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LinearPartition: linear-time approximation of RNA folding partition function and base-pairing probabilities.

He Zhang1,2, Liang Zhang2, David H Mathews3,4,5

  • 1Baidu Research, Sunnyvale, CA 94089, USA.

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|July 14, 2020
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Summary

A new algorithm, LinearPartition, dramatically speeds up RNA secondary structure prediction by approximating partition functions in linear time. This method is significantly faster than existing tools and improves accuracy for long RNA sequences.

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Area of Science:

  • Computational Biology
  • Bioinformatics
  • Molecular Biology

Background:

  • RNA secondary structure prediction is crucial for understanding RNA function.
  • Current partition function-based methods offer insights into folding ensembles and base-pairing probabilities.
  • Classical partition function algorithms exhibit cubic time complexity, limiting their application to long RNA sequences.

Purpose of the Study:

  • To develop a fast, linear-time heuristic algorithm for approximating RNA partition functions and base-pairing probabilities.
  • To overcome the computational limitations of existing methods for long RNA sequences.

Main Methods:

  • Development of the LinearPartition algorithm, inspired by the LinearFold approach.
  • Linear-time heuristic for approximating the partition function and base-pairing probabilities.
  • Comparison with established tools like Vienna RNAfold and CONTRAfold.

Main Results:

  • LinearPartition achieves orders of magnitude speedup compared to existing methods (e.g., minutes vs. days for long sequences).
  • The algorithm demonstrates improved correlation of base-pairing probabilities with ground-truth structures.
  • Enhanced accuracy in downstream structure prediction for long RNA families (16S and 23S rRNAs) and long-distance base pairs.

Conclusions:

  • LinearPartition provides a computationally efficient and accurate method for RNA secondary structure prediction.
  • The algorithm's speed and accuracy make it suitable for analyzing large RNA molecules.
  • Availability of code and a server facilitates broader adoption and research.