# MARVEL, a tool for prediction of bacteriophage sequences in metagenomic bins

Deyvid Amgarten, Lucas Palma Perez Braga, Aline M. da Silva, Joao C. Setubal

► To cite this version:

Deyvid Amgarten, Lucas Palma Perez Braga, Aline M. da Silva, Joao C. Setubal. MARVEL, a tool for prediction of bacteriophage sequences in metagenomic bins. Frontiers in Genetics, 2018, 9, 10.3389/fgene.2018.00304. [https://doi.org/10.3389/fgene.2018.00304](https://doi.org/10.3389/fgene.2018.00304). [arXiv:2005.02223](https://arxiv.org/abs/2005.02223)

HAL Id: hal-02622235

https://hal.inrae.fr/hal-02622235

Submitted on 26 May 2020

HAL is a multi-disciplinary open access
archive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d'enseignement et de
recherche français ou étrangers, des laboratoires
publics ou privés.

<figure><img src="image_1.png" /></figure>

Distributed under a Creative Commons Attribution 4.0 International License
<figure><img src="image_2.png" /></figure>

Check for
updates

# MARVEL, a Tool for Prediction of Bacteriophage Sequences in Metagenomic Bins

Deyvid Amgarten¹, Lucas P. P. Braga¹,², Aline M. da Silva¹ and João C. Setubal¹,³*

¹Departamento de Bioquímica, Instituto de Química, Universidade de São Paulo, São Paulo, Brazil, ²INRA, UMR 1347, Agroécologie, Dijon, France, ³Biocomplexity Institute of Virginia Tech, Blacksburg, VA, United States

Here we present MARVEL, a tool for prediction of double-stranded DNA bacteriophage sequences in metagenomic bins. MARVEL uses a random forest machine learning approach. We trained the program on a dataset with 1,247 phage and 1,029 bacterial genomes, and tested it on a dataset with 335 bacterial and 177 phage genomes. We show that three simple genomic features extracted from contig sequences were sufficient to achieve a good performance in separating bacterial from phage sequences: gene density, strand shifts, and fraction of significant hits to a viral protein database. We compared the performance of MARVEL to that of VirSorter and VirFinder, two popular programs for predicting viral sequences. Our results show that all three programs have comparable specificity, but MARVEL achieves much better performance on the recall (sensitivity) measure. This means that MARVEL should be able to identify many more phage sequences in metagenomic bins than heretofore has been possible. In a simple test with real data, containing mostly bacterial sequences, MARVEL classified 58 out of 209 bins as phage genomes; other evidence suggests that 57 of these 58 bins are novel phage sequences. MARVEL is freely available at https://github.com/LaboratorioBioinformatica/MARVEL.

**Keywords:** phage, virus, microbiome, machine learning, random forest

## OPEN ACCESS

**Edited by:**
Alfredo Pulvirenti,
Università degli Studi di Catania, Italy

**Reviewed by:**
Ayman Sabry El-Baz,
University of Louisville, United States
Cuncong Zhong,
The University of Kansas,
United States

*Correspondence:
João C. Setubal
joao.c.setubal@gmail.com

**Specialty section:**
This article was submitted to
Bioinformatics and Computational
Biology,
a section of the journal
Frontiers in Genetics

**Received:** 17 May 2018
**Accepted:** 18 July 2018
**Published:** 07 August 2018

**Citation:**
Amgarten D, Braga LPP, da Silva AM and Setubal JC (2018) MARVEL, a Tool for Prediction of Bacteriophage Sequences in Metagenomic Bins. *Front. Genet.* 9:304. doi: 10.3389/fgene.2018.00304

## INTRODUCTION

In the past few decades, our understanding of microbial life has been profoundly changed by techniques of environmental sampling and high-throughput sequencing (Rappé and Giovannoni, 2003; Handelsman, 2004; DeLong, 2009). The uncultured majority of Bacteria and Archaea is slowly being revealed and so is the largely unknown universe of their viruses (Yutin et al., 2018). Viruses are the most abundant biological entities on Earth, outnumbering bacteria and archaea in the oceans by a factor of at least 10, perhaps 100 (Bergh et al., 1989). The majority of environmental viruses infects bacterial hosts and are therefore termed bacteriophages or simply phages. They have been shown to be important drivers of biogeochemical cycles on Earth (Roux et al., 2016), as well as key players in directing and originating bacterial diversity (Falkowski et al., 2008; Koskella and Brockhurst, 2014; Braga et al., 2018).

Isolation is the gold standard for characterizing and assessing phage diversity, and many new phages are isolated every year from diverse environments such as oceans, composting, and human sewage, among many others (Sullivan et al., 2003; Kumari et al., 2009; Amgarten et al., 2017). However, isolation of viruses is constrained by the requirement of cultivable bacterial isolates
as hosts. This hinders the prospection for new phages since the
vast majority of the microbial species are uncultivable under
laboratory conditions (Solden et al., 2016). In this context, tools
for mining viral sequences in large datasets of metagenomic reads
and contigs are crucial to retrieve information about novel phage
genes and genomes (Rosario and Breitbart, 2011).

