Eventos

Speaker:   Dr Julia Zeitlinger, Investigator at the Stowers Institute for Medical Research and Associate Professor at the University of Kansas Medical Center.

Abstract:  

The cis-regulatory code that instructs gene regulation during development, also known as the genome’s second code, is a fundamentally unresolved problem. Recent progress has provided proof-of-principle evidence that this complex cis-regulatory code can be learned with neural networks. The new approach is fundamentally different from traditional methods in that the sequence rules are learned inside a black box directly from genomic sequences through their ability to better predict a specific genomics data set. This dramatically improves the predictive performance, but requires rigorous approaches for extracting, understanding and validating the learned sequence rules to make sure that they represent biology. I will describe how we use this approach using mouse or Drosophila development as model systems and uncover unexpected sequence rules that we can validate with experiments.