dataesr/scientific-paragraphs-categorization huggingface dataset:
Optimization technique of system programming 3. 1. 1 Optimization of system performance This system makes use of buffering technique and selecting buffering time is the crux of performance optimization. As personnel in power department does not frequently update database, data buffering model of ASP. NET is selected to design system page. Data which is usually stored or accessed or relatively important data shall be stored in internal storage so that response time of system is reduced. Buffering time is set to be a proper time and what kind of data on the page are to be buffered as well as when to empty buffering and renew write-in for inquiry are designated. This method enhances performance of the system and brings convenience for working personnel.
Code optimization system Code quality directly influences performance and maintainability of system. Therefore, good programming thought can greatly improve performance and maintainability of system. Some methods are as follows: (1) Reduce connection with database as much as possible. It will take a lot of time to repeatedly connect database. With increase of data amount and rapid increase of internal storage consumption, resources of connection pool will not be released in a short time and finally, the system will break down. (2) Making use of variable distinct. If statement data repeatedly emerge at the process of system development, variable distinct can be used to solve all problems. Codes are as follows: SELECT distinct Level Type FROM WHERE.
Type IV effectors (T4Es) are proteins produced by pathogenic bacteria to manipulate host cell gene expression and processes, divert the cell machinery for their own profit and circumvent the immune responses. T4Es have been characterized for some bacteria but many remain to be discovered. To help biologists identify putative T4Es from the complete genome of a-and c-proteobacteria, we developed a Perl-based command line bioinformatics tool called S4TE (searching algorithm for type-IV secretion system effectors). The tool predicts and ranks T4E candidates by using a combination of 13 sequence characteristics, including homology to known effectors, homology to eukaryotic domains, presence of subcellular localization signals or secretion signals, etc. S4TE software is modular, and specific motif searches are run independently before ultimate combination of the outputs to generate a score and sort the strongest T4Es candidates.
However, all the algorithms used in these works were based on the identification of a limited number of sequence motifs and were designed for specific aor g-proteobacteria. To circumvent the risk of missing important T4Es, we decided to develop a new algorithm combining searches for all motifs that previously predicted T4Es of aand g-proteobacteria. In addition, new sequence motifs were derived from the analysis of the extensive repertoire of T4Es characterized in L. pneumophila  (8)  and included in the algorithm. In this article, we present 'S4TE' (Searching Algorithm for Type-IV secretion system Effectors), a tool for in silico screening of proteobacteria genomes and T4Es prediction based on the combined use of 13 distinctive features. This software was first probed against the comprehensive T4E dataset of L.
pneumophila, strain Philadelphia (8) and subsequently tested on several genomes of aand gproteobacteria. S4TE is both memory-and time-efficient. Although advanced users will be capable of modifying searching parameters of S4TE (e. g. exclusion of modules, change in module weighting, selection threshold or input databases), the common user can easily run the program with default settings. Installation process and basic command lines to launch and run S4TE are detailed in the user guide. S4TE package is freely available to noncommercial users at http://sate. cirad. fr/.
Overview We propose an easy-to-use and customizable algorithm for the prediction of candidate effector proteins secreted by T4SS. The algorithm can be used as a standard pre-selection technique for T4 effectors in genomes of any size. Its modularity will offer a simple and robust alternative to machine learning approach for less-studied pathogenic bacteria. In this section, we describe the algorithm used by S4TE, how the parameters of this software were estimated from the literature and how S4TE performs on different genomes. The essential features of the S4TE program, as depicted in Figure  1 , are the following: (i) genome-wide screening based on 13 different criteria including homology to known T4Es, occurrence of eukaryotic-like domains or motifs and subcellular localization signals; (ii) T4Es prediction and ordering output based on criteria scoring; (iii) information on prediction performance compared with the reference L.
S4TE search modules S4TE is a modular program written in Perl that screens bacterial genomes. The 13 search parameters are listed in Table  1  and are run in 10 modules detailed below. (1) De novo regulatory motif search. In Legionella, the PmrA and CpxR response regulators regulate numerous T4Es  (40). Because cis-acting regulatory sequences have not been described so far in a-proteobacteria, a motif search was performed in the promoter region of the 19 known T4Es of this class of bacteria in Anaplasma, Ehrlichia, Brucella and Bartonella genera. Enriched DNA motifs were searched in a window of 300 nt placed upstream of the start codon, using MEME (41) (http:// meme. nbcr. net/meme/). A consensus motif of 10 nt was identified in 14 promoters. This motif, termed RS-TY, consists of 3 purines (R), 1 strong base G or C (S), any nucleotide (A, T, G, C), 4 thymines (T) and 1 pyrimidine (Y) (Supplementary Figure  S1 ).
