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Lario R, Soley R, White S, Butler J, Del Fiol G, Eilbeck K, Huff S, Kawamoto K. The Business Process Management for Healthcare (BPM+ Health) Consortium: motivation, methodology, and deliverables for enabling clinical knowledge interoperability (CKI). J Am Med Inform Assoc 2024; 31:797-808. [PMID: 38237123 PMCID: PMC10990521 DOI: 10.1093/jamia/ocad242] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2023] [Revised: 11/09/2023] [Accepted: 12/01/2023] [Indexed: 04/05/2024] Open
Abstract
OBJECTIVES To enhance the Business Process Management (BPM)+ Healthcare language portfolio by incorporating knowledge types not previously covered and to improve the overall effectiveness and expressiveness of the suite to improve Clinical Knowledge Interoperability. METHODS We used the BPM+ Health and Object Management Group (OMG) standards development methodology to develop new languages, following a gap analysis between existing BPM+ Health languages and clinical practice guideline knowledge types. Proposal requests were developed based on these requirements, and submission teams were formed to respond to them. The resulting proposals were submitted to OMG for ratification. RESULTS The BPM+ Health family of languages, which initially consisted of the Business Process Model and Notation, Decision Model and Notation, and Case Model and Notation, was expanded by adding 5 new language standards through the OMG. These include Pedigree and Provenance Model and Notation for expressing epistemic knowledge, Knowledge Package Model and Notation for supporting packaging knowledge, Shared Data Model and Notation for expressing ontic knowledge, Party Model and Notation for representing entities and organizations, and Specification Common Elements, a language providing a standard abstract and reusable library that underpins the 4 new languages. DISCUSSION AND CONCLUSION In this effort, we adopted a strategy of separation of concerns to promote a portfolio of domain-agnostic, independent, but integrated domain-specific languages for authoring medical knowledge. This strategy is a practical and effective approach to expressing complex medical knowledge. These new domain-specific languages offer various knowledge-type options for clinical knowledge authors to choose from without potentially adding unnecessary overhead or complexity.
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Affiliation(s)
- Robert Lario
- Department of Biomedical Informatics, University of Utah, Salt Lake City, UT 84112-5775, United States
| | - Richard Soley
- Object Management Group, Milford, MA 01757, United States
| | - Stephen White
- BPM Advantage Consulting, Inc, Irvine, CA 92620, United States
| | - John Butler
- Auxiliumtg, LLC, Mount Airy, MD 21771, United States
| | - Guilherme Del Fiol
- Department of Biomedical Informatics, University of Utah, Salt Lake City, UT 84112-5775, United States
| | - Karen Eilbeck
- Department of Biomedical Informatics, University of Utah, Salt Lake City, UT 84112-5775, United States
| | - Stanley Huff
- Graphite Health, Inc, Albuquerque, NM, 87109, United States
| | - Kensaku Kawamoto
- Department of Biomedical Informatics, University of Utah, Salt Lake City, UT 84112-5775, United States
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Kiser AC, Schliep KC, Hernandez EJ, Peterson CM, Yandell M, Eilbeck K. An artificial intelligence approach for investigating multifactorial pain-related features of endometriosis. PLoS One 2024; 19:e0297998. [PMID: 38381710 PMCID: PMC10881015 DOI: 10.1371/journal.pone.0297998] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2023] [Accepted: 01/16/2024] [Indexed: 02/23/2024] Open
Abstract
Endometriosis is a debilitating, chronic disease that is estimated to affect 11% of reproductive-age women. Diagnosis of endometriosis is difficult with diagnostic delays of up to 12 years reported. These delays can negatively impact health and quality of life. Vague, nonspecific symptoms, like pain, with multiple differential diagnoses contribute to the difficulty of diagnosis. By investigating previously imprecise symptoms of pain, we sought to clarify distinct pain symptoms indicative of endometriosis, using an artificial intelligence-based approach. We used data from 473 women undergoing laparoscopy or laparotomy for a variety of surgical indications. Multiple anatomical pain locations were clustered based on the associations across samples to increase the power in the probability calculations. A Bayesian network was developed using pain-related features, subfertility, and diagnoses. Univariable and multivariable analyses were performed by querying the network for the relative risk of a postoperative diagnosis, given the presence of different symptoms. Performance and sensitivity analyses demonstrated the advantages of Bayesian network analysis over traditional statistical techniques. Clustering grouped the 155 anatomical sites of pain into 15 pain locations. After pruning, the final Bayesian network included 18 nodes. The presence of any pain-related feature increased the relative risk of endometriosis (p-value < 0.001). The constellation of chronic pelvic pain, subfertility, and dyspareunia resulted in the greatest increase in the relative risk of endometriosis. The performance and sensitivity analyses demonstrated that the Bayesian network could identify and analyze more significant associations with endometriosis than traditional statistical techniques. Pelvic pain, frequently associated with endometriosis, is a common and vague symptom. Our Bayesian network for the study of pain-related features of endometriosis revealed specific pain locations and pain types that potentially forecast the diagnosis of endometriosis.
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Affiliation(s)
- Amber C. Kiser
- Department of Biomedical Informatics, University of Utah, Salt Lake City, Utah, United States of America
| | - Karen C. Schliep
- Department of Family and Preventative Medicine, University of Utah, Salt Lake City, Utah, United States of America
| | - Edgar Javier Hernandez
- Department of Biomedical Informatics, University of Utah, Salt Lake City, Utah, United States of America
- Department of Human Genetics, Utah Center for Genetic Discovery, University of Utah, Salt Lake City, Utah, United States of America
| | - C. Matthew Peterson
- Department of Obstetrics and Gynecology, Division of Reproductive Endocrinology and Infertility, University of Utah, Salt Lake City, Utah, United States of America
| | - Mark Yandell
- Department of Human Genetics, Utah Center for Genetic Discovery, University of Utah, Salt Lake City, Utah, United States of America
| | - Karen Eilbeck
- Department of Biomedical Informatics, University of Utah, Salt Lake City, Utah, United States of America
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3
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Roberts AM, DiStefano MT, Riggs ER, Josephs KS, Alkuraya FS, Amberger J, Amin M, Berg JS, Cunningham F, Eilbeck K, Firth HV, Foreman J, Hamosh A, Hay E, Leigh S, Martin CL, McDonagh EM, Perrett D, Ramos EM, Robinson PN, Rath A, Sant DW, Stark Z, Whiffin N, Rehm HL, Ware JS. Toward robust clinical genome interpretation: Developing a consistent terminology to characterize Mendelian disease-gene relationships-allelic requirement, inheritance modes, and disease mechanisms. Genet Med 2024; 26:101029. [PMID: 37982373 PMCID: PMC11039201 DOI: 10.1016/j.gim.2023.101029] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2023] [Revised: 11/09/2023] [Accepted: 11/12/2023] [Indexed: 11/21/2023] Open
Abstract
PURPOSE The terminology used for gene-disease curation and variant annotation to describe inheritance, allelic requirement, and both sequence and functional consequences of a variant is currently not standardized. There is considerable discrepancy in the literature and across clinical variant reporting in the derivation and application of terms. Here, we standardize the terminology for the characterization of disease-gene relationships to facilitate harmonized global curation and to support variant classification within the ACMG/AMP framework. METHODS Terminology for inheritance, allelic requirement, and both structural and functional consequences of a variant used by Gene Curation Coalition members and partner organizations was collated and reviewed. Harmonized terminology with definitions and use examples was created, reviewed, and validated. RESULTS We present a standardized terminology to describe gene-disease relationships, and to support variant annotation. We demonstrate application of the terminology for classification of variation in the ACMG SF 2.0 genes recommended for reporting of secondary findings. Consensus terms were agreed and formalized in both Sequence Ontology (SO) and Human Phenotype Ontology (HPO) ontologies. Gene Curation Coalition member groups intend to use or map to these terms in their respective resources. CONCLUSION The terminology standardization presented here will improve harmonization, facilitate the pooling of curation datasets across international curation efforts and, in turn, improve consistency in variant classification and genetic test interpretation.
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Affiliation(s)
- Angharad M Roberts
- National Heart and Lung Institute and MRC London Institute of Medical Sciences, Imperial College London, London, United Kingdom; Dept of Medical Genetics, Great Ormond Street Hospital, Great Ormond Street, London, United Kingdom.
| | - Marina T DiStefano
- Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA
| | | | - Katherine S Josephs
- National Heart and Lung Institute and MRC London Institute of Medical Sciences, Imperial College London, London, United Kingdom; Royal Brompton and Harefield Hospitals, Guy's and St. Thomas' NHS Foundation Trust, London, United Kingdom
| | - Fowzan S Alkuraya
- Department of Translational Genomics, Center for Genomic Medicine, KFSHRC, Riyadh, Saudi Arabia
| | - Joanna Amberger
- Department of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, MD
| | | | - Jonathan S Berg
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC
| | - Fiona Cunningham
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridgeshire, United Kingdom
| | - Karen Eilbeck
- Department of Biomedical Informatics, University of Utah, Salt Lake City, UT
| | - Helen V Firth
- Dept of Medical Genetics, Cambridge University Hospitals, Cambridge, United Kingdom; Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, United Kingdom
| | - Julia Foreman
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, United Kingdom; European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridgeshire, United Kingdom
| | - Ada Hamosh
- Department of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, MD
| | - Eleanor Hay
- Dept of Medical Genetics, Great Ormond Street Hospital, Great Ormond Street, London, United Kingdom
| | - Sarah Leigh
- Genomics England, Queen Mary University of London, Dawson Hall, London, United Kingdom
| | | | - Ellen M McDonagh
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridgeshire, United Kingdom; Open Targets, Open Targets, Wellcome Genome Campus, Hinxton, Cambridgeshire, United Kingdom
| | - Daniel Perrett
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridgeshire, United Kingdom
| | - Erin M Ramos
- National Human Genome Research Institute, National Institutes of Health, Bethesda, MD
| | | | - Ana Rath
- INSERM, US14-Orphanet, Paris, France
| | - David W Sant
- Department of Biomedical Informatics, University of Utah, Salt Lake City, UT
| | - Zornitza Stark
- Australian Genomics, Melbourne 3052, Australia; Victorian Clinical Genetics Services, Murdoch Children's Research Institute, Melbourne 3052, Australia; University of Melbourne, Melbourne 3052, Australia
| | - Nicola Whiffin
- Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA; Big Data Institute and Wellcome Centre for Human Genetics, University of Oxford, United Kingdom
| | - Heidi L Rehm
- Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA; Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA
| | - James S Ware
- National Heart and Lung Institute and MRC London Institute of Medical Sciences, Imperial College London, London, United Kingdom; Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA; Royal Brompton and Harefield Hospitals, Guy's and St. Thomas' NHS Foundation Trust, London, United Kingdom
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Han S, DiBlasi E, Monson ET, Shabalin A, Ferris E, Chen D, Fraser A, Yu Z, Staley M, Callor WB, Christensen ED, Crockett DK, Li QS, Willour V, Bakian AV, Keeshin B, Docherty AR, Eilbeck K, Coon H. Whole-genome sequencing analysis of suicide deaths integrating brain-regulatory eQTLs data to identify risk loci and genes. Mol Psychiatry 2023; 28:3909-3919. [PMID: 37794117 PMCID: PMC10730410 DOI: 10.1038/s41380-023-02282-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/25/2023] [Revised: 09/14/2023] [Accepted: 09/20/2023] [Indexed: 10/06/2023]
Abstract
Recent large-scale genome-wide association studies (GWAS) have started to identify potential genetic risk loci associated with risk of suicide; however, a large portion of suicide-associated genetic factors affecting gene expression remain elusive. Dysregulated gene expression, not assessed by GWAS, may play a significant role in increasing the risk of suicide death. We performed the first comprehensive genomic association analysis prioritizing brain expression quantitative trait loci (eQTLs) within regulatory regions in suicide deaths from the Utah Suicide Genetic Risk Study (USGRS). 440,324 brain-regulatory eQTLs were obtained by integrating brain eQTLs, histone modification ChIP-seq, ATAC-seq, DNase-seq, and Hi-C results from publicly available data. Subsequent genomic analyses were conducted in whole-genome sequencing (WGS) data from 986 suicide deaths of non-Finnish European (NFE) ancestry and 415 ancestrally matched controls. Additional independent USGRS suicide deaths with genotyping array data (n = 4657) and controls from the Genome Aggregation Database were explored for WGS result replication. One significant eQTL locus, rs926308 (p = 3.24e-06), was identified. The rs926308-T is associated with lower expression of RFPL3S, a gene important for neocortex development and implicated in arousal. Gene-based analyses performed using Sherlock Bayesian statistical integrative analysis also detected 20 genes with expression changes that may contribute to suicide risk. From analyzing publicly available transcriptomic data, ten of these genes have previous evidence of differential expression in suicide death or in psychiatric disorders that may be associated with suicide, including schizophrenia and autism (ZNF501, ZNF502, CNN3, IGF1R, KLHL36, NBL1, PDCD6IP, SNX19, BCAP29, and ARSA). Electronic health records (EHR) data was further merged to evaluate if there were clinically relevant subsets of suicide deaths associated with genetic variants. In summary, our study identified one risk locus and ten genes associated with suicide risk via gene expression, providing new insight into possible genetic and molecular mechanisms leading to suicide.
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Affiliation(s)
- Seonggyun Han
- Department of Biomedical Informatics, University of Utah School of Medicine, Salt Lake City, UT, USA.
| | - Emily DiBlasi
- Department of Psychiatry & Huntsman Mental Health Institute, University of Utah School of Medicine, Salt Lake City, UT, USA
| | - Eric T Monson
- Department of Psychiatry & Huntsman Mental Health Institute, University of Utah School of Medicine, Salt Lake City, UT, USA
| | - Andrey Shabalin
- Department of Psychiatry & Huntsman Mental Health Institute, University of Utah School of Medicine, Salt Lake City, UT, USA
| | - Elliott Ferris
- Department of Neurobiology, University of Utah School of Medicine, Salt Lake City, UT, USA
| | - Danli Chen
- Department of Psychiatry & Huntsman Mental Health Institute, University of Utah School of Medicine, Salt Lake City, UT, USA
| | - Alison Fraser
- Pedigree & Population Resource, Huntsman Cancer Institute, University of Utah, Salt Lake City, UT, USA
| | - Zhe Yu
- Pedigree & Population Resource, Huntsman Cancer Institute, University of Utah, Salt Lake City, UT, USA
| | - Michael Staley
- Office of the Medical Examiner, Utah Department of Health and Human Services, Salt Lake City, UT, USA
| | - W Brandon Callor
- Office of the Medical Examiner, Utah Department of Health and Human Services, Salt Lake City, UT, USA
| | - Erik D Christensen
- Office of the Medical Examiner, Utah Department of Health and Human Services, Salt Lake City, UT, USA
| | - David K Crockett
- Clinical Analytics, Intermountain Health, Salt Lake City, UT, USA
| | - Qingqin S Li
- Neuroscience Therapeutic Area, Janssen Research & Development, LLC, Titusville, NJ, USA
| | - Virginia Willour
- Department of Psychiatry, University of Iowa, Iowa City, IA, USA
| | - Amanda V Bakian
- Department of Psychiatry & Huntsman Mental Health Institute, University of Utah School of Medicine, Salt Lake City, UT, USA
| | - Brooks Keeshin
- Department of Psychiatry & Huntsman Mental Health Institute, University of Utah School of Medicine, Salt Lake City, UT, USA
- Department of Pediatrics, University of Utah, Salt Lake City, UT, USA
| | - Anna R Docherty
- Department of Psychiatry & Huntsman Mental Health Institute, University of Utah School of Medicine, Salt Lake City, UT, USA
| | - Karen Eilbeck
- Department of Biomedical Informatics, University of Utah School of Medicine, Salt Lake City, UT, USA
| | - Hilary Coon
- Department of Psychiatry & Huntsman Mental Health Institute, University of Utah School of Medicine, Salt Lake City, UT, USA
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Kiser AC, Eilbeck K, Bucher BT. Developing an LSTM Model to Identify Surgical Site Infections using Electronic Healthcare Records. AMIA Jt Summits Transl Sci Proc 2023; 2023:330-339. [PMID: 37350879 PMCID: PMC10283140] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Subscribe] [Scholar Register] [Indexed: 06/24/2023]
Abstract
Recently, hospitals and healthcare providers have made efforts to reduce surgical site infections as they are a major cause of surgical complications, a prominent reason for hospital readmission, and associated with significantly increased healthcare costs. Traditional surveillance methods for SSI rely on manual chart review, which can be laborious and costly. To assist the chart review process, we developed a long short-term memory (LSTM) model using structured electronic health record data to identify SSI. The top LSTM model resulted in an average precision (AP) of 0.570 [95% CI 0.567, 0.573] and area under the receiver operating characteristic curve (AUROC) of 0.905 [95% CI 0.904, 0.906] compared to the top traditional machine learning model, a random forest, which achieved 0.552 [95% CI 0.549, 0.555] AP and 0.899 [95% CI 0.898, 0.900] AUROC. Our LSTM model represents a step toward automated surveillance of SSIs, a critical component of quality improvement mechanisms.
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Affiliation(s)
- Amber C Kiser
- Department of Biomedical Informatics, University of Utah School of Medicine, Salt Lake City, UT
| | - Karen Eilbeck
- Department of Biomedical Informatics, University of Utah School of Medicine, Salt Lake City, UT
| | - Brian T Bucher
- Department of Biomedical Informatics, University of Utah School of Medicine, Salt Lake City, UT
- Department of Surgery University of Utah School of Medicine, Salt Lake City, UT
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Engelsma T, Anders C, Oudbier SJ, Eilbeck K, Knaup P, Peute LW, Ganzinger M. Proposing a Novel Hybrid Short-Term Exchange Program in Biomedical and Health Informatics Education. Stud Health Technol Inform 2023; 302:498-499. [PMID: 37203733 DOI: 10.3233/shti230189] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/20/2023]
Abstract
International student exchange is a valuable opportunity for Biomedical and Health Informatics students to gain new perspectives and experiences. In the past, such exchanges have been made possible through international partnerships between universities. Unfortunately, numerous obstacles such as housing, financial concerns, and environmental implications related to travel, have made it difficult to continue international exchange. Experiences with hybrid and online education during covid-19 paved the way for a new approach that allows for short international exchange with a hybrid online-offline supervision model. This will be initiated with an exploration project between two international universities , each related to their respective institute's research focus.
