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Brownian model of transcriptome evolution and phylogenetic network visualization between tissues.
Gu, Xun; Ruan, Hang; Su, Zhixi; Zou, Yangyun.
Afiliação
  • Gu X; Department of Genetics, Development and Cell Biology, Iowa State University, Ames, IA 50011, USA; State Key Laboratory of Genetic Engineering, MOE Key Laboratory of Contemporary Anthropology, School of Life Sciences, Fudan University, Shanghai 200433, China. Electronic address: xgu@iastate.edu.
  • Ruan H; State Key Laboratory of Genetic Engineering, MOE Key Laboratory of Contemporary Anthropology, School of Life Sciences, Fudan University, Shanghai 200433, China.
  • Su Z; State Key Laboratory of Genetic Engineering, MOE Key Laboratory of Contemporary Anthropology, School of Life Sciences, Fudan University, Shanghai 200433, China.
  • Zou Y; State Key Laboratory of Genetic Engineering, MOE Key Laboratory of Contemporary Anthropology, School of Life Sciences, Fudan University, Shanghai 200433, China.
Mol Phylogenet Evol ; 114: 34-39, 2017 09.
Article em En | MEDLINE | ID: mdl-28442318
ABSTRACT
While phylogenetic analysis of transcriptomes of the same tissue is usually congruent with the species tree, the controversy emerges when multiple tissues are included, that is, whether species from the same tissue are clustered together, or different tissues from the same species are clustered together. Recent studies have suggested that phylogenetic network approach may shed some lights on our understanding of multi-tissue transcriptome evolution; yet the underlying evolutionary mechanism remains unclear. In this paper we develop a Brownian-based model of transcriptome evolution under the phylogenetic network that can statistically distinguish between the patterns of species-clustering and tissue-clustering. Our model can be used as a null hypothesis (neutral transcriptome evolution) for testing any correlation in tissue evolution, can be applied to cancer transcriptome evolution to study whether two tumors of an individual appeared independently or via metastasis, and can be useful to detect convergent evolution at the transcriptional level.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Transcriptoma / Modelos Teóricos Limite: Animals / Humans Idioma: En Revista: Mol Phylogenet Evol Ano de publicação: 2017 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Transcriptoma / Modelos Teóricos Limite: Animals / Humans Idioma: En Revista: Mol Phylogenet Evol Ano de publicação: 2017 Tipo de documento: Article