dataesr/scientific-paragraphs-categorization huggingface dataset:
The 'handle databases' button allows the user to view and edit molecule databases from the window that pops up. The format that is supported is based on the one used by Ligbuilder to manage fragment and molecular databases. Every database is a folder consisting of the included molecule files in. mol2 format and an INDEX text file which lists the molecular parameters alongside extra information and properties bound to each molecular entry. There is also a 'preferences' button through which the user can manage some of the DrugOn settings, like module path settings and the system's local folder management. The software paths and installation sites are user defined at the DrugOn automated setup by default. Another frame in the preferences window contains the default parameter files, where the user can set the default parameter file that will be used for any given experiments.
Moreover, the user is provided with the option to create/edit his own configuration file with the parameters that are needed for each experiment. A major issue with most major drug design/pharmacophore suites is the installation process on UNIX/Linux based systems, as the command line is not very popular with the majority of users. This is especially true for people who only use graphically enabled operating systems and avoid using applications or software package that runs on Linux because of its difficulty when the graphical interface is not an option. The DrugOn is a pipelined software package based on Linux-Ubuntu systems and has been specifically designed to provide the user with a seamless setup via a graphical interface that simplifies the installation.
VALIDATION DrugOn is not the first platform designed for pharmacophore modeling. A similar pipeline approach for a complete drug design toolkit (not pharmacophore) has been published by  Vlachakis et al. (2013a)  with the Drugster toolkit. Moreover, a series of different approaches have been made in the past few years which resulted in commercially available suits like Moe (2010), or some free available suits like pharmer  (Koes & Camacho, 2011 ) and open3dqsar  (Tosco & Balle, 2011) ; two efficient software packages. Also, Schrödinger has developed PHASE which is distributed as a commercial module of the Maestro suite  (Dixon et al. , 2006; Dixon, Smondyrev & Rao, 2006). In an effort to quantitatively and qualitatively evaluate the performance of DrugOn, we used two different and quite diverge use cases.
The first use case is the crystal structure of the chimeric protein of 5-HT1B-BRIL, pdb entry: 4IAR (Fig. 4 ) and the second case is the pharmacophore design for PARN (Fig. 5 )  (Vlachakis et al. , 2012). As a benchmark control we compared DrugOn to the rather expensive and commercially available package MOE and its build-in modules (BREED) and then to the Schrödinger suite and its built-in pharmacophore module PHASE. The results have been summarized in Figs. 4  and 5. It is clear that in both cases the DrugOn suite performed as well as the more expensive rival commercial suites. The number, structure and 3D alignment of candidate compounds and 3D pharmacophore model design as it was produced by DrugOn is almost identical to that of MOE and similar to PHASE. As far as accuracy and reliability goes, we are now confident that DrugOn reported a set of pharamcophore models that has been evaluated and confirmed by in vitro assays, as the predicted poly-A-DNP was found active in the sub-milimolar range  (Vlachakis et al.
Background: The advent and democratization of next generation sequencing and genotyping technologies lead to a huge amount of data for the characterization of population genetic diversity in model and non model-species. However, efficient storage, management, cross-analyzing and exploration of such dense genotyping datasets remain challenging. This is particularly true for the bovine species where many SNP datasets have been generated in various cattle populations with different genotyping tools. Description: We developed WIDDE, a Web-Interfaced Next Generation Database that stands as a generic tool applicable to a wide range of species and marker types (http://widde. toulouse. inra. fr). As a first illustration, we hereby describe its first version dedicated to cattle biodiversity, which includes a large and evolving cattle genotyping dataset for over 750,000 SNPs available on 129 (89 public) different cattle populations representative of the world-wide bovine genetic diversity and on 7 outgroup bovid species.
