dataesr/scientific-paragraphs-categorization huggingface dataset:
Marker development DNA amplified from 1BL sorted chromosomes was used for 454 shotgun sequencing as described by Wicker et al. [28]. A total of 2,177,887 good quality reads were obtained with an average read size of 383 bp and a total size of 834 Mb corresponding to a coverage of 1. 6 X. Two homemade Perl scripts, IsbpFinder. pl and ssrFinder. pl, were used to develop ISBP and SSR markers, respectively, from the 454 reads. In total, 775,995 ISBPs and 38,400 SSRswere designed. Other home-made Perl Scripts were developed to automatically discard duplicated markers and select the best quality markers. The quality criteriawere the level of confidence given by IsbpFinder. pl  [29]  for the ISBP markers and more than 10 repeats for dinucleotide repeats for the SSR markers. Finally, 46,194 ISBP and 412 SSR corresponding to a total of 46,606good quality and non-redundant markers were identified.
Then, distances between shared and target loci were calculated as a ratio of the distance of the genetic interval and used, ultimately, to estimate the coordinate of the target loci on the neighbor map. The neighbor map was constructed with segregating data from the following mapping populations: Chinese Spring × Renanas a framework; W7984 × Opata (the ITMI reference population; GrainGenes  [27] ); RL4452 × AC Domain, SC8021-V2 × AC Karma,Wuhan × Nyubai (three populations from Agriculture Canada integrated in a consensus map including the ITMI map; Somers et al. [50] ; Banks et al. [49] ); Courtot × Chinese Spring  [48] ; and a wheat composite map originating from several tens of populations (R. Appels, personal communication).
PCR amplification PCR amplification was carried out in 10 µL reaction volume containing 5 µL of AmpliTaq gold 360 master mix (Applied Biosystems), 0. 4 µL 360 GC enhancer (Applied Biosystems), 1 µL syto9 (2 µM), 1. 6 µL forward and reverse primer (3 µM) and 2µL of 1/200 phi29 MTP pool amplification. PCR conditions were as follows: initial denaturation at 95°C for 10 min followed by 47 cycles of denaturation at 95°C for 30 s, annealing at 62°C for 30 s, extension at 72°C for 30 s, and a final extension at 72°C for 5 min. The PCR plates were then run on LightCycler 480 (Roche Diagnostics, Meylan, France{) to obtain dissociation curves. Results were then analyzed on LightCycler 480 Software release 1. 5. 0.
NimbleGenarray development High confidence ISBP markers were selected from the 46,194 ISBPsdesigned in the section 'markers development'. A mathematically defined repeats index using the Talymer program  [94] and a Perl-based script were used to select specifically ISBP markers comprising a junction between a TE and a stretch of 30mer of low-copy sequence. Selected were 17,788 ISBP markers with an average of five probes per marker (88,470 probes), 27 positive controls (TE from wheat, 211 probes) and 20 negative controls (TE from several other species; 159 probes). Probes were 50 to 58 nucleotides in length (30mers for the low-copy DNA and a varying length of 20 to 28 nucleotides for the TE), with a temperature of melting (Tm) between 68°C and 73°C (Tm = 41 * ((nG + mC) -16. 4)/ L + 64. 9, where L = length of the oligonucleotide), and a GC content between 40% and 60%.
Microarray hybridizations For the 63 1BL chromosome MTP BAC pools,500 ng of DNA was labeled using the NimbleGen Dual color labeling kit (Roche NimbleGen Inc. ) according to the manufacturer's protocol. Dual color hybridizations were performed on each plex of the arrays. For the 1BL sorted chromosomes, 300 ng of DNA was labeled using the same kit. A dye swap was performed for this sample. An updated version [A-MEXP-2314] of thewheat Nim-bleGen 40k unigene microarray  [15] , was hybridized according to the manufacturer's protocol with an additional vortexing step ofthe hybridization solution master mix (chapter 4 steps 1 to 4 of the protocol for hybridization and washing). Hybridization was performed for 72 hours at 42°C instead of 24 hours as in the original protocol. Washing steps were performed according to the manufacturer's protocol for gene expression analysis (Roche NimbleGen Inc.
