Knowledge base and mini-expert platform for the diagnosis of inborn errors of metabolism

Genetics in Medicine

Volumn 20, Issue 1, 2018, Pages 151-158

  Lee, Jessica J Y ^a,b,c   Wasserman, Wyeth W ^a,b   Hoffmann, Georg F ^d   Van Karnebeek, Clara D M ^a,b,e   Blau, Nenad ^c,d  

^a UNIVERSITY OF BRITISH COLUMBIA   (Canada)

^b UNIVERSITY OF BRITISH COLUMBIA   (Canada)

^c UNIVERSITY CHILDREN S HOSPITAL   (Switzerland)

^d UNIVERSITY HOSPITAL HEIDELBERG   (Germany)

^e UNIVERSITY OF AMSTERDAM   (Netherlands)

Author keywords

biochemical genetics; bioinformatics; database; inherited metabolic diseases; phenomics

Indexed keywords

BIOCHEMICAL MARKER;

ALGORITHM; ARTICLE; CLINICAL FEATURE; COMPUTER ASSISTED DIAGNOSIS; DIAGNOSTIC ACCURACY; DIFFERENTIAL DIAGNOSIS; EXPERT SYSTEM; GENETIC ANALYSIS; HUMAN; HYPERORNITHINEMIA HYPERAMMONEMIA HOMOCITRULLINURIA SYNDROME; INBORN ERROR OF METABOLISM; KNOWLEDGE BASE; PHENOTYPE; SOFTWARE; CHROMOSOMAL MAPPING; CLINICAL DECISION MAKING; EXPERT WITNESS; GENETIC ASSOCIATION STUDY; GENETIC DATABASE; GENETIC PREDISPOSITION; GENETICS; GENOTYPE; MEDICAL INFORMATICS; PROCEDURES; RECEIVER OPERATING CHARACTERISTIC; REPRODUCIBILITY;

ALGORITHMS; CHROMOSOME MAPPING; CLINICAL DECISION-MAKING; DATABASES, GENETIC; EXPERT TESTIMONY; GENETIC ASSOCIATION STUDIES; GENETIC PREDISPOSITION TO DISEASE; GENOTYPE; HUMANS; KNOWLEDGE BASES; MEDICAL INFORMATICS; METABOLISM, INBORN ERRORS; PHENOTYPE; REPRODUCIBILITY OF RESULTS; ROC CURVE; SOFTWARE;

EID: 85040454478     PISSN: 10983600     EISSN: 15300366     Source Type: Journal    
DOI: 10.1038/gim.2017.108     Document Type: Article

References (35)

