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Discovering well-ordered folding patterns in nucleotide sequences.

Shu-Yun Le1, Jih-H Chen, Danielle Konings

  • 1Laboratory of Experimental and Computational Biology, NCI Center for Cancer Research, National Cancer Institute, NIH, Bldg 469, Room 151, Frederick, MD 21702, USA. shuyun@orleans.ncifcrf.gov

Bioinformatics (Oxford, England)
|February 14, 2003
PubMed
Summary
This summary is machine-generated.

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This study introduces ed_scan to identify well-ordered folding sequences (WFS) in RNA, revealing their correlation with functional elements like the Rev response element and iron-responsive elements.

Area of Science:

  • Molecular Biology
  • Bioinformatics
  • Genomics

Background:

  • Local RNA structures are crucial for post-transcriptional gene regulation.
  • Predicting well-ordered folding sequences (WFS) aids in identifying structure-dependent RNA elements in messenger RNAs (mRNAs).

Purpose of the Study:

  • To assess the quality of local WFS by evaluating the energy difference (E(diff)) between global minimal and optimal restrained structures (ORS).
  • To develop and apply a novel method, ed_scan, for detecting WFS in various genomic sequences.

Main Methods:

  • Assessed WFS quality using E(diff) between global minimal and optimal restrained structures (ORS).
  • Employed the novel ed_scan method to identify WFS in HIV-1 mRNA, ferritin mRNAs, and genomic sequences containing let-7 RNA.
  • Utilized Zscr(e) (z-score of E(diff)) and random simulations to determine statistical significance.

Related Experiment Videos

Main Results:

  • Detected WFS in HIV-1 mRNA that correspond to the known Rev response element.
  • Identified WFS in ferritin mRNAs that align with known iron-responsive elements.
  • Found WFS in genomic sequences of *C. elegans*, *C. briggsae*, and *D. melanogaster* that match small let-7 RNAs.

Conclusions:

  • Statistically significant WFS identified by ed_scan are likely to have important structural roles directly related to their sequence information.
  • The findings highlight the utility of WFS prediction in understanding RNA function and regulation.