH, Segal M, Sergouniotis PI, Sever R, Smith CL, Straub V, Thompson R, Turner C, Turro E, Veltman MWM, Vulliamy T, Yu J, von Ziegenweidt J, Zankl A, Züchner S, Zemojtel T, Jacobsen JOB, Groza T, Smedley D, Mungall CJ, Haendel M, Robinson PN. The Human Phenotype Ontology in 2017. Nucleic Acids Res 2016; 45:D865-D876. [PMID: 27899602 PMCID: PMC5210535 DOI: 10.1093/nar/gkw1039] [Citation(s) in RCA: 498] [Impact Index Per Article: 62.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2016] [Accepted: 10/28/2016] [Indexed: 12/14/2022] Open
Abstract
Deep phenotyping has been defined as the precise and comprehensive analysis of phenotypic abnormalities in which the individual components of the phenotype are observed and described. The three components of the Human Phenotype Ontology (HPO; www.human-phenotype-ontology.org) project are the phenotype vocabulary, disease-phenotype annotations and the algorithms that operate on these. These components are being used for computational deep phenotyping and precision medicine as well as integration of clinical data into translational research. The HPO is being increasingly adopted as a standard for phenotypic abnormalities by diverse groups such as international rare disease organizations, registries, clinical labs, biomedical resources, and clinical software tools and will thereby contribute toward nascent efforts at global data exchange for identifying disease etiologies. This update article reviews the progress of the HPO project since the debut Nucleic Acids Research database article in 2014, including specific areas of expansion such as common (complex) disease, new algorithms for phenotype driven genomic discovery and diagnostics, integration of cross-species mapping efforts with the Mammalian Phenotype Ontology, an improved quality control pipeline, and the addition of patient-friendly terminology.
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Affiliation(s)
- Sebastian Köhler
- Institute for Medical Genetics and Human Genetics, Charité-Universitätsmedizin Berlin, Augustenburger Platz 1, 13353 Berlin, Germany
| | - Nicole A Vasilevsky
- Library and Department of Medical Informatics and Clinical Epidemiology, Oregon Health & Science University, Portland, OR 97239, USA
| | - Mark Engelstad
- Library and Department of Medical Informatics and Clinical Epidemiology, Oregon Health & Science University, Portland, OR 97239, USA
| | - Erin Foster
- Library and Department of Medical Informatics and Clinical Epidemiology, Oregon Health & Science University, Portland, OR 97239, USA
| | - Julie McMurry
- Library and Department of Medical Informatics and Clinical Epidemiology, Oregon Health & Science University, Portland, OR 97239, USA
| | - Ségolène Aymé
- Institut du Cerveau et de la Moelle épinière-ICM, CNRS UMR 7225-Inserm U 1127-UPMC-P6 UMR S 1127, Hôpital Pitié-Salpêtrière, 47, bd de l'Hôpital, 75013 Paris, France
| | - Gareth Baynam
- Western Australian Register of Developmental Anomalies and Genetic Services of Western Australia, King Edward Memorial Hospital Department of Health, Government of Western Australia, Perth, WA 6008, Australia.,School of Paediatrics and Child Health, University of Western Australia, Perth, WA 6008, Australia
| | - Susan M Bello
- The Jackson Laboratory, 600 Main St, Bar Harbor, ME 04609, USA
| | - Cornelius F Boerkoel
- Imagenetics Research, Sanford Health, PO Box 5039, Route 5001, Sioux Falls, SD 57117-5039, USA
| | - Kym M Boycott
- Children's Hospital of Eastern Ontario Research Institute, University of Ottawa, Ottawa, Ontario, Canada
| | - Michael Brudno
- Department of Computer Science, University of Toronto, Toronto, ON M5S 2E4, Canada Centre for Computational Medicine, Hospital for Sick Children, Toronto, ON M5G 1L7, Canada
| | - Orion J Buske
- Department of Computer Science, University of Toronto, Toronto, ON M5S 2E4, Canada Centre for Computational Medicine, Hospital for Sick Children, Toronto, ON M5G 1L7, Canada
| | - Patrick F Chinnery
- Department of Clinical Neurosciences, School of Clinical Medicine, University of Cambridge, Cambridge CB2 0QQ, UK.,NIHR Rare Diseases Translational Research Collaboration, Cambridge Biomedical Campus, Cambridge CB2 0QQ, UK
| | - Valentina Cipriani
- UCL Institute of Ophthalmology, Department of Ocular Biology and Therapeutics, 11-43 Bath Street, London EC1V 9EL, UK.,UCL Genetics Institute, University College London, London WC1E 6BT, UK
| | | | - Hugh J S Dawkins
- Office of Population Health Genomics, Public Health Division, Health Department of Western Australia, 189 Royal Street, Perth, WA, 6004 Australia
| | - Laura E DeMare
- Cold Spring Harbor Laboratory Press, Cold Spring Harbor, NY, USA
| | - Andrew D Devereau
- Genomics England, Queen Mary University of London, Dawson Hall, Charterhouse Square, London EC1M 6BQ, UK
| | - Bert B A de Vries
- Department of Human Genetics, Radboud University, University Medical Centre, Nijmegen, The Netherlands
| | - Helen V Firth
- Wellcome Trust Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SA, UK
| | - Kathleen Freson
- Department of Cardiovascular Sciences, Center for Molecular and Vascular Biology, University of Leuven, Leuven, Belgium
| | - Daniel Greene
- Department of Haematology, University of Cambridge, NHS Blood and Transplant Centre, Long Road, Cambridge CB2 0PT, UK.,Medical Research Council Biostatistics Unit, Cambridge Institute of Public Health, Cambridge Biomedical Campus, Cambridge, UK
| | - Ada Hamosh
- McKusick-Nathans Institute of Genetic Medicine, Department of Pediatrics, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Ingo Helbig
- Division of Neurology, The Children's Hospital of Philadelphia, 3501 Civic Center Blvd, Philadelphia, PA 19104, USA.,Department of Neuropediatrics, University Medical Center Schleswig-Holstein (UKSH), Kiel, Germany
| | - Courtney Hum
- Centre for Computational Medicine, The Hospital for Sick Children, Toronto, ON M5G 1H3, Canada
| | - Johanna A Jähn
- Department of Neuropediatrics, University Medical Center Schleswig-Holstein (UKSH), Kiel, Germany
| | - Roger James
- NIHR Rare Diseases Translational Research Collaboration, Cambridge Biomedical Campus, Cambridge CB2 0QQ, UK.,Medical Research Council Biostatistics Unit, Cambridge Institute of Public Health, Cambridge Biomedical Campus, Cambridge, UK
| | - Roland Krause
- LuxembourgCentre for Systems Biomedicine, University of Luxembourg, 7, avenue des Hauts-Fourneaux, L-4362 Esch-sur-Alzette, Luxembourg
| | | | - Hanns Lochmüller
- John Walton Muscular Dystrophy Research Centre, MRC Centre for Neuromuscular Diseases, Institute of Genetic Medicine, University of Newcastle, Newcastle upon Tyne, UK
| | - Gholson J Lyon
- Stanley Institute for Cognitive Genomics, Cold Spring Harbor Laboratory, New York, NY 11797, USA
| | - Soichi Ogishima
- Dept of Bioclinical Informatics, Tohoku Medical Megabank Organization, Tohoku University, Tohoku Medical Megabank Organization Bldg 7F room #741,736, Seiryo 2-1, Aoba-ku, Sendai Miyagi 980-8573 Japan
| | - Annie Olry
- Orphanet-INSERM, US14, Plateforme Maladies Rares, 96 rue Didot, 75014 Paris, France
| | - Willem H Ouwehand
- Medical Research Council Biostatistics Unit, Cambridge Institute of Public Health, Cambridge Biomedical Campus, Cambridge, UK
| | - Nikolas Pontikos
- UCL Institute of Ophthalmology, Department of Ocular Biology and Therapeutics, 11-43 Bath Street, London EC1V 9EL, UK.,UCL Genetics Institute, University College London, London WC1E 6BT, UK
| | - Ana Rath
- Orphanet-INSERM, US14, Plateforme Maladies Rares, 96 rue Didot, 75014 Paris, France
| | - Franz Schaefer
- Division of Pediatric Nephrology and KFH Children's Kidney Center, Center for Pediatrics and Adolescent Medicine, 69120 Heidelberg, Germany
| | - Richard H Scott
- Genomics England, Queen Mary University of London, Dawson Hall, Charterhouse Square, London EC1M 6BQ, UK
| | - Michael Segal
- SimulConsult Inc., 27 Crafts Road, Chestnut Hill, MA 02467, USA
| | | | - Richard Sever
- Cold Spring Harbor Laboratory Press, Cold Spring Harbor, NY, USA
| | - Cynthia L Smith
- The Jackson Laboratory, 600 Main St, Bar Harbor, ME 04609, USA
| | - Volker Straub
- John Walton Muscular Dystrophy Research Centre, MRC Centre for Neuromuscular Diseases, Institute of Genetic Medicine, University of Newcastle, Newcastle upon Tyne, UK
| | - Rachel Thompson
- John Walton Muscular Dystrophy Research Centre, MRC Centre for Neuromuscular Diseases, Institute of Genetic Medicine, University of Newcastle, Newcastle upon Tyne, UK
| | - Catherine Turner
- John Walton Muscular Dystrophy Research Centre, MRC Centre for Neuromuscular Diseases, Institute of Genetic Medicine, University of Newcastle, Newcastle upon Tyne, UK
| | - Ernest Turro
- Department of Haematology, University of Cambridge, NHS Blood and Transplant Centre, Long Road, Cambridge CB2 0PT, UK.,Medical Research Council Biostatistics Unit, Cambridge Institute of Public Health, Cambridge Biomedical Campus, Cambridge, UK
| | - Marijcke W M Veltman
- NIHR Rare Diseases Translational Research Collaboration, Cambridge Biomedical Campus, Cambridge CB2 0QQ, UK
| | - Tom Vulliamy
- Blizard Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London E1 2AT, UK
| | - Jing Yu
- Nuffield Department of Clinical Neurosciences, University of Oxford, Level 6, West Wing, John Radcliffe Hospital, Oxford OX3 9DU, UK
| | - Julie von Ziegenweidt
- Department of Haematology, University of Cambridge, NHS Blood and Transplant Centre, Long Road, Cambridge CB2 0PT, UK
| | - Andreas Zankl
- Discipline of Genetic Medicine, Sydney Medical School, The University of Sydney, Australia.,Academic Department of Medical Genetics, Sydney Childrens Hospitals Network (Westmead), Australia
| | - Stephan Züchner
- JD McDonald Department of Human Genetics and Hussman Institute for Human Genomics, University of Miami, Miami, FL, USA
| | - Tomasz Zemojtel
- Institute for Medical Genetics and Human Genetics, Charité-Universitätsmedizin Berlin, Augustenburger Platz 1, 13353 Berlin, Germany
| | - Julius O B Jacobsen
- Genomics England, Queen Mary University of London, Dawson Hall, Charterhouse Square, London EC1M 6BQ, UK
| | - Tudor Groza
- Garvan Institute of Medical Research, Darlinghurst, Sydney, NSW 2010, Australia.,St Vincent's Clinical School, Faculty of Medicine, UNSW Australia
| | - Damian Smedley
- Genomics England, Queen Mary University of London, Dawson Hall, Charterhouse Square, London EC1M 6BQ, UK
| | - Christopher J Mungall
- Environmental Genomics and Systems Biology Division, Lawrence Berkeley National Laboratory, 1 Cyclotron Road, Berkeley, CA 94720, USA
| | - Melissa Haendel
- Library and Department of Medical Informatics and Clinical Epidemiology, Oregon Health & Science University, Portland, OR 97239, USA
| | - Peter N Robinson
- The Jackson Laboratory for Genomic Medicine, 10 Discovery Drive, Farmington, CT 06032, USA .,Institute for Systems Genomics, University of Connecticut, Farmington, CT 06032, USA
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11
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Buske OJ, Girdea M, Dumitriu S, Gallinger B, Hartley T, Trang H, Misyura A, Friedman T, Beaulieu C, Bone WP, Links AE, Washington NL, Haendel MA, Robinson PN, Boerkoel CF, Adams D, Gahl WA, Boycott KM, Brudno M. PhenomeCentral: a portal for phenotypic and genotypic matchmaking of patients with rare genetic diseases. Hum Mutat 2015; 36:931-40. [PMID: 26251998 DOI: 10.1002/humu.22851] [Citation(s) in RCA: 101] [Impact Index Per Article: 11.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2015] [Accepted: 07/28/2015] [Indexed: 01/18/2023]
Abstract
The discovery of disease-causing mutations typically requires confirmation of the variant or gene in multiple unrelated individuals, and a large number of rare genetic diseases remain unsolved due to difficulty identifying second families. To enable the secure sharing of case records by clinicians and rare disease scientists, we have developed the PhenomeCentral portal (https://phenomecentral.org). Each record includes a phenotypic description and relevant genetic information (exome or candidate genes). PhenomeCentral identifies similar patients in the database based on semantic similarity between clinical features, automatically prioritized genes from whole-exome data, and candidate genes entered by the users, enabling both hypothesis-free and hypothesis-driven matchmaking. Users can then contact other submitters to follow up on promising matches. PhenomeCentral incorporates data for over 1,000 patients with rare genetic diseases, contributed by the FORGE and Care4Rare Canada projects, the US NIH Undiagnosed Diseases Program, the EU Neuromics and ANDDIrare projects, as well as numerous independent clinicians and scientists. Though the majority of these records have associated exome data, most lack a molecular diagnosis. PhenomeCentral has already been used to identify causative mutations for several patients, and its ability to find matching patients and diagnose these diseases will grow with each additional patient that is entered.
