dataesr/scientific-paragraphs-categorization huggingface dataset:
Four samples represented pools of different plant species. The vast majority of the datasets were derived from leaf material (69), branches (1), petioles  (10) , or combined leaves + petioles/branches (9), while only a minority of them were derived from other plant parts, such as tubers/rootstocks  (10)  or pollen  (2). At least 9 different RNA extraction methods/kits were used (File S1). About half of the samples (45) were treated with DNase during RNA extraction, and the majority of the samples underwent an rRNA depletion step (70) prior to HTS. Interestingly, one participant entered the same sample twice, once processed with and once without an rRNA depletion step. Most samples were Overview of the different phases in the "RNA-seq community effort", in which total RNA-seq datasets previously analyzed for the presence of viruses were re-analyzed in order to find possible traces of non-viral pathogens and pests.
rRNA Depletion during Library Prep Reduced the Amount of Plant rRNA with Very Variable Efficiencies The participants mapped their datasets against the SILVA Large Subunit (LSU) database and reported the number of mapped reads per database entry. Since each participant independently performed the mapping, data processing steps differed slightly between them (File S1). Most datasets underwent some basic preprocessing in the form of quality filtering (99) and/or adapter removal (92), in some cases followed by merging of forward and reverse reads  (35)  and/or duplicate removal  (12). Of the 101 datasets, 79 were mapped using BWA, 18 using Geneious, and 4 using CLC Genomics Workbench. The percentage of reads that mapped against the rRNA database is shown in Figure  2A. On average, 95 ± 5% (standard deviation) of the reads mapped in case no rRNA depletion step was performed, while only 42 ± 29% of the reads mapped in case an rRNA depletion step was included during library preparation.
This difference proves that rRNA removal kits are useful to boost the number of non rRNA reads, although results are very variable (Figure  2A ). This was also well reflected in the sample from which two datasets were available, one prepared with and one prepared without an rRNA depletion step. In the dataset derived from the library without rRNA depletion, 94% of the reads mapped to the rRNA database, while this was only 3% in the case of the dataset derived from the library with rRNA depletion.
RNA-Seq Community Effort Phase II 2. 3. 1. The Reads from the RNA-Seq Datasets Analyzed in Detail in Phase II Are Primarily Assigned to Fungi and Bacteria, but also to Insects and Mites Based on the results of Phase I, 37 of the 101 datasets were selected to be analyzed in more detail by classifying the reads taxonomically using Kraken2, a k-mer based lowest common ancestor method. Resulting Krona plots showing the taxonomic diversity resulting from these annotation steps are available for all datasets on https://gitlab. com/ilvo/PHBN-WP4-RNAseq_Community_Screening. Figure  3  shows the relative number of reads (in rpm) that were assigned to the following organism categories: fungi, insects, bacteria, spiders and mites, oomycetes, phytoplasmas, and nematodes. The total number of rpm assigned to one of these categories ranged from 4549 to 322,901 rpm, corresponding to 0.
38, p = 0. 021). When the same The sequences that mapped against the rRNA database were assigned to broad categories of organisms in order to study the global composition of the rRNA of all samples. An overview of this rRNA composition is shown in Figure  2B. The vast majority of the mapped rRNA reads belonged to the plants category (on average, 94. 53 ± 1. 03%), followed by bacteria (on average, 2. 33 ± 0. 53%), fungi (on average, 1. 57 ± 0. 58%), insects (on average, 0. 66 ± 0. 22%) and spiders and mites (on average, 0. 24 ± 0. 06%). In some datasets, the rRNA levels for bacteria, fungi, or insects reached levels higher than 3% of the total reads. Other categories (phytoplasmas, oomycetes, and nematodes) all had an average percentage of mapped reads below 0. 1%. Based on the results of Phase I, 37 of the 101 datasets were selected to be analyzed in more detail by classifying the reads taxonomically using Kraken2, a k-mer based lowest common ancestor method.
