Enrico Berkes: Statistical Analysis of ChIP on Chip Experiments

Adviser: Peter Bühlmann

July 2007

Abstract:

With the end of the Human Genome Project, the challenge of the emerging discipline of modern biology is  to determine the role of the newly characterised genes in man and model organisms. This new sequence data represents, for the first time, a realistic opportunity to link the function (and dysfunction) of specific tissues and cell types to the activity of the genes expressed within them, and so to identify genes and gene products that could act as therapeutic targets. The underlying
strategy in the identification and functional characterisation of target genes will rely heavily on the ability to perform high throughput analysis of gene expression, at both the tissue and cellular level. Gene expression is regulated by proteins, specific for every gene, that bind themselves to
the target gene and promote or repress its transcription. In the last years two methods have been refined in order to study the gene regulation mechanism: microarray and ChIP on chip experiments. However the large quantity of data and the uncertainties, due to noise, provided by these experiments make the interpretation of the results difficult and laborious. For this reason many statistical methods have been developed trying to obtain the most relevant information from these data.

Our work consists of modifying Motif Regressor, an already existing method to analyze data of microarray experiments, and using this new algorithm to search the transcription factor DNA-binding motifs of HIF1-alpha, a protein involved in gene regulation under hypoxia. The results show that
our algorithm is fast, effective, does not require many biological experiments and gives important suggestions on where future biological researches could be directed.

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