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Affiliation(s)
- Orion J Buske
- Department of Computer Science, University of Toronto, Toronto, Canada.,Genetics and Genome Biology Program, The Hospital for Sick Children, Toronto, Canada.,Centre for Computational Medicine, The Hospital for Sick Children, Toronto, Canada
| | - Marta Girdea
- Department of Computer Science, University of Toronto, Toronto, Canada.,Genetics and Genome Biology Program, The Hospital for Sick Children, Toronto, Canada.,Centre for Computational Medicine, The Hospital for Sick Children, Toronto, Canada
| | - Sergiu Dumitriu
- Centre for Computational Medicine, The Hospital for Sick Children, Toronto, Canada
| | - Bailey Gallinger
- Centre for Computational Medicine, The Hospital for Sick Children, Toronto, Canada.,Division of Clinical and Metabolic Genetics, The Hospital for Sick Children, Toronto, Ontario, Canada
| | - Taila Hartley
- Children's Hospital of Eastern Ontario Research Institute, Ottawa, Ontario, Canada
| | - Heather Trang
- Centre for Computational Medicine, The Hospital for Sick Children, Toronto, Canada.,Division of Clinical and Metabolic Genetics, The Hospital for Sick Children, Toronto, Ontario, Canada
| | - Andriy Misyura
- Centre for Computational Medicine, The Hospital for Sick Children, Toronto, Canada
| | - Tal Friedman
- Department of Computer Science, University of Toronto, Toronto, Canada
| | - Chandree Beaulieu
- Children's Hospital of Eastern Ontario Research Institute, Ottawa, Ontario, Canada
| | - William P Bone
- Undiagnosed Diseases Program, Common Fund, Office of the Director, National Institutes of Health, Bethesda, Maryland
| | - Amanda E Links
- Undiagnosed Diseases Program, Common Fund, Office of the Director, National Institutes of Health, Bethesda, Maryland
| | - Nicole L Washington
- Genomics Division, Lawrence Berkeley National Laboratory, Berkeley, California
| | - Melissa A Haendel
- Department of Medical Informatics and Clinical Epidemiology, Oregon Health & Science University, Portland, Oregon
| | - Peter N Robinson
- Institute for Medical Genetics and Human Genetics, Charité-Universitätsmedizin Berlin, Berlin, Germany
| | - Cornelius F Boerkoel
- Undiagnosed Diseases Program, Common Fund, Office of the Director, National Institutes of Health, Bethesda, Maryland
| | - David Adams
- Undiagnosed Diseases Program, Common Fund, Office of the Director, National Institutes of Health, Bethesda, Maryland
| | - William A Gahl
- Undiagnosed Diseases Program, Common Fund, Office of the Director, National Institutes of Health, Bethesda, Maryland
| | - Kym M Boycott
- Children's Hospital of Eastern Ontario Research Institute, Ottawa, Ontario, Canada
| | - Michael Brudno
- Department of Computer Science, University of Toronto, Toronto, Canada.,Genetics and Genome Biology Program, The Hospital for Sick Children, Toronto, Canada.,Centre for Computational Medicine, The Hospital for Sick Children, Toronto, Canada
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12
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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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13
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Analysis of the human diseasome using phenotype similarity between common, genetic, and infectious diseases. Sci Rep 2015; 5:10888. [PMID: 26051359 PMCID: PMC4458913 DOI: 10.1038/srep10888] [Citation(s) in RCA: 72] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2014] [Accepted: 04/22/2015] [Indexed: 01/29/2023] Open
Abstract
Phenotypes are the observable characteristics of an organism arising from its response to the environment. Phenotypes associated with engineered and natural genetic variation are widely recorded using phenotype ontologies in model organisms, as are signs and symptoms of human Mendelian diseases in databases such as OMIM and Orphanet. Exploiting these resources, several computational methods have been developed for integration and analysis of phenotype data to identify the genetic etiology of diseases or suggest plausible interventions. A similar resource would be highly useful not only for rare and Mendelian diseases, but also for common, complex and infectious diseases. We apply a semantic text-mining approach to identify the phenotypes (signs and symptoms) associated with over 6,000 diseases. We evaluate our text-mined phenotypes by demonstrating that they can correctly identify known disease-associated genes in mice and humans with high accuracy. Using a phenotypic similarity measure, we generate a human disease network in which diseases that have similar signs and symptoms cluster together, and we use this network to identify closely related diseases based on common etiological, anatomical as well as physiological underpinnings.
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14
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Groza T, Tudorache T, Robinson PN, Zankl A. Capturing domain knowledge from multiple sources: the rare bone disorders use case. J Biomed Semantics 2015; 6:21. [PMID: 25926964 PMCID: PMC4414390 DOI: 10.1186/s13326-015-0008-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2014] [Accepted: 03/02/2015] [Indexed: 12/13/2022] Open
Abstract
Background Lately, ontologies have become a fundamental building block in the process of formalising and storing complex biomedical information. The community-driven ontology curation process, however, ignores the possibility of multiple communities building, in parallel, conceptualisations of the same domain, and thus providing slightly different perspectives on the same knowledge. The individual nature of this effort leads to the need of a mechanism to enable us to create an overarching and comprehensive overview of the different perspectives on the domain knowledge. Results We introduce an approach that enables the loose integration of knowledge emerging from diverse sources under a single coherent interoperable resource. To accurately track the original knowledge statements, we record the provenance at very granular levels. We exemplify the approach in the rare bone disorders domain by proposing the Rare Bone Disorders Ontology (RBDO). Using RBDO, researchers are able to answer queries, such as: “What phenotypes describe a particular disorder and are common to all sources?” or to understand similarities between disorders based on divergent groupings (classifications) provided by the underlying sources. Availability RBDO is available at http://purl.org/skeletome/rbdo. In order to support lightweight query and integration, the knowledge captured by RBDO has also been made available as a SPARQL Endpoint at http://bio-lark.org/se_skeldys.html.
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Affiliation(s)
- Tudor Groza
- School of ITEE, The University of Queensland, St Lucia, Australia
| | - Tania Tudorache
- Stanford Center for Biomedical Informatics Research, Stanford University, Stanford, USA
| | - Peter N Robinson
- Institut für Medizinische Genetik und Humangenetik, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Andreas Zankl
- Children's Hospital, Westmead, The University of Sydney, Sydney, New South Wales Australia
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15
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Groza T, Köhler S, Doelken S, Collier N, Oellrich A, Smedley D, Couto FM, Baynam G, Zankl A, Robinson PN. Automatic concept recognition using the human phenotype ontology reference and test suite corpora. DATABASE-THE JOURNAL OF BIOLOGICAL DATABASES AND CURATION 2015; 2015:bav005. [PMID: 25725061 PMCID: PMC4343077 DOI: 10.1093/database/bav005] [Citation(s) in RCA: 38] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/06/2023]
Abstract
Concept recognition tools rely on the availability of textual corpora to assess their performance and enable the identification of areas for improvement. Typically, corpora are developed for specific purposes, such as gene name recognition. Gene and protein name identification are longstanding goals of biomedical text mining, and therefore a number of different corpora exist. However, phenotypes only recently became an entity of interest for specialized concept recognition systems, and hardly any annotated text is available for performance testing and training. Here, we present a unique corpus, capturing text spans from 228 abstracts manually annotated with Human Phenotype Ontology (HPO) concepts and harmonized by three curators, which can be used as a reference standard for free text annotation of human phenotypes. Furthermore, we developed a test suite for standardized concept recognition error analysis, incorporating 32 different types of test cases corresponding to 2164 HPO concepts. Finally, three established phenotype concept recognizers (NCBO Annotator, OBO Annotator and Bio-LarK CR) were comprehensively evaluated, and results are reported against both the text corpus and the test suites. The gold standard and test suites corpora are available from http://bio-lark.org/hpo_res.html. Database URL:http://bio-lark.org/hpo_res.html
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Affiliation(s)
- Tudor Groza
- School of ITEE, The University of Queensland, St. Lucia, QLD 4072, Australia, Garvan Institute of Medical Research, Darlinghurst, Sydney, NSW 2010, Australia, Institute for Medical Genetics and Human Genetics, Charité-Universitätsmedizin Berlin, 13353 Berlin, Germany, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge, UK, National Institute of Informatics, Hitotsubashi, Tokyo, Japan, Mouse Informatics Group, Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton CB10 1SA, UK, LASIGE, Departamento de Informática, Faculdade de Ciências, Universidade de Lisboa, 1749-016 Lisboa, Portugal, Genetic Services of Western Australia, King Edward Memorial Hospital, WA 6008, Australia, School of Paediatrics and Child Health, University of Western Australia, WA 6008, Australia, Institute for Immunology and Infectious Diseases, Murdoch University, WA 6150, Australia, Office of Population Health, Public Health and Clinical Services Division, Western Australian Department of Health, WA 6004, Australia, Academic Department of Medical Genetics, Sydney Children's Hospitals Network (Westmead), NSW 2145, Australia, Discipline of Genetic Medicine, Sydney Medical School, The University of Sydney, NSW 2006, Australia, Max Planck Institute for Molecular Genetics, 14195 Berlin, Germany, Institute for Bioinformatics, Department of Mathematics and Computer Science, Freie Universität Berlin, 14195 Berlin, Germany and Berlin Brandenburg Center for Regenerative Therapies, 13353 Berlin, Germany School of ITEE, The University of Queensland, St. Lucia, QLD 4072, Australia, Garvan Institute of Medical Research, Darlinghurst, Sydney, NSW 2010, Australia, Institute for Medical Genetics and Human Genetics, Charité-Universitätsmedizin Berlin, 13353 Berlin, Germany, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge, UK, National Institute of Informatics, Hitotsubashi, Tokyo, Japan, Mouse Informa
| | - Sebastian Köhler
- School of ITEE, The University of Queensland, St. Lucia, QLD 4072, Australia, Garvan Institute of Medical Research, Darlinghurst, Sydney, NSW 2010, Australia, Institute for Medical Genetics and Human Genetics, Charité-Universitätsmedizin Berlin, 13353 Berlin, Germany, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge, UK, National Institute of Informatics, Hitotsubashi, Tokyo, Japan, Mouse Informatics Group, Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton CB10 1SA, UK, LASIGE, Departamento de Informática, Faculdade de Ciências, Universidade de Lisboa, 1749-016 Lisboa, Portugal, Genetic Services of Western Australia, King Edward Memorial Hospital, WA 6008, Australia, School of Paediatrics and Child Health, University of Western Australia, WA 6008, Australia, Institute for Immunology and Infectious Diseases, Murdoch University, WA 6150, Australia, Office of Population Health, Public Health and Clinical Services Division, Western Australian Department of Health, WA 6004, Australia, Academic Department of Medical Genetics, Sydney Children's Hospitals Network (Westmead), NSW 2145, Australia, Discipline of Genetic Medicine, Sydney Medical School, The University of Sydney, NSW 2006, Australia, Max Planck Institute for Molecular Genetics, 14195 Berlin, Germany, Institute for Bioinformatics, Department of Mathematics and Computer Science, Freie Universität Berlin, 14195 Berlin, Germany and Berlin Brandenburg Center for Regenerative Therapies, 13353 Berlin, Germany
| | - Sandra Doelken
- School of ITEE, The University of Queensland, St. Lucia, QLD 4072, Australia, Garvan Institute of Medical Research, Darlinghurst, Sydney, NSW 2010, Australia, Institute for Medical Genetics and Human Genetics, Charité-Universitätsmedizin Berlin, 13353 Berlin, Germany, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge, UK, National Institute of Informatics, Hitotsubashi, Tokyo, Japan, Mouse Informatics Group, Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton CB10 1SA, UK, LASIGE, Departamento de Informática, Faculdade de Ciências, Universidade de Lisboa, 1749-016 Lisboa, Portugal, Genetic Services of Western Australia, King Edward Memorial Hospital, WA 6008, Australia, School of Paediatrics and Child Health, University of Western Australia, WA 6008, Australia, Institute for Immunology and Infectious Diseases, Murdoch University, WA 6150, Australia, Office of Population Health, Public Health and Clinical Services Division, Western Australian Department of Health, WA 6004, Australia, Academic Department of Medical Genetics, Sydney Children's Hospitals Network (Westmead), NSW 2145, Australia, Discipline of Genetic Medicine, Sydney Medical School, The University of Sydney, NSW 2006, Australia, Max Planck Institute for Molecular Genetics, 14195 Berlin, Germany, Institute for Bioinformatics, Department of Mathematics and Computer Science, Freie Universität Berlin, 14195 Berlin, Germany and Berlin Brandenburg Center for Regenerative Therapies, 13353 Berlin, Germany
