dataesr/scientific-paragraphs-categorization huggingface dataset:
Each of the 305 files was aligned at nucleotide level by MUSCLE  [22]  that was run locally on the University of York Biology LINUX grid. Each alignment file was checked and gaps were added for strains that had no reads for a given gene. The final results of FASTA alignments were concatenated by strain to form a 305-gene alignment using GALAXY  [23]. A 100-gene alignment was also created using only those genes that were represented in every isolate by at least one read of at least 100 nucleotides.
Phylogenetic analysis Phylogenies were constructed using either neighbour-net or maximum-likelihood (ML) methods. All neighbour-nets were generated using the uncorrected p-distances function of SPLITSTREE v. 4. 11  [24]. All maximum-likelihood analyses were performed by FASTTREE  [25]  with settings: -gamma -gtr, run locally on the University of York Biology LINUX grid. For the 100-gene alignment, an ML tree was constructed using FASTTREE with 100 bootstrap replicates and visualized using SPLITSTREE. An individual ML phylogeny of each of the 100 genes was constructed using PHYML  [26]  with the best-fit model of nucleotide substitution calculated from MODELTEST embedded in TOPALI v. 2  [27]. To compare tree topologies (e. g. single gene trees with 100-gene tree), Shimodaira-Hasegawa (SH) congruence tests implemented in the CONSEL package  [28]  were performed ( p , 0.
Average nucleotide identity and phylogenetic analysis Average nucleotide identity (ANI) was calculated using the JSPECIES package  [30]  using MUMMER (ANIm). The cut-off for per cent similarity between two genomes belonging to the same species is 96%, which generally gives a similar result to the DNA-DNA hybridization threshold value of 70%  [31]. This method was applied to representative strains that were selected based on coverage and to include at least one member from each of the five putative genospecies (A-E) and major subclusters present in genospecies C (gsC). To assign strains from published studies to the genospecies, housekeeping gene sequences were compared by local BLASTN to a database containing all contigs from the 72 strains.
Presence/absence of genes The GSMAPPER 2. 5 software was used, with 90% sequence identity and 40-bp minimum overlap, to perform individual reference-based assembly of R. leguminosarum strains against the combined sequences of all Rlv 3841 replicons. Perl and R scripts were used to extract information based on Rlv 3841 genes from the output file 454RefStatus. txt, which provides information on the number of reads mapping to each reference sequence. The extracted data were converted by Perl scripts into binary presence/absence format based on at least one unique mapped read. These presence/absence matrices were displayed as heat maps using R.
1). Capturing this diversity requires multiple genomes spanning the range of genetic variation across the species or, failing this, a single genome that is complemented by data on polymorphism of a handful of core genes sequenced in multiple strains. Furthermore, our data indicate that simple phenotypes are unlikely to provide reliable diagnostic tests for a species, as commonly used features may not be consistent when enough strains are examined ( §4. 6). There may be sets of genes that are unique to each species ( §4. 4), and we can surmise that these provide Figure  5. Utilization of carbon substrates by the 72 R. leguminosarum strains plus the reference strain 3841 (rows), determined using the Biolog GN microplate. Colour at left indicates the genospecies (gsA-gsE, figure  1 ); the second column indicates symbiovar (red, viciae; blue, trifolii).
Some major suites of correlated adaptive traits, such as symbiotic host range or pathogenicity, merit recognition as 'biovars'. This approach is widely applicable and is consistent with our current understanding of the diversity and evolution of bacteria, which has emerged in the past decade with the abundant availability of genome sequences. Data accessibility. Data are available from the Sequence Read Archive of the International Nucleotide Sequence Database Collaboration as study accession PRJEB7987.
Acknowledgements. We thank  Celina Whalley  and  Naveed Aziz  of the  York Technology Facility , as well as staff at the  Food and Environment Research Agency , for sequencing, and  Ryan Lower  for preliminary analyses of the data. We thank  Alvaro Peix  and  Helena Ramı ´rez-Bahena  for cultures of R. pisi DSM30132 T and R. phaseoli DSM30137 T , and  Changfu Tian  for R. fabae CCBAU33202 T.
