dataesr/scientific-paragraphs-categorization huggingface dataset:
Manteia was initially designed to be able to help annotate these microarrays by importing annotation from other species. Then the possibility to manipulate large lists of genes, probesets and other types of annotations was added together with statistical analysis modules allowing to directly analyze the enrichment in particular annotation categories  (8) (9) (10). New functionalities and vertebrate species annotations were progressively added to the database as requested by investigators with the goal of providing a user-friendly tool, allowing biologists to mine their data without knowledge of bioinformatics. Manteia is designed to annotate and analyze large volumes of data by combining major resources from human and vertebrate model organisms. The system provides new tools and concepts to compare the annotation of ortholog genes and to analyze the information and make insightful testable predictions to help in the understanding of biological and developmental events as well as genetic disease etiology.
Data and software Part of the Manteia architecture is based on the Aniseed Gmod  (11). The data used in Manteia are stored in a PostgreSQL8 database. The user Web site and the administration tools are written in PHP5 and use javascript and AJAX to enhance the interaction with users. Dynamic graphs are built using the D3 JavaScript library. Statistics tools use R to compute the hypergeometric test. Data originating from Ensembl are downloaded using BioMart. Affymetrix probe sets originate from the Human Genome U133 Plus 2. 0 array, the Mouse Genome 430 2. 0 and MoGene 1. 0 st arrays, the Chicken array and the Zebrafish array. The other data are downloaded directly from their respective Web sites. The download, the processing and the storage of the information in the database are automated using the Manteia management tools.
System presentation Manteia is an integrative database that gathers most of the data generated for human and vertebrate model organisms in a same framework. The system is designed to be used by biologists or geneticists and does not require any specific knowledge in bioinformatics. It includes sophisticated data mining tools that allow the combination of data from different origins, nature and species. The data stored in Manteia originate from four vertebrate species: human, mouse, chicken and zebrafish. The information stored in the system covers different aspects of embryonic development and mutations/genetic disorders leading to abnormal phenotypes and diseases in animal models such as zebrafish or mouse and in humans. Data are organized into three main categories: Molecular, Expression and Developmental data, each corresponding to a different menu in Manteia.
Molecular data consist of functional annotations from Gene ontology [GO  (16) ], chromosome location and single-nucleotide polymorphisms (SNPs) from Ensembl, protein motifs from Interpro  (17) , transcription factors from the DNA-binding domain database [DBD  (18) ] and orthology data computed from Inparanoid  (19)  and Compara (4) to link together the genes of different species. In addition, the data from reactome  (20)  are used to describe interactions, complexes and pathways, while data from the Encyclopedia of DNA Elements [ENCODE  (21) ] are used to predict the transcription factors regulating gene expression. Expression data comprise in situ hybridization from the MGI and Embrys  (22)  as well as expressed sequence tags (ESTs) from Unigene. EST data can be used to evaluate the expression level of genes using a digital differential display approach similar to ZooDDD  (23).
Embryological data include phenotype descriptions of mutations for human, mouse and zebrafish originating from OMIM, MGI and ZFIN, respectively. All these descriptions make use of ontologies including the mammalian phenotype ontology  (24) , the HPO  (25)  and the fish anatomical description. HPO is used to provide a semantic description to OMIM data. Comparing the information related to a given gene across data sets remains a challenging task. This is due in part to the fact that the data are associated to gene models that are specific to the database they originate from. To address this problem, Manteia links all the information from the databases described above to the same set of gene models allowing an easy comparison of genes and their annotations across vertebrate species. We chose to use the Ensembl models as our reference set, but gene models from NCBI are also included and can be browsed independently in the system.
Affymetrix microarray probe sets for Human, mouse, fish and chicken are linked to both sets of gene models, allowing one to maximize the information available to describe each sequence and interpret the experiments. All the data are processed automatically by the Manteia management tools (Figure  1 ). For each gene model, Manteia provides a page listing all the available information. The page is analyzed dynamically to generate charts designed to provide an overview of the annotation (Figure  2a  and b ). It is also possible to combine the data from all the orthologs and corresponding gene models so the information can be compared and completed within a same page. This way, users can easily access the most comprehensive data set available. The Manteia system is particularly useful to analyze large lists of data such as those generated by microarray, high-throughput sequencing or proteomics analyses.