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Affiliation(s)
- Thomas Engelsma
- Amsterdam UMC location University of Amsterdam, Department of Medical Informatics, Amsterdam Public Health research institute, Amsterdam, The Netherlands
| | - Carolin Anders
- Heidelberg University Hospital, Institute of Medical Informatics, Heidelberg, Germany
| | - Susan J Oudbier
- Amsterdam UMC location Vrije Universiteit Amsterdam, Outpatient Division, Amsterdam Public Health research institute, Amsterdam, The Netherlands
| | - Karen Eilbeck
- Department of Biomedical Informatics, School of Medicine, University of Utah, Salt Lake City, USA
| | - Petra Knaup
- Heidelberg University Hospital, Institute of Medical Informatics, Heidelberg, Germany
| | - Linda W Peute
- Amsterdam UMC location University of Amsterdam, Department of Medical Informatics, Amsterdam Public Health research institute, Amsterdam, The Netherlands
| | - Matthias Ganzinger
- Heidelberg University Hospital, Institute of Medical Informatics, Heidelberg, Germany
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7
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Roberts AM, DiStefano MT, Riggs ER, Josephs KS, Alkuraya FS, Amberger J, Amin M, Berg JS, Cunningham F, Eilbeck K, Firth HV, Foreman J, Hamosh A, Hay E, Leigh S, Martin CL, McDonagh EM, Perrett D, Ramos EM, Robinson PN, Rath A, van Sant D, Stark Z, Whiffin N, Rehm HL, Ware JS. Towards robust clinical genome interpretation: developing a consistent terminology to characterize disease-gene relationships - allelic requirement, inheritance modes and disease mechanisms. medRxiv 2023:2023.03.30.23287948. [PMID: 37066232 PMCID: PMC10104222 DOI: 10.1101/2023.03.30.23287948] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 04/29/2023]
Abstract
PURPOSE The terminology used for gene-disease curation and variant annotation to describe inheritance, allelic requirement, and both sequence and functional consequences of a variant is currently not standardized. There is considerable discrepancy in the literature and across clinical variant reporting in the derivation and application of terms. Here we standardize the terminology for the characterization of disease-gene relationships to facilitate harmonized global curation, and to support variant classification within the ACMG/AMP framework. METHODS Terminology for inheritance, allelic requirement, and both structural and functional consequences of a variant used by Gene Curation Coalition (GenCC) members and partner organizations was collated and reviewed. Harmonized terminology with definitions and use examples was created, reviewed, and validated. RESULTS We present a standardized terminology to describe gene-disease relationships, and to support variant annotation. We demonstrate application of the terminology for classification of variation in the ACMG SF 2.0 genes recommended for reporting of secondary findings. Consensus terms were agreed and formalized in both sequence ontology (SO) and human phenotype ontology (HPO) ontologies. GenCC member groups intend to use or map to these terms in their respective resources. CONCLUSION The terminology standardization presented here will improve harmonization, facilitate the pooling of curation datasets across international curation efforts and, in turn, improve consistency in variant classification and genetic test interpretation.
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Affiliation(s)
- Angharad M Roberts
- National Heart & Lung Institute & MRC London Institute of Medical Sciences, Imperial College London, London, UK
- Dept of Medical Genetics, Great Ormond Street Hospital, Great Ormond Street, London. WC1N 3JH, UK
| | - Marina T DiStefano
- Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Erin Rooney Riggs
- Geisinger Autism & Developmental Medicine Institute, Danville, PA, USA
| | - Katherine S Josephs
- National Heart & Lung Institute & MRC London Institute of Medical Sciences, Imperial College London, London, UK
- Royal Brompton & Harefield Hospitals, Guy's and St. Thomas' NHS Foundation Trust, London, UK
| | - Fowzan S Alkuraya
- Department of Translational Genomics, Center for Genomic Medicine, KFSHRC, Riyadh, Saudi Arabia
| | - Joanna Amberger
- Online Mendelian Inheritance in Man (OMIM), Johns Hopkins University School of Medicine, USA
| | | | - Jonathan S Berg
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill NC, 27599
| | - Fiona Cunningham
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridgeshire, CB10 1SD, United Kingdom
| | - Karen Eilbeck
- Department of Biomedical Informatics, University of Utah, Salt Lake City, Utah
| | - Helen V Firth
- Dept of Medical Genetics, Cambridge University Hospitals, Cambridge CB2 0QQ, UK
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, CB10 1SA, UK
| | - Julia Foreman
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, CB10 1SA, UK
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridgeshire, CB10 1SD, United Kingdom
| | - Ada Hamosh
- Online Mendelian Inheritance in Man (OMIM), Johns Hopkins University School of Medicine, USA
| | - Eleanor Hay
- Dept of Medical Genetics, Great Ormond Street Hospital, Great Ormond Street, London. WC1N 3JH, UK
| | - Sarah Leigh
- Genomics England, Queen Mary University of London, Dawson Hall, London, EC1M 6BQ, UK
| | | | - Ellen M McDonagh
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridgeshire, CB10 1SD, United Kingdom
- Open Targets, Cambridge, UK
| | - Daniel Perrett
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridgeshire, CB10 1SD, United Kingdom
| | - Erin M Ramos
- National Human Genome Research Institute, National Institutes of Health, USA
| | - Peter N Robinson
- The Jackson Laboratory for Genomic Medicine, Farmington CT 06032, USA
| | - Ana Rath
- INSERM, US14-Orphanet, Paris, France
| | - David van Sant
- Department of Biomedical Informatics, University of Utah, Salt Lake City, Utah
| | - Zornitza Stark
- Australian Genomics, Melbourne 3052, Australia
- Victorian Clinical Genetics Services, Murdoch Children's Research Institute, Melbourne 3052, Australia
- University of Melbourne, Melbourne 3052, Australia
| | - Nicola Whiffin
- Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Big Data Institute and Wellcome Centre for Human Genetics, University of Oxford, UK
| | - Heidi L Rehm
- Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - James S Ware
- National Heart & Lung Institute & MRC London Institute of Medical Sciences, Imperial College London, London, UK
- Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridgeshire, CB10 1SD, United Kingdom
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Lario R, Kawamoto K, Sottara D, Eilbeck K, Huff S, Del Fiol G, Soley R, Middleton B. A method for structuring complex clinical knowledge and its representational formalisms to support composite knowledge interoperability in healthcare. J Biomed Inform 2023; 137:104251. [PMID: 36400330 DOI: 10.1016/j.jbi.2022.104251] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2022] [Revised: 10/08/2022] [Accepted: 11/11/2022] [Indexed: 11/17/2022]
Abstract
INTRODUCTION The use and interoperability of clinical knowledge starts with the quality of the formalism utilized to express medical expertise. However, a crucial challenge is that existing formalisms are often suboptimal, lacking the fidelity to represent complex knowledge thoroughly and concisely. Often this leads to difficulties when seeking to unambiguously capture, share, and implement the knowledge for care improvement in clinical information systems used by providers and patients. OBJECTIVES To provide a systematic method to address some of the complexities of knowledge composition and interoperability related to standards-based representational formalisms of medical knowledge. METHODS Several cross-industry (Healthcare, Linguistics, System Engineering, Standards Development, and Knowledge Engineering) frameworks were synthesized into a proposed reference knowledge framework. The framework utilizes IEEE 42010, the MetaObject Facility, the Semantic Triangle, an Ontology Framework, and the Domain and Comprehensibility Appropriateness criteria. The steps taken were: 1) identify foundational cross-industry frameworks, 2) select architecture description method, 3) define life cycle viewpoints, 4) define representation and knowledge viewpoints, 5) define relationships between neighboring viewpoints, and 6) establish characteristic definitions of the relationships between components. System engineering principles applied included separation of concerns, cohesion, and loose coupling. RESULTS A "Multilayer Metamodel for Representation and Knowledge" (M*R/K) reference framework was defined. It provides a standard vocabulary for organizing and articulating medical knowledge curation perspectives, concepts, and relationships across the artifacts created during the life cycle of language creation, authoring medical knowledge, and knowledge implementation in clinical information systems such as electronic health records (EHR). CONCLUSION M*R/K provides a systematic means to address some of the complexities of knowledge composition and interoperability related to medical knowledge representations used in diverse standards. The framework may be used to guide the development, assessment, and coordinated use of knowledge representation formalisms. M*R/K could promote the alignment and aggregated use of distinct domain-specific languages in composite knowledge artifacts such as clinical practice guidelines (CPGs).
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Affiliation(s)
- Robert Lario
- Department of Biomedical Informatics, University of Utah, Salt Lake City, UT, United States
| | - Kensaku Kawamoto
- Department of Biomedical Informatics, University of Utah, Salt Lake City, UT, United States
| | | | - Karen Eilbeck
- Department of Biomedical Informatics, University of Utah, Salt Lake City, UT, United States
| | - Stanley Huff
- Department of Biomedical Informatics, University of Utah, Salt Lake City, UT, United States; Graphite Health, Salt Lake City, UT, United States
| | - Guilherme Del Fiol
- Department of Biomedical Informatics, University of Utah, Salt Lake City, UT, United States
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Kiser AC, Schliep KC, Yandell M, Eilbeck K. A BAYESIAN NETWORK FOR COMPLEX PAIN-RELATED FEATURES OF ENDOMETRIOSIS. Fertil Steril 2022. [DOI: 10.1016/j.fertnstert.2022.08.629] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
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10
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Kiser AC, Eilbeck K, Ferraro JP, Skarda DE, Samore MH, Bucher B. Standard Vocabularies to Improve Machine Learning Model Transferability With Electronic Health Record Data: Retrospective Cohort Study Using Health Care-Associated Infection. JMIR Med Inform 2022; 10:e39057. [PMID: 36040784 PMCID: PMC9472055 DOI: 10.2196/39057] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2022] [Revised: 08/09/2022] [Accepted: 08/15/2022] [Indexed: 11/13/2022] Open
Abstract
BACKGROUND With the widespread adoption of electronic healthcare records (EHRs) by US hospitals, there is an opportunity to leverage this data for the development of predictive algorithms to improve clinical care. A key barrier in model development and implementation includes the external validation of model discrimination, which is rare and often results in worse performance. One reason why machine learning models are not externally generalizable is data heterogeneity. A potential solution to address the substantial data heterogeneity between health care systems is to use standard vocabularies to map EHR data elements. The advantage of these vocabularies is a hierarchical relationship between elements, which allows the aggregation of specific clinical features to more general grouped concepts. OBJECTIVE This study aimed to evaluate grouping EHR data using standard vocabularies to improve the transferability of machine learning models for the detection of postoperative health care-associated infections across institutions with different EHR systems. METHODS Patients who underwent surgery from the University of Utah Health and Intermountain Healthcare from July 2014 to August 2017 with complete follow-up data were included. The primary outcome was a health care-associated infection within 30 days of the procedure. EHR data from 0-30 days after the operation were mapped to standard vocabularies and grouped using the hierarchical relationships of the vocabularies. Model performance was measured using the area under the receiver operating characteristic curve (AUC) and F1-score in internal and external validations. To evaluate model transferability, a difference-in-difference metric was defined as the difference in performance drop between internal and external validations for the baseline and grouped models. RESULTS A total of 5775 patients from the University of Utah and 15,434 patients from Intermountain Healthcare were included. The prevalence of selected outcomes was from 4.9% (761/15,434) to 5% (291/5775) for surgical site infections, from 0.8% (44/5775) to 1.1% (171/15,434) for pneumonia, from 2.6% (400/15,434) to 3% (175/5775) for sepsis, and from 0.8% (125/15,434) to 0.9% (50/5775) for urinary tract infections. In all outcomes, the grouping of data using standard vocabularies resulted in a reduced drop in AUC and F1-score in external validation compared to baseline features (all P<.001, except urinary tract infection AUC: P=.002). The difference-in-difference metrics ranged from 0.005 to 0.248 for AUC and from 0.075 to 0.216 for F1-score. CONCLUSIONS We demonstrated that grouping machine learning model features based on standard vocabularies improved model transferability between data sets across 2 institutions. Improving model transferability using standard vocabularies has the potential to improve the generalization of clinical prediction models across the health care system.
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Affiliation(s)
- Amber C Kiser
- Department of Biomedical Informatics, School of Medicine, University of Utah, Salt Lake City, UT, United States
| | - Karen Eilbeck
- Department of Biomedical Informatics, School of Medicine, University of Utah, Salt Lake City, UT, United States
| | - Jeffrey P Ferraro
- Department of Medicine, School of Medicine, University of Utah, Salt Lake City, UT, United States
| | - David E Skarda
- Center for Value-Based Surgery, Intermountain Healthcare, Salt Lake City, UT, United States.,Department of Surgery, School of Medicine, University of Utah, Salt Lake City, UT, United States
| | - Matthew H Samore
- Department of Medicine, School of Medicine, University of Utah, Salt Lake City, UT, United States.,Informatics, Decision-Enhancement and Analytic Sciences Center 2.0, Veterans Affairs Salt Lake City Health Care System, Salt Lake City, UT, United States
| | - Brian Bucher
- Department of Biomedical Informatics, School of Medicine, University of Utah, Salt Lake City, UT, United States.,Department of Surgery, School of Medicine, University of Utah, Salt Lake City, UT, United States
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11
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Conway M, Vuong TT, Hart K, Rohrwasser A, Eilbeck K. Pain points in parents' interactions with newborn screening systems: a qualitative study. BMC Pediatr 2022; 22:167. [PMID: 35361157 PMCID: PMC8967687 DOI: 10.1186/s12887-022-03160-1] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/24/2021] [Accepted: 02/08/2022] [Indexed: 11/23/2022] Open
Abstract
Background & Objectives This study aims to explore and elucidate parents’ experience of newborn screening [NBS], with the overarching goal of identifying desiderata for the development of informatics-based educational and health management resources. Methods We conducted four focus groups and four one-on-one qualitative interviews with a total of 35 participants between March and September 2020. Participants were grouped into three types: parents who had received true positive newborn screening results; parents who had received false positive results; and soon-to-be parents who had no direct experience of the screening process. Interview data were subjected to analysis using an inductive, constant comparison approach. Results Results are divided into five sections: (1) experiences related to the process of receiving NBS results and prior knowledge of the NBS program; (2) approaches to the management of a child’s medical data; (3) sources of additional informational and emotional support; (4) barriers faced by parents navigating the health system; and (5) recommendations and suggestions for new parents experiencing the NBS process. Conclusion Our analysis revealed a wide range of experiences of, and attitudes towards the newborn screening program and the wider newborn screening system. While parents’ view of the screening process was – on the whole – positive, some participants reported experiencing substantial frustration, particularly related to how results are initially communicated and difficulties in accessing reliable, timely information. This frustration with current information management and education resources indicates a role for informatics-based approaches in addressing parents’ information needs.
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Affiliation(s)
- Mike Conway
- School of Computing and Information Systems , University of Melbourne, Parkville, VIC 3052, Australia.
| | - Truc Thuy Vuong
- Cell, Molecular, and Cancer Biology Graduate Program and Medical Sciences Graduate Program, Indiana University, School of Medicine, Bloomington, IN 47405, USA
| | - Kim Hart
- Utah Department of Health, Salt Lake City, UT, USA
| | | | - Karen Eilbeck
- Department of Biomedical Informatics, University of Utah, Salt Lake City, UT, 84108, USA.
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12
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Wesołowski S, Lemmon G, Hernandez EJ, Henrie A, Miller TA, Weyhrauch D, Puchalski MD, Bray BE, Shah RU, Deshmukh VG, Delaney R, Yost HJ, Eilbeck K, Tristani-Firouzi M, Yandell M. An explainable artificial intelligence approach for predicting cardiovascular outcomes using electronic health records. PLOS Digit Health 2022; 1:e0000004. [PMID: 35373216 PMCID: PMC8975108 DOI: 10.1371/journal.pdig.0000004] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/31/2021] [Accepted: 11/17/2021] [Indexed: 11/19/2022]
Abstract
Understanding the conditionally-dependent clinical variables that drive cardiovascular health outcomes is a major challenge for precision medicine. Here, we deploy a recently developed massively scalable comorbidity discovery method called Poisson Binomial based Comorbidity discovery (PBC), to analyze Electronic Health Records (EHRs) from the University of Utah and Primary Children's Hospital (over 1.6 million patients and 77 million visits) for comorbid diagnoses, procedures, and medications. Using explainable Artificial Intelligence (AI) methodologies, we then tease apart the intertwined, conditionally-dependent impacts of comorbid conditions and demography upon cardiovascular health, focusing on the key areas of heart transplant, sinoatrial node dysfunction and various forms of congenital heart disease. The resulting multimorbidity networks make possible wide-ranging explorations of the comorbid and demographic landscapes surrounding these cardiovascular outcomes, and can be distributed as web-based tools for further community-based outcomes research. The ability to transform enormous collections of EHRs into compact, portable tools devoid of Protected Health Information solves many of the legal, technological, and data-scientific challenges associated with large-scale EHR analyses.
