Computational Identification of Essential Genes in Prokaryotes and Eukaryotes

  • Dawit NigatuEmail author
  • Werner Henkel
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 881)


Several computational methods were proposed for the identification of essential genes (EGs). The machine learning based methods use features derived from the genetic sequences, gene-expression data, network topology, homology, and domain information. Except for the sequence-based features, the others require additional experimental data which is unavailable for under-studied and newly sequenced organisms. Hence, here, we propose a sequence-based identification of EGs. We performed gene essentiality predictions considering 15 bacteria, 1 archeaon, and 4 eukaryotes. Information-theoretic quantities, such as mutual information, conditional mutual information, entropy, Kullback-Leibler divergence, and Markov models, were used as features. In addition, with the hope of improving the prediction performance, other easily accessible sequence-based features related to stop codon usage, length, and GC content were included. For classification, the Random Forest algorithm was used. The performance of the proposed method is extensively evaluated by employing both intra- and cross-organism predictions. The obtained results were better than most of the previously published EG predictors which rely only on sequence information and comparable to those using additional features derived from network topology, homology, and gene-expression data.


Essential genes Information-theoretic features Machine learning Random Forest Markov model 


  1. 1.
    Koonin, E.V.: How many genes can make a cell: the minimal-gene-set concept 1. Annu. Rev. Genomics Hum. Genet. 1(1), 99–116 (2000)CrossRefGoogle Scholar
  2. 2.
    Itaya, M.: An estimation of minimal genome size required for life. FEBS Lett. 362(3), 257–260 (1995)CrossRefGoogle Scholar
  3. 3.
    Hutchison, C.A., Chuang, R.Y., Noskov, V.N., Assad-Garcia, N., Deerinck, T.J., Ellisman, M.H., Gill, J., Kannan, K., Karas, B.J., Ma, L., et al.: Design and synthesis of a minimal bacterial genome. Science 351(6280), aad6253 (2016)CrossRefGoogle Scholar
  4. 4.
    Chalker, A.F., Lunsford, R.D.: Rational identification of new antibacterial drug targets that are essential for viability using a genomics-based approach. Pharmacol. Ther. 95(1), 1–20 (2002)CrossRefGoogle Scholar
  5. 5.
    Lamichhane, G., Zignol, M., Blades, N.J., Geiman, D.E., Dougherty, A., Grosset, J., Broman, K.W., Bishai, W.R.: A postgenomic method for predicting essential genes at subsaturation levels of mutagenesis: application to Mycobacterium tuberculosis. Proc. Natl. Acad. Sci. 100(12), 7213–7218 (2003)CrossRefGoogle Scholar
  6. 6.
    Chen, L., Ge, X., Xu, P.: Identifying essential Streptococcus sanguinis genes using genome-wide deletion mutation. Gene Essentiality: Methods Protoc., 15–23 (2015)Google Scholar
  7. 7.
    Giaever, G., Chu, A.M., Ni, L., Connelly, C., Riles, L., Veronneau, S., Dow, S., Lucau-Danila, A., Anderson, K., Andre, B., et al.: Functional profiling of the Saccharomyces cerevisiae genome. Nature 418(6896), 387–391 (2002)CrossRefGoogle Scholar
  8. 8.
    Salama, N.R., Shepherd, B., Falkow, S.: Global transposon mutagenesis and essential gene analysis of Helicobacter pylori. J. Bacteriol. 186(23), 7926–7935 (2004)CrossRefGoogle Scholar
  9. 9.
    Cullen, L.M., Arndt, G.M.: Genome-wide screening for gene function using RNAi in mammalian cells. Immunol. Cell Biol. 83(3), 217–223 (2005)CrossRefGoogle Scholar
  10. 10.
    Blomen, V.A., Májek, P., Jae, L.T., Bigenzahn, J.W., Nieuwenhuis, J., Staring, J., Sacco, R., van Diemen, F.R., Olk, N., Stukalov, A., et al.: Gene essentiality and synthetic lethality in haploid human cells. Science 350(6264), 1092–1096 (2015)CrossRefGoogle Scholar
  11. 11.
