dataesr/scientific-paragraphs-categorization huggingface dataset:
Case Study 1: a group collaboration on S. enterica serovar Agama S. enterica subspecies enterica encompasses more than 1586 defined serovars  (Guibourdenche et al. 2010; Issenhuth-Jeanjean et al. 2014). These differ in the antigenic formulas of their lipopolysaccharide (O antigen) and/or two alternative flagellar antigens (H1, H2), which are abbreviated as O:H1:H2. Some serovars are commonly isolated from infections and the environment and have been extensively studied. Others are rare, poorly understood, and often polyphyletic  (Achtman et al. 2012) , including Salmonella that colonize badgers  (Wray et al. 1977; Wilson et al. 2003). In late 2018, serovar Agama (antigenic formula: 4,12:i:1,6) was specified in the Serovar metadata field for only 134/156,347 (0. 09%) genome assemblies in EnteroBase, and all 134 isolates were from humans.
We were therefore interested to learn that the University of Liverpool possessed serovar Agama isolates that had been isolated in 2006-2007 from European badgers (Meles meles) in Woodchester Park, Gloucestershire, England. We sequenced the genomes of 72 such isolates and uploaded the short reads and strain metadata into EnteroBase. This data was used to analyze the population structure of a rare serovar within a single host species over a limited geographical area and to compare Agama genomes from multiple hosts and geographical sources.
Search strains The browser interface to EnteroBase is implemented as a spreadsheet-like window called a "Workspace" that can page through thousands of entries, showing metadata at the left and experimental data at the right (https://enterobase. readthedocs. io/en/latest/ features/using-workspaces. html). However, visual scanning of so many entries is inefficient. EnteroBase therefore offers powerful search functions (https://enterobase. readthedocs. io/en/latest/ enterobase-tutorials/search-agama. html) for identifying isolates that share common phenotypes (metadata) and/or genotypes (experimental data). EnteroBase also predicts serovars from assembled Salmonella genomes and from MLST data. However, the software predictions are not failproof, and many entries lack metadata information or the metadata is erroneous.
Transmission patterns at different levels of HierCC resolution All isolates from badgers were in HierCC cluster HC400_299 (Fig. 3B , dashed box), which also included other isolates from humans and other animals. HC400_299 was investigated by maximumlikelihood trees of core, nonrepetitive SNPs called against a reference draft genome with the help of the EnteroBase Dendrogram GUI. One tree (Fig. 4A ) encompassed 149 isolates from the British Isles which were in EnteroBase before establishing the Agama Study Group. A second tree (Fig. 4B ) contained the final data set of 213 genomes, including isolates from additional badgers and multiple countries. A comparison of the two trees is highly instructive on the effects of sample bias. Almost all of the initial HC400_299 genomes fell into three clades designated HC100_299, HC100_2433, and HC100_67355.
Ancient Y. pestis The number of publications describing ancient Y. pestis genomes has increased over the last few years as ancient plague has been progressively deciphered  (Bos et al. 2011 (Bos et al. , 2016;; Wagner et al. 2014; Rasmussen et al. 2015; Feldman et al. 2016; Spyrou et al. 2016 Spyrou et al. , 2018;; Margaryan et al. 2018; Namouchi et al. 2018; Keller et al. 2019; Spyrou et al. 2019). The metagenomic short reads used to reconstruct these genomes are routinely deposited in the public domain, but the reconstructed ancient genomes are not. This practice has made it difficult for non-bioinformaticians to evaluate the relationships between ancient and modern genomes from Y. pestis. However, EnteroBase now provides a solution to this problem. The EnteroBase EToKi calculation package (Supplemental Code) can reconstruct an ancient genome assembly by unmasking individual nucleotides in a fully masked reference genome based on reliable SNP calls from metagenomic data (Supplemental Fig.
S6 ). We ran EToKi on 56 published ancient metagenomes containing Y. pestis, and the resulting assemblies and metadata were uploaded to EnteroBase. EnteroBase users can now include those ancient genomes together with other reconstructed genomes and modern genomic assemblies in a workspace of their choice and use the EnteroBase SNP dendrogram module to calculate and visualize a maximum-likelihood tree (of up to a current maximum of 200 genomes). Figure  5   over the last 5500 years, including hundreds of unpublished modern genomes. This tree was manually annotated using a User-defined Field and Custom View with population designations for reconstructed ancient genomes that are consistent with the literature on modern isolates. We also assigned consistent population designations to additional modern genomes from central Asia and elsewhere.
