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Colli SL, Cardoso N, Massone CA, Cores M, García Lombardi M, De Matteo EN, Lorenzetti MA, Preciado MV. Molecular alterations in the integrated diagnosis of pediatric glial and glioneuronal tumors: A single center experience. PLoS One 2022; 17:e0266466. [PMID: 35363819 PMCID: PMC8975011 DOI: 10.1371/journal.pone.0266466] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2021] [Accepted: 03/21/2022] [Indexed: 11/20/2022] Open
Abstract
Objectives: Tumors of the central nervous system (CNS) are the most common pediatric solid tumors, where low grade (LGG) and high grade gliomas (HGG) represent up to 55% of CNS tumors. Current molecular classification of these tumors results in a more accurate diagnosis and risk stratification, which ultimately enables individualized treatment strategies. Identifying known alterations is a suitable approach, particularly in developing countries, where NGS approaches are not easily accessible. We sought to assess molecular alterations in BRAF and histone 3 genes. Study design: FISH, IHC and Sanger sequencing were performed in a series of 102 pediatric glial and glioneuronal tumors. We also correlated these results with clinical and histological findings to evaluate their usefulness as diagnostic and/or prognostic tools. Results: We found that the KIAA1549-BRAF gene fusion was a relevant diagnostic tool for pilocytic astrocytoma, but not related to progression free survival (PFS) and overall survival (OS). BRAFV600E mutation was associated with a decreased OS in LGG, and with decreased PFS and OS among pilocytic astrocytomas. All HGG of the midline were H3K27M mutants, while H3G34R mutant cases were located in brain hemispheres. HGG harboring the H3K27M variant were associated with a decreased PFS and OS. Conclusions: Assessing druggable molecular markers with prognostic value is particularly important in those cases where complete resection or further radiation therapy is not possible. These potential diagnostic/prognostic markers may be suitable as further screening tests to reduce the requirement on NGS, which is not available in all laboratories. Furthermore, these results broaden data on BRAF and Histone 3 alterations in children from geographic regions, other than USA and Europe.
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Affiliation(s)
- Sandra Lorena Colli
- División Patología, Hospital de Niños “Dr. Ricardo Gutiérrez”, Buenos Aires, Argentina
| | - Nazarena Cardoso
- División Patología, Hospital de Niños “Dr. Ricardo Gutiérrez”, Buenos Aires, Argentina
- Laboratorio de Biología Molecular, División Patología, Instituto Multidisciplinario de Investigaciones en Patologías Pediátricas (IMIPP), CONICET-GCBA, Hospital de Niños “Dr. Ricardo Gutiérrez”, Buenos Aires, Argentina
| | - Carla Antonella Massone
- Laboratorio de Biología Molecular, División Patología, Instituto Multidisciplinario de Investigaciones en Patologías Pediátricas (IMIPP), CONICET-GCBA, Hospital de Niños “Dr. Ricardo Gutiérrez”, Buenos Aires, Argentina
| | - María Cores
- Unidad de Oncología, Hospital de Niños “Dr. Ricardo Gutiérrez”, Buenos Aires, Argentina
| | | | - Elena Noemí De Matteo
- División Patología, Hospital de Niños “Dr. Ricardo Gutiérrez”, Buenos Aires, Argentina
- Laboratorio de Biología Molecular, División Patología, Instituto Multidisciplinario de Investigaciones en Patologías Pediátricas (IMIPP), CONICET-GCBA, Hospital de Niños “Dr. Ricardo Gutiérrez”, Buenos Aires, Argentina
| | - Mario Alejandro Lorenzetti
- Laboratorio de Biología Molecular, División Patología, Instituto Multidisciplinario de Investigaciones en Patologías Pediátricas (IMIPP), CONICET-GCBA, Hospital de Niños “Dr. Ricardo Gutiérrez”, Buenos Aires, Argentina
| | - María Victoria Preciado
- Laboratorio de Biología Molecular, División Patología, Instituto Multidisciplinario de Investigaciones en Patologías Pediátricas (IMIPP), CONICET-GCBA, Hospital de Niños “Dr. Ricardo Gutiérrez”, Buenos Aires, Argentina
- * E-mail:
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2
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Farrell CM, Goldfarb T, Rangwala SH, Astashyn A, Ermolaeva OD, Hem V, Katz KS, Kodali VK, Ludwig F, Wallin CL, Pruitt KD, Murphy TD. RefSeq Functional Elements as experimentally assayed nongenic reference standards and functional interactions in human and mouse. Genome Res 2021; 32:175-188. [PMID: 34876495 PMCID: PMC8744684 DOI: 10.1101/gr.275819.121] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2021] [Accepted: 12/02/2021] [Indexed: 11/25/2022]
Abstract
