Bayesian inference of chromatin structure from Hi-C data with Simeon Carstens — the bioinformatics chat, a podcast about bioinformatics

Bayesian inference of chromatin structure from Hi-C data with Simeon Carstens (#30)

February 27, 2019

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.

Download MP3

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)

Music: Eric Skiff — Come and Find Me (modified, licensed under CC BY 4.0).

Subscribe to the bioinformatics chat on Apple Podcasts, Pocket Casts, Spotify, or any other podcasting app via the RSS feed link. You can also follow the podcast on Mastodon and Twitter and support it on Patreon.