# Benchmarking of virome metagenomic analysis approaches using a large, 60+ members, viral synthetic community

Deborah Schönegger, Oumaima Moubset, Paolo Margaria, Wulf Menzel, Stephan Winter, Philippe Roumagnac, Armelle Marais, Thierry Candresse

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

► To cite this version:

Deborah Schönegger, Oumaima Moubset, Paolo Margaria, Wulf Menzel, Stephan Winter, et al. Benchmarking of virome metagenomic analysis approaches using a large, 60+ members, viral synthetic community. Journal of Virology, 2023, 97 (11), pp.e0130023. 10.1128/jvi.01300-23. hal-04384795

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https://hal.inrae.fr/hal-04384795

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# Benchmarking of virome metagenomic analysis approaches using a large, 60+ members, viral synthetic community

Deborah Schönegger, Oumaima Moubset, Paolo Margaria, Wulf Menzel, Stephan Winter, Philippe Roumagnac, Armelle Marais, Thierry Candresse

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

► To cite this version:

Deborah Schönegger, Oumaima Moubset, Paolo Margaria, Wulf Menzel, Stephan Winter, et al. Benchmarking of virome metagenomic analysis approaches using a large, 60+ members, viral synthetic community. Journal of Virology, 2023, 97 (11), pp.e0130023. 10.1128/jvi.01300-23. hal-04384795

HAL Id: hal-04384795

https://hal.inrae.fr/hal-04384795v1

Submitted on 10 Jan 2024

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_2.png" /></figure>
Benchmarking of virome metagenomic analysis
approaches using a large, 60+ members, viral synthetic
community

Deborah Schönegger¹, Oumaima Moubset²,³, Paolo Margaria⁴, Wulf Menzel⁴, Stephan
Winter⁴, Philippe Roumagnac²,³, Armelle Marais¹ and Thierry Candresse¹*

¹Univ. Bordeaux, INRAE, UMR BFP, CS20032, 33882, Villenave d'Ornon Cedex, France.

² CIRAD, UMR PHIM, 34090 Montpellier, France

³ PHIM Plant Health Institute, Univ Montpellier, CIRAD, INRAE, Institut Agro, IRD, Montpellier, France

⁴ Plant Virus Department, Leibniz-Institute DSMZ, 38124 Braunschweig, Germany

* Author for correspondence: [thierry.candresse@inrae.fr](mailto:thierry.candresse@inrae.fr) Tel : (+33) 557 12 23 89

**Abstract word count:** 248 words

**Text word count:** 7561 words (Introduction-Materials and Methods-Results-Discussion)

**Tables and Figures:** 5 Tables, 4 Figures, 7 supplementary Tables and Figures

**Running title:** Synthetic viral community for virome benchmarking
Benchmarking of virome metagenomic analysis
approaches using a large, 60+ members, viral synthetic
community

Deborah Schönegger¹, Oumaima Moubset²,³, Paolo Margaria⁴, Wulf Menzel⁴, Stephan
Winter⁴, Philippe Roumagnac²,³, Armelle Marais¹ and Thierry Candresse¹*

¹Univ. Bordeaux, INRAE, UMR BFP, CS20032, 33882, Villenave d'Ornon Cedex, France.

² CIRAD, UMR PHIM, 34090 Montpellier, France

³ PHIM Plant Health Institute, Univ Montpellier, CIRAD, INRAE, Institut Agro, IRD, Montpellier, France

⁴ Plant Virus Department, Leibniz-Institute DSMZ, 38124 Braunschweig, Germany

* Author for correspondence: [thierry.candresse@inrae.fr](mailto:thierry.candresse@inrae.fr) Tel : (+33) 557 12 23 89

**Abstract word count:** 248 words

**Text word count:** 7561 words (Introduction-Materials and Methods-Results-Discussion)

**Tables and Figures:** 5 Tables, 4 Figures, 7 supplementary Tables and Figures

**Running title:** Synthetic viral community for virome benchmarking
1 Abstract

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In contrast to microbial metagenomics, there has still been only limited efforts to benchmark
virome analysis approaches performance in terms of faithfulness to community structure and
of completeness of virome description. While natural communities are more readily accessible,
synthetic communities assembled using well characterized isolates allow more accurate
performance evaluation. Starting from authenticated, quality-controlled reference isolates from
the DSMZ Plant Virus Collection, we have assembled synthetic communities of varying
complexity up to a highly complex community of 72 viral agents (115 viral molecules)
comprising isolates from 21 viral families and 61 genera. These communities were then
analyzed using two approaches frequently used in ecology-oriented plant virus metagenomics:
a virion-associated nucleic acids (VANA) based strategy and a highly purified double-stranded
RNAs (dsRNA) based one. The results obtained allowed to compare diagnostic sensitivity of
these two approaches for groups of viruses and satellites with different genome types and
confirmed that the dsRNA-based approach provides a more complete representation of the RNA
virome. For viromes of low to medium complexity, VANA however appears a reasonable
alternative and would be the preferred choice, in particular if analysis of DNA viruses is of
importance. They also allowed to identify several important parameters and to propose
hypotheses to explain differences in performance, in particular differences in the imbalance in
the representation of individual viruses using each approach. Remarkably, these analyses
highlight a strong direct relationship between the completeness of virome description and
sample sequencing depth which should prove useful in further virome analysis efforts.
1 Abstract

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In contrast to microbial metagenomics, there has still been only limited efforts to benchmark
virome analysis approaches performance in terms of faithfulness to community structure and
of completeness of virome description. While natural communities are more readily accessible,
synthetic communities assembled using well characterized isolates allow more accurate
performance evaluation. Starting from authenticated, quality-controlled reference isolates from
the DSMZ Plant Virus Collection, we have assembled synthetic communities of varying
complexity up to a highly complex community of 72 viral agents (115 viral molecules)
comprising isolates from 21 viral families and 61 genera. These communities were then
analyzed using two approaches frequently used in ecology-oriented plant virus metagenomics:
a virion-associated nucleic acids (VANA) based strategy and a highly purified double-stranded
RNAs (dsRNA) based one. The results obtained allowed to compare diagnostic sensitivity of
these two approaches for groups of viruses and satellites with different genome types and
confirmed that the dsRNA-based approach provides a more complete representation of the RNA
virome. For viromes of low to medium complexity, VANA however appears a reasonable
alternative and would be the preferred choice, in particular if analysis of DNA viruses is of
importance. They also allowed to identify several important parameters and to propose
hypotheses to explain differences in performance, in particular differences in the imbalance in
the representation of individual viruses using each approach. Remarkably, these analyses
highlight a strong direct relationship between the completeness of virome description and
sample sequencing depth which should prove useful in further virome analysis efforts.
23 **Importance**

We report here efforts to benchmark performance of two widespread approaches for virome analysis, which target either virion-associated nucleic acids (VANA) or highly purified double-stranded RNAs (dsRNA). This was achieved using synthetic communities of varying complexity levels, up to a highly complex community of 72 viral agents (115 viral molecules) comprising isolates from 21 families and 61 genera of plant viruses. The results obtained confirm that the dsRNA-based approach provides a more complete representation of the RNA virome, in particular for high complexity ones. For viromes of low to medium complexity, VANA however appears a reasonable alternative and would be the preferred choice if analysis of DNA viruses is of importance. Several parameters impacting performance were identified as well as a direct relationship between the completeness of virome description and sample sequencing depth. The strategy, results and tools used here should prove useful in a range of virome analysis efforts.

36 **Keywords:** virome, VANA, dsRNA, synthetic community, metagenome, double-stranded RNA, high-throughput sequencing
23 **Importance**

We report here efforts to benchmark performance of two widespread approaches for virome
analysis, which target either virion-associated nucleic acids (VANA) or highly purified double-
stranded RNAs (dsRNA). This was achieved using synthetic communities of varying
complexity levels, up to a highly complex community of 72 viral agents (115 viral molecules)
comprising isolates from 21 families and 61 genera of plant viruses. The results obtained
confirm that the dsRNA-based approach provides a more complete representation of the RNA
virome, in particular for high complexity ones. For viromes of low to medium complexity,
VANA however appears a reasonable alternative and would be the preferred choice if analysis
of DNA viruses is of importance. Several parameters impacting performance were identified as
well as a direct relationship between the completeness of virome description and sample
sequencing depth. The strategy, results and tools used here should prove useful in a range of
virome analysis efforts.

36 **Keywords:** virome, VANA, dsRNA, synthetic community, metagenome, double-stranded
37 RNA, high-throughput sequencing
38 INTRODUCTION

Significant advances in the development of molecular methods have been made in the last 39 decades, including innovative sequencing technologies based on DNA/RNA approaches such 40 as targeted (RT-)PCR or non-targeted High-Throughput Sequencing (HTS). HTS, also known 41 as next generation sequencing (NGS), enables high-speed, high-throughput sequencing of 42 native DNA/RNA or amplified DNA, generating enormous amounts of sequencing data. These 43 developments led to major advances in the field of metagenomics, i.e. the sequencing of the 44 entire genetic material of a sample, and to a new understanding of microbial diversity [1, 2]. 45

Viral metagenomics has revealed the immense diversity and ubiquity of viruses in nature and 46 thus revolutionized our vision of these biological agents [1, 3-8]. Specifically, these 47 metagenomics studies have revealed that virus sequence data available in public databases are 48 biased toward human viruses or viruses of anthropological significance, with e.g. influenza-like 49 viruses found in fish and amphibian hosts [9] or more than 75% of the plant virus species 50 characterized up to 2006 having been isolated from crops [10]. These findings, together with 51 reports on viruses associated with hosts different from those known for the vast majority of 52 their relatives, such as flavi-like viruses found in plants [11, 12], have raised novel questions 53 about virus-hosts co-divergence or host switching. 54

In plant virology in particular, advances in the development of viral metagenome analyses have 55 been of great importance in terms of early detection of known viruses and discovery of novel 56 plant viruses [4, 7, 13-14], as more than half of emerging diseases in plants are thought to be 57 caused by viruses [15]. HTS has a huge potential in plant virus diagnostics because it allows to 58 picture the complete phytosanitary status of a plant and to differentiate between virus variants 59 that may contribute differentially to disease etiology [14]. For example, in a metagenomic 60 analysis of sour cherry showing symptoms of Shirofugen stunt disease (SSD), a divergent 61 isolate of little cherry virus 1 (LChV1) was identified in the absence of any other viral agent, 62
38 INTRODUCTION

Significant advances in the development of molecular methods have been made in the last 39 decades, including innovative sequencing technologies based on DNA/RNA approaches such 40 as targeted (RT-)PCR or non-targeted High-Throughput Sequencing (HTS). HTS, also known 41 as next generation sequencing (NGS), enables high-speed, high-throughput sequencing of 42 native DNA/RNA or amplified DNA, generating enormous amounts of sequencing data. These 43 developments led to major advances in the field of metagenomics, i.e. the sequencing of the 44 entire genetic material of a sample, and to a new understanding of microbial diversity [1, 2]. 45

Viral metagenomics has revealed the immense diversity and ubiquity of viruses in nature and 46 thus revolutionized our vision of these biological agents [1, 3-8]. Specifically, these 47 metagenomics studies have revealed that virus sequence data available in public databases are 48 biased toward human viruses or viruses of anthropological significance, with e.g. influenza-like 49 viruses found in fish and amphibian hosts [9] or more than 75% of the plant virus species 50 characterized up to 2006 having been isolated from crops [10]. These findings, together with 51 reports on viruses associated with hosts different from those known for the vast majority of 52 their relatives, such as flavi-like viruses found in plants [11, 12], have raised novel questions 53 about virus-hosts co-divergence or host switching. 54

In plant virology in particular, advances in the development of viral metagenome analyses have 55 been of great importance in terms of early detection of known viruses and discovery of novel 56 plant viruses [4, 7, 13-14], as more than half of emerging diseases in plants are thought to be 57 caused by viruses [15]. HTS has a huge potential in plant virus diagnostics because it allows to 58 picture the complete phytosanitary status of a plant and to differentiate between virus variants 59 that may contribute differentially to disease etiology [14]. For example, in a metagenomic 60 analysis of sour cherry showing symptoms of Shirofugen stunt disease (SSD), a divergent 61 isolate of little cherry virus 1 (LChV1) was identified in the absence of any other viral agent, 62
suggesting that LChV1 could be responsible for the SSD disease [16]. However, metagenomics approaches have also revealed that plants are often infected by more than one virus [17], complicating the unravelling of the etiology of plant viral diseases.

HTS has also renewed the link between classical plant virology and ecology [4, 18]. Viromes identified from both cultivated and uncultivated plant populations enabled the study of ecological processes such as the movement of viruses between different host reservoirs, the effects of management practices or of the anthropological simplification of ecosystems [19-23].

For the efficient characterization of complex plant-associated viromes, there is generally a need to enrich viral sequences and conversely reduce the amount of host plant sequences that are generated. Different target nucleic acid populations have been used for virome studies but, coupled with the virus enrichment constraint, the most widely used approaches have targeted virion-associated nucleic acids (VANA) or double stranded RNAs (dsRNAs) [4, 7]. For single plant samples or low complexity samples, the use of total RNA or small interfering RNA (siRNA) sequencing are considered the most universal and straightforward options [24, 25] but when the viromes of entire plant communities are analyzed from complex plant pools, VANA or dsRNAs enrichment methods are generally preferred [4, 7, 19, 21, 26]. A huge number of bioinformatic tools are available for HTS data analysis and have been, together with nucleic acid preparation strategies, extensively reviewed [13, 27-28]. The choice of a specific viral enrichment method or bioinformatic pipeline depends on the experimental objectives. Even though there have been some efforts towards performance comparisons of different virome analysis approaches [29, 30], there is a need to better benchmark them and assess their respective efficiency at providing a faithful and comprehensive description of complex viromes, without introducing biases. In a virus discovery study on single quarantine plants, VANA was shown to assemble longer contigs compared to siRNA for a novel DNA mastrevirus [31], while in a study investigating the virome of native plants in Oklahoma, more viral Operational
suggesting that LChV1 could be responsible for the SSD disease [16]. However, metagenomics approaches have also revealed that plants are often infected by more than one virus [17], complicating the unravelling of the etiology of plant viral diseases.

HTS has also renewed the link between classical plant virology and ecology [4, 18]. Viromes identified from both cultivated and uncultivated plant populations enabled the study of ecological processes such as the movement of viruses between different host reservoirs, the effects of management practices or of the anthropological simplification of ecosystems [19-23].

For the efficient characterization of complex plant-associated viromes, there is generally a need to enrich viral sequences and conversely reduce the amount of host plant sequences that are generated. Different target nucleic acid populations have been used for virome studies but, coupled with the virus enrichment constraint, the most widely used approaches have targeted virion-associated nucleic acids (VANA) or double stranded RNAs (dsRNAs) [4, 7]. For single plant samples or low complexity samples, the use of total RNA or small interfering RNA (siRNA) sequencing are considered the most universal and straightforward options [24, 25] but when the viromes of entire plant communities are analyzed from complex plant pools, VANA or dsRNAs enrichment methods are generally preferred [4, 7, 19, 21, 26]. A huge number of bioinformatic tools are available for HTS data analysis and have been, together with nucleic acid preparation strategies, extensively reviewed [13, 27-28]. The choice of a specific viral enrichment method or bioinformatic pipeline depends on the experimental objectives. Even though there have been some efforts towards performance comparisons of different virome analysis approaches [29, 30], there is a need to better benchmark them and assess their respective efficiency at providing a faithful and comprehensive description of complex viromes, without introducing biases. In a virus discovery study on single quarantine plants, VANA was shown to assemble longer contigs compared to siRNA for a novel DNA mastrevirus [31], while in a study investigating the virome of native plants in Oklahoma, more viral Operational
Taxonomy Units (OTUs) could be detected with dsRNA compared to VANA [26]. Ma *et al.* [32] provided a more comprehensive comparison of these two approaches using the natural viral communities present in complex plant pools from managed and unmanaged sites. The authors found significant differences with more viral contigs and, on average, longer contigs assembled from libraries prepared from dsRNA. With regard to viral richness, more OTUs were detected by the dsRNA approach compared to the VANA one. However, most DNA viruses were only detected using VANA.

Standardization is fundamental for the reliable representation of microbiome/virome in metagenomic studies and is challenged by the rapid development of sequencing platforms, protocols and bioinformatic pipelines [33]. Benchmarking is a powerful tool to provide standards that can be used to compare and evaluate the performance of the different steps required in metagenomic studies, including target nucleic acids population extraction, library preparation, sequencing (and sequencing platform) and finally bioinformatics sequence analysis. In this context, benchmarking studies in metagenomics are often based on mock communities that are microbial assemblages of known composition which can be used to compare the actual vs the expected performance of a process. Besides the use of actual empirical phytoviromes [32], the use of synthetic communities could therefore provide a more precise and detailed benchmarking of HTS-based virome description strategies. Bacterial and fungal mock communities have thus been developed and used to compare the performance of different sequencing platforms, e.g. short read Illumina or long read PacBio SMRT sequencing [34-36]. In recent years, viral mock communities have also been developed, especially in the medical and clinical field, to benchmark protocols in human virome studies. For example, the nucleic acid preparation step for the virome analysis of fecal samples was optimized using a combination of both viral and bacterial mock communities [37]. In another study, the bias introduced by viral enrichment or random amplification were assessed using a DNA virus mock
Taxonomy Units (OTUs) could be detected with dsRNA compared to VANA [26]. Ma *et al.* [32] provided a more comprehensive comparison of these two approaches using the natural viral communities present in complex plant pools from managed and unmanaged sites. The authors found significant differences with more viral contigs and, on average, longer contigs assembled from libraries prepared from dsRNA. With regard to viral richness, more OTUs were detected by the dsRNA approach compared to the VANA one. However, most DNA viruses were only detected using VANA.

Standardization is fundamental for the reliable representation of microbiome/virome in metagenomic studies and is challenged by the rapid development of sequencing platforms, protocols and bioinformatic pipelines [33]. Benchmarking is a powerful tool to provide standards that can be used to compare and evaluate the performance of the different steps required in metagenomic studies, including target nucleic acids population extraction, library preparation, sequencing (and sequencing platform) and finally bioinformatics sequence analysis. In this context, benchmarking studies in metagenomics are often based on mock communities that are microbial assemblages of known composition which can be used to compare the actual vs the expected performance of a process. Besides the use of actual empirical phytoviromes [32], the use of synthetic communities could therefore provide a more precise and detailed benchmarking of HTS-based virome description strategies. Bacterial and fungal mock communities have thus been developed and used to compare the performance of different sequencing platforms, e.g. short read Illumina or long read PacBio SMRT sequencing [34-36]. In recent years, viral mock communities have also been developed, especially in the medical and clinical field, to benchmark protocols in human virome studies. For example, the nucleic acid preparation step for the virome analysis of fecal samples was optimized using a combination of both viral and bacterial mock communities [37]. In another study, the bias introduced by viral enrichment or random amplification were assessed using a DNA virus mock
community [38]. Viral synthetic communities have also been used to benchmark library preparation approaches in environmental [39] and insect [40] virome studies. However, the use of synthetic communities in plant virome studies is lagging behind. So far, the only study using a defined mix of plant viruses to assess different nucleic acid preparation protocols was performed by Gafaar and Ziebell [30]. This study revealed a better performance of enriched dsRNAs as compared to ribodepleted total RNA or siRNAs for virus detection. However, only low complexity synthetic communities have been used so far, whereas most of the viral metagenomes associated with natural plant communities are composed of a complex and diverse mixture of DNA and RNA viruses that are studied from pooled plant samples. In the present work, we used a total of 22 synthetic plant virus communities of varying degrees of complexity to compare the diagnostic performance of VANA and dsRNA-based approaches for virome description and analyzed how this performance is affected by sequencing depth and other parameters. In parallel, a first attempt at contrasting the performance of VANA and dsRNA approaches with those of RNASeq was conducted, using synthetic datasets assembled *in silico* from single-isolate RNASeq data.