Only alignments with a score >130 are selected. (2) Homology. Effectors were shown not only to share local sequence similarities but also to diverge rapidly  (8). BLAST 2. 2 (44) was used for protein comparisons to look for homologies with known T4Es. The command line application can be downloaded from the NCBI web page. The blastp software allows the search of local similarity regions between two protein sequences. S4TE compares the database containing all known T4Es with the query proteome and returns all proteins containing a region that has local similarity with a cutoff of expected value (E) <0. 01. This E-value cutoff of 0. 01 was selected to show similarities between phylogenetically distant bacterial species  (6). The majority of known T4Es are from g-proteobacteria, and S4TE was expected to work also with a-proteobacteria.
The blastcut. pl program was written to reformat the output file of blastp program for user-friendly reading and for S4TE compiler. The blastcut. pl returns only blastp positive alignments. (3) and (  4 ) Eukaryotic-and prokaryotic-like domains. The search for eukaryotic-and prokaryotic-like domains  is done by Pfam-scan. pl in the PfamScan package (http:// pfam. sanger. ac. uk)  (45). To run properly, Pfam-scan. pl needs several softwares: the Moose module in CPAN (http://search. cpan. org/$ether/Moose-2. 0801/lib/Moose. pm), hmmer3. 0rc2 and the BioPerl-1. 6. 1. tar. gz package. In addition, Pfam-scan. pl needs motif database Pfam-A. hmm (http://pfam. sanger. ac. uk). For file size purpose and memory saving, hmmcut. pl was designed to generate a motif database with the Pfam ID of each motif of interest.
This motif database contains 58 eukaryotic-like domains previously found in effectors (Supplementary Table  S1 ) and 3617 prokaryotic-like DUF domains  (29). PfamManager. pl will reformat the output file of Pfam program for user convenience by separating search results for eukaryotic-like domains and for DUF domains. (5) Nuclear localization signals (NLS). NLS are protein sequences that target proteins in the nucleus of eukaryotic cells  (1). We assumed that the occurrence of NLS in a bacterial protein sequence would be a good indicator of secretion. There are two classes of NLS. Monopartite NLS consist of the PKKKRKV motif  (30). Bipartite NLS are more complex and consist of two alkaline clusters (K and R) separated by a variable spacer  (31). We wrote NLS.
pl to search for monopartite NLS with the [KR]-[KR]-[KR]-[KR]-[KR] motif and for bipartite NLS with the K-[KR]-X(6,20)-[KR]-[KR]-X-[KR] motif. The latter motif was derived from multiple alignments of known eukaryotic protein sequences containing NLS  (31). (6) Mitochondrial localization signals (MLS). MLS are signal sequences located in the N-terminus of proteins that are targeted to mitochondria. This sequence is cleaved after translocation of the protein inside the mitochondria  (1, 22). To predict MLS and extract the predicted signal, we used the Mitoprot. pm package of Bioperl  (32). Mitoprot. pl and Mitocut. pl were developed to use the Perl module Mitoprot. pm and to format the output file, respectively. Only MLS with P > 0. 95 are selected by S4TE. (7) Prenylation domains. Prenylation is a permanent post-translational modification that is required for protein stability  (1).
Most proteins containing validated coiled-coil domains are of eukaryotic origin  (47). Interestingly, coiled-coil domains are frequently found in secreted virulence effector proteins  (47). To search for coiled-coil domains, we used pepcoil software of Emboss package (http://emboss. bioinformatics. nl/cgi-bin/emboss/pepcoil). The module pepcoilcut. pl of S4TE extracts coiled-coil domains with P > 0. 95 and formats the output file. (  9 ) and (  10 ) C-terminal basicity and charge. T4Es often have a positive charge and a large number of alkaline amino acids (HRK) in the 25 C-terminal amino acids  (3, 7, 22). In S4TE, these two features are investigated with charge. pl module. In a-proteobacteria, all known T4Es have three or more alkaline amino acids (HRK) in the 25 C-terminal amino acids. This feature was used as a threshold to select positives.
