dataesr/scientific-paragraphs-categorization huggingface dataset:
Dedicated Genome Browsers complement this information by providing a visualization of genetic elements in their chromosomal context. One of the originalities of the system is to go beyond the classical hierarchical textual representation of anatomical entities found in most model organism databases and to provide a description of the shapes, neighborhood and area of cell contacts up to the early gastrula stage. The system initially focused on the embryonic development of Ciona intestinalis. Complete manually-curated data from 175 published articles were formalized into individual article pages (e. g. http://www. aniseed. cnrs. fr/v3/view-article. php?id = 219), which presented in--a single layout--all extracted data, irrespective of the journal in which the article was initially published.
Overall, ANISEED pursues two aims. First, it provides a service to the worldwide community of developmental biologists working on ascidians, who want to plan their next bench experiments in light of existing information. Biologists from other fields may also use it to compare results obtained in their favorite model organism to the ascidian situation. Paramount to fulfil this first aim is the simplicity and ergonomy of the user interfaces and the quality of the D810 Nucleic Acids Research, 2016, Vol. 44, Database issue links to other taxa, via gene orthology or tissue homology relationships. A second--much more ambitious--aim is to integrate and formalize available molecular and anatomical data, thereby contributing to making the embryo 'computable'  (49). Here, the challenge is to use semantic web technology to structure the data set to make it machineunderstandable.
Improved database architecture The ANISEED 2010 Developmental Browser used a custom database schema, which did not make full use of ontologies, and made extensions to new data types difficult. We thus refactored ANISEED, using the highly modular and ontology-based Chado relational database schema, used by most major model organism databases  (54). The choice of Chado was also motivated by the extensive set of companion tools developed by the GMOD consortium, including genome browsers  (55, 56) , genome annotation editors  (57)  and workflow and analysis frameworks  (58). This switch to Chado made it possible to extend the use of general ontologies. For example, ANISEED 2015 now uses qualifiers from the PATO ontology to describe morphological phenotypes (http://www. aniseed. cnrs. fr/aniseed/ experiment/show morphogen?experiment id = 31782).
Embryological perturbations are defined using a set of molecular tools generally specific for a species and are described by a sequence and a Sequence Ontology (  59 ) term (e. g. morpholino oligonucleotides, TALE nucleases, CRISPR/Cas9 constructs). In addition, active chemical compounds (e. g. pharmacological inhibitors of signaling pathways) that cannot be described using sequence information or associated to a specific species are described using the Chemical Entities of Biological Interest (ChEBI) ontology  (60). The second step of the experiment describes, using the Evidence Ontology (ECO)  (61) , the molecular analyses that were carried out on control or experimental embryos (e. g. in situ hybridization, morphological phenotype characterization). This data structure allows the extraction from the database of the specific phenotypic effect of a precisely described experimental perturbation.
In the future, we will extend this strategy to associate several analyses carried out on the same biomaterial (e. g. double in situ hybridization; combined ISH, RNAseq or epigenetic profiling). The second major modification we introduced aimed at facilitating both comparative analyses between species, and the independent update of the database for each species. For this, the ANISEED Developmental Browser groups in a single database multiple copies of the same basic database schema. Each replicate schema hosts all attributes solely associated to a given species including genome, genes, transcripts, proteins, regulatory sequences, morpholinos, anatomical territory and gene expression patterns. An additional schema contains all data common to several species including articles, pharmacological reagents, and general controlled vocabulary terms such as Interpro domains, Gene Ontology, ECO, PATO, ChEBI.
Design of new user and biocuration graphic interfaces In parallel to the above changes in the schema and database controller, the user, biocuration and back end administration interfaces were entirely redesigned with a new graphic charter, to improve their ergonomy. The landing page shows tips and news, as well as six entry tiles ("Blast", "Genome Browser", "Genome annotations", "Anatomy", "Gene expression and function" and "Literature"). Each tile gives access to a single intuitive search page (Figure  2 ), which centralizes all the information needed for most searches. The use of autocomplete functions in search boxes facilitates searches while ensuring the correct spelling of controlled vocabulary terms such as anatomical entities. Some of the proposed complex queries are computationally challenging, in particular searches for experiments in which a gene is expressed in a list of territories, but expression is excluded from others.