    Hart, T., Chandrashekhar, M., Aregger, M., Steinhart, Z., Brown, K.R., MacLeod, G., Mis, M., Zimmermann, M., Fradet-Turcotte, A., Sun, S., et al.: High-resolution CRISPR screens reveal fitness genes and genotype-specific cancer liabilities. Cell 163(6), 1515–1526 (2015)CrossRefGoogle Scholar
  12. 12.
    Wang, T., Birsoy, K., Hughes, N.W., Krupczak, K.M., Post, Y., Wei, J.J., Lander, E.S., Sabatini, D.M.: Identification and characterization of essential genes in the human genome. Science 350(6264), 1096–1101 (2015)CrossRefGoogle Scholar
  13. 13.
    Mushegian, A.R., Koonin, E.V.: A minimal gene set for cellular life derived by comparison of complete bacterial genomes. Proc. Natl. Acad. Sci. 93(19), 10268–10273 (1996)CrossRefGoogle Scholar
  14. 14.
    Ning, L., Lin, H., Ding, H., Huang, J., Rao, N., Guo, F.: Predicting bacterial essential genes using only sequence composition information. Genet. Mol. Res. 13, 4564–4572 (2014)CrossRefGoogle Scholar
  15. 15.
    Song, K., Tong, T., Wu, F.: Predicting essential genes in prokaryotic genomes using a linear method: ZUPLS. Integr. Biol. 6(4), 460–469 (2014)CrossRefGoogle Scholar
  16. 16.
    Yu, Y., Yang, L., Liu, Z., Zhu, C.: Gene essentiality prediction based on fractal features and machine learning. Mol. BioSyst. 13(3), 577–584 (2017)CrossRefGoogle Scholar
  17. 17.
    Plaimas, K., Eils, R., König, R.: Identifying essential genes in bacterial metabolic networks with machine learning methods. BMC Syst. Biol. 4(1), 1 (2010)CrossRefGoogle Scholar
  18. 18.
    Acencio, M.L., Lemke, N.: Towards the prediction of essential genes by integration of network topology, cellular localization and biological process information. BMC Bioinf. 10(1), 1 (2009)CrossRefGoogle Scholar
  19. 19.
    Lu, Y., Deng, J., Rhodes, J.C., Lu, H., Lu, L.J.: Predicting essential genes for identifying potential drug targets in Aspergillus fumigatus. Comput. Biol. Chem. 50, 29–40 (2014)MathSciNetCrossRefGoogle Scholar
  20. 20.
    Cheng, J., Xu, Z., Wu, W., Zhao, L., Li, X., Liu, Y., Tao, S.: Training set selection for the prediction of essential genes. PLoS ONE 9(1), e86805 (2014)CrossRefGoogle Scholar
  21. 21.
    Palaniappan, K., Mukherjee, S.: Predicting essential genes across microbial genomes: a machine learning approach. In: 2011 10th International Conference on Machine Learning and Applications and Workshops (ICMLA), vol. 2, pp. 189–194. IEEE (2011)Google Scholar
  22. 22.
    Liu, X., Wang, B.J., Xu, L., Tang, H.L., Xu, G.Q.: Selection of key sequence-based features for prediction of essential genes in 31 diverse bacterial species. PLoS ONE 12(3), e0174638 (2017)CrossRefGoogle Scholar
  23. 23.
    Deng, J., Deng, L., Su, S., Zhang, M., Lin, X., Wei, L., Minai, A.A., Hassett, D.J., Lu, L.J.: Investigating the predictability of essential genes across distantly related organisms using an integrative approach. Nucleic Acids Res. 39(3), 795–807 (2011)CrossRefGoogle Scholar
  24. 24.
    Li, Y., Lv, Y., Li, X., Xiao, W., Li, C.: Sequence comparison and essential gene identification with new inter-nucleotide distance sequences. J. Theor. Biol. 418, 84–93 (2017)MathSciNetCrossRefGoogle Scholar
  25. 25.