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Affiliation(s)
- Sergiusz Wesołowski
- Department of Human Genetics and Utah Center for Genetic Discovery, University of Utah, Salt Lake City, UT, United States of America
| | - Gordon Lemmon
- Department of Human Genetics and Utah Center for Genetic Discovery, University of Utah, Salt Lake City, UT, United States of America
| | - Edgar J. Hernandez
- Department of Human Genetics and Utah Center for Genetic Discovery, University of Utah, Salt Lake City, UT, United States of America
| | - Alex Henrie
- Department of Human Genetics and Utah Center for Genetic Discovery, University of Utah, Salt Lake City, UT, United States of America
| | - Thomas A. Miller
- Division of Pediatric Cardiology, University of Utah School of Medicine, Salt Lake City, UT, United States of America
| | - Derek Weyhrauch
- Division of Pediatric Cardiology, University of Utah School of Medicine, Salt Lake City, UT, United States of America
| | - Michael D. Puchalski
- Division of Pediatric Cardiology, University of Utah School of Medicine, Salt Lake City, UT, United States of America
| | - Bruce E. Bray
- Division of Cardiovascular Medicine, University of Utah School of Medicine, Salt Lake City, UT, United States of America
- University of Utah, Biomedical Informatics, Salt Lake City, UT 84108, United States of America
| | - Rashmee U. Shah
- Division of Cardiovascular Medicine, University of Utah School of Medicine, Salt Lake City, UT, United States of America
| | - Vikrant G. Deshmukh
- University of Utah Health Care CMIO Office, Salt Lake City, UT, United States of America
| | - Rebecca Delaney
- Department of Population Health Sciences, University of Utah, Salt Lake City, UT, United States of America
| | - H. Joseph Yost
- Molecular Medicine Program, University of Utah, Salt Lake City, UT, United States of America
| | - Karen Eilbeck
- Department of Population Health Sciences, University of Utah, Salt Lake City, UT, United States of America
| | - Martin Tristani-Firouzi
- Division of Pediatric Cardiology, University of Utah School of Medicine, Salt Lake City, UT, United States of America
- Nora Eccles Harrison CVRTI, University of Utah School of Medicine, Salt Lake City, UT, United States of America
| | - Mark Yandell
- Department of Human Genetics and Utah Center for Genetic Discovery, University of Utah, Salt Lake City, UT, United States of America
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13
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Rehm HL, Page AJ, Smith L, Adams JB, Alterovitz G, Babb LJ, Barkley MP, Baudis M, Beauvais MJ, Beck T, Beckmann JS, Beltran S, Bernick D, Bernier A, Bonfield JK, Boughtwood TF, Bourque G, Bowers SR, Brookes AJ, Brudno M, Brush MH, Bujold D, Burdett T, Buske OJ, Cabili MN, Cameron DL, Carroll RJ, Casas-Silva E, Chakravarty D, Chaudhari BP, Chen SH, Cherry JM, Chung J, Cline M, Clissold HL, Cook-Deegan RM, Courtot M, Cunningham F, Cupak M, Davies RM, Denisko D, Doerr MJ, Dolman LI, Dove ES, Dursi LJ, Dyke SO, Eddy JA, Eilbeck K, Ellrott KP, Fairley S, Fakhro KA, Firth HV, Fitzsimons MS, Fiume M, Flicek P, Fore IM, Freeberg MA, Freimuth RR, Fromont LA, Fuerth J, Gaff CL, Gan W, Ghanaim EM, Glazer D, Green RC, Griffith M, Griffith OL, Grossman RL, Groza T, Guidry Auvil JM, Guigó R, Gupta D, Haendel MA, Hamosh A, Hansen DP, Hart RK, Hartley DM, Haussler D, Hendricks-Sturrup RM, Ho CW, Hobb AE, Hoffman MM, Hofmann OM, Holub P, Hsu JS, Hubaux JP, Hunt SE, Husami A, Jacobsen JO, Jamuar SS, Janes EL, Jeanson F, Jené A, Johns AL, Joly Y, Jones SJ, Kanitz A, Kato K, Keane TM, Kekesi-Lafrance K, Kelleher J, Kerry G, Khor SS, Knoppers BM, Konopko MA, Kosaki K, Kuba M, Lawson J, Leinonen R, Li S, Lin MF, Linden M, Liu X, Liyanage IU, Lopez J, Lucassen AM, Lukowski M, Mann AL, Marshall J, Mattioni M, Metke-Jimenez A, Middleton A, Milne RJ, Molnár-Gábor F, Mulder N, Munoz-Torres MC, Nag R, Nakagawa H, Nasir J, Navarro A, Nelson TH, Niewielska A, Nisselle A, Niu J, Nyrönen TH, O’Connor BD, Oesterle S, Ogishima S, Ota Wang V, Paglione LA, Palumbo E, Parkinson HE, Philippakis AA, Pizarro AD, Prlic A, Rambla J, Rendon A, Rider RA, Robinson PN, Rodarmer KW, Rodriguez LL, Rubin AF, Rueda M, Rushton GA, Ryan RS, Saunders GI, Schuilenburg H, Schwede T, Scollen S, Senf A, Sheffield NC, Skantharajah N, Smith AV, Sofia HJ, Spalding D, Spurdle AB, Stark Z, Stein LD, Suematsu M, Tan P, Tedds JA, Thomson AA, Thorogood A, Tickle TL, Tokunaga K, Törnroos J, Torrents D, Upchurch S, Valencia A, Guimera RV, Vamathevan J, Varma S, Vears DF, Viner C, Voisin C, Wagner AH, Wallace SE, Walsh BP, Williams MS, Winkler EC, Wold BJ, Wood GM, Woolley JP, Yamasaki C, Yates AD, Yung CK, Zass LJ, Zaytseva K, Zhang J, Goodhand P, North K, Birney E. GA4GH: International policies and standards for data sharing across genomic research and healthcare. Cell Genom 2021; 1:100029. [PMID: 35072136 PMCID: PMC8774288 DOI: 10.1016/j.xgen.2021.100029] [Citation(s) in RCA: 64] [Impact Index Per Article: 21.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
Abstract
The Global Alliance for Genomics and Health (GA4GH) aims to accelerate biomedical advances by enabling the responsible sharing of clinical and genomic data through both harmonized data aggregation and federated approaches. The decreasing cost of genomic sequencing (along with other genome-wide molecular assays) and increasing evidence of its clinical utility will soon drive the generation of sequence data from tens of millions of humans, with increasing levels of diversity. In this perspective, we present the GA4GH strategies for addressing the major challenges of this data revolution. We describe the GA4GH organization, which is fueled by the development efforts of eight Work Streams and informed by the needs of 24 Driver Projects and other key stakeholders. We present the GA4GH suite of secure, interoperable technical standards and policy frameworks and review the current status of standards, their relevance to key domains of research and clinical care, and future plans of GA4GH. Broad international participation in building, adopting, and deploying GA4GH standards and frameworks will catalyze an unprecedented effort in data sharing that will be critical to advancing genomic medicine and ensuring that all populations can access its benefits.
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Affiliation(s)
- Heidi L. Rehm
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Massachusetts General Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
| | - Angela J.H. Page
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Global Alliance for Genomics and Health, Toronto, ON, Canada
| | - Lindsay Smith
- Global Alliance for Genomics and Health, Toronto, ON, Canada
- Ontario Institute for Cancer Research, Toronto, ON, Canada
| | - Jeremy B. Adams
- Global Alliance for Genomics and Health, Toronto, ON, Canada
- Ontario Institute for Cancer Research, Toronto, ON, Canada
| | - Gil Alterovitz
- Brigham and Women’s Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
| | | | | | - Michael Baudis
- University of Zurich, Zurich, Switzerland
- SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Michael J.S. Beauvais
- Global Alliance for Genomics and Health, Toronto, ON, Canada
- McGill University, Montreal, QC, Canada
| | - Tim Beck
- University of Leicester, Leicester, UK
| | | | - Sergi Beltran
- CNAG-CRG, Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology, Barcelona, Spain
- Universitat Pompeu Fabra (UPF), Barcelona, Spain
- Universitat de Barcelona, Barcelona, Spain
| | - David Bernick
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | | | | | - Tiffany F. Boughtwood
- Australian Genomics, Parkville, VIC, Australia
- Murdoch Children’s Research Institute, Parkville, VIC, Australia
| | - Guillaume Bourque
- McGill University, Montreal, QC, Canada
- Canadian Center for Computational Genomics, Montreal, QC, Canada
| | | | | | - Michael Brudno
- Canadian Center for Computational Genomics, Montreal, QC, Canada
- University of Toronto, Toronto, ON, Canada
- University Health Network, Toronto, ON, Canada
- Vector Institute, Toronto, ON, Canada
- Canadian Distributed Infrastructure for Genomics (CanDIG), Toronto, ON, Canada
| | | | - David Bujold
- McGill University, Montreal, QC, Canada
- Canadian Center for Computational Genomics, Montreal, QC, Canada
- Canadian Distributed Infrastructure for Genomics (CanDIG), Toronto, ON, Canada
| | - Tony Burdett
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Hinxton, UK
| | | | | | - Daniel L. Cameron
- Walter and Eliza Hall Institute of Medical Research, Parkville, VIC, Australia
- University of Melbourne, Melbourne, VIC, Australia
| | | | | | | | - Bimal P. Chaudhari
- Nationwide Children’s Hospital, Columbus, OH, USA
- The Ohio State University, Columbus, OH, USA
| | - Shu Hui Chen
- National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD, USA
| | | | - Justina Chung
- Global Alliance for Genomics and Health, Toronto, ON, Canada
- Ontario Institute for Cancer Research, Toronto, ON, Canada
| | - Melissa Cline
- UC Santa Cruz Genomics Institute, Santa Cruz, CA, USA
| | | | | | - Mélanie Courtot
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Hinxton, UK
| | - Fiona Cunningham
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Hinxton, UK
| | | | | | | | | | | | | | - L. Jonathan Dursi
- University Health Network, Toronto, ON, Canada
- Canadian Distributed Infrastructure for Genomics (CanDIG), Toronto, ON, Canada
| | | | | | | | | | - Susan Fairley
- Global Alliance for Genomics and Health, Toronto, ON, Canada
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Hinxton, UK
| | - Khalid A. Fakhro
- Sidra Medicine, Doha, Qatar
- Weill Cornell Medicine - Qatar, Doha, Qatar
| | - Helen V. Firth
- Wellcome Sanger Institute, Hinxton, UK
- Addenbrooke’s Hospital, Cambridge, UK
| | | | | | - Paul Flicek
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Hinxton, UK
| | - Ian M. Fore
- National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Mallory A. Freeberg
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Hinxton, UK
| | | | - Lauren A. Fromont
- Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology, Barcelona, Spain
| | | | - Clara L. Gaff
- Australian Genomics, Parkville, VIC, Australia
- Murdoch Children’s Research Institute, Parkville, VIC, Australia
- Walter and Eliza Hall Institute of Medical Research, Parkville, VIC, Australia
- University of Melbourne, Melbourne, VIC, Australia
| | - Weiniu Gan
- National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD, USA
| | - Elena M. Ghanaim
- National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA
| | - David Glazer
- Verily Life Sciences, South San Francisco, CA, USA
| | - Robert C. Green
- Brigham and Women’s Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
| | - Malachi Griffith
- Washington University School of Medicine in St. Louis, St. Louis, MO, USA
| | - Obi L. Griffith
- Washington University School of Medicine in St. Louis, St. Louis, MO, USA
| | | | | | | | - Roderic Guigó
- Universitat Pompeu Fabra (UPF), Barcelona, Spain
- Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology, Barcelona, Spain
| | - Dipayan Gupta
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Hinxton, UK
| | | | - Ada Hamosh
- Johns Hopkins University, Baltimore, MD, USA
| | - David P. Hansen
- Australian Genomics, Parkville, VIC, Australia
- The Australian e-Health Research Centre, CSIRO, Herston, QLD, Australia
| | - Reece K. Hart
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Invitae, San Francisco, CA, USA
- MyOme, Inc, San Bruno, CA, USA
| | | | - David Haussler
- UC Santa Cruz Genomics Institute, Santa Cruz, CA, USA
- Howard Hughes Medical Institute, University of California, Santa Cruz, CA, USA
| | | | | | | | - Michael M. Hoffman
- University of Toronto, Toronto, ON, Canada
- University Health Network, Toronto, ON, Canada
- Vector Institute, Toronto, ON, Canada
| | - Oliver M. Hofmann
- University of Toronto, Toronto, ON, Canada
- University of Melbourne, Melbourne, VIC, Australia
| | - Petr Holub
- BBMRI-ERIC, Graz, Austria
- Masaryk University, Brno, Czech Republic
| | | | | | - Sarah E. Hunt
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Hinxton, UK
| | - Ammar Husami
- Cincinnati Children’s Hospital Medical Center, Cincinnati, OH, USA
| | | | - Saumya S. Jamuar
- SingHealth Duke-NUS Genomic Medicine Centre, Singapore, Republic of Singapore
- SingHealth Duke-NUS Institute of Precision Medicine, Singapore, Republic of Singapore
| | - Elizabeth L. Janes
- Global Alliance for Genomics and Health, Toronto, ON, Canada
- University of Waterloo, Waterloo, ON, Canada
| | | | - Aina Jené
- Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology, Barcelona, Spain
| | - Amber L. Johns
- Garvan Institute of Medical Research, Darlinghurst, NSW, Australia
| | - Yann Joly
- McGill University, Montreal, QC, Canada
| | - Steven J.M. Jones
- Canada’s Michael Smith Genome Sciences Centre, BC Cancer, Vancouver, BC, Canada
| | - Alexander Kanitz
- SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland
- University of Basel, Basel, Switzerland
| | | | - Thomas M. Keane
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Hinxton, UK
- University of Nottingham, Nottingham, UK
| | - Kristina Kekesi-Lafrance
- Global Alliance for Genomics and Health, Toronto, ON, Canada
- McGill University, Montreal, QC, Canada
| | | | - Giselle Kerry
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Hinxton, UK
| | - Seik-Soon Khor
- National Center for Global Health and Medicine Hospital, Tokyo, Japan
- University of Tokyo, Tokyo, Japan
| | | | | | | | | | | | - Rasko Leinonen
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Hinxton, UK
| | - Stephanie Li
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Global Alliance for Genomics and Health, Toronto, ON, Canada
| | | | - Mikael Linden
- CSC–IT Center for Science, Espoo, Finland
- ELIXIR Finland, Espoo, Finland
| | | | - Isuru Udara Liyanage
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Hinxton, UK
| | | | | | | | - Alice L. Mann
- Global Alliance for Genomics and Health, Toronto, ON, Canada
- Wellcome Sanger Institute, Hinxton, UK
| | | | | | | | - Anna Middleton
- Wellcome Connecting Science, Hinxton, UK
- University of Cambridge, Cambridge, UK
| | - Richard J. Milne
- Wellcome Connecting Science, Hinxton, UK
- University of Cambridge, Cambridge, UK
| | | | - Nicola Mulder
- H3ABioNet, Computational Biology Division, IDM, Faculty of Health Sciences, Cape Town, South Africa
| | | | - Rishi Nag
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Hinxton, UK
| | - Hidewaki Nakagawa
- Japan Agency for Medical Research & Development (AMED), Tokyo, Japan
- RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
| | | | - Arcadi Navarro
- Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology, Barcelona, Spain
- Institute of Evolutionary Biology (UPF-CSIC), Universitat Pompeu Fabra (UPF), Barcelona, Spain
- Institució Catalana de Recerca i Estudis Avançats, Barcelona, Spain
- Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation, Barcelona, Spain
| | | | - Ania Niewielska
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Hinxton, UK
| | - Amy Nisselle
- Murdoch Children’s Research Institute, Parkville, VIC, Australia
- University of Melbourne, Melbourne, VIC, Australia
- Human Genetics Society of Australasia Education, Ethics & Social Issues Committee, Alexandria, NSW, Australia
| | - Jeffrey Niu
- University Health Network, Toronto, ON, Canada
| | - Tommi H. Nyrönen
- CSC–IT Center for Science, Espoo, Finland
- ELIXIR Finland, Espoo, Finland
| | | | - Sabine Oesterle
- SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | | | - Vivian Ota Wang
- National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | | | - Emilio Palumbo
- Universitat Pompeu Fabra (UPF), Barcelona, Spain
- Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology, Barcelona, Spain
| | - Helen E. Parkinson
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Hinxton, UK
| | | | | | | | - Jordi Rambla
- Universitat Pompeu Fabra (UPF), Barcelona, Spain
- Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology, Barcelona, Spain
| | | | - Renee A. Rider
- National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA
| | - Peter N. Robinson
- The Jackson Laboratory, Farmington, CT, USA
- University of Connecticut, Farmington, CT, USA
| | - Kurt W. Rodarmer
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD, USA
| | | | - Alan F. Rubin
- Walter and Eliza Hall Institute of Medical Research, Parkville, VIC, Australia
- University of Melbourne, Melbourne, VIC, Australia
| | - Manuel Rueda
- Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology, Barcelona, Spain
| | | | | | | | - Helen Schuilenburg
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Hinxton, UK
| | - Torsten Schwede
- SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland
- University of Basel, Basel, Switzerland
| | | | | | | | - Neerjah Skantharajah
- Global Alliance for Genomics and Health, Toronto, ON, Canada
- Ontario Institute for Cancer Research, Toronto, ON, Canada
| | | | - Heidi J. Sofia
- National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA
| | - Dylan Spalding
- CSC–IT Center for Science, Espoo, Finland
- ELIXIR Finland, Espoo, Finland
| | | | - Zornitza Stark
- Australian Genomics, Parkville, VIC, Australia
- Murdoch Children’s Research Institute, Parkville, VIC, Australia
- University of Melbourne, Melbourne, VIC, Australia
| | - Lincoln D. Stein
- Ontario Institute for Cancer Research, Toronto, ON, Canada
- University of Toronto, Toronto, ON, Canada
| | | | - Patrick Tan
- SingHealth Duke-NUS Genomic Medicine Centre, Singapore, Republic of Singapore
- Precision Health Research Singapore, Singapore, Republic of Singapore
- Genome Institute of Singapore, Singapore, Republic of Singapore
| | | | - Alastair A. Thomson
- National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD, USA
| | - Adrian Thorogood
- McGill University, Montreal, QC, Canada
- University of Luxembourg, Esch-sur-Alzette, Luxembourg
| | | | - Katsushi Tokunaga
- University of Tokyo, Tokyo, Japan
- National Center for Global Health and Medicine, Tokyo, Japan
| | - Juha Törnroos
- CSC–IT Center for Science, Espoo, Finland
- ELIXIR Finland, Espoo, Finland
| | - David Torrents
- Institució Catalana de Recerca i Estudis Avançats, Barcelona, Spain
- Barcelona Supercomputing Center, Barcelona, Spain
| | - Sean Upchurch
- California Institute of Technology, Pasadena, CA, USA
| | - Alfonso Valencia
- Institució Catalana de Recerca i Estudis Avançats, Barcelona, Spain
- Barcelona Supercomputing Center, Barcelona, Spain
| | | | - Jessica Vamathevan
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Hinxton, UK
| | - Susheel Varma
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Hinxton, UK
- Health Data Research UK, London, UK
| | - Danya F. Vears
- Murdoch Children’s Research Institute, Parkville, VIC, Australia
- University of Melbourne, Melbourne, VIC, Australia
- Human Genetics Society of Australasia Education, Ethics & Social Issues Committee, Alexandria, NSW, Australia
- Melbourne Law School, University of Melbourne, Parkville, VIC, Australia
| | - Coby Viner
- University of Toronto, Toronto, ON, Canada
- University Health Network, Toronto, ON, Canada
| | | | - Alex H. Wagner
- Nationwide Children’s Hospital, Columbus, OH, USA
- The Ohio State University, Columbus, OH, USA
| | | | | | | | - Eva C. Winkler
- Section of Translational Medical Ethics, University Hospital Heidelberg, Heidelberg, Germany
| | | | | | | | | | - Andrew D. Yates
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Hinxton, UK
| | - Christina K. Yung
- Ontario Institute for Cancer Research, Toronto, ON, Canada
- Indoc Research, Toronto, ON, Canada
| | - Lyndon J. Zass
- H3ABioNet, Computational Biology Division, IDM, Faculty of Health Sciences, Cape Town, South Africa
| | - Ksenia Zaytseva
- McGill University, Montreal, QC, Canada
- Canadian Centre for Computational Genomics, Montreal, QC, Canada
| | - Junjun Zhang
- Ontario Institute for Cancer Research, Toronto, ON, Canada
| | - Peter Goodhand
- Global Alliance for Genomics and Health, Toronto, ON, Canada
- Ontario Institute for Cancer Research, Toronto, ON, Canada
| | - Kathryn North
- Murdoch Children’s Research Institute, Parkville, VIC, Australia
- University of Toronto, Toronto, ON, Canada
- University of Melbourne, Melbourne, VIC, Australia
| | - Ewan Birney
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Hinxton, UK
- European Molecular Biology Laboratory, Heidelberg, Germany
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14
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Sant DW, Sinclair M, Mungall CJ, Schulz S, Zerbino D, Lovering RC, Logie C, Eilbeck K. Sequence ontology terminology for gene regulation. Biochim Biophys Acta Gene Regul Mech 2021; 1864:194745. [PMID: 34389511 DOI: 10.1016/j.bbagrm.2021.194745] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Received: 01/30/2021] [Revised: 07/17/2021] [Accepted: 08/05/2021] [Indexed: 01/12/2023]
Abstract
The Sequence Ontology (SO) is a structured, controlled vocabulary that provides terms and definitions for genomic annotation. The Gene Regulation Ensemble Effort for the Knowledge Commons (GREEKC) initiative has gathered input from many groups of researchers, including the SO, the Gene Ontology (GO), and gene regulation experts, with the goal of curating information about how gene expression is regulated at the molecular level. Here we discuss recent updates to the SO reflecting current knowledge. We have developed more accurate human-readable terms (also known as classes), including new definitions, and relationships related to the expression of genes. New findings continue to give us insight into the biology of gene regulation, including the order of events, and participants in those events. These updates to the SO support logical reasoning with the current understanding of gene expression regulation at the molecular level.
