English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Motivation: Predictive modelling of gene expression is a powerful frameworkfor the in silico exploration of transcriptional regulatory interactionsthrough the integration of high-throughput -omics data. A major limitation ofprevious approaches is their inability to handle conditional and synergisticinteractions that emerge when collectively analysing genes subject to differentregulatory mechanisms. This limitation reduces overall predictive power andthus the reliability of downstream biological inference.  Results: We introduce an analytical modelling framework (TREEOME: tree ofmodels of expression) that integrates epigenetic and transcriptomic data byseparating genes into putative regulatory classes. Current predictive modellingapproaches have found both DNA methylation and histone modification epigeneticdata to provide little or no improvement in accuracy of prediction oftranscript abundance despite, for example, distinct anti-correlation betweenmRNA levels and promoter-localised DNA methylation. To improve on this, inTREEOME we evaluate four possible methods of formulating gene-level DNAmethylation metrics, which provide a foundation for identifying gene-levelmethylation events and subsequent differential analysis, whereas most previoustechniques operate at the level of individual CpG dinucleotides. We demonstrateTREEOME by integrating gene-level DNA methylation (bisulfite-seq) and histonemodification (ChIP-seq) data to accurately predict genome-wide mRNA transcriptabundance (RNA-seq) for H1-hESC and GM12878 cell lines.  Availability: TREEOME is implemented using open-source software and madeavailable as a pre-configured bootable reference environment. All scripts anddata presented in this study are available online athttp://sourceforge.net/projects/budden2015treeome/.

TREEOME: A framework for epigenetic and transcriptomic data integration to explore regulatory interactions controlling transcription