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RNA Secondary Structure Prediction Using High-throughput SHAPE
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DNAshapeR: an R/Bioconductor package for DNA shape prediction and feature encoding.

Tsu-Pei Chiu1, Federico Comoglio2, Tianyin Zhou1

  • 1Molecular and Computational Biology Program, Departments of Biological Sciences, Chemistry, Physics, and Computer Science, University of Southern California, Los Angeles, CA 90089, USA.

Bioinformatics (Oxford, England)
|December 16, 2015
PubMed
Summary

DNAshapeR rapidly predicts DNA shape features from genomic data. This tool encodes sequence and shape into matrices for machine learning, accelerating biological modeling.

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Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • Understanding DNA's three-dimensional structure is crucial for deciphering its biological functions.
  • Accurate prediction of DNA shape features from sequence data is essential for various genomic analyses.
  • Existing methods may lack the speed and throughput required for large-scale genomic studies.

Purpose of the Study:

  • To develop an ultra-fast, high-throughput tool for predicting DNA shape features.
  • To provide a flexible framework for encoding DNA sequence and shape information.
  • To facilitate the integration of DNA shape features into machine learning models.

Main Methods:

  • The DNAshapeR software package was implemented in the R programming language.
  • Input can be either nucleotide sequences or genomic coordinates.
  • Generates graphical representations for visualization and analysis.
  • Encodes DNA sequence and shape features using user-defined combinations of k-mers and shape features.

Main Results:

  • DNAshapeR achieves ultra-fast and high-throughput prediction of DNA shape features.
  • The software generates diverse graphical representations for data visualization.
  • Output feature matrices are compatible with various machine learning software packages.
  • Enables the creation of user-defined feature combinations.

Conclusions:

  • DNAshapeR provides an efficient and versatile tool for DNA shape analysis.
  • The software accelerates genomic studies by enabling rapid feature extraction and integration with machine learning.
  • Facilitates advanced modeling of DNA sequence-structure-function relationships.