dataesr/scientific-paragraphs-categorization huggingface dataset:
b: Caption from the 'Sequence View' tab. Statistics and DNA sequence of the contig are presented on this this screen. An embedded tool enables the user to search ORFs and get their translation. c: Caption from the 'JBrowse View' tab. This browser enables a quick overview of the contig and its corresponding counterparts in other databases. d: Caption from the 'Depth View' tab that contains expression data as the mean coverage and number of reads of the contig in all the libraries that can be chosen and displayed on a graph using a point-and-click interface. As an example, the contig CHOYP_41. 3. 3 is displayed in the Amu (in red) and Dgl (in blue) libraries. The structure of the corresponding protein has been added to the graph with the domain architecture. Discrepancies in mean depth coverage between library assembly variants may indicate splice variants at these positions (green gradient frame) easy to use, point-and-click interface.
Our searchable online database enables a high versatility tool both in terms of request criteria and retrieved attributes that can be easily downloaded and further analyzed using dedicated software packages. In parallel, specific contig characteristics such as transcript variants, polymorphism and expression can be browsed online in the graphic interface. Furthermore, the included browser allows a convenient view of the target contig counterparts in numerous other databases at a single glance (Fig. 4c ), without any particular The GigaTON contigs were translated using TransDecoder (http://transdecoder. github. io/) and resulted in 41445 proteins. They were aligned with the Ensembl protein from the oyster genome. The relative length of the 16787 alignments displaying at least 95 % identity between sets was plotted.
The vertical smear on the right reflects longer proteins in the GigaTON database. For example, the protein with coordinates (X = 100, Y = 50) has its full Ensembl sequence covering 50 % of its GigaTON counterpart need for the user to be skilled in bioinformatics. Therefore, the GigaTON database is most likely to be extensively accessed by an increasing scientific community studying evolutionary, environmental or aquaculture related aspects in C. gigas. Together with the current genome assembly, it constitutes one of its strong assets as an emerging model species.
Conclusions In this work, we assembled 114 RNA-seq libraries, mostly generated aside of the oyster genome project, into a publicly accessible database that includes userfriendly 'search, browse and retrieve' online tools, the GigaTON database. To date, the GigaTON database constitutes the most extensive transcriptomic resource in a marine invertebrate to our knowledge, and sets the new reference transcriptome in C. gigas, one of the most important shellfish aquaculture resources worldwide.
xlsx). Contig search and browser: the BioMart portal. [22]ants was stored in the database and the complete variant files containing 2,144,528 SNPs and 77,274 INDELs can be found in the download section of the website. The polymorphisms have been annotated using snpEff v4. 15[22]using the transdecoder open reading frame annotations made on the contigs. The complete annotated VCF files have been added to the download page of the website, one for SNPs (ALL_SNP. oysterAssem-bly1. 0_annot1. vcf)and another one for INDELs (ALL_INDEL. oysterAssembly1. 0_annot1. vcf). The results indicate that out of the 2,181,748 SNPs found, 33,98 % cause synonymous changes. The impact of SNPs and INDELS are detailed in Table1and.
Along this line, a recently developed MATLAB-based toolset called TIAM allows for integration of data such as area of attachment to the underlying substrate, cell polarity and fluorescence intensity from two fluorescence channels  (26). Finally, the part of the review dedicated to the actin cytoskeleton regulators studied in T lymphocytes reveals that the function of many proteins remains to be characterized in these cells. Screening approaches combined to high-content automated imaging would accelerate our discovery of novel molecules controlling specific aspects of actin cytoskeleton remodeling during T lymphocyte migration. This knowledge could then be exploited to identify targets for the tuning of T lymphocyte activation in the context of infection, auto-immunity and cancer.
Our prototype is developed in Ocaml for the computation, and in PHP and JavaScript for the web interface. Binaries of the application can be found on the same web page. As the main difference between ATL and ATL * comes from path formulae, we mainly focus our test on that point and use the list of tests proposed by Reynolds for CTL * in  [7]. This allows us to check that our application gives the same results in term of satisfiability and that our running times for these examples are satisfactory. Moreover, other tests using formulae with non trivial coalitions have been done. Nevertheless a serious benchmark has still to be done, which is a non trivial work, left for the future. Also, we plan to compare theoretically and experimentally our approach with the automata-decision based procedure of  [8].