) Hybridization and washing of the NimbleGen 17k 1BL ISBP arraywere performed according to manufacturer's procedure except that the hybridization time was extended to 120 hours. The arrays were dried by centrifugation at 1,200 × g for 1 min. The arrays were scanned using the InnoScan 900AL scanner (Innopsys, Carbonne, France). Data were extracted from scanned images using the NimbleScan 2. 5 Software (Roche NimbleGen Inc. ) that allows for automated grid alignment, extraction and generation of data files.
Microarray hybridization results analysis For the wheat NimbleGen 40k unigene microarray,the normalization of the MTP pool data was done using the methods developed by Rustenholz et al. [15, 16]. Two thresholds were calculated:the 'mean + × x Standard Deviation' with the following coefficient for the plates, columns and rows:plate: 2. 7, 2. 6, 2. 5, 2. 4, 2. 3, 2. 2, 2. 1 and 2; row: 2. 8, 2. 7, 2. 6, 2. 5, 2. 4, 2. 3 2. 2 and 2. 1; column: 3. 0, 2. 9, 2. 8, 2. 7, 2. 6, 2. 5, 2. 4, 2. 3 and 2. 2, andthe 't-test' method using the same thresholds as Rustenholz et al. [16]. All probes with a probe signal above these thresholds were considered positive. The normalization of the sorted 1BL chromosome data was done using automated script developed with the R software  [95]. A lowess correction was used to correct the dye biases.
Afterwards the corrected intensity values were checked for each gene and the aberrant values deleted. Then, for each value, the median value of all genes was subtracted and divided by their standard deviation. Finally, the positive genes were identified with the 'mean + × x Standard Deviation' as described above. For the 1BL ISBP NimbleGen array,the normalization was performed using automated scripts developed with the R software. The background intensity was estimated using the median of the intensities of the empty spots and subtracted fromthe intensity of each spot. After a log2 transformation, linear-lowess normalization was performed. Data were then subtracted by the median of the total spot intensity and divided by the standard deviation. For each probe,two thresholdswere calculated:the mean + 2 × the standard deviation and a Student's t-test at a Pvalue threshold of 0.
Three-dimensional MTP pools and plate pools data deconvolution A home-made Perl script, called DSMP. pl was developed for the deconvolution of molecular marker screening data produced on the three-dimensionalBAC pools of the MTP. Essentially, the script checks if two overlapping BACs of the physical map explain all the positive pools and can alsodeconvolute three others types of results: two non-overlapping BACs from two different contigs with fingerprints matching at acut-off of 1e -25 , a tolerance of 12, and a MTP addresses that explain all the true positive pools; two overlapping BACs with one fingerprint matching a third BAC, included in a contig other than the two overlapping BACs, at a cut-off of 1e -25 , a tolerance of 12, and a MTP addressing these three BACs that explain all the real positive pools;and a pair of overlapping BACs with one fingerprint matching a BAC in a second pair of overlapping BACs, included in a contig other than the two first overlapping BACs, at a cut-off of 1e -25 , a tolerance of 12, and a MTP addressing these fourBACs that explain all the true positive pools.
TheDSMP. pl script is available upon request from the corresponding author. The molecular marker screening data produced on the plate pools of the whole 1BL BAC library were deconvoluted with the Elephantsoftware and the default parameters  [33]. Briefly, Elephantpartitions the contigs into short sections by splitting the contig at each branching point and establishes a list of clones for each segment; for each marker, it combines the results from pool screening with the pool composition to establish a list of candidate clones harboring the markers; it compares the two lists and scores each segment; and finally, for each marker, if a unique segment had a score above the threshold of 13, it assigns the marker to the segment.
Sequence analysis Sequences were analyzed using Basic Local Alignment Search Tool (BLAST) software  [96]. BLASTN analyses of all the probes of the wheat NimbleGen 40k unigene microarray  [15]  against all the Roche454 reads of sorted chromosome1BL were performed to identify the 1BL unigenes present on the NimbleGen microarray. The results were parsed to keep the best hit with at least 98% of sequence identity on at least 57 bp. Every unigene with a hit meeting these criteria wasconsidered as aunigene originating from chromosome 1BL. BLASTXanalyses of the 39,179 unigenes sequences represented on the wheat NimbleGen 40k microarray against the databases of all rice (Oryza sativa)  [97] , B. distachyon  [98]  and sorghum (Sorghum bicolour) peptides  [99]  were performed to identify orthologous genes to the genes mapped on wheat chromosome 1BL.
sequences and making them available for analysis in advance of publication. We are grateful to Kellye Eversole for critical editing of the manuscript. Hybridization experiments were performed at Institut National de la Recherche Agronomique Clermont-Ferrand on the GENTYANE genotyping platform  [105]. This work was supported by the European Community's Seventh Framework Programme TriticeaeGenome (grant agreement n°FP7-212019).