    Wei, W., Ning, L.W., Ye, Y.N., Guo, F.B.: Geptop: a gene essentiality prediction tool for sequenced bacterial genomes based on orthology and phylogeny. PLoS ONE 8(8), e72343 (2013)CrossRefGoogle Scholar
  26. 26.
    Guo, F.B., Dong, C., Hua, H.L., Liu, S., Luo, H., Zhang, H.W., Jin, Y.T., Zhang, K.Y.: Accurate prediction of human essential genes using only nucleotide composition and association information. Bioinformatics 33(12), 1758–1764 (2017)CrossRefGoogle Scholar
  27. 27.
    Sharp, P.M., Li, W.H.: The Codon Adaptation Index-a measure of directional synonymous codon usage bias, and its potential applications. Nucleic Acids Res. 15(3), 1281–1295 (1987)CrossRefGoogle Scholar
  28. 28.
    Cheng, J., Wu, W., Zhang, Y., Li, X., Jiang, X., Wei, G., Tao, S.: A new computational strategy for predicting essential genes. BMC Genom. 14(1), 910 (2013)CrossRefGoogle Scholar
  29. 29.
    Chen, Y., Xu, D.: Understanding protein dispensability through machine-learning analysis of high-throughput data. Bioinformatics 21(5), 575–581 (2005)CrossRefGoogle Scholar
  30. 30.
    Seringhaus, M., Paccanaro, A., Borneman, A., Snyder, M., Gerstein, M.: Predicting essential genes in fungal genomes. Genome Res. 16(9), 1126–1135 (2006)CrossRefGoogle Scholar
  31. 31.
    Yuan, Y., Xu, Y., Xu, J., Ball, R.L., Liang, H.: Predicting the lethal phenotype of the knockout mouse by integrating comprehensive genomic data. Bioinformatics 28(9), 1246–1252 (2012)CrossRefGoogle Scholar
  32. 32.
    Lloyd, J.P., Seddon, A.E., Moghe, G.D., Simenc, M.C., Shiu, S.H.: Characteristics of plant essential genes allow for within-and between-species prediction of lethal mutant phenotypes. Plant Cell 27(8), 2133–2147 (2015)CrossRefGoogle Scholar
  33. 33.
    Guo, F.B., Ou, H.Y., Zhang, C.T.: ZCURVE: a new system for recognizing protein-coding genes in bacterial and archaeal genomes. Nucleic Acids Res. 31(6), 1780–1789 (2003)CrossRefGoogle Scholar
  34. 34.
    Nigatu, D., Henkel, W.: Prediction of essential genes based on machine learning and information theoretic features. In: Proceedings of BIOSTEC 2017 - BIOINFORMATICS, pp. 81–92 (2017)Google Scholar
  35. 35.
    Nigatu, D., Henkel, W., Sobetzko, P., Muskhelishvili, G.: Relationship between digital information and thermodynamic stability in bacterial genomes. EURASIP J. Bioinf. Syst. Biol. 2016(1), 1 (2016)CrossRefGoogle Scholar
  36. 36.
    Bauer, M., Schuster, S.M., Sayood, K.: The average mutual information profile as a genomic signature. BMC Bioinf. 9(1), 1 (2008)CrossRefGoogle Scholar
  37. 37.
    Date, S.V., Marcotte, E.M.: Discovery of uncharacterized cellular systems by genome-wide analysis of functional linkages. Nat. Biotechnol. 21(9), 1055–1062 (2003)CrossRefGoogle Scholar
  38. 38.
    Hagenauer, J., Dawy, Z., Göbel, B., Hanus, P., Mueller, J.: Genomic analysis using methods from information theory. In: Information Theory Workshop, pp. 55–59. IEEE (2004)Google Scholar
  39. 39.
    Luo, H., Lin, Y., Gao, F., Zhang, C.T., Zhang, R.: DEG 10, an update of the database of essential genes that includes both protein-coding genes and noncoding genomic elements. Nucleic Acids Res. 42(D1), D574–D580 (2014)CrossRefGoogle Scholar
  40. 40.