Thus the obtained PCR products are the result of the amplification of one single molecule of cDNA. These PCR products are then sequenced directly without cloning. Nevertheless, these three methods are time intensive and tedious. Furthermore, the limited coverage that can be sampled (generally <100 clones and 100 K nucleotides per population) is far from the range of variants (105-109) typically found in virus samples. Newer high-throughput sequencing (HTS) technologies have expedited research in microbiology in general, including virology, due to their high throughput sequence data production  (Capobianchi et al. , 2013; Didelot et al. , 2012; Radford et al. , 2012; Shomron, 2013). The ultra-deep coverage afforded by HTS technologies provides obvious improvements to the characterization of RNA viruses.
This dataset also highlights the need for sequencing-error aware methods in order to reduce false positive calls such as the ones produced by naı ¨ve pileup. detection, we see that ViVan demonstrates the highest reproducibility (>91. 7%) among methods in variants with frequencies >0. 5%. Lower variant frequencies thresholds resulted in lower reproducibility across tools, with ViVan detecting the highest number of variants but at the cost of lower reproducibility (Fig. 2D ). We suspected that this decrease in reproducibility is due to coverage limitation and in fact represents false negative calls in extreme low frequency variants in the disagreeing replicates in oppose to false positive calls in the replicate with detected variant. We therefore reviewed the allele frequencies in the variants not reproduced and found that the majority of them (93/133) did demonstrate a higher frequency than expected in the disagreeing replicates (P < 0.
In this work, wild type chikungunya virus that normally cycles between mosquito and mammalian hosts, was adapted to either only mammalian host cells, or cycled between mammalian and mosquito cells  (Coffey and Vignuzzi, 2011). We Applied ViVan on three sequenced samples: the initial virus population (stock virus); following seven passages in HeLa cells (p7 HeLa); and after alternating passages between HeLa cells and mosquito C6/36 cells (p7 alt). In the original study using molecular cloning data  (Coffey and Vignuzzi, 2011) , mammalianhost adapted virus presented the highest amount of genetic diversity compared with the initial starting population, while mammalianmosquito passage had an intermediate diversity. Analysis with ViVan corroborated these observations. Setting a diversity threshold of >0.
4262) (one tailed t-test, P ¼ 0. 00004) and the starting stock virus (1. 3587) (one tailed t-test, P ¼ 0. 00018). Another powerful feature of the ViVan pipeline, is the ease with which it allows comparing and identifying minority variants in one group of samples that are common to that group and not found in a second group of samples. In analyzing this batch of samples, we assessed all the positions across the viral genome in which there was a significant variant in the alternating passage sample that had cycled through mosquito cells, which was not found in any of the other samples (that encountered only mammalian cells) above a rate of 0. 001. We detected 396 such variants (Table  2  and Supplementary Data 1). Interestingly, only nine variants had frequencies above 1% of the total population, and the variant exhibiting the highest rate (62%), was an aspargine to lysine change, position 112 of the non-structural protein 4 (nsp4).
Funding This study was supported in part by a fellowship from the  Edmond J. Safra Center for Bioinformatics at Tel-Aviv University , by a grant from the  France-Israel High Council for Research Scientific and Technological Cooperation  and from the  European Union Seventh Framework Programme  [FP7/2007-2013] under Grant Agreement n 278433-PREDEMICS. The  Shomron laboratory  is supported by the  Israel Cancer Research Fund (ICRF) ,  Research Career Development Award (RCDA) ;  Wolfson Family Charitable Fund; Earlier. org-Friends for an Earlier Breast Cancer Test; Claire and Amedee Maratier Institute for the Study of Blindness and Visual Disorders; I-CORE Program of the Planning and Budgeting Committee, The Israel Science Foundation  [ 41/11 ]; The  Israeli Ministry of Defense, Office of Assistant Minister of Defense for Chemical, Biological, Radiological and Nuclear (CBRN) Defense ;  Foundation Fighting Blindness; Saban Family Foundation ,  Melanoma Research Alliance; Binational Science Foundation (BSF) ;  ICRF Acceleration Grant ;  Israel Cancer Association (ICA) ; Donation from the  Kateznik K.
