Evolutionary Biology

Simulation of bacterial populations with SLiM

10.24072/pcjournal.72 - Peer Community Journal, Volume 2 (2022), article no. e7.

Get full text PDF Peer reviewed and recommended by PCI
Simulation of genomic data is a key tool in population genetics, yet, to date, there is no forward-in-time simulator of bacterial populations that is both computationally efficient and adaptable to a wide range of scenarios. Here we demonstrate how to simulate bacterial populations with SLiM, a forward-in-time simulator built for eukaryotes. SLiM has gained many users in recent years, due to its speed and power, and has extensive documentation showcasing various scenarios that it can simulate. This paper focuses on a simple demographic scenario, to explore unique aspects of modeling bacteria in SLiM’s scripting language. In addition, we illustrate the flexibility of SLiM by simulating the growth of bacteria on a Petri dish with antibiotic. To foster the development of bacterial simulations based upon this recipe, we explain the inner workings of its code. We also validate the simulator, by extensively testing the results of simulations against existing simulators, and against theoretical expectations for some summary statistics. This protocol, with the flexibility and power of SLiM, will enable the community to simulate bacterial populations efficiently under a wide range of evolutionary scenarios.
Published online:
DOI: 10.24072/pcjournal.72
Cury, Jean 1; Haller, Benjamin C. 2; Achaz, Guillaume 3, 4; Jay, Flora 1

1 Université Paris-Saclay, CNRS, INRIA, Laboratoire Interdisciplinaire des Sciences du Numérique, UMR 9015 Orsay, France
2 Department of Computational Biology, Cornell University, USA
3 UMR7206 Eco-Anthropologie, Université de Paris, CNRS, MNHN, Paris, France
4 UMR7241 Centre Interdisciplinaire de Recherche en Biologie, College de France, CNRS, IN-SERM, Paris, France
@article{10_24072_pcjournal_72,
     author = {Cury, Jean and Haller, Benjamin C. and Achaz, Guillaume and Jay, Flora},
     title = {Simulation of bacterial populations with {SLiM}},
     journal = {Peer Community Journal},
     eid = {e7},
     publisher = {Peer Community In},
     volume = {2},
     year = {2022},
     doi = {10.24072/pcjournal.72},
     url = {https://peercommunityjournal.org/articles/10.24072/pcjournal.72/}
}
TY  - JOUR
TI  - Simulation of bacterial populations with SLiM
JO  - Peer Community Journal
PY  - 2022
DA  - 2022///
VL  - 2
PB  - Peer Community In
UR  - https://peercommunityjournal.org/articles/10.24072/pcjournal.72/
UR  - https://doi.org/10.24072/pcjournal.72
DO  - 10.24072/pcjournal.72
ID  - 10_24072_pcjournal_72
ER  - 
%0 Journal Article
%T Simulation of bacterial populations with SLiM
%J Peer Community Journal
%D 2022
%V 2
%I Peer Community In
%U https://doi.org/10.24072/pcjournal.72
%R 10.24072/pcjournal.72
%F 10_24072_pcjournal_72
Cury, Jean; Haller, Benjamin C.; Achaz, Guillaume; Jay, Flora. Simulation of bacterial populations with SLiM. Peer Community Journal, Volume 2 (2022), article  no. e7. doi : 10.24072/pcjournal.72. https://peercommunityjournal.org/articles/10.24072/pcjournal.72/

Peer reviewed and recommended by PCI : 10.24072/pci.evolbiol.100123

[1] Achaz, G. Frequency Spectrum Neutrality Tests: One for All and All for One, Genetics, Volume 183 (2009) no. 1, pp. 249-258 | Article

[2] Akita, T.; Takuno, S.; Innan, H. Coalescent framework for prokaryotes undergoing interspecific homologous recombination, Heredity, Volume 120 (2018) no. 5, pp. 474-484 | Article

[3] Battey, C.; Ralph, P. L.; Kern, A. D. Predicting geographic location from genetic variation with deep neural networks, eLife, Volume 9 (2020) | Article

