dataesr/scientific-paragraphs-categorization huggingface dataset:
A few TE databases already exist, but none provides evidence to justify TE classification. Moreover, biological information about the sequences remains globally poor. Results: We present here the RepetDB database developed in the framework of GnpIS, a genetic and genomic information system. RepetDB is designed to store and retrieve detected, classified and annotated TEs in a standardized manner. RepetDB is an implementation with extensions of InterMine, an open-source data warehouse framework used here to store, search, browse, analyze and compare all the data recorded for each TE reference sequence. InterMine can display diverse information for each sequence and allows simple to very complex queries. Finally, TE data are displayed via a worldwide data discovery portal. RepetDB is accessible at urgi.
TE detection and annotation is, thus, now considered essential for any genome study. With the development of new high-throughput sequencing technologies, a large number of genomes have been sequenced, resulting in very large amounts of sequence data. Automated TE de novo detection software can provide up to thousands of TE reference sequences per genome, generally in the form of consensuses. TEs are then classified into two classes, with various subclasses, orders and superfamilies defined on the basis of mechanistic, enzymatic and sequence similarity criteria. The two classes of TEs are defined on the basis of their transposition mechanisms: with (Class I) or without (Class II) an RNA intermediate  [8]. TE identification often makes use of popular pipelines, such as RepeatModeler  [9]  and TEdenovo pipeline from the REPET package  [10] , including tools for the identification of repeats, their grouping into TE families, and the generation of multiple alignments to build TE consensus sequences.
TE classification is usually based on similarity to known TEs and protein domains and/or according sequence structure  [11]. Several databases for describing TE reference sequences already exist. Repbase Update  [12, 13]  contains (i) complete consensus sequences for TE families (70% of the database) built with homemade pipelines based principally on Recon  [14]  and LTR-FINDER  [15] , and (ii) TE copy sequences (complete or incomplete) extracted from individual genomic loci. DFAM  [16] , another database for repeats DNA families is based on profiles HMM constructed from multiple sequence alignment. The entries of Dfam are Repbase-derived library (humans, mouse, zebrafish, fly and nematodes). The Gypsy database (GyDB)  [17]  stores LTR-retroelements, including those of the Gypsy/ Copia superfamilies in particular, and Retroviridae-like elements including sequences from the Caulimoviridae (plants Endoviruses).
The SINE database (SINEBase) is dedicated to SINE elements prediction  [18]. The P-MITE database hosts Miniature inverted-repeat transposable elements (MITEs) from 41 plant genomes  [19]. The TIGR Plant Repeat Database  [20]  is a set of resources for the identification of repeats, including TEs, rDNAs, and telomere-associated sequences, populated with repeat sequences from 12 plant genomes from GenBank. This initial dataset is extended on the basis of sequence similarities between these sequences and GenBank sequences. The Transposable Elements Platform (TREP) is a curated TE database (http://botserv2. uzh. ch/kelldata/trep-db/index. html) mostly for monocotyledons and fungi. The MIPS Repeats database (PGSB-REdat) and Catalog (PGSC-REcat) are parts of the Plant Genome and Systems Biology platform (PGSB) including PlantsDB  [21].
They contain TEs retrieved from TREP, TIGR repeats, Repbase and detected de novo in the genome sequences stored in PlantsDB. All these databases are accessed through web browsers and/or quick or advanced search forms. The curation of TE reference sequences is generally poor and based on automatic procedures. However, intrinsic automated TE classification is an error-prone process. There was, therefore, an urgent need for annotation with TE consensus sequences together with the evidence justifying TE classification. None of the existing TE databases can provide the evidence on which the proposed classification is based. We filled this gap by developing the RepetDB database in the framework of GnpIS, a genetic and genomic Information System  [22]. RepetDB is an instance of InterMine, a public open-source data warehouse that has been specially customized and enhanced  [23, 24]  with JBrowse  [25]  for searching, browsing, analyzing and comparing all the data provided for each TE consensus sequence in the many genomes analyzed.
