CentroFinder: accurate de novo identification of centromeres in fungal genomes

Abstract

MotivationCentromeres are essential chromosomal loci, yet their computational identification remains challenging due to rapid sequence evolution, high repeat content, and the absence of conserved defining motifs. This challenge is particularly pronounced in fungi, where centromere architectures vary widely in size, sequence composition, and chromatin organization, limiting the effectiveness of single-feature or motif-based prediction approaches. ResultsWe present CentroFinder, a fungal-specific computational framework for de novo centromere prediction from long-read sequencing-based genome assemblies. CentroFinder integrates multiple genomic and long-read-derived features into a weighted scoring model to identify loci where centromere-associated signals converge. Benchmarking against experimentally mapped centromeres in Cryptococcus deuterogattii, Magnaporthe oryzae, and Neurospora crassa demonstrates that CentroFinder consistently predicts a single centromeric region per chromosome, fully nested within CENP-A-defined domains despite substantial diversity in centromere size, sequence composition, and chromatin context. Availability and ImplementationCentroFinder is freely available as open-source software at https://github.com/RahnamaLab/CentroFinder. The pipeline is designed for high-performance computing environments and leverages features derived from long-read sequencing data.