Simulation-based inference of yeast centromeres
This work addresses the challenge of accurately identifying centromeres in yeast species, which is crucial for understanding chromosome segregation and folding, but it appears incremental as it builds on existing Hi-C methods.
The researchers tackled the problem of inferring centromere locations in budding yeast, which are often unknown, by developing a stochastic method that combines experimental Hi-C data with simulated contact maps, resulting in a novel approach that does not rely on pre-localization.
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.