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Search for SINE repeats in the rice genome using correlation-based position weight matrices.

Yulia M Suvorova1, Anastasia M Kamionskaya2, Eugene V Korotkov2

  • 1Research Center of Biotechnology of the Russian Academy of Sciences, 60 let Oktjabrja pr-t, 7, bld. 1, Moscow, Russia. suvorovay@gmail.com.

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|February 3, 2021
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Summary

The Highly Divergent Repeat Search Method (HDRSM) effectively identifies divergent Short Interspersed Nuclear Elements (SINEs) in rice genomes. This new method surpasses RepeatMasker in finding more SINE copies and accurately defining their boundaries.

Keywords:
Distant similarityPWMSINETransposons

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Area of Science:

  • Genomics
  • Bioinformatics
  • Molecular Biology

Background:

  • Transposable elements (TEs), including Short Interspersed Nuclear Elements (SINEs), comprise a substantial portion of eukaryotic genomes.
  • SINEs are widespread in mammalian genomes and also present in plants.
  • TEs accumulate mutations post-insertion, complicating identification and annotation via current bioinformatics approaches.

Purpose of the Study:

  • To identify highly divergent SINE copies within the rice (Oryza sativa subsp. japonica) genome.
  • To evaluate the efficacy of the Highly Divergent Repeat Search Method (HDRSM) for SINE discovery.

Main Methods:

  • The HDRSM utilizes correlations of neighboring symbols to build position weight matrices (PWMs) for SINE families.
  • A simulated dataset with SINE copies featuring substitutions and indels was used to assess HDRSM accuracy against RepeatMasker.
  • The HDRSM was applied to search for 39 SINE families in the rice genome.

Main Results:

  • The HDRSM demonstrated superior performance in identifying inserted repeats and accurately defining their boundaries compared to RepeatMasker.
  • The HDRSM identified 14,030 SINE copies in the rice genome, with 5,704 copies not detected by RepeatMasker.
  • HDRSM successfully located divergent SINE copies with high statistical significance.

Conclusions:

  • The HDRSM is effective for finding divergent SINE copies and accurately determining their genomic boundaries.
  • RepeatMasker excels at identifying shorter, more similar SINE copies, while HDRSM is better suited for more diverged elements.
  • A combined approach using both HDRSM and RepeatMasker is recommended for a comprehensive analysis of SINE distribution in genomes.