dataesr/scientific-paragraphs-categorization huggingface dataset:
Dataset integration into a unified coupling map -Normalization and log(Emax/EC50) values To enable quantitative correlation of the Bouvier and Inoue couplings, we further filtered the 70 common GPCRs to yield 51 common class A GPCRs tested with the same ligand (excluding 29 receptors tested with different ligands and two class B1 GPCRs). To assess the value of normalization, we calculated the average 'similarity' (Bouvier/Inoue ratio) of individual values (tabs ending with '-sim' in Spreadsheet S4) and the 'linear correlation' (r2 value) of each receptor across all G proteins (reported below as averages of individual couplings and G proteins, respectively) (tabs ending with '-r2' in Spreadsheet S4). Linear correlation was only done for receptors with at least three common G proteins/families.
Data and materials availability All data are available in GPCRdb (https://review. gpcrdb. org), GitHub (https://github. com/protwis/gpcrdb_data) and Spreadsheets S1-5. No code was developed for this manuscript which instead used the existing GPCRdb (33) resources, including a new G protein resource  (5). All open-source code can be obtained from GitHub (https://github. com/protwis/protwis) under the permissive Apache 2. 0 License (https://www. apache. org/licenses/LICENSE-2. 0).
16 A living review of risk prediction models has reported that current models are highly susceptible to bias and are poorly reported. 14 More recently, the ISARIC 4C model has been developed using data from 57,824 patients in the United Kingdom to develop a score on the basis of clinical/ laboratory parameters. 15 Although patients with a history of cancer were included in model development, it was not specifically built to predict the risk of severe COVID-19 disease in this high-risk population, and it is unclear how well it performs in patients with active cancer. We investigated clinical, hematologic, and biochemical features in patients with active cancer presenting to hospital with COVID-19 disease. Crucially, we wanted to create a pragmatic tool with parameters easily obtained through routine clinical history, examination, and laboratory assessment that can be readily applied in hospitals.
We developed a model that aimed to predict the potential for safe discharge without serious sequelae versus severe disease requiring oxygen (O 2 ) or leading to death. Data were used from international cohorts of patients to increase the generalizability of the model. Using this model, we built an online tool, COVID-19 Risk in Oncology Evaluation Tool (CORONET), to guide HCPs and systems in decision making regarding the need for admission and to provide information regarding the likely severity of illness. This is the first step of an iterative process whereby the tool will have ongoing refinement as more data and knowledge regarding COVID-19 disease and its treatment in patients with cancer are obtained.
There were several limitations in our model development. First, the cohort is relatively small; despite that, we obtained reasonable EPV, and according to the learning curves, we expect minimal improvement of the model accuracy with a larger data set size. Second, although we included data from 25 countries, the majority of these were higher-income countries, and therefore, the model should be interpreted with caution by users in middle-/low-income countries. Furthermore, through focusing on patients presenting to hospital, we selected a population biased toward more severe COVID-19 disease. In addition, although we excluded patients with more than one important numeric feature missing and managed remaining missing data through imputation (for , 20% missing), we still did not have sufficient data on features shown to be important in other cohorts such as ethnicity and LDH to incorporate into the analysis.
PRIOR PRESENTATION Presented in part as a poster at ESMO 2020 and in part at ASCO 2021 (oral, merit award). SUPPORT R. J. L. , T. R. , and J. W. were supported by the  National Institute for Health Research as Clinical Lecturers. T. B. was supported by the  National Institute for Health Research  as an academic clinical fellow. U. T. K. is an  MRC Clinical Training Fellow  based at the  University of Liverpool  supported by the  North West England Medical Research Council Fellowship Scheme in Clinical Pharmacology and Therapeutics , which was funded by the  Medical Research Council  ( Award Ref. MR/N025989/ 1 ), The  Liverpool Experimental Cancer Medicine Centre  for providing infrastructure support (Grant Reference:  C18616/A25153 ), and  The Clatterbridge Cancer charity (North West Cancer Research).
