Current Methods for Automated Filtering of Multiple Sequence Alignments Frequently Worsen Single-Gene Phylogenetic Inference.

Details

Serval ID
serval:BIB_0502FCD9284A
Type
Article: article from journal or magazin.
Collection
Publications
Title
Current Methods for Automated Filtering of Multiple Sequence Alignments Frequently Worsen Single-Gene Phylogenetic Inference.
Journal
Systematic Biology
Author(s)
Tan G., Muffato M., Ledergerber C., Herrero J., Goldman N., Gil M., Dessimoz C.
ISSN
1076-836X (Electronic)
ISSN-L
1063-5157
Publication state
Published
Issued date
2015
Peer-reviewed
Oui
Volume
64
Number
5
Pages
778-791
Language
english
Abstract
Phylogenetic inference is generally performed on the basis of multiple sequence alignments (MSA). Because errors in an alignment can lead to errors in tree estimation, there is a strong interest in identifying and removing unreliable parts of the alignment. In recent years several automated filtering approaches have been proposed, but despite their popularity, a systematic and comprehensive comparison of different alignment filtering methods on real data has been lacking. Here, we extend and apply recently introduced phylogenetic tests of alignment accuracy on a large number of gene families and contrast the performance of unfiltered versus filtered alignments in the context of single-gene phylogeny reconstruction. Based on multiple genome-wide empirical and simulated data sets, we show that the trees obtained from filtered MSAs are on average worse than those obtained from unfiltered MSAs. Furthermore, alignment filtering often leads to an increase in the proportion of well-supported branches that are actually wrong. We confirm that our findings hold for a wide range of parameters and methods. Although our results suggest that light filtering (up to 20% of alignment positions) has little impact on tree accuracy and may save some computation time, contrary to widespread practice, we do not generally recommend the use of current alignment filtering methods for phylogenetic inference. By providing a way to rigorously and systematically measure the impact of filtering on alignments, the methodology set forth here will guide the development of better filtering algorithms.

Keywords
Algorithms, Classification/methods, Genome/genetics, Phylogeny, Reproducibility of Results, Sequence Alignment
Pubmed
Web of science
Open Access
Yes
Create date
02/09/2015 9:16
Last modification date
20/08/2019 13:26
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