Publications [#228872] of Francois M. Lutzoni

search PubMed.

Papers Published

  1. Lutzoni, F; Wagner, P; Reeb, V; Zoller, S, Integrating ambiguously aligned regions of DNA sequences in phylogenetic analyses without violating positional homology., Systematic biology, vol. 49 no. 4 (December, 2000), pp. 628-651 [doi] .
    (last updated on 2024/03/29)

    Abstract:
    Phylogenetic analyses of non-protein-coding nucleotide sequences such as ribosomal RNA genes, internal transcribed spacers, and introns are often impeded by regions of the alignments that are ambiguously aligned. These regions are characterized by the presence of gaps and their uncertain positions, no matter which optimization criteria are used. This problem is particularly acute in large-scale phylogenetic studies and when aligning highly diverged sequences. Accommodating these regions, where positional homology is likely to be violated, in phylogenetic analyses has been dealt with very differently by molecular systematists and evolutionists, ranging from the total exclusion of these regions to the inclusion of every position regardless of ambiguity in the alignment. We present a new method that allows the inclusion of ambiguously aligned regions without violating homology. In this three-step procedure, first homologous regions of the alignment containing ambiguously aligned sequences are delimited. Second, each ambiguously aligned region is unequivocally coded as a new character, replacing its respective ambiguous region. Third, each of the coded characters is subjected to a specific step matrix to account for the differential number of changes (summing substitutions and indels) needed to transform one sequence to another. The optimal number of steps included in the step matrix is the one derived from the pairwise alignment with the greatest similarity and the least number of steps. In addition to potentially enhancing phylogenetic resolution and support, by integrating previously nonaccessible characters without violating positional homology, this new approach can improve branch length estimations when using parsimony.