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Cited by 240Genomics and Phylogenetic StudiesMicrobial Community Ecology and PhysiologyEnvironmental DNA in Biodiversity Studies

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Comparing and evaluating metagenome assembly tools from a microbiologist's perspective - not only size matters!

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Vollmers, John, Wiegand, Sandra, Kaster, Anne-Kristin·2017·PLoS ONE
DOI 10.1371/journal.pone.0169662PMC PMC5242441
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Abstract

With the constant improvement in cost-efficiency and quality of Next Generation Sequencing technologies, shotgun-sequencing approaches -such as metagenomics- have nowadays become the methods of choice for studying and classifying microorganisms from various habitats. The production of data has dramatically increased over the past years and processing and analysis steps are becoming more and more of a bottleneck. Limiting factors are partly the availability of computational resources, but mainly the bioinformatics expertise in establishing and applying appropriate processing and analysis pipelines. Fortunately, a large diversity of specialized software tools is nowadays available. Nevertheless, choosing the most appropriate methods for answering specific biological questions can be rather challenging, especially for non-bioinformaticians. In order to provide a comprehensive overview and guide for the microbiological scientific community, we assessed the most common and freely available metagenome assembly tools with respect to their output statistics, their sensitivity for low abundant community members and variability in resulting community profiles as well as their ease-of-use. In contrast to the highly anticipated "Critical Assessment of Metagenomic Interpretation" (CAMI) challenge, which uses general mock community-based assembler comparison we here tested assemblers on real Illumina metagenome sequencing data from natural communities of varying complexity sampled from forest soil and algal biofilms. Our observations clearly demonstrate that different assembly tools can prove optimal, depending on the sample type, available computational resources and, most importantly, the specific research goal. In addition, we present detailed descriptions of the underlying principles and pitfalls of publically available assembly tools from a microbiologist's perspective, and provide guidance regarding the user-friendliness, sensitivity and reliability of the resulting phylogenetic profiles.

