dataesr/scientific-paragraphs-categorization huggingface dataset:
Experiment 1: Input Coverage We selected 12 widely known biomedical databases and extracted 600 biomedical texts from them, with 127 words on average, and 600 lists of biomedical keywords, with 17 keywords on average, producing a total of 1200 inputs (100 inputs per database). The databases used are listed in Table  6. Given the importance of input coverage, we first executed both systems for all inputs and compared the coverage provided by the top-ranked ontology. We focused on the top-ranked ontology because the majority of users always select the first result obtained  [40]. The strategy we used to calculate the ontology coverage differed depending on the input type: • For texts, the coverage was computed as the percentage of input words covered by the ontology with respect to the total number of words that could be covered using all BioPortal ontologies together.
• For keywords, the coverage was computed as the percentage of keywords covered by the ontology divided by the total number of keywords. Figure  6  and Figure  7  show a representation of the coverage provided by both systems for each database and input type. Table  7  and Table  8  provide a summary of the evaluation results. a Mean of the number of words for the inputs extracted from the database. b Percentage of executions where the coverage of the top recommended ontology was lower than 20%. c Mean coverage provided by the top ranked ontology. * Ontology Recommender 1. 0; ** Ontology Recommender 2. 0; *** Ontology Recommender 2. 0 (ontology sets output). For some inputs, the first ontology suggested by Ontology Recommender 1. 0 provides very low coverage (under 20%). This results from one of the shortcomings previously described: Ontology Recommender 1.
0 occasionally assigns a high score to ontologies that provide low coverage because they contain several classes matching the input. The new recommendation approach used by Ontology Recommender 2. 0 addresses this problem: Virtually none of its executions provide such low coverage. For example, Table  9  shows the ontologies recommended if we input the following description of a disease, extracted from the Integrated Disease View (IDV) database: Chronic fatigue syndrome refers to severe, continued tiredness that is not relieved by rest and is not directly caused by other medical conditions. See also: Fatigue. The exact cause of chronic fatigue syndrome (CFS) is unknown. The following may also play a role in the development of CFS: CFS most commonly occurs in women ages 30 to 50. Ontology Recommender 1.
0 suggests the Bone Dysplasia Ontology (BDO), whereas Ontology Recommender 2. 0 suggests the NCI Thesaurus (NCIT). Because BDO covers only 4 of the input terms, while NCIT covers 17, the recommendation provided by Ontology Recommender 2. 0 is more appropriate than that of its predecessor. Ontology Recommender 2. 0 also provides better mean coverage for both input types (i. e. , text and keywords) across all the biomedical databases included in the evaluation. Compared to Ontology Recommender 1. 0, the mean coverage reached using Ontology Recommender 2. 0 was 14. 9% higher for texts and 19. 3% higher for keywords. That increase was even greater using the "ontology sets" output type provided by Ontology Recommender 2. 0, which reached a mean coverage of 92. 1% for texts (31. 3% higher than the Ontology Recommender 1.
Our specialization criterion uses the content of the ontology and the ontology 13 https://en. wikipedia. org/wiki/Anatomy size to discriminate between large ontologies and small ontologies that have better specialization. However, ontologies that provide multiple annotations for the input data are not always specialized to deal with the input domain. Sometimes very specialized ontologies for a domain may provide low coverage for a particular text from the domain. In this scenario, metadata about the domain of the ontology (e. g. , 'anatomy' in the case of FMA) could be used to enhance our ontology specialization criterion by limiting the suggestions to those ontologies whose domain matches the input data domain. We are currently refining, in collaboration with the Center for Expanded Data Annotation and Retrieval (CEDAR)  [41]  and the AgroPortal ontology repository  [42] , the way BioPortal handles metadata for ontologies in order to support even more ontology recommendation scenarios.
Funding This work was supported in part by the  National Center for Biomedical Ontology  as one of the  National Centers for Biomedical Computing , supported by the  NHGRI , the  NHLBI , and the  NIH Common Fund  under grant  U54 HG004028  from the  U. S. National Institutes of Health. Additional support was provided by  CEDAR , the Center for Expanded Data Annotation and Retrieval ( U54 AI117925 ) awarded by the  National Institute of Allergy and Infectious Diseases  through funds provided by the trans- NIH  Big Data to Knowledge ( BD2K ) initiative. This project has also received support from the  European Union 's  Horizon 2020 research and innovation programme  under the  Marie Sklodowska-Curie  grant agreement No  701771  and the  French National Research Agency  (grant  ANR-12-JS02-01001 ).