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Affiliation(s)
- David W Sant
- Department of biomedical informatics, University of Utah, Salt Lake City, UT, USA; Department of Biomedical Sciences, Noorda College of Osteopathic Medicine, Provo, UT, USA.
| | - Michael Sinclair
- Department of biomedical informatics, University of Utah, Salt Lake City, UT, USA
| | - Christopher J Mungall
- Environmental Genomics and Systems Biology, Lawrence Berkeley National Laboratory: Berkeley, CA, US.
| | - Stefan Schulz
- Institute for Medical Informatics, Statistics and Documentation, Medical University of Graz, Austria.
| | - Daniel Zerbino
- European Molecular Biology Laboratory, European Bioinformatics Institute, Hinxton, Cambridge, UK.
| | - Ruth C Lovering
- Functional Gene Annotation, Preclinical and Fundamental Science, UCL Institute of Cardiovascular Science, University College London, London, UK.
| | - Colin Logie
- Radboud Institute for Molecular Life Sciences, Geert Grooteplein Zuid 28, 6525, GA Nijmegen, Netherlands.
| | - Karen Eilbeck
- Department of biomedical informatics, University of Utah, Salt Lake City, UT, USA.
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15
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Watkins M, Au A, Vuong T, Wallis H, Hart K, Rohrwasser A, Eilbeck K. ResultsMyWay: combining Fast Healthcare Interoperability Resources (FHIR), Clinical Quality Language (CQL), and informational resources to create a newborn screening application. AMIA Jt Summits Transl Sci Proc 2021; 2021:615-623. [PMID: 34457177 PMCID: PMC8378617] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
Newborn screening (NBS) can be life-changing for the families of infants who test positive for a rare condition. While resources exist to support these families, there can be delays in sharing these resources due to communication lag between the laboratory, result interpreting clinician, family of the newborn, and additional care providers. This delay can also be exacerbated when additional health history is required from the mother and infant. ResultsMyWay is a proof-of-concept application that uses Clinical Quality Language (CQL) to automate the search for this additional health history. It also translates the NBS results into Fast Healthcare Interoperability Resources (FHIR), increasing both the ease of exchange and the future utility of these data points. After the families are given the NBS results, ResultsMyWay then acts as a hub for several types of informational resources about the recently diagnosed condition.
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Affiliation(s)
- Michael Watkins
- Department of Biomedical Informatics, University of Utah, Salt Lake City, Utah
| | - Alex Au
- Department of Biomedical Informatics, University of Utah, Salt Lake City, Utah
| | - Truc Vuong
- Department of Biomedical Informatics, University of Utah, Salt Lake City, Utah
| | | | - Kim Hart
- Utah Department of Health, Salt Lake City, Utah
| | | | - Karen Eilbeck
- Department of Biomedical Informatics, University of Utah, Salt Lake City, Utah
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16
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Lario R, Hasley S, White SA, Eilbeck K, Soley R, Huff S, Kawamoto K. Utilization of BPM+ Health for the Representation of Clinical Knowledge: A Framework for the Expression and Assessment of Clinical Practice Guidelines (CPG) Utilizing Existing and Emerging Object Management Group (OMG) Standards. AMIA Annu Symp Proc 2021; 2020:687-696. [PMID: 33936443 PMCID: PMC8075494] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
Clinical Practice Guidelines (CPG), meant to express best practices in healthcare, are commonly presented as narrative documents communicating care processes, decision making, and clinical case knowledge. However, these narratives in and of themselves lack the specificity and conciseness in their use of language to unambiguously express quality clinical recommendations. This impacts the confidence of clinicians, uptake, and implementation of the guidance. As important as the quality of the clinical knowledge articulated, is the quality of the language(s) and methods used to express the recommendations. In this paper, we propose the BPM+ family of modeling languages as a potential solution to this challenge. We present a formalized process and framework for translating CPGs into a standardized BPM+ model. Further, we discuss the features and characteristics of modeling languages that underpin the quality in expressing clinical recommendations. Using an existing CPG, we defined a systematic series of steps to deconstruct the CPG into knowledge constituents, assign CPG knowledge constituents to BPM+ elements, and re-assemble the parts into a clear, precise, and executable model. Limitations of both the CPG and the current BPM+ languages are discussed.
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Affiliation(s)
- Robert Lario
- Department of Biomedical Informatics, University of Utah, Salt Lake City, UT
- Department of Veterans Affairs Knowledge Based Systems, Salt Lake City, UT
| | - Steve Hasley
- Department of OB/GYN/RS, University of Pittsburgh Medical Center, Pittsburgh, PA
| | | | - Karen Eilbeck
- Department of Biomedical Informatics, University of Utah, Salt Lake City, UT
| | | | - Stan Huff
- Department of Biomedical Informatics, University of Utah, Salt Lake City, UT
- Intermountain Health, Salt Lake City, UT
| | - Kensaku Kawamoto
- Department of Biomedical Informatics, University of Utah, Salt Lake City, UT
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17
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Ruiz-Schultz N, Sant D, Norcross S, Dansithong W, Hart K, Asay B, Little J, Chung K, Oakeson KF, Young EL, Eilbeck K, Rohrwasser A. Methods and feasibility study for exome sequencing as a universal second-tier test in newborn screening. Genet Med 2021; 23:767-776. [PMID: 33442025 DOI: 10.1038/s41436-020-01058-w] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2020] [Revised: 11/24/2020] [Accepted: 11/25/2020] [Indexed: 11/09/2022] Open
Abstract
PURPOSE Newborn screening disorders increasingly require genetic variant analysis as part of second-tier or confirmatory testing. Sanger sequencing and gene-specific next-generation sequencing (NGS)-based tests, the current methods of choice, are costly and lack scalability when expanding to new conditions. We describe a scalable, exome sequencing-based NGS pipeline with a priori analysis restriction that can be universally applied to any NBS disorder. METHODS De-identified abnormal newborn screening specimens representing severe combined immune deficiency (SCID), cystic fibrosis (CF), VLCAD deficiency, metachromatic leukodystrophy (MLD), and in silico sequence read data sets were used to validate the pipeline. To support interpretation and clinical decision-making within the bioinformatics pipeline, variants from multiple databases were curated and validated. RESULTS CFTR variant panel analysis correctly identified all variants. Concordance compared with diagnostic testing results for targeted gene analysis was between 78.6% and 100%. Validation of the bioinformatics pipeline with in silico data sets revealed a 100% detection rate. Varying degrees of overlap were observed between ClinVar and other databases ranging from 3% to 65%. Data normalization revealed that 11% of variants across the databases required manual curation. CONCLUSION This pipeline allows for restriction of analysis to variants within a single gene or multiple genes, and can be readily expanded to full exome analysis if clinically indicated and parental consent is granted.
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Affiliation(s)
| | - David Sant
- Department of Biomedical Informatics, University of Utah, Salt Lake City, UT, USA
| | | | | | - Kim Hart
- Utah Public Health Laboratory, Salt Lake City, UT, USA
| | - Bryce Asay
- Utah Public Health Laboratory, Salt Lake City, UT, USA
| | - Jordan Little
- Department of Biomedical Informatics, University of Utah, Salt Lake City, UT, USA
| | - Krystal Chung
- Department of Biomedical Informatics, University of Utah, Salt Lake City, UT, USA
| | | | - Erin L Young
- Utah Public Health Laboratory, Salt Lake City, UT, USA
| | - Karen Eilbeck
- Department of Biomedical Informatics, University of Utah, Salt Lake City, UT, USA
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18
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Mejía-Almonte C, Busby SJW, Wade JT, van Helden J, Arkin AP, Stormo GD, Eilbeck K, Palsson BO, Galagan JE, Collado-Vides J. Redefining fundamental concepts of transcription initiation in bacteria. Nat Rev Genet 2020; 21:699-714. [PMID: 32665585 PMCID: PMC7990032 DOI: 10.1038/s41576-020-0254-8] [Citation(s) in RCA: 76] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 05/29/2020] [Indexed: 12/15/2022]
Abstract
Despite enormous progress in understanding the fundamentals of bacterial gene regulation, our knowledge remains limited when compared with the number of bacterial genomes and regulatory systems to be discovered. Derived from a small number of initial studies, classic definitions for concepts of gene regulation have evolved as the number of characterized promoters has increased. Together with discoveries made using new technologies, this knowledge has led to revised generalizations and principles. In this Expert Recommendation, we suggest precise, updated definitions that support a logical, consistent conceptual framework of bacterial gene regulation, focusing on transcription initiation. The resulting concepts can be formalized by ontologies for computational modelling, laying the foundation for improved bioinformatics tools, knowledge-based resources and scientific communication. Thus, this work will help researchers construct better predictive models, with different formalisms, that will be useful in engineering, synthetic biology, microbiology and genetics.
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Affiliation(s)
- Citlalli Mejía-Almonte
- Programa de Genómica Computacional, Centro de Ciencias Genómicas, Universidad Nacional Autónoma de México, Morelos, Cuernavaca, México
| | | | - Joseph T Wade
- Division of Genetics, Wadsworth Center, New York State Department of Health, Albany, NY, USA
| | - Jacques van Helden
- Aix-Marseille University, INSERM UMR S 1090, Theory and Approaches of Genome Complexity (TAGC), Marseille, France
- CNRS, Institut Français de Bioinformatique, IFB-core, UMS 3601, Evry, France
| | - Adam P Arkin
- Department of Bioengineering, University of California, Berkeley, Berkeley, CA, USA
| | - Gary D Stormo
- Department of Genetics, Washington University School of Medicine, St Louis, MO, USA
| | - Karen Eilbeck
- Department of Biomedical Informatics, University of Utah School of Medicine, Salt Lake City, UT, USA
| | - Bernhard O Palsson
- Department of Bioengineering, University of California, San Diego, La Jolla, CA, USA
| | - James E Galagan
- Department of Biomedical Engineering, Boston University, Boston, MA, USA
| | - Julio Collado-Vides
- Programa de Genómica Computacional, Centro de Ciencias Genómicas, Universidad Nacional Autónoma de México, Morelos, Cuernavaca, México.
- Department of Biomedical Engineering, Boston University, Boston, MA, USA.
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19
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Desvignes T, Loher P, Eilbeck K, Ma J, Urgese G, Fromm B, Sydes J, Aparicio-Puerta E, Barrera V, Espín R, Thibord F, Bofill-De Ros X, Londin E, Telonis AG, Ficarra E, Friedländer MR, Postlethwait JH, Rigoutsos I, Hackenberg M, Vlachos IS, Halushka MK, Pantano L. Unification of miRNA and isomiR research: the mirGFF3 format and the mirtop API. Bioinformatics 2020; 36:698-703. [PMID: 31504201 DOI: 10.1093/bioinformatics/btz675] [Citation(s) in RCA: 28] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2019] [Revised: 07/17/2019] [Accepted: 08/28/2019] [Indexed: 02/07/2023] Open
Abstract
MOTIVATION MicroRNAs (miRNAs) are small RNA molecules (∼22 nucleotide long) involved in post-transcriptional gene regulation. Advances in high-throughput sequencing technologies led to the discovery of isomiRs, which are miRNA sequence variants. While many miRNA-seq analysis tools exist, the diversity of output formats hinders accurate comparisons between tools and precludes data sharing and the development of common downstream analysis methods. RESULTS To overcome this situation, we present here a community-based project, miRNA Transcriptomic Open Project (miRTOP) working towards the optimization of miRNA analyses. The aim of miRTOP is to promote the development of downstream isomiR analysis tools that are compatible with existing detection and quantification tools. Based on the existing GFF3 format, we first created a new standard format, mirGFF3, for the output of miRNA/isomiR detection and quantification results from small RNA-seq data. Additionally, we developed a command line Python tool, mirtop, to create and manage the mirGFF3 format. Currently, mirtop can convert into mirGFF3 the outputs of commonly used pipelines, such as seqbuster, isomiR-SEA, sRNAbench, Prost! as well as BAM files. Some tools have also incorporated the mirGFF3 format directly into their code, such as, miRge2.0, IsoMIRmap and OptimiR. Its open architecture enables any tool or pipeline to output or convert results into mirGFF3. Collectively, this isomiR categorization system, along with the accompanying mirGFF3 and mirtop API, provide a comprehensive solution for the standardization of miRNA and isomiR annotation, enabling data sharing, reporting, comparative analyses and benchmarking, while promoting the development of common miRNA methods focusing on downstream steps of miRNA detection, annotation and quantification. AVAILABILITY AND IMPLEMENTATION https://github.com/miRTop/mirGFF3/ and https://github.com/miRTop/mirtop. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Thomas Desvignes
- Institute of Neuroscience, University of Oregon, Eugene, OR 97403, USA
| | - Phillipe Loher
- Computational Medicine Center, Thomas Jefferson University, Philadelphia, PA 19144, USA
| | - Karen Eilbeck
- University of Utah, Biomedical Informatics, Salt Lake City, UT 84108, USA
| | - Jeffery Ma
- Computational Medicine Center, Thomas Jefferson University, Philadelphia, PA 19144, USA
| | - Gianvito Urgese
- Department of Control and Computer Engineering, Politecnico di Torino, Torino 10129, Italy
| | - Bastian Fromm
- Science for Life Laboratory, Department of Molecular Biosciences, The Wenner-Gren Institute, Stockholm University, Stockholm 114 18, Sweden
| | - Jason Sydes
- Institute of Neuroscience, University of Oregon, Eugene, OR 97403, USA
| | - Ernesto Aparicio-Puerta
- Computational Epigenomics Laboratory, Genetics Department and Biotechnology Institute and Biosanitary Institute, University of Granada, Granada 18002, Spain
| | - Victor Barrera
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA
| | - Roderic Espín
- Universitat Oberta de Catalunya, Barcelona 08018, Spain
| | - Florian Thibord
- Sorbonne Université, Pierre Louis Doctoral School of Public Health, Paris 75006, France.,Institut National pour la Santé et la Recherche Médicale (INSERM) Unité Mixte de Recherche en Santé (UMR_S), University of Bordeaux, Bordeaux 33076, France
| | - Xavier Bofill-De Ros
- RNA Biology Laboratory, Center for Cancer Research, National Cancer Institute, Frederick, MD 21702, USA
| | - Eric Londin
- Computational Medicine Center, Thomas Jefferson University, Philadelphia, PA 19144, USA
| | - Aristeidis G Telonis
- Computational Medicine Center, Thomas Jefferson University, Philadelphia, PA 19144, USA
| | - Elisa Ficarra
- Department of Control and Computer Engineering, Politecnico di Torino, Torino 10129, Italy
| | - Marc R Friedländer
- Science for Life Laboratory, Department of Molecular Biosciences, The Wenner-Gren Institute, Stockholm University, Stockholm 114 18, Sweden
| | | | - Isidore Rigoutsos
- Computational Medicine Center, Thomas Jefferson University, Philadelphia, PA 19144, USA
| | - Michael Hackenberg
- Computational Epigenomics Laboratory, Genetics Department and Biotechnology Institute and Biosanitary Institute, University of Granada, Granada 18002, Spain
| | - Ioannis S Vlachos
- Non-coding Research Lab, Department of Pathology, Cancer Research Institute, Harvard Medical School Initiative for RNA Medicine, Beth Israel Deaconess Medical Center, Boston, MA 02115, USA
| | - Marc K Halushka
- Department of Pathology, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
| | - Lorena Pantano
- Bioinformatics Core, The Picower Institute for Learning and Memory, Cambridge, MA 02139, USA
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20
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Watkins M, Eilbeck K. FHIR Lab Reports: using SMART on FHIR and CDS Hooks to increase the clinical utility of pharmacogenomic laboratory test results. AMIA Jt Summits Transl Sci Proc 2020; 2020:683-692. [PMID: 32477691 PMCID: PMC7233102] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
Laboratory tests are a common aspect of clinical care and are the primary source of clinical genomic data. However, most laboratories use PDF documents to store and exchange the results of these tests. This locks the data into a static format and leaves the results only human-readable. The ordering clinician uses the results, but after that the information is unlikely to be used again. Future use would require a clinician to know that the test was performed, know where to find the PDF report, and take the time to open it and determine relevance to that future scenario. New computational standards such as SMART on FHIR and CDS Hooks present opportunities to better utilize these results, both physically upon receipt and asynchronously in future clinical encounters for that patient. Full app available at https://github.com/mwatkin8/FHIR-Lab-Reports-App. Demo available at http://hematite.genetics.utah.edu/FHIR-Lab-Reports/.
