Abstract
DNA is quintessential to carry out basic functions by organisms as it encodes information necessary for metabolomics and proteomics, among others. In particular, it is common nowadays to use DNA for profiling living organisms based on their phenotypic traits. These traits are the outcomes of the genetic makeup constrained by the interaction between living organisms and their surrounding environment over time. For environmental conditions, however, the conventional assumption is that they are too random and ephemeral to be encoded in the DNA of an organism. Here, we demonstrate that, to the contrary, genomic DNA may also encode sufficient information about some environmental features of an organism’s habitat for a machine learning model to reveal them, although there seem to be exceptions, i.e. some environmental features do not appear to be coded in DNA, unless our methods miss that information. Nevertheless, we demonstrate that these features can be used to train better models for better predictions of other environmental factors. These results lead directly to the question of whether over evolutionary history, DNA itself is actually also a repository of information related to the environment where the lineage has developed, perhaps even more cryptically than the way it encodes phenotypic information.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Adleman, L.M.: Molecular computation of solutions to combinatorial problems. Science 266(5), 1021–1024 (1994)
Barberán, A., Ramirez, K.S., Leff, J.W., Bradford, M.A., Wall, D.H., Fierer, N.: Why are some microbes more ubiquitous than others? Predicting the habitat breadth of soul bacteria. Ecol. Lett. 17(7), 794–802 (2014)
Candel, A., Parmar, V., LeDell, E., Arora, A.: Deep learning with H2O. H2O. ai Inc (2016)
Chuine, I.: Why does phenology drive species distribution? Philos. Trans. R. Soc. B Biol. Sci. 365(1555), 3149–3160 (2010)
Colorado-Garzón, F.A., Adler, P.H., García, L.F., Muñoz de Hoyos, P., Bueno, M.L., Matta, N.E.: Estimating diversity of black flies in the Simulium ignescens and Simulium tunja complexes in Colombia: chromosomal rearrangements as the core of integrative taxonomy. J. Hered. 108(1), 12–24 (2017)
Cook-Deegan, R., DeRienzo, C., Carbone, J., Chandrasekharan, S., Heaney, C., Conover, C.: Impact of gene patents and licensing practices on access to genetic testing for inherited susceptibility to cancer: comparing breast and ovarian cancers with colon cancers. Genet. Med. 12, S15–S38 (2010)
Darlington, P.J.: The cost of evolution and the imprecision of adaptation. Proc. Natl. Acad. Sci. 74(4), 1647–1651 (1977)
Elith, J., Leathwick, J.R.: Species distribution models: ecological explanation and prediction across space and time. Annu. Rev. Ecol. Evol. Syst. 40, 677–697 (2009)
Garzon, M.H., Bobba, K.C.: A geometric approach to gibbs energy landscapes and optimal DNA codeword design. In: Stefanovic, D., Turberfield, A. (eds.) DNA 2012. LNCS, vol. 7433, pp. 73–85. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-32208-2_6
Garzon, M.H., Mainali, S.: Towards reliable microarray analysis and design. In: The 9th International Conference on Bioinformatics and Computational Biology, ISCA (2017)
Garzon, M.H., Mainali, S.: Towards a universal genomic positioning system: phylogenetics and species IDentification. In: Rojas, I., Ortuño, F. (eds.) IWBBIO 2017. LNCS, vol. 10209, pp. 469–479. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-56154-7_42
Garzon, M.H., Pham, D.T.: Genomic solutions to hospital-acquired bacterial infection identification. In: Rojas, I., Ortuño, F. (eds.) IWBBIO 2018. LNCS, vol. 10813, pp. 486–497. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-78723-7_42
