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A local multiple alignment method for detection of non-coding RNA sequences.

Yasuo Tabei1, Kiyoshi Asai

  • 1Department of Computational biology, Graduate School of Frontier Sciences, University of Tokyo, Kashiwa, Chiba, Japan. scarna@m.aist.go.jp

Bioinformatics (Oxford, England)
|April 21, 2009
PubMed
Summary
This summary is machine-generated.

A new local multiple alignment method, SCARNA_LM, detects locally conserved non-coding RNAs (ncRNAs) by integrating sequence and secondary structure information. This approach improves ncRNA detection in genomic sequences.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Non-coding RNAs (ncRNAs) evolve with correlated base substitutions to maintain secondary structure.
  • Existing multiple alignment methods for ncRNA detection are primarily global, limiting their effectiveness for identifying local ncRNA conservation within genomic sequences.
  • A need exists for efficient local multiple alignment methods tailored for ncRNA detection.

Purpose of the Study:

  • To develop and evaluate a novel local multiple alignment method for detecting ncRNAs.
  • To address the limitations of global alignment methods in identifying locally conserved ncRNAs.

Main Methods:

  • A local multiple alignment construction procedure inspired by ProDA for protein sequences.
  • A new alignment model incorporating secondary structure features using conditional random fields and a gamma-centroid estimator.
  • Clustering of locally aligned subsequences into blocks for subsequent multiple alignment using MXSCARNA.

Main Results:

  • The proposed method, implemented as SCARNA_LM software, demonstrates high ability in local multiple alignment for ncRNA detection.
  • Benchmark experiments validate the effectiveness of SCARNA_LM.

Conclusions:

  • SCARNA_LM offers an effective solution for identifying locally conserved ncRNAs.
  • The method successfully integrates primary sequence and secondary structure information for improved ncRNA detection.