IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0174638.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0174638
    Download Restriction: no
    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0174638&type=printable
    Download Restriction: no
    File URL: https://libkey.io/10.1371/journal.pone.0174638?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. 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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Jiarui Lu & Pixu Shi & Hongzhe Li, 2019. "Generalized linear models with linear constraints for microbiome compositional data," Biometrics, The International Biometric Society, vol. 75(1), pages 235-244, March.
    2. Jacob Fiksel & Scott Zeger & Abhirup Datta, 2022. "A transformation‐free linear regression for compositional outcomes and predictors," Biometrics, The International Biometric Society, vol. 78(3), pages 974-987, September.
    3. Srinivasan, Arun & Xue, Lingzhou & Zhan, Xiang, 2023. "Identification of microbial features in multivariate regression under false discovery rate control," Computational Statistics & Data Analysis, Elsevier, vol. 181(C).
    4. Zemin Zheng & Jinchi Lv & Wei Lin, 2021. "Nonsparse Learning with Latent Variables," Operations Research, INFORMS, vol. 69(1), pages 346-359, January.
    5. Huiwen Wang & Zhichao Wang & Shanshan Wang, 2021. "Sliced inverse regression method for multivariate compositional data modeling," Statistical Papers, Springer, vol. 62(1), pages 361-393, February.
    6. Rieser, Christopher & Filzmoser, Peter, 2023. "Extending compositional data analysis from a graph signal processing perspective," Journal of Multivariate Analysis, Elsevier, vol. 198(C).
    7. Xiaofei Wu & Rongmei Liang & Hu Yang, 2022. "Penalized and constrained LAD estimation in fixed and high dimension," Statistical Papers, Springer, vol. 63(1), pages 53-95, February.
    8. Licai Huang & Paul Little & Jeroen R. Huyghe & Qian Shi & Tabitha A. Harrison & Greg Yothers & Thomas J. George & Ulrike Peters & Andrew T. Chan & Polly A. Newcomb & Wei Sun, 2021. "A Statistical Method for Association Analysis of Cell Type Compositions," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 13(3), pages 373-385, December.
    9. Bingkai Wang & Brian S. Caffo & Xi Luo & Chin‐Fu Liu & Andreia V. Faria & Michael I. Miller & Yi Zhao & for the Alzheimer's Disease Neuroimaging Initiative*, 2022. "Regularized regression on compositional trees with application to MRI analysis," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 71(3), pages 541-561, June.
    10. Haixiang Zhang & Jun Chen & Zhigang Li & Lei Liu, 2021. "Testing for Mediation Effect with Application to Human Microbiome Data," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 13(2), pages 313-328, July.
    11. Jeon, Jong-June & Kim, Yongdai & Won, Sungho & Choi, Hosik, 2020. "Primal path algorithm for compositional data analysis," Computational Statistics & Data Analysis, Elsevier, vol. 148(C).
    12. Cristofari, Andrea, 2023. "A decomposition method for lasso problems with zero-sum constraint," European Journal of Operational Research, Elsevier, vol. 306(1), pages 358-369.
    13. Liangliang Zhang & Yushu Shi & Robert R. Jenq & Kim‐Anh Do & Christine B. Peterson, 2021. "Bayesian compositional regression with structured priors for microbiome feature selection," Biometrics, The International Biometric Society, vol. 77(3), pages 824-838, September.
    14. Mishra, Aditya & Müller, Christian L., 2022. "Robust regression with compositional covariates," Computational Statistics & Data Analysis, Elsevier, vol. 165(C).
    15. Enora Belz & Arthur Charpentier, 2019. "Aggregated Data and Compositional Variables: Methodological Note [Données Agrégées et Variables Compositionnelles : Note Méthodologique]," Working Papers hal-02097031, HAL.
    16. Lingjing Jiang & Niina Haiminen & Anna‐Paola Carrieri & Shi Huang & Yoshiki Vázquez‐Baeza & Laxmi Parida & Ho‐Cheol Kim & Austin D. Swafford & Rob Knight & Loki Natarajan, 2022. "Utilizing stability criteria in choosing feature selection methods yields reproducible results in microbiome data," Biometrics, The International Biometric Society, vol. 78(3), pages 1155-1167, September.
    17. Yuan, Panxu & Jin, Changhan & Li, Gaorong, 2024. "FDR control for linear log-contrast models with high-dimensional compositional covariates," Computational Statistics & Data Analysis, Elsevier, vol. 197(C).
    18. Jordi Saperas-Riera & Glòria Mateu-Figueras & Josep Antoni Martín-Fernández, 2024. "L p -Norm for Compositional Data: Exploring the CoDa L 1 -Norm in Penalised Regression," Mathematics, MDPI, vol. 12(9), pages 1-16, May.
    19. Juan José Egozcue & Vera Pawlowsky-Glahn, 2019. "Compositional data: the sample space and its structure," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 28(3), pages 599-638, September.
    20. Zachary D Kurtz & Christian L Müller & Emily R Miraldi & Dan R Littman & Martin J Blaser & Richard A Bonneau, 2015. "Sparse and Compositionally Robust Inference of Microbial Ecological Networks," PLOS Computational Biology, Public Library of Science, vol. 11(5), pages 1-25, May.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:plo:pone00:0174638. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: plosone (email available below). General contact details of provider: https://journals.plos.org/plosone/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.