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Generating benchmarks for multiple sequence alignments and phylogenetic reconstructions

J Stoye1, D Evers, F Meyer

  • 1Research Center for Interdisciplinary Studies on Structure Formation (FSPM), University of Bielefeld, Germany. stoye@Mathematik.Uni-Bielefeld.DE

Proceedings. International Conference on Intelligent Systems for Molecular Biology
|January 1, 1997
PubMed
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We developed a novel probabilistic model and tool (rose) to simulate the evolutionary process of RNA, DNA, and protein sequences. This model aids in evaluating multiple sequence alignment methods and phylogenetic prediction.

Area of Science:

  • Computational Biology
  • Bioinformatics
  • Evolutionary Biology

Background:

  • Accurate multiple sequence alignment (MSA) and phylogenetic analysis are crucial for understanding biological sequences.
  • Existing methods for evaluating MSA and phylogenetic prediction often lack realistic evolutionary simulation data.

Purpose of the Study:

  • To introduce a new probabilistic model for simulating the evolution of RNA, DNA, and protein sequences.
  • To present a computational tool, named 'rose', that implements this evolutionary model.
  • To generate benchmark datasets for evaluating multiple sequence alignment algorithms and phylogenetic inference methods.

Main Methods:

  • Development of a probabilistic model incorporating insertions, deletions, and substitutions.
  • Implementation of the model in a tool called 'rose'.

Related Experiment Videos

  • Simulation of artificial evolutionary processes from a common ancestor, logging the true evolutionary history.
  • Simultaneous generation of correct multiple sequence alignments.
  • Inclusion of varying mutation rates to model sequence motifs.

    • Main Results:

    • The 'rose' tool successfully simulates sequence evolution, creating families of related sequences.
    • The simulation process accurately logs the evolutionary history and generates correct multiple sequence alignments.
    • The model's ability to incorporate varying mutation rates allows for the creation of sequence motifs.

    Conclusions:

    • The developed probabilistic model and 'rose' tool provide a valuable resource for the computational biology community.
    • The generated datasets are suitable for rigorous evaluation of multiple sequence alignment and phylogenetic prediction methods.
    • This approach facilitates advancements in understanding sequence evolution and relationships.