Details of talk
|Title||Bioinformatic challenges in the analysis of CLIP Experiments|
|Presenter||Emily Jane Hackett-Jones (University of South Australia)|
|Author(s)||Emily Hackett-Jones, John Toubia, Katherine Pillman, Kate Dredge, Andrew Bert, Cameron Bracken and Greg Goodall|
MicroRNAs are small non-coding RNAs known to bind to messenger RNAs. They are known to act repressively on the translation of the mRNAs to cellular proteins. Much of the interest in microRNAs concerns their role in cancer, as dysregulation of microRNAs is common in cancer. CLIP-Seq experiments are a relatively new method to determine the binding of microRNAs to mRNAs. They involve high-throughput next generation sequencing of RNA cross-linked to microRNAs. There are a number of statistical challenges in analyzing this type of data. Generally we look for``peaks'' in the data to show where the microRNA is binding to the mRNA. These are regions where there are more reads over and above the background. However, assessing the statistical significance of the peaks can be challenging: often there are very many peaks and they are highly sensitive to the background input. Filtering out what is actually a true positive peak is of key importance to the experimentalist, who wants to use the data to design further (expensive and time-consuming) experiments.