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S O F T W A R E Open Access
MetaWRAP—a flexible pipeline for
genome-resolved metagenomic data
analysis
Gherman V. Uritskiy, Jocelyne DiRuggiero
*
and James Taylor
*
Abstract
Background: The study of microbiomes using whole-metagenome shotgun sequencing enables the analysis of
uncultivated microbial populations that may have important roles in their environments. Extracting individual draft
genomes (bins) facilitates metagenomic analysis at the single genome level. Software and pipelines for such analysis
have become diverse and sophisticated, resulting in a significant burden for biologists to access and use
them. Furthermore, while bin extraction algorithms are rapidly improving, there is still a lack of tools for their
evaluation and visualization.
Results: To address these challenges, we present metaWRAP, a modular pipeline software for shotgun metagenomic
data analysis. MetaWRAP deploys state-of-the-art software to handle metagenomic data processing starting from raw
sequencing reads and ending in metagenomic bins and their analysis. MetaWRAP is flexible enough to give
investigators control over the analysis, while still being easy-to-install and easy-to-use. It includes hybrid
algorithms that leverage the strengths of a variety of software to extract and refine high-quality bins from
metagenomic data through bin consolidation and reassembly. MetaWRAP’s hybrid bin extraction algorithm
outperforms individual binning approaches and other bin consolidation programs in both synthetic and real
data sets. Finally, metaWRAP comes with numerous modules for the analysis of metagenomic bins, including
taxonomy assignment, abundance estimation, functional annotation, and visualization.
Conclusions: MetaWRAP is an easy-to-use modular pipeline that automates the core tasks in metagenomic
analysis, while contributing significant improvements to the extraction and interpretation of high-quality
metagenomic bins. The bin refinement and reassembly modules of metaWRAP consistently outperform
other binning approaches. Each module of metaWRAP is also a standalone component, making it a flexible
and versatile tool for tackling metagenomic shotgun sequencing data. MetaWRAP is open-source software
available at https://github.com/bxlab/metaWRAP.
Keywords: Metagenomics, WGS, Metagenome, Binning, Bin, Draft genome, Pipeline, Reassembly
Background
The study of microbial communities through whole-
metagenome (WMG) shotgun sequencing opens new ave-
nues for the investigation of the metabolic potentials of
microbiomes, in addition to their taxonomic composition
[1–3]. This greatly improves the ability to interpret and
predict functional interactions, antibiotic resistance, and
population dynamics of microbiomes, with applications in
human health, waste treatment, agriculture, and environ-
mental stewardship [4–6]. WMG shotgun sequencing
reads from hundreds to thousands of community mem-
bers generate unique challenges for data analysis and in-
terpretation [3,7]. Software for WMG data analysis have
grown in number and complexity, improving our ability to
process, analyze, and interpret such data [8–12]. However,
these tools are burdensome for biologists to work with. As
the field of WMG expands, comprehensive and accessible
software for unified analysis of metagenomic data is
needed [7,11].
* Correspondence: jdiruggiero@jhu.edu;james@taylorlab.org
Department of Biology, Johns Hopkins University, 3400 N Charles St.,
Baltimore, MD 21218, USA
© The Author(s). 2018 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.
Uritskiy et al. Microbiome (2018) 6:158
https://doi.org/10.1186/s40168-018-0541-1
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Running a WMG analysis requires investigators to find
the best currently available tools, install and configure
them, address conflicting libraries and environment vari-
ables, and write scripts to convert outputs from one tool
into the correct format to input into the next tool [13,
14]. These challenges present a major burden to anyone
attempting metagenomic analysis, especially for investi-
gators without computational experience, hindering pro-
gress of microbial genomics as a field [15]. Existing
automated pipelines and cloud services lack modularity,
do not give users control over the analysis, and often
lack functions for genome-resolved metagenomics, the
extraction of putative genomes (bins) through the bin-
ning of metagenomic assemblies [14,16–19].
Genome-resolved metagenomics allows for recon-
struction of the functional potential of individual taxa
and microbiome comparison at a finer scale. While a
number of sophisticated tools such as CONCOCT, Max-
Bin, and metaBAT have been developed to address bin-
ning, it is still an actively improving field [9,19–21].
Qualities of a metagenomic bin are (1) completion, the
level of coverage of a population genome, and (2) con-
tamination, the amount of sequence that does not be-
long to this population from another genome. These
metrics can be estimated by counting universal
single-copy genes within each bin [22,23]. CheckM im-
proves on this by checking for single-copy genes that a
genome of the bin’s taxonomy is expected to have [24].
The percentage of expected single-copy genes that is
found in a bin is interpreted as its completion, while the
contamination is estimated from the percentage of
single-copy genes that are found in duplicate.