Improvement and extension of Ciona intestinalis data The description of the Ciona intestinalis developmental programme in ANISEED 2010 was based on the original JGI assembly of the genome of a C. intestinalis type A individual (  8 ) and a set of gene models built by integrating transcript models of various sources  (48). ANISEED 2015 uses as reference the improved, more contiguous, KH genome assembly and a set of 15 284 manually-curated gene models  (62). Genetic elements can first be explored in their genomic context via the ANISEED genome browser (Figure  3 ). Besides gene and transcript models, this tool provides tracks for miRNAs and other non-coding RNAs, for operons (15% of Ciona intestinalis genes are grouped into operons) for ESTs and for RNA-seq at 6 embryonic developmental stages (stranded RNA-seq: egg, 64-cell, early gastrula, mid gastrula, mid neurula, mid tailbud and hatched larvae) and in adults  (63).
The annotation process first identifies conserved domains in each protein using InterproScan 5  (68). It then computes multi-species ortholog groups between all available sequenced ascidian genomes, three vertebrates (Man, mouse and chicken) and two deeper-branching deuterostomes (the American sea urchin S. purpuratus and The search interface for expression patterns. Note the Anisearch field at the top of the screen, which searches the whole database for keywords, genes, anatomical entities, etc. The expression search interface permits to look for genes expressed in a set of terriories, but whose expression is excluded from another set. Searches can be restricted to a gene, or an article. Gene expression patterns in manipulated embryos can be excluded from the results. the Asian lancelet Branchiostoma belcheri).
In addition to the annotation of genetic elements, ANISEED provides a detailed ontology-based description of the anatomy of developing embryos and postmetamorphic animals. In ANISEED 2010, the Ciona in- testinalis developmental anatomy was described in 24 distinct ontologies, one for each developmental stage defined at the time. This fractionation of the anatomical description into stage-specific ontologies made it difficult to have an overview of the developmental program and to relate equivalent terms (e. g. notochord) present at successive developmental stages, and represented by different database IDs. In addition, the stages defined at the time imperfectly matched the reference Ciona intestinalis Hotta developmental table  (73). The Ciona intestinalis developmental anatomy is represented in ANISEED 2015 by a single ontology, based on the Hotta developmental table, and which groups the description of developmental stages and anatomical entities.
Again, the structure of this part of the ontology is similar to the Zebrafish Anatomy (ZFA) Ontology  (74)  As in ANISEED 2010, the description provided by the anatomical ontology is complemented up to the early gastrula stage by a description of the spatial cell neighborhood relationships and a measure of the area of contact between neighbor cells, as these elements have been shown to be crucial predictors of early cell inductions in ascidians  (76). Using the refined genome assembly, genome models and anatomical ontology, we migrated all expression, functional perturbations and cis-regulatory sequence data from ANISEED 2010. Spatio temporal patterns of gene or cisregulatory sequence activity are described by associating a cDNA probe or constructs to a list of anatomical entities. In addition, manual curation of 42 additional articles led to the insertion of 2,431 new gene expression patterns, 426 cis-regulatory sequences, 167 cis-regulatory sequence activity patterns and 150 molecular tools.
In total, ANISEED 2015 hosts 27,707 Ciona intestinalis gene expression profiles by in situ hybridisation for around 4500 genes. These data were reported in 217 manually curated articles, include 1294 gene expression profiles in experimentallymanipulated conditions using 769 molecular tools, and 944 staining patterns describing the spatio-temporal activity of 875 cis-regulatory sequences in transgenic experiments. Spatio-temporal gene expression, cis-regulatory sequence activity and a limited number of immunohistochemistry patterns can be explored via the "Gene expression" menu of the Developmental Browser (http://www. aniseed. cnrs. fr/ aniseed/experiment/find insitu).
