Orakov et al.

Genome Biology (2021) 22:178

https://doi.org/10.1186/s13059-021-02393-0

METHOD Open Access

GUNC: detection of chimerism and

contamination in prokaryotic genomes
Askarbek Orakov1† , Anthony Fullam1†, Luis Pedro Coelho2,3, Supriya Khedkar1, Damian Szklarczyk4,5,
Daniel R. Mende6, Thomas S. B. Schmidt1* and Peer Bork1,7,8,9*

* Correspondence: sebastian.
schmidt@embl.de; peer.bork@embl. Abstract
org
†
Askarbek Orakov and Anthony Genomes are critical units in microbiology, yet ascertaining quality in prokaryotic
Fullam contributed equally to this genome assemblies remains a formidable challenge. We present GUNC (the Genome
work. UNClutterer), a tool that accurately detects and quantifies genome chimerism based
1
Structural and Computational
Biology Unit, European Molecular on the lineage homogeneity of individual contigs using a genome’s full complement
Biology Laboratory, 69117 of genes. GUNC complements existing approaches by targeting previously
Heidelberg, Germany underdetected types of contamination: we conservatively estimate that 5.7% of
Full list of author information is
available at the end of the article genomes in GenBank, 5.2% in RefSeq, and 15–30% of pre-filtered “high-quality”
metagenome-assembled genomes in recent studies are undetected chimeras. GUNC
provides a fast and robust tool to substantially improve prokaryotic genome quality.
Keywords: Genome quality, Genome contamination, Metagenomics, Metagenome-
assembled genomes, Bioinformatics

Background
Genomes are the genetic blueprint of prokaryotic lineages, a fundamental unit of
microbiology [1] at the heart of the ongoing census of the microbial world [2, 3] and
essential to the study of microbial ecology and evolution [4]. Twenty-five years after
the first release of a complete bacterial genome in 1995 [5], more than 700,000
prokaryote genomes have been deposited to NCBI GenBank [accessed 30th of July
2020], doubling almost yearly as genome-based analyses have become the backbone of
many disciplines in microbiology. Historically, the vast majority of microbial genomes
have been derived from cultured isolates which directly links genome sequences to a
physical sample, but excludes the significant number of species that cannot be easily
cultivated [4, 6].
A promising approach to overcome this deficiency is the delineation of genomes
from complex microbial communities using metagenomic data. As early as 2004,
nearly complete metagenome-assembled genomes (MAGs) were used to chart the di-
versity of an acid mine drainage microbial biofilm [7]. Since then, algorithmic advances
in binning tools such as canopy clustering [8], CONCOCT [9], MaxBin [10], ABA-
WACA [11], or metaBAT [12] have enabled the automated recovery of MAGs at large
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Orakov et al. Genome Biology (2021) 22:178 Page 2 of 19

scales, with individual studies now routinely reporting tens of thousands of novel ge-
nomes [13–15]. MAGs have led to the discovery of novel deep-branching lineages pre-
viously eluding cultivation-based approaches, such as the Asgardarchaeota [16] or the
bacterial Candidate Phyla Radiation [11, 17], thereby substantially expanding the micro-
bial tree of life [18, 19]. Moreover, MAGs can be taxonomically resolved to strain level
[20, 21] which is particularly beneficial in undersampled environments where reference
genomic coverage is scarce [22, 23].
Analyses in many microbiological disciplines now critically depend on high-quality
genomes, and the sheer amount of accruing genomic data calls for an automated rapid
and accurate quality assessment. A substantial fraction of deposited genomes, even
those of supposedly high quality in dedicated databases (e.g., Refseq, [24]), contain for-
eign genome fragments [25] that can originate both in vitro and in silico (Fig. 1a). Er-
rors in isolate-derived genomes are typically introduced during physical sample
processing, e.g., due to contamination of reagents or culture media [25]. In contrast,
the principal error sources in MAGs, usually derived from metagenomic samples with
high microbial loads, are expected to be computational [22]: misassembly (i.e., genomic
fragments from multiple sources are wrongly assembled together, resulting in chimeric
contigs) and mis-binning (contiguous fragments from different sources are erroneously
assigned to the same genomic bin, resulting in chimeric genomes). Of these two, mis-
binning is expected to be the major source of errors, as misassemblies are relatively rare
[26]. Genome quality is mainly assessed based on fragmentation (i.e., the size distribu-
tion of assembled contigs, with “closed” genomes as the optimum), completeness (the
fraction of the source genome captured), and contamination (“surplus” genomic frag-
ments originating from other sources), frequently estimated based on ubiquitous and
single-copy marker genes (SCGs), as provided by BUSCO [27] or CheckM [28].
Erroneous genomes can affect analyses in different ways: whereas errors introduced
due to missing or truncated genetic elements in fragmented or incomplete genomes
(i.e., “false negatives”) can usually be mitigated, contaminating fragments are detrimen-
tal to biological interpretation, as they may cause false inferences about a genome’s
functional repertoire or structure [22, 25]. Operationally, two types of genome contam-
ination can be distinguished (Fig. 1b). Redundant contamination involves surplus gen-
omic fragments (e.g., close orthologs to genes genuinely present in the focal genome)
from related sources (often within the same or related lineages). In contrast, non-redun-
dant contamination involves foreign fragments (e.g., gene families not encoded in the
focal genome’s lineage or distant orthologs) that replace or extend part of the source
genome with unrelated, non-overlapping material, leading to chimeric genomes. Intui-
tively, redundant contamination can be thought of as an addition of “more of the same”
genomic material, whereas non-redundant contamination adds “something new.”
Genome contamination can be difficult to estimate, particularly for previously unde-
scribed lineages that are not well represented by existing references. By design, SCG-
based estimators of genome quality can detect redundant contamination with high sen-
sitivity, but they are less sensitive towards non-redundant contamination, since they
only consider inventories of expected SCGs as a whole, remaining agnostic to conflict-
ing lineage assignments between individual genes [28, 29]. The widely used CheckM al-
gorithm [28] first places a query genome into a reference phylogeny, then defines a
clade-specific set of expected SCGs to estimate genome completeness and
Orakov et al. Genome Biology (2021) 22:178 Page 3 of 19

