Please use this identifier to cite or link to this item: https://hdl.handle.net/1959.11/9021
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dc.contributor.authorHudson, Nicholas Jen
dc.contributor.authorReverter, Antonioen
dc.contributor.authorWang, YongHongen
dc.contributor.authorGreenwood, Paulen
dc.contributor.authorDalrymple, Brian Pen
dc.date.accessioned2011-12-09T15:14:00Z
dc.date.issued2009en
dc.identifier.citationPLoS One, v.4 (10)en
dc.identifier.issn1932-6203en
dc.identifier.urihttps://hdl.handle.net/1959.11/9021en
dc.description.abstractBackground: Despite modern technologies and novel computational approaches, decoding causal transcriptional regulation remains challenging. This is particularly true for less well studied organisms and when only gene expression data is available. In muscle a small number of well characterised transcription factors are proposed to regulate development. Therefore, muscle appears to be a tractable system for proposing new computational approaches. Methodology/Principal Findings: Here we report a simple algorithm that asks "which transcriptional regulator has the highest average absolute co-expression correlation to the genes in a co-expression module?" It correctly infers a number of known causal regulators of fundamental biological processes, including cell cycle activity (E2F1), glycolysis (HLF), mitochondrial transcription (TFB2M), adipogenesis (PIAS1), neuronal development (TLX3), immune function (IRF1) and vasculogenesis (SOX17), within a skeletal muscle context. However, none of the canonical pro-myogenic transcription factors (MYOD1, MYOG, MYF5, MYF6 and MEF2C) were linked to muscle structural gene expression modules. Co-expression values were computed using developing bovine muscle from 60 days post conception (early foetal) to 30 months post natal (adulthood) for two breeds of cattle, in addition to a nutritional comparison with a third breed. A number of transcriptional landscapes were constructed and integrated into an always correlated landscape. One notable feature was a 'metabolic axis' formed from glycolysis genes at one end, nuclear-encoded mitochondrial protein genes at the other, and centrally tethered by mitochondrially-encoded mitochondrial protein genes. Conclusions/Significance: The new module-to-regulator algorithm complements our recently described Regulatory Impact Factor analysis. Together with a simple examination of a co-expression module's contents, these three gene expression approaches are starting to illuminate the in vivo transcriptional regulation of skeletal muscle development.en
dc.languageenen
dc.publisherPublic Library of Science (PLoS)en
dc.relation.ispartofPLoS Oneen
dc.titleInferring the Transcriptional Landscape of Bovine Skeletal Muscle by Integrating Co-Expression Networksen
dc.typeJournal Articleen
dc.identifier.doi10.1371/journal.pone.0007249en
dc.subject.keywordsAnimal Physiology - Systemsen
local.contributor.firstnameNicholas Jen
local.contributor.firstnameAntonioen
local.contributor.firstnameYongHongen
local.contributor.firstnamePaulen
local.contributor.firstnameBrian Pen
local.subject.for2008060603 Animal Physiology - Systemsen
local.subject.seo2008970106 Expanding Knowledge in the Biological Sciencesen
local.profile.emailpaul.greenwood@industry.nsw.gov.auen
local.output.categoryC1en
local.record.placeauen
local.record.institutionUniversity of New Englanden
local.identifier.epublicationsrecordune-20111202-121913en
local.publisher.placeSan Francisco, United States of Americaen
local.identifier.runningnumbere7249en
local.peerreviewedYesen
local.identifier.volume4en
local.identifier.issue10en
local.contributor.lastnameHudsonen
local.contributor.lastnameReverteren
local.contributor.lastnameWangen
local.contributor.lastnameGreenwooden
local.contributor.lastnameDalrympleen
dc.identifier.staffune-id:pgreenw2en
local.profile.roleauthoren
local.profile.roleauthoren
local.profile.roleauthoren
local.profile.roleauthoren
local.profile.roleauthoren
local.identifier.unepublicationidune:9211en
local.title.maintitleInferring the Transcriptional Landscape of Bovine Skeletal Muscle by Integrating Co-Expression Networksen
local.output.categorydescriptionC1 Refereed Article in a Scholarly Journalen
local.description.statisticsepubsVisitors: 224<br />Views: 242<br />Downloads: 0en
local.search.authorHudson, Nicholas Jen
local.search.authorReverter, Antonioen
local.search.authorWang, YongHongen
local.search.authorGreenwood, Paulen
local.search.authorDalrymple, Brian Pen
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