Extension to additional ascidian species ANISEED 2010 focused on the description of Ciona intestinalis development, with some additional data provided for the stolidobranchian Halocynthia roretzi based on gene models obtained by the de novo assembly of ESTs, as the genome of this species had not been sequenced at the time. Although the Ciona savignyi genome had been published (77), this species was not supported. Over the past 5 years, annotated draft genome assemblies for 7 additional solitary species have been generated (Figure  4 , Table  1 ). The genomes of 3 species of the genus Molgula (Stolidobranchia) were reported  (40) , while those of two Phallusia (Phlebobranchia) and two Halocynthia (Stolidobranchia) species are being prepared for publication (Dantec et al. unpublished). In addition to these solitary species, an annotated genome draft for the stolidobranchian colonial species, Botryllus schlosseri, was recently published  (44).
ANISEED 2015 provides a Blast-searchable genome browser for each of these species, which includes for Halocynthia roretzi and Phallusia mammillata tracks for developmental RNA-seq and intra-genus patterns of sequence conservation. In addition, a Developmental Browser with detailed functional genome annotation and anatomical ontologies is provided for each of the six species with highest quality genome assemblies (Ciona savignyi, Phallusia mammillata, Halocynthia roretzi, Halocynthia aurantium, Molgula oculata, Botryllus schlosseri). Intra-ascidian orthology relationships allow the exploration of evolutionary conservation of sequences and expression patterns across ascidians. Table  1  presents for each species the genome assemblies and the genomic data and functional annotations presented by ANISEED 2015.
Computing ascidian development: internal reasoning engines and data accessibility ANISEED's new architecture and data structure is a significant step toward the aim of making ascidian data computable, as the quasi-exclusive use of ontologies to describe data opens the way to semantic reasoning. To allow scientists to go beyond the current Graphical User Interface (GUI) and to reason on the data set, the download section of the system contains parsable formatted text or XML files for most data, including expression data available under the MISFISHIE format  (78). To further improve data accessibility, we will provide the community with ways to dynamically extract targeted subsets of the data. We will first implement a Biomart server to retrieve complex data sets without the need to write complex SQL queries.
NISEED, a generic system applicable to other taxa With the advent of cheap genome sequencing, an increasing number of non-vertebrate species, in particular marine invertebrates, are accessing the status of emerging model organism in the field of developmental biology. The communities working on these species are however often small, precluding the development of specific computational infrastructures necessary to organize molecular embryological data. ANISEED is the ascidian implementation of a generic system, NISEED, which can be easily adapted to any taxon for which a suitable anatomical ontology has been developed. The use of a software architectural pattern and cascading style sheets (CSS) structures the code thereby facil-itating the customization of existing interfaces and the creation of new ones.
The current biocuration pipeline consists in entering data by authors or dedicated biological annotators, followed by data verification by the system's biocurator. Entering data from the literature is much simplified when authors follow the tunicate nomenclature guidelines  (72)  and the Article Minimum Information Standard for tunicates  (48) , indicate in materials and methods the gene model IDs and the precise cDNA clones used to perform in situ hybridization experiments, and follow the new nomenclature for transgenic constructs  (72). Priority will continue to be given to articles reporting the effect of gene loss or gain of function on the developmental programme, taking into account the willingness of authors to contribute to the insertion of their work. Several laboratories are now actively entering data from their publications, a process that will continue to be rewarded by an authorship in ANISEED update papers.
The second main challenge will be to integrate detailed molecular descriptions of the developmental programme with upcoming live imaging data sets. Some ascidians have either translucent (Ciona) or transparent (Phallusia, Ascidiella) embryos that are ideally suited to whole embryo imaging approaches using confocal or light-sheet microscopy. Such approaches coupled to semi-automated or automated segmentation of cell membranes have already been applied to cleavage and gastrula embryos  (76, 81) , to tailbud embryos  (82)  and to individual tissues  (83). ANISEED 2015 already represents cell neighborhood and areas of contact up to the early gastrula stage. The description of later stages with cellular resolution will solely require the adaptation of the current anatomical ontologies to reach cellular resolution beyond the gastrula stages.
