Téléchargez l’affiche de la rencontre
Titre de la Présentation :
Statistical Issues Involved in the Analysis of High Throughput ChIP Assays

Date / Heure / Emplacement:
Jeudi, 11 Décembre 2008 - 18:00
Salle S1-151 de l’IRIC

Affiliation :
IRCM, Institut de Recherches Cliniques de Montréal

URL
Raphaël Gottardo

Abstract :
DNA-binding proteins play a key role in human health and disease, yet our current understanding of these proteins is limited by our knowledge of their binding sites in the genome. Chromatin immunoprecipitation (ChIP) is a powerful tool for determining whether a given protein binds to a specific DNA sequence in vivo. Recently, we have seen the emergence of high throughput assays (tiling arrays, high throughput sequencing) that query all of the non-repetitive genomic sequences of human and other eukaryotes. When combined with ChIP, these assays permit the unbiased mapping of in vivo DNA-protein binding sites across a given genome. During this talk, I will describe some of the statistical issues involved in the analysis of high throughput ChIP data and will present some of the work we have done to address these issues. I will use both ChIP-chip and ChIP-Seq data to illustrate our methods.

Téléchargez l’affiche de la rencontre
Titre de la Présentation :
The Breast Cancer Tumor Microenvironment

Date / Heure / Emplacement:
Jeudi, 13 Novembre 2008 - 18:00
Salle S1-151 de l’IRIC

Affiliation :
McGill Center for Bioinformatics

URL
Michael Hallett

Abstract :
It is increasingly evident that breast cancer outcome is strongly influenced by signals emanating from tumor-associated stroma. However, little is known about how gene expression changes in this tissue affect tumor progression. In this talk, we compare gene expression profiles from laser capture-microdissected tumor-associated versus matched normal stroma, and derive transcriptional profiles strongly associated with clinical outcome. We present a stroma-derived predictor that generates new information to stratify disease endpoint, independent of standard clinical prognostic factors and previously published predictors.

Genes represented in the stroma-derived predictor reveal the strong prognostic capacity of differential immune responses as well as angiogenic and hypoxic responses. The computational and statistical aspects underpinning this work are built upon a new approach to analyzing gene expression data that in some sense is “orthologonal” to traditional clustering based tools, and is general in the sense that a wide range of data types can be easily integrated into the system.We show how this tool stratifies patients in an interesting and clinically relevant way.