scholarly journals OPA1: 516 unique variants and 831 patients registered in an updated centralized Variome database

2019 ◽  
Vol 14 (1) ◽  
Author(s):  
Bastien Le Roux ◽  
Guy Lenaers ◽  
Xavier Zanlonghi ◽  
Patrizia Amati-Bonneau ◽  
Floris Chabrun ◽  
...  
Keyword(s):  
Clinical Data ◽  
Optic Atrophy ◽  
Neurological Disorders ◽  
Large Scale ◽  
Phenotype Ontology ◽  
Human Phenotype ◽  
Variation Database ◽  
Source Variation ◽  
Gene Panel Sequencing ◽  
Panel Sequencing

Abstract Background The dysfunction of OPA1, a dynamin GTPase involved in mitochondrial fusion, is responsible for a large spectrum of neurological disorders, each of which includes optic neuropathy. The database dedicated to OPA1 ( https://www.lovd.nl/OPA1 ), created in 2005, has now evolved towards a centralized and more reliable database using the Global Variome shared Leiden Open-source Variation Database (LOVD) installation. Results The updated OPA1 database, which registers all the patients from our center as well as those reported in the literature, now covers a total of 831 patients: 697 with isolated dominant optic atrophy (DOA), 47 with DOA “plus”, and 83 with asymptomatic or unclassified DOA. It comprises 516 unique OPA1 variants, of which more than 80% (414) are considered pathogenic. Full clinical data for 118 patients are documented using the Human Phenotype Ontology, a standard vocabulary for referencing phenotypic abnormalities. Contributors may now make online submissions of phenotypes related to OPA1 mutations, giving clinical and molecular descriptions together with detailed ophthalmological and neurological data, according to an international thesaurus. Conclusions The evolution of the OPA1 database towards the LOVD, using unified nomenclature, should ensure its interoperability with other databases and prove useful for molecular diagnoses based on gene-panel sequencing, large-scale mutation statistics, and genotype-phenotype correlations.

scholarly journals PHENOstruct: Prediction of human phenotype ontology terms using heterogeneous data sources

F1000Research ◽  
2015 ◽  
Vol 4 ◽  
pp. 259 ◽  
Author(s):  
Indika Kahanda ◽  
Christopher Funk ◽  
Karin Verspoor ◽  
Asa Ben-Hur
Keyword(s):  
Large Scale ◽  
Heterogeneous Data ◽  
Human Phenotype Ontology ◽  
Data Sources ◽  
Literature Mining ◽  
Phenotype Ontology ◽  
Ontology Term ◽  
Protein Coding ◽  
Human Phenotype ◽  
Heterogeneous Data Sources

The human phenotype ontology (HPO) was recently developed as a standardized vocabulary for describing the phenotype abnormalities associated with human diseases. At present, only a small fraction of human protein coding genes have HPO annotations. But, researchers believe that a large portion of currently unannotated genes are related to disease phenotypes. Therefore, it is important to predict gene-HPO term associations using accurate computational methods. In this work we demonstrate the performance advantage of the structured SVM approach which was shown to be highly effective for Gene Ontology term prediction in comparison to several baseline methods. Furthermore, we highlight a collection of informative data sources suitable for the problem of predicting gene-HPO associations, including large scale literature mining data.

scholarly journals Encoding Clinical Data with the Human Phenotype Ontology for Computational Differential Diagnostics

2019 ◽  
Vol 103 (1) ◽  
Author(s):  
Sebastian Köhler ◽  
N. Christine Øien ◽  
Orion J. Buske ◽  
Tudor Groza ◽  
Julius O. B. Jacobsen ◽  
...  
Keyword(s):  
Clinical Data ◽  
Human Phenotype Ontology ◽  
Differential Diagnostics ◽  
Phenotype Ontology ◽  
Human Phenotype

scholarly journals Best Paper Selection

2018 ◽  
Vol 27 (01) ◽  
pp. 144-145
Keyword(s):  
Large Scale ◽  
Ensemble Methods ◽  
Semantic Relations ◽  
Phenotype Ontology ◽  
Human Phenotype ◽  
Data Cubes ◽  
Genomic Variants ◽  
Genome Association ◽  
Rdf Data ◽  
Plos Comput Biol

Boudellioua I, Mahamad Razali RB, Kulmanov M, Hashish Y, Bajic VB, Goncalves-Serra E, Schoenmakers N, Gkoutos GV, Schofield PN, Hoehndorf R. Semantic prioritization of novel causative genomic variants. PLoS Comput Biol 2017;13(4):e1005500 https://www.ncbi.nlm.nih.gov/pmc/articles/pmid/28414800/ Galeota E, Pelizzola M. Ontology-based annotations and semantic relations in large-scale (epi)genomics data. Brief Bioinform 2017;18(3):403-12 https://www.ncbi.nlm.nih.gov/pmc/articles/pmid/27142216/ Khan Y, Saleem M, Mehdi M, Hogan A, Mehmood Q, Rebholz-Schuhmann D, Sahay R. SAFE: SPARQL Federation over RDF Data Cubes with Access Control. J Biomed Semantics 2017;8(1):5 https://www.ncbi.nlm.nih.gov/pmc/articles/pmid/28148277/ Notaro M, Schubach M, Robinson PN, Valentini G. Prediction of Human Phenotype Ontology terms by means of hierarchical ensemble methods. BMC Bioinformatics 2017;18(1):449 https://www.ncbi.nlm.nih.gov/pmc/articles/pmid/29025394/ Petegrosso R, Park S, Hwang TH, Kuang R. Transfer learning across ontologies for phenome-genome association prediction. Bioinformatics 2017;33(4):529-36 https://academic.oup.com/bioinformatics/article-lookup/doi/10.1093/bioinformatics/btw649

