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Related Experiment Videos

Pair hidden Markov models on tree structures.

Yasubumi Sakakibara1

  • 1Department of Biosciences and Informatics, Keio University, 3-14-1 Hiyoshi, Kohoku-ku, Yokohama, 223-8522, Japan. yasu@bio.keio.ac.jp

Bioinformatics (Oxford, England)
|July 12, 2003
PubMed
Summary

We introduce Pair HMMs on Tree Structures (PHMMTSs) for improved RNA structural alignment. This method extends Pair HMMs to effectively align RNA sequences with known secondary structures, enhancing computational RNA analysis.

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Area of Science:

  • Computational Biology
  • Bioinformatics
  • Genomics

Background:

  • Identifying non-coding RNA regions computationally is challenging, unlike protein-coding regions.
  • Comparative sequence analysis is effective for non-coding RNA detection, necessitating efficient alignment methods.
  • Pair Hidden Markov Models (PHMMs) are established for sequence analysis and gene finding.

Purpose of the Study:

  • To develop an extended computational framework for RNA structural alignment.
  • To provide a unifying model for tree alignments, structural alignments, and context-free grammars.
  • To address the challenge of aligning linear RNA sequences with known secondary structures.

Main Methods:

  • Extension of Pair HMMs to Pair HMMs on Tree Structures (PHMMTSs) for tree alignments.
  • Development of PHMMTSs as pair stochastic tree automata for aligning trees.
  • Modification of PHMMTSs to accept a linear RNA sequence and a tree representing secondary structure for structural alignment.

Main Results:

  • PHMMTSs provide a framework for various tree alignments, including affine-gap alignments.
  • PHMMTSs enable structural alignment of RNA sequences by integrating linear sequences with tree structures.
  • PHMMTSs with linear sequence inputs are mathematically equivalent to pair stochastic context-free grammars, validated by computational experiments.

Conclusions:

  • PHMMTSs offer an effective automata-theoretic model for RNA structural alignment.
  • The proposed method demonstrates effectiveness in computational experiments for structural alignment tasks.
  • This work advances computational methods for analyzing non-coding RNA and their structures.

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