Machine learning is a general technique that has gained
in popularity in the last few years (Hastie et al., 2009; James
et al., 2013). A machine learning algorithm can be trained to
recognize a specific biological attribute once a list of example
bona fide features is provided. Attributes are commonly referred
to as *labels* in supervised learning. A machine learning problem
generally consists of trying to assign labels to new objects,
given a list of features on which the algorithm was trained.
In the case of DNA sequences, commonly used features are
GC content, oligonucleotide frequency profiles, and codon
usage.

Two popular tools have been developed for prediction of
viral sequences in a dataset of DNA sequences (Roux et al.,
2015; Ren et al., 2017). VirSorter is a tool for prediction of viral
contigs in metagenomic datasets, which uses alignments and
similarity search in a database of known viruses (Roux et al.,
2015). VirFinder uses a machine learning classifier for the same
purpose, but in this case, *k*-mer frequency profiles (frequency
of nucleotide words of length *k*) are extracted from contigs and
given as input to a previously trained model (Ren et al., 2017).
Both tools have good performance and are shedding light into
the viral dark matter (Nigro et al., 2017; Hurwitz et al., 2018).
However, these tools do not perform well in terms of recall
(sensitivity), and therefore they might be missing an overly large
fraction of true viral sequences (Roux et al., 2015; Ren et al.,
2017).

Here we present MARVEL (Metagenomic Analysis and
Retrieval of Viral Elements), a tool for prediction of dsDNA
phage sequences in metagenomic bins. MARVEL uses a machine
learning approach and three simple genomic features extracted
from contig sequences. MARVEL considers a contig sequence to
be predicted as part of a previously determined bin (as opposed
to treating contigs as isolated objects), seeking to leverage the
information that all contigs in a bin are, in principle, part of the
same organism.

## MATERIALS AND METHODS

### Training and Testing Datasets

To build and test MARVEL, the RefSeq microbial dataset was
downloaded (January 2018) and only genomes belonging to the
Bacteria domain (NCBI txid: 2) and to dsDNA viruses from the
*Caudovirales* order (NCBI txid:28883) were selected (this is the
*baseline dataset*). Tailed phages were selected at this step as a
representative group given that they constitute the majority of
viruses present in most environmental samples (Ashelford et al.,
2003; Filée et al., 2005; Ackermann, 2007). The baseline dataset
was split into two subsets according to the GenBank record
date: before January 2016; and January 2016 and thereafter. This
time-based division is usually applied in classifiers to simulate

the use of the tool on newly isolated sequences (Roux et al.,
2015; Ren et al., 2017). We refer to the before-2016 subset as
the *training dataset*, and to the 2016-and-later subset as the
*testing dataset*. The training dataset has 1,247 phage genomes and
1,029 bacterial genomes, and it was used to train and generate
a model for prediction of phage bins. The testing dataset has
335 bacterial genomes and 177 phage genomes. Training and
testing datasets have no overlap and are available in MARVEL's
repository page<fnref n="1" />.

Training and testing datasets were further processed to
generate mock datasets of contigs with specific lengths. For
each fragment length analyzed in this study (2, 4, 8, 12, and
16 kbp), complete genomes were randomly fragmented in 10
contigs of the specified length that may or may not have overlap.
Next, contigs belonging to the same organism were clustered
to form a simulated bin. This process was performed for both
training and testing sets, and the resulting bins were used
to train the machine learning algorithm, to assess MARVEL's
performance, and to compare MARVEL against VirSorter and
VirFinder.

## Feature Extraction and Classifier
Development

As previous studies have shown, genomic features such as
DNA *k*-mer profiles and GC content can be strong signals in
linking or differentiating genome sequences from bacteria and
viruses (Edwards et al., 2016; Ren et al., 2017). However, it
is known that phages try to mimic host genome sequences in
order to overcome their defenses (Carbone, 2008; Bahir et al.,
2009). This causes classifiers based on *k*-mer frequencies to have
poor performance in terms of overall accuracy and especially
recall. In other words, when one of these classifiers identifies
a phage genome, it is almost always correct, but it is likely
to miss a majority of new phages present in environmental
samples.

Seeking more robust features, we focused our efforts
on characteristics related to genome structure and protein
translational mechanisms of each organism. Such characteristics
require a second layer of information, which may be added by
utilization of results from gene prediction programs, such as
Prodigal (Hyatt et al., 2010) and GeneMark (Besemer et al., 2001).
Therefore, we evaluated phage and bacterial genomes according
to six of these genomic features extracted from the baseline
dataset of RefSeq complete genomes.