We use the powerful capacities of InterMine to create user-friendly interfaces allowing researchers to search and query data in multiple manners. Data can be exported in many commonly used formats, including fasta, GFF, BED or other tab-delimited formats. In addition, InterMine provides APIs in various languages, including Perl, Java, Python, Ruby, and JavaScript, for accessing its features via web services. The full capabilities of these web services are described at http://iodocs. apps. intermine. org/.
InterMine instantiation and customization RepetDB is an InterMine-based data warehouse. It was built with InterMine 1. 4. 2, with the InterMine web application used as the basis for data integration and presentation. Various comprehensive categories of reports through user-friendly interfaces for queries and visualization were developed with the templates provided by InterMine. RepetDB data integration is based on the InterMine data integration process built with Apache Ant 1. 8. 4, running in the Oracle Java 1. 7. 0_79 SE Runtime Environment and building a PostgreSQL 9. 4. 15 database. The RepetDB web application is deployed on Apache Tomcat 7. 0. 59 running in the same Java environment as used for data integration. In addition to traditional InterMine configurations, some more advanced customizations were developed in Java, JSP (Java server page) and JavaScript.
All of the RepetDB programs are hosted on a CentOS 6. 8 virtual machine. The central object in RepetDB is the TE reference sequence, which is a consensus sequence. The main user interface visualization is a "TE reference page" referred to hereafter as the "Consensus card" (Fig. 1 ), on which information is gathered together and organized into five sections: (i) the "Header information" panel containing basic information, such as the consensus identifier, its length, a link to its sequence, its classification according to Wicker nomenclature  [8]  and basic occurrence metrics concerning annotation with this consensus in the genome (ii) a "Materials and Methods" section with a short description of the dataset (genome assembly version) and workflow used to provide the reference and other relevant information (from the point of view of the person submitting the dataset) (iii) a "TE copies statistics" section with various metrics concerning the genome copies annotated with the current TE reference, (iv) an embedded browser (JBrowse 1.
An implementation suitable for standardized TE detection, classification and annotation It is now common practice to propose gene structures with all the multiple sources of evidence used by the predictors to combine them. We have designed RepetDB to function in a similar manner. In databases such as Repbase, useful evidences such as coding sequence, terminal repeats evidences are usually presented in "full text description fields" of the consensus. In addition to this "description field", DFAM, a database based on DNA profile prediction  [16] , also presents for each entry, the overlapping Repbase entries. The added value of RepetDB is that it displays the multiple sources of structural or similarity-based evidence used by PASTEC. Indeed, all the evidences used to automatically detect and classify TEs are presented by category in a text form, included coordinates and external links to Repbase, GyDB and PFAM and also displayed as annotation tracks in a Browser.
The TEdenovo pipeline  [10]  is used for the de novo detection of repeats. It uses Blaster  [26] , and groups repeats in a combined approach using Grouper  [26] , Recon  [14]  and Piler  [27]. It then generates multiple alignments from the sequence clusters with MAP  [28]  and builds consensuses. Each consensus representing a structural variant of a TE family is classified with PAS-TEC  [11] , according to its structural and functional features (LTR, TIR, ORF, polyA tail), based on functional domain similarities to HMM profiles from the protein family databases PFAM  [29]  and GyDB  [17] ), and characterized TEs from the Repbase Update database  [13]. Consensus sequences with no known structure or similarity are classified as noCat (i. e. "unknown"). Note that PASTEC may also be used on a TE library not provided by the TEdenovo pipeline.
tsv) containing the consensus copy statistics. When the TEannot pipeline is used, the PostAnalyse. py script (included in the REPET package) can be used to provide the statistics file in the appropriate format used by RepetDB (http://urgi. versailles. inra. fr/repetdb/docs/ Data-submission. html#i. 3. a. -consensus-copy-statistics). However, although any TE genome annotation pipeline can provide data in a suitable format for input into this system, the easiest way to insert data in RepetDB is to use outputs from the TEannot pipeline  [30]. TEannot is based on three alignment methods (Blaster, Censor and RepeatMasker  [31] ). The HSPs obtained are filtered and combined. TE copies covered with SSRs over more than 75% (by default) of their length are then removed. Finally, a "long join procedure"  [10]  is used to address the problem of nested TEs.