## 128 MATERIALS AND METHODS

### 129 Mock viral communities design

A list of 61 different viruses (assigned to 59 different genera from 18 different families plus one unassigned virus) was selected among those kept in collection and available at the Leibniz-Institute DSMZ - German Collection of Microorganisms and Cell Cultures (Braunschweig, Germany), taking into consideration three main criteria: (i) maximizing viral diversity by including viruses with all genome types (ssDNA, dsDNA-RT, dsRNA, +ssRNA, -ssRNA), (ii) including (with one exception) only a single representative virus per viral genus and (iii) selecting viruses/isolates for which a complete or near complete genomic sequence is available.

In some cases, these genomic sequences had been determined previously, while in other cases they were developed specifically in the frame of efforts to further improve the characterization
community [38]. Viral synthetic communities have also been used to benchmark library preparation approaches in environmental [39] and insect [40] virome studies. However, the use of synthetic communities in plant virome studies is lagging behind. So far, the only study using a defined mix of plant viruses to assess different nucleic acid preparation protocols was performed by Gafaar and Ziebell [30]. This study revealed a better performance of enriched dsRNAs as compared to ribodepleted total RNA or siRNAs for virus detection. However, only low complexity synthetic communities have been used so far, whereas most of the viral metagenomes associated with natural plant communities are composed of a complex and diverse mixture of DNA and RNA viruses that are studied from pooled plant samples. In the present work, we used a total of 22 synthetic plant virus communities of varying degrees of complexity to compare the diagnostic performance of VANA and dsRNA-based approaches for virome description and analyzed how this performance is affected by sequencing depth and other parameters. In parallel, a first attempt at contrasting the performance of VANA and dsRNA approaches with those of RNASeq was conducted, using synthetic datasets assembled *in silico* from single-isolate RNASeq data.

## 128 MATERIALS AND METHODS

### 129 Mock viral communities design

A list of 61 different viruses (assigned to 59 different genera from 18 different families plus one unassigned virus) was selected among those kept in collection and available at the Leibniz-Institute DSMZ - German Collection of Microorganisms and Cell Cultures (Braunschweig, Germany), taking into consideration three main criteria: (i) maximizing viral diversity by including viruses with all genome types (ssDNA, dsDNA-RT, dsRNA, +ssRNA, -ssRNA), (ii) including (with one exception) only a single representative virus per viral genus and (iii) selecting viruses/isolates for which a complete or near complete genomic sequence is available.

In some cases, these genomic sequences had been determined previously, while in other cases they were developed specifically in the frame of efforts to further improve the characterization
139 of isolates distributed by the DSMZ through the EU-funded EVA-Global initiative 140 (https://www.european-virus-archive.com/). Quality controlled samples were obtained from the 141 DSMZ in the form of infected, lyophilized plant material in vacuum-sealed vials. The complete 142 list of the isolates used, together with their properties and the propagation host in which they 143 were provided, are given in Table 1.

144 Initial low complexity pools were generated by assembling 30 mg of virus-infected samples 145 into 12 viral communities comprising five viruses each (150 mg of plant material each) and 146 containing at least one virus with a genome type different from +ssRNA (Supplementary Table 147). Pea enation mosaic virus was counted as one virus, when it is in fact a co-infection of pea 148 enation mosaic virus 1 (*Enamovirus*) and pea enation mosaic virus 2 (*Umbravirus*). Stepwise 149 combinations of these five viruses mock communities were then assembled to create 150 communities of increasing degrees of complexity (Supplementary Fig. S1), yielding a total of 151 22 communities with complexity ranging from five to 60 viruses.

152 **Double-stranded RNA extraction**

153 Double-stranded RNAs were purified from pooled samples according to [41] with some minor 154 modifications. Briefly, instead of 75 mg, 150 mg dried plant material (representing a pool of 155 five plants, Supplementary Table S1) was used as starting material and buffer volumes 156 increased proportionally. Plants were ground in liquid nitrogen until a fine powder was obtained 157 which was then mixed with the phenol-extraction buffer. Following gentle agitation for 30 min 158 and centrifugation, the supernatant was decanted and half of it directly further processed, while 159 the other half was used for the stepwise gradual assembly of pairs of communities used to 160 generate more complex viral communities. In this way, six communities of 10 viruses each, 161 then three communities of 20 viruses and finally a single community of 60 viruses could be 162 assembled. Between each step, assembled samples were vortexed for at least 30s for optimal 163 homogenization. A detailed scheme of the pooling strategy to form communities of different
139 of isolates distributed by the DSMZ through the EU-funded EVA-Global initiative 140 (https://www.european-virus-archive.com/). Quality controlled samples were obtained from the 141 DSMZ in the form of infected, lyophilized plant material in vacuum-sealed vials. The complete 142 list of the isolates used, together with their properties and the propagation host in which they 143 were provided, are given in Table 1.

144 Initial low complexity pools were generated by assembling 30 mg of virus-infected samples 145 into 12 viral communities comprising five viruses each (150 mg of plant material each) and 146 containing at least one virus with a genome type different from +ssRNA (Supplementary Table 147). Pea enation mosaic virus was counted as one virus, when it is in fact a co-infection of pea 148 enation mosaic virus 1 (*Enamovirus*) and pea enation mosaic virus 2 (*Umbravirus*). Stepwise 149 combinations of these five viruses mock communities were then assembled to create 150 communities of increasing degrees of complexity (Supplementary Fig. S1), yielding a total of 151 22 communities with complexity ranging from five to 60 viruses.

152 **Double-stranded RNA extraction**

153 Double-stranded RNAs were purified from pooled samples according to [41] with some minor 154 modifications. Briefly, instead of 75 mg, 150 mg dried plant material (representing a pool of 155 five plants, Supplementary Table S1) was used as starting material and buffer volumes 156 increased proportionally. Plants were ground in liquid nitrogen until a fine powder was obtained 157 which was then mixed with the phenol-extraction buffer. Following gentle agitation for 30 min 158 and centrifugation, the supernatant was decanted and half of it directly further processed, while 159 the other half was used for the stepwise gradual assembly of pairs of communities used to 160 generate more complex viral communities. In this way, six communities of 10 viruses each, 161 then three communities of 20 viruses and finally a single community of 60 viruses could be 162 assembled. Between each step, assembled samples were vortexed for at least 30s for optimal 163 homogenization. A detailed scheme of the pooling strategy to form communities of different
complexities is shown in Supplementary Figure S1. Irrespective of its complexity, a supernatant
volume corresponding to an initial input of 75 mg of plant sample was thus obtained and further
processed as per the protocol of Marais *et al.* [41] which involves two rounds of CC41 cellulose
(Whatman) chromatography followed by a nuclease treatment (DNase RQ1 plus RNaseA under
high salt conditions) to remove any remaining host DNA and single-stranded RNA. A negative
extraction control using only buffer was systematically included. Purified dsRNAs were finally
converted to cDNA and randomly amplified while simultaneously adding MID tags [41-42].

171 **VANA extraction**

172 VANA extractions were performed on pools of five viruses similarly prepared as for dsRNA,
173 using the protocol of François *et al.* [42] with minor modifications. Briefly, 150 mg of
174 lyophilized plant material (representing a pool of five plants, Supplementary Table S1) were
175 ground in Hank's buffered salt solution (HBSS) (1:10) with four metal beads within a grinding
176 machine (Fastprep 24, MP Biomedicals). Following two centrifugation steps (4000g for 5 min
177 at 4°C and 8000g at 4°C for 3 min), the supernatants were split and used in the same stepwise
178 assembly of more complex communities as for the dsRNA approach (Supplementary Figure
179 S1). A negative, buffer only, extraction control was systematically included. Each of the thus
180 generated samples, representing different degrees of community complexity, was filtered
181 through a 0.45µm filter and centrifuged at 148,000g for 2.5 hours at 4°C to concentrate the
182 virus particles. Unprotected nucleic acids were eliminated by DNase and RNase treatment at
183 37°C for 1.5 hours. Viral RNA and DNA were then isolated using the NucleoSpin Virus kit
184 (Macherey Nagel, Hoerdt, France), using only 80 µl of sample in the first lysis step and omitting
185 the addition of proteinase K. Extracted RNAs were transformed to cDNA using Superscript III
186 reverse transcriptase (ThermoFisher Scientific/Invitrogen), cDNAs were further purified with
187 the QIAquick PCR purification Kit (Qiagen, Courtaboeuf, France) and a complementary strand
188 was synthesized using the Klenow fragment of DNA polymerase I. Finally, a random PCR
complexities is shown in Supplementary Figure S1. Irrespective of its complexity, a supernatant
volume corresponding to an initial input of 75 mg of plant sample was thus obtained and further
processed as per the protocol of Marais *et al.* [41] which involves two rounds of CC41 cellulose
(Whatman) chromatography followed by a nuclease treatment (DNase RQ1 plus RNaseA under
high salt conditions) to remove any remaining host DNA and single-stranded RNA. A negative
extraction control using only buffer was systematically included. Purified dsRNAs were finally
converted to cDNA and randomly amplified while simultaneously adding MID tags [41-42].

171 **VANA extraction**

172 VANA extractions were performed on pools of five viruses similarly prepared as for dsRNA,
173 using the protocol of François *et al.* [42] with minor modifications. Briefly, 150 mg of
174 lyophilized plant material (representing a pool of five plants, Supplementary Table S1) were
175 ground in Hank's buffered salt solution (HBSS) (1:10) with four metal beads within a grinding
176 machine (Fastprep 24, MP Biomedicals). Following two centrifugation steps (4000g for 5 min
177 at 4°C and 8000g at 4°C for 3 min), the supernatants were split and used in the same stepwise
178 assembly of more complex communities as for the dsRNA approach (Supplementary Figure
179 S1). A negative, buffer only, extraction control was systematically included. Each of the thus
180 generated samples, representing different degrees of community complexity, was filtered
181 through a 0.45µm filter and centrifuged at 148,000g for 2.5 hours at 4°C to concentrate the
182 virus particles. Unprotected nucleic acids were eliminated by DNase and RNase treatment at
183 37°C for 1.5 hours. Viral RNA and DNA were then isolated using the NucleoSpin Virus kit
184 (Macherey Nagel, Hoerdt, France), using only 80 µl of sample in the first lysis step and omitting
185 the addition of proteinase K. Extracted RNAs were transformed to cDNA using Superscript III
186 reverse transcriptase (ThermoFisher Scientific/Invitrogen), cDNAs were further purified with
187 the QIAquick PCR purification Kit (Qiagen, Courtaboeuf, France) and a complementary strand
188 was synthesized using the Klenow fragment of DNA polymerase I. Finally, a random PCR
189 amplification adding barcoded dodeca-linkers and corresponding MID primers during reverse
190 transcription and PCR, respectively was performed [42].

191 Illumina sequencing

192 PCR products from all communities analyzed using the dsRNA and VANA procedures were
193 finally purified using the MinElute PCR purification kit (Qiagen) and equimolar quantities of
194 amplification products were sent to Illumina sequencing in multiplexed format (2 × 150 bp) on
195 two lanes (one for VANA and one for dsRNA, respectively) on a NovaSeq 6000 system at the
196 GetPlaGe platform (GenoToul INRAE Toulouse, France).

197 Generation of synthetic datasets for viral communities using single-isolate RNASeq data

198 For all but one of the viral isolates used to build the synthetic communities, available single-
199 isolate ribodepleted RNASeq datasets (Leibniz-Institute DSMZ) were used to reconstruct *in
200 silico* datasets corresponding to the different communities with reads number and average reads
201 length paralleling those from the VANA and dsRNA datasets. These reconstructed datasets,
202 mimicking the analysis of the various communities by RNASeq, were analyzed in parallel to
203 those generated by the VAN A and dsRNA approaches.

204 HTS data analysis

205 Sequencing reads were imported into CLC Genomics Workbench v. 21.0.3. (CLC-GW,
206 Qiagen) and adapters were removed from reads followed by trimming on quality and length
207 using default settings and a minimum read length of 60 nucleotides (nt). Final trimmed reads
208 were on average 111-113 nt long for the various datasets. Datasets were normalized by
209 resampling at varying depth as needed, using the random reads sampling tool in CLC-GW.
210 To analyze virus detection performance as a function of contig size, de novo assembly was
211 performed with CLC-GW (word size, 50; bubble size, 300) using various minimum contig
212 lengths (125, 175, 250, 350, 500, 1000 nt). In order to identify viruses possibly present in the
189 amplification adding barcoded dodeca-linkers and corresponding MID primers during reverse
190 transcription and PCR, respectively was performed [42].

191 **Illumina sequencing**

192 PCR products from all communities analyzed using the dsRNA and VANA procedures were
193 finally purified using the MinElute PCR purification kit (Qiagen) and equimolar quantities of
194 amplification products were sent to Illumina sequencing in multiplexed format (2 × 150 bp) on
195 two lanes (one for VANA and one for dsRNA, respectively) on a NovaSeq 6000 system at the
196 GetPlaGe platform (GenoToul INRAE Toulouse, France).

197 **Generation of synthetic datasets for viral communities using single-isolate RNASeq data**

198 For all but one of the viral isolates used to build the synthetic communities, available single-
199 isolate ribodepleted RNASeq datasets (Leibniz-Institute DSMZ) were used to reconstruct *in
200 silico* datasets corresponding to the different communities with reads number and average reads
201 length paralleling those from the VANA and dsRNA datasets. These reconstructed datasets,
202 mimicking the analysis of the various communities by RNASeq, were analyzed in parallel to
203 those generated by the VAN A and dsRNA approaches.

204 **HTS data analysis**

205 Sequencing reads were imported into CLC Genomics Workbench v. 21.0.3. (CLC-GW,
206 Qiagen) and adapters were removed from reads followed by trimming on quality and length
207 using default settings and a minimum read length of 60 nucleotides (nt). Final trimmed reads
208 were on average 111-113 nt long for the various datasets. Datasets were normalized by
209 resampling at varying depth as needed, using the random reads sampling tool in CLC-GW.
210 To analyze virus detection performance as a function of contig size, *de novo* assembly was
211 performed with CLC-GW (word size, 50; bubble size, 300) using various minimum contig
212 lengths (125, 175, 250, 350, 500, 1000 nt). In order to identify viruses possibly present in the
samples used, in addition to the expected reference viruses, contigs were annotated by a BlastX
analysis [43] against the viral RefSeq portion of the non-redundant (nr/nt) NCBI GenBank
database. For the additional viruses thus identified, a genomic scaffold was reconstructed and
extended by repeated rounds of residual reads mapping using CLC-GW, thus yielding near
complete genome sequences that were used as reference for the relevant virus (Table 2). In a
few cases, these assemblies were considered too incomplete and the closest complete genomic
sequence in GenBank was selected as reference sequence (Table 2).

In order to determine virus detection performance, unassembled reads or *de novo* assembled contigs were mapped against the reference genome segment(s) for each virus (Tables 1 and 2) using very stringent mapping parameters (length fraction 100%, minimal similarity fraction 90%) in CLC-GW. In order to take into account inter-sample crosstalk due to index jumping [44-45], a threshold of positive detection was computed for each viral molecule by calculating the average plus 3 standard deviations (SD) of background virus reads observed in libraries generated from communities that did not contain the corresponding virus. Assuming a normal distribution of background reads, the use of such a positivity threshold would provide a <1% risk of reporting a false positive detection (https://en.wikipedia.org/wiki/68%E2%80%9395%E2%80%9399.7_rule).

Comparison of parameters (number, average length) for *de novo* assembled viral contigs
obtained from VANA and dsRNA datasets normalized at different sequencing depths were
performed with five resampling repeats at each depth. Statistically significant differences were
identified using a two-sample t-test.

Data availability

Trimmed sequencing reads for all viral communities analyzed by dsRNA or VANA approaches
are available from the French Recherche Data Gouv multidisciplinary repository at
https://doi.org/10.57745/42WNRJ. The normalized 10M reads dsRNA or VANA datasets
samples used, in addition to the expected reference viruses, contigs were annotated by a BlastX
analysis [43] against the viral RefSeq portion of the non-redundant (nr/nt) NCBI GenBank
database. For the additional viruses thus identified, a genomic scaffold was reconstructed and
extended by repeated rounds of residual reads mapping using CLC-GW, thus yielding near
complete genome sequences that were used as reference for the relevant virus (Table 2). In a
few cases, these assemblies were considered too incomplete and the closest complete genomic
sequence in GenBank was selected as reference sequence (Table 2).

In order to determine virus detection performance, unassembled reads or *de novo* assembled contigs were mapped against the reference genome segment(s) for each virus (Tables 1 and 2) using very stringent mapping parameters (length fraction 100%, minimal similarity fraction 90%) in CLC-GW. In order to take into account inter-sample crosstalk due to index jumping [44-45], a threshold of positive detection was computed for each viral molecule by calculating the average plus 3 standard deviations (SD) of background virus reads observed in libraries generated from communities that did not contain the corresponding virus. Assuming a normal distribution of background reads, the use of such a positivity threshold would provide a <1% risk of reporting a false positive detection (https://en.wikipedia.org/wiki/68%E2%80%9395%E2%80%9399.7_rule).

Comparison of parameters (number, average length) for *de novo* assembled viral contigs
obtained from VANA and dsRNA datasets normalized at different sequencing depths were
performed with five resampling repeats at each depth. Statistically significant differences were
identified using a two-sample t-test.

234 **<u>Data availability</u>**

235 Trimmed sequencing reads for all viral communities analyzed by dsRNA or VANA approaches
236 are available from the French Recherche Data Gouv multidisciplinary repository at
237 https://doi.org/10.57745/42WNRJ. The normalized 10M reads dsRNA or VANA datasets
generated using the 60-viruses community have also been made available together with the
community composition and the complete or near complete reference genomic sequences used
are also available from the same repository at https://doi.org/10.57745/T4UYPC.

241

242 RESULTS

243 Viruses or virus-like agents identified from viral communities HTS data

244 The analysis of reads from both the VANA and dsRNA approaches for all communities
245 revealed the presence of all expected viruses, although a few viruses were only represented by
246 a limited number of reads or were only detected using one of the two approaches. Overall, only
247 lettuce ring necrosis virus turned out to be fully absent from VANA reads, while banana bunchy
248 top virus was only represented by a single dsRNA read. It should also be noted that not all
249 viruses could be detected in all the communities of different complexity in which they were
250 expected.