These six features are: average gene length, average spacing
between genes, density of genes, frequency of strand shifts
between neighboring genes, ATG relative frequency, and fraction
of genes with significant hits against the pVOGs database
(Grazziotin et al., 2017). *Average gene length* was computed by
adding up the length of all predicted CDSs in the genome or in the
contigs in a bin (in bp) divided by the total number of predicted
CDSs. *Average spacing* was calculated as the mean length in bp
of regions between two CDSs. *Density of genes* was calculated as
the total number of CDSs divided by genome length measured
in kbp. *Frequency of strand shifts* was computed by adding up

<footnote n="1">¹https://github.com/laboratoriobioinformatica/MARVEL</footnote>
the number of strand shifts between neighboring genes, and dividing by the total number of CDSs in the genome. ATG relative frequency was computed by counting the number of ATG triplets in one of the strands, in all contigs in a bin or in the complete genome, divided by the total number of 3-mers in that sequence (one strand). Finally, each CDS in a genome was searched using HMMscan (Eddy, 2011) against the pVOGs database of viral HMM profiles (Grazziotin et al., 2017) (downloaded in January 2018); a significant hit was noted when the e-value was less than or equal to 10⁻¹⁰. The number of significant hits was divided by the total number of CDSs to generate the fraction of genes with significant hits against the pVOGs database. All values based on predicted CDSs were extracted from GenBank files as available for download in January 2018 (exploratory step) or predicted in simulated fragments by Prodigal (Hyatt et al., 2010) as driven by Prokka (Seemann, 2014).

Using Python Scikit Learn libraries (Pedregosa et al., 2011), we tried different machine learning approaches based on the six features listed above. Specifically: support vector machine (SVM), logistic regression, neural networks, and random forest. Classifiers were evaluated using the training set as well as k-fold cross-validation (k = 20), with the result that random forest was the best approach for our target prediction. Similar findings about suitability of random forest classifiers in bioinformatics have also been reported (Boulesteix et al., 2012; Zhang et al., 2017).

The relative weight of each feature on a given dataset was calculated by the ID3 implementation of random forest (Quinlan, 1986). Features with low gain of information were removed from the final model, in order to simplify feature extraction in the final version of the tool. The following features were selected as more informative: gene density, strand shifts, and fraction of genes with significant hits against pVOGs database (see section "Results"). We then extracted these three informative features from a complete training set of 8 kbp simulated bins, and a random forest classifier was trained to be MARVEL's prediction core. The random forest model was trained with 50 initial tree estimators and leaf pruning; other parameters were set to their default values.

## Tests With Simulated Metagenomic Bins

Simulated bins containing different fragment lengths were generated for genomes of the testing set as previously described to assess MARVEL's performance. Each test corresponding to a specific fragment length was performed in five randomly sampled replicates of 150 bins (75 bacteria and 75 dsDNA phages). Bins were submitted to MARVEL and predictions were evaluated for true positive rates, specificity, accuracy, and F1 score according to the following standard formulae:

$$ 
\begin{aligned}
TPR &= \frac{TP}{TP + FN} \\
SPC &= \frac{TN}{TN + FP} \\
ACC &= \frac{TP + TN}{TP + FP + TN + FN}
\end{aligned}
$$

$$ F_1 = \frac{2TP}{2TP + FP + FN} $$

$$ 
\begin{aligned}
\text{Where:} & \quad TPR = \text{True positive rate, SPC = Specificity,} \\
& \quad ACC = \text{Accuracy,} \\
& \quad TP = \text{True positive count, FP = False positive count,} \\
& \quad TN = \text{True negative count, FN = False negative count}
\end{aligned}
$$

## Tests With Real Metagenomic Data From Composting Samples

A dataset of Illumina raw reads from composting samples generated by our group (Antunes et al., 2016) was used to test MARVEL's performance in real metagenomic data. Five samples were extracted from a composting unit, and whole community DNA was extracted to generate shotgun metagenomic reads; this dataset contains mostly bacterial sequences. Raw reads for all five samples were cross-assembled with metaSpades (Nurk et al., 2017) generating a set of contigs. Metabat2 (Kang et al., 2015) was used for binning with parameters: -m 1500 -s 10000. Other parameters remained with their default values. Resulting bins were evaluated regarding quality and the presence of Bacterial and Archaeal marker genes using CheckM (Parks et al., 2015).

## Pipeline Implementation

MARVEL was coded in Python 3 and uses Prokka (Seemann, 2014) and HMMscan (Eddy, 2011) as important dependencies. As input, MARVEL requires a directory with metagenomic bins in FASTA format; it generates a results directory containing bins predicted as phages. An auxiliary script was made available to generate bins from Illumina paired-end reads using standard tools and methods (Breitwieser et al., 2017).

## Performance Comparison of MARVEL, VirSorter, and VirFinder

Each contig of a simulated bin (10 contigs in total) was individually given as input to VirSorter and VirFinder. For a given tool, an entire bin was considered to be a positive prediction in case at least one of its contigs were predicted as viral (note that in our experimental set-up, there are no bins with both bacterial and viral sequences). A contig was considered viral if predicted in categories I and II for VirSorter, and if the q-value was less than or equal to 0.01 for VirFinder. Tests were performed for different fragment lengths and in 30 randomly sampled replicates of 100 bins (50 bacteria and 50 dsDNA phages). Average values of true positive rate, specificity, and accuracy were compared using the Wilcoxon signed-rank test and were considered significant if the p-value was less than 0.001.