Background Next Generation Sequencing (NGS) and genotyping (NGG) technologies have revolutionized variant genotyping and now allow cost-effective and genome-wide characterization of genetic diversity in a growing number of species including non-model species  [1]. In livestock species, based on low to high density SNP chips, a growing amount of genomic information on several dozens of local breeds have been generated as exemplified by cattle population studies  [2] [3] [4] [5] [6] [7]  and studies in other species  [8] [9] [10]. However, efficient storage of the huge resulting datasets for management, sharing and routine exploration purposes remains challenging. We thus developed WIDDE, a Web accessible NoSQL Database, dedicated to the storage and management of dense genotyping datasets (e.
g. up to hundreds of thousands of markers genotyped on thousands of individuals), coupled with various user friendly tools for (i) data selection, (ii) data exploration, (iii) export into various popular formats and (iv) population assignment. Via a web interface managing access to public (freely accessible) and private (accessible via login and password) data, users can therefore select (on a population and/or marker location basis) data subsets, perform basic data quality checking and standard population genetic analyses via a test for Hardy-Weinberg equilibrium and principal component analysis (PCA), and export the resulting datasets into various popular formats. Users can also jointly analyze their own genotyping data with WIDDE data subsets, in order to explore genetic proximity between populations by allele sharing distance (ASD) calculation, PCA and supervised clustering, to perform an estimation of ancestry composition of the samples and population assignment.
Database architecture and implementation details The WIDDE architecture diagram is shown on Fig. 1. From a technical point of view, we used a NoSQL database engine to store and efficiently query millions of genotypes. MongoDB (http://www. mongodb. org/) was chosen as an open-source solution supporting complex queries and easy scalability. MongoDB achieves relationship management by providing a concept of collections able to store documents with a flexible structure and that can be embedded in one another. Thereby, it uses BSON (Binary JSON; http://bsonspec. org/) as data storage format. Defining a data structure for use with NoSQL relies on preliminary analysis of the queries that the targeted application will need to execute. Therefore, WIDDE data structure was centered on variants (Additional file 1: Figure  S1 ) due to the higher expected number of variants to be stored compared to the number of individuals.
The genotyping data documents are stored in a collection where keys consist in triplets (variant, project, run). Such documents, which are the most basic unit of data stored in the database, embed marker genotypes for all samples involved in the given run. The server application was written in Java making use of the industry-standard Spring framework (http://spring. io/). Opal Toolkit (http://nbcr. ucsd. edu/data/docs/opal/) allows submitting jobs to a computer cluster running Sun Grid Engine (SGE), to perform either PCA or individual assignment. The client interface was developed in JSP with the jQuery JavaScript library (http://jquery. com/), and relies on the D3. js library for PCA result display (http://d3js. org/). Fig. 1  WIDDE architecture diagram. This high-level diagram illustrates the WIDDE architecture.
Methods PCA of individuals based on SNP genotyping data is performed with the smartpca software package  [11]. Assignment of new individuals provided by users to WIDDE populations is performed using both distance method  [12]  and supervised clustering  [13]. Allele sharing distance (ASD), defined as 1-x ij where x ij represents the proportion of allele alike in state averaged over all genotyped SNPs, are calculated between individuals submitted by users and all public individuals included in WIDDE  [6]. For each submitted individual, the average ASD with all individuals of each population is also calculated and the top 5 or 10 genetically closest populations are summarized. Supervised clustering is used to estimate ancestry proportions of samples relative to each reference population represented in the database (world dataset).
Data source for WIDDE-cattle The first WIDDE version includes bovine genotyping data, obtained with medium to high density Illumina SNP chips (54K and 770K), from different breeds arising from biodiversity studies  [6, 7, [15] [16] [17] [18]. Genotyping data from  [6, 7, 15, 18]  were already stored in the Dryad Digital Repository  [19] [20] [21]. Genotyping data produced by Gautier & Naves were available as online supporting information  [16]  and data from  [17]  that we produced, were added to the database. HD genotyping data were obtained from Illumina. Only cattle populations with at least 5 individuals were stored in WIDDE and those with at least 15 individuals were chosen as reference populations for population assignment. SNPchiMp was used to obtain consistent marker-lists and identify marker synonyms  [22]  and all markers were mapped on the current reference genome assembly bosTau6 UMD3.
