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Deep learning for DNase I hypersensitive sites identification.

Chuqiao Lyu1, Lei Wang1, Juhua Zhang2,3

  • 1School of Life Science, Beijing Institute of Technology, South Zhongguancun Street, Beijing, 100081, China.

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|January 2, 2019
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Summary

This study introduces a deep learning method to accurately identify DNase I hypersensitive sites (DHSs), crucial for understanding chromatin accessibility. The novel approach enhances the prediction of these regulatory DNA elements across multiple species.

Keywords:
Convolutional neural networkDNase I hypersensitive sitesDeep learning

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Area of Science:

  • Genomics
  • Computational Biology
  • Epigenetics

Background:

  • DNase I hypersensitive sites (DHSs) are key cis-regulatory DNA elements.
  • Understanding chromatin accessibility relies on efficient DHS identification.
  • Current resources for DHS analysis are extensive but understanding remains incomplete.

Purpose of the Study:

  • To develop an efficient and accurate method for identifying multi-scale DHSs.
  • To leverage machine learning for predicting regulatory DNA elements.
  • To improve the understanding of chromatin accessibility through advanced computational tools.

Main Methods:

  • A novel convolutional neural network (CNN) incorporating Inception-like networks and a gating mechanism was developed.
  • The CNN was designed to capture multiple patterns and long-term associations in DNA sequences.
  • The method was applied to predict multi-scale DHSs in *Arabidopsis*, rice, and *Homo sapiens*.

Main Results:

  • The method achieved high accuracy, with an Area Under Curve (AUC) of 0.961 for *Arabidopsis*.
  • An AUC of 0.969 was obtained for rice, and 0.918 for *Homo sapiens*.
  • These results demonstrate the method's effectiveness across different species.

Conclusions:

  • The developed deep learning method offers an efficient and accurate approach for identifying multi-scale DHS sequences.
  • This advancement aids in the study of cis-regulatory elements and chromatin accessibility.
  • The findings contribute to a deeper understanding of genomic regulation through computational predictions.