C. D. was funded by  CRUK Core funding  to  Manchester Institute  ( C5757/A27412 ) and was supported by the  CRUK Manchester Centre Award  ( C5759/ A25254 ) and by the  NIHR Manchester Biomedical Research Centre. C. Z. was funded by the  CRUK   Manchester Center Award  ( C5759/A25254 ), J. S. and P. F. were funded by the  CRUK   Accelerator Award  ( 29374 ). This research was funded in part by the  Wellcome Trust  ( 205228/Z/16/Z ). L. T. was also supported by the  National Institute for Health Research Health Protection Research Unit (HPRU) in Emerging and Zoonotic Infections  ( NIHR200907 ) at the  University of Liverpool  in partnership with  Public Health England (PHE) , in collaboration with  Liverpool School of Tropical Medicine  and the  University of Oxford. L. T. is based at  University of Liverpool.
Bacterial genome data is accumulating at an unprecedented speed due the routine use of sequencing in clinical diagnoses, public health surveillance and population genetics studies. Genealogical reconstruction in fundamental to many of these uses, however, inferring genealogy from large-scale genome datasets quickly, accurately, and flexibly is still a challenge. Here, we extend an alignment-and annotation-free method, PopPUNK, to increase its flexibility and interpretability across datasets. Our method, iterative-PopPUNK, rapidly produces multiple consistent cluster assignments across a range of sequence identities. By constructing a partially resolved genealogical tree with respect to these clusters, users can select a resolution most appropriate for their needs. We demonstrated the accuracy of clusters at all levels of similarity and genealogical inference of iterative-PopPUNK based on simulated data and obtained phylogeneticallyconcordant results in real datasets from seven bacterial species.
In practice, the information that genome sequences provide on genealogical relationships is both incomplete and time consuming to extract. Moreover, a full genealogy is rarely necessary for most practical uses of genetic relationships. This has led to the development of methods that attempt to identify clusters of closely related organisms. A simple interpretation of each cluster is that it corresponds to the set of strains descended from a single node of the genealogical tree. Amongst these possible methods, PopPUNK is notable because it works from raw sequence data and does not require a genome alignment  (Lees et al. 2019). This means that the clustering can be obtained with significantly less computational resources and is much faster than methods based on sequence alignments. Moreover, PopPUNK is easily extendable, as new batches of genomes can be integrated into an existing database without needing to recalculate all pairwise distances and model fit.
PopPUNK was originally designed to identify a single level of clusters, attempting to make this choice optimal by adapting a novel machine learning algorithm. However, in practice, multiple levels of clusters with different genetic similarities are often required to achieve different purposes. For example, in the disease outbreak scenario, epidemiologists require clusters of very closely related or indistinguishable strains to help identify and track outbreaks. For routine surveillance and subtyping, clusters with higher diversity are required to represent strains from a sequence type or clonal group. Population geneticists usually require clusters of population or sub-population level to conduct species-level studies. Two state-of-the-art pipelines for large-scale genome datasets that allow multi-level clustering are Fastbaps  (Tonkin-Hill et al.
2019) and HierCC, as implemented in Enterobase  (Zhou et al. 2021). Fastbaps is based on a core genome alignment, which is slow to generate for datasets of even modest sizes. HierCC requires an appropriate curated database of genes and alleles or a stable strain nomenclature and cannot be the general solution for most bacterial species. Here we present iterative-PopPUNK -an extension of the PopPUNK software -that allows estimation of a partially resolved genealogical tree and provides clustering consistent with that tree at different levels of sequence similarity. This enhancement provides greater flexibility than the original PopPUNK algorithm, allowing the fineness of the clustering to be adjusted according to the purpose at hand. Moreover, the clusters can be interpreted more readily, by reference to the full (partially resolved) genealogy.
The speed of iterative-PopPUNK is similar to PopPUNK, which can complete the analysis of thousands of bacterial genomes in a few minutes, and can scale even to very large-scale genome datasets (e. g. >100,000). Using simulated data, we demonstrate the accuracy of the clusters from iterative-PopPUNK at all output levels of similarity. Furthermore, we show that for real data, the produced clusters are normally highly concordant with those obtained by phylogenetic methods, but without requiring an alignment.
