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iNuc-PhysChem: A Sequence-Based Predictor for Identifying Nucleosomes via Physicochemical Properties

Author

Listed:
  • Wei Chen
  • Hao Lin
  • Peng-Mian Feng
  • Chen Ding
  • Yong-Chun Zuo
  • Kuo-Chen Chou

Abstract

Nucleosome positioning has important roles in key cellular processes. Although intensive efforts have been made in this area, the rules defining nucleosome positioning is still elusive and debated. In this study, we carried out a systematic comparison among the profiles of twelve DNA physicochemical features between the nucleosomal and linker sequences in the Saccharomyces cerevisiae genome. We found that nucleosomal sequences have some position-specific physicochemical features, which can be used for in-depth studying nucleosomes. Meanwhile, a new predictor, called iNuc-PhysChem, was developed for identification of nucleosomal sequences by incorporating these physicochemical properties into a 1788-D (dimensional) feature vector, which was further reduced to a 884-D vector via the IFS (incremental feature selection) procedure to optimize the feature set. It was observed by a cross-validation test on a benchmark dataset that the overall success rate achieved by iNuc-PhysChem was over 96% in identifying nucleosomal or linker sequences. As a web-server, iNuc-PhysChem is freely accessible to the public at http://lin.uestc.edu.cn/server/iNuc-PhysChem. For the convenience of the vast majority of experimental scientists, a step-by-step guide is provided on how to use the web-server to get the desired results without the need to follow the complicated mathematics that were presented just for the integrity in developing the predictor. Meanwhile, for those who prefer to run predictions in their own computers, the predictor's code can be easily downloaded from the web-server. It is anticipated that iNuc-PhysChem may become a useful high throughput tool for both basic research and drug design.

Suggested Citation

  • Wei Chen & Hao Lin & Peng-Mian Feng & Chen Ding & Yong-Chun Zuo & Kuo-Chen Chou, 2012. "iNuc-PhysChem: A Sequence-Based Predictor for Identifying Nucleosomes via Physicochemical Properties," PLOS ONE, Public Library of Science, vol. 7(10), pages 1-9, October.
    • Handle: RePEc:plo:pone00:0047843
    DOI: 10.1371/journal.pone.0047843
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    References listed on IDEAS

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    1. Tao Huang & Shen Niu & Zhongping Xu & Yun Huang & Xiangyin Kong & Yu-Dong Cai & Kuo-Chen Chou, 2011. "Predicting Transcriptional Activity of Multiple Site p53 Mutants Based on Hybrid Properties," PLOS ONE, Public Library of Science, vol. 6(8), pages 1-8, August.
    2. Guo-Cheng Yuan & Jun S Liu, 2008. "Genomic Sequence Is Highly Predictive of Local Nucleosome Depletion," PLOS Computational Biology, Public Library of Science, vol. 4(1), pages 1-11, January.
    3. Eran Segal & Yvonne Fondufe-Mittendorf & Lingyi Chen & AnnChristine Thåström & Yair Field & Irene K. Moore & Ji-Ping Z. Wang & Jonathan Widom, 2006. "A genomic code for nucleosome positioning," Nature, Nature, vol. 442(7104), pages 772-778, August.
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    Cited by:

    1. Yan Xu & Jun Ding & Ling-Yun Wu & Kuo-Chen Chou, 2013. "iSNO-PseAAC: Predict Cysteine S-Nitrosylation Sites in Proteins by Incorporating Position Specific Amino Acid Propensity into Pseudo Amino Acid Composition," PLOS ONE, Public Library of Science, vol. 8(2), pages 1-7, February.
    2. Bin Liu & Longyun Fang & Fule Liu & Xiaolong Wang & Junjie Chen & Kuo-Chen Chou, 2015. "Identification of Real MicroRNA Precursors with a Pseudo Structure Status Composition Approach," PLOS ONE, Public Library of Science, vol. 10(3), pages 1-20, March.
    3. Shohreh Ariaeenejad & Maryam Mousivand & Parinaz Moradi Dezfouli & Maryam Hashemi & Kaveh Kavousi & Ghasem Hosseini Salekdeh, 2018. "A computational method for prediction of xylanase enzymes activity in strains of Bacillus subtilis based on pseudo amino acid composition features," PLOS ONE, Public Library of Science, vol. 13(10), pages 1-16, October.

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