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Affiliation(s)
- Michael Watkins
- The Department of Biomedical Informatics, 421 Wakara Way, University of Utah, Salt Lake City, Utah 84108
| | - Karen Eilbeck
- The Department of Biomedical Informatics, 421 Wakara Way, University of Utah, Salt Lake City, Utah 84108
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21
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Watkins M, Rynearson S, Henrie A, Eilbeck K. Implementing the VMC Specification to Reduce Ambiguity in Genomic Variant Representation. AMIA Annu Symp Proc 2020; 2019:1226-1235. [PMID: 32308920 PMCID: PMC7153148] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
Current methods used for representing biological sequence variants allow flexibility, which has created redundancy within variant archives and discordance among variant representation tools. While research methodologies have been able to adapt to this ambiguity, strict clinical standards make it difficult to use this data in what would otherwise be useful clinical interventions. We implemented a specification developed by the GA4GH Variant Modeling Collaboration (VMC), which details a new approach to unambiguous representation of variants at the allelic level, as a haplotype, or as a genotype. Our implementation, called the VMC Test Suite (http://vcfclin.org), offers web tools to generate and insert VMC identifiers into a VCF file and to generate a VMC bundle JSON representation of a VCF file or HGVS expression. A command line tool with similar functionality is also introduced. These tools facilitate use of this standard-an important step toward reliable querying of variants and their associated annotations.
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Affiliation(s)
- Michael Watkins
- Biomedical Informatics, 421 Wakara Way, University of Utah, Salt Lake City, Utah 84108
| | - Shawn Rynearson
- Biomedical Informatics, 421 Wakara Way, University of Utah, Salt Lake City, Utah 84108
| | - Alex Henrie
- Biomedical Informatics, 421 Wakara Way, University of Utah, Salt Lake City, Utah 84108
| | - Karen Eilbeck
- Biomedical Informatics, 421 Wakara Way, University of Utah, Salt Lake City, Utah 84108
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22
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Zastrow DB, Baudet H, Shen W, Thomas A, Si Y, Weaver MA, Lager AM, Liu J, Mangels R, Dwight SS, Wright MW, Dobrowolski SF, Eilbeck K, Enns GM, Feigenbaum A, Lichter-Konecki U, Lyon E, Pasquali M, Watson M, Blau N, Steiner RD, Craigen WJ, Mao R. Unique aspects of sequence variant interpretation for inborn errors of metabolism (IEM): The ClinGen IEM Working Group and the Phenylalanine Hydroxylase Gene. Hum Mutat 2019; 39:1569-1580. [PMID: 30311390 DOI: 10.1002/humu.23649] [Citation(s) in RCA: 37] [Impact Index Per Article: 7.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2018] [Revised: 08/28/2018] [Accepted: 09/06/2018] [Indexed: 11/09/2022]
Abstract
The ClinGen Inborn Errors of Metabolism Working Group was tasked with creating a comprehensive, standardized knowledge base of genes and variants for metabolic diseases. Phenylalanine hydroxylase (PAH) deficiency was chosen to pilot development of the Working Group's standards and guidelines. A PAH variant curation expert panel (VCEP) was created to facilitate this process. Following ACMG-AMP variant interpretation guidelines, we present the development of these standards in the context of PAH variant curation and interpretation. Existing ACMG-AMP rules were adjusted based on disease (6) or strength (5) or both (2). Disease adjustments include allele frequency thresholds, functional assay thresholds, and phenotype-specific guidelines. Our validation of PAH-specific variant interpretation guidelines is presented using 85 variants. The PAH VCEP interpretations were concordant with existing interpretations in ClinVar for 69 variants (81%). Development of biocurator tools and standards are also described. Using the PAH-specific ACMG-AMP guidelines, 714 PAH variants have been curated and will be submitted to ClinVar. We also discuss strategies and challenges in applying ACMG-AMP guidelines to autosomal recessive metabolic disease, and the curation of variants in these genes.
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Affiliation(s)
- Diane B Zastrow
- Palo Alto Medical Foundation, Palo Alto, California.,Stanford University, Stanford, California
| | - Heather Baudet
- University of North Carolina, Chapel Hill, North Carolina
| | - Wei Shen
- ARUP Laboratories, Salt Lake City, Utah.,University of Utah, Salt Lake City, Utah
| | - Amanda Thomas
- Department of Pathology and Cell Biology, Columbia University Irving Medical Center, New York, New York
| | - Yue Si
- GeneDx, Gaithersburg, Maryland
| | - Meredith A Weaver
- American College of Medical Genetics and Genomics, Bethesda, Maryland
| | - Angela M Lager
- Section of Hematology/Oncology, Department of Medicine, University of Chicago, Chicago, Illinois
| | - Jixia Liu
- Marshfield Clinic Research Institute, Marshfield, Wisconsin
| | | | | | | | | | | | | | - Annette Feigenbaum
- Rady Children's Hospital and University of California, San Diego, California
| | - Uta Lichter-Konecki
- Children's Hospital of Pittsburg of UPMC, University of Pittsburgh, Pittsburgh, Pennsylvania
| | - Elaine Lyon
- ARUP Laboratories, Salt Lake City, Utah.,University of Utah, Salt Lake City, Utah
| | - Marzia Pasquali
- ARUP Laboratories, Salt Lake City, Utah.,University of Utah, Salt Lake City, Utah
| | - Michael Watson
- American College of Medical Genetics and Genomics, Bethesda, Maryland
| | - Nenad Blau
- Dietmar-Hopp Metabolic Center, University Children's Hospital, Department of General Pediatrics, Heidelberg, Germany
| | - Robert D Steiner
- Marshfield Clinic Research Institute, Marshfield, Wisconsin.,University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin
| | | | - Rong Mao
- ARUP Laboratories, Salt Lake City, Utah.,University of Utah, Salt Lake City, Utah
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23
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Zastrow DB, Baudet H, Shen W, Thomas A, Si Y, Weaver MA, Lager AM, Liu J, Mangels R, Dwight SS, Wright MW, Dobrowolski SF, Eilbeck K, Enns GM, Feigenbaum A, Lichter‐Konecki U, Lyon E, Pasquali M, Watson M, Blau N, Steiner RD, Craigen WJ, Mao R. Cover Image, Volume 39, Issue 11. Hum Mutat 2018. [DOI: 10.1002/humu.23662] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
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24
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Henrie A, Hemphill SE, Ruiz-Schultz N, Cushman B, DiStefano MT, Azzariti D, Harrison SM, Rehm HL, Eilbeck K. ClinVar Miner: Demonstrating utility of a Web-based tool for viewing and filtering ClinVar data. Hum Mutat 2018; 39:1051-1060. [PMID: 29790234 DOI: 10.1002/humu.23555] [Citation(s) in RCA: 62] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2018] [Revised: 04/20/2018] [Accepted: 05/19/2018] [Indexed: 11/12/2022]
Abstract
ClinVar Miner is a Web-based suite that utilizes the data held in the National Center for Biotechnology Information's ClinVar archive. The goal is to render the data more accessible to processes pertaining to conflict resolution of variant interpretation as well as tracking details of data submission and data management for detailed variant curation. Here, we establish the use of these tools to address three separate use cases and to perform analyses across submissions. We demonstrate that the ClinVar Miner tools are an effective means to browse and consolidate data for variant submitters, curation groups, and general oversight. These tools are also relevant to the variant interpretation community in general.
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Affiliation(s)
- Alex Henrie
- Department of Biomedical Informatics, University of Utah School of Medicine, Salt Lake City, Utah
| | - Sarah E Hemphill
- Laboratory for Molecular Medicine, Partners Healthcare Personalized Medicine, Cambridge, Massachusetts
| | - Nicole Ruiz-Schultz
- Department of Biomedical Informatics, University of Utah School of Medicine, Salt Lake City, Utah
| | - Brandon Cushman
- Laboratory for Molecular Medicine, Partners Healthcare Personalized Medicine, Cambridge, Massachusetts
| | - Marina T DiStefano
- Laboratory for Molecular Medicine, Partners Healthcare Personalized Medicine, Cambridge, Massachusetts
| | - Danielle Azzariti
- Laboratory for Molecular Medicine, Partners Healthcare Personalized Medicine, Cambridge, Massachusetts
| | - Steven M Harrison
- Laboratory for Molecular Medicine, Partners Healthcare Personalized Medicine, Cambridge, Massachusetts
| | - Heidi L Rehm
- Laboratory for Molecular Medicine, Partners Healthcare Personalized Medicine, Cambridge, Massachusetts.,Department of Pathology, Brigham & Women's Hospital, Harvard Medical School, Boston, Massachusetts
| | - Karen Eilbeck
- Department of Biomedical Informatics, University of Utah School of Medicine, Salt Lake City, Utah
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25
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Flygare S, Hernandez EJ, Phan L, Moore B, Li M, Fejes A, Hu H, Eilbeck K, Huff C, Jorde L, G Reese M, Yandell M. The VAAST Variant Prioritizer (VVP): ultrafast, easy to use whole genome variant prioritization tool. BMC Bioinformatics 2018; 19:57. [PMID: 29463208 PMCID: PMC5819680 DOI: 10.1186/s12859-018-2056-y] [Citation(s) in RCA: 20] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2017] [Accepted: 02/13/2018] [Indexed: 11/24/2022] Open
Abstract
Background Prioritization of sequence variants for diagnosis and discovery of Mendelian diseases is challenging, especially in large collections of whole genome sequences (WGS). Fast, scalable solutions are needed for discovery research, for clinical applications, and for curation of massive public variant repositories such as dbSNP and gnomAD. In response, we have developed VVP, the VAAST Variant Prioritizer. VVP is ultrafast, scales to even the largest variant repositories and genome collections, and its outputs are designed to simplify clinical interpretation of variants of uncertain significance. Results We show that scoring the entire contents of dbSNP (> 155 million variants) requires only 95 min using a machine with 4 cpus and 16 GB of RAM, and that a 60X WGS can be processed in less than 5 min. We also demonstrate that VVP can score variants anywhere in the genome, regardless of type, effect, or location. It does so by integrating sequence conservation, the type of sequence change, allele frequencies, variant burden, and zygosity. Finally, we also show that VVP scores are consistently accurate, and easily interpreted, traits not shared by many commonly used tools such as SIFT and CADD. Conclusions VVP provides rapid and scalable means to prioritize any sequence variant, anywhere in the genome, and its scores are designed to facilitate variant interpretation using ACMG and NHS guidelines. These traits make it well suited for operation on very large collections of WGS sequences. Electronic supplementary material The online version of this article (10.1186/s12859-018-2056-y) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Steven Flygare
- Department of Human Genetics, University of Utah, Salt Lake City, UT, USA.,Present address: IDbyDNA Inc., San Francisco, CA, USA
| | - Edgar Javier Hernandez
- Department of Human Genetics, University of Utah, Salt Lake City, UT, USA.,USTAR Center for Genetic Discovery, Salt Lake City, UT, USA
| | - Lon Phan
- National Center for Biotechnology Information, Bethesda, MD, USA
| | - Barry Moore
- Department of Human Genetics, University of Utah, Salt Lake City, UT, USA.,USTAR Center for Genetic Discovery, Salt Lake City, UT, USA
| | - Man Li
- Department of Human Genetics, University of Utah, Salt Lake City, UT, USA
| | | | - Hao Hu
- Department of Epidemiology, M.D. Anderson Cancer Center, Houston, TX, USA
| | - Karen Eilbeck
- USTAR Center for Genetic Discovery, Salt Lake City, UT, USA.,Department of Biomedical Informatics, University of Utah, Salt Lake City, UT, USA
| | - Chad Huff
- Department of Epidemiology, M.D. Anderson Cancer Center, Houston, TX, USA
| | - Lynn Jorde
- Department of Human Genetics, University of Utah, Salt Lake City, UT, USA.,USTAR Center for Genetic Discovery, Salt Lake City, UT, USA
| | | | - Mark Yandell
- Department of Human Genetics, University of Utah, Salt Lake City, UT, USA. .,USTAR Center for Genetic Discovery, Salt Lake City, UT, USA.
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26
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Abstract
When investigating Mendelian disease using exome or genome sequencing, distinguishing disease-causing genetic variants from the multitude of candidate variants is a complex, multidimensional task. Many prioritization tools and online interpretation resources exist, and professional organizations have offered clinical guidelines for review and return of prioritization results. In this Review, we describe the strengths and weaknesses of widely used computational approaches, explain their roles in the diagnostic and discovery process and discuss how they can inform (and misinform) expert reviewers. We place variant prioritization in the wider context of gene prioritization, burden testing and genotype-phenotype association, and we discuss opportunities and challenges introduced by whole-genome sequencing.
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Affiliation(s)
- Karen Eilbeck
- Department of Biomedical Informatics, School of Medicine, University of Utah, 421 Wakara Way, Suite 120, Salt Lake City, Utah 84108, USA
| | - Aaron Quinlan
- Department of Biomedical Informatics, School of Medicine, University of Utah, 421 Wakara Way, Suite 120, Salt Lake City, Utah 84108, USA
- Department of Human Genetics, Eccles Institute of Human Genetics, School of Medicine, University of Utah, 15 S 2030 E, Salt Lake City, Utah 84112, USA
| | - Mark Yandell
- Department of Human Genetics, Eccles Institute of Human Genetics, School of Medicine, University of Utah, 15 S 2030 E, Salt Lake City, Utah 84112, USA
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27
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Schlaberg R, Ampofo K, Tardif KD, Stockmann C, Simmon KE, Hymas W, Flygare S, Kennedy B, Blaschke A, Eilbeck K, Yandell M, McCullers JA, Williams DJ, Edwards K, Arnold SR, Bramley A, Jain S, Pavia AT. Human Bocavirus Capsid Messenger RNA Detection in Children With Pneumonia. J Infect Dis 2017; 216:688-696. [PMID: 28934425 PMCID: PMC5853397 DOI: 10.1093/infdis/jix352] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2017] [Accepted: 07/19/2017] [Indexed: 12/14/2022] Open
Abstract
Background The role of human bocavirus (HBoV) in respiratory illness is uncertain. HBoV genomic DNA is frequently detected in both ill and healthy children. We hypothesized that spliced viral capsid messenger RNA (mRNA) produced during active replication might be a better marker for acute infection. Methods As part of the Etiology of Pneumonia in the Community (EPIC) study, children aged <18 years who were hospitalized with community-acquired pneumonia (CAP) and children asymptomatic at the time of elective outpatient surgery (controls) were enrolled. Nasopharyngeal/oropharyngeal specimens were tested for HBoV mRNA and genomic DNA by quantitative polymerase chain reaction. Results HBoV DNA was detected in 10.4% of 1295 patients with CAP and 7.5% of 721 controls (odds ratio [OR], 1.4 [95% confidence interval {CI}, 1.0–2.0]); HBoV mRNA was detected in 2.1% and 0.4%, respectively (OR, 5.1 [95% CI, 1.6–26]). When adjusted for age, enrollment month, and detection of other respiratory viruses, HBoV mRNA detection (adjusted OR, 7.6 [95% CI, 1.5–38.4]) but not DNA (adjusted OR, 1.2 [95% CI, .6–2.4]) was associated with CAP. Among children with no other pathogens detected, HBoV mRNA (OR, 9.6 [95% CI, 1.9–82]) was strongly associated with CAP. Conclusions Detection of HBoV mRNA but not DNA was associated with CAP, supporting a pathogenic role for HBoV in CAP. HBoV mRNA could be a useful target for diagnostic testing.
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Affiliation(s)
- Robert Schlaberg
- Department of Pathology.,ARUP Institute for Clinical and Experimental Pathology, Salt Lake City, Utah
| | | | - Keith D Tardif
- ARUP Institute for Clinical and Experimental Pathology, Salt Lake City, Utah
| | | | | | - Weston Hymas
- ARUP Institute for Clinical and Experimental Pathology, Salt Lake City, Utah
| | | | | | | | | | - Mark Yandell
- Department of Human Genetics, University of Utah
| | - Jon A McCullers
- Department of Pediatrics, University of Tennessee Health Sciences Center.,Nashville and Le Bonheur Children's Hospital.,St. Jude Children's Research Hospital, Memphis
| | - Derek J Williams
- Vanderbilt University School of Medicine.,Division of Infectious Diseases, Department of Pediatrics, Monroe Carell Jr. Children's Hospital, Vanderbilt University.,Vanderbilt Vaccine Research Program, Nashville, Tennessee
| | - Kathryn Edwards
- Division of Infectious Diseases, Department of Pediatrics, Monroe Carell Jr. Children's Hospital, Vanderbilt University.,Vanderbilt Vaccine Research Program, Nashville, Tennessee
| | - Sandra R Arnold
- Department of Pediatrics, University of Tennessee Health Sciences Center.,Nashville and Le Bonheur Children's Hospital
| | - Anna Bramley
- Centers for Disease Control and Prevention, Atlanta, Georgia
| | - Seema Jain
- Centers for Disease Control and Prevention, Atlanta, Georgia
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28
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Schlaberg R, Queen K, Simmon K, Tardif K, Stockmann C, Flygare S, Kennedy B, Voelkerding K, Bramley A, Zhang J, Eilbeck K, Yandell M, Jain S, Pavia AT, Tong S, Ampofo K. Viral Pathogen Detection by Metagenomics and Pan-Viral Group Polymerase Chain Reaction in Children With Pneumonia Lacking Identifiable Etiology. J Infect Dis 2017; 215:1407-1415. [PMID: 28368491 PMCID: PMC5565793 DOI: 10.1093/infdis/jix148] [Citation(s) in RCA: 65] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023] Open
Abstract
Background. Community-acquired pneumonia (CAP) is a leading cause of pediatric hospitalization. Pathogen identification fails in approximately 20% of children but is critical for optimal treatment and prevention of hospital-acquired infections. We used two broad-spectrum detection strategies to identify pathogens in test-negative children with CAP and asymptomatic controls. Methods. Nasopharyngeal/oropharyngeal (NP/OP) swabs from 70 children <5 years with CAP of unknown etiology and 90 asymptomatic controls were tested by next-generation sequencing (RNA-seq) and pan viral group (PVG) PCR for 19 viral families. Association of viruses with CAP was assessed by adjusted odds ratios (aOR) and 95% confidence intervals controlling for season and age group. Results. RNA-seq/PVG PCR detected previously missed, putative pathogens in 34% of patients. Putative viral pathogens included human parainfluenza virus 4 (aOR 9.3, P = .12), human bocavirus (aOR 9.1, P < .01), Coxsackieviruses (aOR 5.1, P = .09), rhinovirus A (aOR 3.5, P = .34), and rhinovirus C (aOR 2.9, P = .57). RNA-seq was more sensitive for RNA viruses whereas PVG PCR detected more DNA viruses. Conclusions. RNA-seq and PVG PCR identified additional viruses, some known to be pathogenic, in NP/OP specimens from one-third of children hospitalized with CAP without a previously identified etiology. Both broad-range methods could be useful tools in future epidemiologic and diagnostic studies.