[4] Bellais, S.; Six, A.; Fouet, A.; Longo, M.; Dmytruk, N.; Glaser, P.; Trieu-Cuot, P.; Poyart, C. Capsular Switching in Group B Streptococcus CC17 Hypervirulent Clone: A Future Challenge for Polysaccharide Vaccine Development, Journal of Infectious Diseases, Volume 206 (2012) no. 11, pp. 1745-1752 | Article

[5] Bobay, L.-M. CoreSimul: a forward-in-time simulator of genome evolution for prokaryotes modeling homologous recombination, BMC Bioinformatics, Volume 21 (2020) no. 1 | Article

[6] Bobay, L.-M.; Ochman, H. Factors driving effective population size and pan-genome evolution in bacteria, BMC Evolutionary Biology, Volume 18 (2018) no. 1 | Article

[7] Bradburd, G. S.; Ralph, P. L. Spatial Population Genetics: It's About Time, Annual Review of Ecology, Evolution, and Systematics, Volume 50 (2019) no. 1, pp. 427-449 | Article

[8] Brochet, M.; Rusniok, C.; Couve, E.; Dramsi, S.; Poyart, C.; Trieu-Cuot, P.; Kunst, F.; Glaser, P. Shaping a bacterial genome by large chromosomal replacements, the evolutionary history of Streptococcus agalactiae, Proceedings of the National Academy of Sciences, Volume 105 (2008) no. 41, pp. 15961-15966 | Article

[9] Brown, T.; Didelot, X.; Wilson, D. J.; Maio, N. D. SimBac: simulation of whole bacterial genomes with homologous recombination, Microbial Genomics, Volume 2 (2016) no. 1 | Article

[10] Bruford, M.; Ancrenaz, M.; Chikhi, L.; Lackmann-Ancrenaz, I.; Andau, M.; Ambu, L.; Goossens, B. Projecting genetic diversity and population viability for the fragmented orang-utan population in the Kinabatangan floodplain, Sabah, Malaysia, Endangered Species Research, Volume 12 (2010) no. 3, pp. 249-261 | Article

[11] Chikhi, L.; Sousa, V. C.; Luisi, P.; Goossens, B.; Beaumont, M. A. The Confounding Effects of Population Structure, Genetic Diversity and the Sampling Scheme on the Detection and Quantification of Population Size Changes, Genetics, Volume 186 (2010) no. 3, pp. 983-995 | Article

[12] Croucher, N. J.; Harris, S. R.; Barquist, L.; Parkhill, J.; Bentley, S. D. A High-Resolution View of Genome-Wide Pneumococcal Transformation, PLoS Pathogens, Volume 8 (2012) no. 6 | Article

[13] Croucher, N. J.; Finkelstein, J. A.; Pelton, S. I.; Mitchell, P. K.; Lee, G. M.; Parkhill, J.; Bentley, S. D.; Hanage, W. P.; Lipsitch, M. Population genomics of post-vaccine changes in pneumococcal epidemiology, Nature Genetics, Volume 45 (2013) no. 6, pp. 656-663 | Article

[14] Croucher, N. J.; Chewapreecha, C.; Hanage, W. P.; Harris, S. R.; McGee, L.; van der Linden, M.; Song, J.-H.; Ko, K. S.; de Lencastre, H.; Turner, C.; Yang, F.; Sá-Leão, R.; Beall, B.; Klugman, K. P.; Parkhill, J.; Turner, P.; Bentley, S. D. Evidence for Soft Selective Sweeps in the Evolution of Pneumococcal Multidrug Resistance and Vaccine Escape, Genome Biology and Evolution, Volume 6 (2014) no. 7, pp. 1589-1602 | Article

[15] Csilléry, K.; Blum, M. G.; Gaggiotti, O. E.; François, O. Approximate Bayesian Computation (ABC) in practice, Trends in Ecology & Evolution, Volume 25 (2010) no. 7, pp. 410-418 | Article

[16] Da Cunha, V.; Davies, M. R.; Douarre, P.-E.; Rosinski-Chupin, I.; Margarit, I.; Spinali, S.; Perkins, T.; Lechat, P.; Dmytruk, N.; Sauvage, E.; Ma, L.; Romi, B.; Tichit, M.; Lopez-Sanchez, M.-J.; Descorps-Declere, S.; Souche, E.; Buchrieser, C.; Trieu-Cuot, P.; Moszer, I.; Clermont, D.; Maione, D.; Bouchier, C.; McMillan, D. J.; Parkhill, J.; Telford, J. L.; Dougan, G.; Walker, M. J.; Holden, M. T. G.; Poyart, C.; Glaser, P. Streptococcus agalactiae clones infecting humans were selected and fixed through the extensive use of tetracycline, Nature Communications, Volume 5 (2014) no. 1 | Article