An interactive version of this tree and all related metadata in EnteroBase is publicly available (http ://enterobase. warwick. ac. uk/a/21977/g), thus enabling its detailed interrogation by a broad audience from multiple disciplines  (Green 2018)  and providing a common language for scientific discourse. Case Study 3: Thinking big. An overview of the core genomic diversity of Escherichia/Shigella Escherichia coli has long been one of the primary workhorses of molecular biology. Most studies of Escherichia have concentrated on a few well-characterized strains of E. coli, but the genus Escherichia includes other species: E. fergusonii, E. albertii, E. marmotae  (Liu et al. 2015) , and E. ruysiae (van der  Putten et al. 2019 ). E. coli itself includes the genus Shigella  (Pupo et al. 2000) , which was assigned a distinctive genus name because it causes dysentery.
Discussion EnteroBase was originally developed as a genome-based successor to the legacy MLST websites for Escherichia  (Wirth et al. 2006) , Salmonella  (Achtman et al. 2012) , Yersinia pseudotuberculosis  (Laukkanen-Ninios et al. 2011) , and Moraxella catarrhalis  (Wirth et al. 2007). Its underlying infrastructure is sufficiently generic that EnteroBase was readily extended to Clostridioides, Helicobacter, and Vibrio, and could in principle be extended to other taxa. EnteroBase was intended to provide a uniform and reliable pipeline that can assemble consistent draft genomes from the numerous short-read sequences in public databases  (Achtman and Zhou 2014)  and to link those assemblies with metadata and genotype predictions. It was designed to provide access to an unprecedentedly large global set of draft genomes to users at both extremes of the spectrum of informatics skills.
EnteroBase has also been extended to support analyses of metagenomic data from ancient genomes  (Zhou et al. 2018c; Achtman and Zhou 2019 ) by implementing a subset of the functionality of SPARSE  (Zhou et al. 2018b ) within the stand-alone EToKi package. Case Study 2 illustrates this capability for Y. pestis. Additional EnteroBase databases are under development for ancient and modern genomes of S. enterica and biofilms within dental calculus. EnteroBase has also shown its capacities for providing overviews of the core genome diversity of entire genera, with currently extant examples consisting of Salmonella  (Alikhan et al. 2018)  and Escherichia (Case Study 3). EnteroBase is already being used by the community to identify genetically related groups of isolates  (Diemert and Yan 2019; Haley et al.
2019; Johnson et al. 2019; Miller et al. 2019; Numberger et al. 2019) , and HierCC has been used to mark international outbreaks of S. enterica serovar Poona  (Jones et al. 2019b ) and E. coli O26  (Jones et al. 2019a). Case Study 1 illustrates how to explore HierCC genomic relationships at multiple levels, ranging from HC2000 (superlineages) for intercontinental dispersion down to HC5-10 for detecting local transmission chains. Case Study 1 confirms that although S. enterica serovar Agama is rare, it has been isolated from multiple hosts and countries and is clearly not harmless for humans. The results also document that an enormous sample bias exists in current genomic databases because they largely represent isolates that are relevant to human disease from a limited number of geographic locations.
As indicated above, HC5 clusters in Salmonella are associated with recent transmission chains between badgers and across European borders. These additional observations suggest that E. coli from diverse ST Complexes which encode AMR have also been recently transmitted between humans and wild birds and domesticated animals. This user's guide provides an overview of what EnteroBase can do now. With time, we hope to include additional, currently missing features, such as community annotation of the properties of bacterial populations, predicting antimicrobial resistance/ sensitivity, and distributing core pipelines to multiple mirror sites. However, EnteroBase is already able to help a broad community of users with a multitude of tasks for the selected genera it supports. More detailed instructions are available in the online documentation (https://enterobase.
Figure1. Overview of EnteroBase Features. (A) Data uploads. Data are imported from public databases, user uploads, and existing legacy MLST and rMLST databases at PubMLST (https://pubmlst. org/). (B) Spreadsheet Interface. The browser-based interface visualizes sets of strains (one Uberstrain plus any number of substrains) each containing metadata, and their associated experimental data and custom views. Post-release data can be exported (downloaded) as genome assemblies or tab-delimited text files containing metadata and experimental data. Metadata can be imported to entries for which the user has editing rights by uploading tab-delimited text files. (C) Search Strains supports flexible (AND/OR) combinations of metadata and experimental data for identifying entries to load into the spreadsheet.