| | - Nigel Collier
- School of ITEE, The University of Queensland, St. Lucia, QLD 4072, Australia, Garvan Institute of Medical Research, Darlinghurst, Sydney, NSW 2010, Australia, Institute for Medical Genetics and Human Genetics, Charité-Universitätsmedizin Berlin, 13353 Berlin, Germany, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge, UK, National Institute of Informatics, Hitotsubashi, Tokyo, Japan, Mouse Informatics Group, Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton CB10 1SA, UK, LASIGE, Departamento de Informática, Faculdade de Ciências, Universidade de Lisboa, 1749-016 Lisboa, Portugal, Genetic Services of Western Australia, King Edward Memorial Hospital, WA 6008, Australia, School of Paediatrics and Child Health, University of Western Australia, WA 6008, Australia, Institute for Immunology and Infectious Diseases, Murdoch University, WA 6150, Australia, Office of Population Health, Public Health and Clinical Services Division, Western Australian Department of Health, WA 6004, Australia, Academic Department of Medical Genetics, Sydney Children's Hospitals Network (Westmead), NSW 2145, Australia, Discipline of Genetic Medicine, Sydney Medical School, The University of Sydney, NSW 2006, Australia, Max Planck Institute for Molecular Genetics, 14195 Berlin, Germany, Institute for Bioinformatics, Department of Mathematics and Computer Science, Freie Universität Berlin, 14195 Berlin, Germany and Berlin Brandenburg Center for Regenerative Therapies, 13353 Berlin, Germany School of ITEE, The University of Queensland, St. Lucia, QLD 4072, Australia, Garvan Institute of Medical Research, Darlinghurst, Sydney, NSW 2010, Australia, Institute for Medical Genetics and Human Genetics, Charité-Universitätsmedizin Berlin, 13353 Berlin, Germany, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge, UK, National Institute of Informatics, Hitotsubashi, Tokyo, Japan, Mouse Informa
| | - Anika Oellrich
- School of ITEE, The University of Queensland, St. Lucia, QLD 4072, Australia, Garvan Institute of Medical Research, Darlinghurst, Sydney, NSW 2010, Australia, Institute for Medical Genetics and Human Genetics, Charité-Universitätsmedizin Berlin, 13353 Berlin, Germany, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge, UK, National Institute of Informatics, Hitotsubashi, Tokyo, Japan, Mouse Informatics Group, Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton CB10 1SA, UK, LASIGE, Departamento de Informática, Faculdade de Ciências, Universidade de Lisboa, 1749-016 Lisboa, Portugal, Genetic Services of Western Australia, King Edward Memorial Hospital, WA 6008, Australia, School of Paediatrics and Child Health, University of Western Australia, WA 6008, Australia, Institute for Immunology and Infectious Diseases, Murdoch University, WA 6150, Australia, Office of Population Health, Public Health and Clinical Services Division, Western Australian Department of Health, WA 6004, Australia, Academic Department of Medical Genetics, Sydney Children's Hospitals Network (Westmead), NSW 2145, Australia, Discipline of Genetic Medicine, Sydney Medical School, The University of Sydney, NSW 2006, Australia, Max Planck Institute for Molecular Genetics, 14195 Berlin, Germany, Institute for Bioinformatics, Department of Mathematics and Computer Science, Freie Universität Berlin, 14195 Berlin, Germany and Berlin Brandenburg Center for Regenerative Therapies, 13353 Berlin, Germany
| | - Damian Smedley
- School of ITEE, The University of Queensland, St. Lucia, QLD 4072, Australia, Garvan Institute of Medical Research, Darlinghurst, Sydney, NSW 2010, Australia, Institute for Medical Genetics and Human Genetics, Charité-Universitätsmedizin Berlin, 13353 Berlin, Germany, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge, UK, National Institute of Informatics, Hitotsubashi, Tokyo, Japan, Mouse Informatics Group, Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton CB10 1SA, UK, LASIGE, Departamento de Informática, Faculdade de Ciências, Universidade de Lisboa, 1749-016 Lisboa, Portugal, Genetic Services of Western Australia, King Edward Memorial Hospital, WA 6008, Australia, School of Paediatrics and Child Health, University of Western Australia, WA 6008, Australia, Institute for Immunology and Infectious Diseases, Murdoch University, WA 6150, Australia, Office of Population Health, Public Health and Clinical Services Division, Western Australian Department of Health, WA 6004, Australia, Academic Department of Medical Genetics, Sydney Children's Hospitals Network (Westmead), NSW 2145, Australia, Discipline of Genetic Medicine, Sydney Medical School, The University of Sydney, NSW 2006, Australia, Max Planck Institute for Molecular Genetics, 14195 Berlin, Germany, Institute for Bioinformatics, Department of Mathematics and Computer Science, Freie Universität Berlin, 14195 Berlin, Germany and Berlin Brandenburg Center for Regenerative Therapies, 13353 Berlin, Germany
| | - Francisco M Couto
- School of ITEE, The University of Queensland, St. Lucia, QLD 4072, Australia, Garvan Institute of Medical Research, Darlinghurst, Sydney, NSW 2010, Australia, Institute for Medical Genetics and Human Genetics, Charité-Universitätsmedizin Berlin, 13353 Berlin, Germany, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge, UK, National Institute of Informatics, Hitotsubashi, Tokyo, Japan, Mouse Informatics Group, Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton CB10 1SA, UK, LASIGE, Departamento de Informática, Faculdade de Ciências, Universidade de Lisboa, 1749-016 Lisboa, Portugal, Genetic Services of Western Australia, King Edward Memorial Hospital, WA 6008, Australia, School of Paediatrics and Child Health, University of Western Australia, WA 6008, Australia, Institute for Immunology and Infectious Diseases, Murdoch University, WA 6150, Australia, Office of Population Health, Public Health and Clinical Services Division, Western Australian Department of Health, WA 6004, Australia, Academic Department of Medical Genetics, Sydney Children's Hospitals Network (Westmead), NSW 2145, Australia, Discipline of Genetic Medicine, Sydney Medical School, The University of Sydney, NSW 2006, Australia, Max Planck Institute for Molecular Genetics, 14195 Berlin, Germany, Institute for Bioinformatics, Department of Mathematics and Computer Science, Freie Universität Berlin, 14195 Berlin, Germany and Berlin Brandenburg Center for Regenerative Therapies, 13353 Berlin, Germany
| | - Gareth Baynam
- School of ITEE, The University of Queensland, St. Lucia, QLD 4072, Australia, Garvan Institute of Medical Research, Darlinghurst, Sydney, NSW 2010, Australia, Institute for Medical Genetics and Human Genetics, Charité-Universitätsmedizin Berlin, 13353 Berlin, Germany, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge, UK, National Institute of Informatics, Hitotsubashi, Tokyo, Japan, Mouse Informatics Group, Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton CB10 1SA, UK, LASIGE, Departamento de Informática, Faculdade de Ciências, Universidade de Lisboa, 1749-016 Lisboa, Portugal, Genetic Services of Western Australia, King Edward Memorial Hospital, WA 6008, Australia, School of Paediatrics and Child Health, University of Western Australia, WA 6008, Australia, Institute for Immunology and Infectious Diseases, Murdoch University, WA 6150, Australia, Office of Population Health, Public Health and Clinical Services Division, Western Australian Department of Health, WA 6004, Australia, Academic Department of Medical Genetics, Sydney Children's Hospitals Network (Westmead), NSW 2145, Australia, Discipline of Genetic Medicine, Sydney Medical School, The University of Sydney, NSW 2006, Australia, Max Planck Institute for Molecular Genetics, 14195 Berlin, Germany, Institute for Bioinformatics, Department of Mathematics and Computer Science, Freie Universität Berlin, 14195 Berlin, Germany and Berlin Brandenburg Center for Regenerative Therapies, 13353 Berlin, Germany School of ITEE, The University of Queensland, St. Lucia, QLD 4072, Australia, Garvan Institute of Medical Research, Darlinghurst, Sydney, NSW 2010, Australia, Institute for Medical Genetics and Human Genetics, Charité-Universitätsmedizin Berlin, 13353 Berlin, Germany, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge, UK, National Institute of Informatics, Hitotsubashi, Tokyo, Japan, Mouse Informa
| | - Andreas Zankl
- School of ITEE, The University of Queensland, St. Lucia, QLD 4072, Australia, Garvan Institute of Medical Research, Darlinghurst, Sydney, NSW 2010, Australia, Institute for Medical Genetics and Human Genetics, Charité-Universitätsmedizin Berlin, 13353 Berlin, Germany, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge, UK, National Institute of Informatics, Hitotsubashi, Tokyo, Japan, Mouse Informatics Group, Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton CB10 1SA, UK, LASIGE, Departamento de Informática, Faculdade de Ciências, Universidade de Lisboa, 1749-016 Lisboa, Portugal, Genetic Services of Western Australia, King Edward Memorial Hospital, WA 6008, Australia, School of Paediatrics and Child Health, University of Western Australia, WA 6008, Australia, Institute for Immunology and Infectious Diseases, Murdoch University, WA 6150, Australia, Office of Population Health, Public Health and Clinical Services Division, Western Australian Department of Health, WA 6004, Australia, Academic Department of Medical Genetics, Sydney Children's Hospitals Network (Westmead), NSW 2145, Australia, Discipline of Genetic Medicine, Sydney Medical School, The University of Sydney, NSW 2006, Australia, Max Planck Institute for Molecular Genetics, 14195 Berlin, Germany, Institute for Bioinformatics, Department of Mathematics and Computer Science, Freie Universität Berlin, 14195 Berlin, Germany and Berlin Brandenburg Center for Regenerative Therapies, 13353 Berlin, Germany School of ITEE, The University of Queensland, St. Lucia, QLD 4072, Australia, Garvan Institute of Medical Research, Darlinghurst, Sydney, NSW 2010, Australia, Institute for Medical Genetics and Human Genetics, Charité-Universitätsmedizin Berlin, 13353 Berlin, Germany, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge, UK, National Institute of Informatics, Hitotsubashi, Tokyo, Japan, Mouse Informa
| | - Peter N Robinson
- School of ITEE, The University of Queensland, St. Lucia, QLD 4072, Australia, Garvan Institute of Medical Research, Darlinghurst, Sydney, NSW 2010, Australia, Institute for Medical Genetics and Human Genetics, Charité-Universitätsmedizin Berlin, 13353 Berlin, Germany, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge, UK, National Institute of Informatics, Hitotsubashi, Tokyo, Japan, Mouse Informatics Group, Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton CB10 1SA, UK, LASIGE, Departamento de Informática, Faculdade de Ciências, Universidade de Lisboa, 1749-016 Lisboa, Portugal, Genetic Services of Western Australia, King Edward Memorial Hospital, WA 6008, Australia, School of Paediatrics and Child Health, University of Western Australia, WA 6008, Australia, Institute for Immunology and Infectious Diseases, Murdoch University, WA 6150, Australia, Office of Population Health, Public Health and Clinical Services Division, Western Australian Department of Health, WA 6004, Australia, Academic Department of Medical Genetics, Sydney Children's Hospitals Network (Westmead), NSW 2145, Australia, Discipline of Genetic Medicine, Sydney Medical School, The University of Sydney, NSW 2006, Australia, Max Planck Institute for Molecular Genetics, 14195 Berlin, Germany, Institute for Bioinformatics, Department of Mathematics and Computer Science, Freie Universität Berlin, 14195 Berlin, Germany and Berlin Brandenburg Center for Regenerative Therapies, 13353 Berlin, Germany School of ITEE, The University of Queensland, St. Lucia, QLD 4072, Australia, Garvan Institute of Medical Research, Darlinghurst, Sydney, NSW 2010, Australia, Institute for Medical Genetics and Human Genetics, Charité-Universitätsmedizin Berlin, 13353 Berlin, Germany, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge, UK, National Institute of Informatics, Hitotsubashi, Tokyo, Japan, Mouse Informa
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16
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Hoehndorf R, Gruenberger M, Gkoutos GV, Schofield PN. Similarity-based search of model organism, disease and drug effect phenotypes. J Biomed Semantics 2015; 6:6. [PMID: 25763178 PMCID: PMC4355138 DOI: 10.1186/s13326-015-0001-9] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2014] [Accepted: 01/24/2015] [Indexed: 12/17/2022] Open
Abstract
Background Semantic similarity measures over phenotype ontologies have been demonstrated to provide a powerful approach for the analysis of model organism phenotypes, the discovery of animal models of human disease, novel pathways, gene functions, druggable therapeutic targets, and determination of pathogenicity. Results We have developed PhenomeNET 2, a system that enables similarity-based searches over a large repository of phenotypes in real-time. It can be used to identify strains of model organisms that are phenotypically similar to human patients, diseases that are phenotypically similar to model organism phenotypes, or drug effect profiles that are similar to the phenotypes observed in a patient or model organism. PhenomeNET 2 is available at http://aber-owl.net/phenomenet. Conclusions Phenotype-similarity searches can provide a powerful tool for the discovery and investigation of molecular mechanisms underlying an observed phenotypic manifestation. PhenomeNET 2 facilitates user-defined similarity searches and allows researchers to analyze their data within a large repository of human, mouse and rat phenotypes.