Correction of Autofluoresence in the gfp Signal To correct for autofluorescence in the GFP signal, we measured the fluorescence of the unlabeled PA14 strain, which lacks the P rhlAB -gfp reporter, in numerous experiments across multiple days and a range of conditions, and performed a linear regression on the logarithm of these data (S1A Fig). We found the OD data (OD 600 ) to be a good predictor of the autofluorescence. The fit of the autofluorescence (AF) is then a function of the OD 600 of the form: AF ¼ e ðaþbÂlogðOD 600 ÞÞ We found a correlation coefficient r 2 = 0. 97 using all data points that were OD 600 >0. 01. Using this function, we estimated the amount of autofluorescence in our reporter strains by using the OD 600. We then subtracted the calculated autofluorescence value from the total GFP signal to obtain a corrected GFP (S1B Fig).
Promoter Activity Calculation Promoter activity was calculated as the change in population GFP per unit time divided by OD leading to the following expression  [40, 60] : P rhlAB ¼ 1 OD dGFP dt OD and GFP data are smoothed before calculating promoter activity using the 1-D digital filter using the function filter in Matlab with a window size of 5 timepoints. This calculation accounts for GFP dilution by cell division.
Mathematical Modeling and Parameter Fitting The mathematical model was implemented as a system of ordinary differential equations in Matlab (the Mathworks, Natick, MA) and solved numerically using the ode45 function. Parameter fitting was carried out using the fminbnd function in a step-wise manner. The OD time series were fitted initially, since these data are independent of the GFP time series data. The goal function to be minimized was defined as: err OD ¼ X N i¼1 log X i OD i 2 where i 2 {1, N} represents all the data points used for the fit. The GFP data was fitted by first calculating the promoter activity from the data as explained in the main text followed by fitting with fminbnd to minimize the following function err GFP ¼ X N i¼1 ðP i À p i Þ 2 where P i is the promoter activity calculated from the data and p i is the promoter activity predicted by the model.
The IUPHAR/BPS Guide to PHARMACOLOGY (GtoPdb, http://www. guidetopharmacology. org) provides expert-curated molecular interactions between successful and potential drugs and their targets in the human genome. Developed by the International Union of Basic and Clinical Pharmacology (IUPHAR) and the British Pharmacological Society (BPS), this resource, and its earlier incarnation as IUPHAR-DB, is described in our 2014 publication. This update incorporates changes over the intervening seven database releases. The unique model of content capture is based on established and new target class subcommittees collaborating with in-house curators. Most information comes from journal articles, but we now also index kinase cross-screening panels. Targets are specified by UniProtKB IDs. Small molecules are defined by PubChem Compound Identifiers (CIDs); ligand capture also includes peptides and clinical antibodies.
We have extended the capture of ligands and targets linked via published quantitative binding data (e. g. K i , IC 50 or K d ). The resulting pharmacological relationship network now defines a data-supported druggable genome encompassing 7% of human proteins. The database also provides an expanded substrate for the biennially published compendium, the Concise Guide to PHARMACOLOGY. This article covers content increase, entity analysis, revised curation strategies, new website features and expanded download options.
INTRODUCTION As demonstrated by this journal special issue, open databases have become indispensable for pharmacology, drug discovery, metabolism and chemical biology, and are increasingly important across other biomedical domains. The amount of structural information now freely available is immensely useful to researchers, but navigating the resources is becoming problematic for database users  (1). UniChem and PubChem now exceed 90 and 60 million entries respectively, with nearly 14 million structures added in 2014 alone  (2, 3). Of these, however, only 0. 4% have been tested experimentally. Thus, while just over 2 million of the current PubChem compounds have BioAssay results (with ≈50% tagged as active) (4), the increase in submitted structures is accelerating way beyond the community capacity to generate bioactivity measurements, extract them manually from papers and patents, crowd-source representations for structural correctness, or to curate synonym mappings.
