dataesr/scientific-paragraphs-categorization huggingface dataset:
Statistical Analysis Values are expressed as mean ± SEM. n indicates the number of animals. Statistical analyses were performed with Student's t-test for unpaired data. When the variances were significantly different, we used the Student's unpaired t-test with Welch's correction. For the lactate rates comparison statistical significance was measured by ANOVA followed by the post-test Newman Keuls (GraphPad Prism 5, Graph Pad Software, Inc. , San Diego, CA, USA). Values of p < 0. 05 were considered to be statistically significant.
Our platform integrates all the complete Listeria species genomes, transcriptomes, and proteomes published to date. This website allows navigation among all these data sets with enriched metadata in a user-friendly format and can be used as a central database for systems biology analysis. IMPORTANCE In the last decades, Listeria has become a key model organism for the study of host-pathogen interactions, noncoding RNA regulation, and bacterial adaptation to stress. To study these mechanisms, several genomics, transcriptomics, and proteomics data sets have been produced. We have developed Listeriomics, an interactive web platform to browse and correlate these heterogeneous sources of information. Our website will allow listeriologists and microbiologists to decipher key regulation mechanism by using a systems biology approach.
2001, the genomes of L. monocytogenes strain EGD-e and one Listeria innocua strain were sequenced  (11). Since then, many other Listeria genomes, covering all the lineages, have been sequenced  (12) (13) (14) (15) (16) (17). Currently, the NCBI refSeq database contains 83 complete Listeria genomes, including 70 L. monocytogenes genomes. The number of Listeria strains sequenced will probably grow exponentially in the coming years. Efforts have been made to summarize all these genomes on specific databases like ListiList  (11) , GenoList  (18) , GECO-LisDB server  (16) , and ListeriaBase  (19)  to find common gene features and to develop pangenome studies of Listeria species. The first Listeria transcriptomic data set was published in 2007  (20). Since that report, 64 ArrayExpress studies, corresponding to 362 different biological conditions, have been produced  (21).
Only seven of them are transcriptome sequencing (RNA-Seq) studies, and all the others correspond to transcription profiling by microarrays, with the EGD-e strain being the most frequently used strain. Listeria is also a key organism in the study of bacterial regulatory small noncoding RNAs (sRNAs). Despite the high number of studies on Listeria noncoding RNAs, only two websites with Listeria-related data sets have been published. The first one is a genome viewer published along with a transcription start site (TSS) study of Listeria  (22). The second is the sRNAdb database  (23) , which provides tools to visualize the conservation of gene loci surrounding noncoding RNAs in different Gram-positive bacteria. The ability of L. monocytogenes to enter into various types of cells is due to the variety of proteins it secretes or anchors to its cell wall and external membrane.
Consequently, many proteomic studies have been performed to analyze the exoproteome of Listeria  (24) (25) (26) (27) (28) (29) (30) (31) (32) (33) (34) (35). Other studies have focused on cytoplasmic proteins  (27, 33, (35) (36) (37) (38) (39) (40) (41) (42) (43) (44). To our knowledge, 74 proteome studies have been conducted to decipher the production and localization of Listeria proteins. Nevertheless, no database exists that combines all these proteomics data sets into a single, user-friendly resource. The number of omics data sets produced has increased exponentially. The number of tools to analyze these data, as well as the diversity of databases to store them, has also burgeoned. In parallel with this increase, many efforts have been made to develop accurate web-based tools to integrate diverse omics data for each model organism.
One of the most complete resources is certainly the University of California Santa Cruz Encyclopedia of DNA Elements (ENCODE at UCSC) Genome Browser  (45) , which allows the visualization of a large variety of human and mouse omics data sets. For prokaryotic organisms, the BioCyc  (46, 47)  and Pathosystems Resource Integration Center  (48)  websites have been created. These websites connect all the published genomic and transcriptomic data sets for prokaryotic organisms to metabolic pathways. Such wideranging web resources are useful for microbiologists, but for in-depth analyses, the development of individual web resources with curated metadata per model organism is also required. In the case of bacteria, few heterogeneous omics data sets are available  (49)  and few model organisms have dedicated web resources, including Escherichia coli, with RegulonDB  (50)  and PortEco  (51) , and Bacillus subtilis, with SubtiWiki  (52).