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Affiliation(s)
- Robert Hoehndorf
- Computational Bioscience Research Center, King Abdullah University of Science and Technology, 4700 KAUST, Thuwal, 23955-6900 Saudi Arabia ; Computer, Electrical and Mathematical Sciences & Engineering Division, King Abdullah University of Science and Technology, 4700 KAUST, Thuwal, 23955-6900 Saudi Arabia
| | - Michael Gruenberger
- Department of Computer Science, Aberystwyth University, Llandinam Building, Aberystwyth, SY23 3DB UK
| | - Georgios V Gkoutos
- Department of Physiology, Development & Neuroscience, University of Cambridge, Downing Street, Cambridge, CB2 3EG UK
| | - Paul N Schofield
- Department of Computer Science, Aberystwyth University, Llandinam Building, Aberystwyth, SY23 3DB UK
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17
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Collier N, Oellrich A, Groza T. Toward knowledge support for analysis and interpretation of complex traits. Genome Biol 2015; 14:214. [PMID: 24079802 PMCID: PMC4053827 DOI: 10.1186/gb-2013-14-9-214] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/02/2022] Open
Abstract
The systematic description of complex traits, from the organism to the cellular level, is important for hypothesis generation about underlying disease mechanisms. We discuss how intelligent algorithms might provide support, leading to faster throughput.
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18
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Deans AR, Lewis SE, Huala E, Anzaldo SS, Ashburner M, Balhoff JP, Blackburn DC, Blake JA, Burleigh JG, Chanet B, Cooper LD, Courtot M, Csösz S, Cui H, Dahdul W, Das S, Dececchi TA, Dettai A, Diogo R, Druzinsky RE, Dumontier M, Franz NM, Friedrich F, Gkoutos GV, Haendel M, Harmon LJ, Hayamizu TF, He Y, Hines HM, Ibrahim N, Jackson LM, Jaiswal P, James-Zorn C, Köhler S, Lecointre G, Lapp H, Lawrence CJ, Le Novère N, Lundberg JG, Macklin J, Mast AR, Midford PE, Mikó I, Mungall CJ, Oellrich A, Osumi-Sutherland D, Parkinson H, Ramírez MJ, Richter S, Robinson PN, Ruttenberg A, Schulz KS, Segerdell E, Seltmann KC, Sharkey MJ, Smith AD, Smith B, Specht CD, Squires RB, Thacker RW, Thessen A, Fernandez-Triana J, Vihinen M, Vize PD, Vogt L, Wall CE, Walls RL, Westerfeld M, Wharton RA, Wirkner CS, Woolley JB, Yoder MJ, Zorn AM, Mabee P. Finding our way through phenotypes. PLoS Biol 2015; 13:e1002033. [PMID: 25562316 PMCID: PMC4285398 DOI: 10.1371/journal.pbio.1002033] [Citation(s) in RCA: 124] [Impact Index Per Article: 13.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2023] Open
Abstract
Despite a large and multifaceted effort to understand the vast landscape of phenotypic data, their current form inhibits productive data analysis. The lack of a community-wide, consensus-based, human- and machine-interpretable language for describing phenotypes and their genomic and environmental contexts is perhaps the most pressing scientific bottleneck to integration across many key fields in biology, including genomics, systems biology, development, medicine, evolution, ecology, and systematics. Here we survey the current phenomics landscape, including data resources and handling, and the progress that has been made to accurately capture relevant data descriptions for phenotypes. We present an example of the kind of integration across domains that computable phenotypes would enable, and we call upon the broader biology community, publishers, and relevant funding agencies to support efforts to surmount today's data barriers and facilitate analytical reproducibility.
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Affiliation(s)
- Andrew R. Deans
- Department of Entomology, Pennsylvania State University, University Park, Pennsylvania, United States of America
| | - Suzanna E. Lewis
- Genome Division, Lawrence Berkeley National Lab, Berkeley, California, United States of America
| | - Eva Huala
- Department of Plant Biology, Carnegie Institution for Science, Stanford, California, United States of America
- Phoenix Bioinformatics, Palo Alto, California, United States of America
| | - Salvatore S. Anzaldo
- School of Life Sciences, Arizona State University, Tempe, Arizona, United States of America
| | - Michael Ashburner
- Department of Genetics, University of Cambridge, Cambridge, United Kingdom
| | - James P. Balhoff
- National Evolutionary Synthesis Center, Durham, North Carolina, United States of America
| | - David C. Blackburn
- Department of Vertebrate Zoology and Anthropology, California Academy of Sciences, San Francisco, California, United States of America
| | - Judith A. Blake
- The Jackson Laboratory, Bar Harbor, Maine, United States of America
| | - J. Gordon Burleigh
- Department of Biology, University of Florida, Gainesville, Florida, United States of America
| | - Bruno Chanet
- Muséum national d'Histoire naturelle, Département Systématique et Evolution, Paris, France
| | - Laurel D. Cooper
- Department of Botany and Plant Pathology, Oregon State University, Corvallis, Oregon, United States of America
| | - Mélanie Courtot
- Molecular Biology and Biochemistry Department, Simon Fraser University, Burnaby, British Columbia, Canada
| | - Sándor Csösz
- MTA-ELTE-MTM, Ecology Research Group, Pázmány Péter sétány 1C, Budapest, Hungary
| | - Hong Cui
- School of Information Resources and Library Science, University of Arizona, Tucson, Arizona, United States of America
| | - Wasila Dahdul
- Department of Biology, University of South Dakota, Vermillion, South Dakota, United States of America
| | - Sandip Das
- Department of Botany, University of Delhi, Delhi, India
| | - T. Alexander Dececchi
- Department of Biology, University of South Dakota, Vermillion, South Dakota, United States of America
| | - Agnes Dettai
- Muséum national d'Histoire naturelle, Département Systématique et Evolution, Paris, France
| | - Rui Diogo
- Department of Anatomy, Howard University College of Medicine, Washington D.C., United States of America
| | - Robert E. Druzinsky
- Department of Oral Biology, College of Dentistry, University of Illinois, Chicago, Illinois, United States of America
| | - Michel Dumontier
- Stanford Center for Biomedical Informatics Research, Stanford, California, United States of America
| | - Nico M. Franz
- School of Life Sciences, Arizona State University, Tempe, Arizona, United States of America
| | - Frank Friedrich
- Biocenter Grindel and Zoological Museum, Hamburg University, Hamburg, Germany
| | - George V. Gkoutos
- Department of Computer Science, Aberystwyth University, Aberystwyth, Ceredigion, United Kingdom
| | - Melissa Haendel
- Department of Medical Informatics & Epidemiology, Oregon Health & Science University, Portland, Oregon, United States of America
| | - Luke J. Harmon
- Department of Biological Sciences, University of Idaho, Moscow, Idaho, United States of America
| | - Terry F. Hayamizu
- Mouse Genome Informatics, The Jackson Laboratory, Bar Harbor, Maine, United States of America
| | - Yongqun He
- Unit for Laboratory Animal Medicine, Department of Microbiology and Immunology, Center for Computational Medicine and Bioinformatics, and Comprehensive Cancer Center, University of Michigan Medical School, Ann Arbor, Michigan, United States of America
| | - Heather M. Hines
- Department of Entomology, Pennsylvania State University, University Park, Pennsylvania, United States of America
| | - Nizar Ibrahim
- Department of Organismal Biology and Anatomy, University of Chicago, Chicago, Illinois, United States of America
| | - Laura M. Jackson
- Department of Biology, University of South Dakota, Vermillion, South Dakota, United States of America
| | - Pankaj Jaiswal
- Department of Botany and Plant Pathology, Oregon State University, Corvallis, Oregon, United States of America
| | - Christina James-Zorn
- Cincinnati Children's Hospital, Division of Developmental Biology, Cincinnati, Ohio, United States of America
| | - Sebastian Köhler
- Institute for Medical Genetics and Human Genetics, Charité-Universitätsmedizin Berlin, Berlin, Germany
| | - Guillaume Lecointre
- Muséum national d'Histoire naturelle, Département Systématique et Evolution, Paris, France
| | - Hilmar Lapp
- National Evolutionary Synthesis Center, Durham, North Carolina, United States of America
| | - Carolyn J. Lawrence
- Department of Genetics, Development and Cell Biology and Department of Agronomy, Iowa State University, Ames, Iowa, United States of America
| | | | - John G. Lundberg
- Department of Ichthyology, The Academy of Natural Sciences, Philadelphia, Pennsylvania, United States of America
| | - James Macklin
- Eastern Cereal and Oilseed Research Centre, Ottawa, Ontario, Canada
| | - Austin R. Mast
- Department of Biological Science, Florida State University, Tallahassee, Florida, United States of America
| | | | - István Mikó
- Department of Entomology, Pennsylvania State University, University Park, Pennsylvania, United States of America
| | - Christopher J. Mungall
- Genome Division, Lawrence Berkeley National Lab, Berkeley, California, United States of America
| | - Anika Oellrich
- European Molecular Biology Laboratory - European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, United Kingdom
| | - David Osumi-Sutherland
- European Molecular Biology Laboratory - European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, United Kingdom
| | - Helen Parkinson
- European Molecular Biology Laboratory - European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, United Kingdom
| | - Martín J. Ramírez
- Division of Arachnology, Museo Argentino de Ciencias Naturales - CONICET, Buenos Aires, Argentina
| | - Stefan Richter
- Allgemeine & Spezielle Zoologie, Institut für Biowissenschaften, Universität Rostock, Universitätsplatz 2, Rostock, Germany
| | - Peter N. Robinson
- Institut für Medizinische Genetik und Humangenetik Charité – Universitätsmedizin Berlin, Berlin, Germany
| | - Alan Ruttenberg
- School of Dental Medicine, University at Buffalo, Buffalo, New York, United States of America
| | - Katja S. Schulz
- Smithsonian Institution, National Museum of Natural History, Washington, D.C., United States of America
| | - Erik Segerdell
- Knight Cancer Institute, Oregon Health & Science University, Portland, Oregon, United States of America
| | - Katja C. Seltmann
- Division of Invertebrate Zoology, American Museum of Natural History, New York, New York, United States of America
| | - Michael J. Sharkey
- Department of Entomology, University of Kentucky, Lexington, Kentucky, United States of America
| | - Aaron D. Smith
- Department of Biological Sciences, Northern Arizona University, Flagstaff, Arizona, United States of America
| | - Barry Smith
- Department of Philosophy, University at Buffalo, Buffalo, New York, United States of America
| | - Chelsea D. Specht
- Department of Plant and Microbial Biology, Integrative Biology, and the University and Jepson Herbaria, University of California, Berkeley, California, United States of America
| | - R. Burke Squires
- Bioinformatics and Computational Biosciences Branch, Office of Cyber Infrastructure and Computational Biology, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, Maryland, United States of America
| | - Robert W. Thacker
- Department of Biology, University of Alabama at Birmingham, Birmingham, Alabama, United States of America
| | - Anne Thessen
- The Data Detektiv, 1412 Stearns Hill Road, Waltham, Massachusetts, United States of America
| | | | - Mauno Vihinen
- Department of Experimental Medical Science, Lund University, Lund, Sweden
| | - Peter D. Vize
- Department of Biological Sciences, University of Calgary, Calgary, Alberta, Canada
| | - Lars Vogt
- Universität Bonn, Institut für Evolutionsbiologie und Ökologie, Bonn, Germany
| | - Christine E. Wall
- Department of Evolutionary Anthropology, Duke University, Durham, North Carolina, United States of America
| | - Ramona L. Walls
- iPlant Collaborative University of Arizona, Thomas J. Keating Bioresearch Building, Tucson, Arizona, United States of America
| | - Monte Westerfeld
- Institute of Neuroscience, University of Oregon, Eugene, Oregon, United States of America
| | - Robert A. Wharton
- Department of Entomology, Texas A & M University, College, Station, Texas, United States of America
| | - Christian S. Wirkner
- Allgemeine & Spezielle Zoologie, Institut für Biowissenschaften, Universität Rostock, Universitätsplatz 2, Rostock, Germany
| | - James B. Woolley
- Department of Entomology, Texas A & M University, College, Station, Texas, United States of America
| | - Matthew J. Yoder
- Illinois Natural History Survey, University of Illinois, Champaign, Illinois, United States of America
| | - Aaron M. Zorn
- Cincinnati Children's Hospital, Division of Developmental Biology, Cincinnati, Ohio, United States of America
