Simulating metagenomic stable isotope probing datasets with MetaSIPSim

Citations Over Time

Abstract

Abstract Background 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. Results 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. Conclusions 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.

Related Papers

• → Functional metagenomics for enzyme discovery: challenges to efficient screening(2009)347 cited
• → Ultrahigh-throughput discovery of promiscuous enzymes by picodroplet functional metagenomics(2015)281 cited
• → Screening for novel enzymes from metagenome and SIGEX, as a way to improve it(2005)100 cited
• → An Alignment-Free “Metapeptide” Strategy for Metaproteomic Characterization of Microbiome Samples Using Shotgun Metagenomic Sequencing(2016)52 cited
• → Metagenomic Approaches for the Discovery of Pollutant-Remediating Enzymes: Recent Trends and Challenges(2022)2 cited