Garzon, M.H., Wong, T.Y.: DNA chips for species identification and biological phylogenies. Nat. Comput. 10, 375–389 (2011)
Garzon, M., Neathery, P., Deaton, R., Murphy, R.C., Franceschetti, D.R., Stevens Jr., S.E.: A new metric for DNA computing. In: Proceedings of the 2nd Genetic Programming Conference, pp. 472–478. Morgan-Kaufmann (1997)
Guisan, A., et al.: Predicting species distributions for conservation decisions. Ecol. Lett. 16(12), 1424–1435 (2013)
Haykin, S.: Neural Networks and Learning Machines. Prenctice-Hall, New Jersey (2018)
Hoegh-Guldberg, O., et al.: Assisted colonization and rapid climate change. Science 321, 345–346 (2008)
Li, X., Qian, B., Wei, J., Zhang, X., Chen, S., Zheng, Q.: Domain knowledge guided deep atrial fibrillation classification and its visual interpretation. In: Proceedings of the 28th ACM International Conference on Information and Knowledge Management, pp. 129–138. ACM (2019)
Mainali, S., Colorado, F.A., Garzon, M.H.: Foretelling the phenotype of a genomic sequence. In: IEEE Transactions on Computational Biology and Bioinformatics, revision under review (2020)
Marcus, G.: Innateness, alphazero, and artificial intelligence. arXiv preprint arXiv:1801.05667 (2018)
Maturana, H.R., Varela, F.J.: Autopoiesis and Cognition. BSPHS, vol. 42. Springer, Dordrecht (1980). https://doi.org/10.1007/978-94-009-8947-4
Radovanović, S., Delibašić, B., Jovanović, M., Vukićević, M., Suknović, M.: Framework for integration of domain knowledge into logistic regression. In: Proceedings of the 8th International Conference on Web Intelligence, Mining and Semantics, p. 24. ACM (2018)
Ricklefs, R.: Phyletic gradualism vs. punctuated equilibrium: applicability of neontological data. Paleobiology 6(3), 271–275 (1980). https://doi.org/10.1017/s0094837300006795
Seeman, N.C.: Nucleic acid junctions and lattices. J. Theor. Biol. 99(2), 237–247 (1982)
Seeman, N.C.: DNA in a material world. Nature 421(6921), 427 (2003)
Sober, E.: What is wrong with intelligent design? Q. Rev. Biol. 82(1), 3–8 (2007)
Vasseur, F., et al.: Adaptive diversification of growth allometry in the plant Arabidopsis thaliana. PNAS 115:13 3416-3421 (2018)
Wang, J.X., Wu, J.L., Xiao, H.: Physics-informed machine learning approach for reconstructing Reynolds stress modeling discrepancies based on DNS data. Phys. Rev. Fluids 2(3), 034603 (2017)
Watson, J.D., Crick, F.: A structure for deoxyribose nucleic acid. Nature 171, 737–738 (1953)
Weigel, D., Mott, R.: The 1001 genomes project for Arabidopsis thaliana. Genome Biol. 10(5), 107 (2009)
Yin, C., Zhao, R., Qian, B., Lv, X., Zhang, P.: Domain Knowledge guided deep learning with electronic health records. In: IEEE International Conference on Data Mining (ICDM) (2019)
Acknowledgement
We would like to thank the labs of professors Nubia Matta and Fernando Garcia at the National University for their work in collecting some of the sample data for blackfly used in this paper. The use of the High Performance Computing Center (HPC) at the U of Memphis is also gratefully acknowledged.
Author information
Authors and Affiliations
Corresponding authors
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Mainali, S., Garzon, M.H., Colorado, F.A. (2020). Profiling Environmental Conditions from DNA. In: Rojas, I., Valenzuela, O., Rojas, F., Herrera, L., Ortuño, F. (eds) Bioinformatics and Biomedical Engineering. IWBBIO 2020. Lecture Notes in Computer Science(), vol 12108. Springer, Cham. https://doi.org/10.1007/978-3-030-45385-5_58
Download citation
DOI: https://doi.org/10.1007/978-3-030-45385-5_58
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-45384-8
Online ISBN: 978-3-030-45385-5
eBook Packages: Computer ScienceComputer Science (R0)