| | - Paula Mabee
- Department of Biology, University of South Dakota, Vermillion, South Dakota, United States of America
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19
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Oellrich A, Koehler S, Washington N, Mungall C, Lewis S, Haendel M, Robinson PN, Smedley D. The influence of disease categories on gene candidate predictions from model organism phenotypes. J Biomed Semantics 2014; 5:S4. [PMID: 25093073 PMCID: PMC4108905 DOI: 10.1186/2041-1480-5-s1-s4] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/05/2023] Open
Abstract
BACKGROUND The molecular etiology is still to be identified for about half of the currently described Mendelian diseases in humans, thereby hindering efforts to find treatments or preventive measures. Advances, such as new sequencing technologies, have led to increasing amounts of data becoming available with which to address the problem of identifying disease genes. Therefore, automated methods are needed that reliably predict disease gene candidates based on available data. We have recently developed Exomiser as a tool for identifying causative variants from exome analysis results by filtering and prioritising using a number of criteria including the phenotype similarity between the disease and mouse mutants involving the gene candidates. Initial investigations revealed a variation in performance for different medical categories of disease, due in part to a varying contribution of the phenotype scoring component. RESULTS In this study, we further analyse the performance of our cross-species phenotype matching algorithm, and examine in more detail the reasons why disease gene filtering based on phenotype data works better for certain disease categories than others. We found that in addition to misleading phenotype alignments between species, some disease categories are still more amenable to automated predictions than others, and that this often ties in with community perceptions on how well the organism works as model. CONCLUSIONS In conclusion, our automated disease gene candidate predictions are highly dependent on the organism used for the predictions and the disease category being studied. Future work on computational disease gene prediction using phenotype data would benefit from methods that take into account the disease category and the source of model organism data.
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Affiliation(s)
- Anika Oellrich
- Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, CB10 1SA Hinxton, UK
| | - Sebastian Koehler
- Institute for Medical Genetics and Human Genetics, Universitaetsklinikum Charite, Augustenburger Platz 1, 13353 Berlin, Germany
| | - Nicole Washington
- Berkeley Bioinformatics Open-Source Projects, Lawrence Berkeley National Laboratory, 1 Cyclotron Road, CA 94720 Berkeley, USA
| | - Sanger Mouse Genetic Project
- Berkeley Bioinformatics Open-Source Projects, Lawrence Berkeley National Laboratory, 1 Cyclotron Road, CA 94720 Berkeley, USA
| | - Chris Mungall
- Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, CB10 1SA Hinxton, UK
| | - Suzanna Lewis
- Berkeley Bioinformatics Open-Source Projects, Lawrence Berkeley National Laboratory, 1 Cyclotron Road, CA 94720 Berkeley, USA
| | - Melissa Haendel
- Ontology Development Group, OHSU Library, Oregon Health & Science University, 3181 S.W. Sam Jackson Park Rd, OR 97239 Portland, USA
| | - Peter N Robinson
- Institute for Medical Genetics and Human Genetics, Universitaetsklinikum Charite, Augustenburger Platz 1, 13353 Berlin, Germany
| | - Damian Smedley
- Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, CB10 1SA Hinxton, UK
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20
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Haendel MA, Balhoff JP, Bastian FB, Blackburn DC, Blake JA, Bradford Y, Comte A, Dahdul WM, Dececchi TA, Druzinsky RE, Hayamizu TF, Ibrahim N, Lewis SE, Mabee PM, Niknejad A, Robinson-Rechavi M, Sereno PC, Mungall CJ. Unification of multi-species vertebrate anatomy ontologies for comparative biology in Uberon. J Biomed Semantics 2014; 5:21. [PMID: 25009735 PMCID: PMC4089931 DOI: 10.1186/2041-1480-5-21] [Citation(s) in RCA: 97] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2013] [Accepted: 03/25/2014] [Indexed: 12/25/2022] Open
Abstract
BACKGROUND Elucidating disease and developmental dysfunction requires understanding variation in phenotype. Single-species model organism anatomy ontologies (ssAOs) have been established to represent this variation. Multi-species anatomy ontologies (msAOs; vertebrate skeletal, vertebrate homologous, teleost, amphibian AOs) have been developed to represent 'natural' phenotypic variation across species. Our aim has been to integrate ssAOs and msAOs for various purposes, including establishing links between phenotypic variation and candidate genes. RESULTS Previously, msAOs contained a mixture of unique and overlapping content. This hampered integration and coordination due to the need to maintain cross-references or inter-ontology equivalence axioms to the ssAOs, or to perform large-scale obsolescence and modular import. Here we present the unification of anatomy ontologies into Uberon, a single ontology resource that enables interoperability among disparate data and research groups. As a consequence, independent development of TAO, VSAO, AAO, and vHOG has been discontinued. CONCLUSIONS The newly broadened Uberon ontology is a unified cross-taxon resource for metazoans (animals) that has been substantially expanded to include a broad diversity of vertebrate anatomical structures, permitting reasoning across anatomical variation in extinct and extant taxa. Uberon is a core resource that supports single- and cross-species queries for candidate genes using annotations for phenotypes from the systematics, biodiversity, medical, and model organism communities, while also providing entities for logical definitions in the Cell and Gene Ontologies. THE ONTOLOGY RELEASE FILES ASSOCIATED WITH THE ONTOLOGY MERGE DESCRIBED IN THIS MANUSCRIPT ARE AVAILABLE AT: http://purl.obolibrary.org/obo/uberon/releases/2013-02-21/ CURRENT ONTOLOGY RELEASE FILES ARE AVAILABLE ALWAYS AVAILABLE AT: http://purl.obolibrary.org/obo/uberon/releases/
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Affiliation(s)
- Melissa A Haendel
- Department of Medical Informatics & Epidemiology, Oregon Health & Science University, Portland, OR, USA
| | - James P Balhoff
- Department of Biology, University of North Carolina, Chapel Hill, NC 27599-3280, USA ; National Evolutionary Synthesis Center, Durham, NC, USA
| | - Frederic B Bastian
- Department of Ecology and Evolution, University of Lausanne, Lausanne, Switzerland ; Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - David C Blackburn
- Department of Vertebrate Zoology and Anthropology, California Academy of Sciences, San Francisco, CA 94118, USA
| | | | - Yvonne Bradford
- The Zebrafish Model Organism Database, University of Oregon, Eugene, OR 97403, USA
| | - Aurelie Comte
- Department of Ecology and Evolution, University of Lausanne, Lausanne, Switzerland ; Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Wasila M Dahdul
- National Evolutionary Synthesis Center, Durham, NC, USA ; Department of Biology, University of South Dakota, Vermillion, SD 57069, USA
| | - Thomas A Dececchi
- Department of Biology, University of South Dakota, Vermillion, SD 57069, USA
| | - Robert E Druzinsky
- Department of Oral Biology, University of Illinois-Chicago, Chicago, IL 60612, USA
| | | | - Nizar Ibrahim
- Department of Organismal Biology and Anatomy, University of Chicago, Chicago, IL 60637, USA
| | - Suzanna E Lewis
- Lawrence Berkeley National Laboratory, 1 Cyclotron Rd, Berkeley, CA 94720, USA
| | - Paula M Mabee
- Department of Biology, University of South Dakota, Vermillion, SD 57069, USA
| | - Anne Niknejad
- Department of Ecology and Evolution, University of Lausanne, Lausanne, Switzerland ; Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Marc Robinson-Rechavi
- Department of Ecology and Evolution, University of Lausanne, Lausanne, Switzerland ; Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Paul C Sereno
- Department of Organismal Biology and Anatomy, University of Chicago, Chicago, IL 60637, USA
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Oellrich A, Smedley D. Linking tissues to phenotypes using gene expression profiles. DATABASE-THE JOURNAL OF BIOLOGICAL DATABASES AND CURATION 2014; 2014:bau017. [PMID: 24634472 PMCID: PMC3982582 DOI: 10.1093/database/bau017] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/03/2023]
Abstract
Despite great biological and computational efforts to determine the genetic causes
underlying human heritable diseases, approximately half (3500) of these diseases are still
without an identified genetic cause. Model organism studies allow the targeted
modification of the genome and can help with the identification of genetic causes for
human diseases. Targeted modifications have led to a vast amount of model organism data.
However, these data are scattered across different databases, preventing an integrated
view and missing out on contextual information. Once we are able to combine all the
existing resources, will we be able to fully understand the causes underlying a disease
and how species differ. Here, we present an integrated data resource combining tissue
expression with phenotypes in mouse lines and bringing us one step closer to consequence
chains from a molecular level to a resulting phenotype. Mutations in genes often manifest
in phenotypes in the same tissue that the gene is expressed in. However, in other cases, a
systems level approach is required to understand how perturbations to gene-networks
connecting multiple tissues lead to a phenotype. Automated evaluation of the predicted
tissue–phenotype associations reveals that 72–76% of the phenotypes are
associated with disruption of genes expressed in the affected tissue. However,
55–64% of the individual phenotype-tissue associations show spatially
separated gene expression and phenotype manifestation. For example, we see a correlation
between ‘total body fat’ abnormalities and genes expressed in the
‘brain’, which fits recent discoveries linking genes expressed in the
hypothalamus to obesity. Finally, we demonstrate that the use of our predicted
tissue–phenotype associations can improve the detection of a known
disease–gene association when combined with a disease gene candidate prediction
tool. For example, JAK2, the known gene associated with Familial
Erythrocytosis 1, rises from the seventh best candidate to the top hit
when the associated tissues are taken into consideration. Database URL:http://www.sanger.ac.uk/resources/databases/phenodigm/phenotype/list
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Affiliation(s)
- Anika Oellrich
- Mouse Informatics Group, Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SA, UK
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22
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Van Slyke CE, Bradford YM, Westerfield M, Haendel MA. The zebrafish anatomy and stage ontologies: representing the anatomy and development of Danio rerio. J Biomed Semantics 2014; 5:12. [PMID: 24568621 PMCID: PMC3944782 DOI: 10.1186/2041-1480-5-12] [Citation(s) in RCA: 41] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2013] [Accepted: 02/07/2014] [Indexed: 01/07/2023] Open
Abstract
Background The Zebrafish Anatomy Ontology (ZFA) is an OBO Foundry ontology that is used in conjunction with the Zebrafish Stage Ontology (ZFS) to describe the gross and cellular anatomy and development of the zebrafish, Danio rerio, from single cell zygote to adult. The zebrafish model organism database (ZFIN) uses the ZFA and ZFS to annotate phenotype and gene expression data from the primary literature and from contributed data sets. Results The ZFA models anatomy and development with a subclass hierarchy, a partonomy, and a developmental hierarchy and with relationships to the ZFS that define the stages during which each anatomical entity exists. The ZFA and ZFS are developed utilizing OBO Foundry principles to ensure orthogonality, accessibility, and interoperability. The ZFA has 2860 classes representing a diversity of anatomical structures from different anatomical systems and from different stages of development. Conclusions The ZFA describes zebrafish anatomy and development semantically for the purposes of annotating gene expression and anatomical phenotypes. The ontology and the data have been used by other resources to perform cross-species queries of gene expression and phenotype data, providing insights into genetic relationships, morphological evolution, and models of human disease.
