Simulating metagenomic stable isotope probing datasets with MetaSIPSim

• Published in: BMC bioinformatics Vol. 21; no. 1; p. 37
• Main Authors: Barnett, Samuel E, Buckley, Daniel H
• Format: Journal Article
• Language: English
• Published: England BioMed Central Ltd 30.01.2020; BioMed Central; BMC
• Subjects: Analysis; Assembly; Base Composition; BASIC BIOLOGICAL SCIENCES; Complexity; Databases, Genetic; Datasets; Deoxyribonucleic acid; DNA; DNA - chemistry; DNA - genetics; DNA sequencing; Empirical analysis; Experiments; Gene Library; Genomes; Genomics; Hypotheses; Isotope analysis; Isotope Labeling - methods; Isotopes - analysis; Mathematical analysis; Metagenome; Metagenomics; Metagenomics - methods; Methods; Microorganisms; Nucleotide sequencing; Optimization; Parameters; Simulation; SIP; Software; Stable isotope probing; Stable isotopes; Test procedures; United States; Metagenomics; Stable isotope probing; SIP; Simulation
• ISSN: 1471-2105; 1471-2105
• DOI: 10.1186/s12859-020-3372-6

DNA-stable isotope probing (DNA-SIP) links microorganisms to their in-situ function in diverse environmental samples. Combining DNA-SIP and metagenomics (metagenomic-SIP) allows us to link genomes from complex communities to their specific functions and improves the assembly and binning of these targeted genomes. However, empirical development of metagenomic-SIP methods is hindered by the complexity and cost of these studies. We developed a toolkit, 'MetaSIPSim,' to simulate sequencing read libraries for metagenomic-SIP experiments. MetaSIPSim is intended to generate datasets for method development and testing. To this end, we used MetaSIPSim generated data to demonstrate the advantages of metagenomic-SIP over a conventional shotgun metagenomic sequencing experiment. Through simulation we show that metagenomic-SIP improves the assembly and binning of isotopically labeled genomes relative to a conventional metagenomic approach. Improvements were dependent on experimental parameters and on sequencing depth. Community level G + C content impacted the assembly of labeled genomes and subsequent binning, where high community G + C generally reduced the benefits of metagenomic-SIP. Furthermore, when a high proportion of the community is isotopically labeled, the benefits of metagenomic-SIP decline. Finally, the choice of gradient fractions to sequence greatly influences method performance. Metagenomic-SIP is a valuable method for recovering isotopically labeled genomes from complex communities. We show that metagenomic-SIP performance depends on optimization of experimental parameters. MetaSIPSim allows for simulation of metagenomic-SIP datasets which facilitates the optimization and development of metagenomic-SIP experiments and analytical approaches for dealing with these data.