It accepts inputs including lists of genes, ESTs, probe sets, SNPs and chromosome regions with the main identifiers used in the community, which can be pasted from their original files directly into the Manteia interface. Genes returned by the system can be exported with their annotation features as tabulated text files using the 'Refine' tool described below. Similarly, a crossreference system allows the user to convert the gene identifiers to ensure compatibility with other databases and software.
Refine Each type of data is accessible in the system through its own dedicated interface, providing the most relevant options for each query. However, one of the strengths of Manteia comes from its ability to combine different sources of information to address complex biological questions. A particularly useful tool of the Manteia interface is called 'Refine' and it is designed to filter the results from a query with another tool of the system (see all the options in Table  1 ). For example, suppose one wishes to find which transcription factors are responsible for a muscle phenotype in mouse and are known to lead to a genetic disease in man. To that end, one can use the tool 'transcription factor' in the 'Molecular data' menu of Manteia to list all the known mouse transcription factors, then use 'Refine' (selecting 'Phenotype' and then entering the term 'muscle phenotype') to keep only those factors whose knockout in the mouse shows a muscle defect.
The resulting table allows the user to retrieve the list of genes associated with each annotation by a simple click, making it possible to select them specifically for further analysis. Additional features allow one to dynamically filter the results and highlight annotation categories that are related together; this greatly eases the analysis of the results. The Manteia statistics tools have been successfully used to characterize the function of cyclic genes (8), highlight the role of genes expressed in the presomitic mesoderm (9) and identify genes involved in sex determination in the chicken  (10). The scope of application of statistics tools has been extended with a conditional probability module that makes it possible to study the correlation between different annotations. For example, to identify the biological pathways involved in somitogenesis, it is possible to select the genes responsible for an 'abnormal somite development' using the 'Phenotype' search tool, then perform a statistical analysis on GO annotations and filter the GO terms related to 'signaling pathway' (Figure  3 ).
Using mouse phenotypes to predict candidate genes for human genetic diseases We have also developed tools allowing the use of mouse annotation to identify candidate genes for specific human genetic diseases. This tool that allows to ask complex queries is called 'Querybuilder'. It enables the user to query the database by typing a complex question (Figure  4 ). Questions combine the search of specific features with logical operators (and, or, not). For example, '[SPECIES(''Mus musculus'') and PATHWAY (''Signaling by NOTCH'') and GO(''nervous system development'')] not IP(''Notch domain'')' will return all the mouse genes from the Notch signaling pathway that are involved in the nervous system development but not coding for a Notch domain. Questions to the system can take advantage of the multiplicity of information available to complement certain data with others.
One is called 'medical diagnostic' and allows to find a genetic disease based on clinical features. The second, called 'Annotation distribution', counts how many genes correspond to each query defined by the user (Figure  5 ). This is particularly useful to analyze high-throughput experiments and quantify how many genes belong to each category (e. g. cell cycle, signaling, metabolism) and see how these values evolve over different experimental conditions. Boolean tools can be used in combination with 'Refine', thereby providing unlimited ways to combine the information contained in Manteia. Altogether, these tools provide a highly sophisticated way to perform a global search using several types of data from different species. Here the GO annotation enrichment is exemplified. The P-value column gives the significance of the enrichment.
The two following columns correct this value for multiple testing. The blue color indicates a statistical significance. Terms that are related in the ontology can be highlighted to ease the analysis. Here GO terms related to the NOTCH and WNT signaling pathways are colored in beige and green, respectively. Statistics tools can be used in combination with other exploration tools to compute a correlation between different types of data. In this example, 'Phenotype search' (on top) is used to assemble a list of genes leading to an abnormal somite development. The resulting GO statistics are filtered with the keyword 'signaling pathway'. The conditional probability module (right hand side on the GO statistics screenshot) is used to compute the probability of having a gene annotated with a given GO term when it is already annotated with the 'abnormal somite development' phenotype (first column), and conversely (second column).
