domain: scco phys

text:
M Roux

C Philipps-Bertin

O Chiello

P Aumond

A Can

A Kacem

G Lemaitre

USING EXPERIENCE SAMPLING METHOD TO STUDY RAILWAY NOISE ANNOYANCE: A REVIEW

Keywords:

Thus, this article then proposes an adaptation of the experience sampling method (ESM) to study annoyance caused by the passage of high-

The goal of this article is to report on the development of a study of the acoustic factors driving the short-term annoyance experienced by neighbors of high-speed train lines. In fact, annoyance caused by high-speed trains (> 250 km/h) is not completely modeled by current indicators (e.g., LDEN). For example, the suddenness, spectral content, temporal fluctuations of pass-by noises, and the density of train passages seem to significantly impact annoyance in a way that is not fully understood [START_REF] Di | Annoyance and activity disturbance induced by high-speed railway and conventional railway Interne noise: a contrastive case study[END_REF]. To investigate these aspects, we first review different approaches used to study annoyance caused by transportation noise. For railway noise, two main approaches are reported in the literature: insitu social surveys exploring the contributions of acoustic and non-acoustic factors to long-term annoyance; laboratory experiments, in which controlled stimuli (individual pass-by noises) are played back to participants who rate their annoyance. Another approach is also used in transportation noise studies, but not yet for railway noise: diary, or experience sampling methods, whereby neighbors report their annoyance, at home, at different times over a longer time span (usually several weeks) while noise exposure is simultaneously recorded [START_REF] Bartels | The influence of acoustical and non-acoustical factors on short-term annoyance due to aircraft noise in the field -The COSMA study[END_REF]. Such in situ approach allows experimenters to focus on the p recise characteristics of each pass-by noise and consider annoyance in the context of participants' real environment ---------

INTRODUCTION

Railway transportation has many environmental advantages: in countries (such as France) where electricity production mainly relies on nuclear energy or renewable energy sources, train operations have a very low carbon footprint compared to other transportation means. Still, it has an environmental impact: the noise exposure imposed to the surrounding communities. Even if this impact is lower than for road or air transportation, the social cost of railway noise has still been estimated to be 11.2 billion euros per year in France [START_REF]Agence de la transition écologique[END_REF]. Furthermore, public acceptance of annoyance caused by railway noise appears to decline, particularly in the case of high-speed trains (HST), potentially creating hurdles to the development of new projects. It is thus utterly important for train operators, infrastructure managers and transportation authorities to understand precisely how the acoustic characteristics of these trains influence annoyance experienced by residents living nearby railway infrastructures , in order to design effective mitigation measures. Noise emitted by high-speed trains (>250 km/h) is dominated by aero-acoustic sources, generating louder low frequencies than classical-speed trains. Moreover, higher speeds create specific temporal characteristics (see below). However, current indicators and thresholds used in French and European regulations are Interne only based on energy noise levels which are calculated by averaging the equivalent sound pressure level over several hours, with different weights for different periods of the day (e.g., LDEN). Thus, it appears that these indicators may be insufficient to account for more detailed aspects of the noise signatures that may influence annoyance, and regulations may evolve to include new indicators more representative of noise annoyance [START_REF]Ministère de la transition écologique, Loi d'Orientation des Mobilités[END_REF]. This article is part of a study aimed at better understanding the acoustic factors driving annoyance experienced by neighbors of HST lines. To this aim, next paragraphs review current methods used to assess railway noise annoyance, and list the main results gathered using these methods.

CLASSICAL METHODS USED TO STUDY RAILWAY NOISE ANNOYANCE

Two main approaches are reported in the literature to assess annoyance caused by railway noise: in-situ social surveys and laboratory experiments.

In situ social surveys

Method

In situ social surveys ask residents living alongside railway tracks to rate annoyance experienced at home over a certain, long time period (hence the term long-term annoyance).

Their judgment is therefore retrospective (e.g., the last 12 months), global (not related to a specific event), and contextualized [START_REF] Lim | The relationship between civil aircraft noise and community annoyance in Korea[END_REF]- [START_REF] Lambert | Annoyance from high-speed train noise: a social survey[END_REF]. Social surveys usually use standardized response scales [START_REF] Fields | Standardized general-purpose noise reaction questions for community noise surveys: Research and a recommendation[END_REF], [START_REF]15666:2003 Acoustique -Évaluation de la gêne causée par le bruit au moyen d'enquêtes sociales et d'enquêtes socio-acoustiques[END_REF]. For example, two of these standardized response scales are recommended by the International Commission on Biological Effects of Noise (ICBEN [START_REF] Fields | Standardized general-purpose noise reaction questions for community noise surveys: Research and a recommendation[END_REF]): 11-point numerical and 5-point verbal rating scales. Respondents judge annoyance by selecting one of 11 (or 5) categories from 0 (not annoyed at all) to 10 (or 4, extremely annoyed). Some other questions, such as demographic factors, disturbance during the activities and their locations, sleep disturbance, attitudes towards the noise source, main windows position during the study, may be asked [START_REF] Lim | The relationship between civil aircraft noise and community annoyance in Korea[END_REF], [START_REF] Schreckenberg | The impact of acoustical, operational and non-auditory factors on short-term annoyance due to aircraft noise[END_REF], [START_REF] Licitra | Annoyance evaluation due to overall railway noise and vibration in Pisa urban areas[END_REF], [START_REF] Schreckenberg | Effects of aircraft noise on annoyance and sleep disturbances before and after expansion of Frankfurt airportresults of the NORAH study, WP 1 'Annoyance and quality of life[END_REF]. These judgements are then related to acoustic indicators (and particularly energetic noise levels such as LAeq, LDEN, etc.) and other non-acoustic factors (such as those gathered in the socio-demographic surveys) through statistical analyses.

Examples of results

Social surveys allow experimenters to consider a large range of factors influencing annoyance. Importantly, several social surveys have reported that acoustic factors may account for about a third of the variance of annoyance judgements, whereas non-acoustic factors ( individual, social and situational) may account for another third, and the remaining third of the variance is unaccounted for [START_REF] Lambert | Annoyance from high-speed train noise: a social survey[END_REF], [START_REF] Guski | Psychological methods for evaluating sound quality and assessing acoustic information[END_REF].