This cheminformatics problem is analogous to the situation in bioinformatics, where the gap between the generation of new protein sequences and the Nucleic Acids Research, 2016, Vol. 44 , Database issue D1055 experimental assignment of at least some level of biological function is inexorably widening. For example, while UniProtKB/TrEMBL has mushroomed to nearly 50 million entries, only just over 0. 5 million entries have supporting evidence for the UniProtKB/Swiss-Prot level of expert annotation  (5). While the analogy should not be taken too far, the IUPHAR/BPS Guide to PHARMACOL-OGY (GtoPdb, http://www. guidetopharmacology. org;  (6) ) has some conceptual overlap with Swiss-Prot in that we also seek to maximise the level of data support within our 'small data' resource, to underpin the exploitation of 'big data'.
Targets Our generic use of the term 'target' refers to a record in the database that has been resolved to a UniProtKB/Swiss-Prot ID as our primary identifier. Reasons for this choice include (i) the Swiss-Prot canonical philosophy of protein annotation, (ii) species specificity and (iii) global recipro- *Not all our 7TM receptor records are unequivocally assigned as GPCRs, but for convenience we refer to these generally as GPCRs in the text. cal cross-referencing. Notwithstanding, target records also include RefSeq protein IDs and genomic IDs from Entrez Gene, HGNC and Ensembl. Because NC-IUPHAR oversees the nomenclature of (particularly) receptors and channels, these human protein classes are complete in GtoPdb (with the exception of the olfactory and opsintype GPCRs). The G protein-coupled receptors (GPCRs), ion channels and nuclear hormone receptors (NHRs) were present in the earliest database versions, regardless of the level of molecular pharmacology that could be assigned to them at that time, although they were obviously chosen because they were drug-target rich.
By 2012, the catalytic receptors and transporters had been added. At the end of 2012 we received a Biomedical Resources Grant from the UK Wellcome Trust with the objective of capturing the likely targets of future medicines (i. e. to cover the datasupported druggable genome). We consequently embarked on a major expansion of protein capture, of which enzymes formed the largest part. The current category counts are shown in Table  1  (note that statistics of all content types specified throughout this paper refer to our database release 2015. 2 from August 2015). The total number of targets in Table  1  represents 14% of the current Swiss-Prot human protein count of 20,204; although not all our entries are yet mapped to ligands. While the database is centred on human proteins, information from mouse and rat are also presented because rodent binding data are the most common type encountered in papers, either in addition to or instead of, human data.
We continue to add ligand mappings for both these large target classes (supported by the NC-IUPHAR protease and kinase subcommittees). Users can access data for each of the nine target classes in Table  1  via the GtoPdb website. The ion channel hierarchy is shown as an example (Figure  1 ). Where possible we adhere to the HGNC (10) Gene Families Index (http://www. genenames. org/cgibin/genefamilies/), but there are instances where the NC-IUPHAR classification deviates from these (e. g. catalytic receptors). In the database, the term 'target' includes verified targets for the MMOAs for drugs used to treat human diseases, newer receptor-ligand pairings judged to be credible by a dedicated NC-IUPHAR subcommittee  (11) , and human targets identified by orthologue activity mapping where only non-human binding data are available.
Examples of the latter category include the first generation of approved Angiotensin-converting enzyme (ACE) inhibitors, such as moexiprilat, for which only the rabbit protein has documented quantitative pharmacology. In addition, the database contains the targets of undesirable ligand interactions (sometimes termed 'anti-targets'), for example the HERG channel, K v 11. 1 (KCNH2) as a liability target for cardiac toxicity from the withdrawn drug terfenadine. Target capture also extends to emergent targets--proteins that do not have sufficient validation data to be considered bona fide therapeutic drug targets, but are nonetheless being investigated to both establish their normal function and possible disease involvement. Cathepsin A (CTSA) is an interesting recent example, because not only is compound 8a  [PMID 22861813 ] being explored to treat cardiac hypertrophy, but also an approved antiviral drug telaprevir is now being investigated for repurposing as a Cathepsin A inhibitor.
