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Peer-reviewedCC BYBioinformatics advances

CentroFinder: a multi-feature framework for de novo prediction of fungal regional centromeres.

  • Sahar Salimi1
  • Sharon Colson2
  • Michael Renfro2
  • Li-Jun Ma3
  • Mostafa Rahnama1,4
4 affiliations
  1. 1School of Environmental Studies, Tennessee Technological University, Cookeville, TN 38505, United States.
  2. 2Information Technology Services, Tennessee Technological University, Cookeville, TN 38505, United States.
  3. 3Department of Biochemistry and Molecular Biology, University of Massachusetts Amherst, Amherst, MA 01003, United States.
  4. 4Department of Biology, Tennessee Technological University, Cookeville, TN 38505, United States.
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Abstract

Motivation

Centromeres 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.Results

We 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 showed that 27 of 28 predicted intervals overlapped the corresponding experimental domains, yielding 96.4% chromosome-level detection sensitivity. Application to 11 additional fungal genomes produced one contiguous predicted centromeric region per chromosome, supporting the transferability of the workflow for chromosome-level centromere prediction.Availability and implementation

CentroFinder 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.

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Cite

BibTeXRIS
BibTeX
@article{salimi2026centrofinder,
  title = {{CentroFinder: a multi-feature framework for de novo prediction of fungal regional centromeres.}},
  author = {Sahar Salimi and Sharon Colson and Michael Renfro and Li-Jun Ma and Mostafa Rahnama},
  journal = {Bioinformatics advances},
  year = {2026},
  month = sep,
  doi = {10.1093/bioadv/vbag270},
  url = {https://europepmc.org/article/MED/42904296},
  note = {CC BY},
}
RIS
TY  - JOUR
TI  - CentroFinder: a multi-feature framework for de novo prediction of fungal regional centromeres.
AU  - Sahar Salimi
AU  - Sharon Colson
AU  - Michael Renfro
AU  - Li-Jun Ma
AU  - Mostafa Rahnama
PY  - 2026
DA  - 2026/09/12
JO  - Bioinformatics advances
DO  - 10.1093/bioadv/vbag270
UR  - https://europepmc.org/article/MED/42904296
AB  - Motivation  Centromeres 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.Results  We 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 showed that 27 of 28 predicted intervals overlapped the corresponding experimental domains, yielding 96.4% chromosome-level detection sensitivity. Application to 11 additional fungal genomes produced one contiguous predicted centromeric region per chromosome, supporting the transferability of the workflow for chromosome-level centromere prediction.Availability and implementation  CentroFinder 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.
SN  - 2635-0041
ER  -