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Title: Do Alignment and Trimming Methods Matter for Phylogenomic (UCE) Analyses?
Abstract Alignment is a crucial issue in molecular phylogenetics because different alignment methods can potentially yield very different topologies for individual genes. But it is unclear if the choice of alignment methods remains important in phylogenomic analyses, which incorporate data from dozens, hundreds, or thousands of genes. For example, problematic biases in alignment might be multiplied across many loci, whereas alignment errors in individual genes might become irrelevant. The issue of alignment trimming (i.e. removing poorly aligned regions or missing data from individual genes) is also poorly explored. Here, we test the impact of 12 different combinations of alignment and trimming methods on phylogenomic analyses. We compare these methods using published phylogenomic data from ultraconserved elements (UCEs) from squamate reptiles (lizards and snakes), birds, and tetrapods. We compare the properties of alignments generated by different alignment and trimming methods (e.g., length, informative sites, missing data). We also test whether these datasets can recover well-established clades when analyzed with concatenated (RAxML) and species-tree methods (ASTRAL-III), using the full data (∼5,000 loci) and subsampled datasets (10% and 1% of loci). We show that different alignment and trimming methods can significantly impact various aspects of phylogenomic datasets (e.g. length, informative sites). However, these different methods generally had little impact on the recovery and support values for well-established clades, even across very different numbers of loci. Nevertheless, our results suggest several “best practices” for alignment and trimming. Intriguingly, the choice of phylogenetic methods impacted the results most strongly, with concatenated analyses recovering significantly more well-established clades (with stronger support) than the species-tree analyses.  more » « less
Award ID(s):
1655690
PAR ID:
10198146
Author(s) / Creator(s):
;
Date Published:
Journal Name:
Systematic Biology
ISSN:
1063-5157
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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