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CNETML: maximum likelihood inference of phylogeny from copy number profiles of multiple samples.
Lu, Bingxin; Curtius, Kit; Graham, Trevor A; Yang, Ziheng; Barnes, Chris P.
Afiliação
  • Lu B; Department of Cell and Developmental Biology, University College London, London, UK. b.lu@ucl.ac.uk.
  • Curtius K; UCL Genetics Institute, University College London, London, UK. b.lu@ucl.ac.uk.
  • Graham TA; Barts Cancer Institute, Barts and the London School of Medicine and Dentistry, Queen Mary University of London, London, UK.
  • Yang Z; Division of Biomedical Informatics, Department of Medicine, University of California San Diego, La Jolla, CA, USA.
  • Barnes CP; Barts Cancer Institute, Barts and the London School of Medicine and Dentistry, Queen Mary University of London, London, UK.
Genome Biol ; 24(1): 144, 2023 06 20.
Article em En | MEDLINE | ID: mdl-37340508
Phylogenetic trees based on copy number profiles from multiple samples of a patient are helpful to understand cancer evolution. Here, we develop a new maximum likelihood method, CNETML, to infer phylogenies from such data. CNETML is the first program to jointly infer the tree topology, node ages, and mutation rates from total copy numbers of longitudinal samples. Our extensive simulations suggest CNETML performs well on copy numbers relative to ploidy and under slight violation of model assumptions. The application of CNETML to real data generates results consistent with previous discoveries and provides novel early copy number events for further investigation.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Variações do Número de Cópias de DNA / Neoplasias Limite: Humans Idioma: En Revista: Genome Biol Assunto da revista: BIOLOGIA MOLECULAR / GENETICA Ano de publicação: 2023 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Variações do Número de Cópias de DNA / Neoplasias Limite: Humans Idioma: En Revista: Genome Biol Assunto da revista: BIOLOGIA MOLECULAR / GENETICA Ano de publicação: 2023 Tipo de documento: Article