Most metagenomic binning tools extract bins by clus-
tering together scaffolds that have similar sequence
properties, such as K-mer composition and codon usage,
and similar read coverages across multiple samples [25,
26]. Because no single binning approach is superior in
every case, bin consolidation tools attempt to combine
the strengths and minimize the weaknesses of different
approaches. DAS_Tool predicts single-copy genes in all
the provided bin sets, aggregates bins from different bin-
ning predictions, and extracts a more complete consen-
sus bin from each aggregate such that the resulting bin
has the most single-copy genes while having a reason-
ably low number of duplicate genes [27]. This collapsing
approach significantly improves the completion of the
bins. Binning_refiner, on the other hand, splits the con-
tigs into more bins such that no two contigs are in the
same bin if they were in different bins in any of the ori-
ginal bin sets. This breaks the contigs into many more
bins, reducing contamination [28]. Both of these ap-
proaches consolidate sets of bins from different methods
and result in a superior bin set, but they have limita-
tions—DAS_Tool increases completion at the expense of
introducing contamination, while Binning_refiner priori-
tizes purity but loses completeness. Another way to im-
prove draft genome quality that is relatively unexplored
is bin reassembly—extracting reads that belong to a
given bin and assembling them separately from the rest
of the metagenome. With proper benchmarking, this ap-
proach could significantly improve the quality and
downstream functional annotation of at least some bins
in a microbial community.
Because the field of shotgun metagenomics is relatively
new, there is a lack of software to inspect, analyze,
and visualize metagenomic bins. While there are tools
that can accurately predict the taxonomy of metage-
nomic scaffolds (such as Taxator-tk), there is no tool
to classify entire metagenomic bins [29,30]. Similarly,
there are many ways to estimate the coverage of
scaffolds based on read alignment depth but no way
to find the coverages of entire bins across many sam-
ples [31,32]. Finally, there is no tool to visualize draft
genomes in context of whole metagenomic communi-
ties. The need for an easy-to-use integrated tool for
WMG data analysis, as well as the lack of available
tools for metagenomic bin analysis, motivated the con-
struction of MetaWRAP.
Implementation
Main wrapper function
MetaWRAP is command line software for Unix-based
systems that calls on a collection of modules, each being
a standalone program addressing one aspect of WMG
data processing or analysis (Fig. 1). Each module is a
shell script pipeline that takes in a variety of input file
parameters through command line flags. For detailed
outlines of the algorithms behind each module, see
supplementary material (Additional file 1). The modules
call upon numerous installed software as well as custom
Python 2.7 scripts (Additional file 2: Figure S1). Meta-
WRAP relies on the module folder (metawrap-modules),
the script folder (metaWRAP-scripts), and a file con-
taining paths to databases (config-metawrap) to be avail-
able in the PATH. MetaWRAP is hosted on github
(https://github.com/bxlab/metaWRAP), distributed through
Anaconda [33], and can be easily installed locally and on
remote clusters. The metawrap-mg conda package (https://
anaconda.org/ursky/metawrap-mg) includes metaWRAP
and the necessary software for running any metaWRAP
modules. The databases required by some modules need to
be downloaded and unpackaged as described in the meta-
WRAP database download guide (https://github.com/
bxlab/metaWRAP/blob/master/installation/database_in
stallation.md) and their paths indicated in the config-
metawrap file. MetaWRAP v0.7 was used in all bench-
marking runs.
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Bin_refinement module
The metaWRAP-Bin_refinement module produces a su-
perior bin set from multiple original binning predictions.
First, hybrid bin sets are produced with Binning_refiner
[28], which splits the contigs such that no two contigs
are together if they were in different bins in any of the
original sets. Then, the module goes over the different
variants of each bin found in the original and hybrid-
ized bin sets and choses its best version based on com-
pletion and contamination metrics estimated with
CheckM [24]. The decision of the “best bin”is based
on the user-provided minimum completion and max-
imum contamination parameters. The contigs in the
final bin set are then de-replicated, and a report of
their completion, contamination, and other metrics is
produced (Additional file 3:FigureS2).Seesupplemen-
tary methods (Additional file 1) for more details on the
Bin_refinement module.
Reassemble_bins module
The metaWRAP-Reassemble_bins module improves a
set of bins by individually re-assembling each bin
(Additional file 4: Figure S3). Reads are mapped to the
bins with BWA v0.7.15 [32] strictly (no mismatches) and
permissively (< 5 mismatches) and stored into their
respective FastQ files. Importantly, read pairs will be
pulled out even if only one read is aligned to the bin.
Each read set is then reassembled with SPAdes [34],
which produces more contiguous sequences compared
to metagenomic assemblers such as MegaHit [35] and
metaSPAdes [36] used in the Assembly modules.
CheckM [24] is used to evaluate the completion and
contamination of each of the three versions of each
bin—the original bin, the “strict”re-assembled bin, and
“permissive”re-assembled bin—and the best version of
each bin is chosen for the final bin set based on the
user-defined desired bin quality. See supplementary
methods (Additional file 1) for more details on the
Reassemble_bins module.
Results and discussion
MetaWRAP is a flexible, modular pipeline
The metaWRAP installation produces a bioinformatics
environment with over 150 commonly used bioinformat-
ics software and libraries (Additional file 2: Figure S1).
MetaWRAP itself is a collection of modules, each of
which uses a variety of pre-existing and newly developed
software and databases to accomplish a specific step of
metagenomic analysis. Unlike existing metagenomic
wrappers and cloud services, metaWRAP retains modu-
larity and grants the user control of the analysis pipeline.