Data availability A genome browser of the physical map of the wheat chromosome 1BL is available from the Unité de Recherche Génomique Info website  [34]. All the NimbleGen array design and all microarray data have been deposited to ArrayExpress  [104]  under accession numbers [A-MEXP-2314]for the wheat NimbleGen 40k unigene design, [A-MEXP-2312] for the 1BL ISBP NimbleGen array design and [E-MTAB-1657] for the ISBP hybridization experiment and [E-MTAB-1650]for the wheat NimbleGen 40k unigene hybridization experiment. The Roche454 sequences of the 1BL sorted chromosome areaccessible at the European Bioinformatics Institute short-read archive under the accession number [ERX009439].
Background: The study of the conservation of gene order or synteny constitutes a powerful methodology to assess the orthology of genomic regions and to predict functional relationships between genes. The exponential growth of microbial genomic databases is expected to improve synteny predictions significantly. Paradoxically, this genomic data plethora, without information on organisms relatedness, could impair the performance of synteny analysis programs. Results: In this work, I present SyntTax, a synteny web service designed to take full advantage of the large amount or archaeal and bacterial genomes by linking them through taxonomic relationships. SyntTax incorporates a full hierarchical taxonomic tree allowing intuitive access to all completely sequenced prokaryotes. Single or multiple organisms can be chosen on the basis of their lineage by selecting the corresponding rank nodes in the tree.
Indeed, with over 2000 sequenced micro-organisms often responding to exotic genus names, keeping a mental track of all the lineages is a daunting task. To my knowledge, with the exception of GeCont2  [4]  which offers partial access to phyla, none of the afore mentioned synteny tools allows organism selection on the basis of taxonomy. The SyntTax web service described here aims to assist the analysis of synteny by providing an organized tree exposing the full lineage of every completely sequenced prokaryote in the National Center for Biotechnology Information (NCBI) repository. In this manner, single or multiple organisms can be easily selected by any combination of classification ranks allowing the generation of robust syntenies performed according to taxonomic criteria. The taxonomic and genomic databases are stored locally on the SyntTax server; they are both updated automatically on a daily basis from the respective NCBI resources.
Features The SyntTax interface was designed to be user-friendly and straightforward (Figure  1 ). Up to 100 individual genomes can be selected (Figure  1D ) from the taxonomical tree (Figure  1A ). The genomes selected in this manner are then matched against a user-defined protein sequence (Figure  1C ). Upon user decision, the synteny can be either limited to the best hit for each genome or include all hits (Figure  1E ). The minimal normalized genomic BLAST scores can be user-specified to control the degree of orthology as described by Lerat et al. [9]. The complete taxonomy database can be searched to retrieve complete lineages matching the query term (Figure  1B ). If the SyntTax search is successful, the resulting synteny is displayed as an interactive graphical table (Figure  2 ). The consistent color code used for the genetic maps allows the immediate perception of gene clustering.
Since the gene labels are rarely explicit, contextual tooltips displaying salient GenBank annotation are available for each gene by mouse hovering. Direct access to the NCBI protein sequence repository is also provided by clicking on the respective gene symbol. The results page displays at the bottom the list of selected genomes where the synteny was not found. Syntax reports can be exported either as a high-resolution vector-oriented graphics in Acrobat 'pdf' or in detailed Excel 'csv' formats for storage or further processing. Additional contextual information under the form of help icons is available throughout the web service interface and results page.