Fig. 1 GUNC quantifies chimerism in prokaryotic genomes. a Genome contamination may originate in vitro
(e.g., from culture media, laboratory equipment or kits, index hopping during multiplexed sequencing) or in
silico (contig misassembly, erroneous binning). Genomes are represented as circular chromosomes, contigs
as sequences of genes (dots). b Two types of genome contamination can be distinguished operationally:
redundant contamination by surplus genomic material (“more of the same”) and non-redundant
contamination by non-overlapping fragments from distantly related lineages (“something new,” e.g., novel
or distant orthologs). Different single-copy marker genes (SCGs) are shown as solid shapes, other genes as
dashed circles; colors indicate different source lineages. c GUNC workflow. For a given query genome,
genes are called using prodigal, then mapped to the GUNC reference database (based on proGenomes 2.1)
using diamond to compute GUNC scores and to generate interactive Sankey diagrams to visualize genome
taxonomic composition. GUNC quantifies genome chimerism and reference representation across
taxonomic levels. Clade separation scores (CSS) are high if gene classification to distinct lineages
(represented by different colors) follows contig boundaries. Reference representation scores (RRS) are high if
genes map closely and consistently into the GUNC reference space. The top example illustrates a chimeric
genome with good reference representation, the bottom example a non-contaminated genome that is not
well represented in the GUNC reference

contamination. For genomic chimeras of multiple distantly related lineages, phylogen-

etic placement will be more conservative, nearer the root, limiting the range of consen-
sus SCGs: in the extreme case of root-level placement, quality estimates are based on
only 43 near-universal genes [28], corresponding to just 1–2% of an average prokaryotic
genome. In contrast, small levels of contaminant genomic material may be “overruled”
by the principal lineage, leading to over-confident phylogenetic placement nearer the
tips. Lineage-specific SCGs, in particular those with deep phylogenetic roots, are
Orakov et al. Genome Biology (2021) 22:178 Page 4 of 19

moreover often not evenly distributed across the genome, but locally clustered [30, 31],
additionally limiting their representation of the query genome. These biases were first
noted in the original CheckM study [28]; we confirmed that errors are introduced by
phylogenetic placement using simulated data (Additional file 1: Figure S1). Resulting er-
roneous quality estimates can have a detrimental impact on biological interpretation, as
demonstrated in the case of the novel deeply branching lineage Rokubacteria [29], and
more recently shown anecdotally for manually curated human gut-derived MAGs [22].
As a result, the use of SCG inventories as the exclusive estimator of genome quality
has been questioned, in particular in the context of large-scale automated genome bin-
ning [22].
Here we present GUNC (the Genome UNClutterer), a fully integrated workflow to
estimate genome chimerism based on the full gene complement, using an entropy-
based measure of lineage homogeneity across contigs. In various simulation scenarios,
we demonstrate that GUNC accurately quantifies genome contamination at high sensi-
tivity and can pinpoint problematic contigs within genome bins. We further identify a
substantial fraction of chimeric genomes in GenBank [32], the Genome Taxonomy
Database [33], and recently published large-scale MAG datasets [13–15] for which con-
tig mis-binning also leads to inflated estimates of phylogenetic diversity and taxonomic
novelty. GUNC source code is available under a GPLv3 license at https://github.com/
grp-bork/gunc.

Results
GUNC estimates genome quality based on contig homogeneity using the full
complement of genes
Our rationale in designing GUNC was to estimate genome quality based on the phylo-
genetic homogeneity of contigs with respect to a genome’s entire gene content. Using a
filtered and curated set of high-quality reference genomes derived from proGenomes2.1
[34], GUNC infers each gene’s clade membership across a hierarchy of taxonomic
levels, using taxonomy as a proxy for phylogeny. While ideally a genome would receive
a single dominant label, corresponding to its true classification, there are two main rea-
sons why this may not happen: it may be contaminated or it may lie outside the refer-
ence set and thus, due to prediction limitations, receive a set of inconsistent inferences.
GUNC attempts to distinguish between these two cases by computing a clade separ-
ation score (CSS) which builds upon an entropy-based metric [35]. The CSS measures
how diverse the taxonomic assignments are within each contig, normalized to the di-
versity across the whole genome and to the expected entropy when there is no relation-
ship between taxonomic labels across contigs, thus returning a value between 0 and 1
(see “Methods”). Intuitively, if a genome is composed of contigs that are internally
homogeneous, but disagree with each other, then the metric will return a value closer
to 1. On the other hand, a genome that, because it lies outside the reference set, is
assigned a myriad of labels, but where the labelling does not follow contig boundaries,
will have a CSS closer to 0. A genome with all genes assigning to the same taxonomy,
i.e., free of contamination, will be assigned a CSS score of 0 (see “Methods”). The CSS
quantifies the degree to which a genome is a chimeric mixture of distinct lineages fol-
lowing non-random distributions across contigs. GUNC computes CSS values at all
Orakov et al. Genome Biology (2021) 22:178 Page 5 of 19

major taxonomic levels and can thus indicate the approximate phylogenetic depth at
which distinct source genomes diverged.
Importantly, the CSS is a measure of confidence when labelling a genome as chimeric
and is sensitive even to small portions of contaminant if these are well circumscribed
by contig boundaries (Fig. 2b–e). GUNC additionally quantifies the fraction of genome
contamination in two ways: as the fraction of total genes assigned to non-major clade
labels (GUNC contamination), and as the “effective number of distinct clades” in a gen-
ome, based on the Inverse Simpson Index of the clade size distribution (see “Methods”).
To assess a genome’s quality based on GUNC, both GUNC contamination and CSS
should be taken into account.

Fig. 2 GUNC accurately detects chimerism in incrementally challenging simulation scenarios. a Overview of
different types of simulation scenarios. Genomes (filled circles) were simulated as mixtures of lineages
(horizontal lines) diverging at various taxonomic levels (columns) from clades (void circles) contained in the
GUNC reference (solid lines) or not (dashed). See “Methods” for details. b Median CheckM completeness
and contamination estimates (dashed lines) diverged from true values (solid lines) with increasing levels of
simulated contamination (type 3a in panel a), whereas GUNC estimates of contamination (green;
theoretically expected values as blue solid line) and effective number of surplus lineages (purple) were
highly accurate. See Figure S2 for an equivalent plot on type 3b genomes. c–f Detection accuracy across
simulation scenarios, quantified using F1-scores (y-axis) across increasing levels of simulated contamination
(x-axis). Data shown for scenarios 3a (c, d), 3b (e), 4 (f), and 5a (g); full panels for types 3a and 3b in Figure
S3. MIMAG criteria were defined as CheckM contamination < 10%, completeness ≥ 50% (medium) and
contamination < 5%, completeness > 90% (high); note that MIMAG criteria on rRNA and tRNA presence
were not applied; Cont, CheckM contamination; GUNC, default GUNC CSS cutoff of > 0.45
Orakov et al. Genome Biology (2021) 22:178 Page 6 of 19

Finally, GUNC also estimates how closely a query genome is represented by the under-
lying reference set. The reference representation score (RRS, see “Methods”) is based on
the average identity of query genes to the reference and the number of spurious map-
pings, in order to further inform the interpretation of the CSS and genome contamin-
ation. Beyond mere statistics, GUNC also provides interactive visualizations of a query
genome’s taxonomic composition as alluvial Sankey diagrams at gene-level resolution (see
“Methods”). A full overview of the GUNC workflow is provided in Fig. 1c.

