dataesr/scientific-paragraphs-categorization huggingface dataset:
ORFeome: numbers and classification ORF predictions can be queried from either one or both of the annotations or both from the search or browser page (Figure  2A ). Numbers of identified RefProts, AltProts and Isoforms are displayed in Table  1  for each species. For example in human to date, OpenProt (release 1. 3) predicts 55,053 novel isoforms. These are currently non-annotated in NCBI RefSeq or Ensembl, they originate from transcripts D408 Nucleic Acids Research, 2019, Vol. 47, Database issue present in current annotations and display significant similarity with the gene annotated CDS (see similarity filter arguments). All ORF predictions can be visualized using the genome browser where transcripts, ORFs, and MS-based peptides are displayed. For easy visualization, RefProts are coloured in blue, AltProts are in green, and Isoforms are in orange (Figure  2B ).
Protein characteristics annotation The result table from a query also contains additional information on the predicted ORF (Figure  2A ). The predicted protein characteristics, such as the length (in amino acids), the isoelectric point and molecular weight, are displayed. Each protein annotation is linked to all source transcripts for which accession numbers and type (mRNA or ncRNA) are reported. Localization within the transcript (CDS, 5 or 3 UTR) is also displayed for ORF predictions from mR-NAs. Each ORF can be inspected individually in the details page (Figure  2C ). The details page contains ORF information, such as genomic and transcript coordinates, the presence of a simplified Kozak motif (RNNATGG where R stands for A or G)  (39)  or a high-efficiency translation initiation motif (RYMRMVAUGGC where R stands for A or G, Y for U or C, M for A or C, and V for A, C or G)  (40)  and access to protein and DNA sequences.
The Translation tab collects PRICE (35) reports of identifications with the associated P-value and read count per sample (Figure  2E ). The Mass spectrometry tab contains all identified unique peptides alongside the associated peptide spectrum match (PSM) count within each dataset (Figure  2F ). For transparency purposes, all original datasets are accessible by clicking on the study name. At this time, the database reanalyzed 87 ribosome profiling and 114 mass spectrometry studies across several species and diverse cell lines and tissues (Table  2 ). A complete list of all studies implemented in OpenProt is available from the Help section (supplementary materials S1 and S2).
Applications and downloads Expanded databases, such as OpenProt, are invaluable tools for functional proteomics discoveries  (2, 4, 6, 7). OpenProt is tailored for every need of all researchers, giving them the required tools for a more comprehensive view of eukaryotic genomes' coding potential. That is why in addition to the genome Browser and the advanced query page, the Downloads page allows users to download custom databases. For example, one may choose to focus on only the most confident annotations (previously unannotated proteins detected with at least two unique peptides in mass spectrometry experiments), when another may want to focus on discovery of novel functional proteins (all predictions). Moreover, personalized database generation and download based on custom RNA-seq results is also supported, as detailed under the Help section (supplementary materials S1).
DISCUSSION AND COMPARISON TO EXISTING RE-SOURCES OpenProt annotates thousands of novel predicted proteins supported by experimental evidence and functional predictions. As more RIBO-seq and MS datasets are constantly added to the database, we expect this number to rise. The out-of-focus resolution at which we currently look at eukaryotic coding potential and proteomes is gradually being acknowledged  (1, 3, 5, 18, 20 ), yet we still lack systematic approaches to the problem and this could impede on our understanding of basic biology questions  (6). To the best of our knowledge, OpenProt is the first database that fully endorses a polycistronic model of mammalian genome annotation. OpenProt differs from other smORFs databases in that it does not uphold a maximum length threshold (below 100 codons for smORFs).
Open-Prot also allows for multiple ORFs per transcript, and supports two transcriptome annotations. Furthermore, the OpenProt pipeline allows for the identification and detection of novel isoforms. Thus, OpenProt reaches a deeper ORF annotation throughout the genome. In addition, OpenProt distinguishes itself from UniProt as it provides a graphical interface that allows the user to browse all predicted ORFs, in addition to providing functional annotations (conservation, translation, expression and presence of functional domains). OpenProt does not currently allow for deep functional annotation, such as UniProt does. However, when a novel protein is discovered and sufficiently characterized to meet UniProt annotation requirements, it will then become a RefProt in OpenProt database (with a UniProt accession).
