<p>In this episode, <a href="https://jmschrei.github.io/">Jacob Schreiber</a> interviews <a href="https://dyneofdata.github.io/">Da-Inn Erika Lee</a> about
data and computational methods for making sense of 3D genome structure. They 
begin their discussion by talking about 3D genome structure at a high level 
and the challenges in working with such data. Then, they discuss a method 
recently developed by Erika, named <a href="https://roy-lab.github.io/grinch/">GRiNCH</a>, that mines this data to 
identify spans of the genome that cluster together in 3D space and 
potentially help control gene regulation.</p>


      

      
      <p>Links:</p>
<ul>
  <li><a href="https://genomebiology.biomedcentral.com/articles/10.1186/s13059-021-02378-z">GRiNCH: simultaneous smoothing and detection of topological units of genome organization from sparse chromatin contact count matrices with matrix factorization</a> (Da-Inn Lee and Sushmita Roy)</li>
  <li><a href="https://roy-lab.github.io/grinch/">GRiNCH Project Page</a></li>
  <li><a href="https://www.nature.com/articles/s41467-019-13423-8">In silico prediction of high-resolution Hi-C interaction matrices</a>(Shilu Zhang, Deborah Chasman, Sara Knaack, and Sushmita Roy)</li>
</ul>

#62 Steady states of metabolic networks and Dingo with Apostolos Chalkis

A podcast about computational biology, bioinformatics, and next generation sequencing.