1. We also detected identical SNPs with different Illumina identifiers (98 duplicates and 6 triplicates) based on chromosome position. At these positions, we checked that the genotypes were identical for each individual and stored only one genotype per chromosome position in WIDDE. Each SNP stored in WIDDE is thus unique and ID synonym information was stored. At the time of writing, final data imports into WIDDE of any new population can be proposed by contacting administrators.
Application features The WIDDE application has four main functionalities: (i) storing high density genotyping data for hundreds to thousands of individuals each characterized by their population of origin and genotyping projects, (ii) selecting, filtering and exporting genotyping data subsets in several formats (i. e. plink, eigenstrat, hapmap) for downstream analyses, (iii) exploring directly intra-species genetic diversity via PCA and (iv) exploring user-provided genotyped individuals with WIDDE individuals by assigning them to WIDDE reference populations. This latter step includes a visual assignment through PCA, a distance-based assignment without calibration and an estimation of samples' ancestry composition relative to reference populations by supervised clustering  [12] [13] [14]. WIDDE supports storing information from any type of markers derived from NGS (e.
Web-interface The WIDDE website consists in five sections accessible from the homepage. The "Home" section provides a concise description of the database and gives general information about the tool. The "Tutorial" section contains a didactic step-by-step tutorial illustrated with several screenshots. The "Data sources" section lists the different references and sources of data included in the database and the "Contact us" section contains the name, affiliation and email addresses of the main people involved in database conception and maintenance. The "Cattle data" section gives access to the actual application dedicated to bovine species. At the top of the application's screen, a logo and three icons allow respectively to (i) return to the homepage, (ii) visualize populations' origin on a map, (iii) upload data to launch population assignment and user's genotyping data exploration, (iv) authenticate to have access to private genotyping data.
At the middle of the screen, a user friendly Web interface contains three panels for individual selection, marker selection and quality filtering, successively appearing when previous selection is valid (Fig. 2 ). Indeed, the dataset is defined in two steps. Individuals are selected from the first box, according to their population, genotyping project of origin and possible misidentification (e. g. problematic individuals identified by previous genetic analyses, due to population misidentification on phenotype). While choosing from the population list, the total numbers of currently selected individuals and samples are automatically displayed. A batch selection of individuals by population groups (European taurine, African taurine, zebu, hybrid and outgroup species) and DNA chip model (Illumina BovineSNP50v1, Illumina BovineSNP50v2 and Illumina BovineHD) is also possible.
Selected chips are then displayed in the second box and markers can be selected according to their DNA location (mitochondrial, autosomal and/or sex chromosomes). The number of markers in the current selection is also kept up to date in real-time. An optional quality filtering step is available in a third box where two thresholds fix the minimum genotyping call rate for individuals and markers (95 % and 75 % by default). The order in which these two first filters are applied can be reversed by ticking another checkbox. By carrying out an exact test for Hardy-Weinberg Equilibrium  [23] , a third filter can be applied to discard outlying markers (P < 0. 001 by default). Last, a filter on Minor Allele Frequency computed over all populations of the selected dataset can discard poorly informative markers (MAF < 0.
01 by default). Before or after quality filtering, genotyping data can be exported in popular formats (e. g. plink  [24] , eigenstrat  [25] ). Users may also explore genetic diversity via an online PCA performed with the smartpca software  [11]. The dataset is first converted to eigenstrat format and smarpca is then launched on a computer cluster. Individuals are then plotted by default on the first factorial plan in a new window allowing selection of other components (Fig. 3 ). As this step may take time (few to few dozens of minutes depending on the size of the dataset and the cluster queue status), users have the possibility to enter their email address to be informed of the job completion. After job completion, genotyping data in eigenstrat format, a summary of individual and marker selection (selection.
txt) as well as smartpca output files (output. pca. evec, output. eval and sdtout. txt) may be downloaded. By clicking on the assignment icon, users also have the option through an upload interface to analyze their own genotyping data (in plink format with nucleotide letters) with WIDDE public genotyping data. This process, Fig. 2  Web interface to select individuals and markers, apply quality filter, export data in various formats and launch principal component analysis which can be time consuming, is also detached from the web interface runs on the mentioned computer cluster via Opal and SGE. Based on SNPs in common, user can choose (i) to perform a PCA of genotyping data combined with public genotyping data stored in WIDDE, and (ii) to assign these new individuals to populations of the public reference dataset based on ASD calculation and on estimation of ancestry proportions by supervised clustering  [12] [13] [14].