Availability of data and materials • Project name: The Biomedical Ontology Recommender. • Project home page: http://bioportal. bioontology. org/recommender. • Project GitHub repository: https://github. com/ncbo/ncbo_ontology_recommender. • REST service parameters: http://data. bioontology. org/documentation#nav_recommender. • Operating system(s): Platform independent. • Programming language: Ruby, Javascript, HTML.
The BioPortal website received 306. 9K pageviews/month on average in 2016 (see Additional file 1 for more detailed traffic data). The two main BioPortal papers [3, 4]  accumulate 923 citations at the time of writing this paper, with 145 citations received in 2016. 2  http://bioportal. bioontology. org/ 3 At the time of writing this paper, there are 63 citations to the NCBO Ontology Recommender 1. 0 paper [6]. The Ontology Recommender 1. 0 API received 7. 1K calls/month on average in 2014. The Ontology Recommender webpage received 1. 4K pageviews/month on average in 2014. Detailed traffic data is provided in Additional file 1.
The BioPortal website received 306. 8K pageviews/month on average in 2016 (see Additional file 1 for more detailed traffic data). The two main BioPortal papers [3, 4]  accumulate 923 citations at the time of writing this paper, with 145 citations received in 2016. 2 http://bioportal. bioontology. org/ 3 At the time of writing this paper, there are 6357 citations to the NCBO Ontology Recommender 1. 0 paper [6]. The Ontology Recommender 1. 0 serviceAPI received has received more than 7. 1K95 calls per month on average in 2014. Detailed traffic data is provided in Additional file 1. 4 This formula is slightly different from the scoring method presented in the paper describing the.
To identify the causative mutation, whole-genome sequencing was performed on two affected cattle, one heterozygous carrier and one wild-type (WT). The detected polymorphisms (SNP and small indels) were filtered in several steps. First, the genotype/phenotype correlation had to be perfect, i. e. affected cattle had to be homozygous for the polymorphism, and the WT and carrier cattle had to be homozygous or heterozygous, respectively, for the WT allele. Second, since this mutation is supposedly specific to the Rouge-des-Pres breed, with relatively recent emergence, polymorphisms were discarded if they were already present in the dbSNP database and/or in the Illumina SNP chip. Finally, polymorphisms were filtered according to their predicted effects on transcript and/or protein, based on the hypothesis that this mutation is very deleterious (Table  1 ).
Thus, homozygous mutants from both lines were regarded as functional knock-outs and will now be referred to as Tg -/-mice. Invalidation of Slc25a46 in mouse results in a drastic phenotype, with an impaired growth, neurological symptoms and early death At birth, Tg -/-pups from the two lines were indistinguishable from each other and from their WT and heterozygous littermates, despite reported expression of the Slc25a46 gene early during mouse embryogenesis in various EST databases. However, their growth was reduced compared to the WT pups from the end of the 1 st week of life, and from the 2 nd week, they stopped gaining weight (Fig  3A  and 3B ). The observed reduced growth rate started despite a normal feeding behavior during the first weeks, while the pups were still nursed by their mother, as evidenced by the presence of milk in their stomach ( https://doi.
Ethics statement All procedures involving animals conformed to the Guide for the Care and Use of Laboratory Animals (NIH Publication No. 85-23, revised 1996). All efforts were made to minimize suffering. Blood samples were collected from cattle by veterinarians or by trained and licensed technicians during routine blood sampling for paternity testing, genomic selection or annual prophylaxis. Affected calves were euthanized for ethical reasons, due to the absence of effective treatment. All samples and data were obtained with permission of breeders or breed organizations. For mice, protocols were approved by the Animal Experimentation Ethics Committee and the French Ministry of Research (APAFIS#1227-2015100516164803 v3), and the Haut Conseil des Biotechnologies (HCB n˚6461).
Homozygosity mapping Twelve affected calves were examined clinically and confirmed to have the disease  [29]. Blood samples were collected from these calves and their parents, and DNA was extracted with a Genisol Maxi-Prep kit. Blood samples were also collected from control animals known to be unaffected based on our genotypes database. In total, 123 unaffected adult cattle, all of the Rouge-des-Pres breed, were selected (including eight bulls used for artificial insemination and 115 cows from the La Greleraie INRA experimental facility). All of these cattle were genotyped by Labogena with the Bovine SNP50 Beadchip V1 (Illumina). Mapping was carried out by homozygosity mapping with in-house HOMAP software, as described  [28].
