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Potential and pitfalls of eukaryotic metagenome skimming: a test case for lichens

Identifieur interne : 000336 ( Istex/Curation ); précédent : 000335; suivant : 000337

Potential and pitfalls of eukaryotic metagenome skimming: a test case for lichens

Auteurs : Bastian Greshake [Allemagne] ; Simonida Zehr [Allemagne] ; Francesco Dal Grande [Allemagne] ; Anjuli Meiser [Allemagne] ; Imke Schmitt [Allemagne] ; Ingo Ebersberger [Allemagne]

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RBID : ISTEX:387D63BC2B6BA93BF2A016EA284C49570D55B60A

Abstract

Whole‐genome shotgun sequencing of multispecies communities using only a single library layout is commonly used to assess taxonomic and functional diversity of microbial assemblages. Here, we investigate to what extent such metagenome skimming approaches are applicable for in‐depth genomic characterizations of eukaryotic communities, for example lichens. We address how to best assemble a particular eukaryotic metagenome skimming data, what pitfalls can occur, and what genome quality can be expected from these data. To facilitate a project‐specific benchmarking, we introduce the concept of twin sets, simulated data resembling the outcome of a particular metagenome sequencing study. We show that the quality of genome reconstructions depends essentially on assembler choice. Individual tools, including the metagenome assemblers Omega and MetaVelvet, are surprisingly sensitive to low and uneven coverages. In combination with the routine of assembly parameter choice to optimize the assembly N50 size, these tools can preclude an entire genome from the assembly. In contrast, MIRA, an all‐purpose overlap assembler, and SPAdes, a multisized de Bruijn graph assembler, facilitate a comprehensive view on the individual genomes across a wide range of coverage ratios. Testing assemblers on a real‐world metagenome skimming data from the lichen Lasallia pustulata demonstrates the applicability of twin sets for guiding method selection. Furthermore, it reveals that the assembly outcome for the photobiont Trebouxia sp. falls behind the a priori expectation given the simulations. Although the underlying reasons remain still unclear, this highlights that further studies on this organism require special attention during sequence data generation and downstream analysis.

Url:
DOI: 10.1111/1755-0998.12463

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ISTEX:387D63BC2B6BA93BF2A016EA284C49570D55B60A

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<div type="abstract">Whole‐genome shotgun sequencing of multispecies communities using only a single library layout is commonly used to assess taxonomic and functional diversity of microbial assemblages. Here, we investigate to what extent such metagenome skimming approaches are applicable for in‐depth genomic characterizations of eukaryotic communities, for example lichens. We address how to best assemble a particular eukaryotic metagenome skimming data, what pitfalls can occur, and what genome quality can be expected from these data. To facilitate a project‐specific benchmarking, we introduce the concept of twin sets, simulated data resembling the outcome of a particular metagenome sequencing study. We show that the quality of genome reconstructions depends essentially on assembler choice. Individual tools, including the metagenome assemblers Omega and MetaVelvet, are surprisingly sensitive to low and uneven coverages. In combination with the routine of assembly parameter choice to optimize the assembly N50 size, these tools can preclude an entire genome from the assembly. In contrast, MIRA, an all‐purpose overlap assembler, and SPAdes, a multisized de Bruijn graph assembler, facilitate a comprehensive view on the individual genomes across a wide range of coverage ratios. Testing assemblers on a real‐world metagenome skimming data from the lichen Lasallia pustulata demonstrates the applicability of twin sets for guiding method selection. Furthermore, it reveals that the assembly outcome for the photobiont Trebouxia sp. falls behind the a priori expectation given the simulations. Although the underlying reasons remain still unclear, this highlights that further studies on this organism require special attention during sequence data generation and downstream analysis.</div>
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