Eukaryotic genomes contain many nongenic elements that function in gene regulation, chromosome organization, recombination, repair, or replication, and mutation of those elements can affect genome function and cause disease. Although numerous epigenomic studies provide high coverage of gene regulatory regions, those data are not usually exposed in traditional genome annotation and can be difficult to access and interpret without field-specific expertise. The National Center for Biotechnology Information (NCBI) therefore provides RefSeq Functional Elements (RefSeqFEs), which represent experimentally validated human and mouse nongenic elements derived from the literature. The curated data set is comprised of richly annotated sequence records, descriptive records in the NCBI Gene database, reference genome feature annotation, and activity-based interactions between nongenic regions, target genes, and each other. The data set provides succinct functional details and transparent experimental evidence, leverages data from multiple experimental sources, is readily accessible and adaptable, and uses a flexible data model. The data have multiple uses for basic functional discovery, bioinformatics studies, genetic variant interpretation; as known positive controls for epigenomic data evaluation; and as reference standards for functional interactions. Comparisons to other gene regulatory data sets show that the RefSeqFE data set includes a wider range of feature types representing more areas of biology, but it is comparatively smaller and subject to data selection biases. RefSeqFEs thus provide an alternative and complementary resource for experimentally assayed functional elements, with future data set growth expected.
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Affiliation(s)
- Catherine M Farrell
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, Maryland 20894, USA
| | - Tamara Goldfarb
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, Maryland 20894, USA
| | - Sanjida H Rangwala
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, Maryland 20894, USA
| | - Alexander Astashyn
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, Maryland 20894, USA
| | - Olga D Ermolaeva
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, Maryland 20894, USA
| | - Vichet Hem
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, Maryland 20894, USA
| | - Kenneth S Katz
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, Maryland 20894, USA
| | - Vamsi K Kodali
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, Maryland 20894, USA
| | - Frank Ludwig
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, Maryland 20894, USA
| | - Craig L Wallin
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, Maryland 20894, USA
| | - Kim D Pruitt
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, Maryland 20894, USA
| | - Terence D Murphy
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, Maryland 20894, USA
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3
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Sarkar A, Yang Y, Vihinen M. Variation benchmark datasets: update, criteria, quality and applications. DATABASE-THE JOURNAL OF BIOLOGICAL DATABASES AND CURATION 2020; 2020:5710862. [PMID: 32016318 PMCID: PMC6997940 DOI: 10.1093/database/baz117] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/12/2019] [Revised: 06/03/2019] [Accepted: 07/01/2019] [Indexed: 02/07/2023]
Abstract
Development of new computational methods and testing their performance has to be carried out using experimental data. Only in comparison to existing knowledge can method performance be assessed. For that purpose, benchmark datasets with known and verified outcome are needed. High-quality benchmark datasets are valuable and may be difficult, laborious and time consuming to generate. VariBench and VariSNP are the two existing databases for sharing variation benchmark datasets used mainly for variation interpretation. They have been used for training and benchmarking predictors for various types of variations and their effects. VariBench was updated with 419 new datasets from 109 papers containing altogether 329 014 152 variants; however, there is plenty of redundancy between the datasets. VariBench is freely available at http://structure.bmc.lu.se/VariBench/. The contents of the datasets vary depending on information in the original source. The available datasets have been categorized into 20 groups and subgroups. There are datasets for insertions and deletions, substitutions in coding and non-coding region, structure mapped, synonymous and benign variants. Effect-specific datasets include DNA regulatory elements, RNA splicing, and protein property for aggregation, binding free energy, disorder and stability. Then there are several datasets for molecule-specific and disease-specific applications, as well as one dataset for variation phenotype effects. Variants are often described at three molecular levels (DNA, RNA and protein) and sometimes also at the protein structural level including relevant cross references and variant descriptions. The updated VariBench facilitates development and testing of new methods and comparison of obtained performances to previously published methods. We compared the performance of the pathogenicity/tolerance predictor PON-P2 to several benchmark studies, and show that such comparisons are feasible and useful, however, there may be limitations due to lack of provided details and shared data. Database URL: http://structure.bmc.lu.se/VariBench
