dataesr/scientific-paragraphs-categorization huggingface dataset:
Collectively, these studies allowed important progress in virus assembly and identification from deep sequencing data. However, the existing computational workflows require specialist skills for installation, execution and adaptation to specific research, making them poorly accessible to a broad user base of biologists. In some cases, the tools lack documentation or their source codes are not available. In this context, we developed Metavisitor as an open source set of tools and preset workflows  [15, 16]  which allow effective implementation of the computational strategies in the Galaxy framework, with short read as well as long read sequence datasets. In addition, Metavisitor workflows can be easily adapted to suit specific needs, by adding analysis steps or replacing/ modifying existing ones with the numerous tools available in the Galaxy tool sheds.
Experimental Procedures Metavisitor consists of a set of Galaxy tools (Fig  1 ) that can be combined to (i) retrieve up-todate nucleotide as well as protein sequences of viral genomes deposited in Genbank  [17]  and index these sequences for subsequent alignments; (ii) extract sequencing reads that do not align to the host genomes, known symbionts or parasites; (iii) perform de novo assembly of these reads using assembly tools available in Galaxy, align the de novo contigs against the viral nucleotide or protein blast databases using blastn or blastx, respectively, and generate reports from blast outputs to help in known viruses diagnosis or in candidate virus discovery; (iv) use CAP3 (optional, see Use Case 3-3), blast and viral scaffolds for selected viruses to generate guided final viral sequence assemblies of blast sequence hits.
(i) Get reference viral sequences The "Get reference viral sequences" task is performed using the "Retrieve FASTA from NCBI" tool that sends a query string to the Genbank database  [17]  and retrieves the corresponding nucleotide or protein sequences. For the viral nucleotide and protein sequences referred to as "vir1", we used the tool to query Genbank (oct 2015) and retrieve viruses sequences filtered out from cellular organisms and bacteriophage sequences (see S1 Fig). However, users can change the tool settings by entering query strings that fit their specific needs. As retrieving vir1 from NCBI takes several hours, we allow users to skip the step by directly accessing the nucleotides or protein vir1 datasets on the Mississippi server (http://mississippi. fr) or to download them from figshare (https://dx.
(ii) Prepare data The "Prepare data" task (Fig 1 ) processes Illumina sequencing datasets in order to optimize the subsequent de novo assembly of viral sequencing reads. Fastq files of sequence reads are first clipped from library adapters and converted to fasta format using our tool "Clip adapter" tool (S1 Table ). The clipped reads may be further converted to a fasta file of unique sequences headed by a character string that contains a unique identifier and the number of times that the sequences were found in the dataset, thus reducing the size of the dataset without loss of information. This optional treatment removes sequence duplicates and drastically reduces the workload of the next steps as well as the coverage variations after de novo assembly (see Use Cases 1-1 to 1-3). Clipped reads are then depleted from non-viral sequences by sequential alignments to the host genome, to other genomes from known or potential symbionts and parasites, as well as to PhiX174 genome sequences which are commonly used as internal controls in Illumina sequencing and may contaminate the datasets (Fig  1 ).
Blast. Next, de novo assembled contigs are aligned to both nucleotide and protein vir1 BLAST databases built from the viral reference sequences (Fig  1 ) using the blastn or blastx Galaxy tools  [18]. These tools search nucleotide or protein databases using nucleotide or translated nucleotide queries, respectively  [25]. The default parameters are adjusted in order to report only the 5 best alignments per contig (Maximum hits option is set to 5) and to generate a tabular blast output that includes the 12 standard columns plus a column containing the length of the aligned subject sequences (extended columns option, "slen" checked). Note that this additional column in the blast output is required for subsequent parsing of the blast output by the "Parse blast output and compile hits" tool. Parsing.
Tabular outputs generated by blastn and blastx alignments are processed by the "Parse blast output and compile hits" tool (S1 Table ), which returns 4 files, namely "blast analysis, by subjects", "hits", "Blast aligned sequences" and "Blast unaligned sequences". In the "blast analysis, by subjects" file (S2 Fig) , the subject sequences in the viral nucleotide or protein blast databases that produced significant blast alignments (hits) with de novo assembled contigs are listed, together with those contigs and hit information (% Identity, Alignment Length, start and end coordinates of hits relatively to the subject sequence, percentage of the contig length covered by the hit, E-value and Bit Score of the hit). In addition, for each subject sequence in the list, the length in nucleotide or amino-acid of the subject sequence (Subject Length), the summed coverage of the subject by all contig hits (Total Subject Coverage) as well as the fraction of the subject length that this coverage represents (Relative Subject Coverage), and the best (Best Bit Score) and mean (Mean Bit Score) bit scores produced by contig hits are computed and indicated.