Retrieval of the taxonomic data The main resource for lineage information is provided by the NCBI Taxonomy database  [10]. The genus and species definers for each organism are obtained during the update of the corresponding genome from the NCBI repository (ftp://ftp. ncbi. nih. gov/genbank/genomes/ Bacteria). The taxonomy data originates from the same source and is accessed and retrieved using the Entrez Programming Utilities (E-utilities)  [11]  publicly available at http://eutils. ncbi. nlm. nih. gov/entrez/eutils/. A first 'esearch' request permits the retrieval of the lineage of a particular organism using its genus and species name italicized in the following command: esearch:fcgi?db ¼ taxonomy&term ¼ genus species ½ LNGE ½ The response consist of a list of identification numbers describing the complete taxonomy of the organism.
Taxon processing In order to cover all the organisms included in the NCBI database, particular attention was devoted to maximize the retrieval of taxonomic data. Most lineages originating from the NCBI are complete, displaying a constant rank depth of 7 with all ranks defined. However, a number of organisms labeled 'candidatus' or 'uncultured' often lack one or more rank definitions. The addition to the database of incompletely defined lineages without proper processing would impair the uniqueness of particular ranks and perturb the subsequent recursive addition of new taxa. When at least the superkingdom is defined, the incomplete taxa are shortened to superkindom and phylum definitions (if the latest is available) and added under Bacteria: unclassified or Archaea:unclassified. Severely incomplete lineages are stored under Prokaryote:unclassified.
Storage and update of the taxonomic database On the SyntTax web server, the taxonomical information is stored as an XML document whose hierarchical structure is particularly well suited to store data of this nature. The SyntTax taxonomy database is fully accessible to the user and can be queried for particular organism names. New routines devoted to the management of the taxonomy database have been added to the ancillary Updater program described previously  [8, 12, 13]. This program is now capable of performing automated daily incremental updates of both the taxonomic and genomic databases. Decremental updates are performed as well in order to remove obsolete, redundant or renamed organisms from the local databases.
Improvement of the Absynte synteny algorithm The synteny methodology used in SyntTax is based on the Absynte algorithm described in our previous work  [8]  with significant improvements. The algorithm employs a multiple center star gene clustering topology and can be briefly summarized as follows. The query protein is first compared to itself using BLASTP  [14]  in order to permit normalization of further alignments. The same protein sequence is then compared to the DNA sequence of the selected target genomes using TBLASTN  [14]. The normalized results obtained in such queries allow ranking The taxonomy database can be searched for single terms in order to retrieve the corresponding complete lineages. (C) A user-defined protein sequence can be introduced in any format including FASTA; numerals, blank spaces, and carriage returns are removed automatically.
(D) Up to 100 individual genomes can be selected from the taxonomic tree and are listed for clarity. (E) The genomic BLAST search parameters can be user selected in order to allow all hits or to limit the search to the best hit for each genome. The degree of orthology can be user-selected between 10 and 100%. of the various genomes by decreasing scores and to extract the absolute chromosomal coordinates of the matching hit. DNA segments of 15kb, centered on these coordinates are then extracted from each of these chromosomes and translated according to corresponding GenBank annotation. The proteins of the highest ranking chromosome are compared to each other using the Smith-Waterman-Gotoh (SWG) global alignment algorithm for performance reasons detailed in the Results and Discussion section.
A unique color is assigned to each individual protein or to paralogs, when present. These protein sequences are then compared to those extracted from the remaining chromosomes using SWG and the newly identified orthologs are colored accordingly. The synteny becomes readily apparent upon proper alignment and proportional drawing of the color-coded genetic maps. All the processing mentioned above is executed in real time and does not rely on pre-calculation. Synteny analysis with the Absynte/SyntTax algorithm is therefore a processor-intensive task and a number of measures were taken in order to ensure optimal performance. Faster volatile memory access was favored over slower disk operations. Disk I/O was limited to database reading as no user data is written on the server at any time. Multi-threaded or parallel operations were also developed in most areas of the algorithm in order to take full advantage of the multi-core processors architectures available in modern servers.
A specific area of the synteny algorithm, build around the BLAST executable (v2. 2. 24) was nevertheless still single-threaded in the original Absynte algorithm. A substantial optimization of the algorithm could be achieved in SyntTax by allowing full multi-threaded execution of BLAST v2. 2. 26 in addition to the already parallel SWG routines. The entire data flow and processing can now be distributed among the individual processors. The result report produced by SyntTax was also significantly improved: a list of the selected genomes where the synteny is not observed is now included in the result page and in the Acrobat. pdf and Excel. csv reports. In addition, the sequence of the query protein is also included in the printable reports.