Overview of iterative-PopPUNK An overview of Iterative-PopPUNK pipeline is shown in Figure  1A. The first two steps proceed as usual using the original PopPUNK algorithm: 1) database construction and distance calculation and 2) model fitting using original PopPUNK algorithm. Iterative-PopPUNK then performs four additional steps to achieve multi-level clustering and generate clusters under the given similarity cutoffs.
Step 3: Multi-level clustering by moving decision boundary iteratively After an initial model has been fitted, the PopPUNK identifies two fitted components representing "within-strain" comparisons between closely related isolates and "betweenstrain". However, unlike the original PopPUNK algorithm, which identifies a single boundary for defining a "best" cluster assignment by optimising a heuristic (the network score), iterative-PopPUNK instead ignores the network score, and places the decision boundary at multiple equally spaced positions perpendicular to the line connecting within-and betweenstrain components to create multi-level cluster assignments. The first viable boundary point is where at least one non-trivial cluster (containing at least two samples) is formed, corresponding to the two most closely related sequences in the dataset.
Step 6. Choosing clusters under given similarity cutoffs Considering the large difference in the genetic diversity of different bacterial species, we designed two types of relative cutoffs to unify the genetic similarity across species: 1) Pmaxcluster-dist, the percentage of Maximum value of the Average Core-Distance (MACD) of all the iterative-PopPUNK clusters (extracted from local PopPUNK distance database). 2) Pbet-meandist, the percentage of mean pairwise core-distance between all isolates (extracted from initial model fitting results); When one of these cutoffs is provided, iterative-PopPUNK searches all the nodes except root node in the partially resolved tree and extracts the relative similarity values (Pmax-clusterdist or Pbet-mean-dist) of all the samples under each node (NP) and its parent node (N+1P).
Evaluation of iterative-PopPUNK using simulation data To test the performance of iterative-PopPUNK, we generated seven datasets of simulated genomes where the true genealogy is known. We generated 100 genomes with a length of 1,000 Kb per dataset and their corresponding genealogical trees using FastSimBac (De Maio and Wilson 2017), representing bacteria with different mutation and recombination rates (μ = 0. 01-0. 06, r=0. 003-0. 04, Table  S1 ). We created clusters of these simulated genomes using PopPUNK and iterative-PopPUNK using three different types of model fit (DBSCAN and GMM with two and three components), and mapped the clusters obtained back to the simulated genealogical trees. To assess performance, we compared the consistency of the output clusters to the known clonal relationships from the simulations.
Overall, iterative-PopPUNK generates many more (on average ~6-fold greater) clusters than PopPUNK with similar accuracy across a range of different mutation and recombination rates(Figure  2C, 2D ). A universal model we can recommend for iterative-PopPUNK is GMM with two components (K = 2), because more datasets could be analysed using this setting (DBSCAN fits sometimes fail to converge). Under this model, the average accuracy of clusters remains high across parameter choices (~95%). Therefore, we set it as the default model of iterative-PopPUNK and applied it in further analysis. We next tested the performance of iterative-PopPUNK under different relative genetic similarity (Pmax-cluster-dist) cutoffs. We again use the results obtained for simulated data under a mutation and recombination rate (μ=0.
001, r=0. 001, Figure  3A ) as an example, and mapped iterative-PopPUNK clusters under different cutoffs back to simulated trees. As expected, the genetic diversity of strains within a cluster increases as the cutoff increases. Overall, the number of clusters obtained first increases, and then decreases with cutoff point, except in the high recombination rate scenario (Figure  3B ). For each dataset we analyzed, the accuracy of clusters across similarity cutoffs was similar. However, the accuracy between different datasets varies, with higher average accuracy of clusters in datasets with high recombination rates.
