Analysis of phylogenetic signal in protostomial intron patterns using Mutual Information

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• 作者：Natascha Hill (1)
  Alexander Leow (1)
  Christoph Bleidorn (2)
  Detlef Groth (1)
  Ralph Tiedemann (1)
  Joachim Selbig (1)
  Stefanie Hartmann (1)
  
• 关键词：Mutual Information ; Evolution ; Gene structure
• 刊名：Theory in Biosciences
• 出版年：2013
• 出版时间：June 2013
• 年：2013
• 卷：132
• 期：2
• 页码：93-104
• 全文大小：625KB
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• 作者单位：Natascha Hill (1)
  Alexander Leow (1)
  Christoph Bleidorn (2)
  Detlef Groth (1)
  Ralph Tiedemann (1)
  Joachim Selbig (1)
  Stefanie Hartmann (1)
  
  1. Department of Bioinformatics, Institute for Biochemistry and Biology, University of Potsdam, Potsdam, Germany
  2. Department of Molecular Evolution and Animal Systematics, Institute for Biology II, University of Leipzig, Leipzig, Germany
  

文摘

Many deep evolutionary divergences still remain unresolved, such as those among major taxa of the Lophotrochozoa. As alternative phylogenetic markers, the intron–exon structure of eukaryotic genomes and the patterns of absence and presence of spliceosomal introns appear to be promising. However, given the potential homoplasy of intron presence, the phylogenetic analysis of this data using standard evolutionary approaches has remained a challenge. Here, we used Mutual Information (MI) to estimate the phylogeny of Protostomia using gene structure data, and we compared these results with those obtained with Dollo Parsimony. Using full genome sequences from nine Metazoa, we identified 447 groups of orthologous sequences with 21,732 introns in 4,870 unique intron positions. We determined the shared absence and presence of introns in the corresponding sequence alignments and have made this data available in “IntronBase- a web-accessible and downloadable SQLite database. Our results obtained using Dollo Parsimony are obviously misled through systematic errors that arise from multiple intron loss events, but extensive filtering of data improved the quality of the estimated phylogenies. Mutual Information, in contrast, performs better with larger datasets, but at the same time it requires a complete data set, which is difficult to obtain for orthologs from a large number of taxa. Nevertheless, Mutual Information-based distances proved to be useful in analyzing this kind of data, also because the estimation of MI-based distances is independent of evolutionary models and therefore no pre-definitions of ancestral and derived character states are necessary.