Resulting Krona plots showing the taxonomic diversity resulting from these annotation steps are available for all datasets on https://gitlab. com/ilvo/PHBN-WP4-RNAseq_Community_Screening. Figure  3  shows the relative number of reads (in rpm) that were assigned to the following organism categories: fungi, insects, bacteria, spiders and mites, oomycetes, phytoplasmas, and nematodes. The total number of rpm assigned to one of these categories ranged from 4549 to 322,901 rpm, corresponding to 0. 45-32% of the reads, with a median of 2. 4%. The reads assigned to plants ranged from 30 to 92% (File S1). Similar to the results of Phase I, fungi and bacteria were the categories showing the highest representation, while phytoplasmas and oomycetes were detected only in some samples and at relatively low levels.
34, p = 0. 038). After the removal of one outlier, the bacteria group also showed a significant correlation (PCC = 0. 45, p = 0. 006). Only the nematode and oomycete categories did not show significant correlations between Phase I and Phase II results, probably due to the low number of reads attributed to these categories in both analyses. The Krona plots were carefully examined by participants for the presence of reads assigned to (non-viral) pathogens. Based on the feedback from participants on pathogens that might be present in the datasets, we set a global relative occurrence threshold of a The Krona plots were carefully examined by participants for the presence of reads assigned to (non-viral) pathogens. Based on the feedback from participants on pathogens that might be present in the datasets, we set a global relative occurrence threshold of at least 100 rpm (i.
e. , >0. 01%) in the Kraken2 analyses to consider a pathogen or pest to be present. This threshold was deliberately set relatively high to avoid false positives. Indeed, in our previous analysis of datasets derived from leaves with known infections (see Section 2. 1 and Table  1 ), we noticed that several pathogens were observed at lower abundances. Hence, by using the threshold of 100 rpm, we can expect some false negative results, i. e. , unnoticed pathogens. Nevertheless, we decided to continue with this relatively conservative threshold in order to avoid false positives. An overview of the datasets in which pathogens were thus identified is shown in Table  2. Considering the 37 selected datasets, in total 67 pathogens (39 different taxa) were detected (≥100 rpm) from 29 of the datasets (78%), with an abundance ranging from 100 to 107,450 rpm.
Fungal pathogens were the most abundant, with 31 detections (in 16/37 datasets, 43%), with the most common taxa being Alternaria sp. (found in eight datasets), Botrytis cinerea (found in seven datasets), and Fusarium sp. (found in five datasets). Insect pests were detected 19 times (in 13/37 datasets, 35%): 9 aphids (3 different species), 5 thrips, 4 fruit flies, and 1 mealybug. Mites were detected 9 times (in 9/37 datasets, 24%), followed by oomycetes (in 4/37 datasets, 11%), plant pathogenic bacteria (in 3/37 datasets, 8%), and phytoplasma (in 1/37 dataset, 3%) (Figure  4 ). datasets, 43%), with the most common taxa being Alternaria sp. (found in eight datasets), Botrytis cinerea (found in seven datasets), and Fusarium sp. (found in five datasets). Insect pests were detected 19 times (in 13/37 datasets, 35%): 9 aphids (3 different species), 5 thrips, 4 fruit flies, and 1 mealybug.
Mites were detected 9 times (in 9/37 datasets, 24%), followed by oomycetes (in 4/37 datasets, 11%), plant pathogenic bacteria (in 3/37 datasets, 8%), and phytoplasma (in 1/37 dataset, 3%) (Figure  4 ). The participants completed a questionnaire (File S2) that asked their opinion about the usefulness of and previous experience(s) with these analyses. The questionnaire  The participants completed a questionnaire (File S2) that asked their opinion about the usefulness of and previous experience(s) with these analyses. The questionnaire answers (File S3) revealed that only 6/15 participants were aware that their sample had indications of the presence of non-viral pathogen(s), while, for 11/15 participants, at least one non-viral pathogen was detected in at least one of their datasets. A total of 14/15 participants were aware of the existence of metagenomics techniques applicable to RNA-seq datasets, but only 6/15 had previously used similar approaches to taxonomically classify all reads or contigs against databases that contained cellular organisms and not only viruses.
