dataesr/scientific-paragraphs-categorization huggingface dataset:
Gene expression analysis and disease resistance assays Rice plant growth and inoculation were performed as described previously  (Vergne et al. , 2010). Three-week-old plants were inoculated with the moderately virulent isolate GUY11 of M. oryzae. Five to 7 d after inoculation, susceptible lesions (spots characterized by a grayish center) were counted as a measure of susceptibility. Differences in total lesion numbers between Fig. 3 (b) and 3(e) only reflect differences in inoculum quality. RNA and gene expression was determined using qRT-PCR (expression data were normalized with the Actin gene) were done as described in  Grand et al. (2012). The qRT-PCR primers used for ZBED (accession LOC_Os01g36670) were CCGTTGATTCAGAGGTGCTGAC and TCGTAACAACATGGTAGCCTTGG.
Integrated domains are present in NLR proteins from 31 plant genomes The analysis of cloned R proteins and previous reports suggest that NLRs with integrated decoy domains may be common in many plant genomes  (C esari et al. , 2014). To test this hypothesis in a global manner, we analyzed the NLR repertoires in 30 complete genomes from higher plants and one moss species provided by the Greenphyl database which generated protein family clusters from plant whole-genome sequences by using Tribe MCL and BLAST similarity searches (for details see the Materials and Methods section;  Rouard et al. , 2011). The Greenphyl database records slightly more than 15 500 NLRs in these 31 plant genomes belonging to either the TNL orthology group GP000012 or one of the three major CNL orthology groups GP015065, GP015132 or GP015056.
This set of NLRs has the advantage that the same criteria (see the Materials and Methods section) were used to retrieve proteins from whole-genome data for large and complex gene families. Comparison of the Greenphyl NLR sets provided to the manually curated NLR repertoires from rice  (Luo et al. , 2012)  and A. thaliana  (Tan et al. , 2007)  showed that the Greenphyl repertoire contained 236 of From this, we concluded that the NLR repertoires from Greenphyl provide extensive coverage of the NLR diversity in plant genomes. Conversely, we observed that the Greenphyl NLR data set contained rice and A. thaliana proteins not present in the previously published curated NLR lists  (Tan et al. , 2007; Luo et al. , 2012). This is partially explained by the fact that the Greenphyl NLR data set contains proteins carrying either the LRR or the NB-ARC domain but not both of them.
Therefore, we also selected among the more than 15 500 NLRs recorded by Greenphyl a subset of 2699 canonical NLRs based on the combined presence of the NB-ARC and the LRR Interpro domains (Table  S1 ). We retrieved the Interpro annotations for the c. 15 500 NLR proteins and searched for domains that did not correspond to canonical CNL or TNL domains. This identified 456 NLR proteins (3. 6%) harboring at least one unusual domain (Fig. S3 ). The same analysis was performed with the more stringently defined data set of 2699 canonical NLRs. This identified 94 NLRs (3. 5%) with unusual domains (Fig. 2a ), a value very similar to that obtained with the full Greenphyl set of more than 15 500 NLRs. The two major classes of NLRs showed slight differences, with CNLs displaying fewer unusual domains (2.
Only three domains were found in many NLRs and in different species (Fig. 2c ): the protein kinase domain IPR011009 involved in protein phosphorylation, the WRKY domain IPR003657 involved in DNA-binding and transcription, and the BED domain IPR003656 involved in DNAbinding. These domains were found 11, 10 and nine times and in six, four and four different species, respectively. A particular feature of the BED domain is that it is particularly frequent in the N-terminus part of NLRs, as described previously in poplar  (Germain & S eguin, 2011).
Uncommon domains have been integrated frequently and independently into NLRs during plant evolution Acquisition of domains by NLRs seems to have occurred several times and independently during evolution, as the same domain is present in NLRs from different phylogenetic clades. For instance, WRKY (IPR003657) domains were found in CNLs of the groups GP015132 and GP015056 and in TNLs of group GP000012 (Fig. 2b ; Table  S1 ). Further support for repeated and independent acquisition of integrated domains comes from the observation that the same domain can be integrated at different locations. WRKY domains are, for instance, detected in the Nor the C-terminus of CNLs and the C-terminus of TNLs (Fig. 1b ). This apparent flexibility in the integration of additional domains in NLRs had already been reported for the integration of the RATX1/HMA domain in the rice blast resistance NLRs RGA5 and Pik-1 where it is located, respectively, at the Cterminus or between the CC and the NB domains  (Kanzaki et al.
