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SUMMARY:Modelling the transcriptional response to ER signalling in breast 
 cancer cells - Magnus Rattray (Manchester)
DTSTART:20141006T150000Z
DTEND:20141006T160000Z
UID:TALK53532@talks.cam.ac.uk
CONTACT:Florian Markowetz
DESCRIPTION:We are developing computational models of transcription and it
 s regulation from high-throughput time course data. I will discuss two mod
 els: (1) We model RNA polymerase (pol-II) dynamics using data from pol-II 
 ChIP-Seq time course datasets (wa Maina et al. 2014). Our model is used to
  estimate the elongation speed and promoter activation kinetics for early 
 targets of ER-alpha in MCF7 cells. By clustering the promoter activation p
 rofiles we can associate differences in the kinetics with transcription fa
 ctor binding\, e.g. the earliest clusters are all associated with nearby F
 OXA1 binding. (2) We model the relationship between pol-II dynamics and mR
 NA production by combining 3' pol-II ChIP-Seq data with RNA-Seq data. We f
 ind that 11% of genes where we can fit our model exhibit surprisingly long
  production delays which appear to be associated with splicing. Although t
 ranscription delays are longer than splicing delays for most genes\, there
  are a significant set where splicing is the rate-limiting step. We use a 
 Bayesian non-parametric approach to model temporal profiles such as the po
 l-II activation which allows us to limit the number of free parameters in 
 our model and to capture the uncertainty in our inferences. \n\nC. wa Main
 a\, A. Honkela\, F. Matarese\, K. Grote\, H. Stunnenberg\, G. Reid\, N. La
 wrence and M. Rattray. "Inference of RNA Polymerase II Transcription Dynam
 ics from Chromatin Immunoprecipitation Time Course Data." PLoS computation
 al biology 10\, no. 5 (2014): e1003598.
LOCATION:CRUK CI Lecture Theatre
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