(iv) Blast-Guided Scaffolding This last task allows to integrate hit sequences matching a candidate virus into a virus scaffold (Fig  1 ). First, blastn or blastx hits are retrieved from the "hits" file using the tool "Pick Fasta sequences" (S1 Table ) and the appropriate query string (for instance, "Dengue" will retrieve hit sequences that significantly blast aligned with Dengue virus sequences). Next, these hit sequences can be further clustered in longer contigs using the "cap3 Sequence Assembly" Galaxy tool (S1 Table) adapted from CAP3  [26]. Finally, if there are still multiple unlinked contigs at this stage, they can be integrated (uppercase characters) in the matched viral sequence taken as a scaffold (lowercase characters). This scaffolding is achieved by (a) retrieving the viral sequence from the NCBI nucleotide database to be used as the backbone of the scaffold, generating a blast index from this sequence and aligning the contigs to this index with blastn or tblastx tools (b) running the "blast_to_scaffold" tools (S1 Table ), taking as inputs the contigs, the viral guide sequence and the blastn or blastx output (Fig 1  , bottom ).
The Metavisitor tool codes are accessible in our public GitHub repository (https://github. com/ARTbio/tools-artbio/). We also provide a Docker image artbio/metavisitor:1. 2 as well as an ansible playbook that both allow to deploy a Galaxy server instance with preinstalled Metavisitor tools and workflows in local infrastructures. Extensive documentation on how to install and use Metavisitor is available at https://artbio. github. io/Metavisitor-manual/.
Results The strategy implemented by Metavisitor (Fig  1 ) is to perform de novo assembly of sequencing reads and to detect contigs of viral origin through blast alignments to a nucleotide or protein sequence database of known viruses (vir1). These contig alignments can be further clustered to reconstruct a viral genome. Below, we report use cases to demonstrate the use of Metavisitor in specific situations. For each use case, we briefly present the purpose of the original study from which the datasets originate and we describe an adapted Metavisitor workflow as well as its main outputs. Readers can further examine the workflows (https://mississippi. snv. jussieu. fr/workflow/list_published) and use case analyses (https://mississippi. snv. jussieu. fr/history/list_published) in every detail at http://mississippi.
Detection of known viruses Use Cases 1-1, 1-2 and 1-3: detection and reconstruction of the Nora virus genome in small RNA sequencing datasets. Using small RNA sequencing libraries SRP013822 (EBI ENA) and the Paparazzi software  [4]  we were previously able to propose a novel reference genome (NCBI JX220408) for the Nora virus strain infecting Drosophila melanogaster stocks in laboratories  [3]. This so-called rNora genome differed by 3. 2% nucleotides from the Nora virus reference NC_007919. 3 and improved the alignment rate of viral siRNAs by ~121%. Thus, we first tested Metavisitor on the small RNA sequencing datasets SRP013822 using the Oases de novo assembly tool which is well suited to assembly of short read  [9]. Three Metavisitor workflows were run on the merged SRP013822 small RNA sequence reads and the NC_007919.
The final de novo (capital letters) assemblies of both the Nora_raw_reads and Nora_Median-Norm-reads genomes entirely covered the JX220408 and NC_007919. 3 genomes (both 12333 nt), whereas the de novo assembled part of the Nora_MV genome was marginally shorter (12298 nt, the 31 first 5' nucleotides are in lowercase to indicate that they were not de novo assembled but instead recovered from the guide genome). To evaluate the quality of assemblies, we remapped the SRP013822 reads to the 3 reconstituted Nora virus genomes as well as to the JX220408 guide genome using the "workflow for remapping in Use Thus, Metavisitor reconstructed a Nora virus genome Nora_MV whose sequence maximizes the number of vsiRNA read alignments which suggests it is the most accurate genome for the Nora virus present in the datasets.