, 2012; Cesari et al. , 2013). In rare cases, several domains were integrated in one single NLR. For example, one sorghum (Sorghum bicolor) CNL contains a NAC and two WRKY domains (Fig. 1b ) and an A. thaliana TNL (AT4G12020, also known as AtWRKY19; Fig. 1c ) carries two WRKY domains, one kinase domain (IPR001109) and one PAH domain (IPR002832) involved in protein-protein interactions. Taken together, these examples suggest a high modularity of the NLR proteins.
For our systemic analysis, we used the publicly available data of the GreenPhyl database where a huge number of sequenced genomes have been analyzed with a common set of criteria for establishing protein family clusters  (Rouard et al. , 2011). Comparison of this NLR data set with the published, expert-curated NLR repertoires of A. thaliana and rice showed for both species a good representativeness of the GreenPhyl database and suggested good coverage for the other species. This data set was also used to define a more stringent data set of NLRs. Both NLR data sets were filtered according to the occurrence of Interpro domains, leading to the definition of NLRs with additional, noncanonical domains. It is noteworthy that our approach using Interpro annotation has some limitations. For instance, the CNL domain found in almost half of the TIR-LRR proteins  (Dodds et al.
, 2001)  was not detected in our study because this domain is not defined in the Interpro database. Similarly, the CC domain found in canonical NLRs was not detected. Despite these limitations, our analysis clearly demonstrated that decoy integration is frequent and occurs in all analyzed plant lineages. Indeed, NLRs from many different lineages of annual and perennial dicotyledonous plants, several different lineages of monocotyledonous plants and one moss possess decoy domains at varying frequencies. Decoy domains are found in all classes of NLRs, that is, in the different classes of CNLs and in TNLs, and these NLRs seem tremendously flexible in terms of the integration of decoys, as integration can occur at the boundaries of all the different canonical NLR domains, and multiple integrations at different positions in a single NLR are even observed.
For example, well-known crowdsourcing systems such as Deco  [8] , CrowdDB  [3]  focus on enabling important primitives in database query processing through micro-tasks that are performed by individual workers whose responses are then aggregated. Other frameworks such as PyBossa  [2]  or Hive  [4]  are more generic and can be used for a variety of tasks but are still bound by fixed workflows and micro-tasks with no distinct notion of collaboration. Frameworks such as Argonaut  [5]  enables complex context-heavy data processing tasks (dubbed "macrotasks") but are still performed by individual workers and not by teams. In contrast, Crowd4U is declarative, generic and collaboration-aware. Demo Plan: To illustrate the range of applications that can be enabled by Crowd4U, we organize our demonstration based on the three collaboration types specified above.
Collaboration Architecture Figure  2  shows the major components of Crowd4U. A requester who wants to register tasks into Crowd4U writes a project description in CyLog, a Datalog-like language designed for crowdsourcing applications with complex data flows  [7]. CyLog allows humans to evaluate predicates in rules. The rules describing tasks and their dependency are interpreted and executed by the CyLog processor, which dynamically generates and registers tasks into the task pool. Crowd4U also provides tools to help requesters generate Cy-Log rules by allowing them to define tasks with a form-based user interface and spreadsheets. Crowd4U can use any task decomposition algorithm to break a complex task into micro-tasks. Its key innovations are the reliance of a task-specific assignment algorithm to  find the best group of workers to complete a task, and the implementation of sequential, simultaneous and hybrid worker collaboration, to ensure effective result coordination.