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Smedley D, Kohler S, Bone W, Oellrich A, Jacobsen J, Wang K, Mungall C, Washington N, Bauer S, Seelow D, Krawitz P, Boerkel C, Gilissen C, Haendel M, Lewis SE, Robinson PN. Use of animal models for exome prioritization of rare disease genes. Orphanet J Rare Dis 2014. [PMCID: PMC4249606 DOI: 10.1186/1750-1172-9-s1-o19] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022] Open
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Köhler S, Doelken SC, Mungall CJ, Bauer S, Firth HV, Bailleul-Forestier I, Black GCM, Brown DL, Brudno M, Campbell J, FitzPatrick DR, Eppig JT, Jackson AP, Freson K, Girdea M, Helbig I, Hurst JA, Jähn J, Jackson LG, Kelly AM, Ledbetter DH, Mansour S, Martin CL, Moss C, Mumford A, Ouwehand WH, Park SM, Riggs ER, Scott RH, Sisodiya S, Van Vooren S, Wapner RJ, Wilkie AOM, Wright CF, Vulto-van Silfhout AT, de Leeuw N, de Vries BBA, Washingthon NL, Smith CL, Westerfield M, Schofield P, Ruef BJ, Gkoutos GV, Haendel M, Smedley D, Lewis SE, Robinson PN. The Human Phenotype Ontology project: linking molecular biology and disease through phenotype data. Nucleic Acids Res 2013; 42:D966-74. [PMID: 24217912 PMCID: PMC3965098 DOI: 10.1093/nar/gkt1026] [Citation(s) in RCA: 519] [Impact Index Per Article: 47.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022] Open
Abstract
The Human Phenotype Ontology (HPO) project, available at http://www.human-phenotype-ontology.org, provides a structured, comprehensive and well-defined set of 10,088 classes (terms) describing human phenotypic abnormalities and 13,326 subclass relations between the HPO classes. In addition we have developed logical definitions for 46% of all HPO classes using terms from ontologies for anatomy, cell types, function, embryology, pathology and other domains. This allows interoperability with several resources, especially those containing phenotype information on model organisms such as mouse and zebrafish. Here we describe the updated HPO database, which provides annotations of 7,278 human hereditary syndromes listed in OMIM, Orphanet and DECIPHER to classes of the HPO. Various meta-attributes such as frequency, references and negations are associated with each annotation. Several large-scale projects worldwide utilize the HPO for describing phenotype information in their datasets. We have therefore generated equivalence mappings to other phenotype vocabularies such as LDDB, Orphanet, MedDRA, UMLS and phenoDB, allowing integration of existing datasets and interoperability with multiple biomedical resources. We have created various ways to access the HPO database content using flat files, a MySQL database, and Web-based tools. All data and documentation on the HPO project can be found online.
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Affiliation(s)
- Sebastian Köhler
- Institute for Medical Genetics and Human Genetics, Charité-Universitätsmedizin Berlin, Augustenburger Platz 1, 13353 Berlin, Germany, Berlin-Brandenburg Center for Regenerative Therapies, Charité-Universitätsmedizin Berlin, Augustenburger Platz 1, 13353 Berlin, Germany, Lawrence Berkeley National Laboratory, Mail Stop 84R0171, Berkeley, CA 94720, USA, The Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridgeshire CB10 1SA, UK, Department of Medical Genetics, Cambridge University Addenbrooke's Hospital, Cambridge CB2 2QQ, UK, Université Paul Sabatier, Faculté de Chirurgie Dentaire, CHU Toulouse, France, Centre for Genomic Medicine, Central Manchester University Hospitals NHS Foundation Trust, Manchester Academic Health Sciences Centre (MAHSC), Manchester, UK, Centre for Genomic Medicine, Institute of Human Development, Faculty of Medical and Human Sciences, University of Manchester, MAHSC, Manchester M13 9WL, UK, Institute of Genetic Medicine. Newcastle University, Central Parkway, Newcastle upon Tyne, NE1 3BZ, UK, Department of Computer Science, University of Toronto, Ontario, Canada, Centre for Computational Medicine, Hospital for Sick Children, Toronto, Ontario, Canada, Department of Clinical Genetics, Leeds Teaching Hospitals NHS Trust, Leeds LS2 9NS, UK, MRC Human Genetics Unit, MRC Institute of Genetic and Molecular Medicine, University of Edinburgh, Edinburgh EH4 2XU, UK, The Jackson Laboratory, Bar Harbor, ME 04609, USA, Center for Molecular and Vascular Biology, University of Leuven, Belgium, Department of Neuropediatrics, University Medical Center Schleswig-Holstein, Kiel Campus, 24105 Kiel, Germany, NE Thames Genetics Service, Great Ormond Street Hospital, London WC1N 3JH, UK, Drexel University College of Medicine, Philadelphia, PA 19102, USA, Department of Haematology, University of Cambridge and NHS Blood and Transplant Cambridge, CB2 0PT Cambridge, UK, Autism and Developmental Medicine Institute, Geisinger Health System
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25
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Hoehndorf R, Hiebert T, Hardy NW, Schofield PN, Gkoutos GV, Dumontier M. Mouse model phenotypes provide information about human drug targets. ACTA ACUST UNITED AC 2013; 30:719-25. [PMID: 24158600 PMCID: PMC3933875 DOI: 10.1093/bioinformatics/btt613] [Citation(s) in RCA: 33] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
Motivation: Methods for computational drug target identification use information from diverse information sources to predict or prioritize drug targets for known drugs. One set of resources that has been relatively neglected for drug repurposing is animal model phenotype. Results: We investigate the use of mouse model phenotypes for drug target identification. To achieve this goal, we first integrate mouse model phenotypes and drug effects, and then systematically compare the phenotypic similarity between mouse models and drug effect profiles. We find a high similarity between phenotypes resulting from loss-of-function mutations and drug effects resulting from the inhibition of a protein through a drug action, and demonstrate how this approach can be used to suggest candidate drug targets. Availability and implementation: Analysis code and supplementary data files are available on the project Web site at https://drugeffects.googlecode.com. Contact:leechuck@leechuck.de or roh25@aber.ac.uk Supplementary information:Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Robert Hoehndorf
- Department of Computer Science, University of Aberystwyth, Old College, King Street, Aberystwyth SY23 2AX, Department of Biology, Institute of Biochemistry and School of Computer Science, Carleton University, 1125 Colonel By Drive, Ottawa, Ontario K1S 5B6, Canada and Department of Physiology, Development and Neuroscience, University of Cambridge, Downing Street, Cambridge CB2 3EG, UK
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26
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Oellrich A, Grabmüller C, Rebholz-Schuhmann D. Automatically transforming pre- to post-composed phenotypes: EQ-lising HPO and MP. J Biomed Semantics 2013; 4:29. [PMID: 24131519 PMCID: PMC4016257 DOI: 10.1186/2041-1480-4-29] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2013] [Accepted: 04/12/2013] [Indexed: 01/24/2023] Open
Abstract
Background Large-scale mutagenesis projects are ongoing to improve our understanding about the pathology and subsequently the treatment of diseases. Such projects do not only record the genotype but also report phenotype descriptions of the genetically modified organisms under investigation. Thus far, phenotype data is stored in species-specific databases that lack coherence and interoperability in their phenotype representations. One suggestion to overcome the lack of integration are Entity-Quality (EQ) statements. However, a reliable automated transformation of the phenotype annotations from the databases into EQ statements is still missing. Results Here, we report on our ongoing efforts to develop a method (called EQ-liser) for the automated generation of EQ representations from phenotype ontology concept labels. We implemented the suggested method in a prototype and applied it to a subset of Mammalian and Human Phenotype Ontology concepts. In the case of MP, we were able to identify the correct EQ representation in over 52% of structure and process phenotypes. However, applying the EQ-liser prototype to the Human Phenotype Ontology yields a correct EQ representation in only 13.3% of the investigated cases. Conclusions With the application of the prototype to two phenotype ontologies, we were able to identify common patterns of mistakes when generating the EQ representation. Correcting these mistakes will pave the way to a species-independent solution to automatically derive EQ representations from phenotype ontology concept labels. Furthermore, we were able to identify inconsistencies in the existing manually defined EQ representations of current phenotype ontologies. Correcting these inconsistencies will improve the quality of the manually defined EQ statements.
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Affiliation(s)
- Anika Oellrich
- European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridgeshire, CB10 1SD, UK.
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27
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Brinkley JF, Borromeo C, Clarkson M, Cox TC, Cunningham MJ, Detwiler LT, Heike CL, Hochheiser H, Mejino JLV, Travillian RS, Shapiro LG. The ontology of craniofacial development and malformation for translational craniofacial research. AMERICAN JOURNAL OF MEDICAL GENETICS PART C-SEMINARS IN MEDICAL GENETICS 2013; 163C:232-45. [PMID: 24124010 DOI: 10.1002/ajmg.c.31377] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
Abstract
We introduce the Ontology of Craniofacial Development and Malformation (OCDM) as a mechanism for representing knowledge about craniofacial development and malformation, and for using that knowledge to facilitate integrating craniofacial data obtained via multiple techniques from multiple labs and at multiple levels of granularity. The OCDM is a project of the NIDCR-sponsored FaceBase Consortium, whose goal is to promote and enable research into the genetic and epigenetic causes of specific craniofacial abnormalities through the provision of publicly accessible, integrated craniofacial data. However, the OCDM should be usable for integrating any web-accessible craniofacial data, not just those data available through FaceBase. The OCDM is based on the Foundational Model of Anatomy (FMA), our comprehensive ontology of canonical human adult anatomy, and includes modules to represent adult and developmental craniofacial anatomy in both human and mouse, mappings between homologous structures in human and mouse, and associated malformations. We describe these modules, as well as prototype uses of the OCDM for integrating craniofacial data. By using the terms from the OCDM to annotate data, and by combining queries over the ontology with those over annotated data, it becomes possible to create "intelligent" queries that can, for example, find gene expression data obtained from mouse structures that are precursors to homologous human structures involved in malformations such as cleft lip. We suggest that the OCDM can be useful not only for integrating craniofacial data, but also for expressing new knowledge gained from analyzing the integrated data.