Target statistics One of the benefits of our recently enhanced curation is that it enables more detailed exploration of statistics of database content. This gives us a detailed overview of the database and allows us to compare it with other resources, to communicate results to users and funders, to measure progress and identify areas for future expansion. Target-centric examples of such statistics are shown in Figure  2. While the top-level GO categories are relatively coarse and not exclusive (e. g. some proteins are under both binding and enzymes), they provide a straightforward visual assessment of differences between protein sets. Not surprisingly, the curated set of ligand-binding targets (set B in Figure  2 ), compared to the whole proteome (set A in Figure  2 ), is enriched for receptors, enzymes and transporters.
Another filter was used as the Lipinski Rule-of-Five (ROF) with an extended molecular weight (Mw) range. Thus, 70% of our structures are inside this medicinal chemistry property 'sweet zone' that encompasses both drugs and leads (Figure  3 , Row 7). The BioAssay matches (Figure  3 , Row 8) coincide with the ChEMBL count at 70% but are complementary because of extended connectivity to data sets from the Molecular Libraries initiative  (3). Just 30% of our CIDs have a match to the MeSH term 'Pharmacological Actions' (Figure  3 , Row 9), which means the compound has been assigned pharmacological in vivo mechanisms of action by MeSH curators based on the paper in which it was reported. This total is surprisingly low and indicates a capture gap for this MeSH category. We recorded a 25% intersect of our compounds with the 10,939 CIDs retrieved by the query 'INN (or) USAN' which represent non-proprietary names for either approved drugs or failed clinical candidates (Figure  3 , Row 10).
Interaction mapping Quantitative ligand-to-protein interaction mappings constitute the core of the database. Curated relationship data across all targets is shown in Table  3. The total number of references in GtoPdb has reached 27880, a figure that includes the many target-specific references we also capture. Most (98%) have PubMed IDs but we include a few other reference types judged to be sufficiently provenanced. These include journals not indexed in PubMed, patents, slide sets, meeting abstracts, confirmed PubChem BioAssays and pharmaceutical company open information sheets for (unpublished) repurposing candidates.
Kinases In 2013, we added three published sets of results from crossscreening of kinase panels, to extend data for this important target class  (17) (18) (19). The cumulative set of 406 kinases x 230 ligands includes 158551 data points for users to inspect. An example from the imatinib entry is shown in Supplementary Figure  S1. The constitutive problem with surfacing panel screens in a database is that the assays are balanced to produce mostly negative results (i. e. compounds will be predominantly inactive at the threshold tested). In addition, the Millipore and Reaction Biology sets measure only percentageactivity-remaining at fixed concentrations, rather than doseresponses. For this reason, we separate the kinase panel results from the curated literature values (typically selected as    S1 ; note that only the top 10 targets in each of the screens are displayed on the ligand page, with the option to view the full set).
Single versus multiple versus complex targets As explained above, our capture of ligand-target relationships is founded on citable activity data that define pharmacologically significant molecular interactions. We recently enhanced our mapping precision by introducing the concept of a primary target, identified with a tag, when the publication record indicates that drug or lead has been optimised for a single target. By implication, the in vitro MMOA is likely to be causative for observed therapeutic effect in vivo (e. g. the effect of perindoprilat in lowering blood pressure is due to its substrate-competitive binding potency (IC 50 ) of 1 nM against ACE). Nonetheless this assumption has to be caveated where in vivo target validation data are still pending (e. g. via mouse KO and/or a clear genetic disease association).
The curator-assigned 'primary target' tags delineate a concise drug-to-target set of 312 human proteins for approved drugs. We are well aware of the challenges of setting curatorial stringencies for structure-to-activity-to-target mapping  (20). One aspect of increasing importance is polypharmacology, where evidence suggests that clinical efficacy is mediated by multiple MMOAs. The simplest examples are drugs designed as dual inhibitors, such as fasidotrilat (an antihypertensive agent that acts on both ACE and NEP ) where data support our assignment of two primary target relationships to the ligand. The situation is more complex for kinase inhibitors where in vitro data indicate that certain clinically successful inhibitors have polypharmacologic MMOAs  (9). Nonetheless, for relationship curation it remains difficult to define exactly which binding results are causatively relevant or if their capture is useful for GtoPdb data mining.