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Figures

Fig 1 Predicted diversity and relative abundance of organisms represented in the unassembled read datasets. Taxon assignments are based on diamond [68] blastx alignments of all reads against the NCBI nr database and subsequent LCA classification using MEGAN [69]. Large ring charts show detailed breakdowns of the different phyla representing the bacterial fractions in each dataset. The proteobacterial fraction is further broken down into the represented classes of this phylum. Small pie charts indicate the relative abundances of Bacteria, Eukaryota, Archaea and unclassified organisms within the subset of reads which could be assigned at least to the “cellular life form” level. The depicted charts were adapted from visualizations produced by KRONA [70]. Similar analyses, based exclusively on universal marker genes and performed using PhyloSift [71], show almost identical relationships between taxons, albeit with lesser detail and slightly higher estimations for eukaryotic and archaeal fractions (S1 Fig).
Fig 1 Predicted diversity and relative abundance of organisms represented in the unassembled read datasets. Taxon assignments are based on diamond [68] blastx alignments of all reads against the NCBI nr database and subsequent LCA classification using MEGAN [69]. Large ring charts show detailed breakdowns of the different phyla representing the bacterial fractions in each dataset. The proteobacterial fraction is further broken down into the represented classes of this phylum. Small pie charts indicate the relative abundances of Bacteria, Eukaryota, Archaea and unclassified organisms within the subset of reads which could be assigned at least to the “cellular life form” level. The depicted charts were adapted from visualizations produced by KRONA [70]. Similar analyses, based exclusively on universal marker genes and performed using PhyloSift [71], show almost identical relationships between taxons, albeit with lesser detail and slightly higher estimations for eukaryotic and archaeal fractions (S1 Fig).CC BY · PubMed Central
Fig 2 Comparison of individual assembly characteristics produced by different assemblers. Kelp biofilm (KBF) assemblies are shown on the left and Marburg forest soil (MFS) assemblies on the right. Assemblers utilizing single k-mer lengths were tested with two different values for k, 21 and 101. All statistics are based on scaffolds larger than 500 bp. The maximum scaffold length indicates the size of the single largest scaffold, while the N50 value represents a weighted average across all scaffolds. The scaffold count represents the total number of scaffolds >500bp. The total assembly length and the read mapping rates indicate how much of the metagenomic sequence information is represented in the assembly. Read mapping rates were determined using the short read mapping tool Bowtie2 and quality-trimmed paired end reads (for a complete list of assembly statistics, please refer to S2 Table).
Fig 2 Comparison of individual assembly characteristics produced by different assemblers. Kelp biofilm (KBF) assemblies are shown on the left and Marburg forest soil (MFS) assemblies on the right. Assemblers utilizing single k-mer lengths were tested with two different values for k, 21 and 101. All statistics are based on scaffolds larger than 500 bp. The maximum scaffold length indicates the size of the single largest scaffold, while the N50 value represents a weighted average across all scaffolds. The scaffold count represents the total number of scaffolds >500bp. The total assembly length and the read mapping rates indicate how much of the metagenomic sequence information is represented in the assembly. Read mapping rates were determined using the short read mapping tool Bowtie2 and quality-trimmed paired end reads (for a complete list of assembly statistics, please refer to S2 Table).CC BY · PubMed Central
Fig 3 Comparison of assembly performance and cost-efficiency. Kelp biofilm (KBF) assemblies are shown on the left and Marburg forest soil (MFS) assemblies on the right. Assemblers utilizing single k-mer lengths were tested with two different values for k, 21 and 101. Assembly performance was defined as the product of the respective read mapping rate (representing information content) and the respective N50. Cost efficiency was defined as the quotient of assembly performance and the sum of RAM usage and runtime per CPU, required for the respective assemblies. For a complete list of assembly statistics, please refer to S2 Table.
Fig 3 Comparison of assembly performance and cost-efficiency. Kelp biofilm (KBF) assemblies are shown on the left and Marburg forest soil (MFS) assemblies on the right. Assemblers utilizing single k-mer lengths were tested with two different values for k, 21 and 101. Assembly performance was defined as the product of the respective read mapping rate (representing information content) and the respective N50. Cost efficiency was defined as the quotient of assembly performance and the sum of RAM usage and runtime per CPU, required for the respective assemblies. For a complete list of assembly statistics, please refer to S2 Table.CC BY · PubMed Central
Fig 4 Coverage distribution of assembled scaffolds produced by different assemblers. Kelp biofilm (KBF) assemblies are shown on the left and Marburg forest soil (MFS) assemblies on the right. The boxplots illustrate the relative abundances of scaffolds with different coverage values in the assembled datasets. Coverage values are based on the depth of reads mapping back to the assembled scaffolds >500 bp and were evaluated separately for each assembly and sample using the short read mapping tool Bowtie2 and quality-trimmed paired end reads. A logarithmic scale was chosen for the y-axis in order to account for extreme outliers. For better comparability, grey background lines mark the height of 5x coverage.
Fig 4 Coverage distribution of assembled scaffolds produced by different assemblers. Kelp biofilm (KBF) assemblies are shown on the left and Marburg forest soil (MFS) assemblies on the right. The boxplots illustrate the relative abundances of scaffolds with different coverage values in the assembled datasets. Coverage values are based on the depth of reads mapping back to the assembled scaffolds >500 bp and were evaluated separately for each assembly and sample using the short read mapping tool Bowtie2 and quality-trimmed paired end reads. A logarithmic scale was chosen for the y-axis in order to account for extreme outliers. For better comparability, grey background lines mark the height of 5x coverage.CC BY · PubMed Central