scholarly journals Advancing Drug Discovery for Neurological Disorders Using iPSC-Derived Neural Organoids

2021 ◽  
Vol 22 (5) ◽  
pp. 2659
Author(s):  
Gianluca Costamagna ◽  
Giacomo Pietro Comi ◽  
Stefania Corti
Keyword(s):  
Drug Discovery ◽  
Drug Screening ◽  
Neural Development ◽  
Neurological Disorders ◽  
Large Scale ◽  
Three Dimensional ◽  
Induced Pluripotent Stem Cell ◽  
Cellular Level ◽  
Complex Data ◽  
Complex Data Sets

In the last decade, different research groups in the academic setting have developed induced pluripotent stem cell-based protocols to generate three-dimensional, multicellular, neural organoids. Their use to model brain biology, early neural development, and human diseases has provided new insights into the pathophysiology of neuropsychiatric and neurological disorders, including microcephaly, autism, Parkinson’s disease, and Alzheimer’s disease. However, the adoption of organoid technology for large-scale drug screening in the industry has been hampered by challenges with reproducibility, scalability, and translatability to human disease. Potential technical solutions to expand their use in drug discovery pipelines include Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR) to create isogenic models, single-cell RNA sequencing to characterize the model at a cellular level, and machine learning to analyze complex data sets. In addition, high-content imaging, automated liquid handling, and standardized assays represent other valuable tools toward this goal. Though several open issues still hamper the full implementation of the organoid technology outside academia, rapid progress in this field will help to prompt its translation toward large-scale drug screening for neurological disorders.

scholarly journals Matrilineal analysis of mutations in the DMD gene in a multigenerational South Indian cohort using DMD gene panel sequencing

2021 ◽  
Author(s):  
Arun Shastry ◽  
Sankaramoorthy Aravind ◽  
Meeta Sunil ◽  
Keerthi Ramesh ◽  
Berty Ashley ◽  
...  
Keyword(s):  
Gene Panel ◽  
South Indian ◽  
Dmd Gene ◽  
Gene Panel Sequencing ◽  
Panel Sequencing

scholarly journals Phenotypic homogeneity in childhood epilepsies evolves in gene-specific patterns across 3251 patient-years of clinical data

2021 ◽  
Author(s):  
David Lewis-Smith ◽  
Shiva Ganesan ◽  
Peter D. Galer ◽  
Katherine L. Helbig ◽  
Sarah E. McKeown ◽  
...  
Keyword(s):  
Clinical Data ◽  
Similarity Analysis ◽  
Genetic Studies ◽  
Human Phenotype ◽  
Phenotypic Similarity ◽  
Complex Phenotypes ◽  
Cognitive Framework ◽  
Novel Approach ◽  
Genetic Epilepsies

AbstractWhile genetic studies of epilepsies can be performed in thousands of individuals, phenotyping remains a manual, non-scalable task. A particular challenge is capturing the evolution of complex phenotypes with age. Here, we present a novel approach, applying phenotypic similarity analysis to a total of 3251 patient-years of longitudinal electronic medical record data from a previously reported cohort of 658 individuals with genetic epilepsies. After mapping clinical data to the Human Phenotype Ontology, we determined the phenotypic similarity of individuals sharing each genetic etiology within each 3-month age interval from birth up to a maximum age of 25 years. 140 of 600 (23%) of all 27 genes and 3-month age intervals with sufficient data for calculation of phenotypic similarity were significantly higher than expect by chance. 11 of 27 genetic etiologies had significant overall phenotypic similarity trajectories. These do not simply reflect strong statistical associations with single phenotypic features but appear to emerge from complex clinical constellations of features that may not be strongly associated individually. As an attempt to reconstruct the cognitive framework of syndrome recognition in clinical practice, longitudinal phenotypic similarity analysis extends the traditional phenotyping approach by utilizing data from electronic medical records at a scale that is far beyond the capabilities of manual phenotyping. Delineation of how the phenotypic homogeneity of genetic epilepsies varies with age could improve the phenotypic classification of these disorders, the accuracy of prognostic counseling, and by providing historical control data, the design and interpretation of precision clinical trials in rare diseases.

