Systematic identification of genetic systems associated with phenotypes in patients with rare genomic copy number variations

Jabato, Fernando M.; Seoane, Pedro; Perkins, James R.; Rojano, Elena; Moreno, Adrián García; Chagoyen, Mónica; Pazos, Florencio; Ranea, Juan A. G.

doi:10.1007/s00439-020-02214-7

Cited by 9 publications

(10 citation statements)

References 62 publications

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“…All the above calculations have been implemented as an automated workflow named PhenoClusters that is based on PhenCo ( Díaz-Santiago et al, 2020 ) and uses additional scripts from ( Jabato et al, 2020 ). The Picasso supercomputer of University of Malaga was used for code implementation and testing.…”

Section: Methodsmentioning

confidence: 99%

“…The code is available from GitHub at https://github.com/Elenadisa/PhenoClusters . The main requirements are Python 3, Ruby 2.4.1, R 4.0.0 or higher, Bioconductor 3.4 ( Huber et al, 2015 ) or higher, scripts from sys_bio_lab ( Jabato et al, 2020 ), and Anaconda Individual edition ( https://docs.anaconda.com/anaconda/install/ ) as package and environment manager. The workflow was managed using AutoFlow ( Seoane et al, 2016 ).…”

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Decoding Neuromuscular Disorders Using Phenotypic Clusters Obtained From Co-Occurrence Networks

Díaz-Santiago

Claros

Yahyaoui

et al. 2021

Front. Mol. Biosci.

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Neuromuscular disorders (NMDs) represent an important subset of rare diseases associated with elevated morbidity and mortality whose diagnosis can take years. Here we present a novel approach using systems biology to produce functionally-coherent phenotype clusters that provide insight into the cellular functions and phenotypic patterns underlying NMDs, using the Human Phenotype Ontology as a common framework. Gene and phenotype information was obtained for 424 NMDs in OMIM and 126 NMDs in Orphanet, and 335 and 216 phenotypes were identified as typical for NMDs, respectively. ‘Elevated serum creatine kinase’ was the most specific to NMDs, in agreement with the clinical test of elevated serum creatinine kinase that is conducted on NMD patients. The approach to obtain co-occurring NMD phenotypes was validated based on co-mention in PubMed abstracts. A total of 231 (OMIM) and 150 (Orphanet) clusters of highly connected co-occurrent NMD phenotypes were obtained. In parallel, a tripartite network based on phenotypes, diseases and genes was used to associate NMD phenotypes with functions, an approach also validated by literature co-mention, with KEGG pathways showing proportionally higher overlap than Gene Ontology and Reactome. Phenotype-function pairs were crossed with the co-occurrent NMD phenotype clusters to obtain 40 (OMIM) and 72 (Orphanet) functionally coherent phenotype clusters. As expected, many of these overlapped with known diseases and confirmed existing knowledge. Other clusters revealed interesting new findings, indicating informative phenotypes for differential diagnosis, providing deeper knowledge of NMDs, and pointing towards specific cell dysfunction caused by pleiotropic genes. This work is an example of reproducible research that i) can help better understand NMDs and support their diagnosis by providing a new tool that exploits existing information to obtain novel clusters of functionally-related phenotypes, and ii) takes us another step towards personalised medicine for NMDs.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Decoding Neuromuscular Disorders Using Phenotypic Clusters Obtained From Co-Occurrence Networks

Díaz-Santiago

Claros

Yahyaoui

et al. 2021

Front. Mol. Biosci.

Self Cite

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“…CNVxplorer downstream analyses include assessing the phenotypic similarity between the associated genomic elements and the patient clinical signs coded as human phenotype ontology (HPO) ( 15 ) and graph visualizations of gene–gene associations mediated by regulatory elements and transcription factors. The cumulative effect of the previous aspects on biological processes and molecular pathways ( 16 ) is evaluated through gene-set enrichment analyses. Finally, PubMed publications related to the query CNVs and their underlying network of term co-occurrence are presented.…”

Section: Introductionmentioning

confidence: 99%

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

Requena

Abdallah

García

et al. 2021

Nucleic Acids Research

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Copy Number Variants (CNVs) are an important cause of rare diseases. Array-based Comparative Genomic Hybridization tests yield a ∼12% diagnostic rate, with ∼8% of patients presenting CNVs of unknown significance. CNVs interpretation is particularly challenging on genomic regions outside of those overlapping with previously reported structural variants or disease-associated genes. Recent studies showed that a more comprehensive evaluation of CNV features, leveraging both coding and non-coding impacts, can significantly improve diagnostic rates. However, currently available CNV interpretation tools are mostly gene-centric or provide only non-interactive annotations difficult to assess in the clinical practice. Here, we present CNVxplorer, a web server suited for the functional assessment of CNVs in a clinical diagnostic setting. CNVxplorer mines a comprehensive set of clinical, genomic, and epigenomic features associated with CNVs. It provides sequence constraint metrics, impact on regulatory elements and topologically associating domains, as well as expression patterns. Analyses offered cover (a) agreement with patient phenotypes; (b) visualizations of associations among genes, regulatory elements and transcription factors; (c) enrichment on functional and pathway annotations and (d) co-occurrence of terms across PubMed publications related to the query CNVs. A flexible evaluation workflow allows dynamic re-interrogation in clinical sessions. CNVxplorer is publicly available at http://cnvxplorer.com.

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“…CNVxplorer downstream analyses include assessing the phenotypic similarity between the associated genomic elements and the patient clinical signs coded as Human Phenotype Ontology (HPO) (16) and graph visualizations of gene-gene associations mediated by regulatory elements and transcription factors. The cumulative effect of the previous aspects on biological processes and on molecular pathways (17) is evaluated through gene-set enrichment analyses. Finally, PubMed publications related to the query CNVs and their underlying network of term co-occurrence are presented.…”

Section: Introductionmentioning

confidence: 99%

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

Requena

Abdallah

García

et al. 2021

Preprint

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Copy Number Variants (CNVs) are an important cause of rare diseases. Array-based Comparative Genomic Hybridization tests yield a ~12% diagnostic rate, with ~8% of patients presenting CNVs of unknown significance. CNVs interpretation is particularly challenging on genomic regions outside of those overlapping with previously reported structural variants or disease-associated genes. Recent studies showed that a more comprehensive evaluation of CNV features, leveraging both coding and non-coding impacts can significantly improve diagnostic rates. However, currently available CNV interpretation tools are mostly gene-centric or provide only non-interactive annotations difficult to assess in the clinical practice. Here we present CNVxplorer, a web server suited for the functional assessment of CNVs in a clinical diagnostic setting. CNVxplorer mines a comprehensive set of clinical, genomic, and epigenomic features associated with CNVs. It provides sequence constraint metrics, impact on regulatory elements and topologically associating domains, as well as expression patterns. Analyses offered cover (a) agreement with patient phenotypes; (b) visualizations of associations among genes, regulatory elements and transcription factors; (c) enrichment on functional and pathway annotations; and (d) co-occurrence of terms across PubMed publications related to the query CNVs. A flexible evaluation workflow allows dynamic re-interrogation in clinical sessions. CNVxplorer is publicly available at http://cnvxplorer.com

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Systematic identification of genetic systems associated with phenotypes in patients with rare genomic copy number variations

Cited by 9 publications

References 62 publications

Decoding Neuromuscular Disorders Using Phenotypic Clusters Obtained From Co-Occurrence Networks

Decoding Neuromuscular Disorders Using Phenotypic Clusters Obtained From Co-Occurrence Networks

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

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

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