The user may follow the intuitive workflow starting from
raw metagenomic shotgun sequencing reads all the way
to high-quality draft genomes and their functional anno-
tation or use only specific functions, as each module is
also a standalone program (Fig. 1).
First, the metaWRAP-Read_qc module trims the raw
sequence reads, removes human contamination, and
produces quality reports for each of the sequenced sam-
ples. The reads from all given samples can then be as-
sembled with the metaWRAP-Assembly module using
MegaHit [35] or metaSPAdes [36], which also produces
an assembly report. Both the reads from each sample
and the assembly can be rapidly taxonomically profiled
with the Kraken [29] module, producing interactive kro-
nagrams [37] of community taxonomy. It should be
Fig. 1 Overall workflow of metaWRAP. Modules (red), metagenomic data (green), intermediate (orange) and final bin sets (yellow), and data reports
and figures (blue)
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noted that while Kraken is fast, post-classification
standardization may be needed to obtain a more accur-
ate community composition estimate [38]. The assembly
is then binned with the metaWRAP-Binning module by
three metagenomic binning software—MaxBin2, meta-
BAT2, and CONCOCT [19–21].
The other modules of metaWRAP focus on refining,
analyzing, and visualizing metagenomic bins from either
the Binning module or other sources. The metaWRAP-
Bin_refinement module consolidates multiple binning
predictions into a new, improved bin set, while also prov-
ing metrics of their completion and contamination. Meta-
WRAP-Reassemble_bins can then be used to reassemble
the reads belonging to each bin, improving their N50,
completion, and contamination. The resulting bins can be
visualized by using the metaWRAP-Blobology module
[39], which plots the contigs of the joint assembly on a
blob plot, annotating them with their taxonomy and bin
membership. The metaWRAP-Quant_bins module can be
used to quickly estimate the abundance of each bin in
each of the metagenomic samples. MetaWRAP-Classify_-
bins can be used to conservatively but accurately estimate
their taxonomy. Finally, the bins can be functionally anno-
tated with the metaWRAP-Annotate_bins module.
Compute time of metaWRAP modules
The runtime of each metaWRAP’s modules was evaluated
on a subset of the Human Intestinal Tract (MetaHIT) sur-
vey [40]. The same subset is used in the metaWRAP
tutorial page https://github.com/bxlab/metaWRAP/blob/
master/Usage_tutorial.md. The data contained three
WMG samples, totaling 145.8 million 75 bp paired-end
reads, or 21.9 Gbp of sequencing data. MetaWRAP was
used to analyze this data set on a Linux server with 24
cores and 100 GB of RAM. All modules were run on de-
fault settings, and the total runtime of each module was
recorded (Additional file 5: Module runtime). The entire
pipeline was completed in 5 h and 36 min, with the major-
ity of compute time dedicated to the Read_qc, Binning,
Bin_refinement, and Reassemble_bins modules. With the
exception of CONCOCT [19], the programs wrapped into
metaWRAP can take advantage of multi-core systems and
scale well with larger data sets. MetaWRAP itself also par-
allelizes processes when possible.
MetaWRAP-Bin_refinement improved bin predictions in
synthetic data
To test the efficacy of the metaWRAP-Bin_refinement
module at consolidating and improving bin sets, we used
synthetic metagenomic data sets of varying complexity
from the Critical Assessment of Metagenomic Interpret-
ation (CAMI) study [9]. The “gold standard”assemblies
from the “high,”“medium,”and “low”diversity chal-
lenges were first binned with metaBAT2, Maxbin2, and
CONCOCT [19–21] using the metaWRAP-Binning
module, and the resulting three bin sets were then con-
solidated with DAS_Tool [27], Binning_refiner [28], and
metaWRAP-Bin_refinement. The completion and con-
tamination of the bins in the original and refined bin
sets were evaluated with CheckM [24] (Additional file 6:
Figure S4) and Amber [41] (Additional file 7: Figure S5).
True recall and precision for each bin calculated with
Amber were converted to completion and contamination
percentages to be comparable to the CheckM results
(Fig. 2). We found that metaBAT2 consistently outper-
formed MaxBin2 and CONCOCT, producing a total of
385 high-quality bins between all the challenges (com-
pletion greater than 90% and contamination less than
5%) and 271 near-perfect bins (completion greater than
95% and contamination less than 1%). MaxBin2 came in
second with 275 high-quality bins and 164 near-perfect
bins. CONCOCT performed rather poorly in all but the
smallest CAMI challenge data sets, producing 58
high-quality bins and 40 near-perfect bins.
In the consolidated bin sets, DAS_Tool produced 426
high-quality bins and 263 near-perfect bins across all
CAMI challenges, while Binning_refiner produced 289
and 210 bins, respectively. DAS_Tool consistently pro-
duced high-completion bins; however, these bins had
relatively high contamination, which is a result of the ag-
gregation approach that DAS_Tool takes. Binning_refi-
ner on the other hand produced very pure bins with its
splitting approach; however, it did so at the expense of
significantly reduced completion. MetaWRAP-Bin_re-
finement produced bins that had both high completion
and low contamination. In total, it produced 457 high-
quality bins and 339 near-perfect bins (Fig. 2) due to
both splitting and aggregation steps. These results con-
firmed that metaWRAP not only consistently improved
bin sets through its consolidation approach, but it also
outperformed other consolidation algorithms in data sets
of varying complexity.