FUTURE DIRECTIONS The OpenProt pipeline is automated so that new releases of NCBI RefSeq and/or Ensembl will automatically be taken into account. Following iGenomes update, data will be updated at the beginning of the following year for computational resources access reasons. OpenProt is a release-based platform, developed in accordance to the FAIR guiding principles for scientific data management and stewardship  (41). This ensures an up-to-date, continuous availability of all OpenProt data through time. As the field progresses, OpenProt is expected to elaborate its pipeline, notably taking in account ORFs shorter than 30 codons or non-ATG start codons. For statistical and computational reasons, OpenProt current release (1. 3) still holds these cut-offs. Simultaneously, OpenProt will continue to increase the number of datasets for supporting evidence, as well as supported species.
OpenProt can be contacted through the Contact page for request of species annotations and/or dataset analyses (http://www. openprot. org/ p/about). New tools and metrics will also be added following developments in the field. For instance, a pipeline is being developed to allow AltORFs and novel Isoforms consideration in genome or exome sequencing studies. The quantity and quality of data provided by OpenProt along with its ease of use and transparent data availability hold potential to make it a popular tool.
OpenProt (1. 3) evidence collection output Number of species evaluated for orthology relationships (not counting the queried species); St = number of studies re-analyzed by OpenProt; Ref = currently annotated CDS (RefORF); II = novel isoforms of known CDS; IP = novel CDS from alternative ORF (AltORF); n/a = when no dataset has been re-analysed for this species yet (OpenProt release 1. 3). Conservation evidence = all proteins with at least one ortholog in at least one species. Translation evidence = all ORFs detect in at least one detection by PRICE analysis of RIBO-seq data. Protein evidence = all proteins with at least one unique peptide in at least one study.
ACKNOWLEDGEMENTS We thank our collaborators for providing additional mass spectrometry (MS) datasets. We thank  Mark Vaudel  for the support on our MS identification pipeline (PeptideShaker), and  Florian Erhard  for his help implementing the PRICE pipeline on RIBO-seq data. We also thank  Felix Primeau ,  Christian Landry ,  Darel Hunting , and  Alan A. Cohen  for helpful discussions and contributions. F. M. B. , M. S. and X. R. are members of the  Fonds de Recherche du Québec Santé (FRQS)-supported Centre de Recherche du Centre Hospitalier Universitaire de Sherbrooke. We thank the team at  Calcul Québec and Compute Canada  for their support with the use of the supercomputer mp2 from  Université de Sherbrooke.
Failure to build large enough scaffolds or contigs can reduce the proportions of reads that can be reliably identified as being related to known virus species using alignment-based approaches such as BLAST  24. This technical problem is compounded by the dearth of viral taxa that are represented in public nucleotide sequence databases such as GenBank. A consequence of this is that, up to 70% or more of the reads that are generated by some environmental viral metagenomic studies, end up being labeled as "dark matter" because they have no detectable homology to sequences within the public nucleotide sequence databases  19, 25. Third generation sequencing techniques that are capable of generating much longer reads from individual RNA or DNA molecules promise to eliminate the need to assemble contigs de novo from short sequence reads.
These techniques could therefore avoid the problems mentioned above  26. Among the third-generation sequencing techniques is that implemented in the Oxford Nanopore MinION. The MinION has recently proved effective for delivering, in real time, average reads lengths of >15 kilobases  [27] [28] [29]. While the per nucleotide error rate of the MinION long reads is frequently in excess of 20% -substantially higher than that of the shorter Illumina reads  30  the high degrees of genome coverage that are achievable together with post-sequencing data analysis can still yield consensus sequences that are >99% accurate  31  and can be effective for identifying recombinant viral variants  32. The MinION has been used to detect and determine the full (or nearly full) genome sequences of a range of animal and human viruses, including ebola  33  , dengue  34  , zika  35  , influenza  31  , cowpox  36  , and Ross River virus  37.