As yet, resources for Listeria species are limited. Here, we present Listeriomics (http://listeriomics. pasteur. fr/), a highly interactive web resource summarizing many omics data sets related to the genus Listeria. We have curated and integrated all the available Listeria transcriptomic, proteomic, and genomic data sets to date. The Listeriomics platform was developed not only to integrate these diverse data sets but also to display them in a single viewer. To interactively explore these data sets, our website also provides different tools, i. e. , (i) a genome viewer for displaying gene expression arrays, tiling arrays, and sequencing data, along with proteomic and genomic data sets; (ii) an expression atlas and protein atlas, inspired by the EBI Expression Atlas, that connects genomic elements (genes, small RNAs, antisense RNAs [asRNAs]) to the most relevant omics data; (iii) a specific tool for exploring protein conservation through the Listeria phylogenomic tree; and (iv) a coexpression network analysis tool for the discovery of potential new regulations.
RESULTS The Listeriomics web interface. Genomic, transcriptomic, or proteomic data can be browsed by using the Listeriomics website (http://listeriomics. pasteur. fr/) main page (Fig. 1 ; Table  1 ; see Fig. S1  in the supplemental material). For each type of data, we designed a summary panel to navigate through the different data sets. The top banner of the website gives direct access to them. As summarized in Table  1 , users can search 83 complete Listeria genomes and browse 492 transcriptome and 74 proteome data sets. Listeriomics integrates four tools for omics data management, i. e. , (i) a genome viewer for displaying gene expression array, tiling array, and sequencing data along with proteomics and genomics data; (ii) an expression atlas and protein atlas that connect every genomic element (genes, small RNAs, asRNAs) to the most relevant omics data; (iii) a protein conservation tool for the direct visualization of the presence or absence of a protein in a specific Listeria strain; and (iv) a coexpression network analysis tool for the visualization of genome features with the same expression profile.
The second panel provides an instant view of the conservation of a specific protein in other Listeria strains. This panel dynamically displays homologs on the Listeria reference tree in each existing Listeria strain. It also displays a summary table of all the homologous proteins with their similarity percentages and amino acid sequences. Users can also create a multialignment file of the homologous proteins. With the third panel, the user can visualize the protein locus synteny in all Listeria strains. We built an external synteny website by using the SynTView architecture  (54). A fourth panel uses the expression atlas to show in which transcriptomics data sets the selected gene is differently expressed. The fifth panel displays every proteomics data set in which the protein encoded by the selected gene has been detected.
Finally, from the home webpage, a summary panel with all the small RNAs in L. monocytogenes EGD-e can be accessed (Fig. 1 ). For each noncoding RNA element, one can display its position, its nucleotide sequence, its predicted secondary structure at 37°C, and a table displaying all supplementary information provided in source references  (22, (55) (56) (57) (58). In the transcriptomic interface of the Listeriomics website, researchers can access all the Listeria transcriptomic data sets published so far. A searchable table shows every data set available with a precise description of the biological conditions studied. In total, four different transcriptome technologies (gene expression array, tiling array, RNA-Seq, and TSS) are included for seven different L. monocytogenes strains grown in four different broth media under seven intracellular conditions (Fig.
Pearson correlation coeffi- cients have been calculated for 42 tiling array and RNA-Seq data sets. Once a specific Pearson correlation coefficient cutoff has been selected, this interface filters the coexpression network to show only the selected genome elements and the genome elements having a correlation coefficient above the selected cutoff. Two types of displays are available for the coexpression network, including a standard force-directed graph visualization (Fig. 3B ) and a circular graph visualization (Fig. 3C ). The latter viewer shows the Pearson correlation coefficient between genome elements by using a representation of the circular bacterial genome. This visualization highlights possible coexpression between distant genomic loci. The Listeriomics database contains 74 proteomics data sets (Fig.