GUNC accurately quantifies genome contamination in multiple simulated scenarios

We benchmarked and validated GUNC in multiple scenarios, simulating various degrees
of genome chimerism, source genome relatedness, and reference representation (see Fig.
2a and “Construction of artificial genomes under different scenarios” in the “Methods”
section for an overview). All simulated genomes were generated from a curated high-
quality set derived from proGenomes2.1 [34], which is also the basis for the default refer-
ence set used by GUNC (Fig. 2a “type 1 genomes—in reference”). We simulated decreas-
ing reference representation by iteratively removing entire clades from the GUNC
reference set at varying taxonomic levels (“type 2 genomes—out of reference”). Type 1 ge-
nomes were used as the contamination-free baseline in the subsequent benchmarks.
We simulated chimeric genomes by mixing fragments from two (“type 3a”) or mul-
tiple (“type 3b”) sources, varying the taxonomic level at which sources diverged (“diver-
gence level”), but retaining source lineage representatives in the reference training set
(see “Methods”). Non-redundant contamination was simulated by replacing part of an
acceptor genome by a size-matched contaminant fraction of a donor genome; to simu-
late redundant contamination, surplus donor fragments were added to complete ac-
ceptor genomes. We observed that CheckM systematically overestimated completeness
and underestimated contamination for genomes with simulated non-redundant con-
tamination (Fig. 2b), largely independent of the taxonomic level of source genome di-
vergence. This bias further increased when two source genomes were mixed at more
equal shares (Fig. 2b) and followed a similar trend when multiple source genomes were
mixed (Additional file 1: Figure S2), as noted in the original CheckM study [28]. In
contrast, GUNC accurately estimated the contamination and the effective number of
surplus lineages represented in the query genome.
This difference in quantitative estimates translated into differential accuracy in a
binary classification of genome quality, assessed using the harmonic mean of preci-
sion and recall (F1 score) at different levels of contamination (Fig. 2c–e). We com-
pared GUNC scores at default thresholds (CSS < 0.45, contamination filtered at <
2%; see “Methods” and Additional file 1: Figure S3) to CheckM at commonly used
contamination thresholds (< 5%, < 10%) and the widely used MIMAG standard pa-
rameters [36] for medium (contamination < 10%, completeness ≥ 50%) and high
(contamination < 5%, completeness > 90%) genome quality, likewise based on
CheckM estimates. GUNC generally outperformed these filtering methods across
all benchmarks. GUNC accurately detected both non-redundant (Fig. 2c) and re-
dundant contamination (Fig. 2d) for mixtures of two or more (Fig. 2e) source ge-
nomes (Fig. 2e), with F1 scores of ≥ 0.96 even at simulated contaminant fractions
of just 5%. As expected, GUNC accuracy was consistently high with the only
Orakov et al. Genome Biology (2021) 22:178 Page 7 of 19

exception of species-level chimerism where it performed suboptimally at lower por-

tions of contamination (Additional file 1: Figure S3). In contrast, CheckM-based
classification was less accurate for chimeras of more distantly related lineages,
dropping as low as F1 < 0.5 (F-scores below 0.5 not plotted, Additional file 1: Fig-
ure S3). Interestingly, including the completeness criterion (in MIMAG medium
and high thresholds) provided only mild performance improvements in our simula-
tions when compared to classification based only on CheckM contamination. A
strict CheckM contamination threshold of < 5% slightly outperformed GUNC for
species-level chimeras (Additional file 1: Figure S3a-c), while also occasionally
showing minute performance benefits at much higher degrees of contamination (≥
20%) for higher taxonomic levels, as GUNC performance generally plateaued at
100% sensitivity with a low fraction of residual false positive calls.
The simulation scenarios of types 3a and 3b (Fig. 2c–e) assume that the lineages, but
not the genomes themselves, of both contaminant and acceptor are represented in the
reference training set. In practice, however, this is often not the case, in particular for
novel lineages and MAGs. We therefore simulated scenarios of genome chimerism be-
tween source lineages that are themselves out of reference (types 4, 5a, and 5b in Fig.
2a). Note that by design, such leave-one-out simulations are not possible with CheckM,
as the pre-curated reference phylogeny and marker gene sets included with the soft-
ware cannot be modified accordingly. Genomes of type 4 simulated chimerism between
deeply branching source lineages with limited reference representation at the diver-
gence level and none at subordinate levels. For example, these genomes represent chi-
meras of two novel families or genera within distinct previously described phyla or
classes. GUNC accurately detected such chimeras, even at low fractions of contamin-
ation (5–10%, Fig. 2f). We next simulated even more challenging scenarios in which
one (“type 5a”) or both (“type 5b”) source lineages were not represented even at the
level of divergence, corresponding to chimeras of entirely novel lineages. GUNC accur-
ately detected “type 5a” at contaminations ≥ 10% at a phylum to family level (Fig. 2g),
though performance deteriorated towards lower contamination portions and lower
taxonomic divergence levels.
To confirm that performance was independent of the chosen reference dataset and
taxonomy, we repeated all simulations presented in Fig. 2b–g, using (i) the originally
simulated genomes with an inferred Genome Taxonomy Database (GTDB, [2]) tax-
onomy and (ii) a new simulation set based on GTDB v95 genomes directly (see
“Methods”). Neither the observed F1 scores, nor the tools’ ranking changed significantly
in either of these setups (Additional file 1: Figures S4 & S5).
As expected, GUNC was not able to accurately detect chimerism in scenario “5b,”
i.e., if the clades of both source lineages were out of reference. Instead, GUNC ad-
dresses the challenges posed by novel lineages, both as possible contaminants and as
units of discovery, via reference representation scores (RRS) across taxonomic levels,
based on the average identity of query genes to their closest reference counterparts (see
“Methods”). High RRS values indicate that genomes map confidently into the reference
space at a given taxonomic level, whereas low RRS indicate that a lineage is “novel”
relative to the reference. Using simulations, we confirmed that GUNC RRS can predict
the taxonomic level at which a query genome is novel (Additional file 1: Figure S6), in
particular for deeply branching novel lineages (F1 = 0.98 for novel phyla), and
Orakov et al. Genome Biology (2021) 22:178 Page 8 of 19

regardless of whether the query is itself chimeric (type 5b) or not (type 2). We suggest
that the CSS and RRS be used in conjunction to assess genome quality, depending on
the expected reference representation in the dataset under investigation.

