Integrative data mining highlights candidate genes for monogenic myopathies.

Inherited myopathies are a heterogeneous group of disabling disorders with still barely understood pathological mechanisms. Around 40% of afflicted patients remain without a molecular diagnosis after exclusion of known genes. The advent of high-throughput sequencing has opened avenues to the discove...

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Autores principales:	Osorio Abath Neto, Olivier Tassy, Valérie Biancalana, Edmar Zanoteli, Olivier Pourquié, Jocelyn Laporte
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2014
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/fe83ca2ca40748258b75b18a02cad7b0
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spelling	oai:doaj.org-article:fe83ca2ca40748258b75b18a02cad7b02021-11-25T05:55:09ZIntegrative data mining highlights candidate genes for monogenic myopathies.1932-620310.1371/journal.pone.0110888https://doaj.org/article/fe83ca2ca40748258b75b18a02cad7b02014-01-01T00:00:00Zhttps://doi.org/10.1371/journal.pone.0110888https://doaj.org/toc/1932-6203Inherited myopathies are a heterogeneous group of disabling disorders with still barely understood pathological mechanisms. Around 40% of afflicted patients remain without a molecular diagnosis after exclusion of known genes. The advent of high-throughput sequencing has opened avenues to the discovery of new implicated genes, but a working list of prioritized candidate genes is necessary to deal with the complexity of analyzing large-scale sequencing data. Here we used an integrative data mining strategy to analyze the genetic network linked to myopathies, derive specific signatures for inherited myopathy and related disorders, and identify and rank candidate genes for these groups. Training sets of genes were selected after literature review and used in Manteia, a public web-based data mining system, to extract disease group signatures in the form of enriched descriptor terms, which include functional annotation, human and mouse phenotypes, as well as biological pathways and protein interactions. These specific signatures were then used as an input to mine and rank candidate genes, followed by filtration against skeletal muscle expression and association with known diseases. Signatures and identified candidate genes highlight both potential common pathological mechanisms and allelic disease groups. Recent discoveries of gene associations to diseases, like B3GALNT2, GMPPB and B3GNT1 to congenital muscular dystrophies, were prioritized in the ranked lists, suggesting a posteriori validation of our approach and predictions. We show an example of how the ranked lists can be used to help analyze high-throughput sequencing data to identify candidate genes, and highlight the best candidate genes matching genomic regions linked to myopathies without known causative genes. This strategy can be automatized to generate fresh candidate gene lists, which help cope with database annotation updates as new knowledge is incorporated.Osorio Abath NetoOlivier TassyValérie BiancalanaEdmar ZanoteliOlivier PourquiéJocelyn LaportePublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 9, Iss 10, p e110888 (2014)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Osorio Abath Neto Olivier Tassy Valérie Biancalana Edmar Zanoteli Olivier Pourquié Jocelyn Laporte Integrative data mining highlights candidate genes for monogenic myopathies.
description	Inherited myopathies are a heterogeneous group of disabling disorders with still barely understood pathological mechanisms. Around 40% of afflicted patients remain without a molecular diagnosis after exclusion of known genes. The advent of high-throughput sequencing has opened avenues to the discovery of new implicated genes, but a working list of prioritized candidate genes is necessary to deal with the complexity of analyzing large-scale sequencing data. Here we used an integrative data mining strategy to analyze the genetic network linked to myopathies, derive specific signatures for inherited myopathy and related disorders, and identify and rank candidate genes for these groups. Training sets of genes were selected after literature review and used in Manteia, a public web-based data mining system, to extract disease group signatures in the form of enriched descriptor terms, which include functional annotation, human and mouse phenotypes, as well as biological pathways and protein interactions. These specific signatures were then used as an input to mine and rank candidate genes, followed by filtration against skeletal muscle expression and association with known diseases. Signatures and identified candidate genes highlight both potential common pathological mechanisms and allelic disease groups. Recent discoveries of gene associations to diseases, like B3GALNT2, GMPPB and B3GNT1 to congenital muscular dystrophies, were prioritized in the ranked lists, suggesting a posteriori validation of our approach and predictions. We show an example of how the ranked lists can be used to help analyze high-throughput sequencing data to identify candidate genes, and highlight the best candidate genes matching genomic regions linked to myopathies without known causative genes. This strategy can be automatized to generate fresh candidate gene lists, which help cope with database annotation updates as new knowledge is incorporated.
format	article
author	Osorio Abath Neto Olivier Tassy Valérie Biancalana Edmar Zanoteli Olivier Pourquié Jocelyn Laporte
author_facet	Osorio Abath Neto Olivier Tassy Valérie Biancalana Edmar Zanoteli Olivier Pourquié Jocelyn Laporte
author_sort	Osorio Abath Neto
title	Integrative data mining highlights candidate genes for monogenic myopathies.
title_short	Integrative data mining highlights candidate genes for monogenic myopathies.
title_full	Integrative data mining highlights candidate genes for monogenic myopathies.
title_fullStr	Integrative data mining highlights candidate genes for monogenic myopathies.
title_full_unstemmed	Integrative data mining highlights candidate genes for monogenic myopathies.
title_sort	integrative data mining highlights candidate genes for monogenic myopathies.
publisher	Public Library of Science (PLoS)
publishDate	2014
url	https://doaj.org/article/fe83ca2ca40748258b75b18a02cad7b0
work_keys_str_mv	AT osorioabathneto integrativedatamininghighlightscandidategenesformonogenicmyopathies AT oliviertassy integrativedatamininghighlightscandidategenesformonogenicmyopathies AT valeriebiancalana integrativedatamininghighlightscandidategenesformonogenicmyopathies AT edmarzanoteli integrativedatamininghighlightscandidategenesformonogenicmyopathies AT olivierpourquie integrativedatamininghighlightscandidategenesformonogenicmyopathies AT jocelynlaporte integrativedatamininghighlightscandidategenesformonogenicmyopathies
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Integrative data mining highlights candidate genes for monogenic myopathies.

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