Application of iterative-PopPUNK to real data shows consistency with phylogenetic results We applied iterative-PopPUNK with default settings to analyze the genomic datasets of eight bacterial species with different mutation and recombination rates (Table  S1 ) and obtained the cluster assignments under six relative similarity cutoffs (Pmax-cluster-dist: 1%-99%). In addition, we performed core genome alignments for these datasets, and constructed maximum-likelihood (ML) phylogenetic trees based on core-genome SNPs. To measure the consistency between iterative-PopPUNK and alignment-based method, we mapped the clusters obtained under different cutoffs to the corresponding ML trees, and calculated matching accuracy (i. e. the proportion of matched clusters to all clusters) in the same manner as described above.
We collated the dataset for Escherichia coli by subsampling 500 genomes from 50 largest lineages (10 per lineage), which together represent the entire genomic diversity of human-isolated E. coli  (Horesh et al. 2021). The entire, fully sampled, dataset has previously been analyzed by PopPUNK, and these 50 lineages were defined based on PopPUNK clusters. High matching rates were identified, with the overall rate of 75%. When a small cutoff (Pmax-cluster-dist=7. 9%) was applied, iterative-PopPUNK estimated 44 clusters (singletons excluded; 94 clusters if singletons included), among which 43 of them could be perfectly matched with phylogenetic tree nodes. For this same dataset, 55 STs groups (singletons excluded; 92 STs if singletons included) could be identified, with 26 of them were exactly corresponding to tree nodes.
This example highlighted the advantage of iterative-PopPUNK, which allows multi-level clustering in a unified way and users can select their "level" (e. g. sequence type or phylogroup level) according to their analytic purpose. The datasets of the other six species were assembled by simply combining publicly available genomes in NCBI RefSeq database. Iterative-PopPUNK did not produce useful results for two species with low genetic diversify, namely Mycobacterium tuberculosis and Bacillus anthracis. For the other four species, the average matching rates were 93%, and higher rates were identified in species with high recombination rates such as Helicobater pylori and Vibrio parahaemolyticus. The matching rates at different similarity cutoffs were generally similar (Figure  5 ).
Discussion Over the last two decades, with the development of genome sequencing technology and the rapid decline of costs, genome data is accumulating at an unprecedented speed. For some common pathogens such as Salmonella enterica and Escherichia coli, there are now more than 200,000 genomes in public databases such as Enterobase and Genometracker  (Brown et al. 2021). Analyzing large-scale genome datasets is still challenging, and alignment-free methods such as PopPUNK are one of the best solutions to address this. PopPUNK was initially designed to identify a single level of clusters by optimizing a heuristic, but in practice different cluster resolutions are required, depending on the purpose. To solve this problem, we developed iterative-PopPUNK, which allows estimation of a partially resolved genealogical tree and enables clustering at different levels of sequence similarity.
Iterative-PopPUNK inherits the advantages of PopPUNK, which is fast and consumes few computational resources, and it is easily extendable when new data needs to be added. On this basis, Iterative-PopPUNK provides greater flexibility and allows the resolution of the clustering to be adjusted according to the specific purpose. Currently, the most used method for establishing the relationships between bacterial strains is phylogenetic analysis inferred from genomic alignments. This method usually requires a reference genome, and inappropriate reference selection may introduce bias. This approach is also not typically scaleable to large-scale genome datasets (e. g. >10000) and runtimes can become prohibitively long, even with generously provisioned computational resources. For example, on a high-end workstation a phylogenomic analysis of ~10,000 Klebsiella pneumoniae genomes took 14 days and required a maximum of ~500 Gb memory.
Another kind of method for classifying strains is based on gene-based subtyping information, including the classicaly multi-locus sequence typing (MLST) which is based on several housekeeping genes, and more recently developed core-genome MLST (cg-MLST) or wholegenome MLST (wg-MLST). Compared with genome-wide variation such as SNPs, subtyping information is typically much more parsimonious at the loss of some resolution, which can be used to scale for the analysis of large-scale data. There have been methods for multilevel clustering of strains based on subtyping data. HierCC is one of the state-of-the-art pipelines for multi-level clustering analysis of large-scale dataset based on cgMLST. HierCC hierarchical clusters (HC) can be used for multi-level strain assignment. For example, in Salmonella, HC900 (<900 allele difference) and HC2500 (<2500 allele difference) represent strains from the same sequence type complex and species/subspecies, respectively.
