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On Pattern-Cladistic Analyses Based on Complete Plastid Genome Sequences.

Evgeny V MavrodievAlexander Madorsky
Published in: Acta biotheoretica (2023)
The fundamental Hennigian principle, grouping solely on synapomorphy, is seldom used in modern phylogenetics. In the submitted paper, we apply this principle in reanalyzing five datasets comprising 197 complete plastid genomes (plastomes). We focused on the latter because plastome-based DNA sequence data gained dramatic popularity in molecular systematics during the last decade. We show that pattern-cladistic analyses based on complete plastid genome sequences can successfully resolve affinities between plant taxa, simultaneously simplifying both the genomic and analytical frameworks of phylogenetic studies. We developed "Matrix to Newick" (M2N), a program to represent the standard molecular alignment of plastid genomes in the form of trees or relationships directly. Thus, massive plastome-based DNA sequence data can be successfully represented in a relational form rather than as a standard molecular alignment. Application of methods of median supertree construction (the Average Consensus method has been used as an example in this study) or Maximum Parsimony analysis to relational representations of plastome sequence data may help systematist to avoid the complicated assumption-based frameworks of Maximum Likelihood or Bayesian phylogenetics that are most used today in massive plastid sequence data analyses. We also found that significant amounts of pure genomic information that typically accommodate the majority of current plastid phylogenomic studies can be effectively dropped by systematists if they focus on the pattern-cladistics or relational analyses of plastome-based molecular data. The proposed pattern-cladistic approach is a powerful and straightforward heuristic alternative to modern plastome-based phylogenetics.
Keyphrases
  • electronic health record
  • big data
  • single molecule
  • gene expression
  • cell free
  • working memory
  • genome wide
  • machine learning
  • dna methylation
  • artificial intelligence
  • liquid chromatography
  • nucleic acid