    Chen, W.H., Minguez, P., Lercher, M.J., Bork, P.: OGEE: an online gene essentiality database. Nucleic Acids Res. 40(D1), D901–D906 (2011)CrossRefGoogle Scholar
  41. 41.
    Cover, T.M., Thomas, J.A.: Elements of Information Theory. Wiley, Hoboken (2012)zbMATHGoogle Scholar
  42. 42.
    Shannon, C.: A mathematical theory of communication. Bell System Technical Journal 27, 379–423, 623–656 (1948). Mathematical Reviews (MathSciNet): MR10, 133eMathSciNetCrossRefGoogle Scholar
  43. 43.
    SantaLucia, J.: A unified view of polymer, dumbbell, and oligonucleotide DNA nearest-neighbor thermodynamics. Proc. Natl. Acad. Sci. 95(4), 1460–1465 (1998)CrossRefGoogle Scholar
  44. 44.
    Kullback, S., Leibler, R.A.: On information and sufficiency. Ann. Math. Stat. 22(1), 79–86 (1951)MathSciNetCrossRefGoogle Scholar
  45. 45.
    Tong, H.: Determination of the order of a Markov chain by Akaike’s information criterion. J. Appl. Probab. 12, 488–497 (1975)MathSciNetCrossRefGoogle Scholar
  46. 46.
    Katz, R.W.: On some criteria for estimating the order of a Markov chain. Technometrics 23(3), 243–249 (1981)MathSciNetCrossRefGoogle Scholar
  47. 47.
    Peres, Y., Shields, P.: Two new Markov order estimators. ArXiv Mathematics e-prints, June 2005Google Scholar
  48. 48.
    Dalevi, D., Dubhashi, D.: The Peres-Shields order estimator for fixed and variable length Markov models with applications to DNA sequence similarity. In: Casadio, R., Myers, G. (eds.) WABI 2005. LNCS, vol. 3692, pp. 291–302. Springer, Heidelberg (2005). Scholar
  49. 49.
    Menéndez, M., Pardo, L., Pardo, M., Zografos, K.: Testing the order of Markov dependence in DNA sequences. Methodol. Comput. Appl. Probab. 13(1), 59–74 (2011)MathSciNetCrossRefGoogle Scholar
  50. 50.
    Papapetrou, M., Kugiumtzis, D.: Markov chain order estimation with conditional mutual information. Physica A: Stat. Mech. Appl. 392(7), 1593–1601 (2013)MathSciNetCrossRefGoogle Scholar
  51. 51.
    Papapetrou, M., Kugiumtzis, D.: Markov chain order estimation with parametric significance tests of conditional mutual information. Simul. Model. Pract. Theory 61, 1–13 (2016)CrossRefGoogle Scholar
  52. 52.
    Berthold, M.R., et al.: KNIME: the Konstanz information miner. In: Preisach, C., Burkhardt, H., Schmidt-Thieme, L., Decker, R. (eds.) GfKL 2007. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Heidelberg (2007). Scholar
  53. 53.
    Sarmiento, F., Mrázek, J., Whitman, W.B.: Genome-scale analysis of gene function in the hydrogenotrophic methanogenic archaeon Methanococcus maripaludis. Proc. Natl. Acad. Sci. 110(12), 4726–4731 (2013)CrossRefGoogle Scholar
  54. 54.
    Fraser, A.: Essential human genes. Cell Syst. 1(6), 381–382 (2015)CrossRefGoogle Scholar
  55. 55.
    Boone, C., Andrews, B.J.: The indispensable genome. Science 350(6264), 1028–1029 (2015)CrossRefGoogle Scholar
  56. 56.
    Dickinson, M.E., Flenniken, A.M., Ji, X., Teboul, L., Wong, M.D., White, J.K., Meehan, T.F., Weninger, W.J., Westerberg, H., Adissu, H., et al.: High-throughput discovery of novel developmental phenotypes. Nature 537(7621), 508 (2016)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Transmission Systems Group (TrSyS)Jacobs University BremenBremenGermany

Personalised recommendations