אירועים והרצאות בפקולטה למדעי המחשב ע"ש הנרי ומרילין טאוב

As the production of next generation sequencing data is now a relatively trivial component, it is apparent that bioinformatics is becoming the single largest ongoing cost in terms of computing infrastructure and personnel. Moreover, with dozens of computations methods being developed for each step in the data processing, analysis and integration, it is proving to be extremely challenging to design efficient and robust pipeline for next generation sequencing data. This problem is magnified in clinical setting where the goal is to provide quick and accurate personalized treatment options for patients.

In this talk I will present two recent projects. The first one deals with comparing the commonly used differential gene expression analysis methods for RNA-Seq data. Another project studies mechanisms of resistance to MEK inhibitor in uveal melanoma patients. In particular, our recent comprehensive evaluation of the methods for RNA-Seq data (DESeq, edgeR, baySeq, PoissonSeq, limmaVoom, Cuffdiff) uses the SEQC benchmark data set and ENCODE data. The study finds that the methods based on the negative binomial models perform better. In addition, our results from sequencing depth and replication analysis provide important guidelines for experimental design of RNA-Seq studies.

I will also discuss a representative study from an ongoing clinical trial. Selumetinib (AZD6244) is the first drug to ever show a clinical benefit in patients with uveal melanoma but some patients develop resistance to the drug. Analysis of whole-transcriptome sequencing of liver metastases from patients with UM treated with selumetinb identified a biomarker that is overexpressed in patients who failed to derive a clinical benefit from the treatment (patent pending). Detecting biomarkers in patients who develop resistance to MEK inhibitors may provide mechanisms to stratify patients for clinical trials, and offer more personalized treatment options.