Association Holocaust ;  Margot Stoltz Foundation  through the  Faculty of Medicine grants of Tel-Aviv University ; The Varda and  Boaz Dotan Research Center in Hemato-Oncology , Idea Grant. M. Vignuzzi  and  H. Blanc  are supported in part by the  ERC Starting  [242719]. A. B. was supported by the  ANR-09-JCJC-0118. O. Isakov  and  D. Golan  are supported in part by the  Colton Family Foundation.
Nowadays, genomic and proteomic studies produce vast amounts of data. To get the biological meaning of these data and to generate testable new hypothesis, scientists must use several tools often not designed for ruminant studies. Here we present ProteINSIDE: an online tool to analyse lists of protein or gene identifiers from well-annotated species (human, rat, and mouse) and ruminants (cow, sheep, and goat). The aims of ProteINSIDE modules are to gather biological information stores in well-updated public databases, to proceed to annotations according to the Gene Ontology consortium, to predict potentially secreted proteins, and to search for proteins interactions. ProteINSIDE provides results from several software and databases in a single query. From a list of identifiers, ProteINSIDE uses orthologs or homologs to extend analyses and biological information retrieval.
INTRODUCTION Given the increasing amount of genomic and proteomic data produced even in ruminants  [1, 2, 3] , there is a challenge for the bioinformatic data processing, which has not yet been completely solved  [4]. Such bioinformatic data processing has to proceed to data gathering and database searching in order to produce a functional interpretation of large datasets. For this purpose, workflows integrating several bioinformatics analyses are now available  [5] [6] [7] [8]  and were developed to mine dataset from specific species (BioMyn  [9]  for human, DroPNet  [7]  for Drosophila, TAIR  [10]  for Arabidopsis thaliana, EcoCyc  [11]  for Escherichia coli …) or to identify candidate genes related to diseases as ToppGene  [12]  or NetPath  [13]. The few workflows currently used for the bioinformatics data processing of ruminant datasets are multispecies.
Consequently, the data source of the results proposed is not available because of the privacy of databases (as the licensed software Pathway Studio  [14]  or Ingenuity Pathway Analysis (www. ingenuity. com; Redwood City, CA, USA). An alternative for scientists working with ruminant datasets is to use dedicated and complementary bioinformatics tools implemented as web services. These tools are dedicated to one type of analysis, as for example the annotation according to the Gene Ontology (GO)  [15] , the prediction of signal peptide to identify putative secreted proteins  [16] , or the molecular interactions identification  [17]  and visualization as networks  [18, 19]. Whatever the analysis carried out, the first step is to connect a protein name to a unique identifier (ID). Conversely to gene names that have been standardized, protein names or IDs can differ between databases or tools, especially for ruminant data that remains to be largely curated in most of databases.
Thus, the use of several bioinformatics tools to mine ruminant datasets leads to a substantial loss of information and time. A strategy to perform a systematic and integrative analysis of biological protein information from ruminant datasets is to develop an online workflow that integrates several analysis steps in one package and from a unique ID. Thus, we propose ProteINSIDE  [20] , a web service dedicated to a systematic and integrative analysis of protein's biological information from ruminant datasets. Unlike human, mouse or rat, ruminant species are less annotated and protein sequences or information are not always curated. Often, scientists working with ruminant use orthologs or homologs with the aim to increase the meaningful biological contexts for proteins thanks to knowledge available in well-annotated species.
ProteINSIDE's features ProteINSIDE is an online workflow with an interface devoted to accessible and fully customisable analyses from lists of protein or gene IDs. Registered users have access to an analyses manager to run and save analysis, and visualise the results. Unregistered users can use ProteINSIDE, but there is no analyses manager and analyses are deleted each month. ProteINSIDE is divided into three parts: the workflow, the database, and the web interface (Figure  1 ). The web interface, designed to easily use ProteINSIDE, helps the user to create the analyses, to have access to the results thanks to a balance between technical functionalities and visual elements, and to inform about updates (Figure  1 ). ProteINSIDE proposed two types of analysis to be launched: the basic analysis (automatic settings) and the custom analysis (user's settings).
