Your browser doesn't support javascript.
loading
Automated annotation of human centromeres with HORmon.
Kunyavskaya, Olga; Dvorkina, Tatiana; Bzikadze, Andrey V; Alexandrov, Ivan A; Pevzner, Pavel A.
Afiliación
  • Kunyavskaya O; Center for Algorithmic Biotechnology, Institute of Translational Biomedicine, Saint Petersburg State University, Saint Petersburg, Russia, 199034.
  • Dvorkina T; Center for Algorithmic Biotechnology, Institute of Translational Biomedicine, Saint Petersburg State University, Saint Petersburg, Russia, 199034.
  • Bzikadze AV; Graduate Program in Bioinformatics and Systems Biology, University of California, San Diego, California 92093, USA.
  • Alexandrov IA; Center for Algorithmic Biotechnology, Institute of Translational Biomedicine, Saint Petersburg State University, Saint Petersburg, Russia, 199034.
  • Pevzner PA; Department of Computer Science and Engineering, University of California, San Diego, California 92093, USA.
Genome Res ; 32(6): 1137-1151, 2022 06.
Article en En | MEDLINE | ID: mdl-35545449
Recent advances in long-read sequencing opened a possibility to address the long-standing questions about the architecture and evolution of human centromeres. They also emphasized the need for centromere annotation (partitioning human centromeres into monomers and higher-order repeats [HORs]). Although there was a half-century-long series of semi-manual studies of centromere architecture, a rigorous centromere annotation algorithm is still lacking. Moreover, an automated centromere annotation is a prerequisite for studies of genetic diseases associated with centromeres and evolutionary studies of centromeres across multiple species. Although the monomer decomposition (transforming a centromere into a monocentromere written in the monomer alphabet) and the HOR decomposition (representing a monocentromere in the alphabet of HORs) are currently viewed as two separate problems, we show that they should be integrated into a single framework in such a way that HOR (monomer) inference affects monomer (HOR) inference. We thus developed the HORmon algorithm that integrates the monomer/HOR inference and automatically generates the human monomers/HORs that are largely consistent with the previous semi-manual inference.
Asunto(s)

Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Asunto principal: Algoritmos / Centrómero Límite: Humans Idioma: En Revista: Genome Res Asunto de la revista: BIOLOGIA MOLECULAR / GENETICA Año: 2022 Tipo del documento: Article

Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Asunto principal: Algoritmos / Centrómero Límite: Humans Idioma: En Revista: Genome Res Asunto de la revista: BIOLOGIA MOLECULAR / GENETICA Año: 2022 Tipo del documento: Article