Of note, the Nora_MV genome differs from the JX220408 rNora genome generated by Paparazzi by only two mismatches at positions 367 and 10707, and four 2nt-deletions at positions 223, 365, 9059 and 12217 (see S4 File). These variations did not change the amino acid sequence of the 4 ORFs of the Nora virus. We conclude that Metavisitor performs slightly better than Paparazzi for a known virus, using de novo assembly of small RNA reads followed by blast-guided scaffolding. We did not observe any benefits of using raw reads or normalized-by-median reads for de novo assembly with Oases, but rather a decrease in the accuracy of the reconstructed genome as measured by the number of reads re-mapped to the final genomes (Fig 2 ). Use Case 1-4: detection of multiple viruses in small RNA sequencing datasets.
In order to show the ability of Metavisitor in detecting multiple known viruses in small RNA sequencing datasets, we built another workflow Case that performs blastn alignments of Oases contigs on the vir1 reference and reports for all significant alignments without filtering (S7 Fig). Applying this workflow to the SRP013822 sequence datasets produced a list of alignments which contains, as expected, the Nora virus. In addition, contigs were found to align with high significance (Mean BitScore > 200) to the Drosophila A virus and to the Drosophila C virus (S5 File and Table  1 ), strongly suggesting that the fly stocks analyzed in our previous work were also subject to persistent infection by these viruses  [3].
Discovery of novel viruses Use Case 2-1: identification of new viruses in small RNA sequencing datasets. Using Metavisitor, we recently discovered two novel viruses infecting a laboratory colony of Anopheles coluzzii mosquitoes  [28]. In this case, a workflow (S8 Fig) was used to process small RNA datasets from these mosquitoes (EBI SRA ERP012577) and to assemble a number a Oases contigs that show significant blastx hits with Dicistroviridae proteins, including Drosophila C virus (DCV) and Cricket paralysis virus (CrPV) proteins (S6 File). The viral family of Dicistroviridae was named from the dicistronic organisation of their genome with a 5' open reading frame encoding a non-structural polyprotein and a second non-overlapping 3' open reading frame encoding the structural polyprotein. In order to construct a potential new A.
Loci 3 and 46 contigs did not align either to the non-redundant protein database of the NCBI and may therefore be of potential viral origin (S3 Table ). All 5 negative control contigs with unclear or no RNA signature only aligned significantly to non-viral proteins (S3 Table ). Together, these results illustrate the use of Metavisitor to implement a sequence-independent strategy based on siRNAs for virus identification  [12, 13]. Use Case 2-2: identification of new viruses in mRNA sequencing datasets. In our study  [28] , we also used RNAseq libraries from the same A. coluzzii colony (EBI-SRA, ERS977505), demonstrating the use of a Metavisitor workflow for long RNA sequencing read datasets (S11 Fig). Thus, 100nt reads were aligned without adapter clipping to the Anopheles gambiae genome using bowtie2, and unmatched read were subjected to Oases assembly (kmer range 25 to 69, to take into account longer reads).
Oases contigs were then filtered for a size > 5000 nt and aligned to the protein viral reference using blastx. Parsing of blastx alignments with the "blast analysis, by subjects" tool repeatedly pointed to a 8919nt long Oases contig that matched to structural and non-structural polyproteins of DCV and CrPV (S11 File). This 8919nt contig (S12 File) completely includes the contigs generated with the small RNA datasets (S8 File) and shows a dicistronic organization which is typical of Dicistroviridae and is referred to as a novel Anopheles C Virus  [28].
Virus detection in human RNAseq libraries Having illustrated that Metavisitor is able to generate robust genome assemblies from known and novel viruses in Drosophila and Anopheles sequencing datasets, we tested whether it can be used to diagnose viruses in RNA sequencing datasets of human patients from three different studies  [29] [30] [31]. Use Case 3-1. Innate lymphoid cells (ILCs) play a central role in response to viral infection by secreting cytokines crucial for immune regulation, tissue homeostasis, and repair. Therefore, the pathogenic effect of HIV on these cells was recently analyzed in infected or uninfected patients using various approaches, including transcriptome profiling  [30]. ILCs are unlikely to be infected in vivo by HIV as they lack expression of the CD4 co-receptor of HIV and they are refractory in vitro to HIV infection.