251 In addition to the expected 61 viruses, evidence for the presence in some communities of
252 additional viruses or virus-like agents was obtained through the BlastX indexing of de novo
253 assembled contigs from the low complexity, 5-viruses communities. A total of 11 unexpected
254 agents were thus identified (Table 2). These include three linear ssRNA satellites associated
255 with the helper virus isolates included in the communities [turnip crinkle satellite F
256 (TCVsatRNA F), pea enation mosaic satellite RNA (PEMVsatRNA) and strawberry latent
257 ringspot virus satellite RNA (SLRSVsatRNA)], latent viruses associated with the propagation
258 hosts used [Hordeum vulgare endornavirus (HvEV), maize-associated totivirus (MaTV),
259 maize-associated totivirus2 (MaTV-2) and Chenopodium quinoa mitovirus 1 (CqMV1)], as
260 well as viruses in coinfection with some of the viral isolates used [poinsettia mosaic virus
261 (PnMV), tobacco mosaic virus (TMV), turnip yellows virus (TuYV) and maize streak Réunion
262 virus (MSRV)] (Table 2). Taken together, these agents represent three additional viral families,
generated using the 60-viruses community have also been made available together with the
community composition and the complete or near complete reference genomic sequences used
are also available from the same repository at https://doi.org/10.57745/T4UYPC.

241

242 RESULTS

243 Viruses or virus-like agents identified from viral communities HTS data

244 The analysis of reads from both the VANA and dsRNA approaches for all communities
245 revealed the presence of all expected viruses, although a few viruses were only represented by
246 a limited number of reads or were only detected using one of the two approaches. Overall, only
247 lettuce ring necrosis virus turned out to be fully absent from VANA reads, while banana bunchy
248 top virus was only represented by a single dsRNA read. It should also be noted that not all
249 viruses could be detected in all the communities of different complexity in which they were
250 expected.

251 In addition to the expected 61 viruses, evidence for the presence in some communities of
252 additional viruses or virus-like agents was obtained through the BlastX indexing of de novo
253 assembled contigs from the low complexity, 5-viruses communities. A total of 11 unexpected
254 agents were thus identified (Table 2). These include three linear ssRNA satellites associated
255 with the helper virus isolates included in the communities [turnip crinkle satellite F
256 (TCVsatRNA F), pea enation mosaic satellite RNA (PEMVsatRNA) and strawberry latent
257 ringspot virus satellite RNA (SLRSVsatRNA)], latent viruses associated with the propagation
258 hosts used [Hordeum vulgare endornavirus (HvEV), maize-associated totivirus (MaTV),
259 maize-associated totivirus2 (MaTV-2) and Chenopodium quinoa mitovirus 1 (CqMV1)], as
260 well as viruses in coinfection with some of the viral isolates used [poinsettia mosaic virus
261 (PnMV), tobacco mosaic virus (TMV), turnip yellows virus (TuYV) and maize streak Réunion
262 virus (MSRV)] (Table 2). Taken together, these agents represent three additional viral families,
for a total of 21 viral families (plus satellites) used for the assembly of communities. For these additional agents, either a nearly complete genome was reconstructed from sequencing reads and used as the mapping reference or the closest full genome sequence in GenBank was used for further mapping analyses (Table 2). For all other viral isolates included in the communities, complete or nearly complete genomic sequences were available (Table 1).

While the communities of varying complexities analyzed here will be referred to as 5-viruses, 10-viruses, 20-viruses and 60-viruses, it should be kept in mind that the real number of viruses present in a given community might be slightly different because of (i) the presence of one or more of the additional viruses and (ii) the counting of pea enation mosaic virus as one virus when it is in fact a co-infection of pea enation mosaic virus 1 (*Enamovirus*) and pea enation mosaic virus 2 (*Umbravirus*).

## Read mapping analysis of VANA and dsRNA datasets for the communities of various complexities

To be able to compare results between low and high complexity communities, all datasets were normalised by randomly subsampling 120K cleaned reads, the depth of the 5-viruses community with the lowest number of reads. To address the issue of inter-sample crosstalk caused by index jumping [44-45] a threshold of positive detection was computed for each viral molecule by calculating the average + 3 standard deviations (SD) of background reads in libraries generated from communities that did not contain the corresponding virus. Assuming a normal distribution of crosstalk reads numbers, this strategy ensures that the probability of having a mapped reads number higher than the threshold by chance (false positive detection) is lower than 1%.

In general, the proportion of viral reads in both VANA and dsRNA datasets was high (64-89%) and was slightly affected by community complexity, with a general trend to reach higher values when analysing more complex communities (Figure 1A). The proportion of viral reads in the
for a total of 21 viral families (plus satellites) used for the assembly of communities. For these additional agents, either a nearly complete genome was reconstructed from sequencing reads and used as the mapping reference or the closest full genome sequence in GenBank was used for further mapping analyses (Table 2). For all other viral isolates included in the communities, complete or nearly complete genomic sequences were available (Table 1).

While the communities of varying complexities analyzed here will be referred to as 5-viruses, 10-viruses, 20-viruses and 60-viruses, it should be kept in mind that the real number of viruses present in a given community might be slightly different because of (i) the presence of one or more of the additional viruses and (ii) the counting of pea enation mosaic virus as one virus when it is in fact a co-infection of pea enation mosaic virus 1 (*Enamovirus*) and pea enation mosaic virus 2 (*Umbravirus*).

## Read mapping analysis of VANA and dsRNA datasets for the communities of various complexities

To be able to compare results between low and high complexity communities, all datasets were normalised by randomly subsampling 120K cleaned reads, the depth of the 5-viruses community with the lowest number of reads. To address the issue of inter-sample crosstalk caused by index jumping [44-45] a threshold of positive detection was computed for each viral molecule by calculating the average + 3 standard deviations (SD) of background reads in libraries generated from communities that did not contain the corresponding virus. Assuming a normal distribution of crosstalk reads numbers, this strategy ensures that the probability of having a mapped reads number higher than the threshold by chance (false positive detection) is lower than 1%.

In general, the proportion of viral reads in both VANA and dsRNA datasets was high (64-89%) and was slightly affected by community complexity, with a general trend to reach higher values when analysing more complex communities (Figure 1A). The proportion of viral reads in the
dsRNA datasets were slightly higher than in the corresponding VANA datasets, with the strongest differential observed for lower complexity communities of five and 10 viruses (64-65% viral reads as compared to 79-82%, Figure 1A). In contrast, the average proportion of viral reads in RNASeq datasets for individual virus isolates following ribodepletion was 19.6% but with a very large standard deviation of 26.1%.

Using the 12 communities of five viruses and a sequencing depth of 120K reads, 67 viruses were detected with both VANA and dsRNA approaches (with detection of reads for at least one genomic molecule considered as positive detection for a virus with a multipartite genome), out of the total of 72 viruses or virus-like agents present in the 12 communities analyzed (93.1%). However, VANA yielded reads for all six DNA viruses used (100%), while dsRNA yielded reads for only three of them (50%). Conversely, VANA yielded reads for 61 of the 66 RNA viruses or satellites (92.4%), when dsRNA yielded reads for 64 of them (97.0%) (Figure 1B). As expected, and previously reported, the performance of VANA is thus superior for DNA viruses but that of dsRNA slightly superior for RNA viruses. Using the datasets reconstructed from single plant RNASeq data, an overall rate of detection of 97.2% of the 71 viruses was obtained (no RNASeq data was available for one of the isolates used, which was therefore excluded from all computations).

The impact of increasing community complexity is reflected by the diminishing number of viruses detected at an equal sequencing effort of 120K reads. The performance of VANA gradually deteriorated, with detection decreasing from 61 RNA viruses detected to 58 (10-viruses communities) and then to 52 (20-viruses communities) to reach only 34 RNA viruses detected (51.5%) in the most complex community (Figure 1B). The same pattern was observed for DNA viruses, with all six DNA viruses detected in the 10- and 20-viruses communities but only one detected when analysing the 60-viruses community. In the case of the dsRNA approach, performance was marginally reduced for the 10- and 20-viruses communities (65 and
dsRNA datasets were slightly higher than in the corresponding VANA datasets, with the strongest differential observed for lower complexity communities of five and 10 viruses (64-65% viral reads as compared to 79-82%, Figure 1A). In contrast, the average proportion of viral reads in RNASeq datasets for individual virus isolates following ribodepletion was 19.6% but with a very large standard deviation of 26.1%.

Using the 12 communities of five viruses and a sequencing depth of 120K reads, 67 viruses were detected with both VANA and dsRNA approaches (with detection of reads for at least one genomic molecule considered as positive detection for a virus with a multipartite genome), out of the total of 72 viruses or virus-like agents present in the 12 communities analyzed (93.1%). However, VANA yielded reads for all six DNA viruses used (100%), while dsRNA yielded reads for only three of them (50%). Conversely, VANA yielded reads for 61 of the 66 RNA viruses or satellites (92.4%), when dsRNA yielded reads for 64 of them (97.0%) (Figure 1B).
As expected, and previously reported, the performance of VANA is thus superior for DNA viruses but that of dsRNA slightly superior for RNA viruses. Using the datasets reconstructed from single plant RNASeq data, an overall rate of detection of 97.2% of the 71 viruses was obtained (no RNASeq data was available for one of the isolates used, which was therefore excluded from all computations).

The impact of increasing community complexity is reflected by the diminishing number of viruses detected at an equal sequencing effort of 120K reads. The performance of VANA gradually deteriorated, with detection decreasing from 61 RNA viruses detected to 58 (10-viruses communities) and then to 52 (20-viruses communities) to reach only 34 RNA viruses detected (51.5%) in the most complex community (Figure 1B). The same pattern was observed for DNA viruses, with all six DNA viruses detected in the 10- and 20-viruses communities but only one detected when analysing the 60-viruses community. In the case of the dsRNA approach, performance was marginally reduced for the 10- and 20-viruses communities (65 and
63 RNA viruses detected, respectively) and less affected than for the VANA approach for the most complex community, with still 57 of 66 RNA viruses detected (86.4%) (Figure 1B). Remarkably, performance was the least affected for the RNASeq approach using reconstructed communities data, with still 65 viruses (91.5%) detected for the most complex community (5/6 DNA viruses and 59/65 RNA viruses, or 90.7%).

If trying to compensate for community complexity by proportionally increasing the sequencing effort for more complex communities, the erosion in performance is less important for VANA, with still 57 of 66 RNA viruses detected for the 60-virus community (86.4%) and five of the six DNA viruses (83.3%) at a 1.44 M reads depth (12 x 120K). The performance of dsRNA, on the other hand, is no longer impaired, as all 66 RNA viruses (100%) were detected for the most complex community (result not shown). Similarly, the performance of RNASeq was no longer substantially impacted, with all DNA viruses and all but one RNA viruses detected.

The stronger degradation of VANA performance as community complexity increases, correlates with a more uneven distribution of read numbers between viruses and the stronger dominance of a few viruses, in particular turnip yellow mosaic virus (TYMV). In the 60-viruses community VANA dataset, TYMV represented 67% of the reads while the corresponding value for the dsRNA dataset was only 28%. As shown in Figure 2, even if spanning a 5 to 6 logs scale, the percentage of reads for each virus in the total datasets tends to be more evenly distributed between viruses in the dsRNA dataset than in the VANA dataset for the 60-viruses community. By contrast and excluding a single sample showing extremely low viral reads numbers, the variation in the proportion of viral reads in individual viral isolates analyzed by RNASeq showed much less variability as it remained within a 3 logs range of variation.

Although allowing to compare the performance of the VANA and dsRNA approaches, these analyses based on the mapping of reads against cognate reference genomes do not mimic the situation in metagenomic studies, in which a high proportion of viruses are expected to be novel
63 RNA viruses detected, respectively) and less affected than for the VANA approach for the most complex community, with still 57 of 66 RNA viruses detected (86.4%) (Figure 1B). Remarkably, performance was the least affected for the RNASeq approach using reconstructed communities data, with still 65 viruses (91.5%) detected for the most complex community (5/6 DNA viruses and 59/65 RNA viruses, or 90.7%).

If trying to compensate for community complexity by proportionally increasing the sequencing effort for more complex communities, the erosion in performance is less important for VANA, with still 57 of 66 RNA viruses detected for the 60-virus community (86.4%) and five of the six DNA viruses (83.3%) at a 1.44 M reads depth (12 x 120K). The performance of dsRNA, on the other hand, is no longer impaired, as all 66 RNA viruses (100%) were detected for the most complex community (result not shown). Similarly, the performance of RNASeq was no longer substantially impacted, with all DNA viruses and all but one RNA viruses detected.

The stronger degradation of VANA performance as community complexity increases, correlates with a more uneven distribution of read numbers between viruses and the stronger dominance of a few viruses, in particular turnip yellow mosaic virus (TYMV). In the 60-viruses community VANA dataset, TYMV represented 67% of the reads while the corresponding value for the dsRNA dataset was only 28%. As shown in Figure 2, even if spanning a 5 to 6 logs scale, the percentage of reads for each virus in the total datasets tends to be more evenly distributed between viruses in the dsRNA dataset than in the VANA dataset for the 60-viruses community. By contrast and excluding a single sample showing extremely low viral reads numbers, the variation in the proportion of viral reads in individual viral isolates analyzed by RNASeq showed much less variability as it remained within a 3 logs range of variation.

Although allowing to compare the performance of the VANA and dsRNA approaches, these analyses based on the mapping of reads against cognate reference genomes do not mimic the situation in metagenomic studies, in which a high proportion of viruses are expected to be novel
338

and for which therefore no suitable reference genome is available. We therefore analyzed the
performance of these two approaches following the *de novo* assembly of reads into contigs,
which is known to reduce the proportion of un-annotated “dark matter” [46].

339

340

341

**Impact of minimal contig length on the number of detected viruses**

342

We first evaluated the impact of the minimal contig length on the number of detected viruses
using the most complex community of 60-viruses and deep datasets normalized at 10 M reads.

343

344

As expected, and shown in Figure 3, the number of detected viruses decreased as minimal
contig length increased. The pattern observed for RNA viruses is similarly observed for DNA
viruses. The dsRNA approach consistently detected more RNA viruses than the VANA one,
irrespective of the minimal contig length used, but the difference increased as minimal contig
length increased. Using the shortest, 125 nt contig length, VANA identified 54 of the 66 RNA
viruses or satellites present in the community (81.8%), while dsRNA identified 63 of them
(95.5%) (Figure 3). The corresponding values for DNA viruses are respectively 4/6 (66.7%)
and 3/6 (50%).

345

346

347

348

349

350

351

352

On the other hand, the coverage of the detected viruses (fraction of the target molecules
represented in contigs) was much less affected by minimal contig length. While being relatively
stable for the dsRNA approach, for which it varied between 66.5% and 74.9% with no clear
trend, it showed a tendency to increase with contig length for the VANA approach, from 50.2%
(>125 nt contigs) to 76.7% (>1,000 nt contigs) (Supplementary Figure S2).

353

354

355

356

357

For further analyses, an intermediate 250 nt minimal contig length was retained as it
corresponds to an encoded 83 amino acids sequence that was felt sufficient for many conserved
protein domain searches which are often used in virome analysis or annotation [47].

358

359

360
338

339

340

and for which therefore no suitable reference genome is available. We therefore analyzed the
performance of these two approaches following the *de novo* assembly of reads into contigs,
which is known to reduce the proportion of un-annotated “dark matter” [46].

341

Impact of minimal contig length on the number of detected viruses

342

343

We first evaluated the impact of the minimal contig length on the number of detected viruses
using the most complex community of 60-viruses and deep datasets normalized at 10 M reads.

344

345

346

347

348

349

350

351

As expected, and shown in Figure 3, the number of detected viruses decreased as minimal
contig length increased. The pattern observed for RNA viruses is similarly observed for DNA
viruses. The dsRNA approach consistently detected more RNA viruses than the VANA one,
irrespective of the minimal contig length used, but the difference increased as minimal contig
length increased. Using the shortest, 125 nt contig length, VANA identified 54 of the 66 RNA
viruses or satellites present in the community (81.8%), while dsRNA identified 63 of them
(95.5%) (Figure 3). The corresponding values for DNA viruses are respectively 4/6 (66.7%)
and 3/6 (50%).

352

353

354

355

356

On the other hand, the coverage of the detected viruses (fraction of the target molecules
represented in contigs) was much less affected by minimal contig length. While being relatively
stable for the dsRNA approach, for which it varied between 66.5% and 74.9% with no clear
trend, it showed a tendency to increase with contig length for the VANA approach, from 50.2%
(>125 nt contigs) to 76.7% (>1,000 nt contigs) (Supplementary Figure S2).

357

358

359

For further analyses, an intermediate 250 nt minimal contig length was retained as it
corresponds to an encoded 83 amino acids sequence that was felt sufficient for many conserved
protein domain searches which are often used in virome analysis or annotation [47].

