dataesr/scientific-paragraphs-categorization huggingface dataset:
Conclusions and Future Work We have described a highly multilingual collaborative VMWE-dedicated framework meant to unify terminology and annotation methodology, as well as to boost the development of VMWE identification tools. These efforts resulted in (i) the release of openly available VMWE-annotated corpora of over 5 million words, with generally high quality of annotations, in 18 languages, and (ii) a shared task with 7 participating systems. VMWE identification, both manual and automatic, proved a challenging task, and the performance varies greatly among languages and systems. Future work includes a fine-grained linguistic analysis of the annotated corpora on phenomena such as VMWE length, discontinuities, variability, etc. This should allow us to discover similarities and peculiarities among languages, language families and VMWE types.
The increase in available sequence data has advanced the field of microbiology; however, making sense of these data without bioinformatics skills is still problematic. We describe MICRA, an automatic pipeline, available as a web interface, for microbial identification and characterization through reads analysis. MICRA uses iterative mapping against reference genomes to identify genes and variations. Additional modules allow prediction of antibiotic susceptibility and resistance and comparing the results of several samples. MICRA is fast, producing few false-positive annotations and variant calls compared to current methods, making it a tool of great interest for fully exploiting sequencing data.
Big variations in sequencing depth-for example, if bacteria contain plasmids-could make the de novo assembly process difficult and result in a significant increase of the execution time. In MICRA, two mappers, a fast mapper and a robust mapper, are combined and a new adapted and fast variant caller specific for prokaryotes was developed, in order to be effective and to avoid any loss of data  [7]. MICRA is freely available as a web interface developed to be user-friendly for both clinicians and biologists  [8]. Parameters are optimized to offer the user an entirely automatic analysis, requiring only reads as input. For more specialized tasks, MICRA offers the possibility of customizable analyses by giving access to a lot of setting parameters. The outputs are directly readable and usable. MICRA was evaluated on several studies.
Results The MICRA pipeline The structure of the MICRA pipeline, organized in four main parts, is schematically presented in Fig. 1. The pre-processing part, which is optional, allows read quality checking and trimming. The first module uses FastQC  [9]  to check the quality of reads. Cutadapt  [10]  Fig. 1  The MICRA pipeline. Ovals represent the input files. Black boxes represent the four main parts of the MICRA pipeline and blue boxes show the constitutive modules. Dashed lines are used for optional steps. CDS coding sequence is used in the second module for adapter and quality trimming. Detection and trimming of adapters can be done automatically (sequence adapters are identified from FastQC results) or from sequences provided by the user. The reads are trimmed according to quality (Q20 by default) and size (20 nucleotides by default) thresholds; these values can be changed.
The sequence identification part aims to identify the reference sequences which will then be used in the core part of MICRA. The user can choose either to provide their own list of reference sequences (using NCBI identifiers; circle 2 in Fig. 1 ) or to let MICRA automatically select the potential reference sequences from reads (circle 1 in Fig. 1 ). To identify reference genomes and/or plasmids, a subset of the reads is BLASTed against a database containing selected complete genome and/or plasmid sequences (see "Methods"). The sequences showing the greatest numbers of hits are kept in an ID list. The sequence identification strategy was validated using several bacterial strains and sequencing technologies (Additional file 1: part 1). The second module automatically downloads FASTA and GFF files from NCBI or a local server for each entry in the ID list.
The core pipeline part of MICRA, consisting of four modules, takes as inputs the GFF and FASTA files obtained from the previous part and the FASTQ file(s) containing reads. In the first module, the whole set of reads is mapped against all the reference genome sequences with a fast mapper, SNAP  [11]. Mapping metrics are returned for each reference sequence and the percentage of genome coverage value is used to identify the closest reference sequence. Then a robust mapper, SHRiMP2  [12]  for Ion Torrent reads and Bowtie2  [13]  for Illumina reads, is used with the whole set of reads against the closest reference genome, allowing the variant calls and a comparative list of annotations to be obtained (in CSV and FASTA format). For paired-end data, only the concordant pairs of reads are mapped in order to keep reads as pairs in the next steps.