GUNC is robust to the possible confounding effects of horizontal gene transfer

GUNC was designed with the express purpose of detecting artifactual genome contam-
ination, resulting from technical errors in vitro or in silico. Yet many prokaryotic ge-
nomes exhibit a natural form of “chimerism,” as a result of horizontal gene transfer
between evolutionarily distantly related lineages (HGT, [37, 38]) whereby contamin-
ation estimates may be confounded, potentially leading to false positive calls. We there-
fore sought to establish the specificity of GUNC with regard to a recent dataset of
HGT events between 3465 genomes in the proGenomes 2.1 database (Khedkar et al.,
under review; see “Methods”).
As expected, we observed an enrichment of chimerism calls among HGT genomes
(407 out of 3465 genomes, or 11.7%) compared to the overall chimerism frequency in
proGenomes 2.1 (3.8%; Fisher’s exact test, p ~ 10−94, odds ratio = 3.8; Additional file 1:
Figure S7), albeit at very low predicted genomic fractions (only 2.5% of HGT genomes
were ≥ 5% contaminated, the remainder at 2–5%). By comparison, CheckM flagged
5.9% of HGT genomes when applying equivalent criteria (≥ 2% estimated contamin-
ation). Upon further inspection of the 407 HGT genomes flagged by GUNC, we noticed
that a sizeable subset were indeed either correct calls or well-defined artifacts, as de-
tailed below (Additional file 1: Figure S7). In total, 34 of the 100 highest-ranking ge-
nomes were manually confirmed to be correctly called chimeras, in genomic regions
unrelated to the predicted HGT events and involving different contaminant lineages, as
expected based on the chimerism background frequency in proGenomes 2.1. For a fur-
ther 86 genomes, predicted HGT regions were fully integrated into the host genome
(see “Methods”) or taxonomically consistent (i.e., the focal genome’s clade likely acting
as HGT donor rather than recipient), and unrelated to the predicted contamination;
these were likewise considered correctly called chimeras. Moreover, 71 Salmonella spp.
genomes contained defined regions that were classified as Klebsiella sp., attributable to
compatible plasmids between these related lineages, as described previously [39, 40].
The resulting chimerism calls were therefore not due to the method itself, but artifacts
as a consequence of inherited annotation errors in the GUNC reference database which
excludes annotated plasmids by default (see “Methods”); we therefore expect similar er-
rors to be further mitigated in future updates of the GUNC database.
Overall, these results indicate that GUNC scores were generally robust to HGT ef-
fects, with ≤ 10% false positive chimerism calls among 3465 predicted HGT genomes,
corresponding to ≤ 0.5% across the entire proGenomes 2.1 dataset.

Extensive undetected contamination among reference genomes and MAGs

We calculated GUNC scores for various public datasets of both isolate-derived and
metagenome-assembled genomes to detect hitherto overlooked genome chimerism
(Fig. 3a, see Additional file 1: Figure S8 for alternative GUNC parameters). We applied
default GUNC thresholds (CSS > 0.45, see “Methods”), conservatively ignoring species-
level chimerism, i.e., only considering chimerism between genomes involving distinct
Orakov et al. Genome Biology (2021) 22:178 Page 9 of 19

Fig. 3 Extensive undetected chimerism in public genome databases and large-scale MAG datasets. a
Cumulative plots summarizing genome quality for various genome reference and MAG datasets. The y-axis
shows the fraction of genomes passing GUNC filtering at increasing stringency (x-axis), up to the default
CSS threshold of 0.45, conservatively ignoring species-level scores. Note that the Almeida, Pasolli, and
Nayfach sets were pre-filtered using variations of the MIMAG medium criterion based on CheckM estimates.
GTDB, Genome Taxonomy Database; GMGC, Global Microbial Gene Catalog. b Example of detected
contamination in an isolate-derived reference genome for which around one fifth of genes were assigned
to a different phylum, scattered across hundreds of small contigs. c Example of detected contamination in
a MAG for which genes assigned to two major different phyla were well separated into distinct contigs. d
Cumulative plots summarizing the quality of species-level genome bins (SGBs) defined by Pasolli et al. [13].
Lines indicate the fraction of SGBs (y-axis) containing at least one or exclusively chimeric genomes at
increasingly stringent GUNC cutoffs (x-axis) conservatively ignoring species-level scores. For both series,
intervals correspond to edge scenarios in which genomes with limited reference representation are either
conservatively ignored (treated as non-chimeric, upper lines) or aggressively removed (lower lines); the true
fraction of chimeric SGBs likely falls in between. e Differential filtering of MAGs in the GMGC set based on
CheckM contamination (< 5%), CheckM completeness (> 90%), and GUNC (CSS < 0.45, ignoring
species-level scores)

genera and higher taxonomic ranks. The resulting GUNC profiles for 1,375,848 ge-
nomes are available as Supplementary Data (see “Availability of data and materials” sec-
tion). Using these parameters, 5.7% of 701,698 prokaryotic genomes in GenBank
[accessed 30th of July 2020] [32] and 5.2% in the more restrictive RefSeq [24] were
flagged as potentially chimeric. Genomes annotated as “environmental” or “metagen-
ome-derived” (i.e., MAGs) were substantially enriched for chimeras in GenBank, ac-
counting for 18.4% of chimeric genomes even though the overall GenBank MAG share
was only 9.4%. Moreover, by following up genome taxonomic annotations, we observed
Orakov et al. Genome Biology (2021) 22:178 Page 10 of 19

that GenBank contains “cryptic” MAGs that were not annotated as metagenome-
derived by submitters. Indeed, for proGenomes 2.1 [34], a more vetted and curated
GenBank subset totalling 84,095 genomes, the fraction of flagged genomes was only
3.6%.
To confirm that these results were not biased by the used reference database or tax-
onomy, we repeated the analyses shown in Fig. 3a using the GTDBv95 (see “Methods”).
The ranking of genome sets did not change, leading to very similar conclusions (see
Additional file 1: Figure S9).
Among the flagged isolate genomes in GenBank, RefSeq, proGenomes 2.1, and the
Genome Taxonomy Database (GTDB, [2]), we frequently observed patterns consistent
with biological contamination, e.g., of culture media or reagents. For example, Fig. 3b
shows an isolate of the Firmicute Aerococcus urinae, contaminated by a Afipia broo-
meae (phylum Proteobacteria) scattered across many small contigs. The division be-
tween both source genomes clearly follows contig boundaries, indicating that the highly
fragmented Afipia genome may have been partially assembled from a lowly abundant
contamination co-sequenced at low coverage (Additional file 2: Table S1).
While contamination in isolate genomes was usually restricted to small contigs due
to the low abundance of the contaminant species, the sizes of contaminant contigs in
chimeric MAGs were more evenly distributed (cf Fig. 3c for a phylum-level chimera
MAG from a marine metagenome [41]).
As observed in GenBank, genome chimerism was more common among MAGs than
among isolate genomes. The GTDB comprises both types, extending a GenBank-
derived core set with automatically generated MAGs for underrepresented lineages, all
filtered based on CheckM quality estimates and clustered into species-level units by
average nucleotide identity. Among the GTDB, MAGs were more prone to being con-
taminated (8.0%) than single-cell derived (6.7%) or isolate genomes (4.6%). Chimeric
genomes likely inflate estimates of total phylogenetic diversity in the GTDB: 1009
(3.2%) of species-level clusters in the GTDB consisted entirely of contaminated ge-
nomes, and a further 1760 (5.6%) contained at least one.
For the human gut, three recent studies alone generated hundreds of thousands of
MAGs by assembling and locally binning metagenomic data from several thousand
samples [13–15]. All three teams relied on methodologically largely equivalent ap-
proaches for MAG generation and filtering, using variations of the MIMAG “medium”
quality standards, based on CheckM estimates. GUNC identified 17.2%, 15.1%, and
29.9% of the pre-filtered Pasolli, Almeida, and Nayfach MAG sets as putatively chimeric
at genus level or above (Fig. 3a), revealing extensive levels of previously undetected
non-redundant contamination. Among the species-level genome bins (SGBs; clustered
at 95% average nucleotide identity) described by Pasolli et al. [13] 8.5% consisted en-
tirely of chimeras (Fig. 3d), with even higher rates among “novel” SGBs (not containing
any reference genomes) and small clusters: 18% of “novel” singleton SGBs were formed
by chimeric MAGs. Thus, the 17.7% of chimeric genomes in the Pasolli set may have
strongly impacted both SGB clustering and, as a consequence, biological interpretation.
To further quantify the differential effects of CheckM and GUNC filters on MAG
datasets, we re-analyzed the 278,629 MAGs derived from the Global Microbial Gene
Catalog dataset (Coelho et al, in revision). GUNC flagged 23.4%, 14.5%, and 9.4% of the
raw (“GMGC unfiltered”), MIMAG “medium” (“GMGC MQ”) and MIMAG “high”
Orakov et al. Genome Biology (2021) 22:178 Page 11 of 19