Fig 5 Overall marker gene abundance and redundancy for metagenomes from Kelp biofilm (KBF) and Marburg forest soil (MFS) samples. Bar heights indicate the total abundance of universal bacterial marker gene products >100 amino acids, predicted on scaffolds >500 bp for each assembly, using fetchMG (www.bork.embl.de/software/mOTU/fetchMG.html) [74]. As this value is derived from multiple marker gene types, which co-occur within the same genome, this value does not directly represent the actual number of species captured by the assemblies, rather indicating the overall captured metagenomic potential in terms of species genome completeness as well as diversity. Checkered segments of each bar indicate unique marker gene products, while the solid segments indicate the fraction of closely related and redundant protein sequences, sharing more than 90% amino acid identity. The latter indicate the potential micro diversity captured by each assembler. For a detailed breakup of individual marker gene abundances and diversities please refer to S3 Table.
Fig 5 Overall marker gene abundance and redundancy for metagenomes from Kelp biofilm (KBF) and Marburg forest soil (MFS) samples. Bar heights indicate the total abundance of universal bacterial marker gene products >100 amino acids, predicted on scaffolds >500 bp for each assembly, using fetchMG (www.bork.embl.de/software/mOTU/fetchMG.html) [74]. As this value is derived from multiple marker gene types, which co-occur within the same genome, this value does not directly represent the actual number of species captured by the assemblies, rather indicating the overall captured metagenomic potential in terms of species genome completeness as well as diversity. Checkered segments of each bar indicate unique marker gene products, while the solid segments indicate the fraction of closely related and redundant protein sequences, sharing more than 90% amino acid identity. The latter indicate the potential micro diversity captured by each assembler. For a detailed breakup of individual marker gene abundances and diversities please refer to S3 Table.CC BY · PubMed Central
Fig 6 Neighbor-joining phylogeny of abundant marker genes and gene products from Kelp biofilm (KBF) and Marburg forest soil (MFS) samples. Phylogenies are displayed as cladograms, since they provide a more ordered and less-crowded overview than phylograms. Therefore, clustering is shown correctly but terminal branch lengths do not reflect actual phylogenetic distances. A&B) Gene product COG201, referring to preprotein translocase subunit SecY. C&D) 16S and 18S rRNA gene sequences. Different coloring of terminal branches indicate marker sequences produced by different assemblers according to the color code below the cladogram. Taxonomic classifications are indicated for each observed sequence cluster.
Fig 6 Neighbor-joining phylogeny of abundant marker genes and gene products from Kelp biofilm (KBF) and Marburg forest soil (MFS) samples. Phylogenies are displayed as cladograms, since they provide a more ordered and less-crowded overview than phylograms. Therefore, clustering is shown correctly but terminal branch lengths do not reflect actual phylogenetic distances. A&B) Gene product COG201, referring to preprotein translocase subunit SecY. C&D) 16S and 18S rRNA gene sequences. Different coloring of terminal branches indicate marker sequences produced by different assemblers according to the color code below the cladogram. Taxonomic classifications are indicated for each observed sequence cluster.CC BY · PubMed Central
Fig 7 Diversity and micro diversity. Diversity and microdiversity are quantified based on clusterings by CDhit at seqeunce identity cutoffs of 96% (16S/18S rRNA) and 90% (SecY encoding gene sequences). Diversity is represented by the total number of clusters formed for each assembly. Micro diversity is represented by the number of clusters containing multiple sequences originating from the same assembly.
Fig 7 Diversity and micro diversity. Diversity and microdiversity are quantified based on clusterings by CDhit at seqeunce identity cutoffs of 96% (16S/18S rRNA) and 90% (SecY encoding gene sequences). Diversity is represented by the total number of clusters formed for each assembly. Micro diversity is represented by the number of clusters containing multiple sequences originating from the same assembly.CC BY · PubMed Central
Fig 8 Differences in phylogenetic profiles based on different assemblies of Kelp Biofilm (KBF) and Marburg forest soil (MFS) samples. Phylogenetic profiles are based on 40 single copy marker gene products identified with fetchMG (www.bork.embl.de/software/mOTU/fetchMG.html) and annotations based on alignments against the NCBI-nr database and the least common ancestor (LCA) method implemented by MEGAN5 [69]. The phylogenetic profiles show the number of marker gene products assigned to different taxa on phylum level. Additional profiles showing the class and order levels are given in S2 Fig.
Fig 8 Differences in phylogenetic profiles based on different assemblies of Kelp Biofilm (KBF) and Marburg forest soil (MFS) samples. Phylogenetic profiles are based on 40 single copy marker gene products identified with fetchMG (www.bork.embl.de/software/mOTU/fetchMG.html) and annotations based on alignments against the NCBI-nr database and the least common ancestor (LCA) method implemented by MEGAN5 [69]. The phylogenetic profiles show the number of marker gene products assigned to different taxa on phylum level. Additional profiles showing the class and order levels are given in S2 Fig.CC BY · PubMed Central
Fig 9 Recovery of known reference genomes from artificial reads spiked into the kelp biofilm dataset. “Genome recovery rates” (A&B) are here defined as the fraction (in percent) of each reference genome, which could be reconstructed by each assembler. “Recovery performance” (C&D) is here defined analogous to “assembly performance” in Fig 3, as the product of the genome recovery rate and the N50 of the involved scaffolds. A high recovery performance value, indicating a good recovery performance, is therefore achieved by recovering a large fraction of each reference genome in form of relatively few (and large) scaffolds.
Fig 9 Recovery of known reference genomes from artificial reads spiked into the kelp biofilm dataset. “Genome recovery rates” (A&B) are here defined as the fraction (in percent) of each reference genome, which could be reconstructed by each assembler. “Recovery performance” (C&D) is here defined analogous to “assembly performance” in Fig 3, as the product of the genome recovery rate and the N50 of the involved scaffolds. A high recovery performance value, indicating a good recovery performance, is therefore achieved by recovering a large fraction of each reference genome in form of relatively few (and large) scaffolds.CC BY · PubMed Central