Running time was measured for all tools using two sets of bins (100 bins averaging 40 kbp and 100 bins averaging 160 kbp) in a standard desktop computer with a 64-bit Intel Core i7-4770 3.4 GHz × 6 CPUs and 8 GB RAM DDR3, running Linux distribution Ubuntu 16.
<figure><img src="image_3.png" /></figure>

## RESULTS

As mentioned, we tested six different genomic features; the three best features for our target prediction were gene density, strand shifts, and fraction of significant pVOGs hits. The relative weights of each feature (based on gain of information) according to the ID3 implementation on both training and testing datasets are: genes density: 0.32, strand shifts: 0.31, pVogs hits: 0.37. **Figure 1** shows results for two of these features on the baseline dataset; numerical results for all three features are shown in **Table 1**. In **Supplementary Figure S1** we also present a PCA analysis of the three selected features.

For a given length, simulated bins were randomly subsampled and given as input to MARVEL in five replicates. Predictions were performed for each simulated bin and results are shown in

**Figure 2.** Additional results for k-fold cross validation using both training and testing datasets are presented in **Supplementary Table S1**.

The comparison results between MARVEL, VirSorter, and VirFinder are shown in **Figure 3**. **Table 2** shows running times for each tool with two different sets of bins as input and running on a standard desktop computer.

## Identification of Novel Phage Genome Candidates From Composting Samples

Reads from composting samples were assembled and binned, generating 209 bins. These bins were given as input to MARVEL, which classified 58 bins as phage genomes (**Supplementary Table S2**). These 58 bins ranged in length from 10 to 236 kbp (averaging 27 kbp), which are in the expected range of phage genomes (Mahmoudabadi and Phillips, 2018).

We submitted the 209 bins to CheckM. Out of the 58 bins predicted as phages, only one presented bacterial marker genes. This bin contains a CDS predicted to code for a member of the MerR family of transcriptional activators (pfam00376). All other 57 bins were classified by CheckM as “root,” meaning that they had no hits against the set of bacterial marker genes used by CheckM. The potential novelty of the sequences in these 58 bins can be evaluated by observing the number of CDSs in each

<table><caption>**TABLE 1** | Mean values (and respective standard deviations) for three features extracted from the training dataset of dsDNA phage and bacterial genomes.</caption><thead><tr><th rowspan="2"></th><th colspan="3">Features</th></tr><tr><th>Gene density (genes by kbp)</th><th>Strand shifts by total number of genes</th><th>Fraction of pVOGs significant hits</th></tr></thead><tbody><tr><td>Phage</td><td>1.44 (±0.27)</td><td>0.07 (±0.05)</td><td>0.68 (±0.2)</td></tr><tr><td>Bacteria</td><td>0.93 (±0.13)</td><td>0.24 (±0.08)</td><td>0.1 (±0.04)</td></tr></tbody></table>
<figure><img src="image_4.png" /><figcaption>**FIGURE 2** | MARVEL's performance in simulated bins obtained from the testing set of RefSeq genomes. Recall, specificity, accuracy and F1 score are shown for bins composed of different contig lengths.</figcaption></figure>

<figure><img src="image_5.png" /><figcaption>**FIGURE 3** | Performance comparison of MARVEL, VirSorter, and VirFinder. Means were compared using Wilcoxon signed-rank test. Standard deviation of 30 replicates are shown by error bars. \* denotes statistically significant difference.</figcaption></figure>

bin with significant pVOGs hits: the observed range was [14–60]% (Supplementary Table S2). The 209 bins used in this test are available at https://github.com/LaboratorioBioinformatica/MARVEL.

DISCUSSION

**Figure 1** and **Table 1** show that the features chosen can effectively distinguish between bacterial and dsDNA phage sequences.
<table><caption>**TABLE 2** | Running time for two different set of bins.</caption><thead><tr><th rowspan="2"></th><th colspan="2">100 bins of ~40 kbp</th><th colspan="2">100 bins of ~160 kbp</th></tr><tr><th>Wall time</th><th>CPU usage</th><th>Wall time</th><th>CPU usage</th></tr></thead><tbody><tr><td>MARVEL</td><td>11 m 33 s</td><td>17 m 54 s</td><td>36 m 33 s</td><td>70 m 45 s</td></tr><tr><td>VirSorter</td><td>10 m 20 s</td><td>27 m 21 s</td><td>39 m 18 s</td><td>140 m 8 s</td></tr><tr><td>VirFinder</td><td>40 s</td><td>40 s</td><td>42 s</td><td>42 s</td></tr></tbody></table>

Tests were performed in a desktop computer with a 64-bit Intel Core i7-4770 3.4 GHz × 6 CPUs and 8 GB RAM DDR3, running Linux distribution Ubuntu 16. CPU times are the sum of times spent by each CPU.