(“GMGC HQ”) quality filtered GMGC MAGs as chimeric, respectively, comparable to

levels in other tested pre-filtered MAG sets (Fig. 3a). The CheckM 5% contamination
criterion was highly permissive, flagging just 10.3% of all GMGC MAGs (Fig. 3e).
GUNC was more restrictive, flagging 23.4% of total genomes and 20.5% of genomes
passing the CheckM contamination filter (reciprocally, only 7.0% of genomes passing
the GUNC CSS filter were flagged at CheckM contamination > 5%) (Fig. 3e). Overall,
CheckM and GUNC contamination filters agreed on 76% of genomes, at a Pearson cor-
relation between nominal contamination estimates of 0.2. The CheckM completeness
criterion, capturing an entirely orthogonal signal, was the overall most restrictive filter,
flagging 80% of genomes as ≤ 90% and 50% as ≤ 50% complete (Fig. 3e and S10). Relax-
ing the completeness criterion further pronounced the differential impact of GUNC
and CheckM contamination filters (Additional file 1: Figure S10), with GUNC being
consistently more sensitive.

Discussion
Chimerism and contamination can have considerable impact on the biological inter-
pretation of a genome, in particular by causing false inferences about phylogenetic
placement and functional repertoires [22, 25]; thus, there is a need for fast and accurate
methods for automated genome quality control. As we have shown, GUNC quantifies
even small levels of chimerism in prokaryotic genomes, with robust performance even
if one or multiple source lineages of a composite genome are not well represented in
the GUNC reference database.
GUNC is designed to complement existing estimators of prokaryote genome quality
such as the de facto standard in the field, CheckM [28], and addresses error types that
elude marker gene-based methods. GUNC represents a genome as its full gene comple-
ment, not just as an inventory of “expected” core genes and is therefore robust to com-
mon artifacts resulting from erroneously conservative phylogenetic placement.
Moreover, the enhanced resolution of a gene-centric genome representation has been
shown to increase accuracy for related problems, such as, e.g., taxonomic classification
[42, 43] or MAG refinement [14, 18]. GUNC can provide gene-level resolution even for
composite genomes of deeply branching source lineages, a type of chimeras that are no-
toriously difficult to detect automatically as sets of shared marker genes rapidly shrink
with increasing phylogenetic depth. We demonstrated that GUNC scoring was highly
accurate in incrementally challenging simulation scenarios and asserted that the tool
was robust towards the effects of distant horizontal gene transfer, with limited false de-
tections among “natural” genome chimeras as a result of HGT. Moreover, GUNC
quantifies the “novelty” of a query genome relative to its reference set, thus further
qualifying quality estimates, as confidence decreases along with reference representa-
tion. Nevertheless, as demonstrated in incrementally challenging simulation scenarios,
GUNC accurately detects chimerism even among novel lineages.
By design, GUNC does not quantify genome completeness, as it does not attempt to
infer an expected set of a lineage’s core genes. This also means that GUNC does not at-
tempt to resolve redundant contamination between very closely related (or even identi-
cal) lineages where contigs are all taxonomically homogenous—this is a use case at
which marker gene-based methods excel. By the same token, we caution against an
over-interpretation of GUNC CSS and contamination estimates at species or strain
Orakov et al. Genome Biology (2021) 22:178 Page 12 of 19

resolution: GUNC’s underlying gene-wise taxonomy assignments become less precise

between closely related lineages that share substantial genetic material at very high se-
quence similarity, potentially causing an overestimation of contamination. Moreover,
very closely related lineages are prone to recombination and exchange of genomic ma-
terial which can further confuse gene-level classifications. Nevertheless, GUNC reports
scores at all taxonomic levels and in practice accurately detects species-level chimerism
(see Additional file 1: Figures S3 & S11).
Applying permissive GUNC default thresholds, we demonstrated that a substantial
fraction of genomes in public repositories show clear contamination signatures that
were not picked up previously. As expected, metagenome-assembled genomes were
much more prone to chimerism than those derived from isolates, irrespective of a line-
age’s novelty relative to the GUNC reference. Among four large-scale datasets of auto-
matically generated MAGs from human microbiomes [13–15] and various
environments (Coelho et al., in revision), we found extensive levels of undetected gen-
ome contamination, with a disproportionate impact on estimates of “novel” lineages.
As GUNC complements existing tools to estimate genome quality, using orthog-
onal information to address types of genome contamination that are currently
overlooked, a combination of filters based on genome fragmentation, CheckM com-
pleteness, CheckM contamination, and GUNC may greatly refine automatically
generated MAG datasets. GUNC offers a dedicated workflow to accomplish this,
integrating CheckM results with GUNC scores for nuanced estimates of genome
quality at high throughput. We expect that an automated, rapid, and accurate
quantification of genome contamination will further enable genome-centric micro-
biology at large scale and high resolution.