Home-made scripts were developed to compute the mapping metrics, the list of annotations, and to call variants from the SAM alignment file (see "Methods"). A SVG picture of the comparative annotations against the closest reference genome is also produced in this step using CGView  [14]. Each feature from the annotation file is then classified into five classes by MICRA: 1) absent (less than 10% covered), 2) present (more than 80% covered), 3) low complexity sequence (detected with DustMasker  [15] ), 4) partially present (coverage between 40 and 80% distributed among up to two blocks of conservation), 5) divergent (otherwise). When the user asks for plasmid processing, the same steps are performed leading to the identification and characterization of plasmids. In the third module, iterative mapping with the robust mapper is performed: the reads which are not mapped against the closest reference genome and plasmids are then iteratively mapped against the other reference genomes.
This step produces consensus sequences built from the mapping (i. e. , for consecutive covered positions in the reference sequence the consensus sequence is deduced from the reads) and the complete list of covered annotations. The reads that are still unmapped at the end of the iterative mapping steps are then used in the de novo assembly module. MIRA  [16]  is used for de novo assembly of Ion Torrent reads and SPAdes  [17]  for Illumina reads. Resulting contigs are then annotated by BLASTing them against the PATRIC CDS database  [18]. Sequences and annotations produced by the core part of MICRA can then be used in the post-analysis part. The post-analysis part contains two independent modules. The comparison module allows the user to compare the annotations and/or the variant calls from several samples.
A Venn diagram is produced to easily compare the different samples; the genes or variants from the different parts of the Venn diagram are also available. The second module aims to identify potential antibiotic susceptibility and resistance. Susceptibility and resistance genes for a given list of antibiotic drugs can be detected from the sequences produced by the core pipeline. The susceptibility genes are identified by BLASTing the sequences against a local version of DrugBank  [19]  and the resistance genes are identified by BLASTing the sequences against a modified version of the Antibiotic Resistance DataBase  [20]  (see "Methods"). A very easy to interpret table (in HTML format) summarizing the predicted susceptibility and resistance for each drug is returned, as well as the detailed BLAST results.
864 (0. 900 for PE reads) and a precision of 0. 999 (0. 999 for PE reads). These results prove that the in-house variant caller is efficient and fast. The plasmid pSFO157 (NCBI: AF401292. 1) was identified by MICRA with a coverage of 97. 18% (98. 20% for PE reads) and a mean depth of 78× (78× for PE reads), close to the theoretical expected sequencing depth. The comparative genomics results from the mapping against the pSFO157 plasmid showed that two CDSs (w0034 and w0076) were not covered. These two CDSs code for a duplicated IS30 transposase gene. This gene sequence is also present in the K12-DH10B genome, resulting in the mapping of the corresponding reads onto the genomic IS30 transposase sequence during the first step of the pipeline analysis and explaining the non-covered region in the plasmid sequence.
The consensus sequence built from the mapping step produced six sequences (three with PE reads) from K12-DH10B and eight sequences (three with PE reads) from the pSFO157 plasmid. At the end of this step, 415 reads (1334 pairs) were still unmapped and used for de novo assembly, producing only one contig (one contig for PE reads) larger than 500 bp. For both single and paired-end read simulations, automatic annotation of the de novo generated contig identified CDSs, among which were the Shiga-like toxin II subunit A precursor and Shiga-like toxin II subunit B precursor. The de novo assembly step allows MICRA to detect the artificial horizontal transfer of the two shiga-toxin genes. The antibiotic susceptibility and resistance module highlighted two differences in comparison with the prediction from the K12-DH10B genome sequence: the artificial strain is predicted to be sensitive to chloramphenicol due to the deletion of the mdtL gene and to be tetracycline-resistant due to a sequence encoded in the artificially introduced pSFO157 plasmid.