360
361 **Effects of community complexity on virome description performance**

362 We evaluated how, for a given sequencing depth, community complexity affects virome 363 description performance following contigs assembly. For this, all datasets were normalized at 364 a 120K read depth. Similar to the initial analysis using reads mapping, the number of detected 365 viruses was reduced as community complexity increased. Again, dsRNA outperformed VANA 366 at all complexity levels, though the difference in performance remained limited for low to 367 medium community complexities (Supplementary Figure S3). VANA performance degradation 368 was however more drastic at high community complexity, dropping from 44 RNA viruses and 369 four DNA viruses detected for communities of five viruses (66.7% of total viruses) to 11 RNA 370 viruses and one DNA virus detected (16.7%) for the 60-viruses community. The corresponding 371 values for dsRNA were 53 (80.3%) and 26 RNA viruses (39.4%), with no DNA virus detected 372 (Supplementary Figure S3). Remarkably, RNASeq turned out to be the least affected, with 373 respectively 57/71 (80.3%, 5-viruses communities) and 34/71 viruses (47.9%, 60-viruses 374 community) detected. These results indicate that even for limited complexity communities 375 involving only five viruses, read numbers significantly higher than 120K are needed by the 376 various techniques to achieve a 100% detection performance with a wide range of viruses. 377

377 If trying to compensate increased virome complexity by a parallel increase in sequencing depth, 378 a negative impact of complexity is still seen but is much less severe. For example, for the most 379 complex community of 60 viruses at a 1.44M depth (12*120K reads), VANA detected 23 RNA 380 viruses and 2 DNA viruses (compared to 44 RNA viruses and four DNA viruses when analysing 381 individually the 12 pools of five viruses at 120K reads depth), which corresponds to a reduction 382 in performance of 47.9%. For its part, dsRNA detected 42 RNA viruses (no DNA virus), to be 383 compared with 53 viruses when individually analyzing the 12 pools of five viruses, 384 corresponding to a reduction in performance of 20.7% (Supplementary Figure S4). The 385 corresponding value for RNASeq was 55 viruses detected, corresponding to a performance
361 **Effects of community complexity on virome description performance**

362 We evaluated how, for a given sequencing depth, community complexity affects virome 363 description performance following contigs assembly. For this, all datasets were normalized at 364 a 120K read depth. Similar to the initial analysis using reads mapping, the number of detected 365 viruses was reduced as community complexity increased. Again, dsRNA outperformed VANA 366 at all complexity levels, though the difference in performance remained limited for low to 367 medium community complexities (Supplementary Figure S3). VANA performance degradation 368 was however more drastic at high community complexity, dropping from 44 RNA viruses and 369 four DNA viruses detected for communities of five viruses (66.7% of total viruses) to 11 RNA 370 viruses and one DNA virus detected (16.7%) for the 60-viruses community. The corresponding 371 values for dsRNA were 53 (80.3%) and 26 RNA viruses (39.4%), with no DNA virus detected 372 (Supplementary Figure S3). Remarkably, RNASeq turned out to be the least affected, with 373 respectively 57/71 (80.3%, 5-viruses communities) and 34/71 viruses (47.9%, 60-viruses 374 community) detected. These results indicate that even for limited complexity communities 375 involving only five viruses, read numbers significantly higher than 120K are needed by the 376 various techniques to achieve a 100% detection performance with a wide range of viruses. 377

377 If trying to compensate increased virome complexity by a parallel increase in sequencing depth, 378 a negative impact of complexity is still seen but is much less severe. For example, for the most 379 complex community of 60 viruses at a 1.44M depth (12*120K reads), VANA detected 23 RNA 380 viruses and 2 DNA viruses (compared to 44 RNA viruses and four DNA viruses when analysing 381 individually the 12 pools of five viruses at 120K reads depth), which corresponds to a reduction 382 in performance of 47.9%. For its part, dsRNA detected 42 RNA viruses (no DNA virus), to be 383 compared with 53 viruses when individually analyzing the 12 pools of five viruses, 384 corresponding to a reduction in performance of 20.7% (Supplementary Figure S4). The 385 corresponding value for RNASeq was 55 viruses detected, corresponding to a performance
equivalent to the analysis of the 12 communities of five viruses. The loss in performance
resulting from high community complexity is therefore only significant for the dsRNA and
VANA approaches, and strongest in the case of VANA.

389 **Impact of sequencing depth on de novo assembly**

390 The 60-viruses community was used to investigate the influence of sequencing depth on *de novo* assembly performance itself. The VANA and dsRNA datasets were therefore resampled
391 at different depths (100K, 300K, 1M, 3M and 10M reads, five random resampling at each
392 depth), assembled and the obtained contigs mapped against the viral reference genomes to
393 determine the average assembly parameters and viral contigs parameters. The results are shown
394 in Supplementary Table S2 and, for viral contigs alone, in Table 3.
395

396 As expected, all assembly parameters (number of contigs, average contig length, N50, maximal
397 contig length) increased with sequencing depth (Supplementary Table S2). The same tends to
398 be true for viral contigs (number and length, Table 3), while the proportion of viral contigs
399 tended to diminish as sequencing depth increased, likely reflecting increased probability of
400 assembly of non-viral reads (Supplementary Table S2). Although at the lowest 100K reads
401 sequencing depth few assembly parameters were found to be statistically different, both the
402 total number of assembled contigs and the number of viral contigs were found to be highly
403 statistically different, with dsRNA yielding about 3-fold more contigs and 3-fold more viral
404 contigs than VANA (Table 3 and Supplementary Table S2). This trend was observed at all
405 sequencing depth, with 1.3 to 1.8-fold more viral contigs observed for dsRNA.

406 At other sequencing depths, differences between the VANA and dsRNA assemblies proved
407 systematically highly significant, with dsRNA consistently yielding more numerous and longer
408 contigs as well as more numerous and longer viral contigs. On the other hand, the proportion
equivalent to the analysis of the 12 communities of five viruses. The loss in performance
resulting from high community complexity is therefore only significant for the dsRNA and
VANA approaches, and strongest in the case of VANA.

389 **Impact of sequencing depth on de novo assembly**

390 The 60-viruses community was used to investigate the influence of sequencing depth on *de novo* assembly performance itself. The VANA and dsRNA datasets were therefore resampled
391 at different depths (100K, 300K, 1M, 3M and 10M reads, five random resampling at each
392 depth), assembled and the obtained contigs mapped against the viral reference genomes to
393 determine the average assembly parameters and viral contigs parameters. The results are shown
394 in Supplementary Table S2 and, for viral contigs alone, in Table 3.
395

396 As expected, all assembly parameters (number of contigs, average contig length, N50, maximal
397 contig length) increased with sequencing depth (Supplementary Table S2). The same tends to
398 be true for viral contigs (number and length, Table 3), while the proportion of viral contigs
399 tended to diminish as sequencing depth increased, likely reflecting increased probability of
400 assembly of non-viral reads (Supplementary Table S2). Although at the lowest 100K reads
401 sequencing depth few assembly parameters were found to be statistically different, both the
402 total number of assembled contigs and the number of viral contigs were found to be highly
403 statistically different, with dsRNA yielding about 3-fold more contigs and 3-fold more viral
404 contigs than VANA (Table 3 and Supplementary Table S2). This trend was observed at all
405 sequencing depth, with 1.3 to 1.8-fold more viral contigs observed for dsRNA.

406 At other sequencing depths, differences between the VANA and dsRNA assemblies proved
407 systematically highly significant, with dsRNA consistently yielding more numerous and longer
408 contigs as well as more numerous and longer viral contigs. On the other hand, the proportion
409 of viral contigs was found consistently higher in assemblies of the VANA datasets
410 (Supplementary Table S2).

411 It should be noted that the better assembly performance of dsRNA is independent of minimal
412 contig length (Table 4). In particular, using the most complex community and 10 million reads
413 datasets, the higher performance of dsRNA over VANA was observed for all assembly
414 parameters (number of contigs, average length, N50, maximum length) and for both viral
415 contigs parameters (number and average length) at all minimal contigs length (from 125 to 1000
416 nt) with a single exception, the number of viral contigs >125 nt long (1,852 for VANA vs 1,672
417 for dsRNA) (Table 4). At all other minimal contig length, VANA showed from 19.2% (contigs
418 ≥175 nt) to 50.7% (>1 kb contigs) fewer viral contigs than dsRNA and these contigs were 23-
419 33% shorter on average than the dsRNA ones (Table 4).

420 As compared to VANA and dsRNA assemblies, RNASeq assemblies generated more viral
421 contigs at low sequencing depth (ca. 10-30% more than dsRNA for depth of 100K to 1M reads)
422 but ca. 15% fewer viral contigs at the 10M depth. On the other hand, a striking difference in the
423 length of viral contigs was also observed, with RNASeq contigs increasing from an average of
424 1kb (100K depth, 34% longer than dsRNA contigs on average) to 2.1kb (10M depth, 89%
425 longer than for dsRNA).

426 Impact of sequencing depth on virus identification performance

427 We proceeded to evaluate the performance of VANA and dsRNA in identifying the expected
428 viruses or viral molecules as affected by sequencing depth. The contigs obtained for the various
429 datasets resampled at different depths (five resampling per sequencing depth) were mapped on
430 individual reference sequences. This allowed to evaluate both the proportion of detected viruses
431 and the coverage of the detected viral molecules, together with their standard deviation
432 (Supplementary Figure S5). Once again, at all sequencing depths and for both parameters,
409 of viral contigs was found consistently higher in assemblies of the VANA datasets
410 (Supplementary Table S2).

411 It should be noted that the better assembly performance of dsRNA is independent of minimal
412 contig length (Table 4). In particular, using the most complex community and 10 million reads
413 datasets, the higher performance of dsRNA over VANA was observed for all assembly
414 parameters (number of contigs, average length, N50, maximum length) and for both viral
415 contigs parameters (number and average length) at all minimal contigs length (from 125 to 1000
416 nt) with a single exception, the number of viral contigs >125 nt long (1,852 for VANA vs 1,672
417 for dsRNA) (Table 4). At all other minimal contig length, VANA showed from 19.2% (contigs
418 ≥175 nt) to 50.7% (>1 kb contigs) fewer viral contigs than dsRNA and these contigs were 23-
419 33% shorter on average than the dsRNA ones (Table 4).

420 As compared to VANA and dsRNA assemblies, RNASeq assemblies generated more viral
421 contigs at low sequencing depth (ca. 10-30% more than dsRNA for depth of 100K to 1M reads)
422 but ca. 15% fewer viral contigs at the 10M depth. On the other hand, a striking difference in the
423 length of viral contigs was also observed, with RNASeq contigs increasing from an average of
424 1kb (100K depth, 34% longer than dsRNA contigs on average) to 2.1kb (10M depth, 89%
425 longer than for dsRNA).

426 Impact of sequencing depth on virus identification performance

427 We proceeded to evaluate the performance of VANA and dsRNA in identifying the expected
428 viruses or viral molecules as affected by sequencing depth. The contigs obtained for the various
429 datasets resampled at different depths (five resampling per sequencing depth) were mapped on
430 individual reference sequences. This allowed to evaluate both the proportion of detected viruses
431 and the coverage of the detected viral molecules, together with their standard deviation
432 (Supplementary Figure S5). Once again, at all sequencing depths and for both parameters,
dsRNA outperformed VANA for RNA viruses, while VANA outperformed dsRNA for DNA viruses. In all cases, average coverage of detected segments of RNA viruses showed a high standard deviation but dsRNA contigs covered 9% to 22% more of the detected molecules than VANA contigs.

Similarly, and as expected from single reads mapping data, dsRNA outperformed VANA for the identification of RNA viruses present in the most complex, 60-viruses community. For VANA, performance ranged from 17.7% of RNA viruses identified at the 100K reads depth to 60.3% at the 10 million reads depth. The corresponding values for dsRNA are respectively 35.2% and 89.7% and those for RNASeq respectively 46.2% and 90.8%. The performance of RNASeq therefore appears to be nearly identical to that of dsRNA for RNA viruses, and superior for DNA viruses with 5/6 viruses detected for the 3M and 10M reads depth.

A plot of the observed proportion of detected RNA viruses over a logarithmic scale of the sequencing effort is shown in Figure 4. It shows a remarkable pattern with linear regression $r^2$ coefficients of 0.97-0.99, suggesting a very strong and monotonous relationship between sequencing depth and the proportion of the viruses present in the community that are represented by at least one assembled contig. An extension of that trend would suggest that a depth of about 30 million reads would be needed for the dsRNA approach to recover at least one contig for each of the 66 RNA viruses present in the synthetic community, while in excess of 1 billion reads would be needed to achieve a comparable performance using VANA. If taking into account also DNA viruses to calculate a proportion of detected viruses, similar linear relationships are still observed, but the performance of the dsRNA approach is slightly degraded as expected from its poor ability to detect DNA viruses (Figure 4). Analyzed in a similar fashion, the RNASeq data showed the same linear relationship, although with a slightly lower $r^2$ value of 93.7% and a predicted detection of all 71 viruses and satellites with 16-17M reads.
dsRNA outperformed VANA for RNA viruses, while VANA outperformed dsRNA for DNA viruses. In all cases, average coverage of detected segments of RNA viruses showed a high standard deviation but dsRNA contigs covered 9% to 22% more of the detected molecules than VANA contigs.

Similarly, and as expected from single reads mapping data, dsRNA outperformed VANA for the identification of RNA viruses present in the most complex, 60-viruses community. For VANA, performance ranged from 17.7% of RNA viruses identified at the 100K reads depth to 60.3% at the 10 million reads depth. The corresponding values for dsRNA are respectively 35.2% and 89.7% and those for RNASeq respectively 46.2% and 90.8%. The performance of RNASeq therefore appears to be nearly identical to that of dsRNA for RNA viruses, and superior for DNA viruses with 5/6 viruses detected for the 3M and 10M reads depth.

A plot of the observed proportion of detected RNA viruses over a logarithmic scale of the sequencing effort is shown in Figure 4. It shows a remarkable pattern with linear regression $r^2$ coefficients of 0.97-0.99, suggesting a very strong and monotonous relationship between sequencing depth and the proportion of the viruses present in the community that are represented by at least one assembled contig. An extension of that trend would suggest that a depth of about 30 million reads would be needed for the dsRNA approach to recover at least one contig for each of the 66 RNA viruses present in the synthetic community, while in excess of 1 billion reads would be needed to achieve a comparable performance using VANA. If taking into account also DNA viruses to calculate a proportion of detected viruses, similar linear relationships are still observed, but the performance of the dsRNA approach is slightly degraded as expected from its poor ability to detect DNA viruses (Figure 4). Analyzed in a similar fashion, the RNASeq data showed the same linear relationship, although with a slightly lower $r^2$ value of 93.7% and a predicted detection of all 71 viruses and satellites with 16-17M reads.
Due to a more limited number of reads available for virus communities up to the 20-viruses pools, a similar evaluation could not be as extensively performed for these lower complexity communities. However, an analysis at three sequencing depths (100K reads, 300K reads, 875K reads) of the 20-viruses communities data provided comparable results with r² correlation coefficients of 0.95-0.98, suggesting that the linear correlation between the percentage of viruses recovered and the log of the sequencing depth is independent of the complexity of the analyzed community (result not shown).

An analysis performed at the level of individual viral genomic molecules (115 viral molecules) allows to evaluate the performance of the two methods using the most complex, 60-viruses pool, for groups of viruses with different genome types. The numbers of viral molecules are however small for RNA satellites, dsRNA viruses and dsDNA viruses. The results, using a 10 million reads sequencing depth, are summarized in Table 5. Considering individual molecules, VANA had at least one contig for only 50% of the viral molecules present in the most complex synthetic community, to be compared with a 76.5% value for dsRNA. But while the VANA performance was at an intermediate level for all virus groups analyzed, dsRNA showed good performance for +ssRNA viruses (89.5% of molecules), RNA satellites (100%) and dsRNA viruses (100%). The dsRNA performance was however poor for DNA viruses, as expected, but also for -ssRNA viruses (41.7% of detected molecules only).

## DISCUSSION

While synthetic communities have been widely used to benchmark metagenomic processes targeting bacteria and fungi, methodological benchmarking approaches in virome studies are still limited and largely confined to clinical settings [38, 48-49] and, to some extent, to environmental virome studies [50-51]. Such approaches are today largely lacking in plant virology. Here we used well authenticated and sequence characterized plant virus isolates from
Due to a more limited number of reads available for virus communities up to the 20-viruses pools, a similar evaluation could not be as extensively performed for these lower complexity communities. However, an analysis at three sequencing depths (100K reads, 300K reads, 875K reads) of the 20-viruses communities data provided comparable results with r² correlation coefficients of 0.95-0.98, suggesting that the linear correlation between the percentage of viruses recovered and the log of the sequencing depth is independent of the complexity of the analyzed community (result not shown).

An analysis performed at the level of individual viral genomic molecules (115 viral molecules) allows to evaluate the performance of the two methods using the most complex, 60-viruses pool, for groups of viruses with different genome types. The numbers of viral molecules are however small for RNA satellites, dsRNA viruses and dsDNA viruses. The results, using a 10 million reads sequencing depth, are summarized in Table 5. Considering individual molecules, VANA had at least one contig for only 50% of the viral molecules present in the most complex synthetic community, to be compared with a 76.5% value for dsRNA. But while the VANA performance was at an intermediate level for all virus groups analyzed, dsRNA showed good performance for +ssRNA viruses (89.5% of molecules), RNA satellites (100%) and dsRNA viruses (100%). The dsRNA performance was however poor for DNA viruses, as expected, but also for -ssRNA viruses (41.7% of detected molecules only).

## DISCUSSION

While synthetic communities have been widely used to benchmark metagenomic processes targeting bacteria and fungi, methodological benchmarking approaches in virome studies are still limited and largely confined to clinical settings [38, 48-49] and, to some extent, to environmental virome studies [50-51]. Such approaches are today largely lacking in plant virology. Here we used well authenticated and sequence characterized plant virus isolates from
a public bioresource center (Leibniz-Institute DSMZ) that allowed for the simple construction of synthetic viral communities of varying complexity. Although some of the viruses were detected by only very low read numbers, no virus was fully absent from all generated datasets, validating the approach and the samples used. The fact that some viruses were identified only by low read numbers could have a variety of reasons, such as low virus titer in some samples, competition with other viruses for reads representation in the assembled communities, or difficulties in extracting viral nucleic acids from some plant species. In addition, the fact that freeze-dried plant material was used in this study may have had a negative impact on results and the analysis of fresh plant tissues might have provided superior results. In this respect, it should be noted that the two viruses present as infected banana samples, banana streak OL virus (BSOLV) and banana bunchy top virus (BBTV), were only detected by very low read numbers using both VANA and dsRNA, despite the fact that these techniques have successfully been used in the past to analyze banana samples [52-53]. The RNASeq data on the same viral isolates shows about 0.9% of viral reads BSOLV but BBTV was the individual sample with the fewest reads by far in the RNASeq analysis, suggesting a low viral concentration in that particular sample.

A total of 11 additional viruses or viral agents were identified in the constructed communities. In most cases, these correspond to satellites that had not been specifically indexed in the viral isolates used or of viruses latently infecting propagation hosts, such as Hordeum vulgare endornavirus, which is present in many barley varieties, or Chenopodium quinoa mitovirus.

The communities assembled cover all known plant virus genome types, 21 viral families (plus satellites and one virus unassigned in a family) and a total of 61 genera [plus four viruses not currently assigned to a genus and three satellites]. It is thus probable to date the largest scale effort to build synthetic viral communities and use them for the benchmarking of phytoviruses. In some benchmarking studies, the nucleic acid proportions of the
a public bioresource center (Leibniz-Institute DSMZ) that allowed for the simple construction of synthetic viral communities of varying complexity. Although some of the viruses were detected by only very low read numbers, no virus was fully absent from all generated datasets, validating the approach and the samples used. The fact that some viruses were identified only by low read numbers could have a variety of reasons, such as low virus titer in some samples, competition with other viruses for reads representation in the assembled communities, or difficulties in extracting viral nucleic acids from some plant species. In addition, the fact that freeze-dried plant material was used in this study may have had a negative impact on results and the analysis of fresh plant tissues might have provided superior results. In this respect, it should be noted that the two viruses present as infected banana samples, banana streak OL virus (BSOLV) and banana bunchy top virus (BBTV), were only detected by very low read numbers using both VANA and dsRNA, despite the fact that these techniques have successfully been used in the past to analyze banana samples [52-53]. The RNASeq data on the same viral isolates shows about 0.9% of viral reads BSOLV but BBTV was the individual sample with the fewest reads by far in the RNASeq analysis, suggesting a low viral concentration in that particular sample.

A total of 11 additional viruses or viral agents were identified in the constructed communities. In most cases, these correspond to satellites that had not been specifically indexed in the viral isolates used or of viruses latently infecting propagation hosts, such as Hordeum vulgare endornavirus, which is present in many barley varieties, or Chenopodium quinoa mitovirus.