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Affiliation(s)
- Robert Schlaberg
- Department of Pathology.,ARUP Institute for Clinical and Experimental Pathology, Salt Lake City, Utah ; and
| | - Krista Queen
- Centers for Disease Control and Prevention, Atlanta, Georgia
| | | | - Keith Tardif
- ARUP Institute for Clinical and Experimental Pathology, Salt Lake City, Utah; and
| | | | | | - Brett Kennedy
- Department of Human Genetics, University of Utah, and
| | - Karl Voelkerding
- Department of Pathology.,ARUP Institute for Clinical and Experimental Pathology, Salt Lake City, Utah ; and
| | - Anna Bramley
- Centers for Disease Control and Prevention, Atlanta, Georgia
| | - Jing Zhang
- Centers for Disease Control and Prevention, Atlanta, Georgia
| | | | - Mark Yandell
- Department of Human Genetics, University of Utah, and
| | - Seema Jain
- Centers for Disease Control and Prevention, Atlanta, Georgia
| | | | - Suxiang Tong
- Centers for Disease Control and Prevention, Atlanta, Georgia
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29
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Lubin IM, Aziz N, Babb LJ, Ballinger D, Bisht H, Church DM, Cordes S, Eilbeck K, Hyland F, Kalman L, Landrum M, Lockhart ER, Maglott D, Marth G, Pfeifer JD, Rehm HL, Roy S, Tezak Z, Truty R, Ullman-Cullere M, Voelkerding KV, Worthey EA, Zaranek AW, Zook JM. Principles and Recommendations for Standardizing the Use of the Next-Generation Sequencing Variant File in Clinical Settings. J Mol Diagn 2017; 19:417-426. [PMID: 28315672 DOI: 10.1016/j.jmoldx.2016.12.001] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2016] [Revised: 12/05/2016] [Accepted: 12/23/2016] [Indexed: 11/30/2022] Open
Abstract
A national workgroup convened by the Centers for Disease Control and Prevention identified principles and made recommendations for standardizing the description of sequence data contained within the variant file generated during the course of clinical next-generation sequence analysis for diagnosing human heritable conditions. The specifications for variant files were initially developed to be flexible with regard to content representation to support a variety of research applications. This flexibility permits variation with regard to how sequence findings are described and this depends, in part, on the conventions used. For clinical laboratory testing, this poses a problem because these differences can compromise the capability to compare sequence findings among laboratories to confirm results and to query databases to identify clinically relevant variants. To provide for a more consistent representation of sequence findings described within variant files, the workgroup made several recommendations that considered alignment to a common reference sequence, variant caller settings, use of genomic coordinates, and gene and variant naming conventions. These recommendations were considered with regard to the existing variant file specifications presently used in the clinical setting. Adoption of these recommendations is anticipated to reduce the potential for ambiguity in describing sequence findings and facilitate the sharing of genomic data among clinical laboratories and other entities.
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Affiliation(s)
- Ira M Lubin
- Division of Laboratory Systems, Centers for Disease Control and Prevention, Atlanta, Georgia.
| | - Nazneen Aziz
- College of American Pathologists, Chicago, Illinois; Kaiser Permanente Research Bank, Oakland, California
| | - Lawrence J Babb
- Partners Healthcare Personalized Medicine, Cambridge, Massachusetts; GeneInsight, a Sunquest Company, Boston, Massachusetts
| | | | - Himani Bisht
- Center for Devices and Radiological Health, US Food and Drug Administration, Silver Spring, Maryland
| | - Deanna M Church
- Personalis, Menlo Park, California; National Center for Biotechnology Information, NIH, Bethesda, Maryland; 10× Genomics, Pleasanton, California
| | | | - Karen Eilbeck
- Department of Human Genetics, University of Utah School of Medicine, Salt Lake City, Utah
| | | | - Lisa Kalman
- Division of Laboratory Systems, Centers for Disease Control and Prevention, Atlanta, Georgia
| | - Melissa Landrum
- National Center for Biotechnology Information, NIH, Bethesda, Maryland
| | - Edward R Lockhart
- Division of Laboratory Systems, Centers for Disease Control and Prevention, Atlanta, Georgia
| | - Donna Maglott
- National Center for Biotechnology Information, NIH, Bethesda, Maryland
| | - Gabor Marth
- Department of Biomedical Informatics, University of Utah School of Medicine, Salt Lake City, Utah; Boston College, Chestnut Hill, Massachusetts
| | - John D Pfeifer
- Department of Pathology and Immunology, Washington University School of Medicine, St. Louis, Missouri
| | - Heidi L Rehm
- Department of Pathology, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts
| | - Somak Roy
- Division of Molecular and Genomic Pathology, University of Pittsburgh Medical Center, Pittsburgh, Pennsylvania
| | - Zivana Tezak
- Center for Devices and Radiological Health, US Food and Drug Administration, Silver Spring, Maryland
| | - Rebecca Truty
- Complete Genomics, Mountain View, California; Invitae Corporation, San Francisco, California
| | | | - Karl V Voelkerding
- Department of Pathology, University of Utah and the Institute for Clinical and Experimental Pathology, Associated Regional and University Pathologists Laboratories, Salt Lake City, Utah
| | - Elizabeth A Worthey
- Department of Pediatrics, Medical College of Wisconsin, Milwaukee, Wisconsin
| | - Alexander W Zaranek
- Personal Genome Project, Harvard Medical School, Boston, Massachusetts; Curoverse, Inc., Somerville, Massachusetts
| | - Justin M Zook
- Material Measurement Laboratory, National Institute of Standards and Technology, Gaithersburg, Maryland
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30
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Huang J, Eilbeck K, Smith B, Blake JA, Dou D, Huang W, Natale DA, Ruttenberg A, Huan J, Zimmermann MT, Jiang G, Lin Y, Wu B, Strachan HJ, de Silva N, Kasukurthi MV, Jha VK, He Y, Zhang S, Wang X, Liu Z, Borchert GM, Tan M. The development of non-coding RNA ontology. INT J DATA MIN BIOIN 2016; 15:214-232. [PMID: 27990175 DOI: 10.1504/ijdmb.2016.077072] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
Identification of non-coding RNAs (ncRNAs) has been significantly improved over the past decade. On the other hand, semantic annotation of ncRNA data is facing critical challenges due to the lack of a comprehensive ontology to serve as common data elements and data exchange standards in the field. We developed the Non-Coding RNA Ontology (NCRO) to handle this situation. By providing a formally defined ncRNA controlled vocabulary, the NCRO aims to fill a specific and highly needed niche in semantic annotation of large amounts of ncRNA biological and clinical data.
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Affiliation(s)
- Jingshan Huang
- School of Computing, University of South Alabama, Shelby Hall, Room 1123, 150 Jaguar Drive Mobile, AL 36688, USA,
| | - Karen Eilbeck
- Department of Biomedical Informatics, University of Utah School of Medicine, Salt Lake City, Utah, USA,
| | - Barry Smith
- University at Buffalo - SUNY, Buffalo, New York 14260, USA,
| | | | - Dejing Dou
- Computer and Information Science Department, University of Oregon, Eugene, Oregon 97403, USA,
| | - Weili Huang
- Miracle Query, Inc., Eugene, Oregon 97405, USA,
| | - Darren A Natale
- Georgetown University Medical Center, Washington DC 20007, USA,
| | | | - Jun Huan
- Department of Electrical Engineering and Computer Science, University of Kansas, Lawrence, Kansas 66045, USA,
| | - Michael T Zimmermann
- Division of Biomedical Statistics and Informatics, College of Medicine at Mayo Clinic, Rochester, Minnesota 55905, USA,
| | - Guoqian Jiang
- Division of Biomedical Statistics and Informatics, College of Medicine at Mayo Clinic, Rochester, Minnesota 55905, USA,
| | - Yu Lin
- Data Coordination and Integration Center, University of Miami, Miami, Florida 33146, USA,
| | - Bin Wu
- Endocrinology Department, Kunming Medical University, Kunming, Yunnan, 650032 China,
| | - Harrison J Strachan
- School of Computing, University of South Alabama, Mobile, Alabama 36688, USA,
| | - Nisansa de Silva
- Computer and Information Science, University of Oregon, Eugene, Oregon 97403, USA,
| | | | - Vikash Kumar Jha
- School of Computing, University of South Alabama, Mobile, Alabama 36688, USA,
| | - Yongqun He
- Lab Animal Medicine, Microbiology, Immunology and Bioinformatics, University of Michigan, Ann Arbor, Michigan 48109, USA,
| | - Shaojie Zhang
- Department of Computer Science, University of Central Florida, Orlando, Florida 32816, USA,
| | - Xiaowei Wang
- Cancer Biology, Washington University in St. Louis, St. Louis, Missouri 63130, USA,
| | - Zixing Liu
- Mitchell Cancer Institute, University of South Alabama, Mobile, Alabama 36604, USA,
| | - Glen M Borchert
- Department of Biology, University of South Alabama, Mobile, Alabama 36688, USA,
| | - Ming Tan
- Mitchell Cancer Institute, University of South Alabama, Mobile, Alabama 36604, USA,
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31
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Flygare S, Simmon K, Miller C, Qiao Y, Kennedy B, Di Sera T, Graf EH, Tardif KD, Kapusta A, Rynearson S, Stockmann C, Queen K, Tong S, Voelkerding KV, Blaschke A, Byington CL, Jain S, Pavia A, Ampofo K, Eilbeck K, Marth G, Yandell M, Schlaberg R. Taxonomer: an interactive metagenomics analysis portal for universal pathogen detection and host mRNA expression profiling. Genome Biol 2016; 17:111. [PMID: 27224977 PMCID: PMC4880956 DOI: 10.1186/s13059-016-0969-1] [Citation(s) in RCA: 110] [Impact Index Per Article: 13.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2015] [Accepted: 04/27/2016] [Indexed: 02/07/2023] Open
Abstract
Background High-throughput sequencing enables unbiased profiling of microbial communities, universal pathogen detection, and host response to infectious diseases. However, computation times and algorithmic inaccuracies have hindered adoption. Results We present Taxonomer, an ultrafast, web-tool for comprehensive metagenomics data analysis and interactive results visualization. Taxonomer is unique in providing integrated nucleotide and protein-based classification and simultaneous host messenger RNA (mRNA) transcript profiling. Using real-world case-studies, we show that Taxonomer detects previously unrecognized infections and reveals antiviral host mRNA expression profiles. To facilitate data-sharing across geographic distances in outbreak settings, Taxonomer is publicly available through a web-based user interface. Conclusions Taxonomer enables rapid, accurate, and interactive analyses of metagenomics data on personal computers and mobile devices. Electronic supplementary material The online version of this article (doi:10.1186/s13059-016-0969-1) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Steven Flygare
- Department of Human Genetics, University of Utah, Salt Lake City, UT, USA
| | - Keith Simmon
- Department of Biomedical Informatics, University of Utah, Salt Lake City, UT, USA
| | - Chase Miller
- Department of Human Genetics, University of Utah, Salt Lake City, UT, USA
| | - Yi Qiao
- Department of Human Genetics, University of Utah, Salt Lake City, UT, USA
| | - Brett Kennedy
- Department of Human Genetics, University of Utah, Salt Lake City, UT, USA
| | - Tonya Di Sera
- Department of Human Genetics, University of Utah, Salt Lake City, UT, USA
| | - Erin H Graf
- Department of Pathology, University of Utah, Salt Lake City, UT, USA
| | - Keith D Tardif
- ARUP Institute for Clinical and Experimental Pathology, Salt Lake City, UT, USA
| | - Aurélie Kapusta
- Department of Human Genetics, University of Utah, Salt Lake City, UT, USA
| | - Shawn Rynearson
- Department of Human Genetics, University of Utah, Salt Lake City, UT, USA
| | - Chris Stockmann
- Department of Pediatrics, University of Utah, Salt Lake City, UT, USA
| | - Krista Queen
- Centers for Disease Control and Prevention, Atlanta, GA, USA
| | - Suxiang Tong
- Centers for Disease Control and Prevention, Atlanta, GA, USA
| | - Karl V Voelkerding
- Department of Pathology, University of Utah, Salt Lake City, UT, USA.,ARUP Institute for Clinical and Experimental Pathology, Salt Lake City, UT, USA
| | - Anne Blaschke
- Department of Pediatrics, University of Utah, Salt Lake City, UT, USA
| | - Carrie L Byington
- Department of Pediatrics, University of Utah, Salt Lake City, UT, USA
| | - Seema Jain
- Centers for Disease Control and Prevention, Atlanta, GA, USA
| | - Andrew Pavia
- Department of Pediatrics, University of Utah, Salt Lake City, UT, USA
| | - Krow Ampofo
- Department of Pediatrics, University of Utah, Salt Lake City, UT, USA
| | - Karen Eilbeck
- Department of Biomedical Informatics, University of Utah, Salt Lake City, UT, USA.,USTAR Center for Genetic Discovery, Salt Lake City, UT, USA
| | - Gabor Marth
- Department of Human Genetics, University of Utah, Salt Lake City, UT, USA.,USTAR Center for Genetic Discovery, Salt Lake City, UT, USA
| | - Mark Yandell
- Department of Human Genetics, University of Utah, Salt Lake City, UT, USA. .,USTAR Center for Genetic Discovery, Salt Lake City, UT, USA.
| | - Robert Schlaberg
- Department of Pathology, University of Utah, Salt Lake City, UT, USA. .,ARUP Institute for Clinical and Experimental Pathology, Salt Lake City, UT, USA.
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Huang J, Gutierrez F, Strachan HJ, Dou D, Huang W, Smith B, Blake JA, Eilbeck K, Natale DA, Lin Y, Wu B, Silva ND, Wang X, Liu Z, Borchert GM, Tan M, Ruttenberg A. OmniSearch: a semantic search system based on the Ontology for MIcroRNA Target (OMIT) for microRNA-target gene interaction data. J Biomed Semantics 2016; 7:25. [PMID: 27175225 PMCID: PMC4863347 DOI: 10.1186/s13326-016-0064-2] [Citation(s) in RCA: 25] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2015] [Accepted: 04/12/2016] [Indexed: 01/05/2023] Open
Abstract
As a special class of non-coding RNAs (ncRNAs), microRNAs (miRNAs) perform important roles in numerous biological and pathological processes. The realization of miRNA functions depends largely on how miRNAs regulate specific target genes. It is therefore critical to identify, analyze, and cross-reference miRNA-target interactions to better explore and delineate miRNA functions. Semantic technologies can help in this regard. We previously developed a miRNA domain-specific application ontology, Ontology for MIcroRNA Target (OMIT), whose goal was to serve as a foundation for semantic annotation, data integration, and semantic search in the miRNA field. In this paper we describe our continuing effort to develop the OMIT, and demonstrate its use within a semantic search system, OmniSearch, designed to facilitate knowledge capture of miRNA-target interaction data. Important changes in the current version OMIT are summarized as: (1) following a modularized ontology design (with 2559 terms imported from the NCRO ontology); (2) encoding all 1884 human miRNAs (vs. 300 in previous versions); and (3) setting up a GitHub project site along with an issue tracker for more effective community collaboration on the ontology development. The OMIT ontology is free and open to all users, accessible at: http://purl.obolibrary.org/obo/omit.owl. The OmniSearch system is also free and open to all users, accessible at: http://omnisearch.soc.southalabama.edu/index.php/Software.
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Affiliation(s)
- Jingshan Huang
- School of Computing, University of South Alabama, Mobile, Alabama, 36688-0002 USA
| | - Fernando Gutierrez
- Computer and Information Science Department, University of Oregon, Eugene, Oregon, 97403-1202 USA
| | - Harrison J Strachan
- School of Computing, University of South Alabama, Mobile, Alabama, 36688-0002 USA
| | - Dejing Dou
- Computer and Information Science Department, University of Oregon, Eugene, Oregon, 97403-1202 USA
| | - Weili Huang
- Miracle Query, Inc., Eugene, Oregon, 97403-1202 USA
| | - Barry Smith
- Department of Philosophy, University at Buffalo, Buffalo, New York, 14260-4150 USA
| | - Judith A Blake
- Genome Informatics, The Jackson Laboratory, Bar Harbor, Maine, 04609-1523 USA
| | - Karen Eilbeck
- Department of Biomedical Informatics, University of Utah, Salt Lake City, Utah, 84112-5775 USA
| | - Darren A Natale
- Department of Biochemistry and Molecular and Cellular Biology, Georgetown University Medical Center, Washington D.C., 20007-1485 USA
| | - Yu Lin
- Center for Computational Science, University of Miami, Miami, Florida, 33146-2960 U.S.A
| | - Bin Wu
- Department of Microbiology and Immunology, First Affiliated Hospital, Kunming Medical University, Kunming, Yunnan, 650032 China
| | - Nisansa de Silva
- Computer and Information Science Department, University of Oregon, Eugene, Oregon, 97403-1202 USA
| | - Xiaowei Wang
- Department of Radiation Oncology, Washington University School of Medicine, St. Louis, Missouri, 63110-0001 USA
| | - Zixing Liu
- Mitchell Cancer Institute, University of South Alabama, Mobile, Alabama, 36604-1405 USA
| | - Glen M Borchert
- Department of Biology, University of South Alabama, Mobile, Alabama, 36688-0002 USA
| | - Ming Tan
- Mitchell Cancer Institute, University of South Alabama, Mobile, Alabama, 36604-1405 USA
| | - Alan Ruttenberg
- School of Dental Medicine, University at Buffalo, Buffalo, New York, 14214-8006 USA
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Aziz A, Kawamoto K, Eilbeck K, Williams MS, Freimuth RR, Hoffman MA, Rasmussen LV, Overby CL, Shirts BH, Hoffman JM, Welch BM. The genomic CDS sandbox: An assessment among domain experts. J Biomed Inform 2016; 60:84-94. [PMID: 26778834 DOI: 10.1016/j.jbi.2015.12.019] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2015] [Revised: 12/11/2015] [Accepted: 12/29/2015] [Indexed: 01/17/2023]
Abstract
Genomics is a promising tool that is becoming more widely available to improve the care and treatment of individuals. While there is much assertion, genomics will most certainly require the use of clinical decision support (CDS) to be fully realized in the routine clinical setting. The National Human Genome Research Institute (NHGRI) of the National Institutes of Health recently convened an in-person, multi-day meeting on this topic. It was widely recognized that there is a need to promote the innovation and development of resources for genomic CDS such as a CDS sandbox. The purpose of this study was to evaluate a proposed approach for such a genomic CDS sandbox among domain experts and potential users. Survey results indicate a significant interest and desire for a genomic CDS sandbox environment among domain experts. These results will be used to guide the development of a genomic CDS sandbox.
