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SUMMARY:BSU Seminar: “Biomarker discovery through statistical signal pro
 cessing and Bayesian modelling on large-scale quantitative proteomics data
 ” - Professor Andrew Dowsey\, University of Bristol
DTSTART:20180614T133000Z
DTEND:20180614T143000Z
UID:TALK106651@talks.cam.ac.uk
CONTACT:Alison Quenault
DESCRIPTION:Critical to the success of the stratified medicine approach is
  a diagnostic programme that can reliably characterise molecules that act 
 as markers for early disease detection and subsequent drug selection based
  on safety and efficacy. A number of large-scale  facilities worldwide hav
 e been established to systematically discover these biomarkers\, including
  the Stoller Biomarker Discovery Centre (SBDC) for proteomics in Mancheste
 r. Despite strict operating procedures that control sample preparation and
  analysis\, analysis of biomedical proteomics and metabolomics data is cha
 llenged by the biological complexity\, heterogeneity and dynamic range inh
 erent in clinical samples\, requiring large sample sizes for confident dis
 covery\, which in turn leads to issues with reproducibility. To overcome t
 he limitations of existing informatics pipelines for robust identification
 \, quantification and differential analysis\, we have developed a novel wo
 rkflow for biomarker discovery that for the first time extracts peaks and 
 whole biochemical features through statistical signal processing of the un
 processed raw data. Recently\, we have further extended this pipeline with
  a Bayesian modelling approach to assess peptide reproducibility for robus
 t\, reliable protein quantification and significance testing\, and a new f
 ile format for scalable computation. In this talk I will discuss the trans
 lation of these methods into a platform for the SBDC\, plus the potential 
 of adapting these methods for corresponding applications in metabolomics.
LOCATION:Large Seminar Room\, 1st Floor\, Institute of Public Health\, Uni
 versity Forvie Site\, Robinson Way\, Cambridge
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