There is also a pre-set analysis for registered users only who want to make a new analysis with settings of a previous analysis. The basic analysis performs a:  Functional annotation using GO terms by querying QuickGO database  [21]  without electronic annotation.  Prediction of secreted proteins using SignalP  [16]  and TargetP  [22]  software. We improve the prediction by giving GO terms related to the cellular location of the protein and the processes of secretion.  Search of proteins interactions curated and listed in IntAct  [23] , UniProt  [24] , and BioGrid  [25]  databases. The custom analysis performs programs and their options that have to be selected by the user in order to:  Perform a functional annotation using GO terms from QuickGO, with the options to select also electronic annotations (predicted and scripted annotations), and to generate a GOTree view of linked GO terms (pathways of functional annotation).
 Predict secreted proteins with the option to increase the software's sensitivity of prediction and by this way to increase the number of predicted proteins, however with a higher number of false positive results.  Search for protein interaction within (core network) and outside (extended network) the uploaded dataset. Options propose to select interactions stored within 1 to 31 databases gathered by the PSICQUIC website  [17]. User can select the databases depending on the type of interactions (PPi, Nucleic acid-Protein interaction (NPi), and Smallmolecule-Protein interaction (SPi)) and the data (curated, predicted, curated according to the IMEx project  [26]  or the MIMIx curation  [27] ). PSICQUIC service or some databases could be offline, that's why the status of each website is indicated in the table.
To submit an analysis, users either directly paste a list of IDs or upload a file of IDs. Inputs can be protein (e. g. , ADIPO_HUMAN) or gene (e. g. , ADIPO or gi|62022275) ID, or protein accession numbers (e. g. , Q15848) from six species: cow, human, rat, mouse, sheep, and goat (Figure  2 ). A new analysis is run directly or is placed on a waiting list if the workflow is overloaded. Uploaded data and results remain confidential. In addition to the web interface, ProteINSIDE is composed of a database and a workflow. The database (invisible to any user) collects and stores the information required for the proper functioning of ProteINSIDE. It stores analysis settings and results to reduce server load (Figure  1 ). The database stores also a gathering of biological information from the NCBI  [28]  (Gene, Protein, and HomoloGene for known orthologous proteins between the 6 species) and UniProt  [24]  databases (for the ID Mapping module), and QuickGO  [21]  and AmiGO  [29]  (for the GO annotation module).
A script updates automatically and monthly the database by extracting IDs, homologs, biological function, FASTA sequence, and other information from the latest releases of these databases. The workflow uses uploaded data. It is a combination of Perl and R scripts to query databases, recover protein data, perform calculations and run algorithms for signal peptide predictions and network visualisation (Figure  1 ). The workflow is invisible to any user. The workflow is composed of 4 parts: the "ID Mapping", the search of annotations according to GO, the prediction of secreted proteins, and the search of protein-protein interactions (PPi). The workflow always starts by the ID Mapping program which searches the biological information available for each protein or gene of the input within the ProteINSIDE database.
Gathered biological information is required to run the 3 other modules of the workflow: "Gene Ontology", "Secreted Proteins", and "Protein Interaction" (described in the "Results" section of this article). The GO program queries QuickGO and ProteINSIDE's databases to perform the functional annotation. The GO program analyses over-and under-represented terms to highlight the most relevant GO terms related to the input. These statistical calculations are made with an R script performing a Fisher´s exact test (functional enrichment first proposed by FatiGO  [30] ) and the resulting p-value is corrected or not by the Benjamini & Hochberg (BH) test  [31]. The prediction of secreted proteins is made using a local version of SignalP (version 4. 1) that looks for a signal peptide on amino acid sequence of each protein  [16]  (cutoff of 0.
45 and 0. 34 for SignalP prediction, in the basic and custom analysis with the sensitive option selected, respectively; for more information see the tutorials of SignalP a. To ascertain that proteins are secreted, ProteINSIDE uses TargetP  [22]  (version 1. 1) to predict the cellular location of each protein. ProteINSIDE uses a pre-set cutoff option to get a significant prediction (higher than 95%) according to TargetP instructions b. Protein interactions are searched using PSICQUIC service  [17]  and statistical calculations are made with an R script and the package "tnet"  [32]. ProteINSIDE performs sequence alignment thanks to a local version of NCBI BlastP  [33]  against UniProt/Swissprot databases  [24, 34]. Lastly and as an additional valuable tool, ProteINSIDE lists in one table all known IDs for an input of proteins or genes thanks to the ProteCONVERT module.