Regarding acoustic factors, the results of such surveys a re often summarized with dose-response relationships, modeling the percentage of highly annoyed residents as a function of noise exposure. These dose-response relationships are often used to determine the exposure thresholds in national and international regulations [START_REF] Guski | Psychological methods for evaluating sound quality and assessing acoustic information[END_REF].

Beyond these thresholds, railway noise is considered to have an impact on health and/or sleep. For example, Miedema and Oudhsoorn have compiled many different surveys to provide dose-response relationships for various transportation modes [START_REF] Miedema | Annoyance from transportation noise: relationships with exposure metrics DNL and DENL and their confidence intervals[END_REF]. These relationships have been revised over the years to provide a basis for local regulations [START_REF]Environmental noise guidelines for European Region[END_REF]. Some examples of thresholds used in French regulations are reported in Tab. 1.

It should be noted that the thresholds applied to HS lines are lower than those for conventional lines. This acknowledges the fact that, for a same noise level, exposure to HS trains appears to cause more annoyance than conventional trains.

In another example, Lambert et al. used a social survey to assess the annoyance caused by HST noise, at the time of the introduction of a new HS line in France [START_REF] Lambert | Annoyance from high-speed train noise: a social survey[END_REF]. In their study, two hundred and sixty residents were interviewed by a questionnaire about their long-time noise annoyance. Analysis of noise annoyance levels and noise exposure levels suggests that, in addition to energetic noise levels, the number of train passages is also an important factor contributing to annoyance, particularly during evening and morning periods. The impact of the number of events on annoyance has also been reported in other studies of other transportation modes. For example, Schreckenberg and Schuemer showed that the number of aircraft flyovers better models annoyance than the mean maximum sound level, in particular with regard to the hourly annoyance [START_REF] Schreckenberg | The impact of acoustical, operational and non-auditory factors on short-term annoyance due to aircraft noise[END_REF].

Interne

In situ social surveys are designed to encompass a variety of factors that influence the annoyance experienced by residents in their daily environments [START_REF] Licitra | Annoyance evaluation due to overall railway noise and vibration in Pisa urban areas[END_REF].

Laboratory experiments

In contrast, laboratory experiments are designed to focus on the characteristics of a small number of acoustic events, in a laboratory setting. Due to their focus on a shorter duration, annoyance in these experiments is usually described as short-term. The duration can vary from a single pass-by noise to a few hours.

Methods

There are in fact two types of laboratory approaches. First, the psychoacoustics approach requires participants to focus their attention toward short stimuli (pass-by noises) and rate the pleasantness (or unpleasantness) of each stimulus using classical psychoacoustical response methods such as rating scales or pairwise comparisons [START_REF] Terroir | Qualité sonore des trains TGV : influence des facteurs perceptifsanalyse multidimensionnelle et préférentielle[END_REF]. In this approach, participants listen attentively to the sounds and judge them individually or by comparisons. In another approach, participants are not instructed to listen to the sounds, but to relax or perform some task (watch a movie, read a book) while being exposed to some noise [START_REF] Di | Annoyance and activity disturbance induced by high-speed railway and conventional railway Interne noise: a contrastive case study[END_REF]. In this case, noise annoyance is considered as the disturbance to the participants' activities. The disturbance can be measured either subjectively by the participants themselves [START_REF] Coensel | Experimental investigation of noise annoyance caused by high-speed trains[END_REF], by measuring their performance at a cognitive task, or by measuring some physiological indicators. Because this approach does not require the participants to focus on the sounds but to perform some cognitive tasks, it is sometimes referred to as the "distracted listening" or "cognitive tasks" approaches.

The results of laboratory experiments are usually analyzed with multifactorial regression analyses or general linear mixed models (a generalization of the multifactorial regression), allowing experimenter to quantify the influence of various acoustic factors on the pleasantness or the disturbance measures. To ensure the statistical relevance of these analyses, stimuli are usually manipulated (e.g., selected, synthesized, or processed to reach a specific statistical distribution of acoustic characteristics).

Examples of results

Terroir and Lavandier conducted a laboratory experiment to assess the influence of perceptual factors on HST noise quality [START_REF] Terroir | Perceptual impact of distance on high-speed train sound quality[END_REF]. Their study showed that, although sound level appears to be the most representative factor of annoyance due to pass-by noise, temporal fluctuations (i.e., passage length, rhythm) also play a significant role on noise quality.

However, they warn about the relevance of these results in conditions close to residents' daily life.

ANOTHER APPROACH: THE EXPERIENCE SAMPLING METHOD

Laboratory experiments allow a tight and parametric control over the acoustic parameters of the stimuli, but the artificial setting used in these experiments raises concerns about ecological validity and the generalizability of the results outside of the laboratory. In contrast, social surveys are designed to consider the real-life experiences of residents and a variety of factors but are not targeted to assess the impact of fine-grained acoustical factors.

Next paragraphs review another type of method: the experience sampling method (ESM). Although this method has not been applied yet to railway noise, it combines an ecological setting with a precise measurement of the noise exposure related to each annoyance judgement. This method consists of sampling participants' experiences (sound exposure, activities, perceptions) i.e., asking participants about their annoyance in their daily life, while noise exposure is simultaneously recorded.

Definition of the experience sampling method

According to Schreckenberg et al., "the experience sampling method is a method for the assessment of eventrelated, acute or short-term self-reports. ESM involves the Interne repeated measurements of human-beings' daily-life experiences, perceptions, or behavior in situ in different moments across a period of time (e.g., at different times of day, on several different days, etc.)" [START_REF] Schreckenberg | Reflections about the assessment of short-term noise annoyance[END_REF]. It is a form of diary studies whereby participants fill in a questionnaire in response to notifications. These notifications can be sent at random intervals, fixed predetermined intervals or at fixed times. These parameters will be detailed later (c.f. 4.1). This method has been used to study annoyance caused by transportation noise. In this case, the responses are associated with data on sound exposure. For example, in aircraft noise annoyance assessment studies, noise exposure was recorded at each subject's home with an outdoor recording system [START_REF] Bartels | The influence of acoustical and non-acoustical factors on short-term annoyance due to aircraft noise in the field -The COSMA study[END_REF], [START_REF] Page | Quiet Supersonic Flights 2018 (QSF18) test: Galveston, Texas risk reduction for future community testing with a low-boom flight demonstration vehicle[END_REF]- [START_REF] Stearns | A pilot study of human response to general aviation aircraft noise[END_REF].