Each search module was individually designed and refined with dedicated T4Es datasets of different origins to achieve the best specificity (i. e. probability to find a true negative [TN]). We then adjusted S4TE (module weights and threshold) to have the best positive predictive value (PPV; proportion of true positives [TP] detected among all positives) and sensitivity (probability to find a TP) on the dataset of 275 T4Es characterized in L. pneumophila, strain Philadelphia (see Results section). S4TE is also designed to provide two performance indicators attached to any combination of searched parameters. The first indicator is the Sensitivity Index for Legionella (SI L ) of a given combination: SI L is calculated by SI L ¼ TP ðTP+FPÞ and ranges from 0 to 1. SI L gives information on the proportion of TP found in L.
Data compilation The main program S4TE. pl executes the 10 previously described modules to generate 13 result files. Afterward, a compiler (compilation. pl) will collect all information from result files generated during the pipeline execution and will highlight important data (Figure  1 ). For each feature, compilation. pl will search the identified proteins and will count the number of positive hits. Then, compilation. pl will sort the results by top/down scoring. For each analysis, the compiled results are written in CompilationFile. txt with the list of identified proteins, the number of hits, the score and the combination of positive features for each protein.
Databases To analyse a given genome, S4TE needs a genome database with four distinct files: (i) Genome. nucl containing the FASTA genome sequence; (ii) Genome. an, a csv file (with; as separator) containing the gene ID; the position of the first nucleotide of coding sequence; the position of the last nucleotide; the sense or antisense status; before use in S4TE, this file needs to be sorted in ascending order from the first nucleotide position; (iii) Genome. prot, a fasta file containing all the protein sequences of the genome; and (iv) Genome. csv, a file constructed from Genome. prot with nomprot. pl in the folder $/S4TE/DataBases/Genome/Tools/ A database of validated effectors derived from the literature was constructed in a protein sequence file of effectors (effector_db. txt) and formatted by makeblastdb -in effector_db.
txt -dbtype prot The database assembling the eukaryotic Pfam domains found in T4Es was constructed from the downloaded original wget ftp://ftp. sanger. ac. uk/pub/ databases/Pfam/releases/Pfam26. 0/Pfam-A. hmm*. gz The tool hmmcut. pl will construct the Pfam-A. hmm database with the motifs used by S4TE only. The Pfam-A. hmm database is formatted by. hmmpress Pfam-A. hmm.
Software availability S4TE is freely available to non-commercial users at http:// sate. cirad. fr/. Programming was done using Perl 5. 12 and BioPerl 1. 6. The software runs on Linux platforms (Ubuntu 11. 10 and Mac OS X). All required packages and the installation process are described in the user guide included in Supplementary Methods S1. The user guide also details S4TE options for running S4TE. By default, the command line to launch S4TE is. S4TE. pl -g ''name of the genome'' from the S4TE folder (cd way_to_S4TE/S4TE/). Some options are available for the user to launch S4TE: -c, suppression of a module in the pipeline; -w, modification of the weight of each module in the pipeline; -t, imposition of a threshold for effector selection. Each S4TE module creates an. txt file in the folder way_to_S4TE/S4TE/Jobs/job<Name_ of_genome_folder><year><month><day> <hour><min> All the results are compiled in CompilationFile.
On the L. pneumophila dataset, the algorithm with unweighted parameters and the default threshold at 5 selected 151 TP and 2428 TN. However, 238 hits were not T4Es (false positive [FP]), and 124 T4Es were not found by S4TE (false negative  [FN] ). This led to a sensitivity (Se) of 55%, a specificity (Sp) of 91%, PPV [PPV = TP/ (TP+FP)] of 38. 8% and a negative predictive value [NPV = TN/(TN+FN)] of 95. 1%. To improve Se and PPV, different parameter weightings were tested. The best combination (i. e. 1311111111111) led to the selection of 223 TP of the 275 effectors of L. pneumophila (Se = 81%, PPV = 72. 8%); it led to only 83 FP (Sp = 98. 8%, NPV = 98%). The weighting code in brackets is a 13-digit code corresponding to the 13 features in the S4TE scheme and is reported in the head of the compilation file and the results file (Supplementary Figures  S2  and S3 ).