The communities assembled cover all known plant virus genome types, 21 viral families (plus satellites and one virus unassigned in a family) and a total of 61 genera [plus four viruses not currently assigned to a genus and three satellites]. It is thus probable to date the largest scale effort to build synthetic viral communities and use them for the benchmarking of phytoviruses analysis approaches. In some benchmarking studies, the nucleic acid proportions of the
individual viruses involved in the virus community were quantified prior to extraction [36, 39].

The fact that no special effort was made here to normalize or measure the concentration of the different viruses is a limitation for some comparisons. On the other hand, the samples used involved different propagation hosts and actual virus titers in those hosts, so that the communities assembled reflect actual samples from plant virome studies. The results obtained indicate that a range of parameters impact the completeness of the virome description achieved.

Not surprisingly, such parameters include (i) sequencing depth, (ii) community complexity, (iii) use of *de novo* assembled contigs vs use of unassembled reads and (iv) minimal contig length.

The key objective of this work was to compare the performance of the VANA and dsRNA approaches, which are the two techniques most widely used in ecology-oriented viral metagenomics experiments involving the analysis of complex pools of plants. The results provided here for RNAseq following ribodepletion should be considered with caution, since they are not fully comparable with the VANA or dsRNA data. Indeed, the RNAseq datasets for the various communities were assembled *in silico*, from data obtained by single-isolate sequencing. This means that any interactions between plant samples or competition between viruses for representation in the datasets were eliminated, contrary to the situation with the VANA and dsRNA experiments. Given that RNAseq is considered an unbiased approach (hence its use for transcriptome analysis), this should not be a problem but the existence of unforeseen effects affecting the results cannot be completely ruled out. As compared to dsRNA and VANA, the results obtained for RNAseq using the *in silico* assembled communities show: (i) a much lower imbalance in the representation of the various viruses (3 logs variation as opposed to 5-6 logs), (ii) on average significantly longer viral contigs, irrespective of sequencing depth and (iii) an overall excellent performance with 90% of the viruses identified at 10M reads depth for the most complex, 60-viruses community. This last result favourably compares with the dsRNA performance for all viral categories with the exception of viruses
individual viruses involved in the virus community were quantified prior to extraction [36, 39].

The fact that no special effort was made here to normalize or measure the concentration of the different viruses is a limitation for some comparisons. On the other hand, the samples used involved different propagation hosts and actual virus titers in those hosts, so that the communities assembled reflect actual samples from plant virome studies. The results obtained indicate that a range of parameters impact the completeness of the virome description achieved.

Not surprisingly, such parameters include (i) sequencing depth, (ii) community complexity, (iii) use of *de novo* assembled contigs vs use of unassembled reads and (iv) minimal contig length.

The key objective of this work was to compare the performance of the VANA and dsRNA approaches, which are the two techniques most widely used in ecology-oriented viral metagenomics experiments involving the analysis of complex pools of plants. The results provided here for RNAseq following ribodepletion should be considered with caution, since they are not fully comparable with the VANA or dsRNA data. Indeed, the RNAseq datasets for the various communities were assembled *in silico*, from data obtained by single-isolate sequencing. This means that any interactions between plant samples or competition between viruses for representation in the datasets were eliminated, contrary to the situation with the VANA and dsRNA experiments. Given that RNAseq is considered an unbiased approach (hence its use for transcriptome analysis), this should not be a problem but the existence of unforeseen effects affecting the results cannot be completely ruled out. As compared to dsRNA and VANA, the results obtained for RNAseq using the *in silico* assembled communities show: (i) a much lower imbalance in the representation of the various viruses (3 logs variation as opposed to 5-6 logs), (ii) on average significantly longer viral contigs, irrespective of sequencing depth and (iii) an overall excellent performance with 90% of the viruses identified at 10M reads depth for the most complex, 60-viruses community. This last result favourably compares with the dsRNA performance for all viral categories with the exception of viruses
with dsRNA genomes (Table 5). This performance comes as a surprise given the absence of enrichment (besides ribodepletion) in RNASeq. However, the relatively narrow range of variation in the proportion of viral reads for different viruses, possibly implying reduced competition for representation between viruses, and the even distribution of RNASeq reads along viral genomes, possibly favouring a more efficient genome assembly, could have contributed to the RNASeq performance. In any case, these results surprisingly suggest that RNASeq could have a very good potential for the analysis of complex viral communities and clearly call for direct benchmarking efforts using RNASeq and complex synthetic or natural communities in order to unambiguously validate this potential.

As previously reported using natural communities (Ma *et al.*, 2019), the dsRNA approach provided in all comparisons a more complete description of the RNA virome than the VANA approach but performed very poorly with DNA viruses. However, the differential with VANA is more limited for the less complex communities of five or 10 viruses. According to our own experience, this level of complexity is most often seen when analyzing single plants or pools of 5-20 plants of the same species, with vegetatively propagated plants tending to have more complex viromes. Higher complexity levels are usually encountered when analyzing larger pools composed of plants belonging to different species. The dsRNA approach is therefore recommended whenever analysing complex viromes or when an emphasis on RNA viruses is of importance, in particular since dsRNA allows comparable levels of completeness with a lower sequencing effort. On the other hand, for viromes of low to medium complexity, the results reported here show VANA to be a reasonable alternative. For example, at 480K reads depth, VANA detected 57.4% of all viruses for the 20-viruses communities as compared to 61.8% for dsRNA (result not shown, see also Supplementary Figure 4 for the compared rates of detection of RNA viruses only). VANA should of course be the preferred choice if analysis of DNA viruses is of importance. The reason for the better performance of the dsRNA approach
with dsRNA genomes (Table 5). This performance comes as a surprise given the absence of enrichment (besides ribodepletion) in RNASeq. However, the relatively narrow range of variation in the proportion of viral reads for different viruses, possibly implying reduced competition for representation between viruses, and the even distribution of RNASeq reads along viral genomes, possibly favouring a more efficient genome assembly, could have contributed to the RNASeq performance. In any case, these results surprisingly suggest that RNASeq could have a very good potential for the analysis of complex viral communities and clearly call for direct benchmarking efforts using RNASeq and complex synthetic or natural communities in order to unambiguously validate this potential.

As previously reported using natural communities (Ma *et al.*, 2019), the dsRNA approach provided in all comparisons a more complete description of the RNA virome than the VANA approach but performed very poorly with DNA viruses. However, the differential with VANA is more limited for the less complex communities of five or 10 viruses. According to our own experience, this level of complexity is most often seen when analyzing single plants or pools of 5-20 plants of the same species, with vegetatively propagated plants tending to have more complex viromes. Higher complexity levels are usually encountered when analyzing larger pools composed of plants belonging to different species. The dsRNA approach is therefore recommended whenever analysing complex viromes or when an emphasis on RNA viruses is of importance, in particular since dsRNA allows comparable levels of completeness with a lower sequencing effort. On the other hand, for viromes of low to medium complexity, the results reported here show VANA to be a reasonable alternative. For example, at 480K reads depth, VANA detected 57.4% of all viruses for the 20-viruses communities as compared to 61.8% for dsRNA (result not shown, see also Supplementary Figure 4 for the compared rates of detection of RNA viruses only). VANA should of course be the preferred choice if analysis of DNA viruses is of importance. The reason for the better performance of the dsRNA approach
for high complexity viromes is not fully clear but might result from a lower level of competition
between viral nucleic acid molecules for representation in complex pools, resulting in a
somewhat less imbalanced distribution of read numbers between viruses (Figure 2). Different
human microbiome studies have shown that different steps of RNA/DNA extraction such as
homogenization, centrifugation, filtration and chloroform treatment, can have a major impact
on the quantitative and qualitative composition of identified viral communities, skewing viral
metagenome assemblies [37-38, 54]. Another critical step is library preparation, which often
involves a random amplification PCR to increase virus genetic material and to add linkers,
allowing samples multiplexing during HTS sequencing and thus reducing sequencing costs.
The amplification step may alter the relative abundance of viruses and can lead to uneven
coverage if random primers do not anneal randomly on viral genomes. Indeed, in the case of
faba bean necrotic stunt virus, the relative frequencies of the different genome segments
determined by qPCR was significantly different before and after a rolling circle amplification
step used prior to HTS sequencing [55]. Furthermore, different library preparation techniques
have been found to require different sequencing depths to achieve the same genome coverage
[56]. Regardless of the experiment, it is advisable to develop an estimate of the sequencing
depth needed, so as to be able to answer the biological question at hand while avoiding
excessive sequencing costs. Here we identified a very robust correlation between the percentage
of viruses identified in complex communities and the log of the sequencing depth. This is an
interesting result, since it allows to gauge the sequencing effort needed for a particular level of
virome description or, conversely, to gauge the extent of virome description that can be
expected from a particular sequencing depth. Besides metagenomic studies, this finding might
have practical implications for diagnostics since many plants, in particular vegetatively
propagated ones, frequently display complex mixed infections involving a range of viruses.
for high complexity viromes is not fully clear but might result from a lower level of competition
between viral nucleic acid molecules for representation in complex pools, resulting in a
somewhat less imbalanced distribution of read numbers between viruses (Figure 2). Different
human microbiome studies have shown that different steps of RNA/DNA extraction such as
homogenization, centrifugation, filtration and chloroform treatment, can have a major impact
on the quantitative and qualitative composition of identified viral communities, skewing viral
metagenome assemblies [37-38, 54]. Another critical step is library preparation, which often
involves a random amplification PCR to increase virus genetic material and to add linkers,
allowing samples multiplexing during HTS sequencing and thus reducing sequencing costs.
The amplification step may alter the relative abundance of viruses and can lead to uneven
coverage if random primers do not anneal randomly on viral genomes. Indeed, in the case of
faba bean necrotic stunt virus, the relative frequencies of the different genome segments
determined by qPCR was significantly different before and after a rolling circle amplification
step used prior to HTS sequencing [55]. Furthermore, different library preparation techniques
have been found to require different sequencing depths to achieve the same genome coverage
[56]. Regardless of the experiment, it is advisable to develop an estimate of the sequencing
depth needed, so as to be able to answer the biological question at hand while avoiding
excessive sequencing costs. Here we identified a very robust correlation between the percentage
of viruses identified in complex communities and the log of the sequencing depth. This is an
interesting result, since it allows to gauge the sequencing effort needed for a particular level of
virome description or, conversely, to gauge the extent of virome description that can be
expected from a particular sequencing depth. Besides metagenomic studies, this finding might
have practical implications for diagnostics since many plants, in particular vegetatively
propagated ones, frequently display complex mixed infections involving a range of viruses.
Virus detection in metagenomic studies is constrained by the degree of complexity of the virus communities analyzed. Our results suggest that the detection efficiency of either mapping of unassembled reads or analysis of *de novo* assembled contigs were affected by community complexity with a general trend of detecting a lower proportion of viruses in more complex communities. However, the read mapping strategy was more efficient at all complexities (Figure 1B and Supplementary Figure S3), confirming results obtained through performance testing of sequence analysis strategies [57]. This may be due to the complexity of *de novo* assembly of complex communities, linked with insufficient coverage or uneven coverage of low abundance viruses within such communities. Correspondingly, we observed a lower virus detection rate when using longer minimal contig sizes in the *de novo* assembly, which again might be attributed to difficulties in assembling reads from more complex communities for example when coexisting viruses share highly similar regions in their genomes, leading to higher fragmentation and reduced contig sizes [58].

Lastly, it has been reported that the quality and completeness of virome description is also affected by the bioinformatic analysis used [58-61]. The normalized 10M reads datasets generated in the present study with the 60-viruses community, which are available at https://doi.org/10.57745/T4UYPC, together with the community composition and the complete or near complete reference genomic sequences used here should prove very useful tools to benchmark virome characterization pipelines.

599
Virus detection in metagenomic studies is constrained by the degree of complexity of the virus communities analyzed. Our results suggest that the detection efficiency of either mapping of unassembled reads or analysis of *de novo* assembled contigs were affected by community complexity with a general trend of detecting a lower proportion of viruses in more complex communities. However, the read mapping strategy was more efficient at all complexities (Figure 1B and Supplementary Figure S3), confirming results obtained through performance testing of sequence analysis strategies [57]. This may be due to the complexity of *de novo* assembly of complex communities, linked with insufficient coverage or uneven coverage of low abundance viruses within such communities. Correspondingly, we observed a lower virus detection rate when using longer minimal contig sizes in the *de novo* assembly, which again might be attributed to difficulties in assembling reads from more complex communities for example when coexisting viruses share highly similar regions in their genomes, leading to higher fragmentation and reduced contig sizes [58].

Lastly, it has been reported that the quality and completeness of virome description is also affected by the bioinformatic analysis used [58-61]. The normalized 10M reads datasets generated in the present study with the 60-viruses community, which are available at https://doi.org/10.57745/T4UYPC, together with the community composition and the complete or near complete reference genomic sequences used here should prove very useful tools to benchmark virome characterization pipelines.

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**Acknowledgements:** The authors wish to thank the INRAE GetPlaGe Platform (GenoToul, Toulouse, France) for Illumina sequencing.

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**Funding:** This study was funded by the European Union through a Horizon 2020 Marie Skłodowska-Curie Actions Innovative Training Network (H2020 MSCA- 60 ITN) project "INEXTVIR" (GA 813542). The individual sequencing of some of the isolates used in this study was performed as part of the EVA-Global project which has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No. 871029. OM is recipient of a PhD fellowship from CIRAD and the ANR (Phytovirus project number: ANR-19-CE35-0008-02).

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**Conflict of interest:** The authors declare that they have no conflict of interest.

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**Ethical approval:** This article does not contain any studies with human participants or animals performed by any of the authors.

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**Acknowledgements:** The authors wish to thank the INRAE GetPlaGe Platform (GenoToul, Toulouse, France) for Illumina sequencing.

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**Funding:** This study was funded by the European Union through a Horizon 2020 Marie Skłodowska-Curie Actions Innovative Training Network (H2020 MSCA- 60 ITN) project "INEXTVIR" (GA 813542). The individual sequencing of some of the isolates used in this study was performed as part of the EVA-Global project which has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No. 871029. OM is recipient of a PhD fellowship from CIRAD and the ANR (Phytovirus project number: ANR-19-CE35-0008-02).

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**Conflict of interest:** The authors declare that they have no conflict of interest.

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**Ethical approval:** This article does not contain any studies with human participants or animals performed by any of the authors.

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801 P, Acinas SG, Wincker P, Sullivan MB (2016) Ecogenomics and potential
802 biogeochemical impacts of globally abundant ocean viruses. Nature, 537, 689-693.
803 https://doi.org/10.1038/nature19366

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809
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819
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833 https://doi.org/10.1094/PHYTO-02-18-0067-R

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838
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845 https://doi.org/10.1186/s40168-019-0626-5

846
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Figures

<figure><img src="image_3.png" /></figure>

<figure><img src="image_4.png" /></figure>

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**Figure 1.** Average proportion of viral reads (DNA and RNA viruses) in VANA (grey) and dsRNA (blue) datasets from viral communities of different complexities (A) and number of viruses detected at an even 120K read depth for communities of different complexities (B). In figure 1B RNA viruses are indicated by solid bars while DNA viruses are indicated by dashed bars.

<figure><img src="image_5.png" /></figure>

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**Figure 2:** Distribution of percentage of mapped VANA (grey) and dsRNA (blue) reads for each detected virus in the 60-viruses community using a normalized 1.44 million reads sequencing depth. The percentages of mapped reads for each virus are shown on a logarithmic scale, from 1, E+00 (100%) to 1, E-07 (0.000001%).
847

Figures

<figure><img src="image_3.png" /></figure>

<figure><img src="image_4.png" /></figure>

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**Figure 1.** Average proportion of viral reads (DNA and RNA viruses) in VANA (grey) and dsRNA (blue) datasets from viral communities of different complexities (A) and number of viruses detected at an even 120K read depth for communities of different complexities (B). In figure 1B RNA viruses are indicated by solid bars while DNA viruses are indicated by dashed bars.

<figure><img src="image_5.png" /></figure>

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**Figure 2:** Distribution of percentage of mapped VANA (grey) and dsRNA (blue) reads for each detected virus in the 60-viruses community using a normalized 1.44 million reads sequencing depth. The percentages of mapped reads for each virus are shown on a logarithmic scale, from 1, E+00 (100%) to 1, E-07 (0.000001%).
<figure><img src="image_6.png" /></figure>

**Figure 3.** Number of detected viruses using VANA (grey) or dsRNA (blue) in the 60-viruses community (over a total of 69 viruses plus 3 satellites) as a function of minimal contig length at a sequencing depth of 10M reads. RNA viruses are indicated by solid bars while DNA viruses are indicated by dashed bars.
<figure><img src="image_6.png" /></figure>