ESM applied to assess aircraft noise annoyance

So far, the ESM has mostly been used to asses aircraft noise annoyance [START_REF] Fujiwara | Experience sampling in and around airports. Momentary subjective wellbeing, a irports, and aviation noise in England[END_REF]- [START_REF] Schreckenberg | Measuring short-term noise annoyance to determine the impact of low sonic boom noise[END_REF]. These studies have yielded the following points:

• There is a significant negative association between noise level and both happiness and relaxation [START_REF] Fujiwara | Experience sampling in and around airports. Momentary subjective wellbeing, a irports, and aviation noise in England[END_REF]; • The number of overflights has a strong impact on short-term annoyance, especially regarding on the period of the day [START_REF] Bartels | The influence of acoustical and non-acoustical factors on short-term annoyance due to aircraft noise in the field -The COSMA study[END_REF], [START_REF] Quehl | Short-term annoyance from nocturnal aircraft noise exposure: results of the NORAH and STRAIN sleep studies[END_REF]. Therefore, the ESM appears particularly well-suited to assess the impact of the number of events. However, Großarth describes an ESM study as "a demanding exercise. Many steps in the process are methodical-specific and bear pitfalls, if not considered carefully. […] There is reason to assume that a diary study can explain more variety in data than the usual retrospective surveys combined with regression derived approaches, by considering in-situ raised data and the discrimination of fixed and random effects when calculating the models" [START_REF] Großarth | Assessment of short-term annoyance due to shooting noise using the experience sampling method[END_REF]. Setting up an ESM study to assess railway noise annoyance thus requires careful consideration.

DESIGNING AN EXPERIENCE SAMPLING PILOT TO STUDY RAILWAY NOISE ANNOYANCE

In fact, and to the best of our knowledge, the ESM has not yet been applied to railway noise. Therefore, the following paragraph will outline a set of methodological considerations for adapting this method to railway noise research.

To this aim, we start with discussing the recommendations of Christensen et al., who have discussed the different techniques available to the experience sampling researcher in their practical guide to experience sampling procedures [START_REF] Conner Christensen | A practical guide to experience-sampling procedures[END_REF]. These discussions and techniques also form the bases of the noise assessment studies reported by Schreckenberg [START_REF] Schreckenberg | Reflections about the assessment of short-term noise annoyance[END_REF]. The following checklist is proposed: (A) decide whether you need experience sampling, (B) determine your resources, (C) set study parameters, (D) choose software and equipment, (E) implement security measures, (F) implement the study, (G) data issues.

Decide whether you need experience sampling

This method is time-and resource-intense for researchers and participants. It is thus important to consider how useful an ESM study is. For example, it may not be necessary to assess annoyance repeatedly. It depends on the research question and the characteristics of the exposure, which are specific to the particular noise source being investigated.

Here, we want to conduct a study to assess annoyance caused by railway noise in the participants' real environment, considering the influence of the number of train passages, and the trains' acoustic characteristics. The ESM seems a promising approach to reach this goal.

Determine your resources

The financial resources to provide incentives to the participants and to use the necessary devices for data collection (e.g., computerized vs. paper and pencil instruments) has also to be considered. The more timeconsuming an experience-sampling study is for the participants, the more substantial the remuneration should be. These incentives help maintain participants' motivat ion throughout the duration of long studies.

Set study parameters

The study parameters are determined by the sampling protocol and the sampling period. Three types of sampling protocols can be distinguished: interval-contingent, signalcontingent, and event-contingent. Interval-contingent protocols involve assessing annoyance at fixed times (either by the participant or not) throughout the day (e.g., at 10 am and 10 pm on a daily basis). These protocols are well suited for studying relatively frequent events as the event is likely to occur at the time of the report. Reports are made at predictable times, enabling participants to configure their schedule around them and prepare themselves cognitively and emotionally. The predictability of the requests makes the interval-contingent protocols the least burdensome for the participants. Signal-contingent protocols involve reporting experiences in response to a signal at various times throughout the day. They are appropriate for studying experiences that are Interne susceptible to retrospective memory bias, or cognitive or emotional regulation if they were reported on later from memory. In noise annoyance research, signal-contingent protocols seem to be suitable for assessing noise responses to sources with a higher number of events per day (e.g., road traffic noise) and during ongoing activities is of interest. However, the main disadvantage of this protocol is its burden to participants, who are interrupted by the signal and must stop their activities to report their experience.

Even-contingent protocols involve reporting experiences immediately or within a short-time after the event of interest. This protocol is appropriate for studying events that are less common in daily life. In noise annoyance research, it is suitable when the single noise event that the annoyance judgment refers to is distinct and detectable from the background noise. These procedures also minimize the retrospective bias that might occur when the judgment is from memory, but only to the extent that reports are made close in time to the event. Event-contingent protocols can be challenging to participants, especially if the events are frequent.

Choose software and equipment

Nowadays, the number of ESM studies that use mobile devices for collecting data has increased [START_REF] Van Berkel | Recommendations for conducting longitudinal experience sampling studies[END_REF]. Purchasing the devices and handing them out to the participants can be quite cost-intensive but allows the researcher to control the software that is used.

Implement security measures

Considering the high cost of the equipment, this point aims to protect it from damages, by setting up a deposit for example.

Implement the study

The implementation of an ESM survey involves the development of a pilot study. This pilot includes thorough extensive testing of the procedure and the equipment, an easy-to-read document to guide the installation and use of the ESM devices and procedure.

Data issues

The data collection and storage process must comply with applicable data protection regulations, such as the EU General Data Protection Regulation (GDPR). All data should be uploaded to a secure server as soon as possible, and the research team must ensure that appropriate measures are in place to prevent data loss and protect the privacy of research participants in accordance with GDPR requirements.

