RESEARCH ARTICLE


Nucleosome Positioning with Set of Key Positions and Nucleosome Affinity



Jia Wang1, Shuai Liu2, 3, *, Weina Fu2
1 Experimental Instrument Center, Dalian Polytechnic University, Dalian, Liaoning, 116034, China
2 College of Computer Science, Inner Mongolia University, Hohhot, Inner Mongolia, 010012, China
3 School of Physical Science and Technology, Inner Mongolia University, Inner Mongolia, 010012, China


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Creative Commons License
© Duoqi et al.; Licensee Bentham Open.

open-access license: This is an open access articles licensed under the terms of the Creative Commons Attribution-Non-Commercial 4.0 International Public License (CC BY-NC 4.0) (https://creativecommons.org/licenses/by-nc/4.0/legalcode), which permits unrestricted, non-commercial use, distribution and reproduction in any medium, provided that the work is properly cited.

* Address correspondence to this author at the College of Computer Science, Inner Mongolia University, Hohhot, Inner Mongolia, 010012, China; E-mail: cs_liushuai@imu.edu.cn


Abstract

The formation and precise positioning of nucleosome in chromatin occupies a very important role in studying life process. Today, there are many researchers who discovered that the positioning where the location of a DNA sequence fragment wraps around a histone octamer in genome is not random but regular. However, the positioning is closely relevant to the concrete sequence of core DNA. So in this paper, we analyzed the relation between the affinity and sequence structure of core DNA, and extracted the set of key positions. In these positions, the nucleotide sequences probably occupy mainly action in the binding. First, we simplified and formatted the experimental data with the affinity. Then, to find the key positions in the wrapping, we used neural network to analyze the positive and negative effects of nucleosome generation for each position in core DNA sequences. However, we reached a class of weights with every position to describe this effect. Finally, based on the positions with high weights, we analyzed the reason why the chosen positions are key positions, and used these positions to construct a model for nucleosome positioning prediction. Experimental results show the effectiveness of our method.

Keywords: Affinity, DNA sequence, key position, neural network, nucleosome positioning.