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Affiliation(s)
- Anasua Sarkar
- Department of Experimental Medical Science, BMC B13, Lund University, SE-22 184 Lund, Sweden
| | - Yang Yang
- School of Computer Science and Technology, Soochow University, No1. Shizi Street, Suzhou, 215006 Jiangsu, China.,Provincial Key Laboratory for Computer Information Processing Technology, No1. Shizi Street, Soochow University, Suzhou, 215006 Jiangsu, China
| | - Mauno Vihinen
- Department of Experimental Medical Science, BMC B13, Lund University, SE-22 184 Lund, Sweden
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4
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Pastor Ó, León AP, Reyes JFR, García AS, Casamayor JCR. Using conceptual modeling to improve genome data management. Brief Bioinform 2020; 22:45-54. [PMID: 32533135 DOI: 10.1093/bib/bbaa100] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2019] [Revised: 03/12/2020] [Accepted: 05/04/2020] [Indexed: 12/18/2022] Open
Abstract
With advances in genomic sequencing technology, a large amount of data is publicly available for the research community to extract meaningful and reliable associations among risk genes and the mechanisms of disease. However, this exponential growth of data is spread in over thousand heterogeneous repositories, represented in multiple formats and with different levels of quality what hinders the differentiation of clinically valid relationships from those that are less well-sustained and that could lead to wrong diagnosis. This paper presents how conceptual models can play a key role to efficiently manage genomic data. These data must be accessible, informative and reliable enough to extract valuable knowledge in the context of the identification of evidence supporting the relationship between DNA variants and disease. The approach presented in this paper provides a solution that help researchers to organize, store and process information focusing only on the data that are relevant and minimizing the impact that the information overload has in clinical and research contexts. A case-study (epilepsy) is also presented, to demonstrate its application in a real context.
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5
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A Method to Identify Relevant Genome Data: Conceptual Modeling for the Medicine of Precision. CONCEPTUAL MODELING 2018. [DOI: 10.1007/978-3-030-00847-5_44] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
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6
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Claustres M, Thèze C, des Georges M, Baux D, Girodon E, Bienvenu T, Audrezet MP, Dugueperoux I, Férec C, Lalau G, Pagin A, Kitzis A, Thoreau V, Gaston V, Bieth E, Malinge MC, Reboul MP, Fergelot P, Lemonnier L, Mekki C, Fanen P, Bergougnoux A, Sasorith S, Raynal C, Bareil C. CFTR-France, a national relational patient database for sharing genetic and phenotypic data associated with rare CFTR variants. Hum Mutat 2017; 38:1297-1315. [PMID: 28603918 DOI: 10.1002/humu.23276] [Citation(s) in RCA: 61] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2017] [Revised: 05/31/2017] [Accepted: 06/04/2017] [Indexed: 11/09/2022]
Abstract
Most of the 2,000 variants identified in the CFTR (cystic fibrosis transmembrane regulator) gene are rare or private. Their interpretation is hampered by the lack of available data and resources, making patient care and genetic counseling challenging. We developed a patient-based database dedicated to the annotations of rare CFTR variants in the context of their cis- and trans-allelic combinations. Based on almost 30 years of experience of CFTR testing, CFTR-France (https://cftr.iurc.montp.inserm.fr/cftr) currently compiles 16,819 variant records from 4,615 individuals with cystic fibrosis (CF) or CFTR-RD (related disorders), fetuses with ultrasound bowel anomalies, newborns awaiting clinical diagnosis, and asymptomatic compound heterozygotes. For each of the 736 different variants reported in the database, patient characteristics and genetic information (other variations in cis or in trans) have been thoroughly checked by a dedicated curator. Combining updated clinical, epidemiological, in silico, or in vitro functional data helps to the interpretation of unclassified and the reassessment of misclassified variants. This comprehensive CFTR database is now an invaluable tool for diagnostic laboratories gathering information on rare variants, especially in the context of genetic counseling, prenatal and preimplantation genetic diagnosis. CFTR-France is thus highly complementary to the international database CFTR2 focused so far on the most common CF-causing alleles.