These results suggest that higher gene density and lower rates of strand shift are important phage genomic hallmarks when compared with bacterial genomes. The length of phage genomes is physically constrained by the size of the capsid, which imposes a limited space for genes in the genome (Chirico et al., 2010), favoring increased gene density when compared to bacteria. Evidence supporting very compact phage genomes has also been reported by previous studies (O'Connell, 2005; Roux et al., 2015; Mahmoudabadi and Phillips, 2018). The lower rates of strand shifts can be interpreted as giving phages more efficiency in transcription/translation processes. Such efficiency helps ensure competitive superiority of phage genes over host genes and is essential for phage control of host transcription/translation machinery and cellular resources (Mrázek and Karlin, 1998; Miller et al., 2003). MARVEL's results have also indicated that the pVOGs database of HMM profiles is comprehensive enough to capture the signal of conserved phage proteins, such as DNA polymerases, helicases, and terminases. These proteins were often identified in the newly discovery genomes, which is in agreement with previous reports from the literature (Rohwer and Edwards, 2002; Comeau et al., 2007). In sum, gene density, strand shifts and pVOGs hits combined as features in a machine learning approach allow more accurate and more sensible prediction of phage genomes compared to other features reported in the literature. Moreover, the relative weights of these three features are approximately the same, suggesting that our model is robust, and should perform well even when a new phage genome has few or no hits to the pVOGs database.

In terms of performance metrics, MARVEL has high F1 scores and accuracy for all bin lengths analyzed, but especially for bins composed of contigs 4 kbp long and longer (Figure 2). True positive rates were particularly high for all fragment lengths. As already mentioned, VirFinder and VirSorter do not in general have good recall values, as opposed to specificity, for which their performance is usually very good (Roux et al., 2015; Ren et al., 2017). Altogether, these performance results in simulated data suggest that MARVEL is effectively able to predict dsDNA phage genomes in metagenomic bins.

All three tools have comparable results for specificity in most of the fragment lengths studied, with the exception of 2 kbp-fragments (Figure 2). On the other hand, MARVEL's true positive rates (recall) were significantly higher in all cases (p-value < 0.001). MARVEL's better true positive rates resulted in better overall accuracy compared to the two other tools in all

scenarios. Short contigs (2 kbp or less in length) represent a clear limitation, since MARVEL uses CDS predictions as primary information in all three features that we selected. Sequences too short will contain very few or no CDSs, and at least two CDSs are required for calculating the features gene density and frequency of strand shifts. On the other hand, reports in the literature indicate that viral bins are often composed of large contigs, and in some cases contain almost complete viral genomes (Dutilh et al., 2014; Paez-Espino et al., 2017), suggesting that this limitation may not be serious.

The use of MARVEL in one dataset of real data, with contigs having widely varying lengths, yielded promising results, resulting in 57 potentially novel phage sequences.

Upstream processing such as assembly and binning are two major factors that also influence MARVEL's performance. Chimeric contigs, as well as poorly binned bins may generate noisy data, which will certainly increase the number of erroneous predictions. Therefore, it is important to choose thresholds and parameter values to ensure quality of upstream processing (Mavromatis et al., 2007; Roux et al., 2017). There are several tools available for assembly and binning which generate good quality contigs and bins (Kang et al., 2015; Li et al., 2016; Wu et al., 2016; Nurk et al., 2017). We emphasize, however, that assessing quality of viral bins is not an easy task. CheckM is a tool for assessing marker genes, contamination and completeness of metagenomic bins, but unfortunately only bacterial and archaeal datasets of marker genes are available (Parks et al., 2015).

Our results (Table 2) show that MARVEL and VirSorter have comparable running times, while VirFinder is much faster than either. For all programs, wall time was under an hour for what we believe are realistic-sized datasets.

## CONCLUSION

To our knowledge, MARVEL is the first tool capable to effectively separate metagenomic bins containing dsDNA phage sequences from those containing bacterial sequences. By doing this, it facilitates downstream metagenomic analyses aiming to characterize phage phylogenetic and functional diversity. VirSorter and VirFinder are two excellent tools optimized to analyze single contigs. Although it would be possible to use these tools in a pipeline to generate whole bin predictions, this would certainly require substantial additional work. Furthermore, we present results in simulated data showing significantly better true positive rates and accuracy for MARVEL's predictions. These improvements were achieved by the implementation and use of three specific genomic features, shown here to be highly suitable for viral sequence prediction.

In its present incarnation, as described here, MARVEL is able to effectively predict tailed phages of the *Caudovirales* order only. Tailed phages constitute the majority of viruses present in most environmental samples, and we believe this fact justifies our choice (Ashelford et al., 2003; Filée et al., 2005; Ackermann, 2007). On the other hand, the features that we used for predictions in this work may not be as effective for viruses in general (Mahmoudabadi and Phillips, 2018). This may be one
reason why recall rates in our tests were lower for VirSorter and
VirFinder as compared to MARVEL, since those other tools are
generic viral sequence finders.

