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Related Experiment Videos

RAG: RNA-As-Graphs web resource.

Daniela Fera1, Namhee Kim, Nahum Shiffeldrim

  • 1Department of Chemistry, New York University, New York, NY 10003, USA. df448@nyu.edu

BMC Bioinformatics
|July 9, 2004
PubMed
Summary
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This study introduces RAG (RNA-As-Graphs), a novel database for cataloguing RNA secondary motifs using graph theory. RAG systematically organizes and ranks RNA structures, aiding in the discovery of new RNA motifs.

Area of Science:

  • Molecular Biology
  • Bioinformatics
  • Computational Biology

Background:

  • RNA structural studies reveal a diverse and expanding repertoire of structural motifs.
  • Systematic cataloguing of these RNA structures is essential for understanding their functions.
  • Previous work established a graphical approach for organizing RNA secondary structures.

Purpose of the Study:

  • To present RAG (RNA-As-Graphs), a database for cataloguing and ranking all theoretically possible RNA secondary motifs.
  • To provide a systematic classification of existing, candidate, and hypothetical RNA motifs.
  • To facilitate the identification of structural and functional properties of RNA sequences.

Main Methods:

  • Utilizing graph theory to represent and analyze RNA secondary structures.

Related Experiment Videos

  • Developing a clustering algorithm to classify RNA graphs into RNA-like and non-RNA categories.
  • Filing RNA motifs by graph vertex number (RNA length) and ranking by topological complexity.

    • Main Results:

    • The RAG database catalogues a comprehensive set of RNA secondary motifs.
    • Candidate motifs are identified using a clustering algorithm distinguishing RNA-like structures.
    • RNA motifs are organized and ranked based on structural complexity and length.

    Conclusions:

    • RAG enables quantitative cataloguing and facile retrieval of all RNA secondary motif classes.
    • The database aids in identifying structural and functional properties of user-provided RNA sequences.
    • RAG stimulates the search for novel RNAs through the prediction of candidate motifs.