ACKNOWLEDGEMENTS We thank  Marlène Guillemette  ( CRBM ) for her work on the initial version of a curator,  Isabelle Guiguon  ( CRBM ) for her work on the annotation pipeline,  Edwin Jacox  ( CRBM ) for sharing scripts,  Philippe Dru  (LBDV),  Julie Poulain  (Genoscope) and  Corinne Da Silva (Genoscope) for Phallusia mammillata ESTs ,  Gaku Kumano  and  Hidehiko Hashimoto  ( Osaka University ) for their contribution to the Halocynthia section. We are grateful for IT support by  Patrice Langlois  and  Marc Romero  ( CRBM/IGMM/CPBS , Montpellier).
FUNDING Fondation pour la Recherche Médicale (FRM)  [ ING20121226356 ,  ING20100118205 ] to  D. D, M. B , and P. L. ;  Agence Nationale de la Recherche (ANR)  [ TED ,  ANR-13-BSV2-0011-01 ] to P. L. and  E. D. ;  Centre National de la Recherche Scientifique  [PIME grant] to P. L. ;  NIH/NIGMS   GM100466  (to A. D. G. ),  NIH/NIGMS  grant  R01GM096032  and  NIH/NHLBI  grant  R01HL108643  (C. R. ); a  Long Term EMBO fellowship  (C. R. ); subcontract from the TEFOR infrastructure (tefor. net/) to P. L. Funding for open access charge:  Centre National de la Recherche Scientifique.
Business Process. The business process layer refers to the organisation structure represented by process, activity and actors. Functional. This layer organises the functionalities from business services in nested blocks called Zone, District and Plot  (Longépé, 2003). Application. The application layer is used to model software components and services with their interrelationships. Technical. The infrastructure layer represents all resources for the storage, communication and execution; for instance database, servers, networks. The dashed lines between the layers represent an "ideal" alignment where the low-level IT concepts are aligned to high-level business concepts. It is qualified as "ideal" because alignment is not simply the matter of abstraction (e. g. traceability). Moreover, business concepts are not fully comparable with IT concepts.
META-MODELS FOR EA Defining ontologies is often cumbersome. The three meta-models we define are a compromise between expressivity, genericity and simplicity. These qualities enable one to apply the method to a broad diversity of applications but also to support large and complex IT systems. We define three core meta-models and interrelationships (Section 5) in order to achieve alignment. The business and functional models must be convenient to any enterprise organisation. The application model must be generic to capture the software concepts of various programming styles. A Generic Business Process Meta-model. A Business Process is a collection of activities that take one or more inputs and create output values  (Hammer and Champy, 2006)  The three meta-models for EA are implemented using the Eclipse Modeling Framework (EMF) 1  in order to provide method and user-friendly tools required by for EA stakeholders and by users from the business world or from IT world.
while a Platform Independent Model (PIM) is a formal specification of the structure and the function of a system that abstracts away technical details. S1. 1 -Source Code Reverse Engineering. Reverse engineering consists in analyzing the source code files of a set of programs in order to get a representation of the code in a model (PSM). For this purpose, reverse engineering tools include a meta-model for each target programming language. In EA projects, the environment is heterogeneous; the reverse engineering tool should be able to cope with several languages like Java, C++, Smalltalk, Cobol, etc. Ex: Java Reverse Engineering with Modisco The program from MIc case study is written in Java. The reverse engineering has been executed using the Modisco 5 tool including a Java meta-model. This tool discovers the Java model with the assistance of the Java development tools (JDT) which parses the source code and computes an Abstract Syntax Tree (AST).
S1. 2 -Intermediate Transformation. This step targets to produce an instance of the Knowledge Discovery Meta-model (KDM) 6  intermediate model (PIM). KDM includes many layers to save different aspects of common programming languages and more generally it defines common concepts for software assets and their operational environments. We borrow only a part of KDM, mainly the code package which contains all the characteristics of the current programming language. Ex: Java Model to KDM Transformation Modisco provides this transformation, but we faced the problem of scaling the size of the model. Indeed Modisco is not dedicated to the EA alignment problem and too much information was stored, so we had to write our own transformation using the Mia-transformation tool.
