mlsanityData to 14 Oct 2026

Simulation-based inference of yeast centromeres

arXiv 2509.00200 ↗First posted 29 Aug 2025stat.ML cs.LG q-bio.QMcited by 1

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Abstract (arXiv)

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.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 3 of its 19 references that are in the map. First posted on arXiv 29 Aug 2025.

Builds on

3 of 19 references.

References · 3

  1. 2017Attention Is All You Needcited by 60,361
  2. 2019Automatic Posterior Transformation for Likelihood-Free Inferencecited by 219
  3. 2015Learning Summary Statistic for Approximate Bayesian Computation via Deep Neural Networkcited by 39

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