Simulation-based inference of yeast centromeres

Eloïse Touron, Pedro L. C. Rodrigues, Julyan Arbel, Nelle Varoquaux, Michael Arbel

Submission Track: Short papers presenting ongoing research or work submitted to other venues (up to 5 pages, excluding references)

Keywords: chromosome conformation capture, Hi-C map, centromeres, simulation-based inference, Bayesian inference, Approximate Bayesian Computation, neural posterior estimation, stochastic simulator

TL;DR: We present a novel approach that stochastically infers the locations of all centromeres in budding yeast from an experimental Hi-C map using Bayesian inference and simulated contact maps.

Abstract:

The chromatin folding and the spatial arrangement of chromosomes in the cell play
a crucial role in DNA replication and genes expression. An improper chromatin
folding could lead to malfunctions and, over time, diseases. For eukaryotes,
centromeres are essential for proper chromosome segregation and folding. Despite
extensive research using de novo sequencing of genomes and annotation analysis,
centromere locations in yeasts remain difficult to infer and are still unknown in
most species. Recently, genome-wide chromosome conformation capture coupled
with next-generation sequencing (Hi-C) has become one of the leading methods
to investigate chromosome structures. Some recent studies have used Hi-C data
to give a point estimate of each centromere, but those approaches highly rely
on a good pre-localization. Here, we present a novel approach that infers in a
stochastic manner the locations of all centromeres in budding yeast based on both
the experimental Hi-C map and simulated contact maps.

Submission Number: 6