ASD between submitted individuals and each individual from the reference dataset are calculated and the top five or ten populations of the WIDDE reference dataset with the weakest ASD average are listed (along with ASD minimum and maximum within populations) for each new individual. The supervised clustering step determines for each new individual the proportion of ancestry attributed to each population of the WIDDE reference dataset. Users, who may enter their email address to be informed of job completion, can download: (i) a summary and the complete results of ASD calculation (asd_summary. tsv and asd_results. tsv), (ii) a summary and the complete results of the supervised clustering (ancestry_summary. tsv and ancestry_results. tsv) and (iii) the merged dataset used in the analysis in eigenstrat format.
Illustration with cattle data As an illustration of WIDDE functionalities, we hereby detailed the cattle module currently containing 783,640 SNPs and 3951 (2827 publicly available) individuals belonging to 129 (89 public) different cattle populations and 8 (7 public) populations of outgroup species (two Bos javanicus populations, Bison bison, Syncerus caffer, Bos gaurus, Bubalus depressicornis, Bos grunniens), that were thoroughly selected and curated. These various local cattle populations are representative of the bovine genetic diversity and belong to the three main cattle groups, i. e. European (EUT) and African (AFT) taurine (Bos taurus) and zebus (ZEB; Bos indicus). Figure  3  describes the PCA results of a data subset including 685 individuals from 22 cattle populations representative of EUT, AFT and ZEB, genotyped on 44,554 SNPs after quality filtering using default settings (Fig.
Briefly, each main cattle group is positioned at the three apexes of the and admixed populations lie at intermediate positions. A world reference dataset including all WIDDE public populations representative of the world-wide genetic diversity of the bovine species with at least 15 individuals was defined to assign user-uploaded individuals to WIDDE public populations. To illustrate this step, we estimated ancestry proportions of 2250 individuals from 45 public populations of the world reference dataset against the world reference dataset itself using supervised clustering. We started from 33K SNP (i. e. the highest number of variants taken into account in the analysis), 10K SNP and 1K SNP randomly chosen within the 33K list, and considered different EM stopping criteria (ε = 0. 01, ε = 0.
As expected, the proportion of assigned individuals increased with the number of selected markers and with the stringency of the stopping criterion (Fig. 4a ). For instance, at an ancestry threshold of 0. 8, the proportions of assigned individuals were above 80 % (respectively 60 %) with 33K (respectively 10K) SNPs whatever the stopping criterion value. Conversely, the misassignment rates always remained under 2 % with both 33K and 10K SNPs (Fig. 4b ). Note however that for a small number of SNPs (e. g. 1K) the misassignment rates sometimes reached values above 5 %. As a rule of thumb, one may thus recommend using for assignment purposes at least 10K SNPs and an ancestry threshold above 0. 75. We also applied WIDDE diversity exploration and population assignment tools to a test dataset with individuals not included in the database and to two European taurine breeds i.
e. Montbeliard (2 individuals) and Tarentaise (5 individuals)  [7]. Tarentaise is simply another name for the Tarine although considered as a separate breed in  [7]. We first checked that these populations were positioned near the EUT group as expected on the first factorial plan of PCA (Additional file 2: Figure  S2 ). In order to have an idea of the WIDDE populations presenting the strongest genetic proximity with the uploaded individuals, we then launched the assignment module using the reference dataset (ε = 0. 01). Additional file 3: Tables  S1  and S2  resume for each new individual the top five nearest WIDDE populations based on average ASD calculation and the proportion of ancestry attributed to each population of the reference dataset, respectively. We thus checked that the Montbéliard and Tarentaise individuals were properly assigned as the corresponding populations were already present in WIDDE.
