Genomes from uncultivated prokaryotes: a comparison of metagenome-assembled and single-amplified genomes

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Abstract

Background

Prokaryotes dominate the biosphere and regulate biogeochemical processes essential to all life. Yet, our knowledge about their biology is for the most part limited to the minority that has been successfully cultured. Molecular techniques now allow for obtaining genome sequences of uncultivated prokaryotic taxa, facilitating in-depth analyses that may ultimately improve our understanding of these key organisms.

Results

We compared results from two culture-independent strategies for recovering bacterial genomes: single-amplified genomes and metagenome-assembled genomes. Single-amplified genomes were obtained from samples collected at an offshore station in the Baltic Sea Proper and compared to previously obtained metagenome-assembled genomes from a time series at the same station. Among 16 single-amplified genomes analyzed, seven were found to match metagenome-assembled genomes, affiliated with a diverse set of taxa. Notably, genome pairs between the two approaches were nearly identical (average 99.51% sequence identity; range 98.77–99.84%) across overlapping regions (30–80% of each genome). Within matching pairs, the single-amplified genomes were consistently smaller and less complete, whereas the genetic functional profiles were maintained. For the metagenome-assembled genomes, only on average 3.6% of the bases were estimated to be missing from the genomes due to wrongly binned contigs.

Conclusions

The strong agreement between the single-amplified and metagenome-assembled genomes emphasizes that both methods generate accurate genome information from uncultivated bacteria. Importantly, this implies that the research questions and the available resources are allowed to determine the selection of genomics approach for microbiome studies.

Electronic supplementary material

The online version of this article (10.1186/s40168-018-0550-0) contains supplementary material, which is available to authorized users.

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Most cited references 33

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Community structure and metabolism through reconstruction of microbial genomes from the environment.

Gene W. Tyson, Jarrod Chapman, Philip Hugenholtz … (2004)

Microbial communities are vital in the functioning of all ecosystems; however, most microorganisms are uncultivated, and their roles in natural systems are unclear. Here, using random shotgun sequencing of DNA from a natural acidophilic biofilm, we report reconstruction of near-complete genomes of Leptospirillum group II and Ferroplasma type II, and partial recovery of three other genomes. This was possible because the biofilm was dominated by a small number of species populations and the frequency of genomic rearrangements and gene insertions or deletions was relatively low. Because each sequence read came from a different individual, we could determine that single-nucleotide polymorphisms are the predominant form of heterogeneity at the strain level. The Leptospirillum group II genome had remarkably few nucleotide polymorphisms, despite the existence of low-abundance variants. The Ferroplasma type II genome seems to be a composite from three ancestral strains that have undergone homologous recombination to form a large population of mosaic genomes. Analysis of the gene complement for each organism revealed the pathways for carbon and nitrogen fixation and energy generation, and provided insights into survival strategies in an extreme environment.

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The NumPy array: a structure for efficient numerical computation

Gael Varoquaux, S. Chris Colbert, Stefan van der Walt (2011)

In the Python world, NumPy arrays are the standard representation for numerical data. Here, we show how these arrays enable efficient implementation of numerical computations in a high-level language. Overall, three techniques are applied to improve performance: vectorizing calculations, avoiding copying data in memory, and minimizing operation counts. We first present the NumPy array structure, then show how to use it for efficient computation, and finally how to share array data with other libraries.

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WebMGA: a customizable web server for fast metagenomic sequence analysis

Sitao Wu, Zhengwei Zhu, Liming Fu … (2011)

Background The new field of metagenomics studies microorganism communities by culture-independent sequencing. With the advances in next-generation sequencing techniques, researchers are facing tremendous challenges in metagenomic data analysis due to huge quantity and high complexity of sequence data. Analyzing large datasets is extremely time-consuming; also metagenomic annotation involves a wide range of computational tools, which are difficult to be installed and maintained by common users. The tools provided by the few available web servers are also limited and have various constraints such as login requirement, long waiting time, inability to configure pipelines etc. Results We developed WebMGA, a customizable web server for fast metagenomic analysis. WebMGA includes over 20 commonly used tools such as ORF calling, sequence clustering, quality control of raw reads, removal of sequencing artifacts and contaminations, taxonomic analysis, functional annotation etc. WebMGA provides users with rapid metagenomic data analysis using fast and effective tools, which have been implemented to run in parallel on our local computer cluster. Users can access WebMGA through web browsers or programming scripts to perform individual analysis or to configure and run customized pipelines. WebMGA is freely available at http://weizhongli-lab.org/metagenomic-analysis. Conclusions WebMGA offers to researchers many fast and unique tools and great flexibility for complex metagenomic data analysis.