Whole-genome sequencing and analysis Whole genome sequencing was performed at the Get-PlaGe platform (http://genomique. genotoul. fr/) on a HiSeq 2000 Illumina sequencer producing 100-bp long, paired end reads. Four animals had their entire genomic DNA sequence determined (two affected, one carrier and one healthy). Reads were quality checked and mapped on the UMD3. 1 reference genome using BWA aln software (version 0. 5. 9-r16). Alignments were filtered with a minimum MAPQ value of 30. Reads that mapped to multiple localizations were removed. The target region was selected on each produced. bam file using Samtools (Version 0. 1. 18). Local indel realignment and base quality recalibration were applied using GATK toolkit. The SNPs were predicted with samtools mpileup and bcftools, and annotated with Ensembl Variant Effect Predictor tool and SNPs were filtered according to the animals' phenotype-genotype correlation.
TALEN plasmid constructs Exon3 of mouse Slc25a46 gene was targeted at the site of the original mutation associated with phenotype (chromosome:GRCm38:18:31604753:31606764:1 (reverse complement)). The target sequence was chosen with the ZiFiT Targeter program (http://zifit. partners. org). A potential TALEN target sequence identified by the program was selected empirically with a preference for an 18-16-18 combination (16 bases for the spacer). The chosen sequences were TGTGCTGGCCCATCCTTG for the left TALEN and CAGTGTCAGGTAAATATA for the right. No homology with the targeted sequence was identified (Blast NCBI) at any other location in the genome that could represent a potential off-target site The TALEN kit used for TALE assembly was a gift from the Keith Joung laboratory (Addgene kit # 1000000017).
d. × 5 mm, 5 μm, Thermo Scientific) at a flow rate of 20 μl. min -1 for 3 min with 2% ACN/ 0. 08% TFA (v/v). Peptides were then separated on a reverse phase nanocolumn (PepMap100, C18, 75 μm i. d. × 150 mm, 2 μm, Thermo Scientific) with a two-step gradient from 1 to 25% B for 42 min and from 25 to 35% B for 5 min at 300 nl. min -1 at 40˚C (buffer A: 2% ACN/0. 1% FA (v/v), buffer B: 80% ACN/0,1% FA (v/v)). Ionization was performed in positive mode (1. 4 kV ionization potential) with a liquid junction and a sillica tip emitter (10 μm id, NewObjective). Peptide ions were analyzed using Xcalibur 2. 0. 7 with a data-dependent method including two steps: (i) full MS scan (m/z 300-1,400) and (ii) MS/MS (normalized collision energy fixed to 35%, dynamic exclusion time set to 45 s). The MS and MS/MS raw data were submitted for protein identification and quantification by spectral counting using the X!TandemPipeline.
3. 3. 4 (version 2015. 06. 03, http://pappso. inra. fr/bioinfo/xtandempipeline/) with X! Tandem search engine (version Piledriver, 2015. 04. 01, http://www. thegpm. org/TANDEM) and Uniprot SwissProt mus musculus database (version 2015. 10. 14; 25248 entries). Search criteria used were trypsin digestion, carbamidomethyl (C) set as fixed modification and oxidation (M) set as variable modification, one missed cleavage allowed, mass accuracy of 10 ppm on the parent ion and 0. 5 Da on the fragment ion. The final search results was filtered using multiple threshold filter: -4. 0 protein log (E-value) identified with at least two different peptides with Evalue < 0. 01.
Acknowledgments We thank  J. M. Nicol  for his early involvement which facilitated identification of the novel bovine polyneuropathy syndrome. We are grateful to the veterinarians and breeders for their help to recruit bovine cases, through the  French National Observatory of Bovine Abnormalities (ONAB). We thank  A. Valais ,  G. Aminot  ( OS Rouge-des-Pres ) and  F. Phocas  for providing semen and blood samples from AI sires and Rouge-des-Pres wild-type animals. We thank  K. Moazami-Goudarzi  for providing the bovine biodiversity panel. We thank  J. Guillemain  for helping analyzing the blood sample results. We are grateful to  O. Bouchez  and colleagues for technical support in sequencing experiments and bioinformatics analyses at the  Toulouse Midi-Pyrenees Genotoul platform , and to the  Plateforme d'Analyse Proteomique Paris Sud-Ouest (PAPPSO)  for assistance with proteomic analyses.
