CNVxplorer: a web tool to assist clinical interpretation of CNVs in rare disease patients

Requena, Francisco; Abdallah, Hamza Hadj; García, Alejandro Dorado; Nitschké, Patrick; Romana, Sergi; Malan, Valérie; Rausell, Antonio

doi:10.1093/nar/gkab347

Cited by 13 publications

(16 citation statements)

References 68 publications

(90 reference statements)

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“…The use of variant data from control groups such as individuals from the 1000 genomes project [ 62 ], or structural data from resources such as the Database of Genomic Variants [ 63 ] could be analyzed alongside the patient cohort, to look for differences. Moreover, tools such as CNVxplorer could be used to help locate specific regions of interest [ 64 ].…”

Section: Discussionmentioning

confidence: 99%

Evaluating, Filtering and Clustering Genetic Disease Cohorts Based on Human Phenotype Ontology Data with Cohort Analyzer

Rojano

Córdoba-Caballero

Jabato

et al. 2021

JPM

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Exhaustive and comprehensive analysis of pathological traits is essential to understanding genetic diseases, performing precise diagnosis and prescribing personalized treatments. It is particularly important for disease cohorts, as thoroughly detailed phenotypic profiles allow patients to be compared and contrasted. However, many disease cohorts contain patients that have been ascribed low numbers of very general and relatively uninformative phenotypes. We present Cohort Analyzer, a tool that measures the phenotyping quality of patient cohorts. It calculates multiple statistics to give a general overview of the cohort status in terms of the depth and breadth of phenotyping, allowing us to detect less well-phenotyped patients for re-examining or excluding from further analyses. In addition, it performs clustering analysis to find subgroups of patients that share similar phenotypic profiles. We used it to analyse three cohorts of genetic diseases patients with very different properties. We found that cohorts with the most specific and complete phenotypic characterization give more potential insights into the disease than those that were less deeply characterised by forming more informative clusters. For two of the cohorts, we also analysed genomic data related to the patients, and linked the genomic data to the patient-subgroups by mapping shared variants to genes and functions. The work highlights the need for improved phenotyping in this era of personalized medicine. The tool itself is freely available alongside a workflow to allow the analyses shown in this work to be applied to other datasets.

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Section: Discussionmentioning

confidence: 99%

Evaluating, Filtering and Clustering Genetic Disease Cohorts Based on Human Phenotype Ontology Data with Cohort Analyzer

Rojano

Córdoba-Caballero

Jabato

et al. 2021

JPM

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“…Increased effort is being devoted to the interpretation of structural variants (SVs), which include copy number variants, chromosomal rearrangements and repeat-rich regions [ 78 ]. Indeed, array-based comparative genomic hybridization tests yield a ~12% diagnostic rate, with ~8% of patients having CNVs of unknown significance [ 79 ]. It should also be mentioned that the development of tools for the detection of all chromosomal rearrangements has developed a lot since this past year and increased effort is made to also perform this on WES data [ 80 ].…”

Section: Emerging Technologies and Methodologies For Reanalyzing Rare...mentioning

confidence: 99%

New Developments and Possibilities in Reanalysis and Reinterpretation of Whole Exome Sequencing Datasets for Unsolved Rare Diseases Using Machine Learning Approaches

Setty¹,

Scott‐Boyer²,

Cuppens³

et al. 2022

IJMS

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Rare diseases impact the lives of 300 million people in the world. Rapid advances in bioinformatics and genomic technologies have enabled the discovery of causes of 20–30% of rare diseases. However, most rare diseases have remained as unsolved enigmas to date. Newer tools and availability of high throughput sequencing data have enabled the reanalysis of previously undiagnosed patients. In this review, we have systematically compiled the latest developments in the discovery of the genetic causes of rare diseases using machine learning methods. Importantly, we have detailed methods available to reanalyze existing whole exome sequencing data of unsolved rare diseases. We have identified different reanalysis methodologies to solve problems associated with sequence alterations/mutations, variation re-annotation, protein stability, splice isoform malfunctions and oligogenic analysis. In addition, we give an overview of new developments in the field of rare disease research using whole genome sequencing data and other omics.

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“…In bulk, the data can be used, for example, for developing new analytical methods, in understanding patterns of polymorphism, and in refining critical intervals to map genes involved in specific phenotypes and diseases. The data set has recently been used to associate phenotypes with functional systems (Jabato et al, 2021 ), and to develop a new tool to assist clinical interpretation of CNVs (Requena et al, 2021 ). DECIPHER also shares the data in bulk for display, subject to a Data Display Agreement.…”

Section: Driving Rare Disease Researchmentioning

confidence: 99%

DECIPHER: Supporting the interpretation and sharing of rare disease phenotype‐linked variant data to advance diagnosis and research

et al. 2022

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DECIPHER (https://www.deciphergenomics.org) is a free web platform for sharing anonymized phenotype‐linked variant data from rare disease patients. Its dynamic interpretation interfaces contextualize genomic and phenotypic data to enable more informed variant interpretation, incorporating international standards for variant classification. DECIPHER supports almost all types of germline and mosaic variation in the nuclear and mitochondrial genome: sequence variants, short tandem repeats, copy‐number variants, and large structural variants. Patient phenotypes are deposited using Human Phenotype Ontology (HPO) terms, supplemented by quantitative data, which is aggregated to derive gene‐specific phenotypic summaries. It hosts data from >250 projects from ~40 countries, openly sharing >40,000 patient records containing >51,000 variants and >172,000 phenotype terms. The rich phenotype‐linked variant data in DECIPHER drives rare disease research and diagnosis by enabling patient matching within DECIPHER and with other resources, and has been cited in >2,600 publications. In this study, we describe the types of data deposited to DECIPHER, the variant interpretation tools, and patient matching interfaces which make DECIPHER an invaluable rare disease resource.

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CNVxplorer: a web tool to assist clinical interpretation of CNVs in rare disease patients

Cited by 13 publications

References 68 publications

Evaluating, Filtering and Clustering Genetic Disease Cohorts Based on Human Phenotype Ontology Data with Cohort Analyzer

Evaluating, Filtering and Clustering Genetic Disease Cohorts Based on Human Phenotype Ontology Data with Cohort Analyzer

New Developments and Possibilities in Reanalysis and Reinterpretation of Whole Exome Sequencing Datasets for Unsolved Rare Diseases Using Machine Learning Approaches

DECIPHER: Supporting the interpretation and sharing of rare disease phenotype‐linked variant data to advance diagnosis and research

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