L. monocytogenes Mutants A webpage form is available to inform of the need to integrate a specific data set in the Listeriomics database. Before the publication of new genomes, transcriptomes, or proteomes to the Listeriomics website, the data sets must first be uploaded on referent repositories such as the Sequence Read Archive  (61)  or ArrayExpress  (21). This process ensures that all the data sets are formatted in accordance with international standards and that minimal information on the experimental design of each study has been provided. The multi-omics genome viewer. One of the key features of the Listeriomics interface is the multi-omics genome viewer. Figure  3A  shows a variety of omics data sets produced for L. monocytogenes EGD-e grown at 37°C to the exponential phase in brain heart infusion (BHI) medium.
RNA-Seq (62), tiling array  (56) , gene expression array  (56) , TSS  (22) , transcription termination site and ribosome profiling  (63) , and proteomics (36) data sets are displayed. To our knowledge, this is the first time that such a variety of omics scales can be browsed together through a genome viewer for a prokaryotic organism. Thus, the user can visualize the correlation of the genome annotation with transcription and translation for a specific coding RNA or sRNA. The genome viewer is dynamic, with zoom-in and zoom-out capabilities. The viewer also has search capability to access a specific position in the selected Listeria strain genome. Every omics data set present in the Listeriomics database can be added to this genome viewer. Easy-access buttons on the home page of the Listeriomics interface allow quick access to three genome viewers with preloaded reference omics data sets.
The first genome viewer is for L. monocytogenes EGD-e grown to the exponential phase at 37°C in BHI (Fig. 3A ), the second is for the same condition but in stationary phase, and the last is for L. monocytogenes EGD-e grown in mouse macrophage cells. Meta-analysis of Listeria transcriptomic data sets. A meta-analysis of the diversity of transcriptomic data sets available in the Listeriomics database offers a striking overview of the variety of studies performed by the Listeria research community. This wide range of studies covers a majority of the different living environments in which Listeria species have been observed to grow. As shown in Fig. 4A , several studies on the effects of different growth environments on Listeria have been performed, i. e. , cold environments, acidic environments, specific gene deletions, common biocides, sugar availability, and intracellular growth.
The other genes on the list are involved in either sugar metabolism (lmo0096, lmo2391, lmo0783, lmo2684) or stress response (lmo2158, lmo2673). Finally, a not-yet-described membrane protein, Lmo2484, is differently expressed in 81 of the 279 available data sets. This protein is conserved with Ͼ90% similarity in all 83 Listeria strains present in the Listeriomics database. We also found six genes that have never been demonstrated to be differently expressed in a data set (Fig. 4C ; see Table  S6 ). As expected, these genes are involved in general bacterial physiology factors like DNA mismatch repair or reductase and transferase enzymes. Finally, we extracted the 651 genes of L. monocytogenes EGD-e that were found to be differently expressed in Ͼ10% of the 279 data sets (see Table  S6 ). We performed a pathway enrichment analysis by using COG (Clusters of Orthologous Groups) information and the Fisher exact test (67) (Fig.
Meta-analysis of Listeria proteomic data sets. An analysis of the variety of proteomic data sets available in the Listeriomics database shows that many reference biological conditions have been studied. As for transcriptomic data, there is a great number of data sets produced by bacteria grown to the exponential phase at 37°C in BHI. Users can also access data sets from intracellular growth, cold environments, and the stationary growth phase. The key information in these proteomic data sets is the protein extraction protocol used that indicates which compartment of the bacterial cells (cytoplasm, membrane, cell wall, and secretome) has been analyzed. The cytoplasmic compartment is the most studied. The second most studied is the secretome compartment, the focus of many publications, since secreted proteins are key components of the Listeria intracellular life cycle.