The algorithm cannot distinguish the right candidate among genes responsible for similar phenotypic features in the genome. To overcome this problem, Manteia allows the investigators to narrow down the number of candidates by using this automatic approach along with their own evidences. For example, one can constrain the search to a chromosomal location given by a CGH array or a linkage analysis. Data integration tool. The 'Querybuilder' makes it possible to address a question to the system using several types of data. The user enters a keyword, selects the best matching suggestion among a list and builds a question using Boolean operators (and, or, not). Several questions can be addressed at the same time using a separator followed by a weight reflecting the relative importance of each query.
DISCUSSION Manteia is an original online resource that provides a cohort of data from different species with a set of powerful tools within a structured environment to unleash the potential of combinatorial large-scale analyses of biological and clinical data. The system is extremely versatile and can be used for many types of analyses, including data retrieval, gene or probe set annotation, information content analysis, candidate gene prediction and prioritization. The user interface has been designed to make all analyses easy and fast, and an online help is provided to familiarize the novice with the system. The system architecture is modular and will allow the addition of new animal models, new data sets and new tools to extend even more the scope of its applications. This tool should prove particularly helpful for investigators interested in finding biologically significant correlations in large lists of genes and proteins generated by modern high-throughput approaches.
ACKNOWLEDGMENTS We thank the members of our laboratory for their numerous inputs and comments about Manteia. We thank  Maria-Elena Torres-Padilla ,  He´le`ne Puccio  and  Jacques Michaud  for there insightful comments about the manuscript. We thank  Philippe Moncuquet  and  Nicode`me Paul  for their invaluable help with the statistics. We are grateful to  Serge Uge ,  Guillaume Seith ,  Mike Newhouse  and  Brandon Young  for the configuration and maintenance of the servers hosting the system. Last, we thank the authors of the resources used by Manteia for sharing their work and data with the scientific community.
UDF12 high-redshift galaxy sample selection New HUDF galaxy samples were selected from the final UDF12+UDF09 data set as follows. First, SEXTRACTOR  (Bertin & Arnouts 1996)   ). The catalogues from these 10 alternative detection runs were next merged to form a parent sample which was then culled by rejecting any source which showed a >2σ detection in any of the three shortest-wavelength (B 435 , V 606 , i 775 ) deep HST ACS optical images of the HUDF. This process means that the resulting galaxy sample should remain complete beyond z = 6. 4 (although it will also yield some galaxies in the redshift range z 6-6. 4; see  McLure et al. 2011 McLure et al. , 2013)). Multi-band aperture photometry was then performed at the position of each object (as determined from the detection image which yielded the highest signal to noise ratio detection), using 'matched' circular apertures designed to contain 70 per cent of the flux density from a point source.
If, when a star particle forms, the metallicity of its parent gas particle is less than Z crit = 10 -4 Z , we use the PopIII SED  (Schaerer 2002) ; if a star particle forms out of a metalenriched gas particle (Z > Z crit ), the SED is computed via the population synthesis code STARBURST99  (Leitherer et al. 1999 ), using its mass, stellar metallicity and age. The composite spectrum for each galaxy is then calculated by summing the SEDs of all its star particles, and the intrinsic continuum luminosity, L int c , is calculated at λ rest = 1500 Å. We also self-consistently compute the dust mass and attenuation for each galaxy in the simulation box assuming Type II SN (SNII) to be the main dust producers  (Maiolino et al. 2006; Stratta et al. 2007). The dust mass is converted into an optical depth to UV photons assuming the dust is made of carbonaceous grains spatially distributed as the gas (see  Dayal, Ferrara & Saro 2010; Dayal & Ferrera 2012 , for complete details of this calculation).
C O N C L U S I O N S We have used the new ultra-deep, near-infrared imaging of the HUDF provided by our UDF12 HST WFC3/IR imaging campaign to explore the rest-frame UV properties of galaxies at redshifts z > 6. 5. In this study we have exploited the final multi-band WFC3/IR imaging (UDF12+UDF09) to select deeper and more reliable galaxy samples at z 7, z 8 and z 9, and to provide improved photometric redshifts. Most importantly, we have used the enhanced data set to provide more accurate photometry, and to base galaxy selection primarily on the new J 140 imaging, which exerts minimal influence on the derivation of the UV spectral index β (f λ ∝ λ β ) from either J 125 -H 160 colour (e. g. Bouwens et al. 2010b; Dunlop et al. 2012) , or J 125 + J 140 + H 160 power-law fitting (as advocated by  Rogers et al.