However, performing gene-by-gene subtyping-based clustering first requires an appropriate curated database of genes, and a stable strain nomenclature. These have not yet constructed for most bacterial species, especially for non-pathogenic species, and require ongoing maintenance, making this method unlikely to be the general solution for microbes. The current version of iterative-PopPUNK has two main limitations. First, it does not perform well on species with low genetic diversity because boundaries tend to connect clusters together in a non-transitive manner. This can be solved by nearest neighbour approaches. Second, the range of the levels of clusters obtained depends on the model fit of within-and between-strains components. Sometimes, only a very limited number of levels can be obtained, especially when using the second type of relative similarity cutoff Pbet-mean- dist.
With the widely use of whole genome sequencing and rapid accumulation of genome sequences, large-scale genome dataset analysis may become normal in the near future. However, there is currently no ideal multi-level clustering method for large-scale dataset analysis. Iterative-PopPUNK is alignment-free, highly efficient even for very large-scale dataset, and does not reply on curated database and nomenclature, which can solve the limitations of the above methods, and we believe has the potential to be the general solution for multi-level clustering of many bacterial species.
Implementation of iterative-PopPUNK algorithm Iterative-PopPUNK procedure is implemented in the 'poppunk_iterate' program installed as part of PopPUNK package. Source code is available on GitHub (https://github. com/johnlees/PopPUNK), and can be installed through conda, pip or manually. Users can run multi-level clustering procedure using the flags '--fit-model refine -multi-boundary' in the PopPUNK software. The subsequent QC procedure is implemented in the 'poppunk_iterate' program and uses output from multi-level clustering step as input.
Application of iterative-PopPUNK in real data The publicly available, assembled genomes of seven species were downloaded from the National Center for Biotechnology Information (NCBI) Reference sequence (RefSeq) database based on the taxonomy identifier (Table  S2 ). Two steps of genome sequences QC were performed. First, we used CheckM  (Parks et al. 2015)  to assess the genome quality and only genome sequences with completeness >90% and contamination <5% were kept in further analysis. Second, we set 70% as whole genome coverage threshold to remove lowquality genomes or outlier samples that may not be part of the species. After QC, the same iterative-PopPUNK pipeline used for simulation data were applied to real data analysis. The 500 E. coli genomes dataset were collected from  Horesh G, et al.
The chosen example dataset has a recombination rate r=0. 001 and mutation rate =0. 001  clusters with the genealogical tree (from 100 samples) at four levels: 1%, 20%, 80%, and 99% of the MACD. These green and orange faces represent matched clusters with tree nodes respectively. For these unmatched clusters, the tree leaves are annotated using different colors. B). 1). The average number of clusters while moving the cutoff lines in iterative-PopPUNK tree. The value is calculated by the total number of estimated clusters divided by total number of genomes at each level; 2). The accuracy of estimated clusters at each level. The accuracy is calculated by the total number of matched clusters (with tree nodes) divided by the total number of estimated clusters. The chosen example dataset has a recombination rate r=0.
Figure2: The performance of iterative-PopPUNK. A1). A genealogical tree with 9 nodes (clusters) estimated by default PopPUNK; A2). The same genealogical tree with 69 clusters estimated by iterative-PopPUNK, among which 68 can be totally matched with genealogical tree nodes while 1 of them cannot be matched and these isolates (leaves) were being annotated using different colors. B1). A line graph shows the average number of estimated clusters by PopPUNK and by iterative-PopPUNK. The solid lines present the results for iterative-PopPUNK, and the dash lines are for default-PopPUNK. B2). The accuracy results for both default PopPUNK and iterative-PopPUNK. The accuracy is calculated from total number of matched nodes divided by total number of estimated clusters. The chosen example dataset has a recombination rate r=0.