However, we reasoned that ILCs samples could still be contaminated by infected cells. This might allow Metavisitor to detect and assemble HIV genomes from patient's ILC sequencing data (EBI SRP068722). We imported 40 ICL sequence datasets from the EBI SRP068722 archive and merged the datasets belonging to the same patients. As the data contained short 32 nt reads that in addition had to be 3' trimmed to 27 nt to retain acceptable sequence quality, we designed a workflow for Use Case 3-1 (S15 Fig) that is similar to the workflows used in cases 1-1 and 2-1 for small RNA sequencing data. Thus, the sequencing datasets were depleted from reads aligning to the human genome (hg19) and viral reads were selected by alignment to the NCBI viral sequences using the sRbowtie tool (S1 Table ). These reads were further submitted to Oases assembly (kmers 11 to 27, to take into account short reads) and the resulting contigs were aligned to the Nucleotide Viral Blast Database using blastn.
Alignments were parsed using the "Parse blast output and compile hits" tool, removing alignments to NCBI sequences related to patents to simplify the report ("Patent" term in the filter option of the "Parse blast output and compile hits" tool). A final report was generated by concatenating the reports produced by this tool for each patient (S14 File and Table  2 ). In summary, we were able to detect HIV RNAs in samples from 3 out of 4 infected patients whereas all samples from control uninfected patients remained negative for HIV. This Metavisitor workflow was able to accurately detect HIV RNA, even in samples where the number of sequence reads was expected to be low. Use Case 3-2. Yozwiak et al. searched the presence of viruses in RNA Illumina sequencing data from serums of children suffering from fevers of unknown origins  [29].
In this study, pairedend sequencing datasets were depleted from reads aligning to the human genome and the human transcriptome using BLAT and BLASTn, respectively, and the remaining reads were aligned to the NCBI nucleotide database using BLASTn. A virus was considered identified when 10 reads or more aligned to a viral genome which was not tagged as a known lab contaminant. For a significant number of Patient IDs reported in Table  1  of the article  [29] , we were not able to find the corresponding sequencing files in the deposited EBI SRP011425 archive. In addition, we did not find the same read counts for these datasets as those indicated by the authors. With these limitations in mind, we downloaded 86 sequencing datasets that could be further concatenated and assigned to 36 patients in Yozwiak et al  [29].
As sequence reads in SRP011425 datasets are 97 nt long, we adapted a workflow for this Use Case 3-2 (S16 Fig) from the one used in the Use Case 3-1 with the following modifications: (i) sequences reads were depleted from human sequences and viral reads were selected by alignment to the NCBI viral sequences using the Galaxy bowtie2 tool (S1 Table ) instead of the sRbowtie tool; (ii) viral reads were submitted to Oases assembly using kmer values ranging from 13 to 69 to take into account long reads; (iii) the SAM file with reads alignments to the vir1 bowtie2 index was parsed using the "join" and "sort" Galaxy tools in order to detect putative false negative datasets with viral reads that fails to produce significant Oases viral contigs. This workflow generated a report file (S15 File) summarized in Table  3.
The results show that Metavisitor detected the same viruses as those reported by Yozwiak et al. in 17 patients. Although viral reads were detected in 16 other patients, they were not covering sufficient portions of viral genomes to produce significant viral assemblies. Finally, in the three remaining patients (patients 363, 330 and 345 in Table  3 ), we detected viruses (Dengue virus 2, Stealth virus 1 and Dengue virus 4, respectively) other than those identified by Yozwiak et al. These discrepancies are most likely due to misannotation of some of the deposited datasets, which precludes further detailed comparisons. The Data of this table were extracted from the Metavisitor report file available as S15 File. Values of the column "Coverage of complete viral genome (%)" correspond to the fractions (in %) of the complete viral genomes that are covered by blast hits of viral contigs to these genomes and values of the column "Mean blast bit score" correspond to the mean values of the bit scores observed for these blast hits.
