Join the Neugebauer Lab

 

Now Hiring!

We are currently searching for a post-graduate student to join our team in studying how the cell nucleus is compartmentalized and how nuclear bodies help cells respond to stress. Click here for more information.

Undergraduate Students

We are actively recruiting undergraduate researchers to join our lab. If you are interested, please submit a 1-page resume and a 1-2-paragraph statement describing why you would like to work with us to karla.neugebauer@yale.edu. We are most interested in students who are able to commit to at least two full years of research.

Graduate Students

Students interested in doing a PhD with us should apply to the BBS program within the Yale Graduate School of Arts and Sciences. If you are already a matriculated Yale graduate student and would like to do a rotation with us, please email Karla directly at karla.neugebauer@yale.edu.  Below are some open questions that rotation students could work on.

  • How do environmental conditions affect complex machineries in living cells? It turns out all sorts of environmental stresses (e.g. heat, oxidative stress, osmotic stress) as well as cancer and viral infections can cause changes in gene expression that are not fully understood. What are the underlying molecular mechanisms and cellular consequences of these phenomena?  We are investigating the coupling between transcription and RNA processing steps (e.g. splicing and polyadenylation) by analyzing nascent RNA. In your rotation project, you could investigate the stress of your choice! As a reference, check out Herzel et al. 2018. Note that you could use budding or fission yeast, human tissue culture cells, zebrafish embryos, or green algae to test your stress!

  • We use a combination of emerging sequencing technologies to analyze nascent RNA and determine splicing kinetics. This involves both short (Illumina) and long (Oxford Nanopore and PacBio) read sequencing of nascent RNA, which reveals splicing precursors, intermediates, and products as transcription proceeds (the 3’ end indicates the position of Pol II). Each gene has a unique kinetic signature. As a reference, check out Carrillo Oesterreich et al. 2016 We have used machine learning to generate predictions about splicing regulation, but there are so many other approaches to explore! We would be interested in modeling and curve fitting approaches that allow us to test the effects of mutations.

postdocs

Please apply by writing directly to karla.neugebauer@yale.edu.