**Figure 3.** Number of detected viruses using VANA (grey) or dsRNA (blue) in the 60-viruses community (over a total of 69 viruses plus 3 satellites) as a function of minimal contig length at a sequencing depth of 10M reads. RNA viruses are indicated by solid bars while DNA viruses are indicated by dashed bars.
<figure><img src="image_7.png" /><figcaption>**Figure 4:** Observed percentages of detected viruses in the 60 viruses community as a function of sequencing depth expressed in million reads per sample and plotted on a logarithmic scale. VANA results are in grey, dsRNA results in blue. Linear regression curves are shown for RNA viruses (round dots, thick lines,) as well as considering both RNA and DNA viruses (square dots, thin lines). Linear r² coefficients are shown only for the RNA viruses curves.</figcaption></figure>
<figure><img src="image_7.png" /><figcaption>**Figure 4:** Observed percentages of detected viruses in the 60 viruses community as a function of sequencing depth expressed in million reads per sample and plotted on a logarithmic scale. VANA results are in grey, dsRNA results in blue. Linear regression curves are shown for RNA viruses (round dots, thick lines,) as well as considering both RNA and DNA viruses (square dots, thin lines). Linear r² coefficients are shown only for the RNA viruses curves.</figcaption></figure>
<table><caption>**Table 1. Viral isolates used to construct mock viral communities of varying complexity.** The taxonomic status of the various viruses is indicated, together with their DSMZ catalogue code, their propagation host and the GenBank accession number(s) of their genomic sequence(s).</caption><thead><tr><td>Family</td><td>Genus</td><td>Virus</td><td>Acronym</td><td>Genome</td><td>Code<sup>a</sup></td><td>Host<sup>b</sup></td><td>Sequence accession number(s)</td></tr></thead><tbody><tr><td>Alphaflexiviridae</td><td>Allexivirus</td><td>Shallot virus X</td><td>ShVX</td><td>ssRNA(+)</td><td>PV-0622</td><td>Chenopodium murale</td><td>MW854280</td></tr><tr><td>Alphaflexiviridae</td><td>Potexvirus</td><td>Lettuce virus X</td><td>LeVX</td><td>ssRNA(+)</td><td>PV-0904</td><td>Nicotiana benthamiana</td><td>MW248356</td></tr><tr><td>Benyviridae</td><td>Benyvirus</td><td>Beet necrotic yellow vein virus</td><td>BNYVV</td><td>ssRNA(+)</td><td>PV-0467</td><td>Chenopodium quinoa</td><td>OK181765-67; M36896</td></tr><tr><td>Betaflexiviridae</td><td>Capillovirus</td><td>Apple stem grooving virus</td><td>ASGV</td><td>ssRNA(+)</td><td>PV-0199</td><td>Chenopodium quinoa</td><td>MW582790</td></tr><tr><td>Betaflexiviridae</td><td>Carlavirus</td><td>Poplar mosaic virus</td><td>PopMV</td><td>ssRNA(+)</td><td>PV-0341</td><td>Nicotiana benthamiana</td><td>ON924213</td></tr><tr><td>Betaflexiviridae</td><td>Trichovirus</td><td>Apple chlorotic leaf spot virus</td><td>ACLSV</td><td>ssRNA(+)</td><td>PV-0998</td><td>Chenopodium quinoa</td><td>OK340218-19<sup>c</sup></td></tr><tr><td>Betaflexiviridae</td><td>Tepovirus</td><td>Potato virus T</td><td>PVT</td><td>ssRNA(+)</td><td>PV-1145</td><td>Nicotiana hesperis</td><td>MZ405665</td></tr><tr><td>Bromoviridae</td><td>Alfamovirus</td><td>Alfalfa mosaic virus</td><td>AMV</td><td>ssRNA(+)</td><td>PV-0779</td><td>Nicotiana tabacum “Samsun nn”</td><td>MZ405653-55</td></tr><tr><td>Bromoviridae</td><td>Anulavirus</td><td>Pelargonium zonate spot virus</td><td>PZSV</td><td>ssRNA(+)</td><td>PV-0259</td><td>Nicotiana glutinosa “24A”</td><td>ON398493-95</td></tr><tr><td>Bromoviridae</td><td>Bromovirus</td><td>Brome mosaic virus</td><td>BMV</td><td>ssRNA(+)</td><td>PV-0194</td><td>Hordeum vulgare</td><td>MW582787-89</td></tr><tr><td>Bromoviridae</td><td>Cucumovirus</td><td>Peanut stunt virus</td><td>PSV</td><td>ssRNA(+)</td><td>PV-0190</td><td>Nicotiana benthamiana</td><td>MW307259-61</td></tr><tr><td>Bromoviridae</td><td>Ilarvirus</td><td>Parietaria mottle virus</td><td>PMoV</td><td>ssRNA(+)</td><td>PV-0400</td><td>Chenopodium quinoa</td><td>MZ405646-48</td></tr><tr><td>Closteroviridae</td><td>Closterovirus</td><td>Beet yellows virus</td><td>BYV</td><td>ssRNA(+)</td><td>PV-1260</td><td>Beta macrocarpa</td><td>MT815988</td></tr><tr><td>Closteroviridae</td><td>Crinivirus</td><td>Tomato chlorosis virus</td><td>ToCV</td><td>ssRNA(+)</td><td>PV-1242</td><td>Solanum lycopersicum</td><td>ON398512-13</td></tr><tr><td>Potyviridae</td><td>Bymovirus</td><td>Barley yellow mosaic virus</td><td>BaYMV</td><td>ssRNA(+)</td><td>PV-0634</td><td>Hordeum vulgare</td><td>OL311692-93</td></tr><tr><td>Potyviridae</td><td>Ipomovirus</td><td>Cucumber vein yellowing virus</td><td>CVYV</td><td>ssRNA(+)</td><td>PV-0776</td><td>Cucumis sativus</td><td>OK181771</td></tr><tr><td>Potyviridae</td><td>Potyvirus</td><td>Bidens mottle virus</td><td>BiMoV</td><td>ssRNA(+)</td><td>PV-0752</td><td>Nicotiana benthamiana</td><td>ON398504</td></tr><tr><td>Potyviridae</td><td>Rymovirus</td><td>Agropyron mosaic virus</td><td>AgMV</td><td>ssRNA(+)</td><td>PV-0729</td><td>Triticum aestivum</td><td>OM471970</td></tr><tr><td>Potyviridae</td><td>Tritimovirus</td><td>Brome streak mosaic virus</td><td>BrSMV</td><td>ssRNA(+)</td><td>PV-0431</td><td>Hordeum vulgare</td><td>OP357935</td></tr></tbody></table>
<table><caption>**Table 1. Viral isolates used to construct mock viral communities of varying complexity.** The taxonomic status of the various viruses is indicated, together with their DSMZ catalogue code, their propagation host and the GenBank accession number(s) of their genomic sequence(s).</caption><thead><tr><td>Family</td><td>Genus</td><td>Virus</td><td>Acronym</td><td>Genome</td><td>Code<sup>a</sup></td><td>Host<sup>b</sup></td><td>Sequence accession number(s)</td></tr></thead><tbody><tr><td>Alphaflexiviridae</td><td>Allexivirus</td><td>Shallot virus X</td><td>ShVX</td><td>ssRNA(+)</td><td>PV-0622</td><td>Chenopodium murale</td><td>MW854280</td></tr><tr><td>Alphaflexiviridae</td><td>Potexvirus</td><td>Lettuce virus X</td><td>LeVX</td><td>ssRNA(+)</td><td>PV-0904</td><td>Nicotiana benthamiana</td><td>MW248356</td></tr><tr><td>Benyviridae</td><td>Benyvirus</td><td>Beet necrotic yellow vein virus</td><td>BNYVV</td><td>ssRNA(+)</td><td>PV-0467</td><td>Chenopodium quinoa</td><td>OK181765-67; M36896</td></tr><tr><td>Betaflexiviridae</td><td>Capillovirus</td><td>Apple stem grooving virus</td><td>ASGV</td><td>ssRNA(+)</td><td>PV-0199</td><td>Chenopodium quinoa</td><td>MW582790</td></tr><tr><td>Betaflexiviridae</td><td>Carlavirus</td><td>Poplar mosaic virus</td><td>PopMV</td><td>ssRNA(+)</td><td>PV-0341</td><td>Nicotiana benthamiana</td><td>ON924213</td></tr><tr><td>Betaflexiviridae</td><td>Trichovirus</td><td>Apple chlorotic leaf spot virus</td><td>ACLSV</td><td>ssRNA(+)</td><td>PV-0998</td><td>Chenopodium quinoa</td><td>OK340218-19<sup>c</sup></td></tr><tr><td>Betaflexiviridae</td><td>Tepovirus</td><td>Potato virus T</td><td>PVT</td><td>ssRNA(+)</td><td>PV-1145</td><td>Nicotiana hesperis</td><td>MZ405665</td></tr><tr><td>Bromoviridae</td><td>Alfamovirus</td><td>Alfalfa mosaic virus</td><td>AMV</td><td>ssRNA(+)</td><td>PV-0779</td><td>Nicotiana tabacum “Samsun nn”</td><td>MZ405653-55</td></tr><tr><td>Bromoviridae</td><td>Anulavirus</td><td>Pelargonium zonate spot virus</td><td>PZSV</td><td>ssRNA(+)</td><td>PV-0259</td><td>Nicotiana glutinosa “24A”</td><td>ON398493-95</td></tr><tr><td>Bromoviridae</td><td>Bromovirus</td><td>Brome mosaic virus</td><td>BMV</td><td>ssRNA(+)</td><td>PV-0194</td><td>Hordeum vulgare</td><td>MW582787-89</td></tr><tr><td>Bromoviridae</td><td>Cucumovirus</td><td>Peanut stunt virus</td><td>PSV</td><td>ssRNA(+)</td><td>PV-0190</td><td>Nicotiana benthamiana</td><td>MW307259-61</td></tr><tr><td>Bromoviridae</td><td>Ilarvirus</td><td>Parietaria mottle virus</td><td>PMoV</td><td>ssRNA(+)</td><td>PV-0400</td><td>Chenopodium quinoa</td><td>MZ405646-48</td></tr><tr><td>Closteroviridae</td><td>Closterovirus</td><td>Beet yellows virus</td><td>BYV</td><td>ssRNA(+)</td><td>PV-1260</td><td>Beta macrocarpa</td><td>MT815988</td></tr><tr><td>Closteroviridae</td><td>Crinivirus</td><td>Tomato chlorosis virus</td><td>ToCV</td><td>ssRNA(+)</td><td>PV-1242</td><td>Solanum lycopersicum</td><td>ON398512-13</td></tr><tr><td>Potyviridae</td><td>Bymovirus</td><td>Barley yellow mosaic virus</td><td>BaYMV</td><td>ssRNA(+)</td><td>PV-0634</td><td>Hordeum vulgare</td><td>OL311692-93</td></tr><tr><td>Potyviridae</td><td>Ipomovirus</td><td>Cucumber vein yellowing virus</td><td>CVYV</td><td>ssRNA(+)</td><td>PV-0776</td><td>Cucumis sativus</td><td>OK181771</td></tr><tr><td>Potyviridae</td><td>Potyvirus</td><td>Bidens mottle virus</td><td>BiMoV</td><td>ssRNA(+)</td><td>PV-0752</td><td>Nicotiana benthamiana</td><td>ON398504</td></tr><tr><td>Potyviridae</td><td>Rymovirus</td><td>Agropyron mosaic virus</td><td>AgMV</td><td>ssRNA(+)</td><td>PV-0729</td><td>Triticum aestivum</td><td>OM471970</td></tr><tr><td>Potyviridae</td><td>Tritimovirus</td><td>Brome streak mosaic virus</td><td>BrSMV</td><td>ssRNA(+)</td><td>PV-0431</td><td>Hordeum vulgare</td><td>OP357935</td></tr></tbody></table>
<table><tr><td>Potyviridae</td><td>Unassigned</td><td>Spartina mottle virus</td><td>SpMV</td><td>ssRNA(+)</td><td>PV-0970</td><td>Spartina sp.</td><td>MN788417</td></tr><tr><td>Secoviridae</td><td>Cheravirus</td><td>Arracacha virus B</td><td>AVB</td><td>ssRNA(+)</td><td>PV-0082</td><td>Chenopodium murale</td><td>MW582785-86</td></tr><tr><td>Secoviridae</td><td>Comovirus</td><td>Squash mosaic virus</td><td>SqMV</td><td>ssRNA(+)</td><td>PV-0581</td><td>Cucurbita pepo</td><td>ON398498-99</td></tr><tr><td>Secoviridae</td><td>Fabavirus</td><td>Broad been wilt virus 1</td><td>BBWV-1</td><td>ssRNA(+)</td><td>PV-0067</td><td>Chenopodium quinoa</td><td>MT663310-11</td></tr><tr><td>Secoviridae</td><td>Nepovirus</td><td>Tomato black ring virus</td><td>TBRV</td><td>ssRNA(+)</td><td>PV-0191</td><td>Nicotiana clevelandii</td><td>MW057704-05</td></tr><tr><td>Secoviridae</td><td>Sequivirus</td><td>Carrot necrotic dieback virus</td><td>CNDV</td><td>ssRNA(+)</td><td>PV-0976</td><td>Nicotiana benthamiana</td><td>MW080951</td></tr><tr><td>Secoviridae</td><td>Stralirivirus</td><td>Strawberry latent ringspot virus</td><td>SLRSV</td><td>ssRNA(+)</td><td>PV-0247</td><td>Chenopodium quinoa</td><td>MZ405640-41</td></tr><tr><td>Solemoviridae</td><td>Sobemovirus</td><td>Rice yellow mottle virus</td><td>RYMV</td><td>ssRNA(+)</td><td>PV-0732</td><td>Oryza sativa</td><td>MT701719</td></tr><tr><td>Solemoviridae</td><td>Enamovirus</td><td>Pea enation mosaic virus 1</td><td>PEMV1</td><td>ssRNA (+)</td><td>PV-0088</td><td>Pisum sativum</td><td>MW961146</td></tr><tr><td>Solemoviridae</td><td>Polerovirus</td><td>Cucurbit aphid-borne yellows virus</td><td>CABYV</td><td>ssRNA(+)</td><td>PV-1017</td><td>Physalis floridana</td><td>MZ202344</td></tr><tr><td>Tombusviridae</td><td>Alphacarmovirus</td><td>Calibrachoa mottle virus</td><td>CbMV</td><td>ssRNA(+)</td><td>PV-0611</td><td>Chenopodium quinoa</td><td>OK181769</td></tr><tr><td>Tombusviridae</td><td>Alphanecrovirus</td><td>Tobacco necrosis virus A</td><td>TNV-A</td><td>ssRNA(+)</td><td>PV-0186</td><td>Chenopodium quinoa</td><td>MT675968</td></tr><tr><td>Tombusviridae</td><td>Aureusvirus</td><td>Johnsongrass chlorotic stripe mosaic virus</td><td>JCSMV</td><td>ssRNA(+)</td><td>PV-0605</td><td>Zea mays</td><td>MT682309</td></tr><tr><td>Tombusviridae</td><td>Betacarmovirus</td><td>Turnip crinkle virus</td><td>TCV</td><td>ssRNA(+)</td><td>PV-0293</td><td>Nicotiana benthamiana</td><td>OK181761</td></tr><tr><td>Tombusviridae</td><td>Betanecrovirus</td><td>Beet black scorch virus</td><td>BBSV</td><td>ssRNA(+)</td><td>PV-0951</td><td>Chenopodium quinoa</td><td>OK058516</td></tr><tr><td>Tombusviridae</td><td>Dianthovirus</td><td>Carnation ringspot virus</td><td>CRSV</td><td>ssRNA(+)</td><td>PV-0097</td><td>Nicotiana clevelandii</td><td>MT682300-01</td></tr><tr><td>Tombusviridae</td><td>Gammacarmovirus</td><td>Melon necrotic spot virus</td><td>MNSV</td><td>ssRNA(+)</td><td>PV-0378</td><td>Cucumis sativus</td><td>ON398496</td></tr><tr><td>Tombusviridae</td><td>Machlomovirus</td><td>Maize chlorotic mottle virus</td><td>MCMV</td><td>ssRNA(+)</td><td>PV-1087</td><td>Zea mays</td><td>OK181780</td></tr><tr><td>Tombusviridae</td><td>Pelarspovirus</td><td>Pelargonium line pattern virus</td><td>PLPV</td><td>ssRNA(+)</td><td>PV-0193</td><td>Chenopodium quinoa</td><td>MW854266</td></tr><tr><td>Tombusviridae</td><td>Tombusvirus</td><td>Tomato bushy stunt virus</td><td>TBSV</td><td>ssRNA(+)</td><td>PV-0268</td><td>Nicotiana clevelandii</td><td>MW582792</td></tr><tr><td>Tombusviridae</td><td>Umbravirus</td><td>Carrot mottle virus</td><td>CMoV</td><td>ssRNA(+)</td><td>PV-0968</td><td>Nicotiana benthamiana</td><td>OK058520</td></tr><tr><td>Tombusviridae</td><td>Umbravirus</td><td>Pea enation mosaic virus 2</td><td>PEMV2</td><td>ssRNA(+)</td><td>PV-0088</td><td>Pisum sativum</td><td>MW961147; MW961148<sup>c</sup></td></tr><tr><td>Tospoviridae</td><td>Orthotospovirus</td><td>Impatiens necrotic spot virus</td><td>INSV</td><td>ssRNA(+/-)</td><td>PV-0280</td><td>Nicotiana benthamiana</td><td>MW582795-97</td></tr><tr><td>Tymoviridae</td><td>Tymovirus</td><td>Turnip yellow mosaic virus</td><td>TYMV</td><td>ssRNA(+)</td><td>PV-0299</td><td>Brassica rapa</td><td>ON924209</td></tr></table>
<table><tr><td>Potyviridae</td><td>Unassigned</td><td>Spartina mottle virus</td><td>SpMV</td><td>ssRNA(+)</td><td>PV-0970</td><td>Spartina sp.</td><td>MN788417</td></tr><tr><td>Secoviridae</td><td>Cheravirus</td><td>Arracacha virus B</td><td>AVB</td><td>ssRNA(+)</td><td>PV-0082</td><td>Chenopodium murale</td><td>MW582785-86</td></tr><tr><td>Secoviridae</td><td>Comovirus</td><td>Squash mosaic virus</td><td>SqMV</td><td>ssRNA(+)</td><td>PV-0581</td><td>Cucurbita pepo</td><td>ON398498-99</td></tr><tr><td>Secoviridae</td><td>Fabavirus</td><td>Broad been wilt virus 1</td><td>BBWV-1</td><td>ssRNA(+)</td><td>PV-0067</td><td>Chenopodium quinoa</td><td>MT663310-11</td></tr><tr><td>Secoviridae</td><td>Nepovirus</td><td>Tomato black ring virus</td><td>TBRV</td><td>ssRNA(+)</td><td>PV-0191</td><td>Nicotiana clevelandii</td><td>MW057704-05</td></tr><tr><td>Secoviridae</td><td>Sequivirus</td><td>Carrot necrotic dieback virus</td><td>CNDV</td><td>ssRNA(+)</td><td>PV-0976</td><td>Nicotiana benthamiana</td><td>MW080951</td></tr><tr><td>Secoviridae</td><td>Stralirivirus</td><td>Strawberry latent ringspot virus</td><td>SLRSV</td><td>ssRNA(+)</td><td>PV-0247</td><td>Chenopodium quinoa</td><td>MZ405640-41</td></tr><tr><td>Solemoviridae</td><td>Sobemovirus</td><td>Rice yellow mottle virus</td><td>RYMV</td><td>ssRNA(+)</td><td>PV-0732</td><td>Oryza sativa</td><td>MT701719</td></tr><tr><td>Solemoviridae</td><td>Enamovirus</td><td>Pea enation mosaic virus 1</td><td>PEMV1</td><td>ssRNA (+)</td><td>PV-0088</td><td>Pisum sativum</td><td>MW961146</td></tr><tr><td>Solemoviridae</td><td>Polerovirus</td><td>Cucurbit aphid-borne yellows virus</td><td>CABYV</td><td>ssRNA(+)</td><td>PV-1017</td><td>Physalis floridana</td><td>MZ202344</td></tr><tr><td>Tombusviridae</td><td>Alphacarmovirus</td><td>Calibrachoa mottle virus</td><td>CbMV</td><td>ssRNA(+)</td><td>PV-0611</td><td>Chenopodium quinoa</td><td>OK181769</td></tr><tr><td>Tombusviridae</td><td>Alphanecrovirus</td><td>Tobacco necrosis virus A</td><td>TNV-A</td><td>ssRNA(+)</td><td>PV-0186</td><td>Chenopodium quinoa</td><td>MT675968</td></tr><tr><td>Tombusviridae</td><td>Aureusvirus</td><td>Johnsongrass chlorotic stripe mosaic virus</td><td>JCSMV</td><td>ssRNA(+)</td><td>PV-0605</td><td>Zea mays</td><td>MT682309</td></tr><tr><td>Tombusviridae</td><td>Betacarmovirus</td><td>Turnip crinkle virus</td><td>TCV</td><td>ssRNA(+)</td><td>PV-0293</td><td>Nicotiana benthamiana</td><td>OK181761</td></tr><tr><td>Tombusviridae</td><td>Betanecrovirus</td><td>Beet black scorch virus</td><td>BBSV</td><td>ssRNA(+)</td><td>PV-0951</td><td>Chenopodium quinoa</td><td>OK058516</td></tr><tr><td>Tombusviridae</td><td>Dianthovirus</td><td>Carnation ringspot virus</td><td>CRSV</td><td>ssRNA(+)</td><td>PV-0097</td><td>Nicotiana clevelandii</td><td>MT682300-01</td></tr><tr><td>Tombusviridae</td><td>Gammacarmovirus</td><td>Melon necrotic spot virus</td><td>MNSV</td><td>ssRNA(+)</td><td>PV-0378</td><td>Cucumis sativus</td><td>ON398496</td></tr><tr><td>Tombusviridae</td><td>Machlomovirus</td><td>Maize chlorotic mottle virus</td><td>MCMV</td><td>ssRNA(+)</td><td>PV-1087</td><td>Zea mays</td><td>OK181780</td></tr><tr><td>Tombusviridae</td><td>Pelarspovirus</td><td>Pelargonium line pattern virus</td><td>PLPV</td><td>ssRNA(+)</td><td>PV-0193</td><td>Chenopodium quinoa</td><td>MW854266</td></tr><tr><td>Tombusviridae</td><td>Tombusvirus</td><td>Tomato bushy stunt virus</td><td>TBSV</td><td>ssRNA(+)</td><td>PV-0268</td><td>Nicotiana clevelandii</td><td>MW582792</td></tr><tr><td>Tombusviridae</td><td>Umbravirus</td><td>Carrot mottle virus</td><td>CMoV</td><td>ssRNA(+)</td><td>PV-0968</td><td>Nicotiana benthamiana</td><td>OK058520</td></tr><tr><td>Tombusviridae</td><td>Umbravirus</td><td>Pea enation mosaic virus 2</td><td>PEMV2</td><td>ssRNA(+)</td><td>PV-0088</td><td>Pisum sativum</td><td>MW961147; MW961148<sup>c</sup></td></tr><tr><td>Tospoviridae</td><td>Orthotospovirus</td><td>Impatiens necrotic spot virus</td><td>INSV</td><td>ssRNA(+/-)</td><td>PV-0280</td><td>Nicotiana benthamiana</td><td>MW582795-97</td></tr><tr><td>Tymoviridae</td><td>Tymovirus</td><td>Turnip yellow mosaic virus</td><td>TYMV</td><td>ssRNA(+)</td><td>PV-0299</td><td>Brassica rapa</td><td>ON924209</td></tr></table>
<table><tr><td>Virgaviridae</td><td>Furovirus</td><td>Soil-borne wheat mosaic virus</td><td>SBWMV</td><td>ssRNA(+)</td><td>PV-0748</td><td>Triticum aestivum</td><td>MZ405651-52</td></tr><tr><td>Virgaviridae</td><td>Hordeivirus</td><td>Barley stripe mosaic virus</td><td>BSMV</td><td>ssRNA(+)</td><td>PV-0330</td><td>Hordeum vulgare</td><td>ON924210-12</td></tr><tr><td>Virgaviridae</td><td>Pecluvirus</td><td>Peanut clump virus</td><td>PCV</td><td>ssRNA(+)</td><td>PV-0291</td><td>Nicotiana benthamiana</td><td>MW961156-57</td></tr><tr><td>Virgaviridae</td><td>Pomovirus</td><td>Potato mop-top virus</td><td>PMTV</td><td>ssRNA(+)</td><td>PV-0582</td><td>Nicotiana benthamiana</td><td>ON398500-02</td></tr><tr><td>Virgaviridae</td><td>Tobamovirus</td><td>Paprika mild mottle virus</td><td>PaMMV</td><td>ssRNA(+)</td><td>PV-0606</td><td>Nicotiana benthamiana</td><td>OK181768</td></tr><tr><td>Virgaviridae</td><td>Tobravirus</td><td>Pea early-browning virus</td><td>PEBV</td><td>ssRNA(+)</td><td>PV-0298</td><td>Chenopodium quinoa</td><td>MW854268-69</td></tr><tr><td>not assigned</td><td>Idaeovirus</td><td>Raspberry bushy dwarf virus</td><td>RBDV</td><td>ssRNA(+)</td><td>PV-0053</td><td>Chenopodium quinoa</td><td>MW582777-78</td></tr><tr><td>Rhabdoviridae</td><td>Cytorhabdovirus</td><td>Lettuce necrotic yellows virus</td><td>LNYV</td><td>ssRNA(-)</td><td>PV-0085</td><td>Nicotiana glutinosa “24A”</td><td>MZ202327</td></tr><tr><td>Rhabdoviridae</td><td>Varicosavirus</td><td>Beet oak leaf virus</td><td>BOLV</td><td>ssRNA(-)</td><td>PV-1034</td><td>Spinacia oleracea</td><td>OQ975887-88</td></tr><tr><td>Rhabdoviridae</td><td>Alphanucleorhabdovirus</td><td>Physostegia chlorotic mottle virus</td><td>PhCMoV</td><td>ssRNA(-)</td><td>PV-1182</td><td>Nicotiana occidentalis “37B”</td><td>KX636164</td></tr><tr><td>Rhabdoviridae</td><td>Betanucleorhabdovirus</td><td>Sonchus yellow net virus</td><td>SYNV</td><td>ssRNA(-)</td><td>PV-0052</td><td>Nicotiana clevelandii</td><td>MT613317</td></tr><tr><td>Aspiviridae</td><td>Ophiovirus</td><td>Lettuce ring necrosis virus</td><td>LRNV</td><td>ssRNA(-)</td><td>PV-0983</td><td>Nicotiana occidentalis “P1”</td><td>ON398506-09</td></tr><tr><td>Partitiviridae</td><td>Alphacryptovirus</td><td>Poinsettia latent virus</td><td>PnLV</td><td>dsRNA</td><td>PV-0629</td><td>Euphorbia pulcherrima</td><td>ON398503</td></tr><tr><td>Caulimoviridae</td><td>Badnavirus</td><td>Banana streak OL virus</td><td>BSOLV</td><td>dsDNA-RT</td><td>PV-0492</td><td>Musa sp.</td><td>OQ102041</td></tr><tr><td>Caulimoviridae</td><td>Caulimovirus</td><td>Cauliflower mosaic virus</td><td>CaMV</td><td>dsDNA-RT</td><td>PV-0229</td><td>Brassica rapa</td><td>OP947586</td></tr><tr><td>Geminiviridae</td><td>Begomovirus</td><td>Squash leaf curl virus</td><td>SLCV</td><td>ssDNA</td><td>PV-1299</td><td>Cucurbita pepo</td><td>MW582809-10</td></tr><tr><td>Geminiviridae</td><td>Mastrevirus</td><td>Maize streak virus</td><td>MSV</td><td>ssDNA</td><td>PV-1103</td><td>Zea mays</td><td>OQ102042-44</td></tr><tr><td>Nanoviridae</td><td>Babuvirus</td><td>Banana bunchy top virus</td><td>BBTV</td><td>ssDNA</td><td>PV-1166</td><td>Musa sp.</td><td>OQ102052-57</td></tr></table>