The ability to visualize these proteomic data sets together at the Listeriomics website will help the Listeria community to investigate further these groups of proteins and their roles in the different compartments of bacterial cells. We investigated the 58 proteomic data sets available for L. monocytogenes EGD-e. We counted the data sets in which each protein was analyzed (see Table  S6 ). As expected, we found that most of the proteins analyzed in half of the data sets come from the translation machinery or the carbohydrate transport and metabolism pathway. Remarkably, the first known virulence factor on the list is the pore-forming toxin LLO (Lmo0202), which is detected in 22 of the 58 proteomic data sets present in the Listeriomics database. We also found that Ͼ80% (2,344/2,859) of the L.
pasteur. fr/) provides a unique, comprehensive, and up-to-date source of information on Listeria, including all the available complete genomes to date; a unified annotation of genes, proteins, and noncoding RNAs; and associated omics data and metadata relevant to genomic comparative and systems biology analyses. The curation process focused on the quality of the metadata provided. We completed the process by searching information in the corresponding publications. To our knowledge, the Listeriomics interface is the first bioinformatics resource designed to help scientists in Listeria pathogenesis research using a systems biology approach. The information integrated into the Listeriomics database is provided through a useable and friendly interface that allows querying and visualization of Listeria genomic, phylogenetic, transcriptomic, and proteomic data sets, along with information on small RNAs and coexpression network of genes and small RNAs.
The user experience and feedback from our collaborators using the Listeriomics interface for the last past 5 years were driving forces in organizing and improving the way to access data and tools  (17, 22, 71). Indeed, through the Listeriomics resource, it is possible to summarize what has been discovered about a specific gene, including information on its distribution in the different Listeria genomes. Users can also identify the different transcriptomics data sets in which a specific gene or small RNA is differently expressed. Similarly, scientists can browse the different proteomics data sets to identify biological conditions in which a specific protein was detected by mass spectrometry. Finally, regulatory networks between genes and small RNAs can be inferred and explored. The field of microbiology is considerably impacted by new technologies, and the role of databases in microbiology research will become even more important shortly  (72).
Until now, most of the model organism resources have focused on the widely studied bacterium E. coli, often with an emphasis on the manual curation of some particular aspect of omics information. Only a few of them, like the RegulonDB resource dedicated to the E. coli transcriptional regulatory network, really address the challenge of integrating knowledge based on experimental high-throughput omics data sets  (50). For B. subtilis, SubtiWiki originally focused on manually curated gene annotation with a collection of pages providing interlinked pages on B. subtilis gene properties, including valuable information such as essentiality or sporulation, obtained from qualified members of the Bacillus community  (73). Interestingly, the latest release of Subtiwiki integrates some omics data, with new modules allowing the linkage of pathway, interaction, and expression information  (52).
MATERIALS AND METHODS Integration of Listeria species genomes and sRNAs. The complete Listeria genomes available in the NCBI RefSeq and GenBank databases (see Table  S1 ) were downloaded and integrated into our the Listeriomics database. Information about serotype, lineage, and the origin of strain isolation was included when possible. We used the PasteurMLST tool  (74)  to search for the sequence type and clonal complex of each strain. For L. monocytogenes EGD-e genes, we integrated their predicted functional group  (11) , COG, operon prediction  (56) , and subcellular protein localization prediction  (27)  (see Fig. S1 ). Many studies have been performed to identify sRNAs in L. monocytogenes  (22, (55) (56) (57) (58). Among these studies, only one concerned strain 10403S (57); the others focused on EGD-e.
Altogether, 304 noncoding RNA elements have now been reported in L. monocytogenes, of which 154 are sRNAs, 104 are asRNAs, and 46 are cis-regulatory elements (cisRegs) including riboswitches (see Table  S2  and Fig. S1 ). We included these elements in our database, along with supplementary information gathered from all related publications. Prediction of secondary structures at 37°C for each noncoding RNA were calculated by using UNAFold software (hybrid-ss-min default parameters on the whole RNA  [76] ). Listeria ortholog gene families. Nucleic and amino acid sequences of all the annotated coding genes were produced for each complete Listeria genome by using GenBank files and a custom-made Python script. Amino acid sequences for each Listeria genome were aligned against those of other proteins of all the Listeria genomes by using BLASTPϩ  (53)  with an E-value threshold of 0.
