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DHSpred: support-vector-machine-based human DNase I hypersensitive sites prediction using the optimal features

Balachandran Manavalan1, Tae Hwan Shin1,2, Gwang Lee1,2

  • 1Department of Physiology, Ajou University School of Medicine, Suwon, Republic of Korea.

Oncotarget
|February 9, 2018
PubMed
Summary
This summary is machine-generated.

A new computational method, DHSpred, accurately predicts DNase I hypersensitive sites (DHSs) using machine learning. This tool aids in identifying regulatory elements and understanding chromatin states in DNA sequences efficiently.

Keywords:
DNase I hypersensitive sitefeature selectionmachine learningrandom forestsupport vector machine

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Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • DNase I hypersensitive sites (DHSs) are crucial genomic regions indicating transcriptional regulatory elements and chromatin states.
  • Identifying DHSs is essential for understanding DNA sequence functions, but experimental methods are costly for genome-wide analysis.

Purpose of the Study:

  • To develop an efficient computational method for predicting DHSs in human DNA sequences.
  • To improve upon existing methods for DHS identification, facilitating large-scale genomic studies.

Main Methods:

  • A support vector machine (SVM)-based predictor, DHSpred, was developed.
  • Optimal features were selected from nucleotide composition and physicochemical properties using a random forest algorithm.
  • The predictor was trained using 174 selected features.

Main Results:

  • DHSpred achieved a Matthews correlation coefficient of 0.660 and an accuracy of 0.871.
  • The method demonstrated a 3% improvement over SVM predictors using non-optimized features.
  • DHSpred outperformed state-of-the-art DHS predictors in performance.

Conclusions:

  • The developed feature selection method enhances the efficiency of DHS prediction.
  • DHSpred offers a superior and cost-effective alternative for identifying DHSs.
  • An online prediction server is available to support the scientific community.