title: Can comprehensive background knowledge be incorporated into substitution models to improve phylogenetic analyses? A case study on major arthropod relationships.
abstract: BACKGROUND
  Whenever different data sets arrive at conflicting phylogenetic hypotheses, only testable causal explanations of sources of errors in at least one of the data sets allow us to critically choose among the conflicting hypotheses of relationships. The large (28S) and small (18S) subunit rRNAs are among the most popular markers for studies of deep phylogenies. However, some nodes supported by this data are suspected of being artifacts caused by peculiarities of the evolution of these molecules. Arthropod phylogeny is an especially controversial subject dotted with conflicting hypotheses which are dependent on data set and method of reconstruction. We assume that phylogenetic analyses based on these genes can be improved further i) by enlarging the taxon sample and ii) employing more realistic models of sequence evolution incorporating non-stationary substitution processes and iii) considering covariation and pairing of sites in rRNA-genes.
  
  
  RESULTS
  We analyzed a large set of arthropod sequences, applied new tools for quality control of data prior to tree reconstruction, and increased the biological realism of substitution models. Although the split-decomposition network indicated a high noise content in the data set, our measures were able to both improve the analyses and give causal explanations for some incongruities mentioned from analyses of rRNA sequences. However, misleading effects did not completely disappear.
  
  
  CONCLUSIONS
  Analyses of data sets that result in ambiguous phylogenetic hypotheses demand for methods, which do not only filter stochastic noise, but likewise allow to differentiate phylogenetic signal from systematic biases. Such methods can only rely on our findings regarding the evolution of the analyzed data. Analyses on independent data sets then are crucial to test the plausibility of the results. Our approach can easily be extended to genomic data, as well, whereby layers of quality assessment are set up applicable to phylogenetic reconstructions in general.
authors.affiliation: Molecular Lab, Zoologisches Forschungsmuseum A, Koenig, Bonn, Germany. bmvr@arcor.de
authors.forename: Björn M
authors.initials: BM
authors.lastname: von Reumont
authors.forename: Karen
authors.initials: K
authors.lastname: Meusemann
authors.forename: Nikolaus U
authors.initials: NU
authors.lastname: Szucsich
authors.forename: Emiliano
authors.initials: E
authors.lastname: Dell'Ampio
authors.forename: Vivek
authors.initials: V
authors.lastname: Gowri-Shankar
authors.forename: Daniela
authors.initials: D
authors.lastname: Bartel
authors.forename: Sabrina
authors.initials: S
authors.lastname: Simon
authors.forename: Harald O
authors.initials: HO
authors.lastname: Letsch
authors.forename: Roman R
authors.initials: RR
authors.lastname: Stocsits
authors.forename: Yun-xia
authors.initials: YX
authors.lastname: Luan
authors.forename: Johann Wolfgang
authors.initials: JW
authors.lastname: Wägele
authors.forename: Günther
authors.initials: G
authors.lastname: Pass
authors.forename: Heike
authors.initials: H
authors.lastname: Hadrys
authors.forename: Bernhard
authors.initials: B
authors.lastname: Misof
mesh_terms: D000818:Animals; D001181:Arthropods / Q000235:genetics*; D019143:Evolution, Molecular; D008957:Models, Genetic; D009690:Nucleic Acid Conformation; D010802:Phylogeny; D012337:RNA, Ribosomal, 18S / Q000235:genetics; D012339:RNA, Ribosomal, 28S / Q000235:genetics; D016415:Sequence Alignment; D017423:Sequence Analysis, RNA / Q000379:methods
chemical_list: D012337:RNA, Ribosomal, 18S; D012339:RNA, Ribosomal, 28S