(q)PCR Confirmation of Pathogen Detection Since a relatively high number of pathogens were found, we decided to try to confirm some of these presumed infections. To have a representative view on different organisms, a bacterium (Pectobacterium sp. ), a phytoplasma ('Ca. Phytoplasma pruni'), and two fungi (Botrytis cinerea and Alternaria sp. ) were selected and tested by (q)PCR on DNA extracted from the same biological material from which the RNA-seq data was derived. Since the two fungi were observed in multiple samples (Table  2 ), we selected a few samples with variable read numbers. Results are shown in Table  3. Both the presence of the bacterium and the phytoplasma were confirmed by the independent assays. Interestingly, for Botrytis cinerea, the samples where a very high rpm was observed in the Kraken2 analyses were PCR positive, but when the abundance was lower (≤131 rpm), the presence could not be confirmed.
g. , designing primers with improved analytical specificity and inclusiveness  [21]. In plant virology, shotgun-based protocols have become the standard for complete virome scanning or for diagnosing difficult samples in case targeted tests fail. Although different types of library preparations exist  [22] , one of the more popular methods is total RNA sequencing because it allows researchers to detect RNA viruses, DNA viruses, and viroids present in a sample. RNA is extracted from the plant tissue, plant rRNA is optionally depleted using specific probes, and the remaining RNA sequenced. To detect viruses, the resulting reads (or contigs, if an assembly is performed) are subsequently compared to a virus reference database  [23]. In other plant pathology disciplines, this method is much less popular since the more traditional detection methods are often quicker and cheaper.
In addition, (meta)genomics is being progressively adopted for the efficient tracing of the origin(s) of outbreaks or for high resolution phylogeographic studies of pathogens, such as Xylella fastidiosa  [35]  or Fusarium graminearum  [36]. In the present study, we demonstrated that it is indeed possible to detect and identify reads from cellular organisms in datasets prepared for plant virus detection. In all datasets examined with confirmed pathogen infections, reads from the pathogen could be retrieved. In total, 101 datasets without information on the possible presence of cellular organisms were also analyzed by mapping the reads against an rRNA database. In this way, by determining the number of reads presumably derived from rRNA, we could evaluate the effectiveness of the ribodepletion step.
Including an rRNA depletion step was generally able to greatly decrease the rRNA fraction of the dataset, from an average of 95 to 42%. However, its efficiency proved highly variable across datasets. This variable efficiency could be due to various factors, such as the biological material (different tissues, different plant species), the use of different kits, or the experience of the laboratories. The (remaining) rRNA fraction also showed that bacteria and fungi were among the most prevalent nonplant sequences. Thirty-seven datasets that showed a substantial amount of non-plant rRNA were selected for more detailed analysis using Kraken2. Overall, the number of non-plant sequences (especially fungal and insect ones) correlated well with the number of mapped rRNA sequences against the SILVA database, making the latter method suitable for a fast first analysis on the (r)RNA composition.
The amount of reads that were not assigned to plants varied greatly between the 37 selected datasets, ranging from 1 to >30% of the total number of reads. In general, bacteria were present in all samples at a relatively stable fraction, presumably representing the plants' endophytic microbiome. Fungi were also represented in all samples, but, in some samples, they reached a relatively high abundance. The same was true for insects (Figure  3 ). When trying to identify plant pathogens, plant pathogenic bacteria were detected much less frequently than fungi. Indeed, they were found in only 4 cases (of which 1 was phytoplasma), while plant pathogenic fungi were detected in 31 of the 37 datasets. Nevertheless, some bacterial pathogens were detected at high abundance, such as 'Ca. Phytoplasma pruni.
Globally, 29 of the 37 selected datasets (78%) showed evidence of plant pathogens or pests, which were mostly fungi, insects, or mites (Figure  4 ). The latter categories were rather surprising, since the presence of pests were, in many cases, not expected and were not thought to leave so many traces of RNA in and on plant tissues. This is especially interesting for virologists, since many viruses are transmitted by insects or mites. Using the same RNA-seq datasets they use for virus detection, virologists could thus potentially look for a vector, or even use the dataset to identify a vector candidate in case none is known yet. Even though it is clear that pathogen and pest reads can be identified in the RNA-seq datasets, the relative number of reads derived from the pathogen (compared to the total amount of reads) was sometimes very low in samples with validated infections.
On the contrary, when the sampled tissue presents primary symptoms of infection, the concentration of the same pathogen can be very high, as observed, for example, for Verticillium dahliae (37,732 rpm). Finally, the bacterial microbiome always corresponds to the most abundant non-plant sequences in the samples. This confirms that a complex community of microorganisms is present inside or at the surface of plant tissues. The composition and diversity of this community can impact the plant pathogen by limiting its development  [40, 41]  and therefore reducing the proportion of reads in the dataset despite its presence. A last observation was that the species assignment by Kraken2 was not always correct. Technical reasons might cause these misassignments, for example, the algorithm used or the incompleteness, biases, or mistakes in the used sequence database.