S1. 3 -Transformation to a High-level Abstraction. The issue here is to transform the KDM model into our App model. App preserves only the architectural information to be aligned later with the business. It is necessary to build an algorithm or a set of rules to detect the different concepts from the application meta-model. This algorithm is more or less complex depending on the architecture used in the source code. Making such an abstraction is based on the inter-relationships described in Section 5. Ex: The KDM to Application Transformation We wrote a Mia-transformation to obtain an application model containing the links between components, interfaces, services, functions and data objects. The source code of the MIc uses a specific file naming based on name prefixing. This naming policy identifies the software architecture role of different Java interfaces and classes.
Interpolation of B. napus CDS models not already represented by orthologues resulted in 52,790 and 63,308 ordered CDS models in the A and C pan-transcriptomes, an increase of 13,676 overall. Comparison of the organisation of this resource with publicly available genome sequences for B. napus showed excellent consistency for the B. napus Darmor-bzh resource, but more breakdown of collinearity for the B. napus ZS11 resource. CDS datasets comprising the pan-transcriptomes.
The predicted genes with CDS models shorter than 150 bp were filtered out. We further performed homology based gene prediction by aligning A. thaliana, Carica papaya, Populus trichocarpa, Vitis vinifera and Oryza sativa protein sequences to the B. rapa genome. TBLASTN was used to do fast alignment (threshold e-value 1E À 5), then Genewise was used to do precise alignment. Additionally, we assembled the Brassica ESTs downloaded from NCBI using PASA and aligned them to B. rapa genome by BLAT. Considering that the fragmented exons in EST data might lead to false results, we filtered out alignments with gaps (introns) that span over 10 kb in length. We then ran GLEAN to merge the gene sets generated from de novo and homology-based predictions, using mRNA-Seq data as the supporting evidence. Finally, the B.
rapa gene set was aligned to the TE protein database of Repbase, those hits with e-value 41E À 5 and coverageZ50% were filtered out. The remaining gene models were reported as Brassica gene set Version 2. 0 (Additional file 1). These CDS models were then used in sequence similarity searches using BLAST to identify the highest-scoring significant hit (threshold e-value 1E À30) for each CDS model in both the version 2 B. rapa Chiifu genome sequence scaffolds and the A genome pseudomolecules reported previously  [6] , based on the version 1 B. rapa Chiifu genome sequence  [7]  that had been reordered relative to the B. napus genome via high density transcriptome SNP linkage mapping  [1]. This enabled the identification of chimeric scaffolds in the version 2 assembly that could be split (Additional file 2) and re-organised (Additional file 3) to form pseudomolecules representative of the organisation of the Brassica A genome.
Data format The data accompanying this article are provided as text files (for B. rapa CDS models and R scripts) and MS Excel spreadsheets providing CDS and scaffold identifiers and sequence similarity coordinates. Specifications Table Subject area Biology More specific Plant genome organisation subject area Type of data CDS gene model Experimental factors n/a Experimental features the WGS section for contigs (accession numbers CCCW010000001 to CCCW010044187) and the CON section for scaffolds, chromosomes, and annotation (accession numbers LK031787 to LK052685). The B. napus ZS11 assembly is available at http://www. ncbi. nlm. nih. gov/Traces/wgs/?val=JMKK01#. The B. oleracea assembly is available via Sequence Read Archive accession number PRJNA158027. The B. rapa version 2 assembly is in the process of publication and in the meantime is available from Xiaowu Wang (wangxiaowu@caas.
rapa genome assembly in order to represent the organisation of the A genome in B. napus. Integration and interpolation of gene models called only in a B. napus genome sequence was undertaken, resulting in the establishment of a pan-transcriptome resource for the Brassica A and C genomes. Collinearity analysis with public B. napus genome sequences was undertaken, based on BLAST similarity hits of CDS models, to compare the order of genes in the pan-transcriptome resource with that of their orthologues in two published B. napus genome sequences. Data source location SRA, NCBI, ENA Data accessibility All genome sequence datasets were provided for analysis prior to publications, but are now available:The B. napus Darmor-bzh assembly is available at ENA (European Nucleotide Archive), in.