This list is the result of a search and of a gathering of IDs thank to the ProteINSIDE biological database. Only registered users have access to the ProteCONVERT module. First, enter a name for the analysis and select the species of study. There are two ways to submit a protein or gene list; you can use an input file or directly paste your IDs. The input file must be less than 250 kb and the file format must be specified. There is also a "Sample" button that loads parameters for an example analysis. Once everything is filled, click on the button "Run the job" to submit.
Implementation ProteINSIDE is freely available online at www. proteinside. org and doesn't require an installation on a computer. ProteINSIDE is completely adapted for any internet browser and tablet. We recommend multiprocessors computer with at least 2 GB of ram to get better performances for huge network visualization and filtering. The web interface is programmed in PHP, HTML, and JavaScript. The workflow has been completely programmed in Perl (version 5. 10. 1; CPAN modules (Comprehensive Perl Archive Network) and BioPerl  [35] ) and R scripts (version 3. 0. 1). The database was made in MySQL (version 5. 5) (Figure  1 ).
Sample dataset We have created a dataset to assess ProteINSIDE performances. This dataset is composed of the UniProt accession numbers of 133 proteins (Table  1 ): 34 proteins related to the glycolysis cycle, 11 proteins from the respiratory chain, 5 proteins from the tricarboxylic acid cycle (TCA), 79 hormones or secreted proteins, and proteins with very specific functions unrelated to the others. We also included a duplicated ID among proteins of the glycolysis to verify its recognition by ProteINSIDE. ProteINSIDE is able to detect duplicate protein even if the IDs are different: a Gene Name, a UniProt accession number, and a Gene Identifier related to a same protein will be taken into account as a single protein. We have created this dataset on bovine species, but the number of annotations and PPi weren't sufficient for a complete overview of the functionalities of ProteINSIDE.
Setting up a Basic Analysis: a standard overview of a dataset ProteINSIDE performs a basic analysis (in which settings are locked and the workflow provides GO terms, list of putative secreted proteins, and PPi data from IntAct  [36] , UniProt  [24] , and BioGrid  [37]  databases). A basic analysis gives a complete overview of a dataset. To set up a basic analysis, user has to follow these steps (Figure  2 ):  Click on "Basic Analysis" menu on the homepage of ProteINSIDE  Fill in "the job name" box  Select the species of study (the same species as the uploaded IDs)  Upload an input file or directly paste IDs  Click on the "Run the job" button to submit a new analysis The analysis status is indicated by the colour of a button: red for "analysis on the waiting list", yellow for "the analysis is running" and green "analysis done".
The blue globe is the link to access to the online results:  Click on the blue globe button to view the results (or use the trash to delete them)  Visualise the results summary produced by the four modules of analysis on the first default page (entitled "Results Summary", Figure  3 )  Navigate between the four module's results pages by clicking on the module's name on the toolbar menu. For our sample dataset, the "Results Summary" page reported that all 133 proteins were recognized by ProteINSIDE and the protein in duplicate was identified and excluded from the analysis (Figure  3 ). Thus, 132 proteins were submitted to the four modules of analysis.
Analyses and data The "ID Mapping's" module aimed to retrieve and gather basic biological knowledge, results are directly viewed on the "ID Resume" web page of ProteINSIDE. This module compares each submitted IDs to the database of ProteINSIDE to ascertain a match with genes or proteins from human, rat, mouse, cow, sheep or goat species. The local biological database of ProteINSIDE is a combination of NCBI Gene/Protein, NCBI HomoloGene  [28] , and UniProt  [24]  databases. These databases were chosen because data are easily extractable, curated and daily updated. For each uploaded ID, ProteINSIDE obtains and summarises as a downloadable table (Figure  4 ): gene or protein ID, gene or protein name, a summary of protein function, gene chromosomal location, and information on tissue expression and cellular location.