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Affiliation(s)
- Mireille Claustres
- Laboratoire de Génétique Moléculaire, Centre Hospitalier Universitaire et Université de Montpellier, Montpellier, France
| | - Corinne Thèze
- Laboratoire de Génétique Moléculaire, Centre Hospitalier Universitaire et Université de Montpellier, Montpellier, France
| | - Marie des Georges
- Laboratoire de Génétique Moléculaire, Centre Hospitalier Universitaire et Université de Montpellier, Montpellier, France
| | - David Baux
- Laboratoire de Génétique Moléculaire, Centre Hospitalier Universitaire et Université de Montpellier, Montpellier, France
| | - Emmanuelle Girodon
- Service de Génétique et Biologie Moléculaires, Groupe Hospitalier Cochin-Broca-Hotel Dieu, Paris, France
| | - Thierry Bienvenu
- Service de Génétique et Biologie Moléculaires, Groupe Hospitalier Cochin-Broca-Hotel Dieu, Paris, France
| | - Marie-Pierre Audrezet
- Laboratoire de Génétique Moléculaire et d'Histocompatibilité, Centre Hospitalier Régional Universitaire, Brest, France
| | - Ingrid Dugueperoux
- Laboratoire de Génétique Moléculaire et d'Histocompatibilité, Centre Hospitalier Régional Universitaire, Brest, France
| | - Claude Férec
- Laboratoire de Génétique Moléculaire et d'Histocompatibilité, Centre Hospitalier Régional Universitaire, Brest, France
| | - Guy Lalau
- Centre de Biologie Pathologie Génétique, Centre Hospitalier Régional Universitaire, Lille, France
| | - Adrien Pagin
- Centre de Biologie Pathologie Génétique, Centre Hospitalier Régional Universitaire, Lille, France
| | - Alain Kitzis
- Département de Génétique, Centre Hospitalier Universitaire, Poitiers, France
| | - Vincent Thoreau
- Département de Génétique, Centre Hospitalier Universitaire, Poitiers, France
| | - Véronique Gaston
- Service de Génétique Médicale, Centre Hospitalier Universitaire, Toulouse, France
| | - Eric Bieth
- Service de Génétique Médicale, Centre Hospitalier Universitaire, Toulouse, France
| | - Marie-Claire Malinge
- Département de Biochimie Génétique, Institut de Biologie en Santé, Centre Hospitalier Universitaire, Angers, France
| | - Marie-Pierre Reboul
- Laboratoire de Génétique Moléculaire, Centre Hospitalier Régional Universitaire, Bordeaux, France
| | - Patricia Fergelot
- Laboratoire Maladies Rares, Génétique et Métabolisme, Bordeaux, France
| | - Lydie Lemonnier
- Registre français de la mucoviscidose, Vaincre la Mucoviscidose, Paris, France
| | - Chadia Mekki
- Laboratoire de Génétique, Hôpital Henri Mondor, Créteil, France
| | - Pascale Fanen
- Laboratoire de Génétique, Hôpital Henri Mondor, Créteil, France
| | - Anne Bergougnoux
- Laboratoire de Génétique Moléculaire, Centre Hospitalier Universitaire et Université de Montpellier, Montpellier, France
| | - Souphatta Sasorith
- Laboratoire de Génétique Moléculaire, Centre Hospitalier Universitaire et Université de Montpellier, Montpellier, France
| | - Caroline Raynal
- Laboratoire de Génétique Moléculaire, Centre Hospitalier Universitaire et Université de Montpellier, Montpellier, France
| | - Corinne Bareil
- Laboratoire de Génétique Moléculaire, Centre Hospitalier Universitaire et Université de Montpellier, Montpellier, France
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7
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Niroula A, Vihinen M. PON-P and PON-P2 predictor performance in CAGI challenges: Lessons learned. Hum Mutat 2017; 38:1085-1091. [PMID: 28224672 DOI: 10.1002/humu.23199] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2016] [Revised: 01/25/2017] [Accepted: 02/17/2017] [Indexed: 01/14/2023]
Abstract
Computational tools are widely used for ranking and prioritizing variants for characterizing their disease relevance. Since numerous tools have been developed, they have to be properly assessed before being applied. Critical Assessment of Genome Interpretation (CAGI) experiments have significantly contributed toward the assessment of prediction methods for various tasks. Within and outside the CAGI, we have addressed several questions that facilitate development and assessment of variation interpretation tools. These areas include collection and distribution of benchmark datasets, their use for systematic large-scale method assessment, and the development of guidelines for reporting methods and their performance. For us, CAGI has provided a chance to experiment with new ideas, test the application areas of our methods, and network with other prediction method developers. In this article, we discuss our experiences and lessons learned from the various CAGI challenges. We describe our approaches, their performance, and impact of CAGI on our research. Finally, we discuss some of the possibilities that CAGI experiments have opened up and make some suggestions for future experiments.