The minimum number of sequences for a conserved position was set to (n/2) ϩ 1 (where n is the total number of sequences in the aligned data set), the maximum number of contiguous nonconserved positions was set to 50, the minimum length required for a block was set to 5, and gap positions were not allowed. The corresponding nucleic acid sequence alignments were obtained from each cluster amino acid sequence alignment with a custommade Perl script. Listeria species reference phylogenomic tree. Nucleic acid sequence alignments of ortholog gene families were concatenated into a single superalignment. This superalignment was used to compute a maximum-likelihood tree by using FastTree 2 (81), a parallelized and optimized software, to build maximum-likelihood trees. The following parameters were used.
Construction of coexpression networks. We selected 42 transcriptomic data sets for which the absolute expression of genes and small RNAs was available (21 tiling array and 21 RNA-Seq [see Table  S4  and Fig. S1] ). The Pearson correlation coefficient for each genome element across all biological conditions was calculated. A link in the coexpression networks was created only when the absolute Pearson correlation coefficient was Ͼ0. 80. Integration of proteomic data sets. We selected 23 publications by using the PubMed database, in which a mass spectrometry experiment with a Listeria strain was performed (see Table  S5 ). We extracted all the available information on the biological conditions screened in each experiment from associated articles and supplementary files (Fig. 1 ). We also extracted the metadata information in the same format for the transcriptomic data sets.
ACKNOWLEDGMENTS We thank  Francis Impens ,  Jeffrey Mellin , and  Nathalie Rolhion  for their important feedback on the Listeriomics website, which helped us improve the user interface. We are grateful to  Eric Westhof  and  Lilliana Radoshevich  for their insights into the writing and structure of the manuscript. We also thank  Daniel Dar ,  Omri Wurtzel , and  Rotem Sorek  of the  Weizmann Institute  for providing the  TSS, RiboSeq , and TermSeq data sets. This work received financial support from the  European Research Council  (BacCellEpi no. 670823 ), the  French Agence Nationale de la Recherche  (grant  BACNET 10-BINF-02-01 ), the  Human Frontier Science Program  (grant  RGP0011/2013 ),  Laboratoire d'Excellence "Integrative Biology of Emerging Infectious Diseases"  ( ANR-10-LABX-62-IBEID ), the  Institut Pasteur , the  Institut National de la Santé et de la Recherche Médicale , the  Institut National de la Recherche Agronomique , the  Pasteur-Weizmann Council  grant, and the  Fondation le Roch Les Mousquetaires.
A total of 25 μl of mixture was added to each well, which contained lyophilized primer assays. The thermocycling conditions consisted of 10 min at 95 °C and then 40 cycles of 15 s at 95 °C and 1 min at 60 °C of a CFX Connect 96™ thermocycler (Bio-Rad, Hercules, CA, USA), which collected also the fluorescence intensity data after each cycle. Dissociation curves were performed for each well with the following melting curve program: 65 to 95 °C with 0. 5 °C step increase for 5 s each step. The data analysis was performed using the CFX Manager Software 3. 0 (Bio-Rad, Hercules, CA, USA) with the normalized relative quantification method. The normalized expression of genes in the test group was calculated relative to the expression in the control group.
Gene expression The effect of magnesium release on the expression of genes involved in the pathways of osteogenesis and osteoporosis  The stability of the expression of the five reference genes (ACTB, B2M, HPRT1, LDHA, RPLP1) was checked through an algorithm integrated in the CFX Manager software, and ACTB was excluded as its expression varied too much around samples. The other four genes were suitable (coefficient of variance below 0. 5 and M value below 1) and were used for the normalization of the input RNA quantity in each sample. The internal positive controls indicated no amplification interferences. No amplification curves were detected in the genomic DNA control (RGDC). The values of reverse transcription controls were within the limits suggested by the manufacturer, indicating that the reverse transcription was successful.
Acknowledgement The research leading to these results has received funding from the  People Programme  ( Marie Curie Actions ) of the European Union's  Seventh Framework Programme FP7 /2007-2013/ under REA grant agreement n°289163, from the  Forsknings-och forskarutbildningsnämnden (FUN)  and from  King Gustav V and Queen Victoria foundation , the  Swedish  order of Freemasons. The authors.