938 respectively with freebayes). Better recall values were obtained with the real reads rather than with the simulated ones, certainly due to higher error rates in the simulated data  [7]. The mdtL gene, which was deleted in the mutated genome, was identified during the iterative mapping against the MG1655 genome (NCBI:NC_000913. 3). The de novo assembly with the 11,944 remaining unmapped reads resulted in eight contigs greater than 500 bp corresponding to human sequences. The antibiotic susceptibility and resistance module used with these data showed a profile identical to the expected wild strain profile with chloramphenicol resistance mediated by the mdtL gene and tetracycline susceptibility. MICRA was then assessed under real-life conditions and results were compared to external tools.
In addition to the assembly step, IonGAP uses a comparative genomics module and a module for bacterial classification and annotation, producing some results directly comparable with the MICRA results. IonGAP contains a variant caller using raw reads and requiring a reference genome, here K12-DH10B, making this step comparable to the MICRA results with the reference genome. Two mutations were detected with MICRA, one true positive and one false positive. With IonGAP, 2684 mutations were predicted, of which 18 with genotype confidence equal to or greater than 10 could be considered as significant. Among the 18 mutations, 17 were tested by Sanger sequencing and proved to be false positives, and the true positive mutation, the insertion of a T at position 4,272,971, was not identified. No plasmid sequences were present in the sequencing sample.
Sequencing data from these circulating strains represented good material to evaluate the pipeline performance in the context of a real infectious case. In all experiments, we only used the five first runs generated with the Ion Torrent technology  [33]  in order to simulate the situation at the early stage of the outbreak period. The poor quality of the sequencing data is due to the early age of the technology at that time. MICRA was first used with the TY2482 chromosome and plasmids to evaluate the performance of the analysis, independent of the choice of the reference sequences given as input (Additional file 1: part 3-1). MICRA was then run in an automatic fashion. In order to get as close as possible to the original outbreak conditions, the sequences released after 2011 were discarded from the MICRA database, leading to the automatic selection of five genomes and ten plasmids.
The MICRA comparison module was then used to rapidly compare the TY2482, 2009-2050, and 2009-2071 strains. A Venn diagram (Fig. 3d ) showed that the three strains share a large number of CDSs and that the two 2009EL strains share more CDSs. Among the CDSs shared between TY2482 and 2009EL-2050, the tetA gene implicated in the tetracycline resistance was retrieved by MICRA.
Additional application examples In order to confirm the performance of MICRA, we performed two additional complete case studies with Ion torrent and Illumina paired-end data from Staphylococcus aureus and Clostridium autoethanogenum (Additional file 1: part 4). The results show that MICRA produced redundant sequences with pairedend data, especially with selected sequences genetically distant from the studied sequence, but also that it is still able to correctly identify more true CDSs than the other approaches. We also showed that MICRA is able to deal with varying sequencing depth and can be of great interest in cases where the assembly process is difficult (Additional file 1: part 4-1 on S. aureus Ion torrent data). Finally, the results greatly illustrate the potential and power of using MICRA in re-sequencing.
The power of HTS analysis with MICRA is illustrated by the immediate determination of variation points compared to a reference strain, allowing the user to immediately focus on important variations which could explain phenotype differences. This rapidity and efficiency is of particular interest for the characterization of a large number of strains, for instance in a microbial GWAS context  [40]. In the near future, we hope that MICRA will become enriched with additional modules thanks to user feedback. MICRA was developed and optimized for bacterial genomes but can also be adapted to other organisms such as viral or small eukaryotic genomes. Due to its mapping iterative approach, MICRA should deal with sequenced genomes obtained from culture-independent methods. To our knowledge, MICRA is the only automatic HTS pipeline based on mappers and comparative genomics, offering an easy-to-use web interface and readable results.