The CAMI challenge consisted of genomes of varying
degrees of similarity and categorized the genomes into
two broad categories depending on their average nucleo-
tide identity (ANI) to other genomes in the mix.
“Unique strains”are defined as genomes with < 95%
ANI to any other genome and “common strains”as ge-
nomes with ≥95% ANI to another genome in the data
set. [9] This gave us an opportunity to benchmark meta-
WRAP at recovering genomes from contig clusters of
varying complexity. We found that metaWRAP outper-
formed all other binning methods in reconstituting both
closely and distantly related genomes (Additional file 8:
CAMI binning summary table). Interestingly, we found
that Binning_refiner performed almost as well as meta-
WRAP in distantly related genomes but performed
poorly in closely related genomes. On the other hand,
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DAS_Tool recovered almost as many closely related ge-
nomes as metaWRAP but performed relatively poorly in
more discrete genomes.
The use of CheckM (Additional file 6: Figure S4) and
Amber (Fig. 2) to evaluate the binning sets produced
similar results, although overall CheckM slightly overes-
timated both completion and contamination of the pro-
duced bins. More importantly, the relative performance
of the six binning approaches was the same when evalu-
ating with CheckM or Amber. This validated the use of
CheckM for benchmarking binning results in data sets
where the true genomes remain unknown.
Benchmarking metaWRAP on real metagenomes
MetaWRAP’s performance was also assessed with real
WMG Illumina paired read sequencing data, using rep-
resentative metagenomic data sets from water, gut, and
soil microbiomes. The water data set was from a brack-
ish water survey, which investigated the seasonal dynam-
ics and biogeography of the surface bacterioplankton in
the Baltic Sea [42]. This data set included 36 samples for
a total of 196 Gbp of sequencing data. The gut data set
came from the Metagenomic of the Human Intestinal
Tract (MetaHIT) survey, which sequenced the gut
microbiota from volunteers across Europe to explore the
diversity and drivers in individual gut microbiome com-
position. [40]. The benchmarking data set consisted of
50 samples for a total of 144 Gbp of sequencing data.
The soil data came from sequencing the highly diverse
grassland soil microbial communities from Angelo
Coastal Reserve, CA [27]. This data set consisted of six
samples for a total of 481 Gbp of sequencing data.
Samples from each microbiome type were pre-proc-
essed through the metaWRAP-Read_qc module to trim
reads and remove human contamination, and the Kra-
ken and Blobology modules were used to evaluate the
taxonomic profile of the communities. The water sam-
ples were dominated by Proteobacteria, the gut samples
were dominated by Bacteroidetes and Firmicutes, and
the soil samples comprised of a wide variety of Proteo-
bacteria and Actinobacteria (Additional file 9). Notably,
contigs from the soil microbiomes had much higher GC
content compared to those of the gut and water. Also,
soil contigs did not form as many defined clusters on
the GC vs. abundance plot, suggesting that the commu-
nities were comprised of multiple closely related taxa
(Fig. 3). Due to the high GC content and high taxonomic
similarity of soil microbiota, this data set posed signifi-
cant binning challenges compared to the water and gut
microbiomes.
Bin_refinement improved bin predictions in real data
The quality-controlled reads from the representative
metagenomic data sets were then co-assembled with the
metaWRAP-Assembly module and the assemblies
binned with metaBAT2 Maxbin2 and CONCOCT using
the metaWRAP-Binning module. The resulting three bin
sets for each microbiome type were consolidated with
Fig. 2 True completion and contamination of bins recovered from the CAMI’s high, medium, and low complexity synthetic data sets using
original binning software (metaBAT2, MaxBin2, and CONCOCT) and software consolidating the original sets (DAS_Tool, Binning_refiner, and
metaWRAP’s Bin_refinement module). Only bins with ≥50% completion and ≤10% contamination are shown
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DAS_Tool, Binning_refiner, and metaWRAP-Bin_refine-
ment, and the completion and contamination of the
resulting bins were evaluated with CheckM (Fig. 4).
Across the original binning software, metaBAT2 consist-
ently produced the best sets of bins when compared to
MaxBin2 and CONCOCT, with 202, 146, and 88 accept-
able quality bins (comp ≥50%, cont ≤10%) in the water,
gut, and soil samples, respectively. MaxBin2 had 151, 98,
and 40 bins, and CONCOCT 65, 121, and 39 bins.