We believe an effective generic viral model would be hard
to achieve, given the heterogeneity of viral types and genome
structures. Nevertheless, it is our intention to expand MARVEL's
scope to include prediction of other groups of viruses, by
obtaining additional models specific to other viral groups. Such
models would be available to users as parameter choices in
future versions of MARVEL; the program was designed with this
objective in mind. We are also working on a module that will seek
to provide genome completeness and contamination statistics for
each predicted phage genome, similar to what CheckM (Parks
et al., 2015) does for bacterial genomes.

SOFTWARE AVAILABILITY

The MARVEL tool, documentation, usage examples, and training
and testing datasets are freely available through an online
repository¹.

AUTHOR CONTRIBUTIONS

DA conceived, coded, and implemented the tool. JS, AdS, and LB discussed the tool's design and experimental set-up and results, providing feedback that led to improvements. DA and JS wrote

the manuscript. All authors read, revised, and approved the final
draft.

FUNDING

DA was supported in part by fellowship Grant No. 2014/16450-8 from the São Paulo State Research Foundation (FAPESP). DA and LB were supported by a fellowship from Brazilian Federal Agency CAPES. JS and AdS wish to acknowledge their respective research fellowships from CNPq. This work was supported in part by FAPESP Grant No. 2011/50870-6 and by CAPES Grant No. 3385/2013.

ACKNOWLEDGMENTS

We thank Luiz Thiberio Rangel for providing helpful coding tips
and for general discussions, Melline Fontes Noronha for testing
the tool and providing feedback, and Carlos Morais for technical
support.

SUPPLEMENTARY MATERIAL

The Supplementary Material for this article can be found
online at: https://www.frontiersin.org/articles/10.3389/fgene.
2018.00304/full#supplementary-material

REFERENCES

Ackermann, H.-W. (2007). 5500 Phages examined in the electron microscope. *Arch. Virol.* **152**, 227–243. doi: **10.1007/s00705-006-0849-1**

Amgarten, D., Martins, L. F., Lombardi, K. C., Antunes, L. P., de Souza, A. P. S.,  
Nicastro, G. G., et al. (2017). Three novel *Pseudomonas* phages isolated from  
composting provide insights into the evolution and diversity of tailed phages.  
*BMC Genomics* **18**:346. doi: **10.1186/s12864-017-3729-z**

Antunes, L. P., Martins, L. F., Pereira, R. V., Thomas, A. M., Barbosa, D., Lemos, L. N., et al. (2016). Microbial community structure and dynamics in thermophilic composting viewed through metagenomics and metatranscriptomics. *Sci. Rep.* **6**:38915. doi: **10.1038/srep38915**

Ashelford, K. E., Day, M. J., and Fry, J. C. (2003). Elevated abundance of bacteriophage infecting bacteria in soil. *Appl. Environ. Microbiol.* **69**, 285–289. doi: **10.1128/AEM.69.1.285-289.2003**

Bahir, I., Fromer, M., Prat, Y., and Linial, M. (2009). Viral adaptation to host: a proteome based analysis of codon usage and amino acid preferences. *Mol. Syst. Biol.* **5**:311. doi: **10.1038/msb.2009.71**

Bergh, O., Børsheim, K. Y., Bratbak, G., and Heldal, M. (1989). High abundance of viruses found in aquatic environments. *Nature* **340**, 467–468. doi: **10.1038/340467a0**

Besemer, J., Lomsadze, A., and Borodovsky, M. (2001). GeneMarkS: a self-training method for prediction of gene starts in microbial genomes. Implications for finding sequence motifs in regulatory regions. *Nucleic Acids Res.* **29**, 2607–2618. doi: **10.1093/nar/29/12.2607**

Boulesteix, A.-L., Janitza, S., Kruppa, J., and König, I. R. (2012). Overview of random forest methodology and practical guidance with emphasis on computational biology and bioinformatics. *Wiley Interdiscip. Rev. Data Min. Knowl. Discov.* **2**, 493–507. doi: **10.1002/widm.1072**

Braga, L. P. P., Soucy, S. M., Amgarten, D. E., da Silva, A. M., and Setubal, J. C. (2018). Bacterial diversification in the light of the interactions with phages: the genetic symbionts and their role in ecological speciation. *Front. Ecol. Evol.* **6**: doi: **10.3389/fevo.2018.00006**

Breitwieser, F. P., Lu, J., and Salzberg, S. L. (2017). A review of methods and
databases for metagenomic classification and assembly. *Brief. Bioinform.* doi:
10.1093/bib/bbx120 [Epub ahead of print].