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28
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Schofield PN, Sundberg JP, Sundberg BA, McKerlie C, Gkoutos GV. The mouse pathology ontology, MPATH; structure and applications. J Biomed Semantics 2013; 4:18. [PMID: 24033988 PMCID: PMC3851164 DOI: 10.1186/2041-1480-4-18] [Citation(s) in RCA: 30] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2013] [Accepted: 08/19/2013] [Indexed: 12/18/2022] Open
Abstract
BACKGROUND The capture and use of disease-related anatomic pathology data for both model organism phenotyping and human clinical practice requires a relatively simple nomenclature and coding system that can be integrated into data collection platforms (such as computerized medical record-keeping systems) to enable the pathologist to rapidly screen and accurately record observations. The MPATH ontology was originally constructed in 2,000 by a committee of pathologists for the annotation of rodent histopathology images, but is now widely used for coding and analysis of disease and phenotype data for rodents, humans and zebrafish. CONSTRUCTION AND CONTENT MPATH is divided into two main branches describing pathological processes and structures based on traditional histopathological principles. It does not aim to include definitive diagnoses, which would generally be regarded as disease concepts. It contains 888 core pathology terms in an almost exclusively is_a hierarchy nine layers deep. Currently, 86% of the terms have textual definitions and contain relationships as well as logical axioms to other ontologies such the Gene Ontology. APPLICATION AND UTILITY MPATH was originally devised for the annotation of histopathological images from mice but is now being used much more widely in the recording of diagnostic and phenotypic data from both mice and humans, and in the construction of logical definitions for phenotype and disease ontologies. We discuss the use of MPATH to generate cross-products with qualifiers derived from a subset of the Phenotype and Trait Ontology (PATO) and its application to large-scale high-throughput phenotyping studies. MPATH provides a largely species-agnostic ontology for the descriptions of anatomic pathology, which can be applied to most amniotes and is now finding extensive use in species other than mice. It enables investigators to interrogate large datasets at a variety of depths, use semantic analysis to identify the relations between diseases in different species and integrate pathology data with other data types, such as pharmacogenomics.
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Affiliation(s)
- Paul N Schofield
- Department of Physiology, Development and Neuroscience, University of Cambridge, Downing Street, CB2 3EG, Cambridge, UK.
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29
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Maynard SM, Mungall CJ, Lewis SE, Imam FT, Martone ME. A knowledge based approach to matching human neurodegenerative disease and animal models. Front Neuroinform 2013; 7:7. [PMID: 23717278 PMCID: PMC3653101 DOI: 10.3389/fninf.2013.00007] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2012] [Accepted: 04/09/2013] [Indexed: 12/19/2022] Open
Abstract
Neurodegenerative diseases present a wide and complex range of biological and clinical features. Animal models are key to translational research, yet typically only exhibit a subset of disease features rather than being precise replicas of the disease. Consequently, connecting animal to human conditions using direct data-mining strategies has proven challenging, particularly for diseases of the nervous system, with its complicated anatomy and physiology. To address this challenge we have explored the use of ontologies to create formal descriptions of structural phenotypes across scales that are machine processable and amenable to logical inference. As proof of concept, we built a Neurodegenerative Disease Phenotype Ontology (NDPO) and an associated Phenotype Knowledge Base (PKB) using an entity-quality model that incorporates descriptions for both human disease phenotypes and those of animal models. Entities are drawn from community ontologies made available through the Neuroscience Information Framework (NIF) and qualities are drawn from the Phenotype and Trait Ontology (PATO). We generated ~1200 structured phenotype statements describing structural alterations at the subcellular, cellular and gross anatomical levels observed in 11 human neurodegenerative conditions and associated animal models. PhenoSim, an open source tool for comparing phenotypes, was used to issue a series of competency questions to compare individual phenotypes among organisms and to determine which animal models recapitulate phenotypic aspects of the human disease in aggregate. Overall, the system was able to use relationships within the ontology to bridge phenotypes across scales, returning non-trivial matches based on common subsumers that were meaningful to a neuroscientist with an advanced knowledge of neuroanatomy. The system can be used both to compare individual phenotypes and also phenotypes in aggregate. This proof of concept suggests that expressing complex phenotypes using formal ontologies provides considerable benefit for comparing phenotypes across scales and species.
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Affiliation(s)
- Sarah M Maynard
- Department of Neurosciences, Center for Research in Biological Systems, University of California San Diego, San Diego, CA, USA
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30
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Chesler EJ, Logan RW. Opportunities for bioinformatics in the classification of behavior and psychiatric disorders. INTERNATIONAL REVIEW OF NEUROBIOLOGY 2013. [PMID: 23195316 DOI: 10.1016/b978-0-12-398323-7.00008-2] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
A bioinformatics approach to behavioral neuroscience provides both unique opportunities and challenges for research on behavior. A major challenge has been to describe, define, and discriminate among abstract behavioral processes, in large part by distinguishing among the biological mechanisms of unique but not entirely discrete, entities of behavior. Understanding the complexity of neurobiology and behavior requires integration of data across diverse biological systems, types of data, and levels of scale. With the perspective and application of bioinformatics, we can uncover the relationships among these systems and take steps forward in realizing the common and distinct bases of psychiatric disease.
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31
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Smedley D, Oellrich A, Köhler S, Ruef B, Westerfield M, Robinson P, Lewis S, Mungall C. PhenoDigm: analyzing curated annotations to associate animal models with human diseases. DATABASE-THE JOURNAL OF BIOLOGICAL DATABASES AND CURATION 2013; 2013:bat025. [PMID: 23660285 PMCID: PMC3649640 DOI: 10.1093/database/bat025] [Citation(s) in RCA: 96] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Abstract
The ultimate goal of studying model organisms is to translate what is learned into useful knowledge about normal human biology and disease to facilitate treatment and early screening for diseases. Recent advances in genomic technologies allow for rapid generation of models with a range of targeted genotypes as well as their characterization by high-throughput phenotyping. As an abundance of phenotype data become available, only systematic analysis will facilitate valid conclusions to be drawn from these data and transferred to human diseases. Owing to the volume of data, automated methods are preferable, allowing for a reliable analysis of the data and providing evidence about possible gene-disease associations. Here, we propose Phenotype comparisons for DIsease Genes and Models (PhenoDigm), as an automated method to provide evidence about gene-disease associations by analysing phenotype information. PhenoDigm integrates data from a variety of model organisms and, at the same time, uses several intermediate scoring methods to identify only strongly data-supported gene candidates for human genetic diseases. We show results of an automated evaluation as well as selected manually assessed examples that support the validity of PhenoDigm. Furthermore, we provide guidance on how to browse the data with PhenoDigm's web interface and illustrate its usefulness in supporting research. Database URL: http://www.sanger.ac.uk/resources/databases/phenodigm
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Affiliation(s)
- Damian Smedley
- Mouse Informatics Group, Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SA, UK.
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Sun Y, Almomani R, Breedveld GJ, Santen GWE, Aten E, Lefeber DJ, Hoff JI, Brusse E, Verheijen FW, Verdijk RM, Kriek M, Oostra B, Breuning MH, Losekoot M, den Dunnen JT, van de Warrenburg BP, Maat-Kievit AJA. Autosomal recessive spinocerebellar ataxia 7 (SCAR7) is caused by variants in TPP1, the gene involved in classic late-infantile neuronal ceroid lipofuscinosis 2 disease (CLN2 disease). Hum Mutat 2013; 34:706-13. [PMID: 23418007 DOI: 10.1002/humu.22292] [Citation(s) in RCA: 58] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2012] [Accepted: 01/31/2013] [Indexed: 01/15/2023]
Abstract
Spinocerebellar ataxias are phenotypically, neuropathologically, and genetically heterogeneous. The locus of autosomal recessive spinocerebellar ataxia type 7 (SCAR7) was previously linked to chromosome band 11p15. We have identified TPP1 as the causative gene for SCAR7 by exome sequencing. A missense and a splice site variant in TPP1, cosegregating with the disease, were found in a previously described SCAR7 family and also in another patient with a SCAR7 phenotype. TPP1, encoding the tripeptidyl-peptidase 1 enzyme, is known as the causative gene for late infantile neuronal ceroid lipofuscinosis disease 2 (CLN2 disease). CLN2 disease is characterized by epilepsy, loss of vision, ataxia, and a rapidly progressive course, leading to early death. SCAR7 patients showed ataxia and low activity of tripeptidyl-peptidase 1, but no ophthalmologic abnormalities or epilepsy. Also, the slowly progressive evolution of the disease until old age and absence of ultra structural curvilinear profiles is different from the known CLN2 phenotypes. Our findings now expand the phenotypes related to TPP1-variants to SCAR7. In spite of the limited sample size and measurements, a putative genotype-phenotype correlation may be drawn: we hypothesize that loss of function variants abolishing TPP1 enzyme activity lead to CLN2 disease, whereas variants that diminish TPP1 enzyme activity lead to SCAR7.
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Affiliation(s)
- Yu Sun
- Center for Human and Clinical Genetics, Leiden University Medical Center, The Netherlands
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Köhler S, Doelken SC, Ruef BJ, Bauer S, Washington N, Westerfield M, Gkoutos G, Schofield P, Smedley D, Lewis SE, Robinson PN, Mungall CJ. Construction and accessibility of a cross-species phenotype ontology along with gene annotations for biomedical research. F1000Res 2013; 2:30. [PMID: 24358873 DOI: 10.12688/f1000research.2-30.v1] [Citation(s) in RCA: 58] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 01/22/2013] [Indexed: 12/30/2022] Open
Abstract
Phenotype analyses, e.g. investigating metabolic processes, tissue formation, or organism behavior, are an important element of most biological and medical research activities. Biomedical researchers are making increased use of ontological standards and methods to capture the results of such analyses, with one focus being the comparison and analysis of phenotype information between species. We have generated a cross-species phenotype ontology for human, mouse and zebrafish that contains classes from the Human Phenotype Ontology, Mammalian Phenotype Ontology, and generated classes for zebrafish phenotypes. We also provide up-to-date annotation data connecting human genes to phenotype classes from the generated ontology. We have included the data generation pipeline into our continuous integration system ensuring stable and up-to-date releases. This article describes the data generation process and is intended to help interested researchers access both the phenotype annotation data and the associated cross-species phenotype ontology. The resource described here can be used in sophisticated semantic similarity and gene set enrichment analyses for phenotype data across species. The stable releases of this resource can be obtained from http://purl.obolibrary.org/obo/hp/uberpheno/.
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Affiliation(s)
- Sebastian Köhler
- Institute for Medical and Human Genetics, Charité-Universitatsmedizin Berlin, Berlin, 13353, Germany ; Berlin-Brandenberg Center for Regenerative Therapies (BCRT), Charité-Universitatsmedizin Berlin, Berlin, 13353, Germany
| | - Sandra C Doelken
- Institute for Medical and Human Genetics, Charité-Universitatsmedizin Berlin, Berlin, 13353, Germany
| | - Barbara J Ruef
- ZFIN, Institute of Neuroscience, University of Oregon, Eugene OR, 97403-5291, USA
| | - Sebastian Bauer
- Institute for Medical and Human Genetics, Charité-Universitatsmedizin Berlin, Berlin, 13353, Germany
| | | | - Monte Westerfield
- ZFIN, Institute of Neuroscience, University of Oregon, Eugene OR, 97403-5291, USA
| | - George Gkoutos
- Department of Computer Science, University of Aberystwyth, Aberystwyth, SY23 2AX, UK
| | - Paul Schofield
- Department of Genetics, University of Cambridge, Cambridge, CB2 3EH, UK
| | - Damian Smedley
- Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Cambridge, CB10 1SA, UK
| | - Suzanna E Lewis
- Lawrence Berkeley National Laboratory, Berkeley CA, 94720, USA
| | - Peter N Robinson
- Institute for Medical and Human Genetics, Charité-Universitatsmedizin Berlin, Berlin, 13353, Germany ; Berlin-Brandenberg Center for Regenerative Therapies (BCRT), Charité-Universitatsmedizin Berlin, Berlin, 13353, Germany ; Max Planck Institute for Molecular Genetics, Berlin, 14195, Germany
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Köhler S, Doelken SC, Ruef BJ, Bauer S, Washington N, Westerfield M, Gkoutos G, Schofield P, Smedley D, Lewis SE, Robinson PN, Mungall CJ. Construction and accessibility of a cross-species phenotype ontology along with gene annotations for biomedical research. F1000Res 2013; 2:30. [PMID: 24358873 PMCID: PMC3799545 DOI: 10.12688/f1000research.2-30.v2] [Citation(s) in RCA: 55] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 01/20/2014] [Indexed: 12/11/2022] Open
Abstract
Phenotype analyses, e.g. investigating metabolic processes, tissue formation, or organism behavior, are an important element of most biological and medical research activities. Biomedical researchers are making increased use of ontological standards and methods to capture the results of such analyses, with one focus being the comparison and analysis of phenotype information between species. We have generated a cross-species phenotype ontology for human, mouse and zebrafish that contains classes from the Human Phenotype Ontology, Mammalian Phenotype Ontology, and generated classes for zebrafish phenotypes. We also provide up-to-date annotation data connecting human genes to phenotype classes from the generated ontology. We have included the data generation pipeline into our continuous integration system ensuring stable and up-to-date releases. This article describes the data generation process and is intended to help interested researchers access both the phenotype annotation data and the associated cross-species phenotype ontology. The resource described here can be used in sophisticated semantic similarity and gene set enrichment analyses for phenotype data across species. The stable releases of this resource can be obtained from
http://purl.obolibrary.org/obo/hp/uberpheno/.