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Affiliation(s)
- Abhishek Niroula
- Protein Structure and Bioinformatics Group, Department of Experimental Medical Science, Lund University, Lund, Sweden
| | - Mauno Vihinen
- Protein Structure and Bioinformatics Group, Department of Experimental Medical Science, Lund University, Lund, Sweden
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8
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Vihinen M. Establishment of an international database for genetic variants in esophageal cancer. Ann N Y Acad Sci 2016; 1381:45-49. [PMID: 27442983 DOI: 10.1111/nyas.13152] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2016] [Revised: 05/20/2016] [Accepted: 05/25/2016] [Indexed: 11/29/2022]
Abstract
The establishment of a database has been suggested in order to collect, organize, and distribute genetic information about esophageal cancer. The World Organization for Specialized Studies on Diseases of the Esophagus and the Human Variome Project will be in charge of a central database of information about esophageal cancer-related variations from publications, databases, and laboratories; in addition to genetic details, clinical parameters will also be included. The aim will be to get all the central players in research, clinical, and commercial laboratories to contribute. The database will follow established recommendations and guidelines. The database will require a team of dedicated curators with different backgrounds. Numerous layers of systematics will be applied to facilitate computational analyses. The data items will be extensively integrated with other information sources. The database will be distributed as open access to ensure exchange of the data with other databases. Variations will be reported in relation to reference sequences on three levels--DNA, RNA, and protein-whenever applicable. In the first phase, the database will concentrate on genetic variations including both somatic and germline variations for susceptibility genes. Additional types of information can be integrated at a later stage.
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Affiliation(s)
- Mauno Vihinen
- Protein Structure and Bioinformatics Group, Department of Experimental Medical Science, Lund University, Lund, Sweden.
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9
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Pinard A, Miltgen M, Blanchard A, Mathieu H, Desvignes JP, Salgado D, Fabre A, Arnaud P, Barré L, Krahn M, Grandval P, Olschwang S, Zaffran S, Boileau C, Béroud C, Collod-Béroud G. Actionable Genes, Core Databases, and Locus-Specific Databases. Hum Mutat 2016; 37:1299-1307. [PMID: 27600092 DOI: 10.1002/humu.23112] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2016] [Accepted: 08/31/2016] [Indexed: 01/04/2023]
Abstract
Adoption of next-generation sequencing (NGS) in a diagnostic context raises numerous questions with regard to identification and reports of secondary variants (SVs) in actionable genes. To better understand the whys and wherefores of these questioning, it is necessary to understand how they are selected during the filtering process and how their proportion can be estimated. It is likely that SVs are underestimated and that our capacity to label all true SVs can be improved. In this context, Locus-specific databases (LSDBs) can be key by providing a wealth of information and enabling classifying variants. We illustrate this issue by analyzing 318 SVs in 23 actionable genes involved in cancer susceptibility syndromes identified through sequencing of 572 participants selected for a range of atherosclerosis phenotypes. Among these 318 SVs, only 43.4% are reported in Human Gene Mutation Database (HGMD) Professional versus 71.4% in LSDB. In addition, 23.9% of HGMD Professional variants are reported as pathogenic versus 4.8% for LSDB. These data underline the benefits of LSDBs to annotate SVs and minimize overinterpretation of mutations thanks to their efficient curation process and collection of unpublished data.