Methods
GUNC workflow and implementation
The core workflow of GUNC consists of three modules (see Fig. 1c). First, for any query
genome, genes are called using prodigal [44], although per-gene protein sequences can al-
ternatively be supplied by the user directly. Protein sequences are then mapped against
representative genomes in the GUNC database (derived from species-representative ge-
nomes in proGenomes 2.1 [34]) using diamond [45], retaining best hits (-k 1) without ap-
plying an evalue filter (-e 1) as alternative filtering is applied downstream. Annotated
plasmids and other non-chromosomal genomic elements are excluded from the reference
to reduce nonspecific hits between lineages within plasmid host range. Moreover, the ref-
erence set was semi-manually curated, removing clear cases of genomic chimerism.
For each query gene, taxonomic annotations at 7 levels (kingdom, phylum, class,
order, family, genus, species) are inherited from the best hit via the manually curated
proGenomes 2.1 taxonomy. To filter against mapping noise, taxonomic clade labels
recruiting less than 2% of all mapped genes are dropped. GUNC scores (see below) are
then calculated based on inferred taxonomic labels, query gene contig membership, se-
quence identity to database hits, and the fraction of mapped and filtered hits. Finally,
GUNC offers a visualization module to automatically generate interactive Sankey allu-
vial diagrams of contig-level taxonomic annotations to enable manual curation and ex-
ploration of flagged genomes.
Orakov et al. Genome Biology (2021) 22:178 Page 13 of 19

GUNC is implemented in Python3, all code is open source and available at https://
github.com/grp-bork/gunc and through bioconda [46] under a GPLv3+ license. Based
on database size and resource requirements, GUNC can be run locally on a personal
computer but is also highly parallelizable in a cluster environment.

Calculation of GUNC scores

GUNC computes several scores to quantify a query genome’s quality, its representation
in the GUNC reference database and its levels of putative contamination. The GUNC
clade separation score (CSS) is an entropy-based clustering measure to assess how
homogeneously taxonomic clade labels (T) are distributed across a genome’s contigs
(C). It is inspired by the uncertainty coefficient [35],
8
< 0;
UC ¼ H ðT jC Þ if H ðT Þ ¼ 0 i:e:all taxonomic labels are identical
: 1− else
H ðT Þ

A simple estimator for this quantity is the plugin estimator where C is a set of con-
tigs, T is a set of taxonomic clades, act is a number of genes located in contig c and
assigned to taxonomic clade t, and N is the total number of genes in a genome.

X
jC j X
jT j
act act
^ ðT jC Þ ¼ −
H log PjT j
N
c¼1 t¼1 t¼1 act
Xj T j Pj C j PjC j
^ ðT Þ ¼ − c¼1 act act
H log c¼1
t¼1
N N

However, this estimator is known to be biased when the number of samples is small
[47] and adjusting it for chance leads to more interpretable quantities [48]. In our case,
the sums range over the genes in each contig, and, in fragmented genomes, many con-
tigs can contain only a small number of genes. Therefore, we normalize the estimated
conditional entropy by the expected value of this estimation under a null model, lead-
ing to CSS = 1 − Ĥ(T|C)/Ĥ(T|R), where Ĥ(T|R) is the expected value of Ĥ(T|C) keep-
ing the same contig size distribution and assuming no relationship between contig
membership and taxonomic assignment (in the special case where Ĥ(T|C) > Ĥ(T|R),
we set CSS to zero).
The CSS is 0 if the frequency distribution of taxonomic labels in every individual
contig exactly follows that across the entire genome. It is 1 if all contigs are “taxonom-
ically pure,” i.e., if the distribution of taxonomic labels follows contig boundaries.
GUNC outputs CSS scores for every tested taxonomic level, so that users can infer the
approximate phylogenetic depth at which source lineages diverged. By default, GUNC
adjusts CSS to 0 at every level separately when the portion of called genes left after re-
moval of minor clades, i.e., genes retained index < 0.4, because in that case there are
too few remaining genes to calculate scores on at that level. Then, GUNC flags a gen-
ome as putatively contaminated if the “adjusted” CSS > 0.45 at any taxonomic level, a
threshold benchmarked in a series of simulation scenarios (Additional file 1: Figure
S12).
The CSS does not carry information about the scale of contamination (i.e., the frac-
tion of contaminant genome), but about the confidence with which a query genome
Orakov et al. Genome Biology (2021) 22:178 Page 14 of 19

may be considered chimeric. In other words, the CSS assesses whether a genome is
contaminated or not, but not how large the contaminant fraction is. GUNC instead
quantifies the scale of contamination at each tested taxonomic level using two mea-
sures. The total fraction of genes with minority clade labels after filtering (“GUNC con-
tamination”) is an estimate of the total fraction of contamination in the query genome.
Note that this definition differs from that commonly used by tools such as CheckM: de-
signed to quantify non-redundant contamination, GUNC scales by the total query gen-
ome size, whereas CheckM estimates redundant contamination by scaling against a
theoretical “clean” source genome with a single set of SCGs. In practice, this means
that GUNC contamination never exceeds 100%, whereas CheckM contamination esti-
mates the number of (complete) surplus genomes. GUNC further provides a combined
estimate of redundant and non-redundant contamination as the effective number of sur-
plus clades (Teff) in a query genome, calculated as the Inverse Simpson Index minus 1
(as 1 genome is expected):

!−1
X
N
T eff ¼ p2i −1
i¼1

where pi is the fraction of genes assigned to clade i. Teff scales in [0, ∞] and can be
interpreted as the number of surplus clades in the query genome considering the
weighted contributions of all source lineages.
Finally, GUNC computes a reference representation score (RRS) based on the total
fraction of genes mapping to the GUNC database (Portiongenes mapped), the fraction of
genes retained after noise filtering by removing minority labels recruiting ≤ 2% of genes
(see above; Portiongenes retained) and their average similarity to the reference
(Identitymean):

RRS ¼ Portiongenes mapped Portiongenes retained Identitymean

The RRS captures the expectation that out-of-reference genomes will map to the ref-
erence to a lower degree (Portiongenes mapped) and at lower similarity (Identitymean).
Moreover, among simulated out-of-reference genomes, we empirically observed a char-
acteristic pattern of noisy, low confidence hits scattered unspecifically across multiple
clades at very low frequencies; in the RRS, this signature is formalized as the term Por-
tiongenes retained. High RRS values indicate that a query genome maps well within the
GUNC reference space, whereas low RRS indicates poor reference representation to
qualify the interpretation of CSS and contamination estimates. In general, the lower the
RRS, the higher the risk of type 1 errors based on CSS (falsely labelling genomes as
contaminated): this way, GUNC asserts that genome quality is only confidently esti-
mated where sufficient data is available and that genomes potentially representing
deeply branching novel lineages beyond the GUNC reference are flagged for further
(manual) inspection.
Orakov et al. Genome Biology (2021) 22:178 Page 15 of 19

Construction of artificial genomes under different scenarios

Artificial genomes were constructed to simulate different scenarios of genome contam-
ination and reference representation (see Fig. 2a). All simulations were performed using
genomes in the curated and taxonomically annotated proGenomes 2.1 database [34],
serving as a baseline for clean, in-reference genomes (“type 1” in Fig. 2a). Further simu-
lation scenarios are described below. Unless otherwise indicated, simulations were con-
ducted separately for each taxonomic level and at contamination portions of 5%, 10%,
15%, 20%, 30%, 40%, and 50%, with 3000 iterations/genomes per each taxonomic level
and contamination portion. In each simulated genome, source genome contigs were
randomly fragmented such that contig size was inversely proportional to contig fre-
quency, parameterized based on the empirical frequency-size distributions of MAGs in
the Pasolli, Almeida, and Nayfach datasets [13–15]. Simulated genomes were then gen-
erated from these simulated contigs based on the rules set out below:

Type 1: Clean (non-contaminated) genomes, in reference. Taken from progenomes2.1.

