Automated annotation of human centromeres with HORmon.

Area of Science:

• Genomics
• Molecular Biology
• Bioinformatics

Background:

• Long-read sequencing advances enable detailed studies of human centromere architecture and evolution.
• Centromere annotation, partitioning into monomers and higher-order repeats (HORs), is crucial but lacks automated algorithms.

Purpose of the Study:

• To develop a rigorous, automated algorithm for centromere annotation.
• To integrate monomer and HOR inference into a unified framework for accurate centromere analysis.

Main Methods:

• Developed the HORmon algorithm, integrating monomer and HOR inference.
• Applied the algorithm to automatically generate human monomers and HORs.

Main Results:

• The HORmon algorithm successfully integrates monomer and HOR inference.
• Generated human monomers/HORs largely consistent with previous semi-manual studies.
• Automated annotation is now feasible for centromere-related genetic and evolutionary research.

Conclusions:

• The HORmon algorithm provides a robust solution for automated centromere annotation.
• Integrated inference of monomers and HORs is essential for accurate centromere characterization.
• This work facilitates future research into centromere function, evolution, and associated diseases.