[17] De Maio, N.; Wilson, D. J. The Bacterial Sequential Markov Coalescent, Genetics, Volume 206 (2017) no. 1, pp. 333-343 | Article

[18] Flagel, L.; Brandvain, Y.; Schrider, D. R. The Unreasonable Effectiveness of Convolutional Neural Networks in Population Genetic Inference, Molecular Biology and Evolution, Volume 36 (2019) no. 2, pp. 220-238 | Article

[19] Fu, Y. Statistical Properties of Segregating Sites, Theoretical Population Biology, Volume 48 (1995) no. 2, pp. 172-197 | Article

[20] Grad, Y. H.; Lipsitch, M. Epidemiologic data and pathogen genome sequences: a powerful synergy for public health, Genome Biology, Volume 15 (2014) no. 11 | Article

[21] Haller, B. C.; Messer, P. W. Evolutionary Modeling in SLiM 3 for Beginners, Molecular Biology and Evolution, Volume 36 (2019) no. 5, pp. 1101-1109 | Article

[22] Haller, B. C.; Messer, P. W. SLiM 3: Forward Genetic Simulations Beyond the Wright–Fisher Model, Molecular Biology and Evolution, Volume 36 (2019) no. 3, pp. 632-637 | Article

[23] Haller BC, ; Messer PW, SLiM: An Evolutionary Simulation Framework, Manual. URL: http://benhaller.com/slim/SLiM_Manual.pdf, (2016)

[24] Haller, B. C.; Messer, P. W. SLiM 2: Flexible, Interactive Forward Genetic Simulations, Molecular Biology and Evolution, Volume 34 (2017) no. 1, pp. 230-240 | Article

[25] Hernandez, R. D. A flexible forward simulator for populations subject to selection and demography, Bioinformatics, Volume 24 (2008) no. 23, pp. 2786-2787 | Article

[26] Hoban, S. An overview of the utility of population simulation software in molecular ecology, Molecular Ecology, Volume 23 (2014) no. 10, pp. 2383-2401 | Article

[27] Hoggart, C. J.; Chadeau-Hyam, M.; Clark, T. G.; Lampariello, R.; Whittaker, J. C.; De Iorio, M.; Balding, D. J. Sequence-Level Population Simulations Over Large Genomic Regions, Genetics, Volume 177 (2007) no. 3, pp. 1725-1731 | Article

[28] Hudson RR, Ms a Program for Generating Samples under Neutral Models, (2004)

[29] Jay, F.; Manel, S.; Alvarez, N.; Durand, E. Y.; Thuiller, W.; Holderegger, R.; Taberlet, P.; François, O. Forecasting changes in population genetic structure of alpine plants in response to global warming, Molecular Ecology, Volume 21 (2012) no. 10, pp. 2354-2368 | Article

[30] Jay, F.; Boitard, S.; Austerlitz, F. An ABC Method for Whole-Genome Sequence Data: Inferring Paleolithic and Neolithic Human Expansions, Molecular Biology and Evolution, Volume 36 (2019) no. 7, pp. 1565-1579 | Article

[31] Kelleher, J.; Etheridge, A. M.; McVean, G. Efficient Coalescent Simulation and Genealogical Analysis for Large Sample Sizes, PLOS Computational Biology, Volume 12 (2016) no. 5 | Article

[32] Kelleher, J.; Wong, Y.; Wohns, A. W.; Fadil, C.; Albers, P. K.; McVean, G. Inferring whole-genome histories in large population datasets, Nature Genetics, Volume 51 (2019) no. 9, pp. 1330-1338 | Article

[33] Kern, A. D.; Schrider, D. R. diploS/HIC: An Updated Approach to Classifying Selective Sweeps, G3 Genes|Genomes|Genetics, Volume 8 (2018) no. 6, pp. 1959-1970 | Article