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Affiliation(s)
- Sebastian Köhler
- Institute for Medical and Human Genetics, Charité-Universitatsmedizin Berlin, Berlin, 13353, Germany ; Berlin-Brandenberg Center for Regenerative Therapies (BCRT), Charité-Universitatsmedizin Berlin, Berlin, 13353, Germany
| | - Sandra C Doelken
- Institute for Medical and Human Genetics, Charité-Universitatsmedizin Berlin, Berlin, 13353, Germany
| | - Barbara J Ruef
- ZFIN, Institute of Neuroscience, University of Oregon, Eugene OR, 97403-5291, USA
| | - Sebastian Bauer
- Institute for Medical and Human Genetics, Charité-Universitatsmedizin Berlin, Berlin, 13353, Germany
| | | | - Monte Westerfield
- ZFIN, Institute of Neuroscience, University of Oregon, Eugene OR, 97403-5291, USA
| | - George Gkoutos
- Department of Computer Science, University of Aberystwyth, Aberystwyth, SY23 2AX, UK
| | - Paul Schofield
- Department of Genetics, University of Cambridge, Cambridge, CB2 3EH, UK
| | - Damian Smedley
- Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Cambridge, CB10 1SA, UK
| | - Suzanna E Lewis
- Lawrence Berkeley National Laboratory, Berkeley CA, 94720, USA
| | - Peter N Robinson
- Institute for Medical and Human Genetics, Charité-Universitatsmedizin Berlin, Berlin, 13353, Germany ; Berlin-Brandenberg Center for Regenerative Therapies (BCRT), Charité-Universitatsmedizin Berlin, Berlin, 13353, Germany ; Max Planck Institute for Molecular Genetics, Berlin, 14195, Germany
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Abstract
A significant challenge of in-vivo studies is the identification of phenotypes with a method that is robust and reliable. The challenge arises from practical issues that lead to experimental designs which are not ideal. Breeding issues, particularly in the presence of fertility or fecundity problems, frequently lead to data being collected in multiple batches. This problem is acute in high throughput phenotyping programs. In addition, in a high throughput environment operational issues lead to controls not being measured on the same day as knockouts. We highlight how application of traditional methods, such as a Student’s t-Test or a 2-way ANOVA, in these situations give flawed results and should not be used. We explore the use of mixed models using worked examples from Sanger Mouse Genome Project focusing on Dual-Energy X-Ray Absorptiometry data for the analysis of mouse knockout data and compare to a reference range approach. We show that mixed model analysis is more sensitive and less prone to artefacts allowing the discovery of subtle quantitative phenotypes essential for correlating a gene’s function to human disease. We demonstrate how a mixed model approach has the additional advantage of being able to include covariates, such as body weight, to separate effect of genotype from these covariates. This is a particular issue in knockout studies, where body weight is a common phenotype and will enhance the precision of assigning phenotypes and the subsequent selection of lines for secondary phenotyping. The use of mixed models with in-vivo studies has value not only in improving the quality and sensitivity of the data analysis but also ethically as a method suitable for small batches which reduces the breeding burden of a colony. This will reduce the use of animals, increase throughput, and decrease cost whilst improving the quality and depth of knowledge gained.
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Rebholz-Schuhmann D, Oellrich A, Hoehndorf R. Text-mining solutions for biomedical research: enabling integrative biology. Nat Rev Genet 2012; 13:829-39. [DOI: 10.1038/nrg3337] [Citation(s) in RCA: 170] [Impact Index Per Article: 14.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
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Baynam G, Walters M, Claes P, Kung S, LeSouef P, Dawkins H, Gillett D, Goldblatt J. The facial evolution: looking backward and moving forward. Hum Mutat 2012; 34:14-22. [PMID: 23033261 DOI: 10.1002/humu.22219] [Citation(s) in RCA: 25] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2012] [Accepted: 08/30/2012] [Indexed: 01/16/2023]
Abstract
Three-dimensional (3D) facial analysis is ideal for high-resolution, nonionizing, noninvasive objective, high-throughput phenotypic, and phenomic studies. It is a natural complement to (epi)genetic technologies to facilitate advances in the understanding of rare and common diseases. The face is uniquely reflective of the primordial tissues, and there is evidence supporting the application of 3D facial analysis to the investigation of variation and disease including studies showing that the face can reflect systemic health, provides diagnostic clues to disorders, and that facial variation reflects biological pathways. In addition, facial variation has been related to evolutionary factors. The purpose of this review is to look backward to suggest that knowledge of human evolution supports, and may instruct, the application and interpretation of studies of facial morphology for documentation of human variation and investigation of its relationships with health and disease. Furthermore, in the context of advances of deep phenotyping and data integration, to look forward to suggest approaches to scalable implementation of facial analysis, and to suggest avenues for future research and clinical application of this technology.
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Affiliation(s)
- Gareth Baynam
- Genetic Services of Western Australia, Princess Margaret and King Edward Memorial Hospitals, Perth, Australia
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Smith CL, Eppig JT. The Mammalian Phenotype Ontology as a unifying standard for experimental and high-throughput phenotyping data. Mamm Genome 2012; 23:653-68. [PMID: 22961259 PMCID: PMC3463787 DOI: 10.1007/s00335-012-9421-3] [Citation(s) in RCA: 126] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2012] [Accepted: 07/24/2012] [Indexed: 01/16/2023]
Abstract
The Mammalian Phenotype Ontology (MP) is a structured vocabulary for describing mammalian phenotypes and serves as a critical tool for efficient annotation and comprehensive retrieval of phenotype data. Importantly, the ontology contains broad and specific terms, facilitating annotation of data from initial observations or screens and detailed data from subsequent experimental research. Using the ontology structure, data are retrieved inclusively, i.e., data annotated to chosen terms and to terms subordinate in the hierarchy. Thus, searching for "abnormal craniofacial morphology" also returns annotations to "megacephaly" and "microcephaly," more specific terms in the hierarchy path. The development and refinement of the MP is ongoing, with new terms and modifications to its organization undergoing continuous assessment as users and expert reviewers propose expansions and revisions. A wealth of phenotype data on mouse mutations and variants annotated to the MP already exists in the Mouse Genome Informatics database. These data, along with data curated to the MP by many mouse mutagenesis programs and mouse repositories, provide a platform for comparative analyses and correlative discoveries. The MP provides a standard underpinning to mouse phenotype descriptions for existing and future experimental and large-scale phenotyping projects. In this review we describe the MP as it presently exists, its application to phenotype annotations, the relationship of the MP to other ontologies, and the integration of the MP within large-scale phenotyping projects. Finally we discuss future application of the MP in providing standard descriptors of the phenotype pipeline test results from the International Mouse Phenotype Consortium projects.
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Abstract
In medical contexts, the word "phenotype" is used to refer to some deviation from normal morphology, physiology, or behavior. The analysis of phenotype plays a key role in clinical practice and medical research, and yet phenotypic descriptions in clinical notes and medical publications are often imprecise. Deep phenotyping can be defined as the precise and comprehensive analysis of phenotypic abnormalities in which the individual components of the phenotype are observed and described. The emerging field of precision medicine aims to provide the best available care for each patient based on stratification into disease subclasses with a common biological basis of disease. The comprehensive discovery of such subclasses, as well as the translation of this knowledge into clinical care, will depend critically upon computational resources to capture, store, and exchange phenotypic data, and upon sophisticated algorithms to integrate it with genomic variation, omics profiles, and other clinical information. This special issue of Human Mutation offers a number of articles describing computational solutions for current challenges in deep phenotyping, including semantic and technical standards for phenotype and disease data, digital imaging for facial phenotype analysis, model organism phenotypes, and databases for correlating phenotypes with genomic variation.
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Affiliation(s)
- Peter N Robinson
- Institut für Medizinische Genetik und Humangenetik, Charité-Universitätsmedizin Berlin, Berlin, Germany.
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Schofield PN, Hoehndorf R, Gkoutos GV. Mouse genetic and phenotypic resources for human genetics. Hum Mutat 2012; 33:826-36. [PMID: 22422677 DOI: 10.1002/humu.22077] [Citation(s) in RCA: 58] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
The use of model organisms to provide information on gene function has proved to be a powerful approach to our understanding of both human disease and fundamental mammalian biology. Large-scale community projects using mice, based on forward and reverse genetics, and now the pan-genomic phenotyping efforts of the International Mouse Phenotyping Consortium, are generating resources on an unprecedented scale, which will be extremely valuable to human genetics and medicine. We discuss the nature and availability of data, mice and embryonic stem cells from these large-scale programmes, the use of these resources to help prioritize and validate candidate genes in human genetic association studies, and how they can improve our understanding of the underlying pathobiology of human disease.
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Affiliation(s)
- Paul N Schofield
- Department of Physiology, Development and Neuroscience, University of Cambridge, Cambridge, United Kingdom.
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Improving disease gene prioritization by comparing the semantic similarity of phenotypes in mice with those of human diseases. PLoS One 2012; 7:e38937. [PMID: 22719993 PMCID: PMC3375301 DOI: 10.1371/journal.pone.0038937] [Citation(s) in RCA: 19] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2011] [Accepted: 05/16/2012] [Indexed: 12/14/2022] Open
Abstract
Despite considerable progress in understanding the molecular origins of hereditary human diseases, the molecular basis of several thousand genetic diseases still remains unknown. High-throughput phenotype studies are underway to systematically assess the phenotype outcome of targeted mutations in model organisms. Thus, comparing the similarity between experimentally identified phenotypes and the phenotypes associated with human diseases can be used to suggest causal genes underlying a disease. In this manuscript, we present a method for disease gene prioritization based on comparing phenotypes of mouse models with those of human diseases. For this purpose, either human disease phenotypes are “translated” into a mouse-based representation (using the Mammalian Phenotype Ontology), or mouse phenotypes are “translated” into a human-based representation (using the Human Phenotype Ontology). We apply a measure of semantic similarity and rank experimentally identified phenotypes in mice with respect to their phenotypic similarity to human diseases. Our method is evaluated on manually curated and experimentally verified gene–disease associations for human and for mouse. We evaluate our approach using a Receiver Operating Characteristic (ROC) analysis and obtain an area under the ROC curve of up to . Furthermore, we are able to confirm previous results that the Vax1 gene is involved in Septo-Optic Dysplasia and suggest Gdf6 and Marcks as further potential candidates. Our method significantly outperforms previous phenotype-based approaches of prioritizing gene–disease associations. To enable the adaption of our method to the analysis of other phenotype data, our software and prioritization results are freely available under a BSD licence at http://code.google.com/p/phenomeblast/wiki/CAMP. Furthermore, our method has been integrated in PhenomeNET and the results can be explored using the PhenomeBrowser at http://phenomebrowser.net.
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Schofield PN, Hancock JM. Integration of global resources for human genetic variation and disease. Hum Mutat 2012; 33:813-6. [DOI: 10.1002/humu.22079] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2012] [Accepted: 03/02/2012] [Indexed: 01/22/2023]
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Morgan H, Simon M, Mallon AM. Accessing and Mining Data from Large-Scale Mouse Phenotyping Projects. INTERNATIONAL REVIEW OF NEUROBIOLOGY 2012. [DOI: 10.1016/b978-0-12-398323-7.00003-3] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
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The neurobehavior ontology: an ontology for annotation and integration of behavior and behavioral phenotypes. INTERNATIONAL REVIEW OF NEUROBIOLOGY 2012. [PMID: 23195121 DOI: 10.1016/b978-0-12-388408-4.00004-6] [Citation(s) in RCA: 27] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
In recent years, considerable advances have been made toward our understanding of the genetic architecture of behavior and the physical, mental, and environmental influences that underpin behavioral processes. The provision of a method for recording behavior-related phenomena is necessary to enable integrative and comparative analyses of data and knowledge about behavior. The neurobehavior ontology facilitates the systematic representation of behavior and behavioral phenotypes, thereby improving the unification and integration behavioral data in neuroscience research.
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