Around 2000 mature embryos were used and homogenized with metallic beads using TissueLyser (Qiagen) followed by DNA extraction (PureLink DNA purification kit, Life Technologies). DNA purity and quality were controlled using Qubit fluometry and agarose gel electrophoresis. Between 1 and 2 ug of genomic DNA were bisulfite treated (EZ DNA Methylation-Gold kit, Zymo) and pair-end sequenced using BGI Tech Solutions WGBS service/Illumina 2000 sequencing technology. Sequencing data are deposited in GEO with accession GSE85975.
Comparative analysis Visualization of DNA methylation was carried out using IGV software (Broad institute). DMR comparison was illustrated using SignalMap genome viewer (Roche). Venn diagrams were generated using BioVenn web application (  [57]  http://www. cmbi. ru. nl/cdd/biovenn/) and statistical significance tests applied (http://nemates. org/MA/progs/ overlap_stats. html). Expression analysis was carried out using Genevestigator software (Nebion) and publicly available data  [58]. For tissue-specific expression visualization, we made use of the online EFP-browser (www. bar. utoronto. ca/efp/cgi-bin/efpWeb. cgi), based on published expression data  [59, 60]. Global DNA methylation was compared to published data on WGBS of wild type leaf tissue  [61]. Meta-TE analysis TAIR10 annotated TEs were grouped into three categories based on their length.
Funding This work was supported by the  European Union   Seventh Framework Program Network of Excellence EpiGeneSys  ( HEALTH-F4-2010-257082 ), the  CNRS  and the  Agence Nationale de la Recherche  ( Investissements d'Avenir   ANR-10-LABX-54   MEMOLIFE ,  ANR-11-IDEX-0001-02 PSL Research University ,  ANR-12-ADAP-0020-01  and  ANR-14-CE19-0008 ) to VC and by an  European Research Council   Starting Independent Researcher Grant  ( 210867-2 ) to AS.
Availability of data and materials Sequencing data are deposited in GEO with accession GSE85975, containing WGBS as of Col-0 and fie mature embryos, four-day-old seedlings as well as sRNA-seq of Col-0 ten-day-old seedlings. WGBS of Col-0 ten-day-old seedlings is deposited in GEO-accession GSE76651. Seeds of fie/+ and fie/ +;cdka;1/+ mutants  [30]  are available on request.
e. , the PaLoc genes and the sporulationassociated genes (see NCBI GEO accession number GSE85395). However, few genes were upregulated in the tcdR mutant. Among those that were overexpressed, we found the srlR gene encoding the regulator of glucitol/sorbitol-specific PTS system (CDR20291_0690 to CDR20291_0696). PaLoc genes (tcdA, tcdB, tcdR, and tcdE) were downregulated (33-fold, 12-fold, 5-fold, and 3-fold, respectively) in the tcdR mutant, as expected. Autoregulation of TcdR and its need for toxin gene transcription were well characterized previously  (7, 8, 16). However, no report was available on the TcdRmediated transcription of tcdE in the PaLoc. TcdE is a holin-like protein and was found to mediate toxin release from C. difficile cells  (22, 23). Our data suggest that TcdR is also needed to initiate tcdE transcription in C.
Ethanol-treated cells were serially diluted in 1ϫ phosphate-buffered saline (PBS), returned to the anaerobic chamber, and plated onto TY agar-0. 1% taurocholate plates to enumerate spores. After 24 h of growth, CFU were enumerated, and percent sporulation was calculated as the number of ethanol-resistant spores divided by the total number of viable cells (vegetative cells and spores). Spore preparation. Spores were generated and purified as previously described  (26, 27). Details are presented in Text S1. RNA-seq analysis and quantitative reverse transcription-PCR (qRT-PCR). RNA-seq analysis was performed at the DNA Core Facility at the University of Missouri, and the data were analyzed using   (60) (61) (62). Details of the RNA-seq analysis and the qRT-PCR  (19, 55)  are provided in Text S1.
Germination. Purified C. difficile spores were heat activated at 65°C for 30 min and then placed on ice. Ten microliters of the heat-activated spores was added to reach a final optical density at 600 nm (OD 600 ) of 0. 5 in 990 l of BHIS medium (brain heart infusion [Difco] supplemented with 5 g/liter yeast extract and 0. 1% L-cysteine) alone or supplemented with a 2, 5, 10, 20, or 50 mM concentration of taurocholic acid (TA). Germination was monitored at 600 nm for 30 min in a PerkinElmer (Waltham, MA) Lambda25 UV/Vis spectrophotometer. The data points at OD 600 (T x ) were normalized to the starting OD 600 value (T0). The germination rates and the 50% effective concentration (EC 50 ) were calculated using the slopes of the linear portions of the germination plots as described previously  (26, 28).
