Ribosomal RNA secondary structure: compensatory mutations and implications for phylogenetic analysis.

Abstract

Using sequence data from the 28S ribosomal RNA (rRNA) genes of selected vertebrates, we investigated the effects that constraints imposed by secondary structure have on the phylogenetic analysis of rRNA sequence data. Our analysis indicates that characters from both base-pairing regions (stems) and non-base-pairing regions (loops) contain phylogenetic information, as judged by the level of support of the phylogenetic results compared with a well-established tree based on both morphological and molecular data. The best results (the greatest level of support of well-accepted nodes) were obtained when the complete data set was used. However, some previously supported nodes were resolved using either the stem or loop bases alone. Stem bases sustain a greater number of compensatory mutations than would be expected at random, but the number is < 40% of that expected under a hypothesis of perfect compensation to maintain secondary structure. Therefore, we suggest that in phylogenetic analyses, the weighting of stem characters be reduced by no more than 20%, relative to that of loop characters. In contrast to previous suggestions, we do not recommend weighting of stem positions by one-half, compared with that of loop positions, because this overcompensates for the constraints that selection imposes on the secondary structure of rRNA.

K-mer natural vector and its application to the phylogenetic analysis of genetic sequences

“…This phylogenetic tree contains four clades: Birds (green), Crocodilians (blue), Mammals (red) and Amphibians (purple), and the species in each clade are correctly grouped together. The results are similar to those obtained from sequence alignment and what is found in some phylogenetic analyses (Xia et al, 2003; Hedges et al, 1990; Chan et al, 2011; Rzhetsky and Nei, 1992; Hedges, 1994; Seutin et al, 1994; Caspers et al, 1996; Janke and Arnason, 1997; Zardoya and Meyer, 1998; Ausio et al, 1999; Dixon and Hillis, 1993). It can be seen that birds group with crocodilians rather than group with mammals in Figure 5.…”

Molecular Phylogenetic Evaluation of Classification and Scenarios of Character Evolution in Calcareous Sponges (Porifera, Class Calcarea)

“…Most phylogenetic methods assume that characters in a data matrix evolve independently from each other, but this assumption is clearly violated in the helices of rRNA because paired nucleotides coevolve driven by the selection pressure to maintain the secondary structure, which is pivotal for rRNA function within the ribosome [33]–[38]. By neglecting these coevolutionary processes, phylogenetic inferences can be biased and result in suboptimal tree topologies (e.g., [33], [38]–[40]).…”

“…By neglecting these coevolutionary processes, phylogenetic inferences can be biased and result in suboptimal tree topologies (e.g., [33], [38]–[40]). Solutions to this problem are special evolutionary models, which instead of single bases consider the two paired bases of helices, the so-called doublet, as single characters.…”

“…Solutions to this problem are special evolutionary models, which instead of single bases consider the two paired bases of helices, the so-called doublet, as single characters. Such models have been shown to outperform standard 4×4 models of nucleotide evolution in analyses of rDNA data [38]–[44]. Several doublet models that make different assumptions about the evolution of doublets have been proposed (for a comprehensive overview see [37]).…”

A novel approach to represent and compare RNA secondary structures

“…By using only primary sequence information, ‘needle's’ results helped us in discriminating the sequence and structural contribution in reconstructing the alignment. Nevertheless, even in cases where two RNAs share a very similar secondary structure and a less similar sequence, which can frequently happen since the secondary structure evolves more slowly than the primary (9), yet the primary sequence can help in obtaining a better alignment, especially in unstructured regions. For this reason, our algorithm can additionally use a numeric ‘bonus’ to include primary sequence information by favoring the alignment of identical nucleotides, without increasing the algorithm complexity.…”