This was also observed by Yang et al. [34] , who were only able to identify a boxwood blight pathogen in metagenomic data after customizing the database. On the other hand, sequence information in nucleotide databases may be biased towards pathogens, possibly leading to wrong conclusions about a pathogen presence when only a non-pathogenic relative, for which there is no sequence information in the database, is present. Indeed, metagenomic classifiers tend to perform very well when closely related sequences are present in the database, but less so in the case that this information is lacking  [42]. This phenomenon can be reinforced by a low number of generated reads, which limit the coverage and representation of the pathogen genome. The preliminary results of this study have shown the potential of metatranscriptomics as a universal plant pathogen and pest detection method, allowing the detection of all expected pathogens from samples with confirmed infections, although sometimes as a very small proportion of sequencing reads.
We first checked whether different types of non-viral plant pathogens could be detected and identified from plant total RNA-seq data, a method commonly used to detect plant viruses. The datasets used were selected in order to cover a broad range of pathogens (bacteria, fungi, oomycetes) and host plant tissues (leaves, fruits, phloem, flower buds). To do this, we downloaded 13 publicly available datasets from NCBI's Sequence Read Archive (SRA). These datasets were plant-derived RNA-seq datasets with confirmed infection of a plant pathogen. We selected datasets with the following pathogens: the vascular bacteria 'Candidatus Liberibacter sp. ' (SRR5100668: potato leaves, SRR10148792: orange leaves, SRR8295844: orange phloem) and 'Candidatus Phytoplasma sp. ' (SRR9225242: tomato leaves, SRR7186379: soybean flower bud, SRR17253894: Chinese cherry fruit); the fungus Verticillium dahliae (SRR8003868: sticky nightshade roots, SRR6760520: olive roots, SRR6053344: upland cotton roots, SRR1525437: olive leaves); the bacterium Erwinia amylovora (SRR7814393: apple leaves); and the bacterium Xanthomonas sp.
(SRR13488408: pepper leaves, ERR2036424: wheat roots). In addition to the downloaded datasets, we also generated 9 new RNA-seq datasets from plants with known and confirmed infections from different types of pathogens (bacteria, phytoplasmas, fungi, and oomycetes). Leaves were collected from Xylella fastidiosa infected goat willow; 'Candidatus Phytoplasma sp. ' infected carrot and faba bean; Puccinia horiana infected Chrysanthemum sp. ; Alternaria sp. infected Daucus carota and potato; and Phytophthora infestans and tomato and grapevine infected with an unidentified downy mildew. RNA was extracted using the RNeasy Plant Mini kit (Qiagen, Venlo, The Netherlands) and DNase treated following the manufacturer's recommendations (ThermoFisher Scientific, Antwerp, Belgium). Ribosomal RNAs were removed using a RiboMinus Plant Kit for RNA-Seq (ThermoFisher Scientific) before cDNA library synthesis with the Illumina TruSeq Stranded RNA library prep kit (Illumina Inc.
, San Diego, CA, USA) and sequenced on an Illumina NextSeq500 (2 × 150 nt) in a multiplexed format (GIGA-Genomics Facility, Université de Liège, Liège, Belgium). Raw data was submitted to SRA under BioProject number PRJNA961939. Data from both the SRAderived datasets, as well as the newly generated datasets, were further processed as follows. Adapters were removed from the sequencing data using cutadapt  [45]. Next, the reads were classified taxonomically with the k-mer based Kraken2 software  [46] , using the lowest common ancestor method (LCA) for assignments at different taxonomic levels. For the database for the Kraken2 analysis, a local version of the complete Genbank nonredundant Nucleotide database converted to a Kraken2 index was used  [47]. The results were visualized using Krona  [48] , and the rpm of the expected pathogens was calculated.