DISCUSSION

The experience sampling method allows experimenters to assess noise annoyance in-situ as it is experienced in real life. Associated with concomitant recordings of noise exposure, this method also allows a precise measurement of the acoustic factors potentially influencing annoyance. To the best of our knowledge, these studies have been applied to assess annoyance caused by aircraft noise, but never for rail transportation. Nevertheless, some of the parameters listed in paragraph 4 can be readily decided depending on the goal of this study. For example, to associate the participants' responses with the acoustics characteristics of the pass-by noises, the best sampling protocol would be the event-contingent. Moreover, incentives strategies are a powerful tool to encourage participants to remain in the study. According to Musthag et al., micro-incentives strategies seem to have a strong impact on the truthfulness of the answers and on participants behaviors [START_REF] Musthag | Exploring micro-incentive strategies for participant compensation in high-burden studies[END_REF]. However, ethical committees may require that all participants be compensated equally, which forbids such strategies. Other parameters such as the study's length, the time slots, the daily number of notifications and their delay, or the design of weekly interviews have an impact on participants motivation and on data's quality and quantity, but strongly depends on the specificities of the communities being studied [START_REF] Van Berkel | Recommendations for conducting longitudinal experience sampling studies[END_REF], [START_REF] Brink | Effects of scale, question location, order of response alternatives, and season on self-reported noise annoyance using ICBEN scales: a field experiment[END_REF], [START_REF] Eisele | The effects of sampling frequency and questionnaire length on perceived burden, compliance, and careless responding in experience sampling data in a student population[END_REF]. Thus, deciding of these parameters has to rely on a pilot experiment. Despite these methodological difficulties, using the ESM to assess railway noise annoyance seems a promising way to get a realistic insight into people's everyday noise experience.

2. 3

 3 Summary of the main results of social surveys and laboratory experimentsDespite their dissimilarities in scope and methods, both social surveys and laboratory experiments have yielded the following results:• Noise level indicators (with different frequency weightings, averaged over one pass-by noise or over longer periods of times, etc.) are often the main factor contributing to annoyance models[START_REF] Di | Annoyance and activity disturbance induced by high-speed railway and conventional railway Interne noise: a contrastive case study[END_REF],[START_REF] Lambert | Annoyance from high-speed train noise: a social survey[END_REF],[START_REF] Fields | Standardized general-purpose noise reaction questions for community noise surveys: Research and a recommendation[END_REF],[START_REF] Andersen | Reactions to railway noise in Denmark[END_REF]-[START_REF] Vallin | Railway noise annoyance modeling: Accounting for noise sensitivity and different acoustical features[END_REF]; • Annoyance due to high-speed and Maglev trains is higher than conventional trains[START_REF] Di | Annoyance and activity disturbance induced by high-speed railway and conventional railway Interne noise: a contrastive case study[END_REF],[START_REF] Coensel | Experimental investigation of noise annoyance caused by high-speed trains[END_REF],[START_REF] Vos | Annoyance caused by the sounds of a magnetic levitation train[END_REF],[START_REF] Yano | Community response to Shinkansen noise and vibration: a survey in areas along the Sanyo Shinkansen Line[END_REF]; • When considering a sequence with several train passages, the number of passages also influences annoyance[START_REF] Lambert | Annoyance from high-speed train noise: a social survey[END_REF],[START_REF] Gidlöf-Gunnarsson | Railway noise annoyance and the importance of number of trains, ground Interne vibration, and building situational factors[END_REF]-[START_REF] Öhrström | Laboratory annoyance and different traffic noise sources[END_REF]; • There is no clear consensus as to whether indicators based on averaged or maximal levels better model long-term annoyance[START_REF] Di | Annoyance and activity disturbance induced by high-speed railway and conventional railway Interne noise: a contrastive case study[END_REF],[START_REF] Chen | Highspeed maglev noise impacts on residents: A case study in Shanghai[END_REF],[START_REF] Möhler | Community response to railway noise: a review of social surveys[END_REF];

Table 1 .

 1 Examples

		of thresholds used in current
	French regulations for railway noise exposure
		LDEN	LN	LAeq,	LAeq,
		(dBA)	(dBA)	6h-22h	22h-6h
	HT lines	68	62	70	65
	Conventional lines 73	65	73	68

Turin, Italy • 11 th -15 th September 2023 • Politecnico di Torino

ACKNOWLEDGMENTS

This study was funded by the Europe's Rail Joint Undertaking (ERJU) and the Rail4EARTH project under the Horizon Europe programme. This work was performed within the framework of the LABEX CeLyA (ANR-10-LABX-0060) of Université de Lyon, within the program « Investissements d'Avenir » operated by the French National Research Agency (ANR).


Deborah Schönegger

Oumaima Moubset

Paolo Margaria

Wulf Menzel

Stephan Winter

Philippe Roumagnac

Armelle Marais

Thierry Candresse
email: thierry.candresse@inrae.fr

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

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

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.

INTRODUCTION

Significant advances in the development of molecular methods have been made in the last decades, including innovative sequencing technologies based on DNA/RNA approaches such as targeted (RT-)PCR or non-targeted High-Throughput Sequencing (HTS). HTS, also known as next generation sequencing (NGS), enables high-speed, high-throughput sequencing of native DNA/RNA or amplified DNA, generating enormous amounts of sequencing data. These developments led to major advances in the field of metagenomics, i.e. the sequencing of the entire genetic material of a sample, and to a new understanding of microbial diversity [START_REF] Zhang | Expanding the RNA Virosphere by Unbiased Metagenomics[END_REF][START_REF] Jian | Diversity and distribution of viruses inhabiting the deepest ocean on Earth[END_REF].

