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Estimating the strength of expression conservation from high throughput RNA-seq data.
Gu, Xun; Ruan, Hang; Yang, Jingwen.
Afiliación
  • Gu X; Department of Genetics, Development, and Cell Biology, Iowa State University, Ames, IA, USA.
  • Ruan H; MOE Key Laboratory of Contemporary Anthropology, Fudan University, Shanghai, China.
  • Yang J; Department of Biochemistry and Molecular Biology, McGovern Medical School, The University of Texas Health Science Center at Houston, Houston, TX, USA.
Bioinformatics ; 35(23): 5030-5038, 2019 12 01.
Article en En | MEDLINE | ID: mdl-31114853
MOTIVATION: Evolution of gene across species is usually subject to the stabilizing selection to maintain the optimal expression level. While it is generally accepted that the resulting expression conservation may vary considerably among genes, statistically reliable estimation remains challenging, due to few species included in current comparative RNA-seq data with high number of unknown parameters. RESULTS: In this paper, we develop a gamma distribution model to describe how the strength of expression conservation (denoted by W) varies among genes. Given the high throughput RNA-seq datasets from multiple species, we then formulate an empirical Bayesian procedure to estimate W for each gene. Our case studies showed that those W-estimates are useful to study the evolutionary pattern of expression conservation. AVAILABILITY AND IMPLEMENTATION: Our method has been implemented in the R-package software, TreeExp, which is publically available at Github develop site https://github.com/hr1912/TreeExp. It involves three functions: estParaGamma, estParaQ and estParaWBayesian. The manual for software TreeExp is available at https://github.com/hr1912/TreeExp/tree/master/vignettes. For any question, one may contact Dr Hang Ruan (Hang.Ruan@uth.tmc.edu).
Asunto(s)

Texto completo: 1 Bases de datos: MEDLINE Asunto principal: RNA-Seq Tipo de estudio: Prognostic_studies Idioma: En Revista: Bioinformatics Asunto de la revista: INFORMATICA MEDICA Año: 2019 Tipo del documento: Article País de afiliación: Estados Unidos

Texto completo: 1 Bases de datos: MEDLINE Asunto principal: RNA-Seq Tipo de estudio: Prognostic_studies Idioma: En Revista: Bioinformatics Asunto de la revista: INFORMATICA MEDICA Año: 2019 Tipo del documento: Article País de afiliación: Estados Unidos