Project Description

Alexandra Essebier

 

The University of Queensland

Processing, integrating and analysing chromatin immunoprecipitation followed by sequencing (ChIP-seq) data

Bioinformatics methods, models and applications to disease

Tuesday 5 July 2016

High throughput sequencing (HTS) technology has contributed to a number of discoveries in the human genome. One technique which relies on HTS is chromatin immunoprecipitation followed by sequencing (ChIP-seq); a technique that allows us to identify where proteins are binding in vivo. A handful of consortiums have generated thousands of data sets which use ChIP-seq to describe transcription factor (TF) binding and histone modifications (HMs). Individual labs are also generating numerous data sets exploring TFs in specific cell types and cell states using HMs as support. In the last decade, ChIP-seq has become so popular that although we have a standard way of performing the experiment in the wet lab, we have seen the development of a staggering number of processing pipelines and protocols designed to interpret the resulting sequenced reads. This begs the question of which approach is the ‘best’.

Once you have your ChIP-seq peaks, you will quickly discover that despite the challenges, generating them was the easy part and now you’re faced with interpreting the data at hand. To fully appreciate the information stored in your peaks, you have to understand the strengths and weaknesses of ChIP-seq and the power of integrating your ChIP-seq result with other available data.

This talk aims to guide you through the ChIP-seq processing steps exploring the available tools and discussing the important considerations that must be made throughout. It will also cover approaches for analysing ChIP-seq peaks and highlight the importance of data integration in the interpretation of processed ChIP-seq datasets.

Alexandra Essebier completed her undergraduate degrees in Science (Biochemistry and Molecular Biology) and Information Technology at the University of Queensland in 2013 then continued her studies and completed her Master of Bioinformatics in 2015. Alex has undertaken a number of research projects over the last three years as part of A/Prof. Mikael Bodén’s group at UQ. Her main focus is on the use of probabilistic models, specifically Bayesian networks, to analyse high-throughput genomic datasets. She is currently enrolled as a PhD student investigating the application of probabilistic models to the integration of datasets relevant to transcriptional regulation such as chromatin immunoprecipitation followed by sequencing (ChIP-seq) and RNA sequencing.