Statistical analysis of RNA-seq data from next-generation sequencing experiments of cancer.

Genomics data generated by next-generation sequencing (NGS) technologies represent the most recent type of high-throughput data. However, due the novelty of NGS data, our ability to analyse either DNA-seq or RNA-seq data is severely limited. This hampers the biological usage of such data, because without statistically sound methods that allow a robust extraction of information from these large data sets, their biological merit is very limited.

The purpose of this PhD project is to develop novel analysis methods that can be applied to NGS data representing gene transcripts. Such data, also called RNA-seq, are notoriously difficult to analyse because due to high experimentation costs, usually, only very few samples per experimental condition are available. This presents a particular challenge for a statistical analysis.
The aims of the proposed PhD research study are:
1. Development of a statistical method to identify differentially expressed genes from RNA-seq data
2. Application of the method to biological data sets from cancer
3. Understand differences and commonalities between different experimental platforms
4. Provide a user-friendly R-package that can be easily used by the biology and the molecular diagnostic community