Despite incorporating all the binning methods, DAS_-
Tool was unable to improve the original bin sets, produ-
cing 198, 130, and 63 acceptable quality bins in the
water, gut, and soil samples, respectively. DAS_Tool per-
formed relatively well at higher bin completion ranges
(≥80%), although at the expense of increased contamin-
ation. Binning_refiner performed similarly, with 206,
138, and 83 bins in the water, gut, and soil data sets, re-
spectively. The bins from Binning_refiner were less
complete but also had significantly lower contamination
than bins in the original bin sets. MetaWRAP’s Bin_re-
finement module produced 235, 175, and 134 acceptable
quality bins in the water, gut, and soil samples, respect-
ively, significantly outperforming all other tested ap-
proaches. The module uses Binning_refiner in its
pipeline to hybridize the input bin sets and then chooses
the best version of each bin from the original and hy-
bridized sets. Because the Bin_refinement module lever-
ages the strength of Binning_refiner but still has a
collapsing step similar to DAS_Tool, it is able to match
DAS_Tool’s high-completion rankings, while retaining
the low-contamination rankings of Binning_refiner.
Overall, MetaWRAP consistently produced the highest
quality bin sets in all the tested metagenomic data sets,
which ranged greatly in diversity, taxonomic compos-
ition, and sequencing depths.
It is important to note that the use of metaWRAP’s
Bin_refinement module to improve binning predictions
is not limited to the bin sets produced from the
metaWRAP-Binning module (metaBAT2, MaxBin2, and
CONCOCT). Bin sets from any two or three binning
software may be used as input for the module. Further-
more, because the algorithm leverages the differences
between the input bin predictions, it is also possible to
use bin sets produced from different parameters of the
same software as input.
Bin_refinement adjusts to the desired bin quality
To consolidate the original and hybridized bin sets,
metaWRAP-Bin_refinement chooses the best version of
each bin based on their completion and contamination
values. However, this selection is subjective and depends
on what the user believes to be the “best bin.”The mini-
mum completion (-c) and maximum contamination (-x)
options are key parameters that greatly alter the quality
of the bins produced, as the module will dynamically ad-
just its algorithms to produce the maximum number of
bins in this range.
To demonstrate the effects of changing the -c and -x
parameters of metaWRAP’s Bin_refinement module, we
ran the original bin sets from the water, gut, and soil
Fig. 3 GC vs. abundance plots of contigs from the water, gut, and soil metagenomes, produced with the Blobology module. Abundance
of contigs was calculated from standardized read coverage in each sample. Contigs were annotated with their phylum taxonomy, as
determined by BLAST
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data sets with varying minimum completion (but fixed
maximum contamination) (Additional file 10:Figure
S6) and varying maximum contamination (but fixed
minimum completion) (Additional file 11:FigureS7)
parameters. When compared to the original Bin_refine-
ment run (-c 50 -x 10), the module produced a greater
number of bins at any given threshold when it was
given custom -c and -x parameters. The improvements
were especially noticeable at higher completion and
lower contamination ranges. For example, MetaWRAP-
Bin_refinement -c 90 -x 10 recovered 19, 18, and 1
(water, gut, and soil, respectively) extra bins with a
minimumcompletionof90%,whencomparedtothe
baseline -c 50 -x 10 run. Similarly, MetaWRAP-Bin_re-
finement with -c 50 -x 1 parameters extracted 8, 21,
and 4 (water, gut, and soil, respectively) more bins at a
maximum contamination of 1%, when compared to the
baseline run. Unlike arbitrary and sometime confusing
thresholding parameters in many other software, the
minimum completion and maximum contamination
options offer the user an intuitive way to parameterize
metaWRAP’s Bin_refinement module to their needs.
This leads to significant increases in the number of
quality bins they are able to extract from their data.
It is important to note that while refinement of bin-
ning predictions results in high-quality bins when evalu-
ated with single-copy gene numbers, they do not
represent the genomes of single individuals in a commu-
nity or even individual strains. In this context, a bin is
simply the optimized taxonomic clustering of contigs,
which themselves are representative consensus resulting
from the clustering of reads belonging to closely related
taxa. In the context of phylogeny, bins may represent in-
dividual strains, species, or even higher-order averaged
taxa, depending on the level of heterogeneity of the
community in question. In the literature, bins are some-
times referred to as population genomes [43], underlying
the complex nature of bins. As described in the context
of the CAMI challenge, the analysis of a community
with mostly “unique strains,”i.e., distantly related organ-
isms, will result in bins potentially representing species
or even strains, whereas the analysis of a community
with mostly “common strains,”i.e., closely related organ-
isms, will result in more hybrid bins. In reality, most
communities are an assemblage of both closely and dis-
tantly related taxa resulting in a range of bin qualities.
Because of this, contamination resulting from strain
heterogeneity is expected [44], and the desired bin
quality can be tailored to the requirements of the down-
stream applications. For accurate taxonomic assignment
of bins, a low contamination is important (1–5%) but a
high completion may not be (20–50% may be sufficient).
For accurate reconstruction of metabolic potential on
the other hand, it is more important to reconstruct the
genome with a higher completion (90–98%), even at the
expense of introducing contamination (5–10%), as long
as the user understands that the resulting bins represent
the averaging of closely related taxa. The parameterization
Fig. 4 Completion and contamination of bins recovered from the water, gut, and soil metagenomes using original binning software (metaBAT2,
MaxBin2, and CONCOCT) and software consolidating the original sets (DAS_Tool, Binning_refiner, and metaWRAP’s Bin_refinement module). Only
bins with ≥50% completion and ≤10% contamination are shown (estimated by CheckM)
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will also be constrained by the characteristics of the
microbiome in question. Communities with relatively low
diversity, low strain heterogeneity, and low GC content
(such as gut microbiomes) will yield bins with lower con-
tamination and higher completion than those extracted
from a community with high diversity, heterogeneity, and
average GC content (such as soil microbiomes).