Conclusions Fast and cost-effective access to HTS data has prompted the development of efficient and reliable solutions for bioinformatics analyses. Here we present MICRA, an automatic pipeline for microbial genome characterization based on iterative read mapping onto annotated reference genomes available in databases. MICRA allows the rapid annotation of genomes and plasmids and the efficient determination of variations relative to close reference genomes thanks to the development of a new variant caller specific for prokaryotes. Additional modules allow for comparison of results and automatic determination of antibiotic resistance profiles. MICRA is freely available  [41, 42]  and easy-to-use as a web interface  [8]  requiring only reads as input. Assessment of MICRA in formal, clinical, and research contexts shows that it is much more rapid (10 minutes in most cases) and accurate than the usual de novo-based assembly methods.
Databases used in MICRA The identification module of MICRA uses homemade databases containing some of the complete sequences extracted from the NCBI and selected for their completeness, for the quality of their annotations, and to avoid similar sequences. We extracted 1533 complete genomes from the ftp summary file for bacterial genomes  [43]. Similarly, an initial list of 4437complete sequences for plasmids was obtained from  [44]. However, some plasmid sequences were very similar. We used PSI-CD-HIT  [45]  (options -c 0. 9 -G 1 -g 1 -prog blastn -circle 1) to cluster sequences showing more than 90% identity and we kept only one representative sequence of each cluster, leading to a final database containing 3807 complete plasmid sequences. The PATRIC CDS database is used during the annotation of the contigs produced by de novo assembly.
The antibiotic susceptibility and resistance module used two databases: the susceptibility is predicted from a local version of DrugBank (http://www. drugbank. ca) and the antibiotic resistance determination is based on a modified version of ARDB (https://ardb. cbcb. umd. edu/). Some links between resistance genes and drugs were added and some others were deleted by comparing the genes and associated drugs with The Comprehensive Antibiotic Resistance Database (CARD)  [46]. The betalactams were re-encoded according to CARD data and the fosfomycin link was deleted in the deoxycholate association from ARDB.
MICRA scripts and external software MICRA contains in-house scripts developed in Java for running, linking, and parsing the results of the steps executed with external software  [41, 42]. In addition to these constitutive scripts, a variant caller was developed to be accurate, fast, and adapted to prokaryotic genomes. The reference sequence is encoded by an array and for each position the mapping data are stored: the corresponding base in the reference genome, the number of reads mapped at this position (a multi-mapped read is counted as 1/n with n the number of mapping positions for this read), and the list of variants with their frequency. The consecutive variant positions are not considered independent: when a deletion of three consecutive bases occurs the mutation is not counted on the three corresponding bases but as a deletion of size 3 in the first position, which decreases the number of false positive variant calls.
In addition to call variants, data encoded in the array-based data structure also allow the genome coverage, the mean depth, and the coverage in percentage of annotations to be computed. MICRA is using several other technologies and external software in its different parts: FastQC  [9]  and cutadapt  [10]  in the pre-processing part, BLAST 2. 2. 28  [47]  in the sequence identification part, core pipeline and antibiotic parts, BioPerl in the sequence identification part to download files from NCBI, readSeq in the core pipeline to format FASTQ file, the bp_genbank2gff3. pl script  [48]  to convert GenBank format to GFF format in the sequence identification part, DustMasker  [15]  to identify low complexity regions in the core pipeline, SHRiMP 2. 2. 3  [12]  for the robust mapping of the Ion Torrent reads, SNAP 0.
Sequencing data The K12-DH10B reads were obtained from Life technology from an Ion Torrent run with a 316 chip. The corresponding FASTQ files are available at  [24]. The Pillemer P134 strain was lab adapted to obtain streptomycin resistance. The obtained resistant mutant was grown in Stainer Scholte (SS) medium + 100 μg/ml streptomycin at 37 °C to reach an OD 600nm of 2. 0. Genomic DNA was extracted using a QIAmp DNA minikit (Qiagen) and a sequencing library was prepared using Ion Shear Plus and Ion Plus Fragment kits (Life Technologies). The library was sequenced on an Ion Torrent PGM using a 314® chip (Life Technologies). The raw reads are available in SRA:SRR4019415  [28]. Cutadapt  [10]  (options -m 50 -q 20) was used to filter and trim the bad quality bases. A total of 623,304 reads were obtained after the quality trimming process.