Data insertion and integration The dataset metadata are integrated in RepetDB in xml format. TEdenovo and TEannot output files can be used for direct insertion, for each dataset: consensus sequences (fasta format), consensus classification file (tabulation-separated format), consensus annotation files including structural and functional annotations (GFF3 format). The data are displayed in the Consensus card JBrowse panel and textual feature categories. Data integration occurs in two major steps: "integrate" and "post process". The "integrate" step involves the loading of data from the provided files into the Inter-Mine database. The "post process" step involves several operations, such as linking objects between data sources (e. g. linking consensuses to their wicker classification ontology terms), and data enrichment (e.
g. protein profile features, blast hits on transposable elements databases, such as Repbase  [13] ). "Potentially chimeric" elements are defined as TEs with an ambiguous classification. We also present a full-text search with the GnpIS  [22]  data discovery tool (https://urgi. versailles. inra. fr/gnpis), which uses autocompletion to assist the user typing keywords for the search. The results are displayed in a table with the source of the data. Results can be restricted to one or several species. Finally, we also provide a web blast tool for searching for similarities to one or more consensus sequences present in RepetDB. All the TE consensus libraries are available through the URGI BLAST  [32]  server (https://urgi. versailles. inra. fr/blast/).
Get data from RepetDB RepetDB data export makes use of the default exporters of InterMine. Common standard formats, such as tabulated/ csv-separated, fasta, BED, and GFF (https://genome. ucsc. edu/FAQ/FAQformat. html) are proposed on the query results and list analysis pages. The user can customize the table (addition or deletion of columns, column sorting etc. ). For each data set, we provide a link to download the genome annotation performed with the consensus library of the dataset.
In RepetDB, we store and provide access to the consensus built for each structural variant. The TE consensus library is used as an input of the TEannot pipeline, for the annotation of copies in the genome. For the automatic curation of some poorly defined consensuses, we run a second TEannot pipeline, using a library of consensuses filtered for sequences without full-length fragments or full-length copies in the genome. A copy may be built with one or more fragments joined by the TEannot long-join procedure. Depending on the context of TE annotation and the additional analyses performed, some of the datasets are manually curated and filtered for unreliable TE consensuses. The version, specific parameters and level of manual curation performed on the datasets are indicated in the "Comments" section of the datasets.
Enhancing the discoverability of RepetDB data This well-structured database should improve the visibility of the wealth of TE reference sequences already identified. In particular, its intrinsic functions, combined with its inclusion in the international data discovery network, make it possible to improve the FAIRness of these data  [33]. Indeed, RepetDB ensures data accessibility, interoperability through the use of standard formats and ontologies, such as Wickers classification terminology, and reusability. Findability is increased through the GnpIS information system data discovery portal (https://urgi. versailles. inra. fr/gnpis), but the content of RepetDB is also indexed in various international discovery portals that are currently emerging in the field of plant biology. These portals can be used to search data with free keywords, across a set of databases displaying indices on several portals based on the same distributed full-text search technology and data model  [34].
Discussion A database for the reuse of rich and often underexploited annotation data The REPET package is one of the most widely used TE annotation tools in eukaryotic genome projects (mainly for plants and fungi). Its outputs are rich and often underexploited, not going beyond initial genome annotation in new genome annotation projects. The TE identification procedure provides access to large amounts of information for each reference sequence that are often lost after publication. We developed RepetDB for the storage and sharing of this information for other potential uses. The database is populated with the results of previous analyses already performed on species of agronomic interest in the framework of several unrelated projects. We group together results obtained in studies on plants (Brassicaceae, wheat, maize, grapevine, apple, oak, Rosaceae), symbiotic fungi and pathogens  [35, 36].