Reassemble_bins significantly improved bin quality
MetaWRAP’s Reassemble_bins module improves a given
set of bins through individual reassembly with SPAdes
[34]. The module only replaces the original bins if the
reassembled ones are better in terms of completion and
contamination. Like the Bin_refinement module, the
Reassemble_bins module takes in minimum completion
(-c) and maximum contamination (-x) parameters to
allow the user to define what they consider a “good”bin.
The bins produced from the water, gut, and soil data
with metaWRAP-Bin_refinement module runs (-c 50 -x
10) were run through the metaWRAP-Reassemble_bins
module (-c 50 -x 10), and the resulting bins were
re-evaluated with CheckM [24].
The Reassemble_bins module improved upon 78%,
98%, and 2% of the bins in the water, gut, and soil bin
sets, respectively. The module significantly improved
the water and gut bins’overall metrics, increasing their
N50 and completion scores. Even more strikingly, the
reassembly process significantly reduced contamination
in these bin sets (Fig. 5). The success of the bin re-
assembly algorithm relies heavily on accurate and spe-
cific recruitment of the correct reads to each bin. In
very diverse and heterogeneous communities such as
those found in soil, the read recruitment may not be
specific enough. This confused the assembler during
the re-assembly stage and resulted in an improvement
for only a small fraction of the bins. However, draft ge-
nomes from the gut and water samples were still sig-
nificantly improved with the Reassemble_bins module
despite their complexity (Fig. 3). Just as with the bin-
ning process, it is important to note that the bins
resulting from the reassembly do not represent the true
genomes of individual organisms found in the commu-
nity but are rather consensus backbones for reads com-
ing from closely related organisms.
Fig. 5 N50, completion, and contamination metrics of original bins extracted from the water, gut, and soil metagenomes with the metaWRAP’s
Bin_refinement module and the same bins reassembled with metaWRAP’s Reassemble_bins module. Only bins with ≥50% completion and
≤10% contamination are shown (estimated with CheckM)
Uritskiy et al. Microbiome (2018) 6:158 Page 8 of 13
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
MetaWRAP produced high-quality draft genomes
We investigated the performance of different binning
approaches (both original binners and bin consolidation
software) when extracting high-quality draft genomes,
with a contamination less than 5% and completion
greater than 70%, 80%, 90%, and 95% (Fig. 6). The de-
fault run of metaWRAP-Bin_refinement consistently
produced the highest number of high-quality draft ge-
nomes in the water, gut, and soil data sets. These num-
bers further improved when re-running the module with
appropriate minimum completion (-c) settings (i.e., run-
ning Bin_refinement -c 90 when benchmarking for bins
with a minimum completion of 90%). This approach sig-
nificantly outperformed every other tested binning and
bin refinement method at every quality threshold.
The reassembly of the metaWRAP-derived bins with
the Reassemble_bins module made a further improve-
ment on the number of high-quality draft genomes
extracted from the gut and water data sets. Even the
default run of Reassemble_bins produced a signifi-
cantly better bin set compared to non-reassembled bin
sets produced by all tested software, including meta-
WRAP’s Bin_refinement. However, just like in the
Bin_refinement runs, the results were further enhanced
when Reassemble_bins was provided with an appropri-
ate -c option.
When comparing to the original binning software
(MaxBin2, metaBAT2, and CONCOCT) and bin consoli-
dation tools (DAS_Tool and Binning_refiner), metaWRAP
produced the largest number of high-quality draft ge-
nomes in all the tested WMG data sets. Additionally, it
should also be considered that metaWRAP is capable of
improving bin sets from any binning software. Therefore,
when new metagenomic binning software are developed,
their outputs can still be used with metaWRAP refine-
ment and reassembly algorithms.
MetaWRAP enables analysis and visualization of
metagenomic bins
The rest of metaWRAP modules address examining and
processing a set of bins in preparation for downstream
analysis. The user may visualize the bins in the context
of the entire community with the Blobology module,
quantify their abundances across samples with the
Quant_bins module, estimate their taxonomy with the
Classify_bins module, and functionally annotate them
with the Annotate_bins module.
The metaWRAP-Quant_bins module was used to esti-
mate bin abundances across samples from their respect-
ive microbiome survey, and the results were shown in a
clustered heatmap (Additional file 12: Figure S8). Clus-
tered heatmaps may be used to infer bin co-abundance
and to identify similarities and differences between
samples. Because this approach considers the abun-
dances of every extracted bin individually, it offers
higher resolution information than when using higher
taxonomic ranks.