Note that blast alignments to incomplete viral genomes were not taken into account. For detection of false positives, reads were aligned to the bowtie2 vir1 index before de novo assembly and counts of these reads were reported in the column "Read mapping to vir1 using bowtie2"). Extracted from Yozwiak et al. Use case 3-3. Matranga et al. recently improved library preparation methods for deep sequencing of Lassa and Ebola viral RNAs in clinical and biological samples  [31]. Accordingly, they were able to generate sequence datasets of 150 nt reads providing high coverage of the viral genomes. We used these datasets, relevant in the context of Lassa and Ebola outbreak and epidemic response, to demonstrate the versatility of Metavisitor as well as its ability to generate high throughput reconstruction of viral genomes.
In order to take into account longer reads and higher viral sequencing depths in the available datasets  [31] , we adapted a Metavisitor workflow for Use Case 3-3 (S17 Fig) as follows: (i) sequencing reads were directly aligned to vir1 sequences using bowtie2, without prior depletion by alignment to the human or rodent hosts; (ii) the Trinity de novo assembler  [23]  that performs well with longer reads was used instead of Oases (S1 Table ); (iii) reconstruction of Lassa and Ebola genomes from the sequences of the blast hits with the nucleotide viral blast database was directly performed with the "blast to scaffold" tool without CAP3 assembly since the Trinity contigs were already covering a significant part of the viral genomes; (iv) the reports generated by our "Parse blast output and compile hits" tool as well as the reconstructed genome generated for each sample were merged in single datasets for easier browsing and subsequent phylogenetic or variant analyses; (v) for adaptability of this workflow to any type of virus, we allowed users to specify two input variables at runtime: the name of the virus to be searched for in the analysis and the identifier of the sequence to be used as guide in genome reconstruction steps.
We imported 63 sequence datasets available in the EBI SRA PRJNA254017 and PRJNA 257197 archives  [31]  and grouped these datasets in Lassa virus (55 fastq files) and Ebola virus (8 fastq files) dataset collections (see Table  4  for description of the sequence datasets). On the one hand, we executed the workflow (S17 Fig)  taking  the Lassa virus dataset collection as input sequences, "Lassa" as a filter term for the "Parse blast output and compile hits" tool and the NCBI sequence NC_004297. 1 as a guide for reconstruction of the Lassa virus segment L. On the other hand, we executed the workflow taking the Ebola virus dataset collection as input sequences, "Ebola" as a filter term for the "Parse blast output and compile hits" tool and the NCBI sequence NC_002549. 1 as a guide for reconstruction of the Ebola virus genome.
The results of both analyses are summarized in Table  4. Metavisitor was able to detect Ebola virus in all corresponding sequence datasets (S16 File) as well as Lassa virus in 53 out the 55 sequence datasets generated from Lassa virus samples (S17 File). Consistently, Matranga et al did not report reconstructed Lassa genomic segments from the two remaining datasets, which likely reflects high read duplication levels in the corresponding libraries  [31]. The reconstructed Lassa virus L segments and Ebola virus genomes are compiled in S18 and S19 Files, respectively. In these sequences, de novo assembled segments in uppercases are integrated in the reference guide sequence (lowercase) used for the reconstruction. To note, for viruses with segmented genomes such as Lassa virus, the workflow has to be used separately with appropriate guide sequences for the segment to be reconstructed.
As an example, we used this workflow with the filter term "Lassa" for the "Parse blast output and compile hits" tool and the Lassa S segmentNC_004296. 1 for guiding the reconstruction (S20 File). At this stage, users can use the genomic fasta sequences for further analyses. For instance, multiple sequence alignments can be performed for phylogenetics or variant analyses, or reads in the original datasets can be realigned to the viral genomes to visualize their coverages, as has been done in Use Cases 1 and 2.
Discussion Metavisitor performs de novo assembly of sequencing reads and detects contigs of viral origin through blast alignments, which then can be clustered to reconstruct a viral genome. On the one hand, this strategy reduces the rate of false positives since the ability to form contigs that align to known viral sequences is a strong evidence of the presence of a full viral genome in the analyzed datasets. In addition, we advise Metavisitor users to remove sequence reads that align to genomes of hosts, symbionts or parasites, if these are known and available (see  Fig 1). Although this treatment can be skipped (as in Use Case 3-3), it avoids chimeric assemblies of viral and nonviral sequences, while speeding up the assembly of contigs of potential viral origin. It also ensures that sequences of the host genome that have been annotated as Endogenous Viral Elements (EVEs) are not retained for viral contigs assembly.