RNA-Seq Community Effort Next, we organized an "RNA-seq community effort" among plant virologists in three phases in order to evaluate the possible presence of non-viral pathogens and pests in some of their previously generated datasets. In the first phase, called "prescreening", participants mapped their data against an rRNA database. This was used as a very rough but easy method to let participants prescreen their own samples for potential presence of non-plant rRNA. In the second phase, referred to as "in depth analysis", a selection of datasets from each participant were transferred to one lab (ILVO) for a detailed taxonomic analysis by a bioinformatics expert. In the third phase, called "feedback and confirmation", results of the detailed analysis were shared with participants for interpretation and further feedback, and the presence of some pathogens was experimentally checked.
Phase I: Prescreening Since we wanted to involve the participants as much as possible with the data analysis, we opted for a prescreening phase that participants could perform in-house. The goal of the prescreening phase was to obtain a general overview of what types of organisms are present in the datasets in order to make a selection of samples interesting for more detailed analysis. This prescreening phase needed to be relatively fast and easily accessible in order to allow participants with minimal experience in bioinformatics to participate as well. Hence, we opted for a mapping approach because mapping is a basic bioinformatics method used in virtually every lab dealing with HTS data, and it is relatively fast and therefore capable of processing many different samples. Participants were allowed to apply their mapping algorithm of choice on as many of their samples as they wanted.
The only prerequisite was that the samples were plant-derived total RNA (no small RNAs) and sequenced with Illumina technology. For the mapping database, we opted for the SILVA LSU rRNA database (v. 132)  [49]  ("SILVA_132_LSURef_tax_silva_trunc. fasta" as downloaded from the ARB-SILVA archive: https://ftp. arb-silva. de/release_132/Exports/, accessed on 26 March 2020). This database was chosen because (1) rRNA is typically very abundant in RNA-seq data (even when rRNA depletion was performed during library preparation); (  2 ) it has a large sequence diversity across different kingdoms; (3) it is curated and non-redundant; and (4) it is relatively small and, hence, easy to distribute. The mapping database was slightly modified to include organism categories (bacteria, fungi, insects, spiders and mites, plants, nematodes, oomycetes, phytoplasmas, and others) in the header of each sequence to facilitate downstream processing.
These organism categories were chosen because they include the most important plant pathogens. The plants category was defined as each sequence which had in its SILVA taxonomy the (partial) word "Plant", "Chloroplast", or "Mitochondr"; the spiders and mites category was assigned when the taxonomy contained the word "Arachnida"; and the oomycetes category was defined as the class Peronosporomycetes. The other categories were straightforward to extract from the SILVA taxonomy. In addition, for each sequence in the SILVA LSU database, the accession number was extracted, and the corresponding NCBI Taxonomy identifier and its complete taxonomy was retrieved using NCBI's E-utilities. The combined accession number, organism category, and NCBI extracted taxonomy were used as a sequence header for the final database.
With the database ready, a detailed tutorial was written explaining how to participate to the community effort, including tutorials on how to perform the mapping using different types of software, such as BWA  [50] , CLC Genomics Workbench (https://digitalinsights. qiagen. com, accessed on 26 March 2020), and Geneious (https://www. geneious. com). These are available on https://gitlab. com/ilvo/PHBN-WP4 -RNAseq_Community_Screening. Invitations to participate were sent to all official and associated partners of the Euphresco "Plant Health Bioinformatics Network", a network of researchers working with HTS data applied to plant pathogen diagnostics. Further distribution to participate among colleagues was encouraged, and an advertisement was made on social media (Twitter). Participants were required to complete a simple metadata file with some basic information on their samples (host plant, host tissue, rRNA removal step (yes/no), read length, number of reads, number of mapped reads) and upload the results of their mappings.
The latter was supplied in a textual format which specifically included the number of mapped reads corresponding to each sequence of the database. This could be easily exported from graphical software (Geneious, CLC Genomics Workbench), or from command line tools using the "idxstats" program of SAMtools  [51]. Finally, the results of each participant were combined in one table and unrealistic values due to errors in reporting were removed (for example, more mapped reads than input reads). Results were summarized as bar plots representing the percentage of mapped reads for each organism category (plants, bacteria, fungi, oomycetes, phytoplasmas, insects, spiders and mites, and others) using the ggplot2 package v. 3. 3. 2  [52]  in R v. 4. 0. 3  [53]  with RStudio v. 1. 3. 1093 [54].