Conclusion RepetDB, a database of consistent TE datasets RepetDB aims to provide a highly homogenized TE classification, with supporting evidence. We therefore integrated into the data submission process the systematic use of PASTEC  [11] , a tool from the REPET TEdenovo pipeline. This tool searches for evidence of TEs through dedicated similarity and structural searches, and uses this evidence to classify TEs automatically. We would therefore expect to obtain similar levels of sequence description and classification for different TEs in different species, thereby facilitating comparative genomics. This consistency of description, classification and annotation makes it possible to perform consistent cross-species or cross-variety searches to compare TE family content between genomes. This tool can be used to explore specific TE families, superfamilies or orders across a large range of species.
Like any automatic procedure, full de novo TE annotation is likely to generate false positives, and ways of assessing correctness of TE classification relative to the corresponding reference sequence are therefore required. RepetDB is designed to display the evidence supporting each consensus classification, so as to facilitate the curation of TE reference libraries. Links to external databases are also available when similarities are detected to sequences from other databases (RepBase, GyDB  [13, 17] ). supervised the project, wrote the paper and prepared several datasets. All authors contributed to the manuscript. All authors read and approved the final manuscript.
Acknowledgements. This work is carried out as part of the  OC-TAVE research program  ( 2017-2021 ). We thank  Météo France  for sharing the AROME output files from forecasts in the southwest  Indian Ocean  which made this work possible. Financial support. This research has been supported by the " Belgian Research Action  through  Interdisciplinary Networks" (BRAIN-be ) through the  Belgian Science Policy Office (BELSPO)  (grant no. BR/175/A2/OCTAVE ). Additional funding from the research federation  OMNCG  ( Observation des Milieux Naturels et des Changements Globaux ) managed by  OSU-R (Observatoire des Sciences de l'Univers à La Réunion)  from the  University of La Réunion Island  was provided for this project. Review statement. This paper was edited by  Christoph Knote  and reviewed by two anonymous referees.
INTRODUCTION The precise and consistent annotation of genes is a prerequisite for all downstream analyses and in particular for comparative genomics and phylogenetics. This seems to be an almost trivial task taking into account the small number of protein-coding genes (PCGs) encoded in a mitochondrial genome (mitogenome). It is indeed not difficult to determine the identity and approximate position of these genes. Due to peculiarities of mitochondrial genetics it remains a challenging problem, however, to determine the precise start and end positions. Given the rapid increase in the number of sequenced mitogenomes--the NCBI organelle genome database lists >8000 animal species at the time of writing (September 2018)--a careful manual annotation of mitogenomes has become infeasible. This requires precise methods that automate this task and reduce the burden of manual curation.
g. ,  (5). Furthermore, in some invertebrate taxa it has been reported that PCGs often start with non-canonical start codons, i. e. codons that are not defined as start codon in the genetic code tables. But for a few cases alternative possibilities to non-canonical start codons have been described. The initiation codon of the cytochrome c oxidase subunit I (cox1) gene in Drosophila, for instance, is out of frame and requires a post-processing of the quadruplet AUAA by frameshift or editing  (6) , see  (7)  for a review. Even if non-canonical start codons are mainly described for invertebrates, the frameshift mechanism recently gained support for the human mitochondrial genome  (8). RefSeq and GenBank  (9)  are the major source of annotated mitochondrial genome sequences. Problems with consistency and quality of annotations in these databases have been described repeatedly  (10, 11)  and have been approached in two different ways.
The first focuses on the manual improvement and reannotation of existing database entries. The OGRe database  (12)  incorporates corrections that have been described in the literature. The manual reannotation performed in MitoZOA  (13)  follows defined rules for detection and correction of errors in databases. The major problem with such manual curation approaches is that they seem to be unsustainable because they do not scale with the ever faster growing amount of available mitochondrial genomes. A second group of methods implement de novo annotation approaches. The first published computational method to annotate mitogenomes was DOGMA  (14). It uses BLAST  (15)  for detecting PCGs as well as ribosomal RNAs (rRNAs) and applies tRNAscan-SE  (16)  for annotating tRNAs. Potential ends of genes are selected by the user.