In silico screening of publically available expressed sequence tag (EST) data from yam. A systematic search of viral sequences in de novo assembled sequences (using the CAP3 sequence assembly program  40  ) from publicly available D. alata expressed sequence tag (EST) resources (GenBank accession numbers: HO809681-HO825421, HO825422-HO840419 and HO850622-HO864016) was performed using BLASTn and BLASTx searches against the GenBank nucleotide collection (nt/nr) sequence databases implemented in the software KoriBlast 3. 1 (KoriLog, Muzillac, France) with a maximum e-value threshold of 10 -3.
1 ). The eluted library was loaded onto a R9. 4 Flow Cell (FLO-MIN106 R9. 4). Sequencing was performed using the MinION Mk1B device (MIN-101B). One strand of the duplex was sequenced at a time, producing 1D reads. The flow cell was run for 48 h using the standard MinKNOW software (version 1. 11. 5) and reads generated were base-called in real-time using the cloud-based Metrichor service provided by Oxford Nanopore Technologies.
Illumina and MinION reads analyses. Illumina and MinION reads and contigs were compared to sequences in the GenBank database using DIAMOND  45  searches for MinION reads or BLASTn and BLASTx 24 searches for Illumina reads with a maximum e-value threshold of 10 -3. Individual macluravirus-related MinION reads were then pairwise aligned with the YCNV Sanger reference genome (hereafter called the reference sequence) using a kmer-based approach (k = 8). Specifically, for each read (hereafter called a query sequence) identified as having detectable homology to the reference sequence, the coordinates of all perfectly matching 8 nt long query sequence fragments were identified on the reference sequence. All perfectly matching 8 nt fragments that were within 20 nucleotides of at least one other perfectly matching 8 nt long fragment on either the query or reference sequences were flagged as being accurately mapped and each of the nucleotides within these fragments was identified as being accurately sequenced (i.
De novo assembly of Illumina sRNA and MinION sequencing data. Illumina reads cleanups and corrections were performed using the CutAdapt version 1. 8. 1 program  47  and de novo assemblies were generated using SPAdes 3. 6. 2 software 48 and CAP3  40. MinION reads de novo assembly was done using Canu v1. 6  49  with the suggested parameters for Nanopore R9 1D sequencing (-nanopore-raw overlapper = mhap utgReAlign = true) using reads >1 kb (minReadLength = 1000) and a genome size of 20 kb (genomeSize = 20 k). Illumina sRNA and MinION sequencing coverage and accuracy. Both sets of reads were independently and jointly mapped to the complete Sanger YCNV genome with Bowtie2 v2. 2. 9  50  using the end-to-end and very sensitive option. All reads mapped by Bowtie2 were kept in the analysis. Resulting bam alignment files were then processed with samtools 1.
3  51  to produce mpileup files. Bases with a phred score lower than 13 were filtered out. The resulting mpileup files were then parsed with custom python and R scripts to extract positions and coverages (second and fourth column, respectively), while the similarity was computed using the fifth column of the file, which displayed the numbers of matches, mismatches and indels at each position in the original bam files. Similarity was reported as percentage of match over the coverage. Means of similarity across 100 nt long sliding windows (step size: one nucleotide), were used for clarity of display. Dataset and scripts are available in a github repository at the address: https://github. com/loire/roumagnac2018_figs. Consensus sequences of the YCNV genome were obtained from mapped sRNA Illumina reads, mapped MinION reads, and jointly mapped sRNA Illumina plus MinION reads.
Pairwise identity analyses of the reference YCNV Sanger sequence and de novo Minion contigs, mapped sRNA Illumina reads-, mapped MinION reads-, and mapped sRNA Illumina/MinION reads-consensus sequences were carried out using the MUSCLE-based pairwise alignment option implemented in SDT v1. 2  52. Pairwise identity and phylogenetic analyses. All available macluravirus full genomes were downloaded from GenBank and aligned using MUSCLE  41. Maximum-likelihood phylogenetic trees were inferred using PhyML 3. 1  53  implemented in MEGA version 6. 06  54  with the nucleotide substitution model GTR + G + I selected as best-fit models by jModelTest  55. Branch supports were tested using 1000 bootstrap replicates.