S4TE successfully predicts T4Es in other bacterial pathogens S4TE was used to analyse the genome of four representative aand g-proteobacteria. Because our validation  The number of true positives (TP), false positives (FP) and the positive predictive value (PPV, expressed in %) is indicated. analysis on L. pneumophila suggested that the software had high accuracy (>90%), we next focused on bacterial pathogens of aand g-classes having various genome sizes and for which some T4Es were already confirmed experimentally. To evaluate the computational requirements of our algorithm, the different runs on these datasets were timed. It took a maximum of 40 min for the bigger genomes (2-3 Mbp). In the annotation of each genome, S4TE indicated the total number of candidate T4Es, and their position relative to the G+C content and to their FIRs (Table  3 ).
ACKNOWLEDGEMENTS The authors thank  T. Lefranc¸ois  and  D. Pleydell  for useful suggestions,  L. Bournez  for her aid with Figure 1 and  S. Gonzalez-Rizzo  for her support and fruitful discussions. D. F. M also thanks P. L. and  M. J. Meyer  for inspiration. The authors are grateful to  G. Burkhart  and anonymous reviewers for critical reading and improvement of the manuscript.
The functional annotation of genes based on sequence homology with genes from model species genomes is time-consuming because it is necessary to mine several unrelated databases. The aim of the present work was to develop a functional annotation database for common wheat Triticum aestivum (L. ). The database, named dbWFA, is based on the reference NCBI UniGene set, an expressed gene catalogue built by expressed sequence tag clustering, and on full-length coding sequences retrieved from the TriFLDB database. Information from good-quality heterogeneous sources, including annotations for model plant species Arabidopsis thaliana (L. ) Heynh. and Oryza sativa L. , was gathered and linked to T. aestivum sequences through BLAST-based homology searches. Even though the complexity of the transcriptome cannot yet be fully appreciated, we developed a tool to easily and promptly obtain information from multiple functional annotation systems (Gene Ontology, MapMan bin codes, MIPS Functional Categories, PlantCyc pathway reactions and TAIR gene families).
Genome-wide analysis of gene expression by means of expression microarrays or transcriptome sequencing (RNA-Seq) is now being adopted in T. aestivum  (5, 6) , but analysing such large datasets requires extensive annotation efforts. Data fragmentation and technical and semantic heterogeneity can severely limit the efficient extraction and interpretation of biological data  (7, 8). More and more genomic information is becoming available for T. aestivum research. Various resources and associated tools grant the user a structural overview of expressed sequence tag (EST) (ITEC, http://avena. pw. usda. gov/genome/)  (9, 10)  or bacterial artificial chromosome clone libraries  (11, 12)  for instance  (13) (14) (15). Important initiatives are underway to facilitate the breeding of improved Triticeae varieties.
The TriticeaeGenome project (www. triticeaegenome. eu) grants access to comprehensive information extracted from experimental data to provide a better understanding of Triticeae genomes  (16). The global database GrainGenes (http://wheat. pw. usda. gov/) provides a variety of services and bioinformatics tools for the Triticeae and Avena sativa research communities. The HarvEST database (http://harvest. ucr. edu/) (17) , dedicated to several crop species, including T. aestivum and Hordeum vulgare, provides access to curated EST assemblies, comparative analysis tools and links to orthologues in related model plant species. Together, these resources compile and cross-reference a great deal of information on physical and genetic mapping, markers, sequence variations and quantitative trait loci.
e. overlapping and partial polyA-tailed expressed sequences are grouped) to provide a reference set of unique expressed genes, NCBI UniGenes (http://www. ncbi. nlm. nih. gov/UniGene). The assembly conditions used to build NCBI UniGenes make it the most comprehensive coding DNA (cDNA) assembly available to date, and UniGene assemblies have been used as a reference set of sequences for many species. An additional effort was made to construct full-length cDNA sequences that were included in the TriFLDB database  (19). Full-length cDNA sequences are most commonly used in genome annotation as a resource for cross-species comparative analyses. Currently, TriFLDB is the most reliable source of full-length cDNA sequences in T. aestivum. TriFLDB includes annotations based on homologies found by searching protein databases, extensive Gene Ontology (GO) annotations and InterProScan results.