Viral metagenomics has revealed the immense diversity and ubiquity of viruses in nature and thus revolutionized our vision of these biological agents [START_REF] Zhang | Expanding the RNA Virosphere by Unbiased Metagenomics[END_REF][START_REF] Lefeuvre | Evolution and ecology of plant viruses[END_REF][START_REF] Maclot | Illuminating an ecological blackbox: using high throughput sequencing to characterize the plant virome across scales[END_REF][START_REF] Roux | Metagenomics in Virology[END_REF][START_REF] Greninger | A decade of RNA virus metagenomics is (not) enough[END_REF][START_REF] Moubset | Virion-associated nucleic acid-based metagenomics: a decade of advances in molecular characterization of plant viruses[END_REF][START_REF] Neri | A five-fold expansion of the global RNA virome reveals multiple new clades of RNA bacteriophages[END_REF]. Specifically, these metagenomics studies have revealed that virus sequence data available in public databases are biased toward human viruses or viruses of anthropological significance, with e.g. influenza-like viruses found in fish and amphibian hosts [START_REF] Shi | The evolutionary history of vertebrate RNA viruses[END_REF] or more than 75% of the plant virus species characterized up to 2006 having been isolated from crops [START_REF] Wren | Plant virus biodiversity and ecology[END_REF]. These findings, together with reports on viruses associated with hosts different from those known for the vast majority of their relatives, such as flavi-like viruses found in plants [START_REF] Kobayashi | Gentian Kobu-sho-associated virus: a tentative, novel double-stranded RNA virus that is relevant to gentian Kobu-sho syndrome[END_REF][START_REF] Schönegger | A new flavi-like virus identified in populations of wild carrots[END_REF], have raised novel questions about virus-hosts co-divergence or host switching.

In plant virology in particular, advances in the development of viral metagenome analyses have been of great importance in terms of early detection of known viruses and discovery of novel plant viruses [START_REF] Maclot | Illuminating an ecological blackbox: using high throughput sequencing to characterize the plant virome across scales[END_REF][START_REF] Moubset | Virion-associated nucleic acid-based metagenomics: a decade of advances in molecular characterization of plant viruses[END_REF][START_REF] Roossinck | Plant virus metagenomics: advances in virus discovery[END_REF][START_REF] Maree | Application of HTS for routine plant virus diagnostics: state of the art and challenges[END_REF], as more than half of emerging diseases in plants are thought to be caused by viruses [START_REF] Anderson | Emerging infectious diseases of plants: pathogen pollution, climate change and agrotechnology drivers[END_REF]. HTS has a huge potential in plant virus diagnostics because it allows to picture the complete phytosanitary status of a plant and to differentiate between virus variants that may contribute differentially to disease etiology [START_REF] Maree | Application of HTS for routine plant virus diagnostics: state of the art and challenges[END_REF]. For example, in a metagenomic analysis of sour cherry showing symptoms of Shirofugen stunt disease (SSD), a divergent isolate of little cherry virus 1 (LChV1) was identified in the absence of any other viral agent, suggesting that LChV1 could be responsible for the SSD disease [START_REF] Candresse | Association of Little cherry virus 1 with the Shirofugen stunt disease and characterization of the genome of a divergent LChV1 isolate[END_REF]. However, metagenomics approaches have also revealed that plants are often infected by more than one virus [START_REF] Moreno | When viruses play team sports: mixed infections in plants[END_REF], complicating the unravelling of the etiology of plant viral diseases.

HTS has also renewed the link between classical plant virology and ecology [START_REF] Maclot | Illuminating an ecological blackbox: using high throughput sequencing to characterize the plant virome across scales[END_REF][START_REF] Malmstrom | The expanding field of plant virus ecology: historical foundations, knowledge gaps, and research directions[END_REF]. 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 [START_REF] Bernardo | Geometagenomics illuminates the impact of agriculture on the distribution and prevalence of plant viruses at the ecosystem scale[END_REF][START_REF] Ma | Metagenomic analysis of virome cross-talk between cultivated Solanum lycopersicum and wild Solanum nigrum[END_REF][START_REF] Ma | Leafassociated fungal and viral communities of wild plant populations differ between cultivated and natural ecosystems[END_REF][START_REF] Susi | Agricultural land use disrupts biodiversity mediation of virus infections in wild plant populations[END_REF][START_REF] Maachi | Genetic differentiation and migration fluxes of viruses from melon crops and crop edge weeds[END_REF].

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) [START_REF] Maclot | Illuminating an ecological blackbox: using high throughput sequencing to characterize the plant virome across scales[END_REF][START_REF] Moubset | Virion-associated nucleic acid-based metagenomics: a decade of advances in molecular characterization of plant viruses[END_REF]. 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 [START_REF] Kreuze | Complete viral genome sequence and discovery of novel viruses by deep sequencing of small RNAs: A generic method for diagnosis, discovery and sequencing of viruses[END_REF][START_REF] Kashif | Detection of viruses in sweet potato from Honduras and Guatemala augmented by deep-sequencing of small RNAs[END_REF] but when the viromes of entire plant communities are analyzed from complex plant pools, VANA or dsRNAs enrichment methods are generally preferred [START_REF] Maclot | Illuminating an ecological blackbox: using high throughput sequencing to characterize the plant virome across scales[END_REF][START_REF] Moubset | Virion-associated nucleic acid-based metagenomics: a decade of advances in molecular characterization of plant viruses[END_REF][START_REF] Bernardo | Geometagenomics illuminates the impact of agriculture on the distribution and prevalence of plant viruses at the ecosystem scale[END_REF][START_REF] Ma | Leafassociated fungal and viral communities of wild plant populations differ between cultivated and natural ecosystems[END_REF][START_REF] Thapa | Determinants of taxonomic composition of plant viruses at the Nature Conservancy's Tallgrass Prairie Preserve[END_REF]. A huge number of bioinformatic tools are available for HTS data analysis and have been, together with nucleic acid preparation strategies, extensively reviewed [START_REF] Roossinck | Plant virus metagenomics: advances in virus discovery[END_REF][START_REF] Villamor | High throughput sequencing for plant virus detection and discovery[END_REF][START_REF] Kutnjak | A primer on the analysis of high-throughput sequencing data for detection of plant viruses[END_REF]. 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 [START_REF] Pecman | Next Generation Sequencing for Detection and Discovery of Plant Viruses and Viroids: Comparison of Two Approaches[END_REF][START_REF] Gaafar | Comparative study on three viral enrichment approaches based on RNA extraction for plant virus/viroid detection using high-throughput sequencing[END_REF], 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 [START_REF] Candresse | Appearances can be deceptive: revealing a hidden viral infection with deep sequencing in a plant quarantine context[END_REF], 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 [START_REF] Thapa | Determinants of taxonomic composition of plant viruses at the Nature Conservancy's Tallgrass Prairie Preserve[END_REF]. Ma et al. [START_REF] Ma | Phytovirome analysis of wild plant populations: comparison of double-stranded RNA and virion-associated nucleic acids metagenomic approaches[END_REF] 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 [START_REF] Massart | Guidelines for the reliable use of high throughput sequencing technologies to detect plant pathogens and pests[END_REF]. 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 [START_REF] Ma | Phytovirome analysis of wild plant populations: comparison of double-stranded RNA and virion-associated nucleic acids metagenomic approaches[END_REF], 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 [START_REF] Kozich | Development of a dual-index sequencing strategy and curation pipeline for analyzing amplicon sequence data on the MiSeq illumina sequencing platform[END_REF][START_REF] Egan | Using mock communities of arbuscular mycorrhizal fungi to evaluate fidelity associated with Illumina sequencing[END_REF][START_REF] Sevim | Shotgun metagenome data of a defined mock community using Oxford Nanopore, PacBio and Illumina technologies[END_REF].