Acknowledgments EnteroBase development was funded by the  Biotechnology and Biological Sciences Research Council  ( BB/L020319/1 ) and the  Wellcome Trust  ( 202792/Z/16/Z ). We thank  Niall Delappe  and  Martin Cormican ,  Salmonella Reference Laboratory, Galway, Ireland , for sharing strains and data, and  Nina Luhmann  and  Jane Charlesworth  for critical comments on the text. Author contributions: M. A. wrote the manuscript with the help of all other authors. Z. Z. , N. -F. A. , K. M. , and Y. F. were responsible for the development of EnteroBase under the guidance of M. A. N. -F. A. and K. M. were responsible for the online manual. Analyses were performed and figures were drawn by Z. Z. and N. -F. A. under the guidance of M. A. The Agama Study Group provided information, bacterial strains, DNAs, and genomic sequences from Agama isolates from all over Europe and was involved in writing the manuscript and evaluating the conclusions.
Data access The Illumina sequence reads for 161 new genomes of S. enterica serovar Agama generated in this study have been submitted to the European Nucleotide Archive database (ENA; https://www. ebi. ac. uk/ena) under study accession numbers ERP114376, ERP114456, ERP114871, and ERP115055. The genomic properties, metadata, and accession codes for the 329 genomic assemblies in HC2000_299 are summarized in Supplemental Table  S3  and in Online Table 1 (https://wrap. warwick. ac. uk/128112). The metadata, genomic assemblies, and annotations are also available from the publicly available workspace "Zhou et al. All Agama Strains" (http://enterobase. warwick. ac. uk/a/21320). The EToKi package and its documentation are accessible at https://github. com/ zheminzhou/EToKi and as Supplemental Code.
Here we describe a proteomic data resource for the NCI-60 cell lines generated by pressure cycling technology and SWATH mass spectrometry. We developed the DIA-expert software to curate and visualize the SWATH data, leading to reproducible detection of over 3,100 SwissProt proteotypic proteins and systematic quantification of pathway activities. Stoichiometric relationships of interacting proteins for DNA replication, repair, the chromatin remodeling NuRD complex, b-catenin, RNA metabolism, and prefoldins are more evident than that at the mRNA level. The data are available in CellMiner (discover. nci. nih. gov/cellminercdb and discover. nci. nih. gov/cellminer), allowing casual users to test hypotheses and perform integrative, cross-database analyses of multi-omic drug response correlations for over 20,000 drugs.
The NCI-60 remains actively used by many academic laboratories and drug companies to assess overall toxicity and drug response selectivity. In addition, many of the NCI-60 cell lines are widely used for cell biology and pharmacology (MCF-7, MDA-MB231, HCT116, HCT15, HT29, HL60, CCR-CEM, K562, etc. ), and 55 and 44 of the NCI-60 cell lines overlap within larger cancer cell line databases GDSC and CCLE, respectively  (Rajapakse et al. , 2018) , providing a unique and highly valuable resource for cross-comparisons. The proteome of the NCI-60 cells has been analyzed previously by data-dependent analysis with a commonly used discovery MS technique  (Gholami et al. , 2013). This proteome dataset was obtained using a sophisticated two-dimensional peptide fractionation strategy. However, peptides and proteins were quantified without technical replicates  (Gholami et al.
1 microgram of total peptide mass). We focused on 3,171 SwissProt proteotypic proteins that were identified across all cell lines, generating a data matrix (120 proteomes vs. 3,171 proteins). Raw signals of each peptide and protein in each sample were curated and visualized with an expert system. The proteomic data expand the existing NCI-60 molecular landscapes  (Holbeck et al. , 2010; Monks et al. , 2018; Rajapakse et al. , 2018; Reinhold et al. , 2012 Reinhold et al. , , 2019) )  and their integration with the larger databases from the Broad-MIT (CCLE, CTRP) and MGH-Sanger (GDSC)  (Rajapakse et al. , 2018) , allowing systematic investigation of the complementarity among genomics, transcriptomics, and proteomics.
DIA-Expert-Curated Results of the NCI-60 Proteome Excluding proteins/peptides that were not technically reproducible resulted in 22,554 proteotypic peptides from 3,171 proteins, with 8% missing values at the peptide level and 0. 1% missing values at the protein level across all MS runs (Table  S1 ). On average, seven peptide precursors and six unique peptide sequences were identified per protein. Several proteins were identified with more than 200 peptides (Figure  1B ). The proteins excluded by DIA-expert may not be incorrect identifications but rather irreproducible quantifications due to either technical (for instance the signal-to-noise ratio) or biological issues (such as post-translational modifications or splicing variants). Improved computational methods may recover more information from this dataset.
