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In this episode, Jacob Schreiber interviews Da-Inn Erika Lee 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...
#61 3D genome organization and GRiNCH with Da-Inn Erika Lee is an episode from the bioinformatics chat by Roman Cheplyaka. In this episode, Jacob Schreiber interviews Da-Inn Erika Lee about data and computational methods for making sense of...
This episode belongs to the bioinformatics chat.
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Published Jun 23, 2021, 1:09:41 long, audio available.
In this episode, Jacob Schreiber interviews Da-Inn Erika Lee 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 GRiNCH , that mines this data to identify spans of the genome that cluster together in 3D space and potentially help control gene regulation. Links: GRiNCH: simultaneous smoothing and detection of topological units of genome organization from sparse chromatin contact count matrices with matrix factorization (Da-Inn Lee and Sushmita Roy) GRiNCH Project Page In silico prediction of high-resolution Hi-C interaction matrices (Shilu Zhang, Deborah Chasman, Sara Knaack, and Sushmita Roy)
You can listen to #61 3D genome organization and GRiNCH with Da-Inn Erika Lee online on Radio and Podcast. Open the player on this page to stream the available audio.
#61 3D genome organization and GRiNCH with Da-Inn Erika Lee is an episode from the bioinformatics chat by Roman Cheplyaka.
This episode is 1:09:41 long.
This episode was published on Jun 23, 2021.
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You can listen to #61 3D genome organization and GRiNCH with Da-Inn Erika Lee on this page when the episode audio is available from the podcast feed.
#61 3D genome organization and GRiNCH with Da-Inn Erika Lee is from the bioinformatics chat by Roman Cheplyaka.
Published Jun 23, 2021 and 1:09:41 long