Please use this identifier to cite or link to this item: https://ahro.austin.org.au/austinjspui/handle/1/12301
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dc.contributor.authorOliver, Karen Len
dc.contributor.authorLukic, Vesnaen
dc.contributor.authorThorne, Natalie Pen
dc.contributor.authorBerkovic, Samuel Fen
dc.contributor.authorScheffer, Ingrid Een
dc.contributor.authorBahlo, Melanieen
dc.date.accessioned2015-05-16T01:57:52Z
dc.date.available2015-05-16T01:57:52Z
dc.date.issued2014-07-09en
dc.identifier.citationPLoS One 2014; 9(7): e102079en
dc.identifier.govdoc25014031en
dc.identifier.otherPUBMEDen
dc.identifier.urihttps://ahro.austin.org.au/austinjspui/handle/1/12301en
dc.description.abstractWe apply a novel gene expression network analysis to a cohort of 182 recently reported candidate Epileptic Encephalopathy genes to identify those most likely to be true Epileptic Encephalopathy genes. These candidate genes were identified as having single variants of likely pathogenic significance discovered in a large-scale massively parallel sequencing study. Candidate Epileptic Encephalopathy genes were prioritized according to their co-expression with 29 known Epileptic Encephalopathy genes. We utilized developing brain and adult brain gene expression data from the Allen Human Brain Atlas (AHBA) and compared this to data from Celsius: a large, heterogeneous gene expression data warehouse. We show replicable prioritization results using these three independent gene expression resources, two of which are brain-specific, with small sample size, and the third derived from a heterogeneous collection of tissues with large sample size. Of the nineteen genes that we predicted with the highest likelihood to be true Epileptic Encephalopathy genes, two (GNAO1 and GRIN2B) have recently been independently reported and confirmed. We compare our results to those produced by an established in silico prioritization approach called Endeavour, and finally present gene expression networks for the known and candidate Epileptic Encephalopathy genes. This highlights sub-networks of gene expression, particularly in the network derived from the adult AHBA gene expression dataset. These networks give clues to the likely biological interactions between Epileptic Encephalopathy genes, potentially highlighting underlying mechanisms and avenues for therapeutic targets.en
dc.language.isoenen
dc.titleHarnessing gene expression networks to prioritize candidate epileptic encephalopathy genes.en
dc.typeJournal Articleen
dc.identifier.journaltitlePLoS Oneen
dc.identifier.affiliationEpilepsy Research Center, Department of Medicine, Austin Health, The University of Melbourne, Heidelberg, Victoria, Australia en
dc.identifier.affiliationDepartment of Paediatrics, University of Melbourne, Royal Children's Hospital, Melbourne, Victoria, Australiaen
dc.identifier.affiliationDepartment of Mathematics and Statistics, University of Melbourne, Melbourne, Victoria, Australiaen
dc.identifier.affiliationBioinformatics Division, The Walter and Eliza Hall Institute of Medical Research, Melbourne, Victoria, Australiaen
dc.identifier.affiliationFlorey Institute, Melbourne, Victoria, Australiaen
dc.identifier.doi10.1371/journal.pone.0102079en
dc.description.pagese102079en
dc.relation.urlhttps://pubmed.ncbi.nlm.nih.gov/25014031en
dc.type.austinJournal Articleen
local.name.researcherBerkovic, Samuel F
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.cerifentitytypePublications-
item.fulltextNo Fulltext-
item.grantfulltextnone-
item.languageiso639-1en-
item.openairetypeJournal Article-
crisitem.author.deptEpilepsy Research Centre-
crisitem.author.deptNeurology-
crisitem.author.deptEpilepsy Research Centre-
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