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Edward J. Oakeley

Researcher at Novartis

Publications -  69
Citations -  13370

Edward J. Oakeley is an academic researcher from Novartis. The author has contributed to research in topics: DNA methylation & Gene. The author has an hindex of 40, co-authored 69 publications receiving 12349 citations. Previous affiliations of Edward J. Oakeley include Friedrich Miescher Institute for Biomedical Research.

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Chromosome-wide and promoter-specific analyses identify sites of differential DNA methylation in normal and transformed human cells.

TL;DR: Analysis of 6,000 CpG islands showed that only a small set of promoters was methylated differentially, suggesting that aberrant methylation of CpGs island promoters in malignancy might be less frequent than previously hypothesized.
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Bacterial disease resistance in Arabidopsis through flagellin perception.

TL;DR: It is shown that treatment of plants with flg22, a peptide representing the elicitor-active epitope of flagellin, induces the expression of numerous defence-related genes and triggers resistance to pathogenic bacteria in wild-type plants, but not in plants carrying mutations in the flageLLin receptor gene FLS2.
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DNA-binding factors shape the mouse methylome at distal regulatory regions

TL;DR: It is shown that DNA-binding factors locally influence DNA methylation, enabling the identification of active regulatory regions and shows that neuronal and stem-cell methylomes are dependent on each other, as cell-type-specific LMRs are occupied by cell- type-specific transcription factors.
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Chemokine receptor CXCR4 downregulated by von Hippel–Lindau tumour suppressor pVHL

TL;DR: It is shown that the von Hippel–Lindau tumour suppressor protein pVHL negatively regulates CX CR4 expression owing to its capacity to target hypoxia-inducible factor (HIF) for degradation under normoxic conditions, resulting in HIF-dependent CXCR4 activation.
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A comprehensive assessment of RNA-seq accuracy, reproducibility and information content by the Sequencing Quality Control Consortium

Zhenqiang Su, +164 more
- 01 Sep 2014 - 
TL;DR: The complete SEQC data sets, comprising >100 billion reads, provide unique resources for evaluating RNA-seq analyses for clinical and regulatory settings, and measurement performance depends on the platform and data analysis pipeline, and variation is large for transcript-level profiling.