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Affiliation(s)
- Ayesha Aziz
- Medical University of South Carolina, Charleston, SC, United States.
| | | | - Karen Eilbeck
- University of Utah, Salt Lake City, UT, United States.
| | | | | | | | | | | | | | - James M Hoffman
- St. Jude Children's Research Hospital, Memphis, TN, United States.
| | - Brandon M Welch
- Medical University of South Carolina, Charleston, SC, United States.
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Osborne E, Blaschke AJ, Ampofo K, Crandall H, Killpack J, Stockmann CR, Farrell AP, Fischer KF, Pavia A, Eilbeck K, Schlaberg R, Yandell M, Byington CL. Shared Genetic Variants Among Streptococcus pneumoniae Isolates Causing Complicated Pneumonia and Empyema in Children. Open Forum Infect Dis 2015. [DOI: 10.1093/ofid/ofv131.20] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
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Desvignes T, Batzel P, Berezikov E, Eilbeck K, Eppig JT, McAndrews MS, Singer A, Postlethwait JH. miRNA Nomenclature: A View Incorporating Genetic Origins, Biosynthetic Pathways, and Sequence Variants. Trends Genet 2015; 31:613-626. [PMID: 26453491 DOI: 10.1016/j.tig.2015.09.002] [Citation(s) in RCA: 134] [Impact Index Per Article: 14.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2015] [Revised: 08/10/2015] [Accepted: 09/04/2015] [Indexed: 12/21/2022]
Abstract
High-throughput sequencing of miRNAs has revealed the diversity and variability of mature and functional short noncoding RNAs, including their genomic origins, biogenesis pathways, sequence variability, and newly identified products such as miRNA-offset RNAs (moRs). Here we review known cases of alternative mature miRNA-like RNA fragments and propose a revised definition of miRNAs to encompass this diversity. We then review nomenclature guidelines for miRNAs and propose to extend nomenclature conventions to align with those for protein-coding genes established by international consortia. Finally, we suggest a system to encompass the full complexity of sequence variations (i.e., isomiRs) in the analysis of small RNA sequencing experiments.
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Affiliation(s)
- T Desvignes
- Institute of Neuroscience, University of Oregon, Eugene, OR 97403, USA
| | - P Batzel
- Institute of Neuroscience, University of Oregon, Eugene, OR 97403, USA
| | - E Berezikov
- European Research Institute for the Biology of Ageing, University of Groningen, University Medical Center Groningen, 9713 AV Groningen, The Netherlands
| | - K Eilbeck
- Utah Science, Technology, and Research Center for Genetic Discovery, University of Utah, Salt Lake City, UT 84112, USA; Department of Biomedical Informatics, University of Utah, Salt Lake City, UT 84112, USA
| | - J T Eppig
- Mouse Genome Informatics, The Jackson Laboratory, 600 Main Street, Bar Harbor, ME 04609, USA
| | - M S McAndrews
- Mouse Genome Informatics, The Jackson Laboratory, 600 Main Street, Bar Harbor, ME 04609, USA
| | - A Singer
- ZFIN, 5291 University of Oregon, Eugene, OR 97403-5291, USA
| | - J H Postlethwait
- Institute of Neuroscience, University of Oregon, Eugene, OR 97403, USA.
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Thibault JC, Roe DR, Eilbeck K, Cheatham TE, Facelli JC. Development of an informatics infrastructure for data exchange of biomolecular simulations: Architecture, data models and ontology. SAR QSAR Environ Res 2015; 26:577-593. [PMID: 26387907 PMCID: PMC4672732 DOI: 10.1080/1062936x.2015.1076515] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/18/2015] [Accepted: 07/22/2015] [Indexed: 06/05/2023]
Abstract
Biomolecular simulations aim to simulate structure, dynamics, interactions, and energetics of complex biomolecular systems. With the recent advances in hardware, it is now possible to use more complex and accurate models, but also reach time scales that are biologically significant. Molecular simulations have become a standard tool for toxicology and pharmacology research, but organizing and sharing data - both within the same organization and among different ones - remains a substantial challenge. In this paper we review our recent work leading to the development of a comprehensive informatics infrastructure to facilitate the organization and exchange of biomolecular simulations data. Our efforts include the design of data models and dictionary tools that allow the standardization of the metadata used to describe the biomedical simulations, the development of a thesaurus and ontology for computational reasoning when searching for biomolecular simulations in distributed environments, and the development of systems based on these models to manage and share the data at a large scale (iBIOMES), and within smaller groups of researchers at laboratory scale (iBIOMES Lite), that take advantage of the standardization of the meta data used to describe biomolecular simulations.
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Affiliation(s)
- J. C. Thibault
- Department of Biomedical Informatics, University of Utah, Salt Lake City, Utah, US
| | - D. R. Roe
- Department of Medicinal Chemistry and Center for High Performance Computing, University of Utah, Salt Lake City, Utah, US
| | - K. Eilbeck
- Department of Biomedical Informatics, University of Utah, Salt Lake City, Utah, US
| | - T. E. Cheatham
- Department of Medicinal Chemistry and Center for High Performance Computing, University of Utah, Salt Lake City, Utah, US
| | - J. C. Facelli
- Department of Biomedical Informatics, University of Utah, Salt Lake City, Utah, US
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Duncan J, Eilbeck K, Narus SP, Clyde S, Thornton S, Staes C. Building an Ontology for Identity Resolution in Healthcare and Public Health. Online J Public Health Inform 2015; 7:e219. [PMID: 26392849 PMCID: PMC4576444 DOI: 10.5210/ojphi.v7i2.6010] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022] Open
Abstract
UNLABELLED Integration of disparate information from electronic health records, clinical data warehouses, birth certificate registries and other public health information systems offers great potential for clinical care, public health practice, and research. Such integration, however, depends on correctly matching patient-specific records using demographic identifiers. Without standards for these identifiers, record linkage is complicated by issues of structural and semantic heterogeneity. OBJECTIVES Our objectives were to develop and validate an ontology to: 1) identify components of identity and events subsequent to birth that result in creation, change, or sharing of identity information; 2) develop an ontology to facilitate data integration from multiple healthcare and public health sources; and 3) validate the ontology's ability to model identity-changing events over time. METHODS We interviewed domain experts in area hospitals and public health programs and developed process models describing the creation and transmission of identity information among various organizations for activities subsequent to a birth event. We searched for existing relevant ontologies. We validated the content of our ontology with simulated identity information conforming to scenarios identified in our process models. RESULTS We chose the Simple Event Model (SEM) to describe events in early childhood and integrated the Clinical Element Model (CEM) for demographic information. We demonstrated the ability of the combined SEM-CEM ontology to model identity events over time. CONCLUSION The use of an ontology can overcome issues of semantic and syntactic heterogeneity to facilitate record linkage.
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Affiliation(s)
- Jeffrey Duncan
- Department of Biomedical Informatics, University of
Utah, Salt Lake City, UT USA
| | - Karen Eilbeck
- Department of Biomedical Informatics, University of
Utah, Salt Lake City, UT USA
| | - Scott P. Narus
- Department of Biomedical Informatics, University of
Utah, Salt Lake City, UT USA
- Intermountain Healthcare, Salt Lake City, UT
USA
| | - Stephen Clyde
- Department of Computer Science, Utah State
University, Logan, UT USA
| | - Sidney Thornton
- Department of Biomedical Informatics, University of
Utah, Salt Lake City, UT USA
- Intermountain Healthcare, Salt Lake City, UT
USA
| | - Catherine Staes
- Department of Biomedical Informatics, University of
Utah, Salt Lake City, UT USA
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Baran J, Durgahee BSB, Eilbeck K, Antezana E, Hoehndorf R, Dumontier M. GFVO: the Genomic Feature and Variation Ontology. PeerJ 2015; 3:e933. [PMID: 26019997 PMCID: PMC4435477 DOI: 10.7717/peerj.933] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2014] [Accepted: 04/14/2015] [Indexed: 01/06/2023] Open
Abstract
Falling costs in genomic laboratory experiments have led to a steady increase of genomic feature and variation data. Multiple genomic data formats exist for sharing these data, and whilst they are similar, they are addressing slightly different data viewpoints and are consequently not fully compatible with each other. The fragmentation of data format specifications makes it hard to integrate and interpret data for further analysis with information from multiple data providers. As a solution, a new ontology is presented here for annotating and representing genomic feature and variation dataset contents. The Genomic Feature and Variation Ontology (GFVO) specifically addresses genomic data as it is regularly shared using the GFF3 (incl. FASTA), GTF, GVF and VCF file formats. GFVO simplifies data integration and enables linking of genomic annotations across datasets through common semantics of genomic types and relations. Availability and implementation. The latest stable release of the ontology is available via its base URI; previous and development versions are available at the ontology's GitHub repository: https://github.com/BioInterchange/Ontologies; versions of the ontology are indexed through BioPortal (without external class-/property-equivalences due to BioPortal release 4.10 limitations); examples and reference documentation is provided on a separate web-page: http://www.biointerchange.org/ontologies.html. GFVO version 1.0.2 is licensed under the CC0 1.0 Universal license (https://creativecommons.org/publicdomain/zero/1.0) and therefore de facto within the public domain; the ontology can be appropriated without attribution for commercial and non-commercial use.
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Affiliation(s)
- Joachim Baran
- Stanford Center for Biomedical Informatics Research, School of Medicine, Stanford University , Stanford, CA , USA
| | | | - Karen Eilbeck
- Department of Biomedical Informatics, School of Medicine, University of Utah , Salt Lake City, UT , USA
| | - Erick Antezana
- Department of Biology, Norwegian University of Science and Technology , Trondheim , Norway
| | - Robert Hoehndorf
- Computer, Electrical and Mathematical Sciences & Engineering Division and Computational Bioscience Research Center, King Abdullah University of Science and Technology , Thuwal , Kingdom of Saudi Arabia
| | - Michel Dumontier
- Stanford Center for Biomedical Informatics Research, School of Medicine, Stanford University , Stanford, CA , USA
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Welch BM, Rodriguez Loya S, Eilbeck K, Kawamoto K. A proposed clinical decision support architecture capable of supporting whole genome sequence information. J Pers Med 2015; 4:176-99. [PMID: 25411644 PMCID: PMC4234046 DOI: 10.3390/jpm4020176] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
Whole genome sequence (WGS) information may soon be widely available to help clinicians personalize the care and treatment of patients. However, considerable barriers exist, which may hinder the effective utilization of WGS information in a routine clinical care setting. Clinical decision support (CDS) offers a potential solution to overcome such barriers and to facilitate the effective use of WGS information in the clinic. However, genomic information is complex and will require significant considerations when developing CDS capabilities. As such, this manuscript lays out a conceptual framework for a CDS architecture designed to deliver WGS-guided CDS within the clinical workflow. To handle the complexity and breadth of WGS information, the proposed CDS framework leverages service-oriented capabilities and orchestrates the interaction of several independently-managed components. These independently-managed components include the genome variant knowledge base, the genome database, the CDS knowledge base, a CDS controller and the electronic health record (EHR). A key design feature is that genome data can be stored separately from the EHR. This paper describes in detail: (1) each component of the architecture; (2) the interaction of the components; and (3) how the architecture attempts to overcome the challenges associated with WGS information. We believe that service-oriented CDS capabilities will be essential to using WGS information for personalized medicine.
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Affiliation(s)
- Brandon M. Welch
- Program in Personalized Health Care, University of Utah, 15 North 2030 East, EIHG Room 2110, Salt Lake City, UT 84112, USA
- Department of Biomedical Informatics, University of Utah, 26 South 2000 East, Room 5775 HSEB, Salt Lake City, UT 84112, USA; E-Mails: (K.E.); (K.K.)
- Author to whom correspondence should be addressed; E-Mail: ; Tel.: +1-585-455-0461
| | - Salvador Rodriguez Loya
- School of Engineering and Informatics, University of Sussex, Shawcross Building, Room Gc4, Falmer, Brighton, East Sussex, BN1 9QT, UK; E-Mail:
| | - Karen Eilbeck
- Department of Biomedical Informatics, University of Utah, 26 South 2000 East, Room 5775 HSEB, Salt Lake City, UT 84112, USA; E-Mails: (K.E.); (K.K.)
| | - Kensaku Kawamoto
- Department of Biomedical Informatics, University of Utah, 26 South 2000 East, Room 5775 HSEB, Salt Lake City, UT 84112, USA; E-Mails: (K.E.); (K.K.)
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Abstract
Next-generation sequencing (NGS) is an effective method for mitochondrial genome (mtDNA) sequencing and heteroplasmy detection. The following protocol describes an mtDNA enrichment method up to library preparation and sequencing on Illumina NGS platforms. A short command line alignment script is available for download via FTP.
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Affiliation(s)
- Shale Dames
- ARUP Laboratories, ARUP Institute for Clinical and Experimental Pathology, 500 Chipeta Way, Salt Lake City, UT, 84108, USA,
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Welch BM, Rodriguez-Loya S, Eilbeck K, Kawamoto K. Clinical decision support for whole genome sequence information leveraging a service-oriented architecture: a prototype. AMIA Annu Symp Proc 2014; 2014:1188-1197. [PMID: 25954430 PMCID: PMC4419907] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 06/04/2023]
Abstract
Whole genome sequence (WGS) information could soon be routinely available to clinicians to support the personalized care of their patients. At such time, clinical decision support (CDS) integrated into the clinical workflow will likely be necessary to support genome-guided clinical care. Nevertheless, developing CDS capabilities for WGS information presents many unique challenges that need to be overcome for such approaches to be effective. In this manuscript, we describe the development of a prototype CDS system that is capable of providing genome-guided CDS at the point of care and within the clinical workflow. To demonstrate the functionality of this prototype, we implemented a clinical scenario of a hypothetical patient at high risk for Lynch Syndrome based on his genomic information. We demonstrate that this system can effectively use service-oriented architecture principles and standards-based components to deliver point of care CDS for WGS information in real-time.
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Affiliation(s)
- Brandon M Welch
- Medical University of South Carolina, Charleston, SC ; University of Utah, Salt Lake City, UT
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Duncan J, Narus SP, Clyde S, Eilbeck K, Thornton S, Staes C. Birth of identity: understanding changes to birth certificates and their value for identity resolution. J Am Med Inform Assoc 2014; 22:e120-9. [PMID: 25080533 DOI: 10.1136/amiajnl-2014-002774] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2014] [Accepted: 07/13/2014] [Indexed: 11/03/2022] Open
Abstract
INTRODUCTION Identity information is often used to link records within or among information systems in public health and clinical settings. The quality and stability of birth certificate identifiers impacts both the success of linkage efforts and the value of birth certificate registries for identity resolution. OBJECTIVE Our objectives were to describe: (1) the frequency and cause of changes to birth certificate identifiers as children age, and (2) the frequency of events (ie, adoptions, paternities, amendments) that may trigger changes and their impact on names. METHODS We obtained two de-identified datasets from the Utah birth certificate registry: (1) change history from 2000 to 2012, and (2) occurrences for adoptions, paternities, and amendments among births in 1987 and 2000. We conducted cohort analyses for births in 1987 and 2000, examining the number, reason, and extent of changes over time. We conducted cross-sectional analyses to assess the patterns of changes between 2000 and 2012. RESULTS In a cohort of 48 350 individuals born in 2000 in Utah, 3164 (6.5%) experienced a change in identifiers prior to their 13th birthday, with most changes occurring before 2 years of age. Cross-sectional analysis showed that identifiers are stable for individuals over 5 years of age, but patterns of changes fluctuate considerably over time, potentially due to policy and social factors. CONCLUSIONS Identities represented in birth certificates change over time. Specific events that cause changes to birth certificates also fluctuate over time. Understanding these changes can help in the development of automated strategies to improve identity resolution.
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Affiliation(s)
- Jeffrey Duncan
- Department of Biomedical Informatics, University of Utah, Salt Lake City, Utah, USA
| | - Scott P Narus
- Department of Biomedical Informatics, University of Utah, Salt Lake City, Utah, USA Intermountain Healthcare, Salt Lake City, Utah, USA
| | - Stephen Clyde
- Department of Computer Science, Utah State University, Logan, Utah, USA
| | - Karen Eilbeck
- Department of Biomedical Informatics, University of Utah, Salt Lake City, Utah, USA
| | - Sidney Thornton
- Department of Biomedical Informatics, University of Utah, Salt Lake City, Utah, USA Intermountain Healthcare, Salt Lake City, Utah, USA
| | - Catherine Staes
- Department of Biomedical Informatics, University of Utah, Salt Lake City, Utah, USA
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Huang J, Dang J, Borchert GM, Eilbeck K, Zhang H, Xiong M, Jiang W, Wu H, Blake JA, Natale DA, Tan M. OMIT: dynamic, semi-automated ontology development for the microRNA domain. PLoS One 2014; 9:e100855. [PMID: 25025130 PMCID: PMC4099014 DOI: 10.1371/journal.pone.0100855] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2014] [Accepted: 05/30/2014] [Indexed: 01/11/2023] Open
Abstract
As a special class of short non-coding RNAs, microRNAs (a.k.a. miRNAs or miRs) have been reported to perform important roles in various biological processes by regulating respective target genes. However, significant barriers exist during biologists' conventional miR knowledge discovery. Emerging semantic technologies, which are based upon domain ontologies, can render critical assistance to this problem. Our previous research has investigated the construction of a miR ontology, named Ontology for MIcroRNA Target Prediction (OMIT), the very first of its kind that formally encodes miR domain knowledge. Although it is unavoidable to have a manual component contributed by domain experts when building ontologies, many challenges have been identified for a completely manual development process. The most significant issue is that a manual development process is very labor-intensive and thus extremely expensive. Therefore, we propose in this paper an innovative ontology development methodology. Our contributions can be summarized as: (i) We have continued the development and critical improvement of OMIT, solidly based on our previous research outcomes. (ii) We have explored effective and efficient algorithms with which the ontology development can be seamlessly combined with machine intelligence and be accomplished in a semi-automated manner, thus significantly reducing large amounts of human efforts. A set of experiments have been conducted to thoroughly evaluate our proposed methodology.