1. Tarailo-Graovac M, Shyr C, Ross CJ, et al. Exome sequencing and the management of neurometabolic disorders. N Engl J Med 2016;374: 2246-2255.
2. Chong JX, Buckingham KJ, Jhangiani SN, et al. The genetic basis of Mendelian phenotypes: discoveries, challenges, and opportunities. Am J Hum Genet 2015;97:199-215.
3. Survey of the delay in diagnosis for 8 rare diseases in Europe ("EurordisCare2"). Eurodis-Rare Diseases Europe. http://www. eurordis.org/IMG/pdf/Fact-Sheet-Eurordiscare2.pdf. Accessed 1 November 2016.
4. Shashi V, McConkie-Rosell A, Rosell B, et al. The utility of the traditional medical genetics diagnostic evaluation in the context of next-generation sequencing for undiagnosed genetic disorders. Genet Med 2014;16: 176-182.
5. Rare diseases: understanding this public health priority. Eurodis-Rare Diseases Europe. http://www.eurordis.org/sites/default/files/publications/ princeps-document-EN.pdf. Accessed 1 November 2016.
6. Leonard JV, Morris AAM. Diagnosis and early management of inborn errors of metabolism presenting around the time of birth. Acta Paediatr 2006;95:6-14.
7. Hawkes CP, Walsh A, O'Sullivan S, Crushell E. Doctors' knowledge of the acute management of Inborn Errors of Metabolism. Acta Paediatr 2011;100:461-463.
8. Garrod A. The incidence of alkaptonuria: a study in chemical individuality. The Lancet 1902;160:1616-1620.
9. Hamosh A, Scott AF, Amberger JS, Bocchini CA, McKusick VA. Online Mendelian Inheritance in Man (OMIM), a knowledgebase of human genes and genetic disorders. Nucleic Acids Res 2005;33:D514-517.
10. Rath A, Olry A, Dhombres F, Brandt MM, Urbero B, Ayme S. Representation of rare diseases in health information systems: the Orphanet approach to serve a wide range of end users. Hum Mutat 2012;33:803-808.
11. Blau N, Duran M, Gibson KM, Dionisi-Vici C (eds). Physician's guide to the diagnosis, treatment, and follow-up of inherited metabolic diseases. Heidelberg, Germany: Springer, 2014.
12. van Karnebeek CDM, Shevell M, Zschocke J, Moeschler JB, Stockler S. The metabolic evaluation of the child with an intellectual developmental disorder: diagnostic algorithm for identification of treatable causes and new digital resource. Mol Genet Metab 2014;111:428-438.
13. Zschocke J, Hoffmann GF. Vademecum metabolicum: diagnosis and treatment of inborn errors of metabolism. Friedrichsdorf, Germany: Schattauer, 2011.
14. The UniProt Consortium. UniProt: a hub for protein information. Nucleic Acids Res 2015;43:D204-D212.
15. Brown GR, Hem V, Katz KS, et al. Gene: a gene-centered information resource at NCBI. Nucleic Acids Res 2015;43:D36-D42.
16. Stelzer G, Rosen N, Plaschkes I, et al. The GeneCards suite: From gene data mining to disease genome sequence analyses. Curr Protoc Bioinformatics 2016;54:1.30.1-1.30.33.
17. Kanehisa M, Sato Y, Kawashima M, Furumichi M, Tanabe M. KEGG as a reference resource for gene and protein annotation. Nucleic Acids Res 2016;44:D457-462.
18. Rubinstein WS, Maglott DR, Lee JM, et al. The NIH genetic testing registry: a new, centralized database of genetic tests to enable access to comprehensive information and improve transparency. Nucleic Acids Res 2013;41:D925-D935.
19. Pagon RA, Adam MP, Ardinger HH, et al. (eds) GeneReviews. Seattle: University of Washington, Seattle, 1993-2017. https://www.ncbi.nlm. nih.gov/books/NBK1116/. Accessed February 27, 2017.
20. Smedley D, Haider S, Durinck S, et al. The BioMart community portal: an innovative alternative to large, centralized data repositories. Nucleic Acids Res 2015;43:W589-W598.
21. Köhler S, Schulz MH, Krawitz P, et al. Clinical diagnostics in human genetics with semantic similarity searches in ontologies. Am J Hum Genet 2009;85:457-464.
22. Köhler S, Doelken SC, Mungall CJ, et al. The Human Phenotype Ontology project: linking molecular biology and disease through phenotype data. Nucleic Acids Res 2014;42:D966-974.
23. Rogers FB. Medical subject headings. Bull Med Libr Assoc 1963;51: 114-116.
24. SNOMED-CT (Systematized Nomenclature of Medicine-Clinical Terms). Copenhagen: International Health Terminology Standards Development Organisation, 2016. http://www.ihtsdo.org/snomed-ct. Accessed October 18, 2016.
25. ICD-10 (International Classification of Diseases-10). World Health Organization, 2016. http://www.who.int/classifications/icd/en/. Accessed October 18, 2016.
26. Kurbatova N, Adamusiak T, Kurnosov P, Swertz MA, Kapushesky M. ontoCAT: an R package for ontology traversal and search. Bioinformatics 2011;27:2468-2470.
27. SPECIALIST Lexical Tools [computer program]. Release 2015. Bethesda, MD: Lexical Systems Group, 2015.
28. Hastings J, de Matos P, Dekker A, et al. The ChEBI reference database and ontology for biologically relevant chemistry: enhancements for 2013. Nucleic Acids Res 2013;41:D456-463.
29. McDonald CJ, Huff SM, Suico JG, et al. LOINC, a universal standard for identifying laboratory observations: a 5-year update. Clin Chem 2003;49: 624-633.
30. Salton G, Wong A, Yang CS. A vector space model for automatic indexing. Commun ACM 1975;18:613-620.
31. Frauendienst-Egger G, Trefz FK. www.metagene.de: online knowledge base for inborn errors of metabolism. J Inherit Metab Dis 2006;29(suppl 1): 84.
32. Töpel T, Scheible D, Trefz F, Hofestädt R. RAMEDIS: a comprehensive information system for variations and corresponding phenotypes of rare metabolic diseases. Hum Mutat 2010;31:E1081-1088.
33. van Karnebeek CDM, Houben RFA, Lafek M, Giannasi W, Stockler S. The treatable intellectual disability APP www.treatable-id.org: a digital tool to enhance diagnosis &care for rare diseases. Orphanet J Rare Dis 2012; 7:47.
34. Tebani A, Afonso C, Marret S, Bekri S. Omics-based strategies in precision medicine: toward a paradigm shift in inborn errors of metabolism investigations. Int J Mol Sci 2016;17:E1555.
35. Sahoo S, Franzson L, Jonsson JJ, Thiele I. A compendium of inborn errors of metabolism mapped onto the human metabolic network. Mol Biosyst 2012;8:2545-2558.