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 [START_REF] Conceição-Neto | Modular approach to customise sample preparation procedures for viral metagenomics: A reproducible protocol for virome analysis[END_REF]. In another study, the bias introduced by viral enrichment or random amplification were assessed using a DNA virus mock community [START_REF] Parras-Moltó | Evaluation of bias induced by viral enrichment and random amplification protocols in metagenomic surveys of saliva DNA viruses[END_REF]. Viral synthetic communities have also been used to benchmark library preparation approaches in environmental [START_REF] Roux | Towards quantitative viromics for both doublestranded and single-stranded DNA viruses[END_REF] and insect [START_REF] Gil | A library preparation optimized for metagenomics of RNA viruses[END_REF] 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 [START_REF] Gaafar | Comparative study on three viral enrichment approaches based on RNA extraction for plant virus/viroid detection using high-throughput sequencing[END_REF]. 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.

MATERIALS AND METHODS

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 of isolates distributed by the DSMZ through the EU-funded EVA-Global initiative (https://www.european-virus-archive.com/). Quality controlled samples were obtained from the DSMZ in the form of infected, lyophilized plant material in vacuum-sealed vials. The complete list of the isolates used, together with their properties and the propagation host in which they were provided, are given in Table 1.

Initial low complexity pools were generated by assembling 30 mg of virus-infected samples into 12 viral communities comprising five viruses each (150 mg of plant material each) and containing at least one virus with a genome type different from +ssRNA (Supplementary Table S1). Pea enation mosaic virus was counted 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). Stepwise combinations of these five viruses mock communities were then assembled to create communities of increasing degrees of complexity (Supplementary Fig. S1), yielding a total of 22 communities with complexity ranging from five to 60 viruses.

Double-stranded RNA extraction

Double-stranded RNAs were purified from pooled samples according to [START_REF] Marais | Viral double-stranded RNAs (dsRNAs) from Plants: Alternative nucleic acid substrates for high-throughput sequencing[END_REF] with some minor modifications. Briefly, instead of 75 mg, 150 mg dried plant material (representing a pool of five plants, Supplementary Table S1) was used as starting material and buffer volumes increased proportionally. Plants were ground in liquid nitrogen until a fine powder was obtained which was then mixed with the phenol-extraction buffer. Following gentle agitation for 30 min and centrifugation, the supernatant was decanted and half of it directly further processed, while the other half was used for the stepwise gradual assembly of pairs of communities used to generate more complex viral communities. In this way, six communities of 10 viruses each, then three communities of 20 viruses and finally a single community of 60 viruses could be assembled. Between each step, assembled samples were vortexed for at least 30s for optimal 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. [START_REF] Marais | Viral double-stranded RNAs (dsRNAs) from Plants: Alternative nucleic acid substrates for high-throughput sequencing[END_REF] 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 [START_REF] Marais | Viral double-stranded RNAs (dsRNAs) from Plants: Alternative nucleic acid substrates for high-throughput sequencing[END_REF][START_REF] François | Viral metagenomics approaches for high-resolution screening of multiplexed arthropod and plant viral communities[END_REF].

VANA extraction

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

Illumina sequencing

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

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

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

HTS data analysis

Sequencing reads were imported into CLC Genomics Workbench v. 21.0.3. (CLC-GW, Qiagen) and adapters were removed from reads followed by trimming on quality and length using default settings and a minimum read length of 60 nucleotides (nt). Final trimmed reads were on average 111-113 nt long for the various datasets. Datasets were normalized by resampling at varying depth as needed, using the random reads sampling tool in CLC-GW.

To analyze virus detection performance as a function of contig size, de novo assembly was performed with CLC-GW (word size, 50; bubble size, 300) using various minimum contig 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 [START_REF] Altschul | Basic local alignment search tool[END_REF] 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 and2) 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 [START_REF] Illumina | Effects of index misassignment on multiplexing and downstream analysis[END_REF][START_REF] Van Der Valk | Index hopping on the Illumina HiseqX platform and its consequences for ancient DNA studies[END_REF], 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. 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.

RESULTS

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

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

In addition to the expected 61 viruses, evidence for the presence in some communities of additional viruses or virus-like agents was obtained through the BlastX indexing of de novo assembled contigs from the low complexity, 5-viruses communities. A total of 11 unexpected agents were thus identified (Table 2). These include three linear ssRNA satellites associated 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 [START_REF] Illumina | Effects of index misassignment on multiplexing and downstream analysis[END_REF][START_REF] Van Der Valk | Index hopping on the Illumina HiseqX platform and its consequences for ancient DNA studies[END_REF] 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 (10viruses 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 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" [START_REF] François | Increase in taxonomic assignment efficiency of viral reads in metagenomic studies[END_REF].

Impact of minimal contig length on the number of detected viruses

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.

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%).

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).

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 [START_REF] Lefebvre | The VirAnnot pipeline: A resource for automated viral diversity estimation and operational taxonomy units assignation for virome sequencing data[END_REF].

Effects of community complexity on virome description performance

We evaluated how, for a given sequencing depth, community complexity affects virome description performance following contigs assembly. For this, all datasets were normalized at a 120K read depth. Similar to the initial analysis using reads mapping, the number of detected viruses was reduced as community complexity increased. Again, dsRNA outperformed VANA at all complexity levels, though the difference in performance remained limited for low to medium community complexities (Supplementary Figure S3). VANA performance degradation was however more drastic at high community complexity, dropping from [START_REF] Illumina | Effects of index misassignment on multiplexing and downstream analysis[END_REF] S4). The 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.