Type 2: Clean (non-contaminated) genomes, out of reference. Simulated by removing
a genome’s entire source lineage from the reference.
Type 3a: Binary chimeric genome from two sources, both in reference. Simulated by
randomly selecting “donor” and “acceptor” genomes whose lineages diverged at any of
the seven tested taxonomic levels (divergence levels). A fraction of the acceptor
genome was either replaced by a matching fraction of donor genome (to simulate non-
redundant contamination), or the corresponding fraction of donor genome was added
to the complete recipient genome (to simulate redundant contamination).
Type 3b: Chimera of multiple (3, 4, or 5) source genomes, all in reference. Source
genomes from different source clades were mixed at equal shares totaling 1 altogether,
. . .
e.g., 1 , 1 , or 1 each.
3 4 5
Type 4: Binary chimera, both source lineages out of reference at subordinate levels.
Source lineage clades removed at subordinate levels (e.g., genus or family) but sister
clades retained in reference within the same parent clades (e.g., class or phylum), so
that both higher-level source clades were represented at divergence level. Simulated
10,000 times for each taxonomic and contamination level.
Type 5a: Binary chimera, one source lineage in reference, one out of reference at
divergence level. Recipient genome (in reference) partially replaced by donor genome
(out of reference at divergence level).
Type 5b: Binary chimera, both source lineages out of reference at divergence level, e.g.,
no genome available from entire clades (at divergence level) containing source genomes.

To check for potential performance bias due to the selected reference set and tax-
onomy an additional round of simulations was done where genomes from GTDB v95
[2] were used for simulation instead of proGenomes2.1. For this purpose, an alternative
GUNC reference set based on GTDB species-representative genomes was generated.
Other than these differences, every aspect of this additional simulation was equivalent
to the original simulation. We also confirmed that optimal GUNC CSS cutoff values
with a GTDB reference did not differ significantly from those originally defined with a
proGenomes-based reference.
Orakov et al. Genome Biology (2021) 22:178 Page 16 of 19

Detection of horizontal gene transfer (HGT)

The data for HGT events was used from Khedkar et al. (under review). Briefly, HGT
events were identified by detecting the presence of marker accessory genes with a mini-
mum of 95% sequence identity across genomes. Only HGT events for broader taxo-
nomic levels were considered.

Reanalysis of public datasets

Pasolli et al. [13]: genome fasta files were accessed from (http://segatalab.cibio.unitn.it/
data/Pasolli_et_al.html) on 2020-03-12. SGB annotations of genomes were taken from
article supplementary files.
Almeida et.al [15].: genome fasta files were accessed from (ftp://ftp.ebi.ac.uk/pub/
databases/metagenomics/umgs_analyses/) on 2020-03-12.
Nayfach et.al [14].: genome fasta files were accessed from (http://bit.ly/HGM_all_6
0664_fna -O HGM_v1.0_all_60664_fna.tar.bz2) on 2020-03-12.
GenBank genomes were accessed on 30.07.2020 (www.ncbi.nlm.nih.gov/genbank/)
[49]. GUNC results for 699,994 GenBank genomes were produced. RefSeq genome set
was subsetted from GenBank. Genomes annotated as “derived from metagenomes” or
“derived from environmental source” in the “excluded from refseq” column of the Gen-
Bank assembly metadata were considered as MAGs.
GTDB [2] genome metadata was accessed from (https://data.ace.uq.edu.au/public/
gtdb/data/releases/release95/95.0/) on 2020-08-17. Genomes were mapped to GenBank
via GenBank accession IDs. Genomes annotated as “derived from metagenomes” or
“derived from environmental sample” in the “ncbi_genome_category” column of the
metadata table were considered as the GTDB MAGs subset.
The unfiltered set of 278,629 MAGs of the Global Microbial Gene Catalog dataset
(GMGC, Coelho et al, in revision) is available at (gmgc.embl.de) [50].

Supplementary Information
The online version contains supplementary material available at https://doi.org/10.1186/s13059-021-02393-0.

Additional file 1: Figure S1. a Percent stacked bar chart of CheckM inferred marker lineage levels (colors) for
type 3a simulated chimeric genomes (see Methods & Fig. 2a) across different: 1) divergence levels of source
genomes (x-axis); 2) simulated portions of contamination (columns); and 3) scenarios of contamination (‘added’ vs
‘replaced’, rows; see Methods). In a and b, the first column (“0”) are clean (non-chimeric) genomes shown for
comparison. b Average inferred CheckM marker lineage depth (y-axis) of simulated chimeric genomes under
different contamination scenarios (‘added’ in dark blue; ‘replaced’ in light blue). The true taxonomic depth of
divergence between source genomes are indicated in green. c Equivalent to a, but using chimeric genomes
simulated from multiple sources (type 3b in Fig. 2a). Columns indicate the number of equally contributing source
genomes (n_sources); rows indicate simulation setups (‘0.5’ if 50% of each source genome was used; ‘1/n_sources’
for equal source parts; see Methods). In c & d, the first column (“1”) are clean (non-chimeric) genomes, the second
column (“2”) are type 3a genomes as in a & b, shown for comparison. d Average inferred CheckM marker lineage
depths (y-axis) with different portions of contamination, equivalent to panel b. Figure S2. Comparison of median
scores from GUNC and CheckM of simulations of genomes type 3a and 3b where source genomes make equal
contributions summing 1 in total (e.g. 0.2 from each of 5 sources or 0.25 from each of 4 sources). This shows that
the trend from Fig. 2b persists when multiple source genomes are mixed in a simulated chimeric genome. Figure
S3. F-scores of distinction between clean and chimeric genomes across all divergence levels of source genomes
for different simulation scenarios. MIMAG medium is CheckM contamination < 10% and CheckM completeness
≥50%. MIMAG high is CheckM contamination <5% and CheckM completeness >90% and due to irrelevance to our
simulations we decided that additional criteria of presence of rRNAs and tRNAs can be ignored here. “Cont” stands
for CheckM contamination and GUNC means GUNC CSS of <0.45 or GUNC contamination <2%. a type 3a non-
redundant contamination. b type 3b redundant contamination. c type 3b. Figure S4. Equivalent to Fig. 2, but
using inferred GTDB taxonomy for GUNC’s default reference DB instead of NCBI taxonomy (default reference ge-
nomes with GTDB taxonomy). Figure S5. Equivalent to Fig. 2, but using an alternative GTDB-based GUNC refer-
ence DB (GTDB v95 genomes and GTDB taxonomy). Figure S6. a ROC-curves and AUCs of separation of genomes
in-reference (type 1) from genomes out-of-reference (types 2 and 5b) at different out-of-reference levels (faceting)
Orakov et al. Genome Biology (2021) 22:178 Page 17 of 19