scholarly journals PATH-11. PROSPECTIVE (EPI-)GENETIC CLASSIFICATION OF > 1,000 PEDIATRIC CNS TUMORS—THE MNP 2.0 STUDY

2020 ◽  
Vol 22 (Supplement_3) ◽  
pp. iii426-iii426
Author(s):  
Dominik Sturm ◽  
Felix Sahm ◽  
Felipe Andreiuolo ◽  
David Capper ◽  
Marco Gessi ◽  
...  
Keyword(s):  
Dna Methylation ◽  
Gene Panel ◽  
Cns Tumors ◽  
Lower Grade ◽  
High Grade ◽  
Sequencing Data ◽  
Cns Tumor ◽  
Gene Panel Sequencing ◽  
Tumor Entities ◽  
Panel Sequencing

Abstract The large variety of CNS tumor entities affecting children and adolescents, some of which are exceedingly rare, results in very diverging patient outcomes and renders accurate diagnosis challenging. To assess the diagnostic utility of routine DNA methylation-based CNS tumor classification and gene panel sequencing, the Molecular Neuropathology 2.0 study prospectively integrated these (epi-)genetic analyses with reference neuropathological diagnostics as an international trial for newly-diagnosed pediatric patients. In a four-year period, 1,215 patients with sufficient tissue were enrolled from 65 centers, receiving a reference neuropathological diagnosis according to the WHO classification in >97%. Using 10 FFPE sections as input, DNA methylation analysis was successfully performed in 95% of cases, of which 78% with sufficient tumor cell content were assigned to a distinct epigenetic tumor class. The remaining 22% did not match any of 82 represented classes, indicating novel rare tumor entities. Targeted gene panel sequencing of >130 genes performed for 96% of patients with matched blood samples detected diagnostically, prognostically, or therapeutically relevant somatic alterations in 48%. Germline DNA sequencing data indicated potential predisposition syndromes in ~10% of patients. Discrepant results by neuropathological and epigenetic classification (29%) were enriched in histological high-grade gliomas and implicated clinical relevance in 5% of all cases. Clinical follow-up suggests improved survival for some patients with high-grade glioma histology and lower-grade molecular profiles. Routine (epi-)genetic profiling at the time of primary diagnosis adds a valuable layer of information to neuropathological diagnostics and will improve clinical management of CNS tumors.

Predicting genes from phenotypes using Human Phenotype Ontology (HPO) terms

2021 ◽  
Vol 132 ◽  
pp. S149
Author(s):  
Anne Slavotinek ◽  
Hannah Prasad ◽  
Hannah Hoban ◽  
Tiffany Yip ◽  
Shannon Rego ◽  
...  
Keyword(s):  
Human Phenotype Ontology ◽  
Phenotype Ontology ◽  
Human Phenotype

BDV-syndrome: An emerging syndrome with profound obesity and neurodevelopmental delay resembling Prader-Willi syndrome

2021 ◽  
Author(s):  
Elisabeth Bosch ◽  
Moritz Hebebrand ◽  
Bernt Popp ◽  
Theresa Penger ◽  
Bettina Behring ◽  
...  
Keyword(s):  
Clinical Features ◽  
Large Scale ◽  
Hypogonadotropic Hypogonadism ◽  
Computational Modelling ◽  
Prader Willi Syndrome ◽  
Loss Of Function ◽  
Human Phenotype ◽  
Neurodevelopmental Delay ◽  
Missense Variants ◽  
Severely Obese

Abstract Context CPE encodes carboxypeptidase E, an enzyme which converts proneuropeptides and propeptide hormones to bioactive forms. It is widely expressed in the endocrine and central nervous system. To date, four individuals from two families with core clinical features including morbid obesity, neurodevelopmental delay and hypogonadotropic hypogonadism, harbouring biallelic loss-of-function CPE variants, were reported. Objective We describe four affected individuals from three unrelated consanguineous families, two siblings of Syrian, one of Egyptian and one of Pakistani descent, all harbouring novel homozygous CPE loss-of-function variants. Methods After excluding Prader-Willi syndrome, exome sequencing was performed in both Syrian siblings. The variants identified in the other two individuals were reported as research variants in a large scale exome study and in ClinVar database. Computational modelling of all possible missense alterations allowed assessing CPE tolerance to missense variants. Results All affected individuals were severely obese with neurodevelopmental delay and other endocrine anomalies. Three individuals from two families shared the same CPE homozygous truncating variant c.361C>T, p.(Arg121*), while the fourth carried the c.994del, p.(Ser333Alafs*22) variant. Comparison of clinical features with previously described cases and standardization according to the Human Phenotype Ontology indicated a recognisable clinical phenotype, which we termed Blakemore-Durmaz-Vasileiou (BDV) syndrome. Computational analysis indicated high conservation of CPE domains and intolerance to missense changes. Conclusions Biallelic truncating CPE variants are associated with BDV syndrome, a clinically recognisable monogenic recessive syndrome with childhood-onset obesity, neurodevelopmental delay, hypogonadotropic hypogonadism and hypothyroidism. BDV syndrome resembles Prader-Willi syndrome. Our findings suggested that missense variants may also be clinically relevant.

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