(a) DSMZ catalogue code

(b) Host in which the virus isolate was propagated and lyophilized

(c) Several variants are present in the propagated sample and accession numbers for the variants are provided
<table><tr><td>Virgaviridae</td><td>Furovirus</td><td>Soil-borne wheat mosaic virus</td><td>SBWMV</td><td>ssRNA(+)</td><td>PV-0748</td><td>Triticum aestivum</td><td>MZ405651-52</td></tr><tr><td>Virgaviridae</td><td>Hordeivirus</td><td>Barley stripe mosaic virus</td><td>BSMV</td><td>ssRNA(+)</td><td>PV-0330</td><td>Hordeum vulgare</td><td>ON924210-12</td></tr><tr><td>Virgaviridae</td><td>Pecluvirus</td><td>Peanut clump virus</td><td>PCV</td><td>ssRNA(+)</td><td>PV-0291</td><td>Nicotiana benthamiana</td><td>MW961156-57</td></tr><tr><td>Virgaviridae</td><td>Pomovirus</td><td>Potato mop-top virus</td><td>PMTV</td><td>ssRNA(+)</td><td>PV-0582</td><td>Nicotiana benthamiana</td><td>ON398500-02</td></tr><tr><td>Virgaviridae</td><td>Tobamovirus</td><td>Paprika mild mottle virus</td><td>PaMMV</td><td>ssRNA(+)</td><td>PV-0606</td><td>Nicotiana benthamiana</td><td>OK181768</td></tr><tr><td>Virgaviridae</td><td>Tobravirus</td><td>Pea early-browning virus</td><td>PEBV</td><td>ssRNA(+)</td><td>PV-0298</td><td>Chenopodium quinoa</td><td>MW854268-69</td></tr><tr><td>not assigned</td><td>Idaeovirus</td><td>Raspberry bushy dwarf virus</td><td>RBDV</td><td>ssRNA(+)</td><td>PV-0053</td><td>Chenopodium quinoa</td><td>MW582777-78</td></tr><tr><td>Rhabdoviridae</td><td>Cytorhabdovirus</td><td>Lettuce necrotic yellows virus</td><td>LNYV</td><td>ssRNA(-)</td><td>PV-0085</td><td>Nicotiana glutinosa “24A”</td><td>MZ202327</td></tr><tr><td>Rhabdoviridae</td><td>Varicosavirus</td><td>Beet oak leaf virus</td><td>BOLV</td><td>ssRNA(-)</td><td>PV-1034</td><td>Spinacia oleracea</td><td>OQ975887-88</td></tr><tr><td>Rhabdoviridae</td><td>Alphanucleorhabdovirus</td><td>Physostegia chlorotic mottle virus</td><td>PhCMoV</td><td>ssRNA(-)</td><td>PV-1182</td><td>Nicotiana occidentalis “37B”</td><td>KX636164</td></tr><tr><td>Rhabdoviridae</td><td>Betanucleorhabdovirus</td><td>Sonchus yellow net virus</td><td>SYNV</td><td>ssRNA(-)</td><td>PV-0052</td><td>Nicotiana clevelandii</td><td>MT613317</td></tr><tr><td>Aspiviridae</td><td>Ophiovirus</td><td>Lettuce ring necrosis virus</td><td>LRNV</td><td>ssRNA(-)</td><td>PV-0983</td><td>Nicotiana occidentalis “P1”</td><td>ON398506-09</td></tr><tr><td>Partitiviridae</td><td>Alphacryptovirus</td><td>Poinsettia latent virus</td><td>PnLV</td><td>dsRNA</td><td>PV-0629</td><td>Euphorbia pulcherrima</td><td>ON398503</td></tr><tr><td>Caulimoviridae</td><td>Badnavirus</td><td>Banana streak OL virus</td><td>BSOLV</td><td>dsDNA-RT</td><td>PV-0492</td><td>Musa sp.</td><td>OQ102041</td></tr><tr><td>Caulimoviridae</td><td>Caulimovirus</td><td>Cauliflower mosaic virus</td><td>CaMV</td><td>dsDNA-RT</td><td>PV-0229</td><td>Brassica rapa</td><td>OP947586</td></tr><tr><td>Geminiviridae</td><td>Begomovirus</td><td>Squash leaf curl virus</td><td>SLCV</td><td>ssDNA</td><td>PV-1299</td><td>Cucurbita pepo</td><td>MW582809-10</td></tr><tr><td>Geminiviridae</td><td>Mastrevirus</td><td>Maize streak virus</td><td>MSV</td><td>ssDNA</td><td>PV-1103</td><td>Zea mays</td><td>OQ102042-44</td></tr><tr><td>Nanoviridae</td><td>Babuvirus</td><td>Banana bunchy top virus</td><td>BBTV</td><td>ssDNA</td><td>PV-1166</td><td>Musa sp.</td><td>OQ102052-57</td></tr></table>

(a) DSMZ catalogue code

(b) Host in which the virus isolate was propagated and lyophilized

(c) Several variants are present in the propagated sample and accession numbers for the variants are provided
<table><caption>Table 2. Additional viruses identified by analysis of the HTS data in the samples used to assemble the synthetic mock communities of varying complexity.</caption><thead><tr><th>Family</th><th>Genus</th><th>Virus</th><th>Acronym</th><th>Genome type</th><th>Reference sequence accession number<sup>a</sup></th></tr></thead><tbody><tr><td>Virgaviridae</td><td>Tobamovirus</td><td>Tobacco mosaic virus</td><td>TMV</td><td>ssRNA(+)</td><td>OQ953825</td></tr><tr><td>Tymoviridae</td><td>unassigned</td><td>Poinsettia mosaic virus</td><td>PnMV</td><td>ssRNA(+)</td><td>OQ953828</td></tr><tr><td>Endornaviridae</td><td>Alphaendornavirus</td><td>Hordeum vulgare endornavirus</td><td>HvEV</td><td>ssRNA(+)</td><td>OQ953829</td></tr><tr><td>Solemoviridae</td><td>Polerovirus</td><td>Turnip yellows virus</td><td>TuYV</td><td>ssRNA(+)</td><td>JQ862472</td></tr><tr><td>Geminiviridae</td><td>Mastrevirus</td><td>Maize streak Réunion virus</td><td>MSRV</td><td>ssDNA</td><td>OQ953826</td></tr><tr><td>Totiviridae</td><td>unassigned</td><td>Maize-associated totivirus</td><td>MATV</td><td>dsRNA</td><td>OQ953827</td></tr><tr><td>Totiviridae</td><td>unassigned</td><td>Maize-associated totivirus 2</td><td>MTV-2</td><td>dsRNA</td><td>MN428829</td></tr><tr><td>Mitoviridae</td><td>Duamitovirus</td><td>Chenopodium quinoa mitovirus 1</td><td>CqMV1</td><td>ssRNA(+)</td><td>MT089917</td></tr><tr><td colspan="2">small linear ssRNA satellite</td><td>Turnip crinkle satellite RNA F</td><td>TCVsatRNA F</td><td>ssRNA</td><td>X12749</td></tr><tr><td colspan="2">small linear ssRNA satellite</td><td>Pea enation mosaic virus satellite RNA</td><td>PEMVsatRNA</td><td>ssRNA</td><td>OQ953831</td></tr><tr><td colspan="2">small linear ssRNA satellite</td><td>Strawberry latent ringspot virus satellite RNA</td><td>SLRSVsatRNA</td><td>ssRNA</td><td>OQ953830</td></tr></tbody></table>

(a) Accession number of the closest sequence in GenBank that was used as reference for reads mapping
<table><caption>Table 2. Additional viruses identified by analysis of the HTS data in the samples used to assemble the synthetic mock communities of varying complexity.</caption><thead><tr><th>Family</th><th>Genus</th><th>Virus</th><th>Acronym</th><th>Genome type</th><th>Reference sequence accession number<sup>a</sup></th></tr></thead><tbody><tr><td>Virgaviridae</td><td>Tobamovirus</td><td>Tobacco mosaic virus</td><td>TMV</td><td>ssRNA(+)</td><td>OQ953825</td></tr><tr><td>Tymoviridae</td><td>unassigned</td><td>Poinsettia mosaic virus</td><td>PnMV</td><td>ssRNA(+)</td><td>OQ953828</td></tr><tr><td>Endornaviridae</td><td>Alphaendornavirus</td><td>Hordeum vulgare endornavirus</td><td>HvEV</td><td>ssRNA(+)</td><td>OQ953829</td></tr><tr><td>Solemoviridae</td><td>Polerovirus</td><td>Turnip yellows virus</td><td>TuYV</td><td>ssRNA(+)</td><td>JQ862472</td></tr><tr><td>Geminiviridae</td><td>Mastrevirus</td><td>Maize streak Réunion virus</td><td>MSRV</td><td>ssDNA</td><td>OQ953826</td></tr><tr><td>Totiviridae</td><td>unassigned</td><td>Maize-associated totivirus</td><td>MATV</td><td>dsRNA</td><td>OQ953827</td></tr><tr><td>Totiviridae</td><td>unassigned</td><td>Maize-associated totivirus 2</td><td>MTV-2</td><td>dsRNA</td><td>MN428829</td></tr><tr><td>Mitoviridae</td><td>Duamitovirus</td><td>Chenopodium quinoa mitovirus 1</td><td>CqMV1</td><td>ssRNA(+)</td><td>MT089917</td></tr><tr><td colspan="2">small linear ssRNA satellite</td><td>Turnip crinkle satellite RNA F</td><td>TCVsatRNA F</td><td>ssRNA</td><td>X12749</td></tr><tr><td colspan="2">small linear ssRNA satellite</td><td>Pea enation mosaic virus satellite RNA</td><td>PEMVsatRNA</td><td>ssRNA</td><td>OQ953831</td></tr><tr><td colspan="2">small linear ssRNA satellite</td><td>Strawberry latent ringspot virus satellite RNA</td><td>SLRSVsatRNA</td><td>ssRNA</td><td>OQ953830</td></tr></tbody></table>