For this reason, we capture non-primary interactions but do not tag them explicitly as 'secondary targets'. We thus generally leave the interpretations of significance (e. g. efficacious polypharmacology, off-target interactions or side effect liabilities) open. An example here would be bosutinib which has 24 curated interactions: only one of these is tagged as primary, while the others are recorded for user interpretation. However, in cases where the pharmacological significance of off-(primary) target binding data is clear we will add a curators comment. For complex targets, we have again taken a parsimonious approach (in line with the primary target concept) in mapping to the minimal, rather than maximal, number of proteins, to increase data mining precision  (21). Examples here include the approved proteasome inhibitor bortezomib and the clinical candidate gamma-secretase inhibitor begacestat.
Strategy In collaboration with our target-family subcommittees, we have enhanced our curation procedures, because they are the primary determinant of database value. Crucially, this includes deciding what to leave out as well as include, and we have introduced more stringent filtering to maximise the utility of our relationship matrix. However, while we make use of established ontologies and terminologies where possible (e. g. see the disease section below), we do not apply rigid rules for content capture. We instead make extensive use of curators' comments that allow us to bridge between structured annotations (i. e. indexed in the database) and the flexibility of unstructured text. For users, we can thus specify new (or low frequency edge-case) relationship types via cross-pointers that are not formalised in the current schema (we may decide later to accommodate these via new structured indexing, if enough examples and an external terminology consensus appear).
Approved drugs Our grant objectives include annotating the targets of approved human medicines (i. e. currently anti-infectives). However, the task is complicated by variation in database molecular structures for approved drugs  (22). For this reason, we have chosen a consensus approach whereby we select the PubChem CID supported by the most submitters (i. e. has the SID 'majority vote'). We realise this approach is not infallible, but it does have pragmatic utility. Specifically, an exact chemical structure match between a majority of sources (at least some of which are manually curated) is more likely to be right than wrong. An example is provided by vapiprost where the CID 6918030 we have selected as (Z)- We have reached a current total of 1222 approved drugs (including antibodies) for which we have been able to curate drug-to-target relationships, and this covers new FDA approvals to 2Q 2015.
A second example is where an approved drug is an enantiomeric mixture (that does not interconvert in vivo), but assay data can be mapped to three different molecular representations (i. e. both the R and S isomers and the mixture or 'flat' form). In this case, we assign the drug tag to the mixture and map data to this. We then add cross-pointers to the CIDs for the R and S if data have been specifically reported and mapped to them. Well known examples are omeprazole as the mixture and esomeprazole as the S isomer, as separately approved drugs. We include both withdrawn and discontinued drugs (the latter being generally superseded by newer drugs) to D1062 Nucleic Acids Research, 2016, Vol. 44, Database issue maximise our capture and cheminformatic analysis of drug sets. The terms are not exclusive (i.
e. a drug can be tagged as both approved and withdrawn) but these can be filtered out of queries if necessary. For a number of reasons, we will not attempt to capture all molecular entities approved for human use. The main reason is because the database is focused on quantitative molecular pharmacology, captured as a ligand-target relationship matrix to facilitate data navigation and mining. It is thus not a pharmacopeia-type compendium (of which many are available), because many substances approved for medicinal purposes would negatively impact the precision of our database if we mapped-in their molecular interactions as 'drugs'. We therefore exclude simple molecules such as acetic acid, ethanol, urea and common inorganic salts. We also omit nutraceuticals that are principally metabolites (e.
An example of the value of patent data is shown in Figure  5  for beta-site APP-cleaving enzyme 2 (BACE2). The BACE2-selective inhibitors claimed specifically as potential anti-diabetes compounds are, as far as we can determine, the only public database instantiation of these activity mappings  (23). In this context, it is important to note that ChEMBL does in fact map 574 compounds to human BACE2 (target ID CHEMBL2525). However, these are all BACE1 inhibitors extracted from journal articles that have included BACE2 cross-screening results, since the first paper specifying the use of BACE2 inhibition for diabetes used a single BACE1 inhibitor and no medicinal chemistry papers have described BACE2-selective inhibitors. Thus, the chemistry is captured in SureChEMBL and GtoPdb, but not ChEMBL.