using GUNC reference representation scores (RRS) at matching taxonomic levels. b F1-scores (y-axis) of separation
of types 2 & 5a from type1 across different RRS cutoffs (x-axis) different out-of-reference (OOR) levels (colors) at
which these genomes have no reference representation. GUNC scores at the taxonomic level identical to OOR level
were used. Vertical lines indicate RRS scores with highest F1-scores at each OOR level. Cutoff of RRS <0.5 is used to
label genomes as “OOR” irrespective of the taxonomic level of max CSS. Figure S7. The Venn diagram on the left
illustrates the overlap (407 genomes) between sets of genomes associated with HGT (3,465 genomes) and those
potentially chimeric (3,162 genomes) according to GUNC (CSS >0.45 & contamination >2%). The stacked bar plot
on the right indicates the numbers of genomes from the overlap in each category. These categories do have over-
laps and therefore genomes in them were counted and removed from the set used to count remaining categories
in the following order of their genome counts: 71 > 34 > 86 > 187 & 29. Figure S8. a Cumulative plot summariz-
ing genome qualities of various sets of genomes represented by lines of different colors. Any point in a plot indi-
cates a portion of genomes retained in a set (y-axis) after filtering out genomes with GUNC CSS higher than the
cutoff (x-axis) & GUNC contamination >5% (ignoring species-level scores). b Cumulative plot illustrating the number
of species-level genome bins (SGBs) (from Pasolli et al. 2019). Lines indicate the portion of unique SGBs retained (y-
axis) after filtering out SGBs where either “all” or “at least one” genome has GUNC CSS score higher than the cutoff
(x-axis) & GUNC contamination >5%. Figure S9. Cumulative plots summarizing genome quality for various gen-
ome reference and MAG datasets. This plot is equivalent to main Fig. 3a, but using a reference set based on GTDB
v95 instead of GUNC’s default based on proGenomes 2.1 (see Methods for details). Note that the Almeida, Pasolli
and Nayfach sets were pre-filtered using variations of the MIMAG medium criterion based on CheckM estimates.
GTDB, Genome Taxonomy Database; GMGC, Global Microbial Gene Catalogue. Figure S10. Alluvial illustration of
the fate of genomes in GMGC based on filters by GUNC and CheckM. Three filters are: 1) CheckM contamination <
10%; 2) CheckM completeness <50%; 3) GUNC CSS <0.45 or GUNC contamination <2% (ignoring species level
scores). The illustration shows the orthogonality and complementarity between GUNC and CheckM filters. Figure
S11. Alluvial illustration of MAG “SRR1779121_bin.6” from Almeida et.al. 2019 that shows that GUNC can detect chi-
merism of related species when both source species have reference representation. The CheckM completeness is
79.31 and contamination is 1.72 for this MAG. Figure S12. Mean F-score of 10 iterations of 10,000 non-chimeric vs
10,000 chimeric genomes across different values of GUNC CSS cutoffs used to separate between chimeric and
non-chimeric genomes. For “All types” genome types 1 and 2 are used as non-chimeric and types 3, 4 and 5a are
used as chimeric (type 5b excluded since it is not expected to be detected at all). For “No OOR” genome type 1
only is used as non-chimeric and types 3 and 4 are used as chimeric. The cutoff with high performance at “No
OOR” was chosen so that its performance is as high as possible in the “All types” setup without any significant loss
to “No OOR” setup performance.
Additional file 2: Table S1. Contig lengths and coverages linked to gene-level taxonomy data underlying the
visualization of genome in Fig. 3b.
Additional file 3. Review history.

Acknowledgements
The authors thank Chris Creevey for insights on phylogenetically deep horizontal gene transfer. We thank Oleksandr M
Maistrenko and Donovan Parks for comments and feedback on the manuscript, as well as all members of the Bork lab
for insightful discussions.

Peer review information

Andrew Cosgrove was the primary editor of this article and managed its editorial process and peer review in
collaboration with the rest of the editorial team.

Review history
The review history is available as Additional file 3.

Authors’ contributions
A.O. and T.S.B.S. conceived the study and designed analyses. L.P.C., D.S., D.R.M., and P.B. contributed to formulating and
implementing core concepts in GUNC scoring. S.K. and D.R.M. provided, curated, and analyzed reference and test
datasets. A.O. performed simulations, benchmarks, and analyses, with support by A.F. A.O., T.S.B.S, and P.B. analyzed the
data, with input from all authors. A.F implemented and optimized the tool for distribution. T.S.B.S. and A.O. wrote the
manuscript, with input from all authors. All authors read and approved the final manuscript.

Authors’ information
Twitter handles: @AskarbekOrakov (Askarbek Orakov); @Fullam_Anthony (Anthony Fullam); @luispedrocoelho (Luis
Pedro Coelho); @pangenomics (Daniel R Mende); @TSBSchm (Thomas SB Schmidt); @BorkLab (Peer Bork).

Funding
This work was partially supported by EMBL, the German Federal Ministry of Education and Research (grant numbers:
031L0181A; 01Kl1706), the Novo Nordisk Foundation (grant number NNF15OC0016692), the H2020 European Research
Council (ERC-AdG-669830), and the German Network for Bioinformatics Infrastructure (de.NBI #031A537B). Open Access
funding enabled and organized by Projekt DEAL.

Availability of data and materials

The datasets generated and/or analyzed during the current study are available in the GUNC webpage (https://grp-
bork.embl-community.io/gunc/datasets.html). Code is available on GitHub under a GNU GPLv3 license (https://github.
com/grp-bork/gunc) [51] with the version used to generate data in this manuscript deposited in zenodo (https://
zenodo.org/record/4733764) [52].
Orakov et al. Genome Biology (2021) 22:178 Page 18 of 19

Declarations
Ethics approval and consent to participate
Ethics approval is not applicable for this work.

Competing interests
The authors declare that they have no competing interests.

Author details
1
Structural and Computational Biology Unit, European Molecular Biology Laboratory, 69117 Heidelberg, Germany.
2
Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China. 3Key Laboratory
of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University), Ministry of Education, Shanghai,
China. 4Institute of Molecular Life Sciences, University of Zurich, Zurich, Switzerland. 5Swiss Institute of Bioinformatics,
Lausanne, Switzerland. 6Department of Medical Microbiology, Amsterdam University Medical Center, Amsterdam, The
Netherlands. 7Max Delbrück Centre for Molecular Medicine, Berlin, Germany. 8Yonsei Frontier Lab (YFL), Yonsei
University, Seoul 03722, South Korea. 9Department of Bioinformatics, Biocenter, University of Würzburg, Würzburg,
Germany.

Received: 16 December 2020 Accepted: 27 May 2021

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