The five Ion Torrent runs from the German outbreak were downloaded from  [33]  (Run 1-5), representing a total of 629,368 reads with a mean length of 105 bp and with qualities varying from Q28 to Q10, resulting in a mean theoretical depth of 10×. No pre-processing steps were performed. Illumina reads for E. coli 2009-2050  [50]  and 2009-2071  [51]  strains were extracted, respectively, from SRR647664 and SRR647666 obtained from the SRA databank. Reads sampling was performed with fastqsample  [52]  to obtain a mean depth of 40 ×.
Acknowledgements We thank  Simon Nguyen  ( Life Technologies ) for the E. coli str. K12 substr. DH10B sequencing data. We thank  Frits Movi  ( RIVM ) for providing the Pillemer strain,  Loïc Coutte  ( CIIL ) for generating the streptomycin-resistant Pillemer strain ( P134S ), and  Dominique Raze  ( CIIL ) for providing the BPZE1 strain.
Availability and requirements Poject name: MICRA Project home page: http://www. pegase-biosciences. com/ MICRA Source code: https://github. com/caboche/MICRA Archived version: https://zenodo. org/record/1045801 Operating system(s): Platform independent Programming language: Java Other requirements: none License: GNU GPLv3 Availability of data and materials HTS data used for Pillemer P134 strain analysis have been submitted to the NCBI Sequence Read Archive under accession number SRR4019415  [28]. The datasets generated and analyzed during the current study are available at http://www. pegase-biosciences. com/MICRA/data. html [24]  and parameters are available in Additional file 1: part 5. The MICRA pipeline is freely accessible at http://www. pegase-biosciences. com/MICRA  [8]. The MICRA source code is available on GitHub (https://github.
We used a purely biomechanical model of growth and chose the poroelastic formalism, because it allows coupling of the osmotic pressure with the stresses induced simultaneously in the cell wall and in the cytoplasm. Elastic constants for the cell wall, necessary to reproduce the observations, have been measured 1 ; unknown values for the soft interior were estimated or varied in our simulations. We tested two models of finite elasticity, the neo-Hookean (n-H) and the Mooney-Rivlin (M-R) models  [19, 20] , and we performed the simulations using FEBio software. 2 With this framework, we show that cell growth, combined with an imposed pressure, explains the morphological changes observed at the cellular level. In addition, branching appears to be the consequence of the accumulation of stresses in the cell wall, exceeding the stability limit and modifying the growth rate locally.
Conclusion The poroelastic model with M-R hyper-elasticity gives a satisfactory representation of the key features occurring during the morphogenesis of E. siliculosus, a brown alga made of uniseriate filaments. Here, we focused on the change from a cylindrical to a spherical cell via an increase in volume and on branching. These cellular events concern mature cells located in the central half of the filament. The simulations, carried out with FEBio software (see endnote 2), give satisfactory information on cellular morphogenetic events for a range of realistic elastic parameters, even if this software does not have the flexibility required for mechano-transduction processes necessary for branching. We would have preferred to introduce the plastic zone continuously as a function of the local stress induced by growth and osmotic pressure.
The IRBAS database integrates and provides free access to these data, contributing to the growing, and global, knowledge base on these ubiquitous and important river systems, for both theoretical and applied advancement. The IRBAS database currently houses over 2000 data samples collected from six countries across three continents, primarily describing aquatic invertebrate taxa inhabiting intermittent rivers during flowing hydrological phases. As such, there is room to expand the biogeographic and taxonomic coverage, for example, through addition of data collected during nonflowing and dry hydrological phases. We encourage contributions and provide guidance on how to contribute and access data. Ultimately, the IRBAS database serves as a portal, storage, standardization, and discovery tool, enabling collation, synthesis, and analysis of data to elucidate patterns in river biodiversity and guide management.