Carbone, A. (2008). Codon bias is a major factor explaining phage evolution in translationally biased hosts. *J. Mol. Evol.* **66**, 210–223. doi: **10.1007/s00239-008-9068-6**

Chirico, N., Vianelli, A., and Belshaw, R. (2010). Why genes overlap in viruses. *Proc. Biol. Sci.* **277**, 3809–3817. doi: **10.1098/rspb.2010.1052**

Comeau, A. M., Bertrand, C., Letarov, A., Tétart, F., and Krisch, H. M. (2007). Modular architecture of the T4 phage superfamily: a conserved core genome and a plastic periphery. *Virology* **362**, 384–396. doi: **10.1016/j.virol.2006.12.031**

DeLong, E. F. (2009). The microbial ocean from genomes to biomes. *Nature* **459**, 200–206. doi: **10.1038/nature08059**

Dutilh, B. E., Cassman, N., McNair, K., Sanchez, S. E., Silva, G. G. Z., Boling, L., et al. (2014). A highly abundant bacteriophage discovered in the unknown sequences of human faecal metagenomes. *Nat. Commun.* **5**:4498. doi: **10.1038/ncomms5498**

Eddy, S. R. (2011). Accelerated profile HMM searches. *PLoS Comput. Biol.* **7**:e1002195. doi: **10.1371/journal.pcbi.1002195**

Edwards, R. A., McNair, K., Faust, K., Raes, J., and Dutilh, B. E. (2016). Computational approaches to predict bacteriophage-host relationships. *FEMS Microbiol. Rev.* **40**, 258–272. doi: **10.1093/femsre/fuv048**

Falkowski, P. G., Fenchel, T., and Delong, E. F. (2008). The microbial engines that drive Earth's biogeochemical cycles. *Science* **320**, 1034–1039. doi: **10.1126/science.1153213**

Filée, J., Tétart, F., Suttle, C. A., and Krisch, H. M. (2005). Marine T4-type bacteriophages, a ubiquitous component of the dark matter of the biosphere. *Proc. Natl. Acad. Sci. U.S.A.* **102**, 12471–12476. doi: **10.1073/pnas.0503404102**

Grazziotin, A. L., Koonin, E. V., and Kristensen, D. M. (2017). Prokaryotic virus orthologous groups (pVOGs): a resource for comparative genomics and protein family annotation. *Nucleic Acids Res.* **45**, D491–D498. doi: **10.1093/nar/gkw975**
Handelsman, J. (2004). Metagenomics: application of genomics to uncultured microorganisms. *Microbiol. Mol. Biol. Rev.* 68, 669-685. doi: 10.1128/MMBR.68.4.669-685.2004

Hastie, T., Tibshirani, R., and Friedman, J. (2009). *The Elements of Statistical Learning: Data Mining, Inference, and Prediction*. New York, NY: Springer. doi: 10.1007/978-0-387-84858-7

Hurwitz, B. L., Ponsero, A., Thornton, J. Jr., and U'Ren, J. M. (2018). Phage hunters: computational strategies for finding phages in large-scale 'omics datasets. *Virus Res.* 244, 110–115. doi: 10.1016/j.virusres.2017.10.019

Hyatt, D., Chen, G.-L., Locascio, P. F., Land, M. L., Larimer, F. W., and Hauser, L. J. (2010). Prodigal: prokaryotic gene recognition and translation initiation site identification. *BMC Bioinformatics* 11:119. doi: 10.1186/1471-2105-11-119

James, G., Witten, D., Hastie, T., and Tibshirani, R. (2013). *An Introduction to Statistical Learning: with Applications in R*. New York, NY: Springer. doi: 10.1007/978-1-4614-7138-7

Kang, D. D., Froula, J., Egan, R., and Wang, Z. (2015). MetaBAT, an efficient tool for accurately reconstructing single genomes from complex microbial communities. *PeerJ* 3:e1165. doi: 10.7717/peerj.1165

Koskella, B., and Brockhurst, M. A. (2014). Bacteria-phage coevolution as a driver of ecological and evolutionary processes in microbial communities. *FEMS Microbiol. Rev.* 38, 916-931. doi: 10.1111/1574-6976.12072

Kumari, S., Harjai, K., and Chhibber, S. (2009). Characterization of *Pseudomonas aeruginosa* PAO specific bacteriophages isolated from sewage samples. *Am. J. Biomed. Sci.* 1, 91-102. doi: 10.5099/aj090200091

Li, D., Luo, R., Liu, C.-M., Leung, C.-M., Ting, H.-F., Sadakane, K., et al. (2016). MEGAHIT v1.0: A fast and scalable metagenome assembler driven by advanced methodologies and community practices. *Methods* 102, 3–11. doi: 10.1016/j.ymeth.2016.02.020

Mahmoudabadi, G., and Phillips, R. (2018). A comprehensive and quantitative exploration of thousands of viral genomes. *eLife* 7:e31955. doi: 10.7554/eLife.31955

Mavromatis, K., Ivanova, N., Barry, K., Shapiro, H., Goltsman, E., McHardy, A. C., et al. (2007). Use of simulated data sets to evaluate the fidelity of metagenomic processing methods. *Nat. Methods* 4, 495–500. doi: 10.1038/nmeth1043

Miller, E. S., Kutter, E., Mosig, G., Arisaka, F., Kunisawa, T., and Rüger, W. (2003). Bacteriophage T4 genome. *Microbiol. Mol. Biol. Rev.* 67, 86–156. doi: 10.1128/MMBR.67.1.86-156.2003

Mrázek, J. A. N., and Karlin, S. (1998). Strand compositional asymmetry in bacterial and large viral genomes. *Proc. Natl. Acad. Sci. U.S.A.* 95, 3720–3725. doi: 10.1073/pnas.95.7.3720

