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Sökning: WFRF:(Walls Ramona L.)

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1.
  • Leebens-Mack, James H., et al. (författare)
  • One thousand plant transcriptomes and the phylogenomics of green plants
  • 2019
  • Ingår i: Nature. - : Nature Publishing Group. - 0028-0836 .- 1476-4687. ; 574:7780, s. 679-
  • Tidskriftsartikel (refereegranskat)abstract
    • Green plants (Viridiplantae) include around 450,000-500,000 species(1,2) of great diversity and have important roles in terrestrial and aquatic ecosystems. Here, as part of the One Thousand Plant Transcriptomes Initiative, we sequenced the vegetative transcriptomes of 1,124 species that span the diversity of plants in a broad sense (Archaeplastida), including green plants (Viridiplantae), glaucophytes (Glaucophyta) and red algae (Rhodophyta). Our analysis provides a robust phylogenomic framework for examining the evolution of green plants. Most inferred species relationships are well supported across multiple species tree and supermatrix analyses, but discordance among plastid and nuclear gene trees at a few important nodes highlights the complexity of plant genome evolution, including polyploidy, periods of rapid speciation, and extinction. Incomplete sorting of ancestral variation, polyploidization and massive expansions of gene families punctuate the evolutionary history of green plants. Notably, we find that large expansions of gene families preceded the origins of green plants, land plants and vascular plants, whereas whole-genome duplications are inferred to have occurred repeatedly throughout the evolution of flowering plants and ferns. The increasing availability of high-quality plant genome sequences and advances in functional genomics are enabling research on genome evolution across the green tree of life.
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2.
  • Deans, Andrew R, et al. (författare)
  • Finding Our Way through Phenotypes.
  • 2015
  • Ingår i: PLoS Biology. - : Public Library of Science (PLoS). - 1545-7885. ; 13:1
  • Tidskriftsartikel (refereegranskat)abstract
    • Despite a large and multifaceted effort to understand the vast landscape of phenotypic data, their current form inhibits productive data analysis. The lack of a community-wide, consensus-based, human- and machine-interpretable language for describing phenotypes and their genomic and environmental contexts is perhaps the most pressing scientific bottleneck to integration across many key fields in biology, including genomics, systems biology, development, medicine, evolution, ecology, and systematics. Here we survey the current phenomics landscape, including data resources and handling, and the progress that has been made to accurately capture relevant data descriptions for phenotypes. We present an example of the kind of integration across domains that computable phenotypes would enable, and we call upon the broader biology community, publishers, and relevant funding agencies to support efforts to surmount today's data barriers and facilitate analytical reproducibility.
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