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Probing RNA Structure with Dimethyl Sulfate Mutational Profiling with Sequencing In Vitro and in Cells
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Combining structure probing data on RNA mutants with evolutionary information reveals RNA-binding interfaces.

Vladimir Reinharz1, Yann Ponty2, Jérôme Waldispühl3

  • 1School of Computer Science, McGill University, Montreal, Québec H3A 0E9, Canada.

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Summary

This study introduces aRNhAck, a new software tool. It combines RNA structure probing data with evolutionary information to reveal nucleotide networks critical for RNA function.

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Area of Science:

  • Computational Biology
  • Molecular Biology
  • Bioinformatics

Background:

  • Non-coding RNA (ncRNA) structures are crucial for their function.
  • Mutate-and-map (MaM) experiments and comparative sequence analysis offer complementary insights into ncRNA structure and function.
  • Understanding genetic robustness and nucleotide interactions is key to deciphering ncRNA mechanisms.

Purpose of the Study:

  • To develop a computational framework integrating biochemical structure probing data with evolutionary information from homologous sequences.
  • To identify long-range nucleotide dependencies in single-stranded RNA using neutral theory principles.
  • To introduce aRNhAck, a software tool for analyzing these nucleotide networks.

Main Methods:

  • Formal framework combining mutate-and-map (MaM) experimental signals with multiple sequence alignment data.
  • Application of neutral theory principles to detect nucleotide dependencies.
  • Development and implementation of the aRNhAck software.

Main Results:

  • aRNhAck successfully integrates MaM data and evolutionary information.
  • The software detects complex, long-range nucleotide dependencies in RNA.
  • Calculated nucleotide networks correlate with known RNA interaction sites (RNA-RNA, RNA-protein, RNA-DNA, RNA-ligand).

Conclusions:

  • The aRNhAck framework provides a powerful method for uncovering functional nucleotide networks in ncRNAs.
  • This approach enhances the understanding of RNA structure-function relationships by combining experimental and evolutionary data.
  • The identified nucleotide networks are biologically relevant, highlighting key interaction interfaces.