Insertions and deletions as phylogenetic signal in an alignment-free context

2022 | journal article. A publication with affiliation to the University of Göttingen.

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​Insertions and deletions as phylogenetic signal in an alignment-free context​
Birth, N.; Dencker, T. & Morgenstern, B. ​ (2022) 
PLOS Computational Biology18(8) pp. e1010303​.​ DOI: https://doi.org/10.1371/journal.pcbi.1010303 

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Authors
Birth, Niklas; Dencker, Thomas; Morgenstern, Burkhard 
Abstract
Most methods for phylogenetic tree reconstruction are based on sequence alignments; they infer phylogenies from substitutions that may have occurred at the aligned sequence positions. Gaps in alignments are usually not employed as phylogenetic signal. In this paper, we explore an alignment-free approach that uses insertions and deletions (indels) as an additional source of information for phylogeny inference. For a set of four or more input sequences, we generate so-called quartet blocks of four putative homologous segments each. For pairs of such quartet blocks involving the same four sequences, we compare the distances between the two blocks in these sequences, to obtain hints about indels that may have happened between the blocks since the respective four sequences have evolved from their last common ancestor. A prototype implementation that we call Gap-SpaM is presented to infer phylogenetic trees from these data, using a quartet-tree approach or, alternatively, under the maximum-parsimony paradigm. This approach should not be regarded as an alternative to established methods, but rather as a complementary source of phylogenetic information. Interestingly, however, our software is able to produce phylogenetic trees from putative indels alone that are comparable to trees obtained with existing alignment-free methods.
Issue Date
2022
Journal
PLOS Computational Biology 
Organization
Abteilung Bioinformatik ; Abteilung Molekulare Mikrobiologie & Genetik ; Universitätsmedizin Göttingen 
eISSN
1553-7358
Language
English
Sponsor
Open-Access-Publikationsfonds 2022

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