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Large Phylogenomic Data sets Reveal Deep Relationships and Trait Evolution in Chlorophyte Green Algae.

Xi LiZheng HouChenjie XuXuan ShiLingxiao YangLouise A LewisBojian Zhong
Published in: Genome biology and evolution (2022)
The chlorophyte green algae (Chlorophyta) are species-rich ancient groups ubiquitous in various habitats with high cytological diversity, ranging from microscopic to macroscopic organisms. However, the deep phylogeny within core Chlorophyta remains unresolved, in part due to the relatively sparse taxon and gene sampling in previous studies. Here we contribute new transcriptomic data and reconstruct phylogenetic relationships of core Chlorophyta based on four large data sets up to 2,698 genes of 70 species, representing 80% of extant orders. The impacts of outgroup choice, missing data, bootstrap-support cutoffs, and model misspecification in phylogenetic inference of core Chlorophyta are examined. The species tree topologies of core Chlorophyta from different analyses are highly congruent, with strong supports at many relationships (e.g., the Bryopsidales and the Scotinosphaerales-Dasycladales clade). The monophyly of Chlorophyceae and of Trebouxiophyceae as well as the uncertain placement of Chlorodendrophyceae and Pedinophyceae corroborate results from previous studies. The reconstruction of ancestral scenarios illustrates the evolution of the freshwater-sea and microscopic-macroscopic transition in the Ulvophyceae, and the transformation of unicellular→colonial→multicellular in the chlorophyte green algae. In addition, we provided new evidence that serine is encoded by both canonical codons and noncanonical TAG code in Scotinosphaerales, and stop-to-sense codon reassignment in the Ulvophyceae has originated independently at least three times. Our robust phylogenetic framework of core Chlorophyta unveils the evolutionary history of phycoplast, cyto-morphology, and noncanonical genetic codes in chlorophyte green algae.
Keyphrases
  • genome wide
  • electronic health record
  • big data
  • single cell
  • dna methylation
  • climate change
  • machine learning
  • rna seq
  • artificial intelligence
  • protein kinase
  • deep learning
  • gram negative
  • genome wide analysis