Literature DB >> 26099258

Can We Identify Genes with Increased Phylogenetic Reliability?

Vinson P Doyle1, Randee E Young2, Gavin J P Naylor3, Jeremy M Brown4.   

Abstract

Topological heterogeneity among gene trees is widely observed in phylogenomic analyses and some of this variation is likely caused by systematic error in gene tree estimation. Systematic error can be mitigated by improving models of sequence evolution to account for all evolutionary processes relevant to each gene or identifying those genes whose evolution best conforms to existing models. However, the best method for identifying such genes is not well established. Here, we ask if filtering genes according to their clock-likeness or posterior predictive effect size (PPES, an inference-based measure of model violation) improves phylogenetic reliability and congruence. We compared these approaches to each other, and to the common practice of filtering based on rate of evolution, using two different metrics. First, we compared gene-tree topologies to accepted reference topologies. Second, we examined topological similarity among gene trees in filtered sets. Our results suggest that filtering genes based on clock-likeness and PPES can yield a collection of genes with more reliable phylogenetic signal. For the two exemplar data sets we explored, from yeast and amniotes, clock-likeness and PPES outperformed rate-based filtering in both congruence and reliability.
© The Author(s) 2015. Published by Oxford University Press, on behalf of the Society of Systematic Biologists. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Entities:  

Keywords:  molecular clock; phylogenomics; posterior prediction; rate of evolution; systematic error

Mesh:

Year:  2015        PMID: 26099258     DOI: 10.1093/sysbio/syv041

Source DB:  PubMed          Journal:  Syst Biol        ISSN: 1063-5157            Impact factor:   15.683


  15 in total

1.  Why is Amazonia a 'source' of biodiversity? Climate-mediated dispersal and synchronous speciation across the Andes in an avian group (Tityrinae).

Authors:  Lukas J Musher; Mateus Ferreira; Anya L Auerbach; Jessica McKay; Joel Cracraft
Journal:  Proc Biol Sci       Date:  2019-04-10       Impact factor: 5.349

2.  More on the Best Evolutionary Rate for Phylogenetic Analysis.

Authors:  Seraina Klopfstein; Tim Massingham; Nick Goldman
Journal:  Syst Biol       Date:  2017-09-01       Impact factor: 15.683

3.  Model Choice, Missing Data, and Taxon Sampling Impact Phylogenomic Inference of Deep Basidiomycota Relationships.

Authors:  Arun N Prasanna; Daniel Gerber; Teeratas Kijpornyongpan; M Catherine Aime; Vinson P Doyle; Laszlo G Nagy
Journal:  Syst Biol       Date:  2020-01-01       Impact factor: 15.683

4.  The Implications of Incongruence between Gene Tree and Species Tree Topologies for Divergence Time Estimation.

Authors:  Tom Carruthers; Miao Sun; William J Baker; Stephen A Smith; Jurriaan M de Vos; Wolf L Eiserhardt
Journal:  Syst Biol       Date:  2022-08-10       Impact factor: 9.160

5.  Evolutionary Rate Variation among Lineages in Gene Trees has a Negative Impact on Species-Tree Inference.

Authors:  Mezzalina Vankan; Simon Y W Ho; David A Duchêne
Journal:  Syst Biol       Date:  2022-02-10       Impact factor: 15.683

6.  A Genome-Scale Investigation of How Sequence, Function, and Tree-Based Gene Properties Influence Phylogenetic Inference.

Authors:  Xing-Xing Shen; Leonidas Salichos; Antonis Rokas
Journal:  Genome Biol Evol       Date:  2016-09-02       Impact factor: 3.416

7.  Expanding anchored hybrid enrichment to resolve both deep and shallow relationships within the spider tree of life.

Authors:  Chris A Hamilton; Alan R Lemmon; Emily Moriarty Lemmon; Jason E Bond
Journal:  BMC Evol Biol       Date:  2016-10-13       Impact factor: 3.260

8.  Differences in Performance among Test Statistics for Assessing Phylogenomic Model Adequacy.

Authors:  David A Duchêne; Sebastian Duchêne; Simon Y W Ho
Journal:  Genome Biol Evol       Date:  2018-06-01       Impact factor: 3.416

9.  PhyKIT: a broadly applicable UNIX shell toolkit for processing and analyzing phylogenomic data.

Authors:  Jacob L Steenwyk; Thomas J Buida; Abigail L Labella; Yuanning Li; Xing-Xing Shen; Antonis Rokas
Journal:  Bioinformatics       Date:  2021-02-09       Impact factor: 6.937

10.  So many genes, so little time: A practical approach to divergence-time estimation in the genomic era.

Authors:  Stephen A Smith; Joseph W Brown; Joseph F Walker
Journal:  PLoS One       Date:  2018-05-17       Impact factor: 3.240

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