Fig. 6 Number of high-purity bins (less than 5% contamination) extracted from the water, gut, and soil metagenomes with 70%, 80%, 90%, and
95% completion (estimated with CheckM) using original binning software (metaBAT2, MaxBin2, and CONCOCT) and bin-refining algorithms
(Binning_refiner, DAS_Tool, metaWRAP-Bin_refinement, and metaWRAP-Reassemble_bins). MetaWRAP modules were run with varying
-c (minimum completion) parameters. MetaWRAP’s Reassemble_bins module was run on the output of the Bin_refinement module
Uritskiy et al. Microbiome (2018) 6:158 Page 9 of 13
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
Bins were also visualized with the metaWRAP-Blobol-
ogy module. The module produces GC vs. abundance
plots of contigs, annotated with their taxonomy [45]
(Fig. 3), bin membership (Fig. 7), or both (Add-
itional file 13: Figure S9). These plots allow for inspec-
tion of the extracted bins in the context of the entire
community that they belong to, as well as visualize the
relative success of the binning process.
The final reassembled bins were taxonomy profiled with
the metaWRAP-Classify_bins module (Additional file 14:
Bin taxonomy) and functionally annotated with the Anno-
tate_bins module. Together, this information may be used
in downstream analysis to investigate complex questions
about functional interactions and metabolic potential of
individual community members.
Conclusions
Genome-level analysis of WMG sequencing data is
essential in understanding the composition and func-
tion of microbiomes. Until now, this rapidly growing
field lacked a unifying platform to utilize the wealth
of currently available software and make them easily
accessible to researchers. MetaWRAP is a flexible
pipeline that can handle common tasks in metage-
nomic data analysis starting from raw read quality
control and ending in bin extraction and analysis.
MetaWRAP is easy to install through Bioconda and
simple to use, and its modularity gives the investiga-
tor flexibility in their analysis approach.
MetaWRAP contributed significant improvements to the
recovery of draft genomes from shotgun metagenomic data
through bin refinement and reassembly. The bin refine-
ment module uses a novel hybrid approach to consolidate
bin predictions from different binning software, producing
a single stronger set. This approach significantly outper-
formed individual binning software, as well as other
consolidation algorithms. The algorithm can adjust to
accommodate specific draft genome quality targets, making
it suitable for many research applications. MetaWRAP’sbin
reassembly module further improved the draft genomes in
both completeness and purity. Finally, metaWRAP contains
multiple modules for analysis and evaluation of metage-
nomic bins—bin taxonomy assignment, abundance estima-
tion, functional annotation, and visualization.
Availability and requirements
Project name: metaWRAP
Project home page: https://github.com/bxlab/metaWRAP
Operating system: Linux64
Programming languages: Shell; Python 2.7
Other requirements: Conda; other packages automatic-
ally installed with metaWRAP: CONCOCT [19], MaxBin2
[20], metaBAT [21], CheckM [24], Binning_refiner [28],
Kraken [29], Taxator-tk [30], BWA [32], SPAdes [34,36],
MegaHIT [35], KronaTools [37], Blobology [39], Mega-
BLAST [45], TrimGalore [46], BMTagger [47], FastQC
[48], Bowtie2 [49], Salmon [50,51], and PROKKA [52].
License: MIT
Fig. 7 GC vs. abundance plots of contigs from the water, gut, and soil metagenomes, produced with the Blobology module. Abundance of
contigs was calculated from standardized read coverage in each sample. The contigs were annotated with the bins that they belong to (bin
colors are chosen at random), allowing for quick inspection of binning success. Bins were produced with metaWRAP’s Bin_Refinement module.
Only bins with ≥70% completion and ≤10% contamination are shown (estimated with CheckM)
Uritskiy et al. Microbiome (2018) 6:158 Page 10 of 13
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
Additional files
Additional file 1: Supplementary methods. Descriptions of analysis
pipelines to process the benchmarking data, and detailed outlines of the
algorithms in each metaWRAP module. (DOCX 170 kb)
Additional file 2: Figure S1. Detailed walkthrough of the data files,
software, databases, and custom scripts that metaWRAP uses. The
components of each metaWRAP module grouped and denoted with
dotted lines. (PNG 2140 kb)
Additional file 3: Figure S2. Logical workflow of the Bin_refinement
modules of metaWRAP. The module takes in three bin sets produced from
the same assembly by different software or different parameters of the
same software. Binning_refiner is used to create hybridized intermediates
(four possible combinations), and the completion and contamination of the
original and hybridized bins are estimated with CheckM. The best version of
each bin is then found in the resulting seven bin sets. (PNG 123 kb)
Additional file 4: Figure S3. Logical workflow of the Reassemble_bins
module, which extracts reads belonging to bins in a given bin set and
individually reassembles them. This process is done for perfectly mapping
reads (strict) and reads mapping with less than three mismatches
(permissive). For each bin, CheckM is used to choose the best bin
between the original and the two reassembled versions. (PNG 164 kb)
Additional file 5: Module runtime. The total real runtime of each module
of metaWRAP when analyzing three samples from the metaHIT gut
metagenomic survey. The modules were tested with default parameters on
a Linux x64 server with 24 cores and 100 GB of RAM. (XLSX 23 kb)
Additional file 6: Figure S4. Completion and contamination (determined
with CheckM) of bins recovered from the CAMI’s high, medium, and low
complexity synthetic data sets using original binning software (metaBAT2,
MaxBin2, CONCOCT) and software consolidating the original sets (DAS_Tool,
Binning_refiner, metaWRAP). Only bins with ≥50 % completion and ≤10%
contamination are shown. (EPS 474 kb)
Additional file 7: Figure S5. True recall and precision (determined with
AMBER) of bins recovered from the CAMI’s high, medium, and low complexity
synthetic data sets using original binning software (metaBAT2, MaxBin2,
CONCOCT) and software consolidating the original sets (DAS_Tool,
Binning_refiner, metaWRAP). Only bins with ≥0.5% recall and ≥0.9%
precision are shown. (EPS 474 kb)
Additional file 8: CAMI binning summary table. The number of bins
recovered at different quality thresholds (determined with AMBER) from the
CAMI challenge with original binning software (metaBAT2, MaxBin2,
CONCOCT) and software consolidating the original sets (DAS_Tool,
Binning_refiner, metaWRAP). MetaWRAP was run with default parameters.