(a) Accession number of the closest sequence in GenBank that was used as reference for reads mapping
<table><caption>**Table 3.** Comparison of the number and average length of *de novo* assembled viral contigs obtained for VANA and dsRNA datasets normalized at different sequencing depths (100K, 300K, 1M, 3M and 10M reads, five resampling repeats at each depth). The standard deviations (SD) and the statistical differences (p-values) are also shown</caption><thead><tr><th></th><th></th><th>VANA average +/- SD</th><th>dsRNA average +/- SD</th><th>Two sample t-test</th></tr></thead><tbody><tr><td rowspan="2">100 K reads</td><td>nb viral contigs</td><td>33.6 +/- 1.9</td><td>101.8 +/- 2.9</td><td>9.2E-11</td></tr><tr><td>Viral contigs average length</td><td>733.4 +/- 23.7</td><td>747.4 +/- 17.1</td><td>0.32</td></tr><tr><td rowspan="2">300K reads</td><td>nb viral contigs</td><td>70.2 +/- 5.4</td><td>129.4 +/- 8.1</td><td>8.0E-07</td></tr><tr><td>Viral contigs average length</td><td>643.4 +/- 27.8</td><td>887.8 +/- 38.2</td><td>2.8E-06</td></tr><tr><td rowspan="2">1M reads</td><td>nb viral contigs</td><td>106.2 +/- 6.3</td><td>159.2 +/- 6.6</td><td>1.1E-06</td></tr><tr><td>Viral contigs average length</td><td>694.8 +/- 30.3</td><td>1019.6 +/- 40.9</td><td>5.7E-07</td></tr><tr><td rowspan="2">3M reads</td><td>nb viral contigs</td><td>129.6 +/- 4.8</td><td>207.6 +/- 3.8</td><td>2.5E-09</td></tr><tr><td>Viral contigs average length</td><td>798.4 +/- 15.9</td><td>1067.6 +/- 11.5</td><td>1.4E-09</td></tr><tr><td rowspan="2">10M reads</td><td>nb viral contigs</td><td>201.2 +/- 4.1</td><td>268 +/- 2.9</td><td>1.8E-09</td></tr><tr><td>Viral contigs average length</td><td>791.2 +/- 11.1</td><td>1121.4 +/- 10.6</td><td>3.9E-11</td></tr></tbody></table>
<table><caption>**Table 3.** Comparison of the number and average length of *de novo* assembled viral contigs obtained for VANA and dsRNA datasets normalized at different sequencing depths (100K, 300K, 1M, 3M and 10M reads, five resampling repeats at each depth). The standard deviations (SD) and the statistical differences (p-values) are also shown</caption><thead><tr><th></th><th></th><th>VANA average +/- SD</th><th>dsRNA average +/- SD</th><th>Two sample t-test</th></tr></thead><tbody><tr><td rowspan="2">100 K reads</td><td>nb viral contigs</td><td>33.6 +/- 1.9</td><td>101.8 +/- 2.9</td><td>9.2E-11</td></tr><tr><td>Viral contigs average length</td><td>733.4 +/- 23.7</td><td>747.4 +/- 17.1</td><td>0.32</td></tr><tr><td rowspan="2">300K reads</td><td>nb viral contigs</td><td>70.2 +/- 5.4</td><td>129.4 +/- 8.1</td><td>8.0E-07</td></tr><tr><td>Viral contigs average length</td><td>643.4 +/- 27.8</td><td>887.8 +/- 38.2</td><td>2.8E-06</td></tr><tr><td rowspan="2">1M reads</td><td>nb viral contigs</td><td>106.2 +/- 6.3</td><td>159.2 +/- 6.6</td><td>1.1E-06</td></tr><tr><td>Viral contigs average length</td><td>694.8 +/- 30.3</td><td>1019.6 +/- 40.9</td><td>5.7E-07</td></tr><tr><td rowspan="2">3M reads</td><td>nb viral contigs</td><td>129.6 +/- 4.8</td><td>207.6 +/- 3.8</td><td>2.5E-09</td></tr><tr><td>Viral contigs average length</td><td>798.4 +/- 15.9</td><td>1067.6 +/- 11.5</td><td>1.4E-09</td></tr><tr><td rowspan="2">10M reads</td><td>nb viral contigs</td><td>201.2 +/- 4.1</td><td>268 +/- 2.9</td><td>1.8E-09</td></tr><tr><td>Viral contigs average length</td><td>791.2 +/- 11.1</td><td>1121.4 +/- 10.6</td><td>3.9E-11</td></tr></tbody></table>
<table><caption>**Table 4.** Performance parameters of *de novo* assembly using different minimal contigs length of normalized, 10M reads, VANA and dsRNA datasets for the 60-viruses synthetic community.</caption><thead><tr><th rowspan="3"></th><th colspan="12">Minimal contig length</th></tr><tr><th colspan="2">125 nt</th><th colspan="2">175 nt</th><th colspan="2">250 nt</th><th colspan="2">350 nt</th><th colspan="2">500 nt</th><th colspan="2">1000 nt</th></tr><tr><th>VANA</th><th>dsRNA</th><th>VANA</th><th>dsRNA</th><th>VANA</th><th>dsRNA</th><th>VANA</th><th>dsRNA</th><th>VANA</th><th>dsRNA</th><th>VANA</th><th>dsRNA</th></tr></thead><tbody><tr><td>nb contigs</td><td>1947</td><td>2212</td><td>416</td><td>784</td><td>220</td><td>437</td><td>144</td><td>276</td><td>86</td><td>182</td><td>37</td><td>88</td></tr><tr><td>average length</td><td>235</td><td>324</td><td>506</td><td>607</td><td>757</td><td>907</td><td>985</td><td>1243</td><td>1355</td><td>1662</td><td>2191</td><td>2696</td></tr><tr><td>N25</td><td>547</td><td>1764</td><td>1836</td><td>3642</td><td>2334</td><td>4007</td><td>2560</td><td>4060</td><td>3449</td><td>4505</td><td>3824</td><td>5671</td></tr><tr><td>N50</td><td>206</td><td>352</td><td>628</td><td>1005</td><td>994</td><td>1521</td><td>1277</td><td>1955</td><td>1709</td><td>2775</td><td>2277</td><td>3705</td></tr><tr><td>N75</td><td>156</td><td>191</td><td>313</td><td>352</td><td>481</td><td>558</td><td>618</td><td>773</td><td>888</td><td>1117</td><td>1653</td><td>1782</td></tr><tr><td>Max</td><td>6549</td><td>13919</td><td>6652</td><td>13919</td><td>6652</td><td>13919</td><td>6549</td><td>13919</td><td>6652</td><td>13919</td><td>6652</td><td>13919</td></tr><tr><td>nb viral contigs</td><td>1852</td><td>1672</td><td>378</td><td>468</td><td>204</td><td>269</td><td>137</td><td>181</td><td>84</td><td>131</td><td>37</td><td>75</td></tr><tr><td>% viral contigs</td><td>95%</td><td>76%</td><td>91%</td><td>60%</td><td>93%</td><td>62%</td><td>95%</td><td>66%</td><td>98%</td><td>72%</td><td>100%</td><td>85%</td></tr><tr><td>Viral contigs average length</td><td>235</td><td>327</td><td>525</td><td>741</td><td>783</td><td>1123</td><td>1008</td><td>1508</td><td>1368</td><td>1921</td><td>2191</td><td>2833</td></tr><tr><td>Bases in viral contigs</td><td>435421</td><td>547507</td><td>198486</td><td>347003</td><td>159827</td><td>302074</td><td>138102</td><td>272883</td><td>114951</td><td>251656</td><td>81077</td><td>212467</td></tr><tr><td>% bases in viral contigs</td><td>95.20%</td><td>76.40%</td><td>94.40%</td><td>72.90%</td><td>96.00%</td><td>76.20%</td><td>97.40%</td><td>79.50%</td><td>98.60%</td><td>83.20%</td><td>100%</td><td>89.50%</td></tr></tbody></table>
<table><caption>**Table 4.** Performance parameters of *de novo* assembly using different minimal contigs length of normalized, 10M reads, VANA and dsRNA datasets for the 60-viruses synthetic community.</caption><thead><tr><th rowspan="3"></th><th colspan="12">Minimal contig length</th></tr><tr><th colspan="2">125 nt</th><th colspan="2">175 nt</th><th colspan="2">250 nt</th><th colspan="2">350 nt</th><th colspan="2">500 nt</th><th colspan="2">1000 nt</th></tr><tr><th>VANA</th><th>dsRNA</th><th>VANA</th><th>dsRNA</th><th>VANA</th><th>dsRNA</th><th>VANA</th><th>dsRNA</th><th>VANA</th><th>dsRNA</th><th>VANA</th><th>dsRNA</th></tr></thead><tbody><tr><td>nb contigs</td><td>1947</td><td>2212</td><td>416</td><td>784</td><td>220</td><td>437</td><td>144</td><td>276</td><td>86</td><td>182</td><td>37</td><td>88</td></tr><tr><td>average length</td><td>235</td><td>324</td><td>506</td><td>607</td><td>757</td><td>907</td><td>985</td><td>1243</td><td>1355</td><td>1662</td><td>2191</td><td>2696</td></tr><tr><td>N25</td><td>547</td><td>1764</td><td>1836</td><td>3642</td><td>2334</td><td>4007</td><td>2560</td><td>4060</td><td>3449</td><td>4505</td><td>3824</td><td>5671</td></tr><tr><td>N50</td><td>206</td><td>352</td><td>628</td><td>1005</td><td>994</td><td>1521</td><td>1277</td><td>1955</td><td>1709</td><td>2775</td><td>2277</td><td>3705</td></tr><tr><td>N75</td><td>156</td><td>191</td><td>313</td><td>352</td><td>481</td><td>558</td><td>618</td><td>773</td><td>888</td><td>1117</td><td>1653</td><td>1782</td></tr><tr><td>Max</td><td>6549</td><td>13919</td><td>6652</td><td>13919</td><td>6652</td><td>13919</td><td>6549</td><td>13919</td><td>6652</td><td>13919</td><td>6652</td><td>13919</td></tr><tr><td>nb viral contigs</td><td>1852</td><td>1672</td><td>378</td><td>468</td><td>204</td><td>269</td><td>137</td><td>181</td><td>84</td><td>131</td><td>37</td><td>75</td></tr><tr><td>% viral contigs</td><td>95%</td><td>76%</td><td>91%</td><td>60%</td><td>93%</td><td>62%</td><td>95%</td><td>66%</td><td>98%</td><td>72%</td><td>100%</td><td>85%</td></tr><tr><td>Viral contigs average length</td><td>235</td><td>327</td><td>525</td><td>741</td><td>783</td><td>1123</td><td>1008</td><td>1508</td><td>1368</td><td>1921</td><td>2191</td><td>2833</td></tr><tr><td>Bases in viral contigs</td><td>435421</td><td>547507</td><td>198486</td><td>347003</td><td>159827</td><td>302074</td><td>138102</td><td>272883</td><td>114951</td><td>251656</td><td>81077</td><td>212467</td></tr><tr><td>% bases in viral contigs</td><td>95.20%</td><td>76.40%</td><td>94.40%</td><td>72.90%</td><td>96.00%</td><td>76.20%</td><td>97.40%</td><td>79.50%</td><td>98.60%</td><td>83.20%</td><td>100%</td><td>89.50%</td></tr></tbody></table>
<table><caption>**Table 5.** Detection performance of VANA, dsRNA and RNASeq methods at the level of individual viral genomic molecules (from a total of 115 viral molecules) using the most complex, 60-virus pool, for groups of viruses with different genome types at 10M reads sequencing depth.</caption><thead><tr><th rowspan="2"></th><th rowspan="2"># viral<br>molecules</th><th colspan="2">VANA</th><th colspan="2">dsRNA</th><th colspan="2">RNASeq</th></tr><tr><th># detected</th><th>% detected</th><th># detected</th><th>% detected</th><th># detected</th><th>% detected</th></tr></thead><tbody><tr><td>+ssRNA viruses</td><td>86</td><td>50</td><td>58.1%</td><td>77</td><td>89.5%</td><td>81</td><td>96.3%</td></tr><tr><td>-ssRNA viruses</td><td>12</td><td>1</td><td>8.3%</td><td>5</td><td>41.7%</td><td>12</td><td>100%</td></tr><tr><td>RNA satellites</td><td>3</td><td>1</td><td>33.3%</td><td>3</td><td>100%</td><td>2</td><td>66.6%</td></tr><tr><td>dsRNA viruses</td><td>2</td><td>0</td><td>0%</td><td>2</td><td>100%</td><td>0</td><td>0.0%</td></tr><tr><td>ssDNA viruses</td><td>10</td><td>4</td><td>40.0%</td><td>0</td><td>0%</td><td>4</td><td>40.0%</td></tr><tr><td>dsDNA viruses</td><td>2</td><td>1</td><td>50.0%</td><td>1</td><td>50.0%</td><td>1</td><td>50.0%</td></tr><tr><td>Total</td><td>115</td><td>57</td><td>49.6%</td><td>88</td><td>76.5%</td><td>100</td><td>87.7%</td></tr></tbody></table>
<table><caption>**Table 5.** Detection performance of VANA, dsRNA and RNASeq methods at the level of individual viral genomic molecules (from a total of 115 viral molecules) using the most complex, 60-virus pool, for groups of viruses with different genome types at 10M reads sequencing depth.</caption><thead><tr><th rowspan="2"></th><th rowspan="2"># viral<br>molecules</th><th colspan="2">VANA</th><th colspan="2">dsRNA</th><th colspan="2">RNASeq</th></tr><tr><th># detected</th><th>% detected</th><th># detected</th><th>% detected</th><th># detected</th><th>% detected</th></tr></thead><tbody><tr><td>+ssRNA viruses</td><td>86</td><td>50</td><td>58.1%</td><td>77</td><td>89.5%</td><td>81</td><td>96.3%</td></tr><tr><td>-ssRNA viruses</td><td>12</td><td>1</td><td>8.3%</td><td>5</td><td>41.7%</td><td>12</td><td>100%</td></tr><tr><td>RNA satellites</td><td>3</td><td>1</td><td>33.3%</td><td>3</td><td>100%</td><td>2</td><td>66.6%</td></tr><tr><td>dsRNA viruses</td><td>2</td><td>0</td><td>0%</td><td>2</td><td>100%</td><td>0</td><td>0.0%</td></tr><tr><td>ssDNA viruses</td><td>10</td><td>4</td><td>40.0%</td><td>0</td><td>0%</td><td>4</td><td>40.0%</td></tr><tr><td>dsDNA viruses</td><td>2</td><td>1</td><td>50.0%</td><td>1</td><td>50.0%</td><td>1</td><td>50.0%</td></tr><tr><td>Total</td><td>115</td><td>57</td><td>49.6%</td><td>88</td><td>76.5%</td><td>100</td><td>87.7%</td></tr></tbody></table>
## Supplementary Figures

**Supplementary Figure S1:** Pooling strategy to generate mock virus communities with different degrees of complexity (5, 10, 20, and 60-viruses communities).

**Supplementary Figure S2:** Percent coverage of detected viral molecules using the VANA or the dsRNA approaches as a function of minimal contig length.

**Supplementary Figure S3:** Number of detected RNA viruses based on *de novo* assembled contigs from the VANA or the dsRNA approaches for datasets normalized at a 120K reads sequencing depth and for viral communities with different degrees of complexity.

**Supplementary Figure S4:** Number of detected RNA viruses for viral communities with different degrees of complexity using *de novo* assembled contigs from the VANA or the dsRNA approaches derived from datasets normalized so as to compensate for community complexity (120K reads for 5 viruses communities, 240K for 10 viruses, 480K for 20 viruses and 1.44M for 60 viruses).

**Supplementary Figure S5:** Average proportion of the length of viral molecules represented by contigs obtained for the VANA or the dsRNA approaches as a function of sequencing depth. For each sequencing depth 5 independent random resamplings were performed and error bars represent the standard deviations of the coverage obtained.

## Supplementary Tables

**Supplementary Table S1.** Pooling strategy to generate the various pools of variable complexity (from 5 to 60 viruses in a pool).

**Supplementary Table S2.** Comparison of *de novo* assembly parameters for VANA and dsRNA datasets normalized at different sequencing depths (100K, 300K, 1M, 3M and 10M reads, 5 resampling repeats at each depth) and corresponding statistical significance.
## Supplementary Figures

**Supplementary Figure S1:** Pooling strategy to generate mock virus communities with different degrees of complexity (5, 10, 20, and 60-viruses communities).

**Supplementary Figure S2:** Percent coverage of detected viral molecules using the VANA or the dsRNA approaches as a function of minimal contig length.

**Supplementary Figure S3:** Number of detected RNA viruses based on *de novo* assembled contigs from the VANA or the dsRNA approaches for datasets normalized at a 120K reads sequencing depth and for viral communities with different degrees of complexity.

**Supplementary Figure S4:** Number of detected RNA viruses for viral communities with different degrees of complexity using *de novo* assembled contigs from the VANA or the dsRNA approaches derived from datasets normalized so as to compensate for community complexity (120K reads for 5 viruses communities, 240K for 10 viruses, 480K for 20 viruses and 1.44M for 60 viruses).

**Supplementary Figure S5:** Average proportion of the length of viral molecules represented by contigs obtained for the VANA or the dsRNA approaches as a function of sequencing depth. For each sequencing depth 5 independent random resamplings were performed and error bars represent the standard deviations of the coverage obtained.

## Supplementary Tables

**Supplementary Table S1.** Pooling strategy to generate the various pools of variable complexity (from 5 to 60 viruses in a pool).

**Supplementary Table S2.** Comparison of *de novo* assembly parameters for VANA and dsRNA datasets normalized at different sequencing depths (100K, 300K, 1M, 3M and 10M reads, 5 resampling repeats at each depth) and corresponding statistical significance.
<figure><img src="image_8.png" /><figcaption>**Supplementary Figure S1:** Pooling strategy to generate mock virus communities with different degrees of complexity (5, 10, 20, and 60-viruses communities)</figcaption></figure>
<figure><img src="image_8.png" /><figcaption>**Supplementary Figure S1:** Pooling strategy to generate mock virus communities with different degrees of complexity (5, 10, 20, and 60-viruses communities)</figcaption></figure>
<figure><img src="image_9.png" /><figcaption>**Supplementary Figure S2:** Percent coverage of detected viral molecules as a function of minimal contig length for the VANA (light grey) or the dsRNA (dark grey) approaches on the 60 viruses community at a 10 millions reads sequencing depth</figcaption></figure>
<figure><img src="image_9.png" /><figcaption>**Supplementary Figure S2:** Percent coverage of detected viral molecules as a function of minimal contig length for the VANA (light grey) or the dsRNA (dark grey) approaches on the 60 viruses community at a 10 millions reads sequencing depth</figcaption></figure>
<figure><img src="image_10.png" /><figcaption>**Supplementary Figure S3:** Number of detected viruses based on *de novo* assembled contigs from the VANA (light grey) or the dsRNA (dark grey) approaches for datasets normalized at a 120K reads sequencing depth and for viral communities with different degrees of complexity. RNA viruses are indicated by solid bars while DNA viruses are indicated by dashed bars.</figcaption></figure>
<figure><img src="image_10.png" /><figcaption>**Supplementary Figure S3:** Number of detected viruses based on *de novo* assembled contigs from the VANA (light grey) or the dsRNA (dark grey) approaches for datasets normalized at a 120K reads sequencing depth and for viral communities with different degrees of complexity. RNA viruses are indicated by solid bars while DNA viruses are indicated by dashed bars.</figcaption></figure>
<figure><img src="image_11.png" /><figcaption>**Supplementary Figure S4:** Number of detected viruses for viral communities with different degrees of complexity using *de novo* assembled contigs from the VANA (light grey) or the dsRNA (dark grey) approaches. Datasets were normalized so as to compensate for community complexity (120K reads for 5 viruses communities, 240K for 10 viruses, 480K for 20 viruses and 1.44M for 60 viruses). RNA viruses are indicated by solid bars while DNA viruses are indicated by dashed bars.</figcaption></figure>
<figure><img src="image_11.png" /><figcaption>**Supplementary Figure S4:** Number of detected viruses for viral communities with different degrees of complexity using *de novo* assembled contigs from the VANA (light grey) or the dsRNA (dark grey) approaches. Datasets were normalized so as to compensate for community complexity (120K reads for 5 viruses communities, 240K for 10 viruses, 480K for 20 viruses and 1.44M for 60 viruses). RNA viruses are indicated by solid bars while DNA viruses are indicated by dashed bars.</figcaption></figure>
<figure><img src="image_12.png" /><figcaption>**Supplementary Figure S5:** Average proportion of the length of detected viral molecules of the 60 viruses community represented by contigs obtained for the VANA (light grey) or the dsRNA (dark grey) approaches at different sequencing depth. For each sequencing depth 5 independent random resamplings were performed and error bars represent the standard deviations of the coverage obtained</figcaption></figure>
<figure><img src="image_12.png" /><figcaption>**Supplementary Figure S5:** Average proportion of the length of detected viral molecules of the 60 viruses community represented by contigs obtained for the VANA (light grey) or the dsRNA (dark grey) approaches at different sequencing depth. For each sequencing depth 5 independent random resamplings were performed and error bars represent the standard deviations of the coverage obtained</figcaption></figure>