However, this did not substantially improve quantitative accuracy (Figure  S4 ). dependence on protein abundance, as evident from the distribution of its values for different expression level quantile groups of the measured proteins (Figure  2B ). We then compared the data with the previously reported DDA-MS proteomic data for the NCI-60  (Gholami et al. , 2013). Whereas the DDA data reported a comparable number of IPI protein groups per cell line as the SwissProt proteotypic protein number from this SWATH dataset (Table  S2 ), the SWATH data exhibited a much higher degree of consistency (Figure  S5 ) and better quantitative accuracy (Figures  S6-S32 ).
Accessibility of the NCI-60 Proteotypes To enable easy data access, visualization, and comparison with other NCI-60 datasets, we have incorporated the SWATH data into the CellMiner databases and web application  (Rajapakse et al. , 2018; Reinhold et al. , 2012; Shankavaram et al. , 2009). This allows direct downloads of the data, as well as direct comparative and integrative analyses with other molecular and pharmacological data, (e. g. sensitivity of each cell line to over 20,000 compounds) and the inspection of specific genes, up to 150 per query. The detailed instructions for using this resource are provided in Figure  S33  and at the project websites (discover. nci. nih. gov/cellminer and discover. nci. nih. gov/cellminercdb). Figure  2E  shows snapshots of data queries for KU70 versus KU80 protein and transcript expression levels (XRCC6 and XRCC5, respectively).
Insights from a Quantitative Comparison of Protein versus Transcript Expression Because of the extensive prior characterization of the NCI-60 transcriptome  (Monks et al. , 2018; Rajapakse et al. , 2018; Reinhold et al. , 2012) , we were able to correlate protein and gene expression for each of the 3,171 proteins quantified across the NCI-60. Table  S3  shows that some proteins exhibit a high correlation with their transcript, indicating the transcripts are the main drivers of protein expression. Correlations and cell line identification can be readily checked with CellMinerCDB (see Figures  S34-S46 ). The most highly correlated proteins include MARCKSL1 (myristyolated alanine-rich C kinase; r = 0. 93), LGALS3 (galectin 3; r = 0. 90), and ITGB1 (integrin-b1; r = 0. 88) (Table  S3  and Figure  S34 ).
We further investigated whether this trend was present when we used different public resources to assign protein complexes. We compared interacting protein pairs or proteins assigned to complexes according to (1) a curated CORUM database of mammalian protein complexes, (2) annotations for stable interactions in the Reactome database, (3) high-quality interaction partners in the STRING database, (4) known and modeled interactions in the Interactome3D based on available protein structures, (5) interaction pairs observed in at least three affinity purification-mass spectrometry experiments (APMS, see Methods), or (6) a small set of curated and annotated protein complexes available from the EMBL-EBI Complex Portal (Figure  2F , top panel). Compared with random protein pairs, the correlation of the measured protein quantities for the annotated interaction partners was strongly shifted to higher values (p value <1.
1 x 10 À10 , Wilcoxon test). Of interest, the shift also reflected the confidence of protein complex assignments with the mean values of 0. 43, 0. 24, 0. 21, 0. 21, 0. 19, and 0. 13 for protein pairs from the EMBL-EBI complex portal, CORUM, Interactome3D, Reactome, STRING databases, and APMS studies, respectively. In addition, for almost all resources, a shoulder with overrepresented negative correlations (r % À0. 5) was visible. Next, we performed the same analysis, substituting transcriptomics data for protein quantities for the same protein pairs (Figure  2F , bottom panel). The co-expression of the interacting protein pairs was verified by the positive shift of correlation values for mRNA quantities. However, this shift was smaller compared with the proteomic comparisons (Figure  2F ), and the right-skewed shoulder reflecting overrepresentation of highly correlating protein interactors was absent using mRNA levels (demarcated by the red vertical dashed line in Figure  2F ).
Using the CellMinerCDB ''Compare Patterns'' tool with RPA3 as the ''identifier'' and selecting ''swa'' as ''Data Type'' yields RPA2 as top correlate (p = 0. 75; r = 6. 58 3 10 À12 ), followed by the two subunits of the MCM replicative helicase MCM5 and MCM7 (p = 0. 63; r = 1 3 10 À7 ) (Figure  S37 ). Such highly significant correlations are not observed for the corresponding transcripts (Figure  S38 ). We also tested proteins co-expressed with PCNA, the essential cofactor for replicative DNA polymerase processivity. Because PCNA is also included in the small number of proteins determined by reverse phase proteomic array (RPPA)  (Nishizuka et al. , 2003) , we were able to establish the reproducibility of the SWATH measurements by plotting PCNA protein expression with SWATH vs. RPPA using CellMinerCDB (r = 0.
Both DHX9 (RNase A) and FUS (Fused in sarcoma and associated with liposarcoma and amyotrophic lateral sclerosis  [AML] ) are stoichiometrically coregulated across the NCI-60 with highly significant correlations at the protein levels (r = 0. 81; p = 4. 7 3 10 À15 ) even more than at the transcript levels (r = 0. 42; p = 0. 001) (Figure  S35 ). Looking further at the cells co-expressing FUS and DHX9 transcripts across the larger MGH-Sanger (GDSC) database  (Garnett et al. , 2012)  using CellMinerCDB  (Rajapakse et al. , 2018)  confirmed the coregulation of these two RNA binding genes across 986 cell lines (r = 0. 52; p = 1. 8e-70), with highest expression in leukemia, lymphomas, and small cell lung cancer cell lines (Figure  S40 ). For large protein complexes, the nucleosome remodeling deacetylase (NuRD) complex  (Basta and Rauchman, 2015)  provides a notable example of protein complex stoichiometry.
Google-Map-Based Visualization of Cancer Signaling Pathways Our NCI-60 proteotypes cover 648 proteins in the Atlas of Cancer Signaling Networks (ACSN), a manually curated pathway database presenting published biochemical reactions involved in cancer using a Google-Maps-style visualization (Figure  S48 )  (Kuperstein et al. , 2015). When mapping the mean protein expression per cancer type, we found that, in different cell types, multiple pathways, including apoptosis, cell survival, motility, and DNA repair, displayed a similar pattern, consistent with the fact that immortal cancer cells retain cancer hallmarks in tissue culture  (Hanahan and Weinberg, 2011). An example of a proteotypic pattern is the delta isoenzyme of protein kinase C, i. e. PRKCD, involved in cancer progression and a drug target  (Mackay and Twelves, 2007).
962 (p = 6 3 10 À34 ), validating data reproducibility. Each of the compounds is categorized by mechanism-of-action annotation (Figure  3A ). The largest group of drugs is DNA damaging agents (including DNA alkylating and cross-linking agents, DNA synthesis and topoisomerase inhibitors). Using the elastic net algorithm  (Barretina et al. , 2012; Garnett et al. , 2012; Rajapakse et al. , 2018) , we developed multivariate linear models to predict the NCI-60 response for each compound based on selected genomic, transcriptomic, and SWATH proteomic features. The Pearson's correlation between observed drug response values and leave-one-out cross-validation-predicted response values was applied to evaluate the performance of each predictive dataset. As different numbers of features were measured for each omics dataset, two strategies were adopted in the modeling analyses.
The SWATH-MS-derived proteotypes displayed a higher percentage of predictive features than mutations and transcripts. Twelve percent of SWATH features were selected in one or more predictive models, whereas the corresponding proportions were 2% for mutation features and 6% for transcript expression features (Table  S7 ). The responsiveness of 49 screened drugs (22%) was best predicted with SWATH data, and 83 compounds (37%) were best predicted by combining SWATH data with transcripts and mutational data. Through an examination of predictive gain by mechanism-of-action (Figure  3G ), we made a few notable observations. Out of six HDAC inhibitors, four showed improvement using the SWATH feature set making it the most improved mechanism category; this observation should be taken with caution given the limited number of compounds.
Kinase inhibitors were one of the least improved categories, but this observation should be revisited using future phospho-proteomic datasets, which would include additional markers of kinase regulation  (Ardito et al. , 2017; Johnson, 2009). Lastly, the diverse set of compounds in the ''Other'' category also showed predictive improvement with the SWATH features and merits further future study. The remaining categories show a largely even distribution in predictive improvement. Next, we compared the distributions of predictive power as measured by Pearson correlation coefficients for models derived with and without the inclusion of SWATH features. The SWATH-included distribution is wider, but spans a comparable range of (higher-accuracy-associated) positive correlations relative to the strictly genomic feature-based distribution (Figures  3C  and 3D ).