In addition, to further improve performance, a significant part of the selection logic is executed locally in the web browser application itself. Contrarily to most synteny programs using BLAST, SyntTax relies instead extensively on the Smith-Waterman-Gotoh algorithm for protein alignments. Whereas BLAST is the most widely used alignment program due to its fast execution speed, several reports pointed out the superior sensitivity of the SWG algorithm  [16, 17]. The choice of SWG, combined with the parallel implementation of this algorithm in SyntTax, contributes significantly to the overall performance of the web service.
Resolution of conserved complex gene clusters Recently, Danielou and co-workers described two particular prokaryotic syntenies with genes involved respectively in the glycogen biosynthesis/degradation pathway and in the biotin biosynthetic pathway  [3]. These complex gene clusters are characterized by the presence of a number of gene permutations or inversions and by rapid sequence divergence within orthologous groups. Using SyntTax, benchmarking experiments were performed on the above syntenies as follows. Commonly used bacterial organisms were selected directly and unfamiliar lineages were retrieved by querying the taxonomy database (Figure  1B ). In the first experiment, the GlgA protein sequence of Escherichia coli was extracted using the BAGET web service  [12]  and entered as query sequence.
The results indicate that SyntTax resolved this synteny successfully (Figure  3A ). Even if the diagram generated by SyntTax resembled closely the one obtained using the OTFQ algorithm  [3] , it is worth noting a few salient differences. Contrarily to OTFQ, SyntTax was capable of connecting all the GlgA proteins in the dataset (BLAST e-value ≤8e-69). Furthermore, the normalized BLAST score obtained in each case was very close or exceeded the recommended discriminant value of 30 used to detect bona fide orthology  [9]. Similarly, the GlgB and GlgX genes encoding respectively the glycogen branching and the glycogen debranching enzymes in the Gammaproteobacteria E. coli and in the Alphaproteobacteria Methylococcus capsulatus were identified as orthologs since they share detectable sequence similarity (BLAST e-value ≤1e-11).
The high proportion of false positives predicted in this way can be substantially reduced by considering only orthologs  [20]. On the basis of these principles, SyntTax provides a refined and systematic approach for the study of the proteins that have evolved by modular assembly of independent domains. In addition to the exhaustive approach provided by its taxonomic capabilities, a second property of SyntTax is instrumental for this type of investigation. The synteny maps produced by this web service are drawn to the same exact scale, on the basis of GenBank annotations provided by the NCBI. This particular feature, which is absent in other resources such as GeConT2  [4] , GCView  [6]  or OTFQ  [3] , allows the immediate observation of the evolution of gene size and the detection of protein fusions.
Interestingly, the archaeal organisms inherited instead the eukaryotic KEOPS/EKC complex  [27, 28]. The precise role of each eukaryotic or prokaryotic protein partner in the tRNA modification pathway is still unknown. In bacteria, gene clusters comprising ygjD, yjeE, yeaZ and rimN encoding the whole YgjD complex are too seldom to be readily observed by systematic synteny analysis. They were nevertheless detected with a C# script in which each sequenced chromosome in the database was scored for a TBLASTN inter-hit distance <8000bp using as query the protein sequence of the four subunits simultaneously. A complete synteny combining ygjD, yeaZ, yjeE and rimN was found in two organisms, Verrucosispora maris and Microlunatus phosphovorus and is illustrated using SyntTax (Figure  4A ). These taxons contain respectively two or three rimN paralogs, and the high degree of conservation of this particular gene cluster contrasts with the variability of the neighboring regions.
The development of new bioinformatics tools that can adapt to continuously expanding genomic datasets is therefore fundamental. The present work has focused on The phylogenetic tree of the bacterial phyla based on 16S RNA sequences was obtained using a PhyML implementation  [32]. Pink labels refer to phyla where several genes encoding the YgjD complex subunits are either neighboring or fused. the development of SyntTax, a new synteny web service whose predictive robustness increases with the amount of available prokaryotic genomes. This property is due to the taxonomic capabilities offered by this web tool and which is not available in comparable implementations. The results presented here show that the quality of complex syntenies produced in real time by SyntTax equals or exceeds those obtained by other methodologies such as OTFQ which requires hours of systematic calculation on a finite set of genomes.