BLASTn comparisons between these two contigs and all sequences in GenBank indicated that the highest similarity scores were detected with YMMV (accession number KJ125479, highest percent nucleotide identity = 85%, query coverage = 99%, e-value = 0) for contig #1 (8791 bp) and YCNV (accession number MG755240, highest percent nucleotide identity = 82%, query coverage = 99%, e-value = 0) for contig #2 (7277 bp).
Characterization of the full-length genome sequence of yam chlorotic necrosis virus. A 8263-nt full length genome sequence of YCNV isolate Kerala (accession number MH341583) was further assembled and annotated, confirming that it possesses a typical macluravirus genome organization (Fig. 1B ), including a large open reading frame (ORF) predicted to translate into a 2628 aa long polyprotein (Fig. 1B ). This large unique ORF, which starts at position 147 and ends at position 8033, was predicted by analogy with other macluraviruses to encode nine functional products after cleavage, including a helper component-proteinase (HC-Pro), P3 (protein of unknown function), 7 K (protein of unknown function), a cylindrical inclusion putative helicase (CI), 9 K (protein of unknown function), a genome-linked protein (VPg), a nuclear inclusion protease (NIa-Pro), a nuclear inclusion polymerase (NIb) and a coat protein (CP)  58, 59.
Acknowledgements D. P. M. has received a research grant from the  National Research Foundation of South Africa. This study was funded by the  Agropolis Fondation  ( E-Space flagship program ). Data available at NCBI. GenBank accessions #8263-nt full length genome sequence of yam chlorotic necrosis virus isolate Kerala, Accession Number  MH341583. Cleaned Illumina and MinION reads have been deposited in the sequence read archive of GenBank (Accession Number:  SRP158898 ).
Data Collection and Refinement The X-ray diffraction (XRD) data were collected by means of a Bruker Kappa Apex II CCD diffractometer with Mo Kα radiation (λ = 0. 71073 Å) and a graphite monochromator. The experiment was conducted at 296 (2) K using φ, and ω scans within 2θ = 52°; 39342 reflections were detected, including 2009 independent ones (Rint = 0. 054). Crystallographic data: a = 9. 0584  (5) , b = 13. 9014 (8) , c = 18. 112 (1) Å, V = 2280. 8(2) Å 3 , space group Pbca, Z = 8, dcalc = 1. 312 g/cm 3 , and μ = 0. 099 mm -1. Absorption corrections were applied empirically in the SADABS software  [21] , which employs multiple measurements of the same reflections at different crystal orientations. The structure model was found using a direct method and was refined in an anisotropic approximation using the SHELXTL software suite  [22, 23].
Data Collection and Refinement The X-ray diffraction (XRD) data were collected by means of a Bruker Kappa Apex II CCD diffractometer with Mo Kα radiation (λ = 0. 71073 Å) and a graphite monochromator. The experiment was conducted at 296 (2) K using ϕ, and ω scans within 2θ = 52 • ; 39342 reflections were detected, including 2009 independent ones (R int = 0. 054). Crystallographic data: a = 9. 0584 (  5 ), b = 13. 9014 (8) , c = 18. 112 (1) Å, V = 2280. 8 (2) Å 3 , space group Pbca, Z = 8, d calc = 1. 312 g/cm 3 , and µ = 0. 099 mm -1. Crystals 2018, 8, 334 3 of 7 Absorption corrections were applied empirically in the SADABS software  [21] , which employs multiple measurements of the same reflections at different crystal orientations. The structure model was found using a direct method and was refined in an anisotropic approximation using the SHELXTL software suite  [22, 23].
Methods: The primary questions addressed by the systematic review protocol are: how antibiotic resistance in the environment is impacted by changes in practice concerning (i) the use of antibiotics, (ii) the management of wastes or (iii) the management of the natural compartment. Bibliographic searches will be made in eleven publication databases as well as in specialist databases. Grey literature will also be searched. Articles will be screened regarding the inclusion and exclusion criteria at title, abstract and full-text levels. Studies where a causal relationship between the intervention and the outcome is made will be retained. After critical appraisal, data from the selected articles will be extracted and saved in a database validated by the expert panel. Study quality will be assessed by critical appraisal.