[34] Lapierre, M.; Blin, C.; Lambert, A.; Achaz, G.; Rocha, E. P. C. The Impact of Selection, Gene Conversion, and Biased Sampling on the Assessment of Microbial Demography, Molecular Biology and Evolution, Volume 33 (2016) no. 7, pp. 1711-1725 | Article

[35] Lefébure, T.; Stanhope, M. J. Evolution of the core and pan-genome of Streptococcus: positive selection, recombination, and genome composition, Genome Biology, Volume 8 (2007) no. 5 | Article

[36] Malécot G, Mathématiques de l’hérédité., Masson et Cie, Paris, (1948)

[37] Martiny, J. B. H.; Bohannan, B. J.; Brown, J. H.; Colwell, R. K.; Fuhrman, J. A.; Green, J. L.; Horner-Devine, M. C.; Kane, M.; Krumins, J. A.; Kuske, C. R.; Morin, P. J.; Naeem, S.; Øvreås, L.; Reysenbach, A.-L.; Smith, V. H.; Staley, J. T. Microbial biogeography: putting microorganisms on the map, Nature Reviews Microbiology, Volume 4 (2006) no. 2, pp. 102-112 | Article

[38] Milkman, R.; Bridges, M. M. Molecular evolution of the Escherichia coli chromosome. III. Clonal frames., Genetics, Volume 126 (1990) no. 3, pp. 505-517 | Article

[39] Ochman, H.; Lawrence, J. G.; Groisman, E. A. Lateral gene transfer and the nature of bacterial innovation, Nature, Volume 405 (2000) no. 6784, pp. 299-304 | Article

[40] Robinson DA et al., Bacterial Population Genetics in Infectious Disease. Wiley-Blackwell. ISBN: 978-0-470-42474-2, (2010)

[41] Rocha, E. P. C. Neutral Theory, Microbial Practice: Challenges in Bacterial Population Genetics, Molecular Biology and Evolution, Volume 35 (2018) no. 6, pp. 1338-1347 | Article

[42] Sackman, A. M.; Harris, R. B.; Jensen, J. D. Inferring Demography and Selection in Organisms Characterized by Skewed Offspring Distributions, Genetics, Volume 211 (2019) no. 3, pp. 1019-1028 | Article

[43] Sanchez, T.; Cury, J.; Charpiat, G.; Jay, F. Deep learning for population size history inference: Design, comparison and combination with approximate Bayesian computation, Molecular Ecology Resources, Volume 21 (2020) no. 8, pp. 2645-2660 | Article

[44] Savageau, M. A. Escherichia coli Habitats, Cell Types, and Molecular Mechanisms of Gene Control, The American Naturalist, Volume 122 (1983) no. 6, pp. 732-744 | Article

[45] Schrider, D. R.; Kern, A. D. Supervised Machine Learning for Population Genetics: A New Paradigm, Trends in Genetics, Volume 34 (2018) no. 4, pp. 301-312 | Article

[46] Sheehan, S.; Song, Y. S. Deep Learning for Population Genetic Inference, PLOS Computational Biology, Volume 12 (2016) no. 3 | Article

[47] Sousa, J. A. M. d.; Rocha, E. P. C. Environmental structure drives resistance to phages and antibiotics during phage therapy and to invading lysogens during colonisation, Scientific Reports, Volume 9 (2019) no. 1 | Article

[48] Takuno, S.; Kado, T.; Sugino, R. P.; Nakhleh, L.; Innan, H. Population Genomics in Bacteria: A Case Study of Staphylococcus aureus, Molecular Biology and Evolution, Volume 29 (2012) no. 2, pp. 797-809 | Article

[49] Wall, J. D. Recombination and the power of statistical tests of neutrality, Genetical Research, Volume 74 (1999) no. 1, pp. 65-79 | Article

[50] Waples, R. S. A bias correction for estimates of effective population size based on linkage disequilibrium at unlinked gene loci*, Conservation Genetics, Volume 7 (2006) no. 2, pp. 167-184 | Article

[51] Wiuf, C. Recombination in Human Mitochondrial DNA?, Genetics, Volume 159 (2001) no. 2, pp. 749-756 | Article

Cited by Sources: