dataesr/scientific-paragraphs-categorization huggingface dataset:
Many biologists and computational biologists establish list of genes of interest (e. g. a list of genes that are differentially expressed between two conditions, such as normal vs disease) and then evaluate if known biological pathways have significant overlap with this list of genes. molecular maps is decomposed in a number of functional modules. The maps themselves are merged into a global ACSN map. Thus the information included in ACSN is organized in three hierarchical levels: a global map, five individual maps, and several functional modules. Each ACSN map covers hundreds of molecular players, biochemical reactions and causal relationships between the molecular players and cellular phenotypes. ACSN represents a large-scale biochemical reaction network of 4,826 reactions involving 2,371 proteins (as of today), and is continuously updated and expanded.
We have included the three hierarchical levels in the ACSNMineR package, in order to be able to calculate enrichments at all three levels. The calculations are made by counting the number of occurences of gene symbols (HUGO gene names) from a given list of genes of interest in all ACSN maps and modules. Table  1  is detailling the number of gene symbols contained in all the ACSN maps. The statistical significance of the counts in the modules is assessed by using either the Fisher exact test  (Fisher, 1922 (Fisher, , 1934) )  or the hypergeometric test, which are equivalent for this purpose  (Rivals et al. , 2007). The current ACSN maps are included in the ACSNMineR package, as a list of character matrices.
> length(ACSN_maps) [1] 6 > names(ACSN_maps) [1] "Apoptosis" "CellCycle" "DNA_repair" "EMT_motility" "Master" [6] "Survival" For each matrix, rows represent a module, with the name of the module in the first column, followed by a description of the module (optional), and then followed by all the gene symbols of the module. The maps will be updated according to every ACSN major release. The main function of the ACSNMineR package is the enrichment function, which is calculating over-representation or depletion of genes in the ACSN maps and modules. We have included a small list of 12 Cell Cycle related genes in the package, named genes_test that can be used to test the main enrichment function and to get familiar with its different options.
> library(ACSNMineR Only the gene list is mandatory to call the enrichment function, all the other arguments have default values. The maps argument can either be a dataframe imported from a GMT file with the format_from_gmt function or a list of dataframes generated by the same procedure. The GMT format corresponds to the Broad Institute's Gene Matrix Transposed file format, a convenient and easy way to encode named sets of genes of interest in tab-delimited text files (it is not a graph or network format). By default, the function enrichment uses the ACSN maps previously described. The correction for multiple testing is set by default to use the method of Benjamini & Hochberg, but can be changed to any of the usual correction methods (Bonferroni, Holm, Hochberg, Holm, or Benjamini & Yekutieli  (Reiner et al.
For example, enrichment of highly expressed pathways can reveal differences between two cancer types or two cell lines. To facilitate such comparisons, ACSNMineR provides a multisample_enrichment function. It relies on the enrichment function but takes a list of character vector genes. The name of each element of the list will be assumed to be the name of the sample for further analysis. Most of the arguments given to multisample_enrichment are the same as the ones passed to enrichment. However, the cohort_threshold is designed to filter out modules which would not pass the significance threshold in all samples. Finally, to facilitate visualization of results, ACSNMineR integrates a representation function based on ggplot2 syntax  (Wickham, 2009). It allows representation of results from enrichment or multisample_enrichment with a limited number of parameters.
RNaviCell The NaviCell Web Service provides a server mode, which allows automating visualization tasks and retrieving data from molecular maps via RESTful (standard http/https) calls. Bindings to different programming languages are provided in order to facilitate the development of data visualization workflows and third-party applications  (Bonnet et al. , 2015). RNaviCell is the R binding to the NaviCell Web Service. It is implemented as a standard R package, using the R object-oriented framework known as Reference Classes  (Wickham, 2015). Most of the work done by the user using graphical point-andclick operations on the NaviCell web interface are encoded as functions in the library encapsulating http calls to the server with appropriate parameters and data. Calls to the NaviCell server are performed using the library RCurl  (Lang and the CRAN team, 2015) , while data encoding/decoding in JSON format is performed with the RJSONIO library  (Lang, 2014).
Once the RNaviCell library is installed and loaded, the first step is to create a NaviCell object and launch the browser session. This will automatically create a unique session ID with the NaviCell server. Once the session is established, various functions can be called to send data to the web session, set graphical options, visualize data on a map or get data from the map. There are 125 The R Journal Vol. 8/2, December 2016 ISSN 2073-4859 functions available in the current version of RNaviCell. All of them are described with their different options in the RNaviCell documentation, and we provide a tutorial on the GitHub repository wiki (https://github. com/sysbio-curie/RNaviCell/wiki/Tutorial). In the simple example detailed below, we create a NaviCell session, then load an expression data set from a local (tab-delimited) file.
The dataset corresponding to this study is available as a Bioconductor package. The code shown below is creating a list of differentially expressed genes between ER positive and ER negative samples, and calculates the enrichment in ACSN maps from this list of genes. As seen in Table  3 , there is one map (DNA repair) and seven modules (belonging to the Cell Cycle, DNA repair and Apoptosis maps) enriched. # load all necessary packages library(breastCancerMAINZ) library(Biobase) library(limma) library(ACSNMineR) library(hgu133a. db) library(RNaviCell) # load data and extract expression and phenotype data data(mainz) eset <-exprs(mainz) pdat <-pData(mainz) # Create list of genes differentially expressed between ER positive and # ER negative samples using moderated t-test statistics design <-model.
matrix(~factor(pdat$er == 1 )) lmFit(eset, design) -> fit eBayes(fit) -> ebayes toptable(ebayes, coef=2,n=25000) -> tt which(tt$adj < 0. 05) -> selection rownames(tt[selection,]) -> probe_list mget(probe_list, env = hgu133aSYMBOL) -> symbol_list symbol_list <-as. character(symbol_list) # calculate enrichment in ACSN maps enrichment(symbol_list) -> results The Molecular Signatures Database (MSigDB) is one of the most widely used repository of wellannotated gene sets representing the universe of biological processes  (Liberzon et al. , 2011). We downloaded the canonical pathways set, counting more than 1,300 gene sets representing canonical pathways compiled by domain experts. The dataset is encoded with the GMT format, and can be imported within ACSNMineR with the format_from_gmt function. We calculate the enrichment for the  # Select ER negative samples and calculate mean expression values apply(eset[probe_list,pdat$er == 0],1,mean) -> er_minus_mean names(er_minus_mean) <-symbol_list er_minus_mean <-as.
Analysis of glioblastoma mutation frequencies Recent years have witnessed a dramatic increase in new technologies for interrogating the activity levels of various cellular components on a genome-wide scale, including genomic, epigenomic, transcriptomic, and proteomic information  (Hawkins et al. , 2010). Integrating these heterogeneous datasets provides more biological insights than performing separate analyses. For instance, international consortia such as The Cancer Genome Atlas (TCGA) have launched large-scale initiatives to characterize The R Journal Vol. 8/2, December 2016 ISSN 2073-4859 multiple types of cancer at different levels on hundreds of samples. These integrative studies have already led to the identification of novel cancer genes  (McLendon et al. , 2008). Malignant gliomas, the most common subtype of primary brain tumors, are aggressive, highly invasive, and neurologically destructive tumors considered to be among the deadliest of human cancers.
Summary and perspectives In this work, we presented the R package ACSNMineR, a novel package for the calculation of pvalues for enrichment or depletion of genes in biological pathways. The package includes the six large-scale molecular maps and 67 functional modules of the Atlas of Cancer Signaling Network (ACSN). Enrichment can be calculated for those maps and modules with several options to play with, but can also be calculated for other databases of molecular pathways, that can be imported from GMT formated files. We also describe in this work the RNaviCell package, a R package convenient to use with ACSN-MineR. This package is dedicated to create web-based and interactive data visualization on ACSN maps. Users can use this tools to represent genes of interest that have been shown to be related to the maps by calculating enrichment with ACSNMineR.
Creating maps with the graphical user interface of the ACSN website can be a tedious task if the user has multiple samples or gene lists, and wants to compare their representations on ACSN maps. The RNaviCell package can be used to automate the process of creating the graphical representations automatically. The maps are displayed in a browser and are interactive, with the possibility for the user to zoom in and out, search for genes or molecular species, and see the details of the molecular reactions (what partners are involved, what is the state of a given species, etc. ). For more details on how to use the interface and the different possibilities, see  Kuperstein et al. (2013) ,  Bonnet et al. (2015)  and  Kuperstein et al. (2015). We have shown how the packages ACSNMineR and RNaviCell can be combined to analyze expression data from breast cancer samples, and also to analyze the frequency of mutated genes in glioblastoma cancer samples.
The R Journal Vol. 8/2, December 2016 ISSN 2073-4859 Of course, ACSNMineR is not the only R package for enrichment calculations. For instance, GOstats  (Falcon and Gentleman, 2007)  is probably one of the first packages that was created to calculate enrichment for Gene Ontology categories. GOstats can also be used to calculate enrichment for other biological pathways categories, such as KEGG pathways (by using an instance of the class KEGGHyperGParams) or PFAM protein families (using PFAMHyperGParams). However, its usage might not be as straightforward as ACSNMineR, and it does not seem possible to test user-defined biological pathways. Furthermore, other authors have pointed out that the KEGG database used by this package has not been updated since 2012. clusterProfiler is a recent R package released for enrichment analysis of Gene Ontology and KEGG with either hypergeometric test or Gene Set Enrichment Analysis (GSEA)  (Yu et al.
, 2012). Via other packages, support for analysis of Disease Ontology and Reactome Pathways is possible. Interestingly, this package also offers the possibility to import user-defined gene set, through tab-delimited pairwise definition files. Other notable packages for enrichment calculations are ReactomePA for Reactome molecular pathways  (Yu and He, 2016) , miRNApath for microRNA pathways  (Cogswell et al. , 2008)  and gage  (Luo et al. , 2009). We believe that the main advantages of ACSNMineR compared to other packages are a direct access to the full set of ACSN maps (updated on a regular basis) and an easy way to test MSigDB gene sets or any user-defined gene set formatted appropriately. In order to improve ACSNMineR, we may in the near future try to improve the speed of calculation, which might be a problem if a very large number of samples or experiments have to be analyzed rapidly.
For instance, we could use the foreach and %dopar% operator to parallelize the most computationally demanding operations. It could also be useful to implement more sensitive methods of gene set enrichment measures, such as the Gene Set Enrichment Analysis (GSEA) method. RNaviCell relies on standard HTTP calls to provide informations and calculations, and we have developped a number of bindings for popular programming languages such as R, Java and Python  (Bonnet et al. , 2015). This open architecture is designed to facilitate the development of utilities by other programmers and to facilitate the integration of ACSN maps in existing frameworks. The development of such services, sometimes called "microservices"  (Fowler, 2014)  is in expansion. Furthermore, this kind of open architecture could clear the way for a more unified and general access to reaction networks database, including for example WikiPathways  (Kelder et al.
, 2012) , Reactome  (Croft et al. , 2014)  and other databases. The PSICQUIC project is a successfull example of such an architecture  (Aranda et al. , 2011). It is an effort of the HUPO proteomics standard initiative to standardize the access to molecular interaction databases programmatically, based on the specification of web services (using REST and SOAP calls) and a common query language (MIQL).
MSigDB canonical pathway database enrichment for genes differentially expressed between ER positive and ER negative samples in breast cancer. This table presents the 10 modules with lowest p-value out of 125 with corrected p-value lower than 0. 05. Module : name of the module. Mod. size: size of the module. Nb genes: number of genes from input which are found in the module. Cor. pval: corrected p-value. At last, we visualize the mean expression values for ER negative samples for all genes differentially expressed on the ACSN master (global) map using RNaviCell commands to create heatmaps.
cancer differentially expressed gene list, simply specifying the MSigDB data we just imported as the maps option. Table4is displaying the pathways having a corrected p-value < 0. 05. The prefix is indicating the database source, so we see that we have pathways from the KEGG, Reactome and PID databases. Consistent with our previous results, most of the enriched pathways are related to the cell cycle regulation. # Import MSigDB canonical pathways and calculate enrichment on this database mtsig <-format_from_gmt( c2. cp. v5. 0. symbols. gmt ) enrichment(symbol_list, maps = mtsig) The R Journal Vol. 8/2, December 2016 ISSN 2073-4859 breast.
Immunostaining on a Masson trichrome counterstained tissue and visualization of fibrillin-1 was performed using the streptavidine/peroxidase method (R. T. U. VECTASTAIN ® Universal ® ABC Kit Vector Laboratories, Inc. , CA, USA). Nuclei were stained using propidium iodide. No primary antibody was added for controlling the nonspecific labeling (data not shown).
19 The DAB reaction product masks the antigen and catalytic sites of the first sequence of immunoreagents, preventing an interaction of the second sequence with the reagents. 20 he stereotaxic atlas of Paxinos and Watson  21  was used for mapping and nomenclature. Photomicrographs were obtained with an Olympus DP50 digital camera attached to a Kyowa Unilux-12 microscope. Using a Leica DMRB photomicroscope/Neurolucida system (8. 0; Microbrightfield-bioscience, Williston, VT, USA) photographs at low-power magnification were also obtained. To improve the visualization of results, only the brightness and contrast of the images were adjusted. Adobe Photoshop Elements software was used.
A fixed amount of amplified DNA was sequenced in an Illumina Hi-seq 2000 sequencer. An average of 30 × 10 6 single reads with 100 bases was obtained for each sample  [28]. The quality of reads was assessed by FastQC, and the sequences were selected according to their quality. All reads corresponding to the mouse genome sequence were filtered by mapping with Bowtie 2. 0 software on a Mus musculus Mn10 sequence. The viral reads corresponding to the RVFV genome were selected by a similarity approach with BLASTN search tools  [30]. All selected viral reads were assembled with Ray software, with k = 25, to obtain the full-length viral genomes  [28]. In order to validate our approach for obtaining the complete sequence of RVFV by highthroughput sequencing, the RNA was extracted from three viral strains (HB74P59, HB1752, and ArB1986), and fragments of the small, medium, and large segments were amplified and sequenced.
Statistics The distribution of serological results for RVFV was analysed by species, season, and human age and sex and presented as proportions. The effect of each variables on the RVFV positivity was examined using the chi-squared test. P values < 0. 05 were considered statistically significant. The variables were then put in a Logistic regression model in stepwise manner. The likelihood ratio test was used to compare the model with and without the variable. In case there was no evidence that the variable fitted, it was dropped (parsimonious model). Statistical analyses were performed with STATA software version 11.
Genetic analysis of complete genome isolated from mosquitoes A third RVFV strain obtained by high-throughput sequencing was isolated in Bangui at the end of December 1984 (HB1752). Genomic analysis of three segments showed that it was identical to a strain isolated in Bangui 3 months earlier; however, RVFV strains isolated from vectors in 1969 and from human cases in the CAR several decades later had different nucleic sequences, even though they belonged to the same cluster (Fig  2 ). The complete genome sequence was made available to GenBank (HB1752 strain small, medium, and large accession numbers KJ782452, KJ782453, and KJ782454 respectively; ArB1986 strain small, medium, and large accession numbers KJ782455, KJ782456, and KJ782457, respectively).
None of the mutations were in pbp5. The most interesting mutation found in Cro1 was in stk (ireK in E. faecalis), which has been identified as involved in the cephalosporin resistance phenotype of E. faecalis  (6). The mutation introduced a premature stop codon 33 amino acids (aa) from the C terminus of the protein, in a region that extends beyond the PASTA domains typical of the extracellular portion of eukaryotelike serine-threonine kinases and is composed predominantly of serines (see Fig. S1  in the supplemental material). A search of the Swiss-Prot database yielded 34 E. faecium sequences, all with the serine tail. Similar serine-rich tails were present on stk homologues in Enterococcus durans, Enterococcus casseliflavus, Enterococcus mundtii, Enterococcus flavescens, and Peptoclostridium difficile Y384.
All 244 available E. faecium genome sequences in the En-sembleBacteria database contained P 5 AP, suggesting that it is part of the E. faecium core genome. The predicted P 5 AP protein is 522 amino acids in length and contains the domains of unknown function DUF4097 (IPR025164) and DUF4098 (IPR025386); these domains are conserved in all identified homologues of P 5 AP in different Enterococcus species. Additionally, we identified proteins with both DUF4097 and DUF4098 domains in other Lactobacillales, including Lactobacillus, Aerococcus, and Carnobacterium. Interestingly, the sequences retrieved from Leuconostoc, Streptococcus, and Lactococcus are highly divergent (Fig. 2 ; see also Fig. S3  and Table  S3  in the supplemental material) and do not cluster with other Lactobacillales. In more distantly related Firmicutes, we identified DUF4097-DUF4098-containing proteins in the Bacillales, including Bacillus, Listeria, and Staphylococcus and in the more distant Clostridia.
Deleting ponA and pbpF results in the interaction between cephalosporins and the allosteric site and hence susceptibility. Exposure to penicillin in these cephalosporin-susceptible strains results in increased expression of P 5 AP, which, we hypothesize, interacts with Pbp5 in a manner that precludes either allosteric binding or the resulting structural  changes. Constitutive acquisition of cephalosporin resistance can occur in this setting through a null mutation in stpA, presumably through increased phosphorylation of a transcription factor for the P 5 AP gene. At this time, we have no compelling data to suggest a specific activity for P 5 AP. It shares little similarity with proteins of known function. Our sequence searches retrieved homologous sequences in Enterococcus species, other Enterococcaceae, other Lactobacillales (Lactobacillus, Streptococcus, Lactococcus), Bacillales (Staphylococcus and Listeria), and Clostridia (Clostridium).
Our search found that proteins containing DUF4097 (IPR025164) and DUF4098 (IPR025386) are not particularly conserved. The DUF4097/DUF4098-containing proteins in Streptococcus and Lactococcus are highly divergent from those in the enterococci. It is interesting that Enterococcus and Listeria are two species that are intrinsically resistant to cephalosporins, but their P 5 AP sequences share only 22. 6% identity. While it is tempting to speculate that P 5 AP serves as a glycosyltransferase, there are no suggestions in the structure that it has such activity. Using the secondary structure prediction program PSIPRED, it was observed that residues 1 to 235 are mostly alpha helical while beta-sheets predominate in the C-terminal domain (residues 240 to 520). There are no obvious transmembrane regions or export signals, though its close association with Pbp5 strongly suggests that it acts in the extracellular milieu, since Pbp5 is predominantly an extracellular protein.
A total of 1. 5 g of labeled material from each sample was hybridized to custom-made E. faecium Affymetrix GeneChips (part number PMDefcma520788F) that contained a total of 4,395 probe sequences representing 3,898 open reading frames and 497 intergenic E. faecium annotated GenBank entries at the time of array design. GeneChips were then processed according to the manufacturer's recommendations for prokaryotic arrays (Affymetrix, Santa Clara, CA). Differentially expressed transcripts were identified as RNA species that demonstrated a 1. 5-fold increase or decrease in expression in penicillin-challenged CV571 cells in comparison to mock treatment (t test, P ϭ 0. 05). Preparation of protein lysates for SDS-PAGE and phosphoprotein analysis. All chemicals were obtained from Sigma unless otherwise specified.
ebi. ac. uk/Tools/hmmer/ search/phmmer) to search for proteins with the DUF4097 and DUF4098 domains. We used default values and used the UniProtKB database. The HMMER search against UniProtKB retrieved 795 proteins, 781 from Bacteria and 705 of those from Firmicutes, mostly concentrated in the Bacilli (558 proteins; see Table  S3  in the supplemental material). We selected representative sequences from Enterococcus, other Lactobacillales, Bacillales, and two Clostridiales for phylogenetic comparison. For phylogenetic analyses, protein sequences were aligned using Clustal Omega. Phylogenetic analyses were then carried out using PhyLM online  (23)  using the LG substitution model, NNI tree searching, and aLRT SH-Like branch support. The resulting tree and matrix were visualized with Geneious 8.
ACKNOWLEDGMENTS Genome library construction was facilitated by the helpful insight of Alina Gutu. Genomic analysis was carried out by  Toshiro Ohsumi  in the  Department of Molecular Biology at Massachusetts General Hospital. Protein sequence analysis and identification of the tandem affinity purification protein complexes were provided by  Ross Tomaino  at the  Taplin Mass Spectrometry Facility at Harvard Medical School, Boston, MA.