A podcast about computational biology, bioinformatics, and next generation sequencing.
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episode 30: Bayesian inference of chromatin structure from Hi-C data with Simeon Carstens
Hi-C is a sequencing-based assay that provides information about the 3-dimensional organization of the genome. In this episode, Simeon Carstens explains how he applied the Inferential Structure Determination (ISD) framework to build a 3D model of chromatin and fit that model to Hi-C data using Hamiltonian Monte Carlo and Gibbs sampling.
Links:
- Bayesian inference of chromatin structure ensembles from population Hi-C data (Simeon Carstens, Michael Nilges, Michael Habeck)
- Inferential Structure Determination of Chromosomes from Single-Cell Hi-C Data (Simeon Carstens, Michael Nilges, Michael Habeck)
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