Abstract
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.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
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)
Itaya, M.: An estimation of minimal genome size required for life. FEBS Lett. 362(3), 257–260 (1995)
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)
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)
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)
Chen, L., Ge, X., Xu, P.: Identifying essential Streptococcus sanguinis genes using genome-wide deletion mutation. Gene Essentiality: Methods Protoc., 15–23 (2015)
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)
Salama, N.R., Shepherd, B., Falkow, S.: Global transposon mutagenesis and essential gene analysis of Helicobacter pylori. J. Bacteriol. 186(23), 7926–7935 (2004)
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)
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)
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)
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)
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)
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)
Song, K., Tong, T., Wu, F.: Predicting essential genes in prokaryotic genomes using a linear method: ZUPLS. Integr. Biol. 6(4), 460–469 (2014)
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)
Plaimas, K., Eils, R., König, R.: Identifying essential genes in bacterial metabolic networks with machine learning methods. BMC Syst. Biol. 4(1), 1 (2010)
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)
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)
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)
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)
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)
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)
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)
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)
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)
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)
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)
Chen, Y., Xu, D.: Understanding protein dispensability through machine-learning analysis of high-throughput data. Bioinformatics 21(5), 575–581 (2005)
Seringhaus, M., Paccanaro, A., Borneman, A., Snyder, M., Gerstein, M.: Predicting essential genes in fungal genomes. Genome Res. 16(9), 1126–1135 (2006)
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)
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)
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)
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)
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)
Bauer, M., Schuster, S.M., Sayood, K.: The average mutual information profile as a genomic signature. BMC Bioinf. 9(1), 1 (2008)
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)
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)
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)
Chen, W.H., Minguez, P., Lercher, M.J., Bork, P.: OGEE: an online gene essentiality database. Nucleic Acids Res. 40(D1), D901–D906 (2011)
Cover, T.M., Thomas, J.A.: Elements of Information Theory. Wiley, Hoboken (2012)
Shannon, C.: A mathematical theory of communication. Bell System Technical Journal 27, 379–423, 623–656 (1948). Mathematical Reviews (MathSciNet): MR10, 133e
SantaLucia, J.: A unified view of polymer, dumbbell, and oligonucleotide DNA nearest-neighbor thermodynamics. Proc. Natl. Acad. Sci. 95(4), 1460–1465 (1998)
Kullback, S., Leibler, R.A.: On information and sufficiency. Ann. Math. Stat. 22(1), 79–86 (1951)
Tong, H.: Determination of the order of a Markov chain by Akaike’s information criterion. J. Appl. Probab. 12, 488–497 (1975)
Katz, R.W.: On some criteria for estimating the order of a Markov chain. Technometrics 23(3), 243–249 (1981)
Peres, Y., Shields, P.: Two new Markov order estimators. ArXiv Mathematics e-prints, June 2005
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). https://doi.org/10.1007/11557067_24
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)
Papapetrou, M., Kugiumtzis, D.: Markov chain order estimation with conditional mutual information. Physica A: Stat. Mech. Appl. 392(7), 1593–1601 (2013)
Papapetrou, M., Kugiumtzis, D.: Markov chain order estimation with parametric significance tests of conditional mutual information. Simul. Model. Pract. Theory 61, 1–13 (2016)
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). https://doi.org/10.1007/978-3-540-78246-9_38
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)
Fraser, A.: Essential human genes. Cell Syst. 1(6), 381–382 (2015)
Boone, C., Andrews, B.J.: The indispensable genome. Science 350(6264), 1028–1029 (2015)
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)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG, part of Springer Nature
About this paper
Cite this paper
Nigatu, D., Henkel, W. (2018). Computational Identification of Essential Genes in Prokaryotes and Eukaryotes. In: Peixoto, N., Silveira, M., Ali, H., Maciel, C., van den Broek, E. (eds) Biomedical Engineering Systems and Technologies. BIOSTEC 2017. Communications in Computer and Information Science, vol 881. Springer, Cham. https://doi.org/10.1007/978-3-319-94806-5_13
Download citation
DOI: https://doi.org/10.1007/978-3-319-94806-5_13
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-94805-8
Online ISBN: 978-3-319-94806-5
eBook Packages: Computer ScienceComputer Science (R0)