Simulation-based inference of yeast centromeres

2025-08-29

AI Summary

Centromere localization in yeast has long been hindered by chromatin conformational complexity and the absence of reliable prior positional information. To address this, we propose a de novo stochastic simulation–statistical inference framework that integrates experimental Hi-C contact maps with physics-based chromosome conformation dynamics simulations, enabling genome-wide, high-confidence centromere prediction through spatial contact pattern matching. Crucially, our method circumvents the strong dependence on initial coordinate guesses inherent in conventional Hi-C analyses, thereby substantially improving robustness and accuracy—particularly in unannotated or low-quality yeast genome assemblies. Validation in *Saccharomyces cerevisiae* demonstrates precise localization of all 16 centromeres, with a mean error of less than 5 kb. This approach establishes a scalable, unbiased paradigm for centromere annotation in non-model yeast species, offering broad applicability to diverse fungal genomes lacking high-resolution structural or epigenetic priors.

Technology Category

• Search and Optimization: Sampling/Simulation-based Search
• Constraint Satisfaction and Optimization: Constraint Learning and Acquisition
• Reasoning under Uncertainty: Stochastic Optimization

Application Category

• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods

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.

Problem

• Inferring centromere locations in yeast species
• Overcoming limitations of Hi-C data pre-localization reliance
• Using stochastic simulation with experimental and simulated maps

Innovation

• Stochastic inference of centromere locations
• Combining experimental Hi-C maps
• Simulated contact maps analysis