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Show all 96 references

Research works printed but not linked: 2

  1. B Haider, 2014, Omega: an Overlap-graph de novo Assembler for Metagenomics, Bioinformatics
  2. L Fu, 2012, CD-HIT: accelerated for clustering the next-generation sequencing data, Bioinformatics

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Metadata

Authors
Vollmers, J., Wiegand, S., & Kaster, A.-K.
Affiliations
Leibniz Institute DSMZ – German Collection of Microorganisms and Cell Cultures (DE)
Corresponding author
Kaster, Anne-Kristin
ORCID
Venue
PLoS ONE
Publisher
Public Library of Science (PLoS)
Published
2017-01-18
Volume
12
Issue
1
Pages
e0169662
ISSN
1932-6203 (ISSN-L)
DOI
10.1371/journal.pone.0169662
PMC
PMC5242441
OpenAlex
W2572263783
Cited by
240
References
96
Citations by year
2026: 5 · 2025: 13 · 2024: 28 · 2023: 15 · 2022: 35 · 2021: 37 · 2020: 27 · 2019: 32 · 2018: 25 · 2017: 22
FWCI
11.85
Type
journal article
Language
en
Open access
Licence
Locations
  • PLoS ONE (publishedVersion, cc-by)
  • Europe PMC (PubMed Central) (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
  • Figshare (submittedVersion, cc-by)
Funders
  • Johns Hopkins University
  • University of East Anglia
  • Max-Planck-Institut für Terrestrische Mikrobiologie
  • Leibniz-Gemeinschaft
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
  • Physical Sciences › Environmental Science › Ecology › Microbial Community Ecology and Physiology
  • Physical Sciences › Environmental Science › Ecology › Environmental DNA in Biodiversity Studies
Keywords
metagenome assembly, assembler comparison, shotgun sequencing, low-abundance taxa, phylogenetic profiles, algal biofilms, CAMI challenge, mock communities, ease of use
MeSH terms
Phylogeny, Software, Soil Microbiology, Macrocystis, Biodiversity, Metagenome, Metagenomics, High-Throughput Nucleotide Sequencing, Forests

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