Nigro, O. D., Jungbluth, S. P., Lin, H.-T., Hsieh, C.-C., Miranda, J. A., Schvarcz, C. R., et al. (2017). Viruses in the oceanic basement. *mBio* 8:e02129-16. doi: 10.1128/mBio.02129-16

Nurk, S., Meleshko, D., Korobeynikov, A., and Pevzner, P. A. (2017). metaSPAdes: a new versatile metagenomic assembler. *Genome Res.* 27, 824–834. doi: 10.1101/gr.213959.116

O'Connell, D. (2005). Viral genomics: Small is beautiful. *Nat. Rev. Microbiol.* 3, 520-520. doi: 10.1038/nrmicro1196

Paez-Espino, D., Chen, I.-M. A., Palaniappan, K., Ratner, A., Chu, K., Szeto, E., et al. (2017). IMG/VR: a database of cultured and uncultured DNA Viruses and retroviruses. *Nucleic Acids Res.* 45, D457–D465. doi: 10.1093/nar/gkw1030

Parks, D. H., Imelfort, M., Skennerton, C. T., Hugenholtz, P., and Tyson, G. W. (2015). CheckM: assessing the quality of microbial genomes recovered from isolates, single cells, and metagenomes. *Genome Res.* 25, 1043–1055. doi: 10.1101/gr.186072.114

Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., et al. (2011). Scikit-learn: machine learning in python. *J. Mach. Learn. Res.* 12, 2825-2830.

Quinlan, J. R. (1986). Induction of decision trees. *Mach. Learn.* 1, 81–106. doi: 10.1007/BF00116251

Rappé, M. S., and Giovannoni, S. J. (2003). The uncultured microbial majority. *Annu. Rev. Microbiol.* 57, 369–394. doi: 10.1146/annurev.micro.57.030502.090759

Ren, J., Ahlgren, N. A., Lu, Y. Y., Fuhrman, J. A., and Sun, F. (2017). VirFinder: a novel k-mer based tool for identifying viral sequences from assembled metagenomic data. *Microbiome* 5:69. doi: 10.1186/s40168-017-0283-5

Rohwer, F., and Edwards, R. (2002). The phage proteomic tree: a genome-based taxonomy for phage. *J. Bacteriol.* 184, 4529–4535. doi: 10.1128/JB.184.16.4529-4535.2002

Rosario, K., and Brechtart, M. (2011). Exploring the viral world through metagenomics. *Curr. Opin. Virol.* 1, 289-297. doi: 10.1016/j.coviro.2011.06.004

Roux, S., Brum, J. R., Dutilh, B. E., Sunagawa, S., Duhaime, M. B., Loy, A., et al. (2016). Ecogenomics and potential biogeochemical impacts of globally abundant ocean viruses. *Nature* 537, 689–693. doi: 10.1038/nature19366

Roux, S., Emerson, J. B., Eloe-Fadrosh, E. A., and Sullivan, M. B. (2017). Benchmarking viromics: an evaluation of metagenome-enabled estimates of viral community composition and diversity. *PeerJ* 5:e3817. doi: 10.7717/peerj.3817

Roux, S., Enault, F., Hurwitz, B. L., and Sullivan, M. B. (2015). VirSorter: mining viral signal from microbial genomic data. *PeerJ* 3:e985. doi: 10.7717/peerj.985

Seemann, T. (2014). Prokka: rapid prokaryotic genome annotation. *Bioinformatics* 30, 2068–2069. doi: 10.1093/bioinformatics/btu153

Solden, L., Lloyd, K., and Wrighton, K. (2016). The bright side of microbial dark matter: lessons learned from the uncultivated majority. *Curr. Opin. Microbiol.* 31, 217–226. doi: 10.1016/j.mib.2016.04.020

Sullivan, M. B., Waterbury, J. B., and Chisholm, S. W. (2003). Cyanophages infecting the oceanic cyanobacterium Prochlorococcus. *Nature* 424, 1047–1051. doi: 10.1038/nature01929

Wu, Y.-W., Simmons, B. A., and Singer, S. W. (2016). MaxBin 2.0: an automated binning algorithm to recover genomes from multiple metagenomic datasets. *Bioinformatics* 32, 605–607. doi: 10.1093/bioinformatics/btv638

Yutin, N., Bäckström, D., Ettema, T. J. G., Krupovic, M., and Koonin, E. V. (2018). Vast diversity of prokaryotic virus genomes encoding double jelly-roll major capsid proteins uncovered by genomic and metagenomic sequence analysis. *Virol. J.* 15:67. doi: 10.1186/s12985-018-0974-y

Zhang, M., Yang, L., Ren, J., Ahlgren, N. A., Fuhrman, J. A., and Sun, F. (2017). Prediction of virus-host infectious association by supervised learning methods. *BMC Bioinformatics* 18:60. doi: 10.1186/s12859-017-1473-7

**Conflict of Interest Statement:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Copyright © 2018 Amgarten, Braga, da Silva and Setubal. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.