AC K N OW L E D G E M E N T S JSD and PD thank their collaborators  U. Maio  and  B. Ciardi  for making available the SPH simulations utilized in Section 5. 3. JSD, PD, VW, RAAB and TAT acknowledge the support of the  European Research Council  via the award of an Advanced Grant. JSD and RJM acknowledge the support of the  Royal Society  via a  Wolfson Research Merit Award , and a University Research Fellowship respectively. ABR and EFCL acknowledge the support of the  UK Science & Technology Facilities Council. US authors acknowledge financial support from the  Space Telescope Science Institute  under award  HST-GO-12498. 01-A. SRF is partially supported by the  David and Lucile Packard Foundation. SC acknowledges the support of the  European Commission  through the  Marie Curie Initial Training Network ELIXIR.
Genome-wide Gene Expression Profiling Affymetrix platform with GeneChip Human Genome U133 Plus 2. 0 arrays were used for microarray analysis of both cell and tissue RNA samples, following manufacturer instructions. Gene-Chip Operating Software (Affymetrix) was used to collect and store the microarray data. CEL files were uploaded to RMAExpress software to assess the quality of the arrays at the image level (http://rmaexpress. bmbolstad. com). Quality assessment of the Affymetrix datasets was performed using affyPLM (http://www. bioconductor. org). NUSE and RLE plots were drawn and outliers with high deviation from the average probe intensity value were excluded from further analyses. The microarray data reported in this paper have been deposited in the Gene Expression Omnibus (GEO) database under accession numbers of GSE17546 (Huh7 clones) and GSE17548 (cirrhosis and HCC tumor samples).
Gene Set Enrichment Analysis Gene set enrichment analyses (GSEA) were performed using GSEA program of the Broad Institute  [31]. For comparing in vitro and in vivo GSEA profiles based on ''C2_ALL'' curated gene list, a custom Matlab ß routine was applied to extract commonly enriched gene sets. Pearson's correlation coefficients were calculated using Matlabß and Fisher's exact test performed using VassarStats (Vassarstats. net).
Generation and Validation of a Senescence-based Genomic Classifier A senescence-based genomic classifier associated with differential diagnosis of HCC from cirrhosis was generated in BRB Array Tools by Prediction Analysis of Microarrays  [34]  using data reported by Wurmbach et al. [35]  as a ''training set''. The resulting classifier was tested using the nearest template prediction (NTP) method  [36]  on a validation set constructed by combining data reported here for Turkish patients with data from Deng et al. [37]  for Japanese patients. Nearest template prediction was performed using NTP module  [36]  of GenePattern program (http://www. broadinstitute. org/cancer/software/genepattern/) using default parameters of the module. The final image was generated using HeatMapImage module of the GenePattern and the output of the NTP.
ATAD2 protein expression was compared by western blot analysis of cell lysates, as described previously  [38] , except that an anti-ATAD2 rabbit polyclonal antibody (Sigma; cat. no: HPA019860) was used at 1:500 dilution as the primary antibody. Anti-calnexin rabbit polyclonal antibody (Sigma; cat. no: C4731) was used at 1:10000 dilution for the loading control. The specificity of anti-ATAD2 antibody was validated by western blot analysis in Hep3B cells after transfection with ATAD2-siRNA1 described by Caron et al. [39]. For comparative analysis of ATAD2 protein expression between immortal and senescent cells, senescence was induced in Huh7 cells by Adriamycin (0. 1 mM) treatment for three days as previously described  [40]. Briefly, Adriamycin-and DMSO vehicle control-treated cells were maintained in culture for three days.
