Evaluating impacts of syntenic block detection strategies on rearrangement phylogeny usingMycobacterium tuberculosisisolates
| Autor: | Afif Elghraoui, Siavash Mirarab, Krister M Swenson, Faramarz Valafar |
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| Rok vydání: | 2023 |
| Předmět: | |
| Zdroj: | Bioinformatics. 39 |
| ISSN: | 1367-4811 1367-4803 |
| DOI: | 10.1093/bioinformatics/btad024 |
| Popis: | MotivationThe phylogenetic signal of structural variation informs a more comprehensive understanding of evolution. As (near-)complete genome assembly becomes more commonplace, the next methodological challenge for inferring genome rearrangement trees is the identification of syntenic blocks of orthologous sequences. In this article, we studied 94 reference quality genomes of primarily Mycobacterium tuberculosis (Mtb) isolates as a benchmark to evaluate these methods. The clonal nature of Mtb evolution, the manageable genome sizes, along with substantial levels of structural variation make this an ideal benchmarking dataset.ResultsWe tested several methods for detecting homology and obtaining syntenic blocks and two methods for inferring phylogenies from them, then compared the resulting trees to the standard method’s tree, inferred from nucleotide substitutions. We found that, not only the choice of methods, but also their parameters can impact results, and that the tree inference method had less impact than the block determination method. Interestingly, a rearrangement tree based on blocks from the Cactus whole-genome aligner was fully compatible with the highly supported branches of the substitution-based tree, enabling the combination of the two into a high-resolution supertree. Overall, our results indicate that accurate trees can be inferred using genome rearrangements, but the choice of the methods for inferring homology requires care.Availability and implementationAnalysis scripts and code written for this study are available at https://gitlab.com/LPCDRP/rearrangement-homology.pub and https://gitlab.com/LPCDRP/syntement.Supplementary informationSupplementary data are available at Bioinformatics online. |
| Databáze: | OpenAIRE |
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