Publication databases Publications will be collected from the following databases, without any limitation on the years of publication: • PubMed. • Web of Science. The following databases will be used depending on the number of publications collected from the first databases and the rate of identification of new articles not previously retrieved: • Agricola (https://www. agricola. nal. usda. gov). • AGRIS (Agricultural database of FAO). • BioOne (https://www. bioone. org). • Directory of Open Access Journals (https://www. doaj. org). • Drug Resistance Updates (https://www. drupjournal. com). • IngentaConnect. • JSTOR. • Scopus. • Wiley Online Library.
Supplementary searches The citation chasing will be used as a method of identifying potentially relevant studies. If not accessible by usual retrieval of articles, authors will be directly contacted to request full texts of publications. Otherwise, members of the Expert Panel will be contacted to know if they have unpublished data or if in their professional network, they know researchers working on solutions to reduce the contamination of the environment by antibiotic resistance. Websites of governmental departments in various countries who might be conducting or be aware of research relevant to this systematic review can be consulted. With the help of an information manager (INRA), a monitoring tool (Digimind ® ) is implemented and will allow us to receive alerts from the scientific publication database (Web of Science) and from different websites.
The use of this tool will start once the bibliographic search on Web of Science is conducted and will end once the screening by full text is done. Alerts from Web of Science will be screened by title and abstract. If included, the corresponding references will be exported in a separate collection of the database (Search record database section), and then will be screened by full text as for articles collected from the searches in the different publication databases. Alerts from different websites (e. g. , press, institutions, governmental) will be screened by title as additional source of grey literature.
Screening process The articles found by searches in databases will be evaluated for inclusion at three levels, i. e. , by title, then by abstract and finally by the full text. For the screening at title level, a subset of 40 articles will be screened by several reviewers. Screening consistency will be evaluated by using Kappa tests and a score ≥ 0. 6 will indicate an acceptable agreement between reviewers. Discrepancies will be discussed between reviewers and refine inclusion/exclusion criteria. A second subset of 40 different articles will be screened again for evidence of improvement of consistency. The same method will be used for the screening at abstract level, with two different subsets of 25 articles, screened by several reviewers. For the screening of articles at full text level, rejection of an article will be decided by the review team upon suggestion of the first reader.
Details regarding the final decision of inclusion/exclusion of articles will be clarified and archived in a database. In cases of uncertainty for the decision to include or exclude an article, the reviewer will include this article for the next level of screening. The documents without abstracts will be screened at the full text level. A list of articles excluded at full text level will be provided in the systematic review, accompanied by reasons for exclusion. Eligibility criteria were developed in consultation with the Expert Panel, for each sub-question and are presented below for each sub-question S1, S2 and S3.
Potential effect modifiers and sources of heterogeneity Potential effect modifiers will be identified to better understand the variations of effects among studies. Indeed, several factors such as the study location (e. g. , regulations for antibiotic use), the climate conditions, the considered antibiotic, can result in heterogeneity of results. The Expert Panel will extract data on potential effect modifiers from studies included at the full text screening. The factors will be recorded in the database. A non-exhaustive list of potential effect modifiers is given hereafter:.
Data extraction strategy All data from included studies will be extracted and recorded in an Excel database by using a predefined spreadsheet and validated by the Expert Panel. The extracted data records will be available as Additional file 1: Table  S1  of the systematic review. The extracted information will be based on the PICO elements and recorded outcomes will be: outcome means, sample sizes and measures of variation such as standard deviation, standard error, confidence intervals. The extracted information will be used to measure the effects of interventions on the change of antibiotic resistance in the environment. If good quality data exist and is in sufficient number, a meta-analysis will be carried out. To ensure that data is correctly extracted, two reviewers of the Expert Panel will extract information from a subset of 10% of all articles.
Data synthesis and presentation A narrative synthesis of data from all documents included in the systematic review will be generated. The results will be summarized into tables or figures as much as possible. A quantitative analysis is the ultimate objective of the systematic review to quantitatively highlight the effects of interventions on the antibiotic-resistance burden in the environment. The quantitative study will be possible only after assessing the content and the quality of full texts. Meta-analyses would be carried out and reported if several studies allow for the calculation of effect sizes. If conducted, heterogeneity or publication bias will be assessed quantitatively in meta-analyses.