Task assignment A feature of Crowd4U is that the task assignment is conducted in a declarative manner. To make that possible, Crowd4U manages three types of relationships between workers and tasks explicitly. (  1 ) Eligible means that a worker is eligible for performing a task. This is computed by the CyLog processor using the project description and worker human factors. For example, in a project description a task requester may specify that only workers who log in to Crowd4U and speak English as a native language are eligible for their tasks. (2)  InterestedIn means that a worker is interested in performing a task. This is declared by each worker when she is shown a list of eligible tasks. (3) Undertakes means that a worker confirms that she performs a task. A (worker,task) pair can go into this relationship status only when the worker is Eligible for that task.
Then, the controller outputs the suggested team and each worker in the team is asked to join the collaborative task. The assignment algorithm combines different human factor such as workers' skills, worker-to-worker affinity and upper critical mass  [9] , to find the right team of workers to complete a task. The worker manager supplies the Cy-Log processor and the task assignment manager with worker human factors (e. g. , languages, countries, and applicationspecific human factors) and the relationship among workers encoded in the worker affinity matrix, which maintains the information on how a pair of workers is expected to work well. For example, in the case of surveillance tasks, if workers live in the same geographic area, their affinity value is larger. The assignment controller waits for a sufficient number of workers to show interest in the task before building a team satisfying the requester's desired human factors.
Interface Design and Worker Interaction Figure  3  is part of screen-shot of the project administration page where a requester specifies the desired human factors for task assignment. The requester also specifies an expiration time for worker recruitment. Figure  4  shows the set of human factors that can be updated by each worker. Those factors are either provided by the worker when creating an Crowd4U account (e. g. , native languages, location) or computed by the system based on previously performed tasks (e. g. , via qualification tests, or by learning workers' profiles as in  [10] ). Figure  5  is a worker's screen in a simultaneous collaboration where she communicates with other workers using Google doc and submits the result for a Crowd4U task. Crowd4U could be combined with any collaboration tool.
ACKNOWLEDGEMENTS The authors are grateful to the contributors to Crowd4U (listed at http://crowd4u. org). This research was partially supported by the  Grant-in-Aid for Scientific Research  (# 25240012 ) from  MEXT, Japan. The work of  Habibur Rahman  and  Gautam Das  was partially supported by  National Science Foundation  under grant  1344152 ,  Army Research Office  under grant  W911NF-15-1-0020  and a grant from  Microsoft Research. The work of  Sihem Amer-Yahia  is supported by  ANR-13-CORD-0020. Any findings, conclusions, or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the sponsors listed above.
However, the use of multiple approaches to genome sequencing and assembly (e. g. , chromosome sorting, GenomeZippers, and genome survey sequencing), the variety of associated data types (e. g. , gene predictions, expression data, and molecular markers), and the existence of alternative coordinate systems (e. g. , genetic map, physical map, and numerical position in molecular sequence) can make it difficult for users to combine different data sets easily and correctly. Storage, integration, and visualization of these heterogenous and complex data are essential to enable efficient research. Here we describe Triticeae genome data resources maintained by partners in the EU-funded transPLANT project. The transPLANT project aims at producing an integrated, coherent data infrastructure shared among dispersed expert resources with strong interconnections between them (including cross-linking and the use of common formats, tools, and datasets) designed to make it easy for users to switch between different resources according to which one best addresses their current point of interest.
transPLANT The transPLANT project (Table  1 , reference no. 1 [Table  1. 1]; hereafter, this format is used to reference Table  1 ) is an integrated infrastructure funded by the Framework 7 program of the European Union. It brings together 11 partners from seven countries with the aim of developing common standards, data, and technologies in the plant genomics area. A major focus of the work is variation data and the development of tools to organize, archive, and analyze this. Another major focus is the definition and use of common reference sequences so that annotation from different resources can be shared and compared. A third focus is the development of a distributed query infrastructure to provide a common point of entry to data held in multiple, dispersed resources. In the context of Triticeae data, five transPLANT partners (The Plant Genome and Systems Biology unit at the Helmholtz Center Munich [PGSB], the European Bioinformatics Institute [EBI], the Unité de Recherche Génomique Info at the Institut National de la Recherche Agronomique [URGI], the Leibniz Institute of Plant Genetics and Crop Plant Research [IPK] and The Genome Analysis Centre  [TGAC] ) are involved and interacting with other partners engaged in the international consortia (IWGSC and IBSC) coordinating the sequencing and assembly of these genomes.