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Affiliation(s)
| | | | | | | | | | | | - Aurélie Fabre
- Aix Marseille Univ, INSERM, GMGF, Marseille, France.,APHM, Hôpital Timone Enfants, Laboratoire de Génétique Moléculaire, Marseille, 13385, France
| | - Pauline Arnaud
- AP-HP, Hôpital Bichat, Centre National de Référence pour le syndrome de Marfan et apparentés, Paris, France.,UFR de Médecine, Diderot Paris Université Paris 7, Paris, France.,Inserm, U1148, Paris, France
| | - Laura Barré
- Aix Marseille Univ, INSERM, GMGF, Marseille, France
| | - Martin Krahn
- Aix Marseille Univ, INSERM, GMGF, Marseille, France.,APHM, Hôpital Timone Enfants, Laboratoire de Génétique Moléculaire, Marseille, 13385, France
| | - Philippe Grandval
- Aix Marseille Univ, INSERM, GMGF, Marseille, France.,AP-HM, Hôpital de la Timone, Gastroentérologie, Marseille, France
| | - Sylviane Olschwang
- Aix Marseille Univ, INSERM, GMGF, Marseille, France.,APHM, Hôpital Timone Enfants, Laboratoire de Génétique Moléculaire, Marseille, 13385, France.,Hôpital Clairval, Ramsay Générale de Santé, Marseille, France.,Hôpital Européen, Fondation Ambroise Paré, Marseille, France
| | | | - Catherine Boileau
- AP-HP, Hôpital Bichat, Centre National de Référence pour le syndrome de Marfan et apparentés, Paris, France.,UFR de Médecine, Diderot Paris Université Paris 7, Paris, France.,Inserm, U1148, Paris, France
| | - Christophe Béroud
- Aix Marseille Univ, INSERM, GMGF, Marseille, France.,APHM, Hôpital Timone Enfants, Laboratoire de Génétique Moléculaire, Marseille, 13385, France
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10
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Abstract
Novel high-throughput sequencing technologies generate large-scale genomic data and are used extensively for disease mapping of monogenic and/or complex disorders, personalized treatment, and pharmacogenomics. Next-generation sequencing is rapidly becoming routine tool for diagnosis and molecular monitoring of patients to evaluate therapeutic efficiency. The next-generation sequencing platforms generate huge amounts of genetic variation data and it remains a challenge to interpret the variations that are identified. Such data interpretation needs close collaboration among bioinformaticians, clinicians, and geneticists. There are several problems that must be addressed, such as the generation of new algorithms for mapping and annotation, harmonization of the terminology, correct use of nomenclature, reference genomes for different populations, rare disease variant databases, and clinical reports.
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Affiliation(s)
- Müge Sayitoğlu
- İstanbul University Faculty of Medicine, Institute of Experimental Medicine, Department of Genetics, İstanbul, Turkey Phone: +90 212 414 22 00-33312, E-mail:
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11
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Retrieving GPCR data from public databases. Curr Opin Pharmacol 2016; 30:38-43. [PMID: 27472010 DOI: 10.1016/j.coph.2016.07.002] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2016] [Revised: 06/30/2016] [Accepted: 07/03/2016] [Indexed: 01/29/2023]
Abstract
Improvements in databases have already impacted GPCR research. The purpose of the review is to give a snapshot of the GPCR data available and provide utility examples. Consequently, this review covers a small set of major databases, including UniProt for proteins, Ensembl for genes, ChEMBL for bioactive chemistry and SureChEMBL for patents. In addition, two portals are outlined, GPCRdb and the IUPHAR/BPS Guide to PHARMACOLOGY (GtoPdb) that are based on expert annotation. The former has an emphasis on structures, sequences, point mutations, analysis tools and visualisation. The latter focuses on endogenous GPCR ligands, pharmacological modulation, approved drugs, clinical candidates and tool compounds. Since data growth is accelerating, those embarking on GPCR projects should not only check databases but also recent journal and patent publications.
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12
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Affiliation(s)
- Garry R. Cutting
- Institute of Genetic Medicine and Department of Pediatrics and Medicine; Johns Hopkins University School of Medicine; Baltimore Maryland
| | - Haig H. Kazazian
- Institute of Genetic Medicine and Department of Pediatrics and Medicine; Johns Hopkins University School of Medicine; Baltimore Maryland
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13
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Niroula A, Vihinen M. Variation Interpretation Predictors: Principles, Types, Performance, and Choice. Hum Mutat 2016; 37:579-97. [DOI: 10.1002/humu.22987] [Citation(s) in RCA: 90] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2015] [Accepted: 03/07/2016] [Indexed: 12/18/2022]
Affiliation(s)
- Abhishek Niroula
- Department of Experimental Medical Science; Lund University; BMC B13 Lund SE-22184 Sweden
| | - Mauno Vihinen
- Department of Experimental Medical Science; Lund University; BMC B13 Lund SE-22184 Sweden
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