Prediction of start and stop codon positions in MITOS The original implementation of MITOS employs a very simple method to predict start and stop codons of PCGs. Given approximate start and stop positions (provided, e. g. by a BLAST search) the proximity (per default in a range of ±6 amino acids) is checked for start and stop codons, respectively, using the genetic code tables of the NCBI Taxonomy  (22)  (https://www. ncbi. nlm. nih. gov/ Taxonomy/Utils/wprintgc. cgi). If no valid start or stop codon can be identified, MITOS chooses the corresponding approximate position as gene boundary.
The 231 internal TAA codons in invertebrate mitogenomes (code table 5) are found in only a small number of species (one mollusk, four hexapods, one crustacean, and one nematode). We further analyzed the appearance of TAR stop codons in all 65526 metazoan mitochondrial genomes currently (2017-06-11) contained in GenBank. The codon TAA was found in 521 mitogenomes, of which only 248 use this codon at least 10 times. Only few of these have code table 14 assigned to them (one annelid, one cestode, three nematodes, and one trematode). With 157 occurrences, TAA is the most abundant codon in the mitogenome of Radopholus similis (Nematoda: Chromadorea) (NCBI accession number NC 013253 = FN313571) and thus code table 14 has been assigned to this species  (23). For many other species only approximate gene positions are annotated in the GenBank files, e.
Precision of predicted start and stop positions In the following, we present the performance of the new probabilistic method for the prediction of gene boundaries and the method originally implemented in MITOS by com-   2 , for arginine in code table 4, and--together with AGY--for serine in code table  5 ). paring their results to the annotations in RefSeq, MitoZOA and MitoAnnotator. While a comparison with reference data and the results of other automatic prediction methods certainly is a good performance indicator, an evaluation with other independent data is desirable. To this end we consider polyadenylated transcripts from the mitochondrion. Using RNA-seq data for six phylogentically diverse species, we determined the exact position of the polyadenylation, which marks the 3 end of the transcript.
For some taxa, e. g. , those with code table 4, the positions differ even in most cases. This problem remained unnoticed in MITOS because differences to reference annotations were analyzed only jointly for all Metazoa. Since the sample set is dominated by vertebrate mitogenomes, systematic, but comparably rare biases were not identified during the performance evaluation of MITOS. In most cases, the positions predicted by our new method are in agreement with RefSeq (77. 5% and 75. 8% of the start and stop positions, respectively). Still, the precision for vertebrates (code table  2 ) is clearly better compared to the results for non-vertebrates (code tables 4, 5, 9 and 13). Here, our new adaptive method profits from the large fraction of vertebrate mitogenomes in the training set (Supplementary Table  S2.
Positive (negative) values correspond to predictions outside (inside) of the annotation. Differences are shown on an inverse hyperbolic sine scale ( f (x) = arcsinh x)). For a comparison to RefSeq 63, see Supplementary Figure  S8. ture differ. In contrast, our new method annotates 6725 of the 54045 (12. 4%) PCGs in the RefSeq 89 data set (excluding the RefSeq 63 data) for which the gene boundaries differ by one or two base pairs from the corresponding reference annotation (compared to 40941 PCGs where the RefSeq annotation and the prediction of our new method match). Comparison with other reference data sets. In addition to a comparison with RefSeq, we compared our predictions to those of MitoZOA and MitoAnnotator. The data in Mito-ZOA contains manually curated annotations of metazoan mitogenomes.
DISCUSSION Problems of MITOS to annotate the correct start and stop codon positions have been reported repeatedly  (7, 18, 25). These inaccuracies are mostly caused by the rather unsophisticated strategy in the previous release of MITOS, which limits the search range to six amino acids around the initial prediction, and the strict use of the NCBI genetic code tables. Our empirical survey of annotated start, stop and internal codons shows, however, that the definition of the NCBI genetic code tables and their usage in RefSeq are not always consistent with the available annotations (which due to the size of the available sample of mitogenomes may be considered as a reasonable approximation to the genetic reality). In particular, the tables do not cover non-canonical start and incomplete stop codons.