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Affiliation(s)
- Jingshan Huang
- School of Computing, University of South Alabama, Mobile, Alabama, United States of America
| | - Jiangbo Dang
- Corporate Technology, Siemens Corporation, Princeton, New Jersey, United States of America
| | - Glen M. Borchert
- Department of Biology, University of South Alabama, Mobile, Alabama, United States of America
| | - Karen Eilbeck
- School of Medicine, University of Utah, Salt Lake City, Utah, United States of America
| | - He Zhang
- School of Computing, University of South Alabama, Mobile, Alabama, United States of America
| | - Min Xiong
- School of Computing, University of South Alabama, Mobile, Alabama, United States of America
| | - Weijian Jiang
- School of Computing, University of South Alabama, Mobile, Alabama, United States of America
| | - Hao Wu
- School of Computing, University of South Alabama, Mobile, Alabama, United States of America
| | - Judith A. Blake
- The Jackson Laboratory, Bar Harbor, Maine, United States of America
| | - Darren A. Natale
- Georgetown University Medical Center, Washington, DC, United States of America
| | - Ming Tan
- Mitchell Cancer Institute, University of South Alabama, Mobile, Alabama, United States of America
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Welch BM, Eilbeck K, Del Fiol G, Meyer LJ, Kawamoto K. Technical desiderata for the integration of genomic data with clinical decision support. J Biomed Inform 2014; 51:3-7. [PMID: 24931434 DOI: 10.1016/j.jbi.2014.05.014] [Citation(s) in RCA: 27] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2013] [Revised: 05/27/2014] [Accepted: 05/29/2014] [Indexed: 12/01/2022]
Abstract
The ease with which whole genome sequence (WGS) information can be obtained is rapidly approaching the point where it can become useful for routine clinical care. However, significant barriers will inhibit widespread adoption unless clinicians are able to effectively integrate this information into patient care and decision-making. Electronic health records (EHR) and clinical decision support (CDS) systems may play a critical role in this integration. A previously published technical desiderata focused primarily on the integration of genomic data into the EHR. This manuscript extends the previous desiderata by specifically addressing needs related to the integration of genomic information with CDS. The objective of this study is to develop and validate a guiding set of technical desiderata for supporting the clinical use of WGS through CDS. A panel of domain experts in genomics and CDS developed a proposed set of seven additional requirements. These desiderata were reviewed by 63 experts in genomics and CDS through an online survey and refined based on the experts' comments. These additional desiderata provide important guiding principles for the technical development of CDS capabilities for the clinical use of WGS information.
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Affiliation(s)
- Brandon M Welch
- Department of Biomedical Informatics, University of Utah, Salt Lake City, UT, United States; Program in Personalized Health Care, University of Utah, Salt Lake City, UT, United States.
| | - Karen Eilbeck
- Department of Biomedical Informatics, University of Utah, Salt Lake City, UT, United States; Department of Human Genetics, University of Utah, Salt Lake City, UT, United States
| | - Guilherme Del Fiol
- Department of Biomedical Informatics, University of Utah, Salt Lake City, UT, United States
| | - Laurence J Meyer
- Departments of Dermatology and Internal Medicine, University of Utah, Salt Lake City, UT, United States; Department of Pediatrics, University of Utah, Salt Lake City, UT, United States
| | - Kensaku Kawamoto
- Department of Biomedical Informatics, University of Utah, Salt Lake City, UT, United States
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Singleton M, Guthery S, Voelkerding K, Chen K, Kennedy B, Margraf R, Durtschi J, Eilbeck K, Reese M, Jorde L, Huff C, Yandell M. Phevor combines multiple biomedical ontologies for accurate identification of disease-causing alleles in single individuals and small nuclear families. Am J Hum Genet 2014; 94:599-610. [PMID: 24702956 DOI: 10.1016/j.ajhg.2014.03.010] [Citation(s) in RCA: 117] [Impact Index Per Article: 11.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2014] [Accepted: 03/13/2014] [Indexed: 10/25/2022] Open
Abstract
Phevor integrates phenotype, gene function, and disease information with personal genomic data for improved power to identify disease-causing alleles. Phevor works by combining knowledge resident in multiple biomedical ontologies with the outputs of variant-prioritization tools. It does so by using an algorithm that propagates information across and between ontologies. This process enables Phevor to accurately reprioritize potentially damaging alleles identified by variant-prioritization tools in light of gene function, disease, and phenotype knowledge. Phevor is especially useful for single-exome and family-trio-based diagnostic analyses, the most commonly occurring clinical scenarios and ones for which existing personal genome diagnostic tools are most inaccurate and underpowered. Here, we present a series of benchmark analyses illustrating Phevor's performance characteristics. Also presented are three recent Utah Genome Project case studies in which Phevor was used to identify disease-causing alleles. Collectively, these results show that Phevor improves diagnostic accuracy not only for individuals presenting with established disease phenotypes but also for those with previously undescribed and atypical disease presentations. Importantly, Phevor is not limited to known diseases or known disease-causing alleles. As we demonstrate, Phevor can also use latent information in ontologies to discover genes and disease-causing alleles not previously associated with disease.
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Brownstein CA, Beggs AH, Homer N, Merriman B, Yu TW, Flannery KC, DeChene ET, Towne MC, Savage SK, Price EN, Holm IA, Luquette LJ, Lyon E, Majzoub J, Neupert P, McCallie D, Szolovits P, Willard HF, Mendelsohn NJ, Temme R, Finkel RS, Yum SW, Medne L, Sunyaev SR, Adzhubey I, Cassa CA, de Bakker PIW, Duzkale H, Dworzyński P, Fairbrother W, Francioli L, Funke BH, Giovanni MA, Handsaker RE, Lage K, Lebo MS, Lek M, Leshchiner I, MacArthur DG, McLaughlin HM, Murray MF, Pers TH, Polak PP, Raychaudhuri S, Rehm HL, Soemedi R, Stitziel NO, Vestecka S, Supper J, Gugenmus C, Klocke B, Hahn A, Schubach M, Menzel M, Biskup S, Freisinger P, Deng M, Braun M, Perner S, Smith RJH, Andorf JL, Huang J, Ryckman K, Sheffield VC, Stone EM, Bair T, Black-Ziegelbein EA, Braun TA, Darbro B, DeLuca AP, Kolbe DL, Scheetz TE, Shearer AE, Sompallae R, Wang K, Bassuk AG, Edens E, Mathews K, Moore SA, Shchelochkov OA, Trapane P, Bossler A, Campbell CA, Heusel JW, Kwitek A, Maga T, Panzer K, Wassink T, Van Daele D, Azaiez H, Booth K, Meyer N, Segal MM, Williams MS, Tromp G, White P, Corsmeier D, Fitzgerald-Butt S, Herman G, Lamb-Thrush D, McBride KL, Newsom D, Pierson CR, Rakowsky AT, Maver A, Lovrečić L, Palandačić A, Peterlin B, Torkamani A, Wedell A, Huss M, Alexeyenko A, Lindvall JM, Magnusson M, Nilsson D, Stranneheim H, Taylan F, Gilissen C, Hoischen A, van Bon B, Yntema H, Nelen M, Zhang W, Sager J, Zhang L, Blair K, Kural D, Cariaso M, Lennon GG, Javed A, Agrawal S, Ng PC, Sandhu KS, Krishna S, Veeramachaneni V, Isakov O, Halperin E, Friedman E, Shomron N, Glusman G, Roach JC, Caballero J, Cox HC, Mauldin D, Ament SA, Rowen L, Richards DR, San Lucas FA, Gonzalez-Garay ML, Caskey CT, Bai Y, Huang Y, Fang F, Zhang Y, Wang Z, Barrera J, Garcia-Lobo JM, González-Lamuño D, Llorca J, Rodriguez MC, Varela I, Reese MG, De La Vega FM, Kiruluta E, Cargill M, Hart RK, Sorenson JM, Lyon GJ, Stevenson DA, Bray BE, Moore BM, Eilbeck K, Yandell M, Zhao H, Hou L, Chen X, Yan X, Chen M, Li C, Yang C, Gunel M, Li P, Kong Y, Alexander AC, Albertyn ZI, Boycott KM, Bulman DE, Gordon PMK, Innes AM, Knoppers BM, Majewski J, Marshall CR, Parboosingh JS, Sawyer SL, Samuels ME, Schwartzentruber J, Kohane IS, Margulies DM. An international effort towards developing standards for best practices in analysis, interpretation and reporting of clinical genome sequencing results in the CLARITY Challenge. Genome Biol 2014; 15:R53. [PMID: 24667040 PMCID: PMC4073084 DOI: 10.1186/gb-2014-15-3-r53] [Citation(s) in RCA: 90] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2013] [Accepted: 03/25/2014] [Indexed: 12/30/2022] Open
Abstract
Background There is tremendous potential for genome sequencing to improve clinical diagnosis and care once it becomes routinely accessible, but this will require formalizing research methods into clinical best practices in the areas of sequence data generation, analysis, interpretation and reporting. The CLARITY Challenge was designed to spur convergence in methods for diagnosing genetic disease starting from clinical case history and genome sequencing data. DNA samples were obtained from three families with heritable genetic disorders and genomic sequence data were donated by sequencing platform vendors. The challenge was to analyze and interpret these data with the goals of identifying disease-causing variants and reporting the findings in a clinically useful format. Participating contestant groups were solicited broadly, and an independent panel of judges evaluated their performance. Results A total of 30 international groups were engaged. The entries reveal a general convergence of practices on most elements of the analysis and interpretation process. However, even given this commonality of approach, only two groups identified the consensus candidate variants in all disease cases, demonstrating a need for consistent fine-tuning of the generally accepted methods. There was greater diversity of the final clinical report content and in the patient consenting process, demonstrating that these areas require additional exploration and standardization. Conclusions The CLARITY Challenge provides a comprehensive assessment of current practices for using genome sequencing to diagnose and report genetic diseases. There is remarkable convergence in bioinformatic techniques, but medical interpretation and reporting are areas that require further development by many groups.
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Schlaberg R, Queen K, Simmon K, Tardif K, Stockmann C, Flygare S, Kennedy B, Voelkerding K, Bramley AM, Eilbeck K, Yandell M, Jain S, Pavia A, Tong S, Ampofo K. 779Viral Pathogen Detection by Metagenomics and Panviral PCR in Children with Pneumonia with no Identifiable Etiology: Preliminary Results from the CDC Etiology of Pneumonia in the Community (EPIC) Study. Open Forum Infect Dis 2014. [DOI: 10.1093/ofid/ofu052.487] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
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O’Rawe JA, Fang H, Rynearson S, Robison R, Kiruluta ES, Higgins G, Eilbeck K, Reese MG, Lyon GJ. Integrating precision medicine in the study and clinical treatment of a severely mentally ill person. PeerJ 2013; 1:e177. [PMID: 24109560 PMCID: PMC3792182 DOI: 10.7717/peerj.177] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2013] [Accepted: 09/16/2013] [Indexed: 01/02/2023] Open
Abstract
Background. In recent years, there has been an explosion in the number of technical and medical diagnostic platforms being developed. This has greatly improved our ability to more accurately, and more comprehensively, explore and characterize human biological systems on the individual level. Large quantities of biomedical data are now being generated and archived in many separate research and clinical activities, but there exists a paucity of studies that integrate the areas of clinical neuropsychiatry, personal genomics and brain-machine interfaces. Methods. A single person with severe mental illness was implanted with the Medtronic Reclaim(®) Deep Brain Stimulation (DBS) Therapy device for Obsessive Compulsive Disorder (OCD), targeting his nucleus accumbens/anterior limb of the internal capsule. Programming of the device and psychiatric assessments occurred in an outpatient setting for over two years. His genome was sequenced and variants were detected in the Illumina Whole Genome Sequencing Clinical Laboratory Improvement Amendments (CLIA)-certified laboratory. Results. We report here the detailed phenotypic characterization, clinical-grade whole genome sequencing (WGS), and two-year outcome of a man with severe OCD treated with DBS. Since implantation, this man has reported steady improvement, highlighted by a steady decline in his Yale-Brown Obsessive Compulsive Scale (YBOCS) score from ∼38 to a score of ∼25. A rechargeable Activa RC neurostimulator battery has been of major benefit in terms of facilitating a degree of stability and control over the stimulation. His psychiatric symptoms reliably worsen within hours of the battery becoming depleted, thus providing confirmatory evidence for the efficacy of DBS for OCD in this person. WGS revealed that he is a heterozygote for the p.Val66Met variant in BDNF, encoding a member of the nerve growth factor family, and which has been found to predispose carriers to various psychiatric illnesses. He carries the p.Glu429Ala allele in methylenetetrahydrofolate reductase (MTHFR) and the p.Asp7Asn allele in ChAT, encoding choline O-acetyltransferase, with both alleles having been shown to confer an elevated susceptibility to psychoses. We have found thousands of other variants in his genome, including pharmacogenetic and copy number variants. This information has been archived and offered to this person alongside the clinical sequencing data, so that he and others can re-analyze his genome for years to come. Conclusions. To our knowledge, this is the first study in the clinical neurosciences that integrates detailed neuropsychiatric phenotyping, deep brain stimulation for OCD and clinical-grade WGS with management of genetic results in the medical treatment of one person with severe mental illness. We offer this as an example of precision medicine in neuropsychiatry including brain-implantable devices and genomics-guided preventive health care.
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Affiliation(s)
- Jason A. O’Rawe
- Stanley Institute for Cognitive Genomics, Cold Spring Harbor Laboratory, NY, USA
- Stony Brook University, Stony Brook, NY, USA
| | - Han Fang
- Stanley Institute for Cognitive Genomics, Cold Spring Harbor Laboratory, NY, USA
- Stony Brook University, Stony Brook, NY, USA
| | - Shawn Rynearson
- Department of Biomedical Informatics, University of Utah, Salt Lake City, UT, USA
| | - Reid Robison
- Utah Foundation for Biomedical Research, Salt Lake City, UT, USA
| | | | | | - Karen Eilbeck
- Department of Biomedical Informatics, University of Utah, Salt Lake City, UT, USA
| | | | - Gholson J. Lyon
- Stanley Institute for Cognitive Genomics, Cold Spring Harbor Laboratory, NY, USA
- Stony Brook University, Stony Brook, NY, USA
- Utah Foundation for Biomedical Research, Salt Lake City, UT, USA
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Dames S, Chou LS, Xiao Y, Wayman T, Stocks J, Singleton M, Eilbeck K, Mao R. The development of next-generation sequencing assays for the mitochondrial genome and 108 nuclear genes associated with mitochondrial disorders. J Mol Diagn 2013; 15:526-34. [PMID: 23665194 DOI: 10.1016/j.jmoldx.2013.03.005] [Citation(s) in RCA: 39] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2013] [Revised: 03/08/2013] [Accepted: 03/15/2013] [Indexed: 01/25/2023] Open
Abstract
Sanger sequencing of multigenic disorders can be technically challenging, time consuming, and prohibitively expensive. High-throughput next-generation sequencing (NGS) can provide a cost-effective method for sequencing targeted genes associated with multigenic disorders. We have developed a NGS clinical targeted gene assay for the mitochondrial genome and for 108 selected nuclear genes associated with mitochondrial disorders. Mitochondrial disorders have a reported incidence of 1 in 5000 live births, encompass a broad range of phenotypes, and are attributed to mutations in the mitochondrial and nuclear genomes. Approximately 20% of mitochondrial disorders result from mutations in mtDNA, with the remaining 80% found in nuclear genes that affect mtDNA levels or mitochondrion protein assembly. In our NGS approach, the 16,569-bp mtDNA is enriched by long-range PCR and the 108 nuclear genes (which represent 1301 amplicons and 680 kb) are enriched by RainDance emulsion PCR. Sequencing is performed on Illumina HiSeq 2000 or MiSeq platforms, and bioinformatics analysis is performed using commercial and in-house developed bioinformatics pipelines. A total of 16 validation and 13 clinical samples were examined. All previously reported variants associated with mitochondrial disorders were found in validation samples, and 5 of the 13 clinical samples were found to have mutations associated with mitochondrial disorders in either the mitochondrial genome or the 108 nuclear genes. All variants were confirmed by Sanger sequencing.
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Affiliation(s)
- Shale Dames
- Institute for Clinical and Experimental Pathology, ARUP Laboratories, Salt Lake City, UT 84108, USA.
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Reese MG, Moore B, Batchelor C, Salas F, Cunningham F, Marth GT, Stein L, Flicek P, Yandell M, Eilbeck K. A standard variation file format for human genome sequences. Genome Biol 2010; 11:R88. [PMID: 20796305 PMCID: PMC2945790 DOI: 10.1186/gb-2010-11-8-r88] [Citation(s) in RCA: 73] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2010] [Revised: 07/26/2010] [Accepted: 08/26/2010] [Indexed: 12/03/2022] Open
Abstract
Here we describe the Genome Variation Format (GVF) and the 10Gen dataset. GVF, an extension of Generic Feature Format version 3 (GFF3), is a simple tab-delimited format for DNA variant files, which uses Sequence Ontology to describe genome variation data. The 10Gen dataset, ten human genomes in GVF format, is freely available for community analysis from the Sequence Ontology website and from an Amazon elastic block storage (EBS) snapshot for use in Amazon's EC2 cloud computing environment.
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Affiliation(s)
- Martin G Reese
- Omicia, 2200 Powell Street, Suite 525, Emeryville, CA 94608, USA.
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