MetaCarvel: linking assembly graph motifs to biological variants

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Abstract

Reconstructing genomic segments from metagenomics data is a highly complex task. In addition to general challenges, such as repeats and sequencing errors, metagenomic assembly needs to tolerate the uneven depth of coverage among organisms in a community and differences between nearly identical strains. Previous methods have addressed these issues by smoothing genomic variants. We present a variant-aware metagenomic scaffolder called MetaCarvel, which combines new strategies for repeat detection with graph analytics for the discovery of variants. We show that MetaCarvel can accurately reconstruct genomic segments from complex microbial mixtures and correctly identify and characterize several classes of common genomic variants.

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The online version of this article (10.1186/s13059-019-1791-3) contains supplementary material, which is available to authorized users.

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Assembly algorithms for next-generation sequencing data.

Jason R. Miller, Sergey Koren, Granger Sutton (2010)

The emergence of next-generation sequencing platforms led to resurgence of research in whole-genome shotgun assembly algorithms and software. DNA sequencing data from the Roche 454, Illumina/Solexa, and ABI SOLiD platforms typically present shorter read lengths, higher coverage, and different error profiles compared with Sanger sequencing data. Since 2005, several assembly software packages have been created or revised specifically for de novo assembly of next-generation sequencing data. This review summarizes and compares the published descriptions of packages named SSAKE, SHARCGS, VCAKE, Newbler, Celera Assembler, Euler, Velvet, ABySS, AllPaths, and SOAPdenovo. More generally, it compares the two standard methods known as the de Bruijn graph approach and the overlap/layout/consensus approach to assembly. Copyright 2010 Elsevier Inc. All rights reserved.

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Metagenomic analysis of a permafrost microbial community reveals a rapid response to thaw.

Rachel Mackelprang, Mark P Waldrop, Kristen DeAngelis … (2011)

Permafrost contains an estimated 1672 Pg carbon (C), an amount roughly equivalent to the total currently contained within land plants and the atmosphere. This reservoir of C is vulnerable to decomposition as rising global temperatures cause the permafrost to thaw. During thaw, trapped organic matter may become more accessible for microbial degradation and result in greenhouse gas emissions. Despite recent advances in the use of molecular tools to study permafrost microbial communities, their response to thaw remains unclear. Here we use deep metagenomic sequencing to determine the impact of thaw on microbial phylogenetic and functional genes, and relate these data to measurements of methane emissions. Metagenomics, the direct sequencing of DNA from the environment, allows the examination of whole biochemical pathways and associated processes, as opposed to individual pieces of the metabolic puzzle. Our metagenome analyses reveal that during transition from a frozen to a thawed state there are rapid shifts in many microbial, phylogenetic and functional gene abundances and pathways. After one week of incubation at 5 °C, permafrost metagenomes converge to be more similar to each other than while they are frozen. We find that multiple genes involved in cycling of C and nitrogen shift rapidly during thaw. We also construct the first draft genome from a complex soil metagenome, which corresponds to a novel methanogen. Methane previously accumulated in permafrost is released during thaw and subsequently consumed by methanotrophic bacteria. Together these data point towards the importance of rapid cycling of methane and nitrogen in thawing permafrost.

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Ray Meta: scalable de novo metagenome assembly and profiling

Sébastien Boisvert, Frédéric Raymond, Élénie Godzaridis … (2012)

Voluminous parallel sequencing datasets, especially metagenomic experiments, require distributed computing for de novo assembly and taxonomic profiling. Ray Meta is a massively distributed metagenome assembler that is coupled with Ray Communities, which profiles microbiomes based on uniquely-colored k-mers. It can accurately assemble and profile a three billion read metagenomic experiment representing 1,000 bacterial genomes of uneven proportions in 15 hours with 1,024 processor cores, using only 1.5 GB per core. The software will facilitate the processing of large and complex datasets, and will help in generating biological insights for specific environments. Ray Meta is open source and available at http://denovoassembler.sf.net.

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Author and article information

Contributors

Mihai Pop:

ORCID: http://orcid.org/0000-0001-9617-5304

mpop@umd.edu

Journal

Journal ID (nlm-ta): Genome Biol

Journal ID (iso-abbrev): Genome Biol

Title: Genome Biology

Publisher: BioMed Central (London )

ISSN (Print): 1474-7596

ISSN (Electronic): 1474-760X

Publication date (Electronic): 26 August 2019

Publication date PMC-release: 26 August 2019

Publication date Collection: 2019

Volume: 20

Electronic Location Identifier: 174

Affiliations

[1 ]ISNI 0000 0001 0941 7177, GRID grid.164295.d, Department of Computer Science, , University of Maryland, ; College Park, MD USA

[2 ]ISNI 0000 0001 0941 7177, GRID grid.164295.d, Center for Bioinformatics and Computational Biology, , University of Maryland, ; College Park, MD USA

[3 ]ISNI 0000 0004 1936 8278, GRID grid.21940.3e, Department of Computer Science, , Rice University, ; Houston, TX USA

[4 ]ISNI 0000 0004 0591 0193, GRID grid.89170.37, Center for Bio/Molecular Science & Engineering, , United States Naval Research Laboratory, ; Washington, DC, USA

Author information

W. Judson Hervey IV http://orcid.org/0000-0003-3285-6754

Mihai Pop http://orcid.org/0000-0001-9617-5304

Article

Publisher ID: 1791

DOI: 10.1186/s13059-019-1791-3

PMC ID: 6710874

PubMed ID: 31451112

SO-VID: a79722b4-f9c4-423a-ac94-84abec08ed55

License:

Open Access This 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 : 5 January 2019

Date accepted : 13 August 2019

Funding

Funded by: FundRef http://dx.doi.org/10.13039/100000060, National Institute of Allergy and Infectious Diseases;

Award ID: R01AI100947

Award Recipient : Mihai Pop

Funded by: FundRef http://dx.doi.org/10.13039/100009917, U.S. Naval Research Laboratory;

Award ID: HASI

Award Recipient : W. Judson Hervey IV

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ScienceOpen disciplines: Genetics

Keywords: metagenomics,variant detection,scaffolding,assembly

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ScienceOpen disciplines: Genetics

Keywords: metagenomics, variant detection, scaffolding, assembly

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MetaCarvel: linking assembly graph motifs to biological variants

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Most cited references 22

Assembly algorithms for next-generation sequencing data.

Metagenomic analysis of a permafrost microbial community reveals a rapid response to thaw.

Ray Meta: scalable de novo metagenome assembly and profiling

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