Impact of sequencing depth on de novo assembly

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 at different depths (100K, 300K, 1M, 3M and 10M reads, five random resampling at each depth), assembled and the obtained contigs mapped against the viral reference genomes to determine the average assembly parameters and viral contigs parameters. The results are shown in Supplementary Table S2 and, for viral contigs alone, in Table 3.

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

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

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

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

Impact of sequencing depth on virus identification performance

We proceeded to evaluate the performance of VANA and dsRNA in identifying the expected viruses or viral molecules as affected by sequencing depth. The contigs obtained for the various datasets resampled at different depths (five resampling per sequencing depth) were mapped on individual reference sequences. This allowed to evaluate both the proportion of detected viruses and the coverage of the detected viral molecules, together with their standard deviation (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 r² 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 r² 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 [START_REF] Parras-Moltó | Evaluation of bias induced by viral enrichment and random amplification protocols in metagenomic surveys of saliva DNA viruses[END_REF][START_REF] Ajami | Maximal viral information recovery from sequence data using VirMAP[END_REF][START_REF] Santiago-Rodriguez | Potential applications of human viral metagenomics and reference materials: considerations for current and future viruses[END_REF] and, to some extent, to environmental virome studies [START_REF] Roux | Ecogenomics and potential biogeochemical impacts of globally abundant ocean viruses[END_REF][START_REF] Zablocki | VirION2: A shortand long-read sequencing and informatics workflow to study the genomic diversity of viruses in nature[END_REF]. 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 [START_REF] Filloux | Metagenomics approaches based on virion-associated nucleic acids (VANA): An innovative tool for assessing without a priori viral diversity of plants[END_REF][START_REF] Teycheney | Viral treasure hunt in European outermost territories: how metagenomics boosts the discovery of novel viral species in tropical and sub-tropical crops germplasm[END_REF]. 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 probably to date the largest scale effort to build synthetic viral communities and use them for the benchmarking of phytovirome analysis approaches. In some benchmarking studies, the nucleic acid proportions of the individual viruses involved in the virus community were quantified prior to extraction [START_REF] Sevim | Shotgun metagenome data of a defined mock community using Oxford Nanopore, PacBio and Illumina technologies[END_REF][START_REF] Roux | Towards quantitative viromics for both doublestranded and single-stranded DNA viruses[END_REF].

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 [START_REF] Ma | Phytovirome analysis of wild plant populations: comparison of double-stranded RNA and virion-associated nucleic acids metagenomic approaches[END_REF], 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 [START_REF] Conceição-Neto | Modular approach to customise sample preparation procedures for viral metagenomics: A reproducible protocol for virome analysis[END_REF][START_REF] Parras-Moltó | Evaluation of bias induced by viral enrichment and random amplification protocols in metagenomic surveys of saliva DNA viruses[END_REF][START_REF] Kleiner | Evaluation of methods to purify virus-like particles for metagenomic sequencing of intestinal viromes[END_REF]. 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 [START_REF] Gallet | The number of target molecules of the amplification step limits accuracy and sensitivity in ultradeep-sequencing viral population studies[END_REF]. Furthermore, different library preparation techniques have been found to require different sequencing depths to achieve the same genome coverage [START_REF] Visser | Next-generation sequencing for virus detection: covering all the bases[END_REF]. 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 [START_REF] Massart | Virus detection by high-throughput sequencing of small RNAs: large-scale performance testing of sequence analysis strategies[END_REF]. 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 [START_REF] Roux | Benchmarking viromics: an in silico evaluation of metagenome-enabled estimates of viral community composition and diversity[END_REF].

Lastly, it has been reported that the quality and completeness of virome description is also affected by the bioinformatic analysis used [START_REF] Roux | Benchmarking viromics: an in silico evaluation of metagenome-enabled estimates of viral community composition and diversity[END_REF][START_REF] Breitwieser | A review of methods and databases for metagenomic classification and assembly[END_REF][START_REF] Rampelli | ViromeScan: a new tool for metagenomic viral community profiling[END_REF][START_REF] Sutton | Choice of assembly software has a critical impact on virome characterisation[END_REF]. 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. (a) Accession number of the closest sequence in GenBank that was used as reference for reads mapping

  with the helper virus isolates included in the communities [turnip crinkle satellite F (TCVsatRNA F), pea enation mosaic satellite RNA (PEMVsatRNA) and strawberry latent ringspot virus satellite RNA (SLRSVsatRNA)], latent viruses associated with the propagation hosts used [Hordeum vulgare endornavirus (HvEV), maize-associated totivirus (MaTV), maize-associated totivirus2 (MaTV-2) and Chenopodium quinoa mitovirus 1 (CqMV1)], as well as viruses in coinfection with some of the viral isolates used [poinsettia mosaic virus (PnMV), tobacco mosaic virus (TMV), turnip yellows virus (TuYV) and maize streak Réunion virus (MSRV)] (Table

FiguresFigure 1 .

 1 Figures

Figure 2 :Figure 3 .

 23 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 4 :

 4 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, think lines). Linear r² coefficients are shown only for the RNA viruses curves.

  (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

  RNA viruses and four DNA viruses detected for communities of five viruses (66.7% of total viruses) to 11 RNA viruses and one DNA virus detected (16.7%) for the 60-viruses community. The corresponding values for dsRNA were 53 (80.3%) and 26 RNA viruses (39.4%), with no DNA virus detected For example, for the most complex community of 60 viruses at a 1.44M depth (12*120K reads), VANA detected 23 RNA viruses and 2 DNA viruses (compared to 44 RNA viruses and four DNA viruses when analysing individually the 12 pools of five viruses at 120K reads depth), which corresponds to a reduction in performance of 47.9%. For its part, dsRNA detected 42 RNA viruses (no DNA virus), to be compared with 53 viruses when individually analyzing the 12 pools of five viruses,

(Supplementary Figure

S3

). Remarkably, RNASeq turned out to be the least affected, with respectively 57/71 (80.3%, 5-viruses communities) and 34/71 viruses (47.9%, 60-viruses community) detected. These results indicate that even for limited complexity communities involving only five viruses, read numbers significantly higher than 120K are needed by the various techniques to achieve a 100% detection performance with a wide range of viruses.