The GenomeZipper concept integrates data from chromosome sorting, second generation sequencing, array hybridization, and systematic exploitation of conserved   (Table 1. 22 ). (B) transPLANT genome resources registry giving access to more than 300 different plant genome resources and databases (Table  1. 3). (C) GenomeZipper overview representation for the barley genome (Table  1. 23). (D) Visualization of syntenic relationships between the genome sequences of Aegilops tauschii and barley within the CrowsNest tool (Table  1. 5 ). synteny with model grasses to construct virtual ordered gene maps  (Mayer et al. , 2009 (Mayer et al. , , 2011)). Besides batch download via FTP, all GenomeZipper data has been integrated into the PGSB PlantsDB database scheme. To assist the interactive exploration of GenomeZippers and the search for anchored elements such as expressed sequence tags (ESTs), genetic markers, and full-length complementary DNA, interfaces for querying and browsing the Geno-meZippers for barley, wheat, and rye were constructed (accessible from Table  1.
tauschii and barley on a whole-genome scale. Wheat genomic subassembly sequences generated in a reference-directed approach  (Brenchley et al. , 2012)  were integrated with their corresponding genes from B. distachyon, sorghum, rice, and barley. Interfaces to query this data include a BLAST server to search for homologous wheat sequences as well as search for reference genes and associated wheat sequences. Genes predicted from chromosome-sorted wheat genome sequence generated within the IWGSC (IWGSC, 2014) have been integrated into PGSB PlantsDB and cross-referenced with the corresponding repositories GnpIS Wheat and Ensembl Plants. To visualize and search the integrated barley physical and genetic maps, dedicated instances of GBrowse and CrowsNest were set up and in cooperation with IPK Gatersleben populated with markers, bacterial artificial chromosome (BAC) end sequences, BAC sequences, and physical map information.
Triticeae Genome Data in Ensembl Plants Ensembl Plants (Table  1. 6) is an integrative web portal for plant genomic data, developed by the EBI. The portal provides interactive and programmatic access to data from 39 species through a variety of interfaces including web browser, Perl and RESTful Application Programming Interfaces, FTP, a publicly accessible relational database server, and a data-mining tool implemented using the data-warehousing framework BioMart optimized for gene and variant-centric queries. In addition to participating in transPLANT, coordination with efforts in the United States is achieved through a formal collaboration with the Gramene project (http://www. gramene. org). Currently, four Triticeae genomes (among 20 cereal genomes) are available in Ensembl Plants: bread wheat, two of its diploid progenitors, A.
tauschii and T. urartu, and barley. In the case of both wheat and barley; additional information (from genetic and physical maps) has been used to assign the genomic contigs to chromosomes and locate them within them. For barley, many of the contiguous sequences generated and assembled by the IBSC have been binned into located clusters according to evidence from the genetic and physical maps, and this information is used to construct a chromosome level view in Ensembl. The initial assembly has recently been revised using POPSEQ  (Mascher et al. , 2013a)  data generated by the IPK. Whole-genome alignments have been performed against B. distachyon, rice, bread wheat, and the bread wheat progenitor genomes; collections of barley and wheat ESTs and RNA-sequencing (RNA-seq) reads have been aligned to the barley reference.
In addition, intervarietal single nucleotide polymorphisms (SNPs) are represented for eleven varieties of barley as well as sites of variation between wild barley (H. spontaneum) and the barley reference. The core wheat data represented is the chromosome survey sequence (CSS) generated, assembled, and annotated by the IWGSC. However, the CSS assembly of chromosome 3B has been replaced by the BAC-by-BAC assembly constructed by  Choulet et al. (2014). In addition to sequence assemblies and gene models, a number of additional data sets have been aligned to the survey sequence, including the complete genomes of B. distachyon and rice, wheat unigene clusters from NCBI, and wheat RNA-seq data deposited in the International Nucleotide Sequence Database Collaboration archives. The wheat genome assemblies previously generated by  Brenchley et al.
