The MAR databases: development and implementation of databases specific for marine metagenomics

Klemetsen, Terje; Raknes, Inge Alexander; Fu, Jing; Agafonov, A. V.; Balasundaram, Sudhagar V.; Tartari, Gabriele; Robertsen, Espen Mikal; Willassen, Nils Peder

doi:10.1093/nar/gkx1036

Cited by 107 publications

(103 citation statements)

References 34 publications

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“…We downloaded all prokaryotic assemblies from RefSeq [65,66], as well as several collections of isolate genomes [29,50,75], MAGs [69,49,41], and SAGs [46]. Where possible, we used perexisting gene annotations provided by NCBI.…”

Section: Eggo Datasetsmentioning

confidence: 99%

Estimating maximal microbial growth rates from cultures, metagenomes, and single cells via codon usage patterns

Weissman

Hou

Fuhrman

2020

Preprint

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Maximal growth rate is a basic parameter of microbial lifestyle that varies over several orders of magnitude, with doubling times ranging from a matter of minutes to multiple days. Growth rates are typically measured using laboratory culture experiments. Yet, we lack sufficient understanding of the ecology and physiology of most microbes to design appropriate culture conditions for them, severely limiting our ability to assess the global diversity of microbial growth rates. Thus, genomic estimators of maximal growth rate provide a practical solution to survey the distribution of microbial growth potential, regardless of cultivation status. Here, we develop an improved maximal growth rate estimator, based on an expanded set of codon usage statistics, and implement this estimator in an easy-to-use R package (gRodon). We show gRodon outperforms the state-of-the-art growth estimator in multiple settings, including in a community context where we implement a novel species abundance correction for metagenomes. Additionally, we estimate maximal growth rates from over 200,000 genomes, metagenome-assembled genomes, and single-cell amplified genomes to survey growth potential across the range of prokaryotic diversity. We provide these compiled maximal growth rates in a publicly-available database (EGGO) and use this database to illustrate how culture collections show a strong bias towards organisms capable of rapid growth. We demonstrate how this database can be used to propagate maximal growth rate predictions to organisms for which we lack genomic information, on the basis of 16S rRNA sequence alone. Finally, we provide a detailed discussion and analysis of potential confounders, and observe a bias in genomic predictions of growth for extremely slow-growing organisms, ultimately leading us to suggest a novel evolutionary definition of oligotrophy based on the selective regime an organism occupies.

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Section: Eggo Datasetsmentioning

confidence: 99%

Estimating maximal microbial growth rates from cultures, metagenomes, and single cells via codon usage patterns

Weissman

Hou

Fuhrman

2020

Preprint

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“…We used MetaQUAST 13 to evaluate the assemblies, using as references all 971 genomes in the MarRef marine genomes database 14 . The parameters used for MetaQUAST and all other software tools can be found in the Supplementary information.…”

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confidence: 99%

Terabase-scale metagenome coassembly with MetaHipMer

Hofmeyr

Egan

Georganas

et al. 2020

Sci Rep

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Metagenome sequence datasets can contain terabytes of reads, too many to be coassembled together on a single shared-memory computer; consequently, they have only been assembled sample by sample (multiassembly) and combining the results is challenging. We can now perform coassembly of the largest datasets using MetaHipMer, a metagenome assembler designed to run on supercomputers and large clusters of compute nodes. We have reported on the implementation of MetaHipMer previously; in this paper we focus on analyzing the impact of very large coassembly. in particular, we show that coassembly recovers a larger genome fraction than multiassembly and enables the discovery of more complete genomes, with lower error rates, whereas multiassembly recovers more dominant strain variation. Being able to coassemble a large dataset does not preclude one from multiassembly; rather, having a fast, scalable metagenome assembler enables a user to more easily perform coassembly and multiassembly, and assemble both abundant, high strain variation genomes, and low-abundance, rare genomes. We present several assemblies of terabyte datasets that could never be coassembled before, demonstrating MetaHipMer's scaling power. MetaHipMer is available for public use under an open source license and all datasets used in the paper are available for public download.

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“…Peptides and genes from DmdA gene family were collected from a set of 771 genomes manually curated and hosted in the MarRef database ( Klemetsen et al, 2018 ). The DmdA orthologs and homologs sequences were obtained as described by González et al (2019) .…”

Section: Methodsmentioning

confidence: 99%

“…Two topologies were tested, the ML topology obtained for the species tree of the genomes here analyzed, and the ML phylogeny of DmdA. To construct the species tree, ribosomal protein 16 small subunit (RPS16) sequences were collected from the MarRef database ( Klemetsen et al, 2018 ), one for each genome ( Table S1 ).…”

Section: Methodsmentioning

confidence: 99%

Evolutionary history of dimethylsulfoniopropionate (DMSP) demethylation enzyme DmdA in marine bacteria

Hernández

Vicens

Eguiarte

et al. 2020

PeerJ

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Dimethylsulfoniopropionate (DMSP), an osmolyte produced by oceanic phytoplankton and bacteria, is primarily degraded by bacteria belonging to the Roseobacter lineage and other marine Alphaproteobacteria via DMSP-dependent demethylase A protein (DmdA). To date, the evolutionary history of DmdA gene family is unclear. Some studies indicate a common ancestry between DmdA and GcvT gene families and a co-evolution between Roseobacter and the DMSP-producing-phytoplankton around 250 million years ago (Mya). In this work, we analyzed the evolution of DmdA under three possible evolutionary scenarios: (1) a recent common ancestor of DmdA and GcvT, (2) a coevolution between Roseobacter and the DMSP-producing-phytoplankton, and (3) an enzymatic adaptation for utilizing DMSP in marine bacteria prior to Roseobacter origin. Our analyses indicate that DmdA is a new gene family originated from GcvT genes by duplication and functional divergence driven by positive selection before a coevolution between Roseobacter and phytoplankton. Our data suggest that Roseobacter acquired dmdA by horizontal gene transfer prior to an environment with higher DMSP. Here, we propose that the ancestor that carried the DMSP demethylation pathway genes evolved in the Archean, and was exposed to a higher concentration of DMSP in a sulfur-rich atmosphere and anoxic ocean, compared to recent Roseobacter eco-orthologs (orthologs performing the same function under different conditions), which should be adapted to lower concentrations of DMSP.

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The MAR databases: development and implementation of databases specific for marine metagenomics

Cited by 107 publications

References 34 publications

Estimating maximal microbial growth rates from cultures, metagenomes, and single cells via codon usage patterns

Estimating maximal microbial growth rates from cultures, metagenomes, and single cells via codon usage patterns

Terabase-scale metagenome coassembly with MetaHipMer

Evolutionary history of dimethylsulfoniopropionate (DMSP) demethylation enzyme DmdA in marine bacteria

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