If trying to compensate increased virome complexity by a parallel increase in sequencing depth, a negative impact of complexity is still seen but is much less severe. corresponding to a reduction in performance of 20.7% (Supplementary Figure

Table 1 . Viral isolates used to construct mock viral communities of varying complexity.

 1 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).

	Family	Genus	Virus	Acronym	Genome	Code a	Host b	Sequence accession number(s)
	Alphaflexiviridae Allexivirus	Shallot virus X	ShVX	ssRNA(+)	PV-0622	Chenopodium murale	MW854280
	Alphaflexiviridae Potexvirus	Lettuce virus X	LeVX	ssRNA(+)	PV-0904	Nicotiana benthamiana	MW248356
	Benyviridae	Benyvirus	Beet necrotic yellow vein virus	BNYVV	ssRNA(+)	PV-0467	Chenopodium quinoa	OK181765-67; M36896
	Betaflexiviridae	Capillovirus	Apple stem grooving virus	ASGV	ssRNA(+)	PV-0199	Chenopodium quinoa	MW582790
	Betaflexiviridae	Carlavirus	Poplar mosaic virus	PopMV	ssRNA(+)	PV-0341	Nicotiana benthamiana	ON924213
	Betaflexiviridae	Trichovirus	Apple chlorotic leaf spot virus	ACLSV	ssRNA(+)	PV-0998	Chenopodium quinoa	OK340218-19 c
	Betaflexiviridae	Tepovirus	Potato virus T	PVT	ssRNA(+)	PV-1145	Nicotiana hesperis	MZ405665
	Bromoviridae	Alfamovirus	Alfalfa mosaic virus	AMV	ssRNA(+)	PV-0779	Nicotiana tabacum "Samsun nn"	MZ405653-55
	Bromoviridae	Anulavirus	Pelargonium zonate spot virus	PZSV	ssRNA(+)	PV-0259	Nicotiana glutinosa "24A"	ON398493-95
	Bromoviridae	Bromovirus	Brome mosaic virus	BMV	ssRNA(+)	PV-0194	Hordeum vulgare	MW582787-89
	Bromoviridae	Cucumovirus	Peanut stunt virus	PSV	ssRNA(+)	PV-0190	Nicotiana benthamiana	MW307259-61
	Bromoviridae	Ilarvirus	Parietaria mottle virus	PMoV	ssRNA(+)	PV-0400	Chenopodium quinoa	MZ405646-48
	Closteroviridae	Closterovirus	Beet yellows virus	BYV	ssRNA(+)	PV-1260	Beta macrocarpa	MT815988
	Closteroviridae	Crinivirus	Tomato chlorosis virus	ToCV	ssRNA(+)	PV-1242	Solanum lycopersicum	ON398512-13
	Potyviridae	Bymovirus	Barley yellow mosaic virus	BaYMV	ssRNA(+)	PV-0634	Hordeum vulgare	OL311692-93
	Potyviridae	Ipomovirus	Cucumber vein yellowing virus	CVYV	ssRNA(+)	PV-0776	Cucumis sativus	OK181771
	Potyviridae	Potyvirus	Bidens mottle virus	BiMoV	ssRNA(+)	PV-0752	Nicotiana benthamiana	ON398504
	Potyviridae	Rymovirus	Agropyron mosaic virus	AgMV	ssRNA(+)	PV-0729	Triticum aestivum	OM471970
	Potyviridae	Tritimovirus	Brome streak mosaic virus	BrSMV	ssRNA(+)	PV-0431	Hordeum vulgare	OP357935

Table 2 . Additional viruses identified by analysis of the HTS data in the samples used to assemble the synthetic mock communities of varying complexity.

 2

	Family	Genus	Virus	Acronym	Genome type	Reference sequence accession number a
	Virgaviridae	Tobamovirus	Tobacco mosaic virus	TMV	ssRNA(+)	OQ953825
	Tymoviridae	unassigned	Poinsettia mosaic virus	PnMV	ssRNA(+)	OQ953828
	Endornaviridae Alphaendornavirus	Hordeum vulgare endornavirus	HvEV	ssRNA(+)	OQ953829
	Solemoviridae	Polerovirus	Turnip yellows virus	TuYV	ssRNA(+)	JQ862472
	Geminiviridae	Mastrevirus	Maize streak Réunion virus	MSRV	ssDNA	OQ953826
	Totiviridae	unassigned	Maize-associated totivirus	MATV	dsRNA	OQ953827
	Totiviridae	unassigned	Maize-associated totivirus 2	MTV-2	dsRNA	MN428829
	Mitoviridae	Duamitovirus	Chenopodium quinoa mitovirus 1	CqMV1	ssRNA(+)	MT089917
	small linear ssRNA satellite	Turnip crinkle satellite RNA F	TCVsatRNA F	ssRNA	X12749
	small linear ssRNA satellite	Pea enation mosaic virus satellite RNA	PEMVsatRNA	ssRNA	OQ953831
	small linear ssRNA satellite	Strawberry latent ringspot virus satellite RNA	SLRSVsatRNA	ssRNA	OQ953830

Table 3 .

 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

Table 4 .

 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.

	Minimal contig length

Table 5 .

 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.

		# viral	VANA		dsRNA	RNASeq
		molecules	# detected % detected # detected	% detected # detected	% detected
	+ssRNA viruses	86	50	58.1%	77	89.5%	81	96.,3%
	-ssRNA viruses	12	1	8.3%	5	41.7%	12	100%
	RNA satellites	3	1	33.3%	3	100%	2	66.,6%
	dsRNA viruses	2	0	0%	2	100%	0	0,0%
	ssDNA viruses	10	4	40.0%	0	0%	4	40.0%
	dsDNA viruses	2	1	50.0%	1	50.0%	1	50.0%
	Total	115	57	49.6%	88	76.5%	100	87.,7%

Acknowledgements:

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

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).

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

Conflict of interest:

The authors declare that they have no conflict of interest.

Ethical approval: This article does not contain any studies with human participants or animals performed by any of the authors. 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). S2.

Supplementary Table