Definition
A simulation is the dynamic modeling of a real process over time. A simulated metagenomic dataset is the product of a single simulation iteration of the sequencing process of a microbial community under a specific set of sequencing-platform model parameters.
Summary
The use of simulations to produce model metagenomic datasets allows to test the performance of technological methodologies and the testing of theoretical hypothesis that cannot be achieved by empirical experimentation. Methodologically, it has been used to evaluate the performance of assembly programs and the effect of differences of read length and error rate on the quality of the resulting datasets. Theoretically, it has revealed biases and heterogeneity in the estimation of several diversity metrics from metagenomic samples. However, the full potential of the implementation of simulated datasets to metagenomics is still to be revealed.
Introduction
The complexity of microbial communities, and the nature of the...
References
Angly FE, Willner D, Rohwer F, Hugenholtz P, Tyson GW. Grinder: a versatile amplicon and shotgun sequence simulator. Nucleic Acids Res. 2012;40(12):e94.
Bent SJ, Forney LJ. The tragedy of the uncommon: understanding limitations in the analysis of microbial diversity. ISME J. 2008;2(7):689–95.
Bonilla-Rosso G, Eguiarte LE, Romero D, Travisano M, Souza V. Understanding microbial community diversity metrics derived from metagenomes: performance evaluation using simulated data sets. FEMS Microbiol Ecol. 2012;82:37–49. doi:10.1111/j.1574-6941.2012.01405.x.
Caswell H. Theory and models in ecology: a different perspective. Ecol Mod. 1988;43(1–2):33–44.
Charuvaka A, Rangwala H. Evaluation of short read metagenomic assembly. BMC Genomics. 2011;12 Suppl 2:S8.
Garfinkel D. Digital computer simulation of ecological systems. Nature. 1962;194(4831):502–7.
Heltshe JF, Forrester NE. Estimating species richness using the jackknife procedure. Biometrics. 1983;39(1):1–11.
Hoff KJ. The effect of sequencing errors on metagenomic gene prediction. BMC Genomics. 2009;10(1):520.
Kuczynski J, Liu Z, Lozupone C, et al. Microbial community resemblance methods differ in their ability to detect biologically relevant patterns. Nat Methods. 2010;7(10):813–9.
Laserson J, Jojic V, Koller D. Genovo: de novo assembly for metagenomes. J Comput Biol. 2011;18(3):429–43.
Liu WT, Marsh TL, Cheng H, Forney LJ. Characterization of microbial diversity by determining terminal restriction fragment length polymorphisms of genes encoding 16S rRNA. Appl Environ Microbiol. 1997;63(11):4516–22.
Mavromatis K, Ivanova N, Barry K, et al. Use of simulated data sets to evaluate the fidelity of metagenomic processing methods. Nat Methods. 2007;4(6):495–500.
Mende DR, Waller AS, Sunagawa S, et al. Assessment of metagenomic assembly using simulated next generation sequencing data. PLoS One. 2012;7(2):e31386.
Mitra S, Schubach M, Huson DH. Short clones or long clones? A simulation study on the use of paired reads in metagenomics. BMC Bioinformatics. 2010;11(Suppl 1):S12
Morgan JL, Darling AE, Eisen JA. Metagenomic sequencing of an in vitro-simulated microbial community. PLoS One. 2010;5(4):e10209.
Namiki T, Hachiya T, Tanaka H, Sakakibara Y. MetaVelvet: an extension of velvet assembler to de novo metagenome assembly from short sequence reads. Nucl Acids Res. 2012;40:e155. doi:10.1093/nar/gks678.
Parks DH, Beiko RG. Measures of phylogenetic differentiation provide robust and complementary insights into microbial communities. ISME J. 2012;7:173–83. doi:10.1038/ismej.2012.88.
Peck SL. The hermeneutics of ecological simulation. Biol Philos. 2008;23(3):383–402.
Peng Y, Leung HCM, Yiu SM, Chin FYL. IDBA-UD: a de novo assembler for single-cell and metagenomic sequencing data with highly uneven depth. Bioinformatics. 2012;28(11):1420–8.
Pignatelli M, Moya A. Evaluating the fidelity of de novo short read metagenomic assembly using simulated data. PLoS One. 2011;6(5):e19984.
Richter DC, Ott F, Auch AF, Schmid R, Huson DH. Metasim – a sequencing simulator for genomics and metagenomics. PLoS One. 2008;3(10):e3373.
Winsberg E. Simulated experiments: methodology for a virtual world. Philos Sci. 2003;70(1):105–25.
Wommack KE, Bhavsar J, Ravel J. Metagenomics: read length matters. Appl Environ Microbiol. 2008;74(5):1453–63.
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Bonilla-Rosso, G. (2013). Lessons Learned from Simulated Metagenomic Datasets. In: Nelson, K. (eds) Encyclopedia of Metagenomics. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-6418-1_716-4
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DOI: https://doi.org/10.1007/978-1-4614-6418-1_716-4
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