Gene Expression Profiles of Hepatocellular Senescence in vitro We profiled four independently established Huh7 clones, subdivided into senescent and immortal phenotypes (i. e. , two clones from each phenotype) using gene expression analysis with pangenomic 54 K Affymetrix microarrays. Three independent biological replicates from each clone were used so that a total of 12 gene chips were performed. Hierarchical clustering of expression values of the top 50 down-and up-regulated genes of each phenotype segregated cell samples based on phenotypic assignment, which suggested a common transcriptional consequence of a switch between senescent and immortal fates (Fig. 2a ). Next we calculated GSEA enrichment scores  [31]  for six senescent cell samples against six immortal cell samples using all curated gene sets (''C2_All'') available at molecular signature database (MSigDB; www.
Both sets of data were subjected to GSEA using ''C2-All'' curated gene sets (''C2_All'') of molecular signature database (MSigDB; www. broadinstitute. org/gsea/). In order to assess senescence-and immortality-related gene expression changes during hepatocellular carcinogenesis, genes differentially expressed in the model of cellular senescence and immortality were identified and the evolution of their expression profiles in pre-neoplastic and neoplastic liver lesions were examined. Finally, a 15-gene senescence-based signature was generated using a training set of cirrhosis and HCC samples, and validated using independently generated test datasets. doi:10. 1371/journal. pone. 0064016. g001 genases, four members of alcohol dehydrogenases, NADP(H)dependent retinol dehydrogenase/reductase (DHRS4) and bcarotene 15,159-monooxygenase 1 (BCMO1), which are all involved in the synthesis of retinal, the immediate precursor of retinoic acid.
The list of senescence-related genes was established by class comparison analysis of in vitro gene expression data. Multivariate permutation tests identified 1220 genes represented by 1813 probe sets with statistically significant expression changes between senescent and immortal clones (Pvalues,10 27 ; fold-changes between senescent and immortal clones:. 2. 0). The selected probe sets were then tested against a publicly available gene expression dataset for tissues at different histological stages of HCC development in HCV patients  [35]. The tissue set was composed of 10 normal liver samples, 13 cirrhotic tissues, 17 dysplastic lesions (originally described low-and high-grade dysplasia cases combined), 17 early HCCs (originally described very early and early HCC cases combined) and 18 advanced HCCs (originally described advanced and very advanced cases combined).
Red: up-regulated; blue: down-regulated; arrow indicates TERT gene whose expression is down-regulated in senescent clones. Previously identified gene sets (available at molecular signature database (MSigDB; www. broadinstitute. org/gsea/) were screened to identify those that are differentially enriched in senescent or immortal Huh7 clones by the analysis of their relative expression levels using GSEA method. (b) Gene set enrichment plots showing the up-regulated expression of two previously known senescence-associated gene sets in senescent Huh7 clones, including genes that are commonly up-regulated in senescent cells (''FRIDMAN_SE-NESCENCE_UP'')  [10]  and p53-responsive genes up-regulated during replicative senescence arrest (''TANG_SENESCENCE_TP53_TARGERTS_UP'')  [41]. In addition, genes known to be down-regulated during immortalization in general (''FRIDMAN_IMMORTALIZATION_DN'')  [10] , and by human papillomavirus 31 (''CHANG_IMMORTALIZED_BY_HPV_DN'')  [42]  were also up-regulated in senescent Huh7 clones.
(c) Genes known to be downregulated by TERT-mediated immortalization (''KANG_IMMORTALIZED_BY_TERT_DN'')  [43]  and TERT-repressed target genes (''SMITH_TERT_TAR-GETS_DN'')  [44]  were also enriched in senescent Huh7 clones. (d) Genes involved in telomere end packaging (''REACTOME_PACKAGING_OF_TE-LOMERE_ENDS''; www. reactome. org) were upregulated in senescent Huh7 clones. (e) In contrast, genes involved in telomere extension (''REACTOME_EXTENSION_OF_TELOMERES''; www. reactome. org) were enriched in immortal Huh7 clones. Enrichment scores (ES) are shown on the yaxis. Positive and negative ES indicate enrichment in immortal and senescent Huh7 clones, respectively. X-axis bars represent individual genes of the indicated gene sets. FDR: False discovery rate, p: nominal p-value. Three biological replicates from each clone were analyzed for genome-wide gene expression using Affymetrix 54 K microarrays and normalized data were used for gene set enrichment analysis (GSEA).