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Author and article information

Contributors

Anders F. Andersson: anders.andersson@scilifelab.se

Jarone Pinhassi:

ORCID: http://orcid.org/0000-0002-6405-1347

jarone.pinhassi@lnu.se

Journal

Journal ID (nlm-ta): Microbiome

Journal ID (iso-abbrev): Microbiome

Title: Microbiome

Publisher: BioMed Central (London )

ISSN (Electronic): 2049-2618

Publication date (Electronic): 28 September 2018

Publication date PMC-release: 28 September 2018

Publication date Collection: 2018

Volume: 6

Electronic Location Identifier: 173

Affiliations

[1 ]ISNI 0000000121581746, GRID grid.5037.1, School of Engineering Sciences in Chemistry, Biotechnology and Health, Department of Gene Technology, Science for Life Laboratory, , KTH Royal Institute of Technology, ; Stockholm, Sweden

[2 ]ISNI 0000 0001 2174 3522, GRID grid.8148.5, Centre for Ecology and Evolution in Microbial Model Systems, EEMiS, , Linnaeus University, ; Kalmar, Sweden

[3 ]ISNI 0000 0004 1936 9457, GRID grid.8993.b, Department of Cell and Molecular Biology, SciLifeLab, , Uppsala University, ; Uppsala, Sweden

[4 ]GRID grid.465198.7, Present address: Science for Life Laboratory, Department of Molecular, Tumour and Cell Biology, Centre for Translational Microbiome Research, , Karolinska Institutet, ; Solna, Sweden

[5 ]ISNI 0000 0001 0930 2361, GRID grid.4514.4, Present address: Department of Biology, , Lund University, ; Lund, Sweden

[6 ]ISNI 0000 0004 1936 9457, GRID grid.8993.b, Department of Ecology and Genetics, Limnology, Science for Life Laboratory, , Uppsala University, ; Uppsala, Sweden

Author information

Jarone Pinhassi http://orcid.org/0000-0002-6405-1347

Article

Publisher ID: 550

DOI: 10.1186/s40168-018-0550-0

PMC ID: 6162917

PubMed ID: 30266101

SO-VID: 21b7bc03-006e-4449-8117-11668679e355

License:

Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

History

Date received : 3 August 2017

Date accepted : 5 September 2018

Funding

Funded by: EU BONUS

Award ID: BLUEPRINT

Award Recipient : Anders F. Andersson Jarone Pinhassi

Funded by: FundRef http://dx.doi.org/10.13039/501100004359, Vetenskapsrådet;

Award ID: 2011-4369

Award ID: 2015-04959

Award ID: 2011-5689

Award Recipient : Anders F. Andersson Jarone Pinhassi

Funded by: FundRef http://dx.doi.org/10.13039/501100001862, Svenska Forskningsrådet Formas;

Funded by: FundRef http://dx.doi.org/10.13039/501100000781, European Research Council;

Award ID: 310039-PUZZLE_CELL

Funded by: FundRef http://dx.doi.org/10.13039/501100001729, Stiftelsen för Strategisk Forskning;

Award ID: SSF-FFL5

Custom metadata

Keywords: single-amplified genomes,metagenome-assembled genomes,metagenomics,binning,single-cell genomics

Data availability:

Keywords: single-amplified genomes, metagenome-assembled genomes, metagenomics, binning, single-cell genomics

Genomes from uncultivated prokaryotes: a comparison of metagenome-assembled and single-amplified genomes

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Abstract

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Results

Conclusions

Electronic supplementary material

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Tick microbiome

Most cited references 33

Community structure and metabolism through reconstruction of microbial genomes from the environment.

The NumPy array: a structure for efficient numerical computation

WebMGA: a customizable web server for fast metagenomic sequence analysis

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