Unifying Gene Duplication, Loss, and Coalescence on Phylogenetic Networks
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), ISSN: 1611-3349, Vol: 11490 LNBI, Page: 40-51
2019
- 8Citations
- 18Captures
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
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Conference Paper Description
Statistical methods were recently introduced for inferring phylogenetic networks under the multispecies network coalescent, thus accounting for both reticulation and incomplete lineage sorting. Two evolutionary processes that are ubiquitous across all three domains of life, but are not accounted for by those methods, are gene duplication and loss (GDL). In this work, we devise a three-piece model—phylogenetic network, locus network, and gene tree—that unifies all the aforementioned processes into a single model of how genes evolve in the presence of ILS, GDL, and introgression within the branches of a phylogenetic network. To illustrate the power of this model, we develop an algorithm for estimating the parameters of a phylogenetic network topology under this unified model. We demonstrate the application of the model and the accuracy of the algorithm on simulated as well as biological data. Our work adds to the biologist’s toolbox of methods for phylogenomic inference by accounting for more complex evolutionary processes.
Bibliographic Details
http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85066860465&origin=inward; http://dx.doi.org/10.1007/978-3-030-20242-2_4; https://link.springer.com/10.1007/978-3-030-20242-2_4; https://dx.doi.org/10.1007/978-3-030-20242-2_4; https://link.springer.com/chapter/10.1007/978-3-030-20242-2_4
Springer Science and Business Media LLC
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