(2012)  have also been aligned to the survey sequence B. distachyon and barley. In addition, a collection of 900,000 polymorphisms from CerealsDB (http://www. cerealsdb. uk. net) have been included in the resource as well as data from the wheat HapMap project  (Jordan et al. , 2015). The chromosome survey sequence (and its annotations), in addition to the complete EST set, are available to search via BLAST and other search alignment algorithms. For all gene models in Ensembl Plants, functional annotation is inferred using GO, InterPro, and homology metrics. Additionally, the evolutionary history of each protein-coding gene is inferred from comparisons to other plant species, and gene trees and protein alignments are available to browse (see Fig. 3 ) and download. The three bread wheat genomes have additionally been aligned to each other and linked to assertions of homeology derived from the gene tree analysis to provide supporting evidence.
Triticeae Genome Data and Tools at the Unité de Recherches en Génomique Info The Official Wheat International Wheat Genome Sequencing Consortium Portal Unité de Recherches en Génomique Info has been chosen by the IWGSC to be the repository for wheat genomic sequences and physical maps (Table  1. 7). To allow users to download, display, and query the IWGSC sequences and physical map data, a section dedicated to wheat genomics data, the sequence repository (Table  1. 8; Fig. 5A ), has been set up. Data stored in the sequence repository includes the wheat survey sequence, the chromosome reference sequence (chromosome 3B), the genes and annotations (gene models, GenomeZipper, and POPSEQ), the physical maps, the RNA-Seq, and the variations (HapMap) data. Users can display the sequence annotation of the 3B reference sequence and the survey sequence in dedicated browsers.
Wheat Data in the GnpIS Information System GnpIS  (Steinbach et al. , 2013 ) is an information system that integrates genomic and genetic data for plants and fungi. A "wheat" filter was implemented within GnpIS that allows interconnecting the wheat genomic data the pl ant genome  m arch 2016  vol. 9, no. 1 evidence-based annotation system enable time-efficient and comprehensive information retrieval. An artificial neural network incorporating user feedback and behavior tracking allows relevance sorting of results. Because this enhanced relevance ranking is one of the major innovations to explore millions of database records, a special focus has been set to its training and the inclusion of user feedback. The current LAILAPS release comprises about 91 million indexed database records of trait knowledge within 13 major life science data collections and more than 60 million associations to -omics data sets.
LAILAPS Search Engine LAILAPS  (Esch et al. , 2014 ) is an integrated information retrieval system to link plant genomic data in the context of phenotypic traits for a detailed forward genetic research (Table  1. 12; Fig. 6 ). LAILAPS is developed in the framework of the transPLANT project and allows exploratory search for candidate genes linked to specific traits over a loosely integrated system of indexed and interlinked genome databases. Query assistance and an  query refinement features. Once the user identifies interesting records, he is guided to the most relevant genomic dataset and is able to rate its quality. In turn, this enables LAILAPS relevance prediction system to improve the relevance prediction network.
The text box (A) enables an interactive and spelling corrected keyword query submission. After query was executed successfully, a list of semantically related phrases is provided for query refinement (B). The query results can be either downloaded as a Microsoft Excel sheet (C) or interactively explored. For this, all relevant hits are displayed as short excerpts in the result panel (D). Connected to each hit is a list of links to associated genomic data (E). Those links can either refer to genome data directly (green) or reflect an indirect, transitive relationship (red). The left hand filter panel enables to restrict the results by fact databases (F1), linked genome databases (F2), direct or indirect linked gene annotations (F3), or synonyms (F4). the pl ant genome  m arch 2016  vol. 9, no.
1 maps. A web-based interface presents data in tabular and graphical format and associates all information and published sequence data with shotgun assemblies, repeat annotations using KMasker (see  Schmutzer et al. , 2014) , physical contigs, and annotated genes. A novel graphbased visualization strategy was implemented to show overlaps between adjacent BACs based on fingerprint and sequence data. BARLEX is publicly accessible at the website listed in Table  1. 14 and is directly connected to the IPK Barley BLAST server as well as the LAILAPS system.