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Selection of key sequence-based features for prediction of essential genes in 31 diverse bacterial species

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  • Xiao Liu
  • Bao-Jin Wang
  • Luo Xu
  • Hong-Ling Tang
  • Guo-Qing Xu

Abstract

Genes that are indispensable for survival are essential genes. Many features have been proposed for computational prediction of essential genes. In this paper, the least absolute shrinkage and selection operator method was used to screen key sequence-based features related to gene essentiality. To assess the effects, the selected features were used to predict the essential genes from 31 bacterial species based on a support vector machine classifier. For all 31 bacterial objects (21 Gram-negative objects and ten Gram-positive objects), the features in the three datasets were reduced from 57, 59, and 58, to 40, 37, and 38, respectively, without loss of prediction accuracy. Results showed that some features were redundant for gene essentiality, so could be eliminated from future analyses. The selected features contained more complex (or key) biological information for gene essentiality, and could be of use in related research projects, such as gene prediction, synthetic biology, and drug design.

Suggested Citation

  • Xiao Liu & Bao-Jin Wang & Luo Xu & Hong-Ling Tang & Guo-Qing Xu, 2017. "Selection of key sequence-based features for prediction of essential genes in 31 diverse bacterial species," PLOS ONE, Public Library of Science, vol. 12(3), pages 1-13, March.
    • Handle: RePEc:plo:pone00:0174638
    DOI: 10.1371/journal.pone.0174638
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    References listed on IDEAS

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    1. Xiao Liu & Baojin Wang & Luo Xu, 2015. "Statistical Analysis of Hurst Exponents of Essential/Nonessential Genes in 33 Bacterial Genomes," PLOS ONE, Public Library of Science, vol. 10(6), pages 1-9, June.
    2. Wei Lin & Pixu Shi & Rui Feng & Hongzhe Li, 2014. "Variable selection in regression with compositional covariates," Biometrika, Biometrika Trust, vol. 101(4), pages 785-797.
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    Cited by:

    1. Zhou, Qian & Qi, Saibing & Ren, Cong, 2021. "Gene essentiality prediction based on chaos game representation and spiking neural networks," Chaos, Solitons & Fractals, Elsevier, vol. 144(C).

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