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January 1, 1998Bioinformatics276 citationsOpen Access

Rose: generating sequence families.

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JSJens StoyeDEDorothea EversFMFolker Meyer

Key Points

  • To develop a probabilistic model for generating families of RNA, DNA, or protein-like sequences based on evolution.
  • Developed a software tool called Rose that implements the probabilistic model.
  • Simulates an evolutionary process using insertions, deletions, and substitutions guided by an evolutionary tree.
  • Logs the history of changes and creates multiple sequence alignments during the sequence generation.
  • Rose produces data suitable for evaluating multiple sequence alignment methods.
  • Facilitates predictions of phylogenetic relationships based on the generated sequences.
  • Useful for teaching and developing models related to sequence evolution.

Abstract

MOTIVATION: We present a new probabilistic model of the evolution of RNA-, DNA-, or protein-like sequences and a software tool, Rose, that implements this model. Guided by an evolutionary tree, a family of related sequences is created from a common ancestor sequence by insertion, deletion and substitution of characters. During this artificial evolutionary process, the 'true' history is logged and the 'correct' multiple sequence alignment is created simultaneously. The model also allows for varying rates of mutation within the sequences, making it possible to establish so-called sequence motifs. RESULTS: The data created by Rose are suitable for the evaluation of methods in multiple sequence alignment computation and the prediction of phylogenetic relationships. It can also be useful when teaching courses in or developing models of sequence evolution and in the study of evolutionary processes. AVAILABILITY: Rose is available on the Bielefeld Bioinformatics WebServer under the following URL: http://bibiserv.TechFak.Uni-Bielefeld.DE/rose/ The source code is available upon request. CONTACT: folker@TechFak.Uni-Bielefeld.DE

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Cite This Study

Stoye et al. (1998) studied this question.

synapsesocial.com/papers/6a0827792c981162dfddea04https://doi.org/10.1093/bioinformatics/14.2.157
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