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rnasim: Simulating RNA evolution

eagle-i ID


Resource Type

  1. Algorithmic software suite


  1. Resource Description
    Simulated nucleotide sequences are widely used in theoretical and empirical molecular evolution studies. Conventional simulators generally use fixed parameter time-homogeneous Markov model for sequence evolution. rnasim simulates RNA evolution on phylogenies based on the model described in the related publication. Briefly, rnasim uses the folding free energy of the secondary structure of an RNA, the energy_of_struct() class in the Vienna RNA package, as a proxy for its phenotypic fitness, and simulates RNA macroevolution by a mutation-selection population genetics model. Because the two-step process is conditioned on an RNA and its mutant ensemble, we no longer have a global substitution matrix, nor do we explicitly assume any for this inhomogeneous stochastic process. The sequences generated by rnasim, have greater statistical complexity than sequences generated by two standard simulators, ROSE and Seq-Gen, and are close to empirical sequences.
  2. Used by
    Kim Laboratory: Computational Evolutionary Biology
  3. Version
  4. Operating System
  5. Data Input
    Tree file
  6. Data Input
    RNA sequence and secondary structure
  7. Data Output
    RNA sequences
  8. Data Output
    RNA secondary structures
  9. Data Output
    Nucleotide positions in the homologous alignment
  10. Data Output
    RNA sequence identifier (node name in the tree)
  11. Data Output
    Scaled tree used in simulation
  12. Software purpose
    Evolutionary modeling objective
  13. Related Publication or Documentation
    Large-scale simulation of RNA macroevolution by an energy-dependent fitness model
  14. Website(s)
  15. Related Technique
    Computational modeling technique
  16. Developed by
    Kim, Junhyong., PhD
  17. Developed by
    Wang, Li-San., PhD
  18. Developed by
    Guo, Sheng
  19. Software license
    Academic software license
  20. Coded in
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Copyright © 2016 by the President and Fellows of Harvard College
The eagle-i Consortium is supported by NIH Grant #5U24RR029825-02 / Copyright 2016