Literature DB >> 28369655

Why Do Phylogenomic Data Sets Yield Conflicting Trees? Data Type Influences the Avian Tree of Life more than Taxon Sampling.

Sushma Reddy1, Rebecca T Kimball2, Akanksha Pandey2, Peter A Hosner2,3, Michael J Braun4,5, Shannon J Hackett6, Kin-Lan Han2, John Harshman7, Christopher J Huddleston8, Sarah Kingston4,5,9, Ben D Marks6, Kathleen J Miglia10, William S Moore10, Frederick H Sheldon11, Christopher C Witt12, Tamaki Yuri2,13, Edward L Braun2,14.   

Abstract

Phylogenomics, the use of large-scale data matrices in phylogenetic analyses, has been viewed as the ultimate solution to the problem of resolving difficult nodes in the tree of life. However, it has become clear that analyses of these large genomic data sets can also result in conflicting estimates of phylogeny. Here, we use the early divergences in Neoaves, the largest clade of extant birds, as a "model system" to understand the basis for incongruence among phylogenomic trees. We were motivated by the observation that trees from two recent avian phylogenomic studies exhibit conflicts. Those studies used different strategies: 1) collecting many characters [$\sim$ 42 mega base pairs (Mbp) of sequence data] from 48 birds, sometimes including only one taxon for each major clade; and 2) collecting fewer characters ($\sim$ 0.4 Mbp) from 198 birds, selected to subdivide long branches. However, the studies also used different data types: the taxon-poor data matrix comprised 68% non-coding sequences whereas coding exons dominated the taxon-rich data matrix. This difference raises the question of whether the primary reason for incongruence is the number of sites, the number of taxa, or the data type. To test among these alternative hypotheses we assembled a novel, large-scale data matrix comprising 90% non-coding sequences from 235 bird species. Although increased taxon sampling appeared to have a positive impact on phylogenetic analyses the most important variable was data type. Indeed, by analyzing different subsets of the taxa in our data matrix we found that increased taxon sampling actually resulted in increased congruence with the tree from the previous taxon-poor study (which had a majority of non-coding data) instead of the taxon-rich study (which largely used coding data). We suggest that the observed differences in the estimates of topology for these studies reflect data-type effects due to violations of the models used in phylogenetic analyses, some of which may be difficult to detect. If incongruence among trees estimated using phylogenomic methods largely reflects problems with model fit developing more "biologically-realistic" models is likely to be critical for efforts to reconstruct the tree of life. [Birds; coding exons; GTR model; model fit; Neoaves; non-coding DNA; phylogenomics; taxon sampling.].
© The Author(s) 2017. Published by Oxford University Press, on behalf of the Society of Systematic Biologists. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Mesh:

Year:  2017        PMID: 28369655     DOI: 10.1093/sysbio/syx041

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


  44 in total

1.  Whole-Genome Analyses Resolve the Phylogeny of Flightless Birds (Palaeognathae) in the Presence of an Empirical Anomaly Zone.

Authors:  Alison Cloutier; Timothy B Sackton; Phil Grayson; Michele Clamp; Allan J Baker; Scott V Edwards
Journal:  Syst Biol       Date:  2019-11-01       Impact factor: 15.683

2.  A phylogenomic rodent tree reveals the repeated evolution of masseter architectures.

Authors:  Mark T Swanson; Carl H Oliveros; Jacob A Esselstyn
Journal:  Proc Biol Sci       Date:  2019-05-15       Impact factor: 5.349

3.  Germline-restricted chromosome (GRC) is widespread among songbirds.

Authors:  Anna A Torgasheva; Lyubov P Malinovskaya; Kira S Zadesenets; Tatyana V Karamysheva; Elena A Kizilova; Ekaterina A Akberdina; Inna E Pristyazhnyuk; Elena P Shnaider; Valeria A Volodkina; Alsu F Saifitdinova; Svetlana A Galkina; Denis M Larkin; Nikolai B Rubtsov; Pavel M Borodin
Journal:  Proc Natl Acad Sci U S A       Date:  2019-04-29       Impact factor: 11.205

4.  Target-capture phylogenomics provide insights on gene and species tree discordances in Old World treefrogs (Anura: Rhacophoridae).

Authors:  Kin Onn Chan; Carl R Hutter; Perry L Wood; L Lee Grismer; Rafe M Brown
Journal:  Proc Biol Sci       Date:  2020-12-09       Impact factor: 5.349

5.  Late Cretaceous neornithine from Europe illuminates the origins of crown birds.

Authors:  Daniel J Field; Juan Benito; Albert Chen; John W M Jagt; Daniel T Ksepka
Journal:  Nature       Date:  2020-03-18       Impact factor: 49.962

6.  Mass estimation of extinct taxa and phylogenetic hypotheses both influence analyses of character evolution in a large clade of birds (Telluraves).

Authors:  Nicholas M A Crouch; Roberta Mason-Gamer
Journal:  Proc Biol Sci       Date:  2019-12-18       Impact factor: 5.349

7.  Sex biases in bird and mammal natural history collections.

Authors:  Natalie Cooper; Alexander L Bond; Joshua L Davis; Roberto Portela Miguez; Louise Tomsett; Kristofer M Helgen
Journal:  Proc Biol Sci       Date:  2019-10-23       Impact factor: 5.349

8.  Differential Splicing of ANP32A in Birds Alters Its Ability to Stimulate RNA Synthesis by Restricted Influenza Polymerase.

Authors:  Steven F Baker; Mitchell P Ledwith; Andrew Mehle
Journal:  Cell Rep       Date:  2018-09-04       Impact factor: 9.423

9.  PhyloWGA: chromosome-aware phylogenetic interrogation of whole genome alignments.

Authors:  Richard H Adams; Todd A Castoe; Michael DeGiorgio
Journal:  Bioinformatics       Date:  2021-07-27       Impact factor: 6.937

10.  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

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