The resin was cured at 60°C for 24 to 48 h, and thin sections (silver to gold color) were cut and absorbed onto on 200-mesh copper grids. Sections were examined with a transmission electron microscope (CM100; FEI Company) at 100 kV, and images were captured using a side-mounted Hamamatsu digital camera (model C8484) with AMT image capture software version 602. 591n. Accession number(s). Sequence data have been deposited in the NCBI GEO database under accession number GSE85395.
ACKNOWLEDGMENTS We thank  Nigel Minton ,  University of Nottingham , for sharing the plasmid pMTL007C-E5 and  Robert Fagan  for providing plasmid pRPF185. We also thank  Jose E. Lopez  for technical assistance throughout the study. R. G. is supported by  1R15AI122173  from  NIAID. Funds from the  Johnson Cancer Center-KSU  and a pilot project to R. G. from  CBID-KU  ( 1P20GM113117-01 ) also supported this work. J. A. S. is supported by award  5R01AI116895  from the  National Institute of Allergy and Infectious Diseases. The content is solely our responsibility and does not necessarily represent the official views of the funding agencies or the National Institutes of Health.
Results: Plasmobase is a unique database designed for the comparative study of Plasmodium genomes. Domain architecture reconstruction in Plasmobase relies on DAMA, the state-of-the-art method in architecture prediction, while domain annotation is realised with CLADE, a novel annotation tool based on a multi-source strategy. Plasmobase significantly increases the Pfam domain coverage of all Plasmodium genomes, it proposes new domain architectures as well as new domain families that have never been reported before for these genomes. It proposes a visualization of domain architectures and allows for an easy comparison among architectures within Plasmodium species and with other species, described in UniProt.
Proteins are composed of one or more "domains", that is structural motifs that can evolve, function, and exist independently of the rest of the protein chain. Domains might be found in different combinations, and the arrangement of these domains in a protein forms the so called "protein architecture". By focalizing on domain recognition, genome annotation can be highly improved. Several approaches and databases have been developed to detect and identify functional domains in a large number of proteins, including Pfam  [3] , SMART  [4] , and PROSITE  [5]. Based on these resources, a number of conflicting domain predictions (potential domains) can be resolved and, for each protein to be annotated, a domain architecture can be proposed. There are different methods for identifying a domain architecture and the most successful ones explore domain co-occurrence for controlling the false discovery rate (FDR) associated with the predictions  [6] [7] [8].
CLADE uses several hundred probabilistic profiles to represent each Pfam domain instead of one profile, based on global consensus, as for mono-source strategies. These probabilistic models are originated from different species, spanning the whole phylogenetic tree, and describe alternative evolutionary pathways for a domain. Tested on the Plasmodium falciparum genome, CLADE outperforms the widely used tools based on a mono-source annotation strategy, HMMer and HHblits  [12]. The new domain annotation obtained by CLADE for P. falciparum 3D7 has been released with  [11]. Plasmobase, presented here, is a novel database reporting known and new protein domains identified by DAMA and CLADE on the 11 fully sequenced genomes in PlasmoDB (see Table  1 ). Plasmobase contains a large number of newly discovered domains in each Plasmodium genome, leading to an enrichment of 18-30% of the total number of domain families when compared to Pfam predictions (with an FDR <1%).
Plasmodium species The 11 fully sequenced genomes present in PlasmoDB  [1]  and considered here are: three human parasites (P. falciparum 3D7, P. falciparum IT, and Plasmodium vivax), two macaques parasites (Plasmodium knowlesi and Plasmodium cynomolgi), one chimpanzee parasite (Plasmodium reichenowi), and five rodent parasites (Plasmodium chabaudi, Plasmodium berghei, and Plasmodium yoelii 17X, Plasmodium yoelii yoelii 17XNL and Plasmodium yoelii YM). All genomes were extracted from http://PlasmoDB. org, the official repository of the Plasmodium proteins used as a reference database by malaria researchers. For each species, the genome size and the number of proteins are shown in Table  1.
GO terms The gene ontology initiative  [14] , besides its value as a database of annotations, maintains and develops a controlled vocabulary of gene and gene product attributes. The gene ontology terms (GO terms)  [15]  is a machinereadable vocabulary that provides a standard output for functional predictions, avoiding the ambiguity of natural language. GO terms describe three aspects of gene product function: molecular function, biological process, and cellular location. pfam2go  [16] , a mapping that associates a specific GO term with a Pfam domain, is used to provide GO terms for Plasmobase domain predictions. In consequence, all proteins containing this domain share the same GO term.