The Wasserman laboratory focuses on the creation, evaluation and application of computational methods for the analysis of genome sequences, with international strength in the study of cis-regulatory elements regulating gene expression. The lab creates widely used software and databases, performs applied analyses of genome sequences, and partners with diverse research teams on projects at the intersection of the computational and life sciences.
Genome Sequencing has disrupted health research. The lab has been developing computational methods and tools to allow researchers and clinicians to identify functional consequences of genetic variations within cis-regulatory elements such as transcription factor binding sites. Alterations in the TF bound DNA sequences can contribute causally to phenotypes, but much work remains to develop the essential computational methods to study them.
Much of the lab's current focus is on the applications of Deep Learning to the understanding of transcriptional regulation. Using such machine learning methods, we attempt to understand how combinations of transcription factors (TFs) control the differentiation of stem cells into mature tissues such as pancreatic beta cells, cardiomyocytes and more.