The two supposed Montbéliard individuals presented the closest genetic distance with MON with a proportion of ancestry above 95 %, as expected (Additional file 3: Tables  S1  and S2 ). The supposed Tarentaise individuals proved genetically close to TAR (Tarine) population but with a weaker proportion of ancestry attributed to this breed (between 48. 8 and 68. 7 %), illustrating a possible admixture with other European taurine breeds. Our results demonstrate the utility of WIDDE in assigning any individual to the genetically closest population and the ability to estimate the ancestry proportion of any individual to the WIDDE reference populations. The assignment method based on supervised clustering is especially accurate when the true population of origin is included in the reference dataset  [12].
When the new individual's true breed population is not present in WIDDE, the tool still provides an estimate of the mostly closely related population. Future work will require the implementation of an exclusion method to measure the confidence that an individual truly belongs to a given population. As these exclusion methods are at the moment difficult to implement with an acceptable computation time compatible with a high number of markers, they were not integrated into WIDDE but output for Geneclass 2. 0 software might be easy to generate and will be available shortly  [26].
Conclusion In summary, the NoSQL next generation database WIDDE represents a biodiversity database able to manage and explore a large amount of genotyping data, and to assign new individuals to populations stored internally. Thus, WIDDE is a generic tool applicable to a wide range of species and marker types. It is a versatile tool and further version of the database will include additional output formats and new tools dedicated to genetic diversity exploration. The first module, WIDDE-cattle, described here, represents the first database dedicated to cattle biodiversity and SNP genotyping data, which allows users to explore not only the WIDDE dataset but also their own genotyping data. It will be very useful for researchers interested in cattle genetic diversity and will be extended to other livestock species shortly.
Acknowledgments The authors wish to thank  Patrice Dehais  ( INRA, SIGENAE, France ) and  Denis Milan  ( INRA, GenPhySE, France ) for their help during database installation on the  Genotoul Bioinformatics platform  (http://www. genotoul. fr/)and the  Southgreen bioinformatics platform team (CIRAD, France)  for providing the development phase's infrastructure. Part of the public and private genotypes included in WIDDE-cattle were obtained thanks to the  INRA DGA Projects  grant  2008  ( BOVDIV  project),  INRA   DGA Projects grant   2009  ( SNPDOM  project),  FRB AAP Innovative Projects.
Proportion of assigned individuals and misassignment rate in assignment tests based on supervised clustering. The 2250 individuals from 45 public populations of the world reference dataset were assigned against the world reference dataset, using 32,966 (33K) SNP, 10K SNP and 1K a b 1. 0 10 33k / ε=0. 01 33k / ε=0. 1 33k / ε=1 10k / ε=0. 01 10k / ε=0. 1 10k / ε=1 0. 8 8 1k / ε=0. 01 1k / ε=0. 1 Proportion of assigned individuals 0. 4 0. 6 33k / ε=0. 01 Misassignment rate (%) 4 6 1k / ε=1 0. 2 33k / ε=0. 1 33k / ε=1 2 10k / ε=0. 01 10k / ε=0. 1 10k / ε=1 1k / ε=0. 01 1k / ε=0. 1 0. 0 1k / ε=1 0 0. 0 0. 2 0. 4 0. 6 0. 8 1. 0 0. 0 0. 2 0. 4 0. 6 0. 8 1. 0 Ancestry threshold Ancestry threshold Fig. 4 SNP, with different values for the EM algorithm's ε stopping criterion (0. 01, 0. 1 and 1). The proportion of assigned individuals a and the misassignment rate b were plotted against ancestry thresholds (0-1).
Case Studies Data from the first five months of the mission were examined to identify candidate ionospheric photoelectron events. Within the time period analysed, photoelectrons are always seen in the sunlit ionosphere, and ionospheric plasma is sometimes seen in the Venus tail, including one example (shown here) of characteristic ionospheric photoelectron energy peaks observed in the tail. Three representative case studies are presented, all from June 2006, including the tail observation. Electron data from the ELS is used to indicate the presence of the photoelectrons. Ion data from the IMA are used to search for possible related ion outflow.