Although H. vulgare (L. ) would be expected to be the most useful reference because it is more closely related to T. aestivum, comprehensive and highquality annotations of gene function are only available for O. sativa and A. thaliana (2), essentially owing to the lengthy and accurate annotation efforts undertaken. To annotate ESTs or transcripts using sequence homology, it is necessary to navigate through unrelated databases. Some tools using this homology approach have been developed. For instance, Blast2GO (31) can be queried using T. aestivum sequences to give GO results. ONDEX  (7) , developed with the challenges of functional annotation of T. aestivum genome in mind, combines data integration from various sources and various mining methods, including graph-based analyses, to annotate wheat gene functions according to a wisely chosen set of annotation standards.
However, ONDEX does not provide easy access to workable static results that are often required in research. To fill this gap, we developed dbWFA, an open-access database relating the T. aestivum UniGene set and the full-length cDNA sequences from TriFLDB to A. thaliana (TAIR10)  (32)  and O. sativa (pseudomolecules version 7. 0)  (33)  annotation through BLAST  (34)  results. dbWFA also includes the inventory database of T. aestivum transcription factors (wDBTF)  (35)  and hand-curated gene families  (36). As an all-in-one interface for the annotation of T. aestivum sequences, dbWFA will be useful to the researchers working on T. aestivum and more generally on cereals, particularly for comparative cereal genomics and functional genomics. The web implementation of dbWFA provides an easy-to-use interface to annotate transcript sequences from T.
aestivum, with functional information from multiple pervasive annotation systems. Here, the use of dbWFA is illustrated with several query examples, and the quality of the annotation method is assessed by comparing the MapMan bin annotation of all the transcripts of the T. aestivum NimbleGen 40 k microarray  (37)  with that of A. thaliana and O. sativa. The use of dbWFA is further illustrated by analysing the annotation of 433 genes specifically expressed during either the early cell division or the late storage polymer accumulation (SPA) phases of grain development.
Data Content, Database Architecture and Web Interface Five functional classification/annotation systems were integrated (Figure  1 ) in dbWFA to offer a fast and efficient functional annotation tool for T. aestivum UniGenes: GO (http://www. geneontology. org) (38) , a nonredundant structured hierarchy of ontologies, which is the most widely used functional annotation system in bioinformatics. The GO project provides an efficient annotation standard that can be applied to numerous species. It is built on a controlled vocabulary of terms for describing gene function. dbWFA includes GO annotation data (OBO version 1. 2) for A. thaliana and O. sativa. (42) , which provides gene family information for the plant model species A. thaliana. The 17 541 full-length cDNA sequences from the TriFLDB and other public databases and all the transcript sequences from the T.
aestivum UniGene set (builds #55, #58, #59 and #60) were processed using the BLASTx algorithm against A. thaliana and O. sativa predicted cDNA sequences (Figure  1 ). Build #55 (the one used to develop the T. aestivum NimbleGen 40 k microarray)  (37)  and the following major releases were retained, as users may have developed resources based on different builds of the UniGene even though NCBI only stores the most recent build. BLAST results with an e-value >10 À3 were not stored in the database, as we considered this would be too poor a match for most research. No other filter was applied to the BLAST results before their insertion into the database. All the parameters from the BLAST tabular results were kept, and >30 Â 10 6 BLAST results for the UniGenes and 95 Â 10 6 BLAST results for the ESTs shaping the UniGene clusters were stored, so they could be rapidly screened when querying the database.
The database also contains curated information on T. aestivum transcription factors (2891 transcripts), E3 ubiquitin ligases of the ubiquitin-proteasome system (876 transcripts), hormone-responsive genes (467 transcripts) and seed storage proteins (55 transcripts; Figure  1 ). Transcription factor UniGenes were retrieved from the wDBTF database  (34). E3 ligase and hormone-responsive UniGenes were recovered from the NCBI and TAIR databases using all A. thaliana and O. sativa E3 ligase and hormone-responsive sequences as the query in homology searches using the BLASTn, BLASTx and tBLASTx programs  (36). The BLAST hits were filtered using an e-value threshold of 10 À5 and an alignment length exceeding 80 bp. All sequences were checked for consistency and for the presence of specific protein signatures using the InterProScan program (http://www.