Performance is shown for “unique strain”(ANI < 95% to any other genome)
and “common strain”(ANI >95% to another genome) genomes. (XLSX 39 kb)
Additional file 9: Taxonomic distribution of reads from water, gut, and
soil metagenomes, estimated with the metaWRAP-Kraken module.
(HTML 972 kb)
Additional file 10: Figure S6. Completion of bins recovered from water,
gut, and soil metagenomes with the metaWRAP-Bin_refinement module
with a varying minimum completion parameter (-c) but constant maximum
contamination parameter (-x 10). The numbers in the brackets indicate
the number of extra bins gained at that threshold compared to the
baseline run (-c 50 -x 10). Only bins with ≥50% completion and ≤10%
contamination are shown. (EPS 394 kb)
Additional file 11: Figure S7. Contamination of bins recovered from
water, gut, and soil metagenomes with the metaWRAP-Bin_refinement
module with a varying maximum contamination parameter (-x) but
constant minimum completion parameter (-c 50). The numbers in the
brackets indicate the number of extra bins gained at that threshold
compared to the baseline run (-c 50 -x 10). Only bins with ≥50%
completion and ≤10% contamination are shown. (EPS 99 kb)
Additional file 12: Figure S8. Clustered heatmaps showing the log of
bin abundance of bins extracted with metaWRAP-Bin_refinement (-c 50
-x 10) across samples in water, gut, and soil metagenomes, calculated
and plotted with metaWRAP’s Quant_bins module. (PNG 974 kb)
Additional file 13: Figure S9. MetaWRAP-Blobology visualization of
water, gut, and soil metagenomes, showing the GC and average coverage
of each successfully binned contig (metaWRAP-Bin_refinement -c 70 -x 10)
in the assemblies and annotated with the taxonomy at the phylum
level and the bins that they belong to (bin colors are chosen at
random). (PNG 2629 kb)
Additional file 14: Bin taxonomy. Distribution of the taxonomy among
bacterial bins extracted from water, gut, and soil metagenomes using
metaWRAP’s Bin_refinement module (-c 50 - x 10). Taxonomy estimated
with metaWRAP’s Classify_bins module. (HTML 167 kb)
Abbreviations
ANI: Average nucleotide identity; -c: Minimum completion parameter;
comp: Completion; cont: Contamination; WMG: Whole metagenome;
-x: Maximum contamination parameter
Acknowledgements
We thank the early users of metaWRAP Alejandro Palomo, Keith Arora-Williams,
and Emine Ertekin for their patience and help with debugging; Bing Ma for
module suggestions; and Michael Sauria for computational support.
Funding
This work was supported by grants NNX15AP18G and NNX15AK57G from
NASA, grant DEB1556574 from the NSF, and grant HG006620 from NIH/
NHGRI.
Availability of data and materials
The data sets supporting the conclusions of this article are available from the
original CAMI challenge (https://data.cami-challenge.org/participate) for the
synthetic data sets, the National Centre for Biotechnology Information under
SRA numbers SRR2053273–SRR2053308 (https://www.ncbi.nlm.nih.gov/
bioproject/PRJNA273799) for the Central Baltic Surface Water Metagenome,
SRA numbers ERR011087-ERR011136 (https://www.ncbi.nlm.nih.gov/bioproject/
PRJEB2054) for the Metagenomic of the Human Intestinal Tract (MetaHIT)
survey, and Joint Genome Institute under Gold Analysis Project IDs
Ga0007435, Ga0007436, Ga0007437, Ga0007438, Ga0007439, and
Ga0007440 (https://gold.jgi.doe.gov/study?id=Gs0110119) for the soil
data. All analysis results and scripts used to generate figures are
available at https://github.com/ursky/metawrap_paper.
Authors’contributions
GU built, released, and maintained the metaWRAP software, ran the benchmarks,
and wrote the manuscript. JDR and JT provided ideas for building and
improving metaWRAP and edited the manuscript. All authors read and
approved the final manuscript.
Ethics approval and consent to participate
Not applicable
Consent for publication
Not applicable
Competing interests
The authors declare that they have no competing interests.
Publisher’sNote
Springer Nature remains neutral with regard to jurisdictional claims in published
maps and institutional affiliations.
Received: 6 March 2018 Accepted: 29 August 2018
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