dataesr/scientific-paragraphs-categorization huggingface dataset:
Analysis of a West Nile Virus outbreak To showcase the importance of these observations with respect to practical epidemiological and phylodynamic investigations, we consider a dataset from a recent West Nile Virus outbreak in North America  [14]. We choose this particular dataset due to availability of the data and of clear instructions on how to repeat the published analyses in BEAST https://beast. community/workshop_continuous_diffusion_wnv (accessed on August 2019), reducing the chances of errors on our part. As described in the tutorial, we include sampling time, sampling location, and genetic sequence data for each sample. We use a separate HKY model for each of the three codon positions, but assume no variation in substitution rates across codons, and we assumed an uncorrelated relaxed molecular clock model  [42]  with an underlying lognormal distribution.
As the tree prior, we employ an exponential growth coalescent model. We assume homogeneous Brownian motion along tree branches. To investigate the possible effects of sampling bias, we consider two datasets: the first including all samples, and the second including only the western-most half of the samples. This second scenario artificially recreates sampling bias, such as the case where only cases from one half of the country are accessible or considered. This sampling scenario might seem extreme, but it's not uncommon for phylogeographic studies to focus, for example, on a single country or area within a continent, see e. g. [43, 44]. We consider the inference of the location of the root (MRCA) of the western half of the samples. The posterior densities of this same ancestor in the two analyses is very different: when using only western samples, this phylogenetic node is confidently placed in western USA, but when using the whole dataset this same node is confidently placed in the eastern USA instead  (Fig 4).
In this analysis, the inferred location of the considered node (the MRCA of the western samples) is now shifted eastward, but it is still very different from the inferred location of the same node from the full analysis (Fig  4 ). It is remarkable that in this dataset, unlike in our simulations, the addition of sequence-free samples does not seem to alleviate the effects of sampling bias very much. One possible explanation for this observation is that, unlike in our BMP simulations, in this case the outbreak seems to migrate westward as time progresses  [14] , a feature that sequence-free samples are insufficient, in this case, to capture, and that a more specific model might be able to address  [45]. This is also hinted at by the fact that performing the same analyses as above but removing the western samples from the full dataset instead of the eastern ones shows almost no effects of the artificially introduced sampling bias (S22 Fig).
Analysing the same datasets with PhyREX also shows different estimates after removing the eastern samples, although this time there is considerable overlap between the different ancestral location estimates and different diffusion rate estimates (Fig 5  , S23 , S24 and S25 Figs). In principle we would not expect to see considerable differences for different subsampling schemes since the ΛFV model should be robust to sampling biases, as shown in our simulations. This further supports the hypothesis that the progressive westward shift of the outbreak plays a major role in the apparent strong effects of sampling bias in this case. A noticeable difference between BEAST and PhyREX results, also observed in simulations, is that the inferred uncertainty in ancestral location is much larger in PhyREX than in BEAST.
Analysis of a Yellow Fever Virus outbreak As a second example of real world epidemic analysis, we considered a recent dataset of Yellow Fever Virus (YFV) from Brazil  [46]. 65 YFV genomes were collected between January and We re-analysed the West Nile Virus North America dataset of Pybus and colleagues  [14]. At top, we show the maximum clade credibility tree. Branch lengths are in years. Green circles represent eastern samples while red squares represent western samples. The red triangle in the tree represents the node whose location is considered here: the most recent common ancestor (MRCA) of all western samples. Below, the sample locations are shown on a map of the USA. Sample numbers are only used to link samples on the map onto the phylogeny. All three kernel density estimate areas (red, orange and blue) on the map represent the posterior densities of the location of the MRCA of all western samples (red triangle in the phylogeny).
The red area represents the posterior from the analysis of only western samples; the blue area is the posterior from the analysis of all samples; the orange area is the posterior from the analysis of the western samples and of sequence-free eastern samples (eastern samples included but without sequence data). https://doi. org/10. 1371/journal. pcbi. 1008561. g004 April 2017, mostly from the Brazilian state of Minas Gerais. Again, we chose this dataset due to availability of data and instructions for repeating the analysis https://beast. community/ workshop_continuous_diffusion_yfv (accessed on August 2019). Following the tutorial, we used the same substitution model as for the West Nile Virus dataset, a skygrid coalescent  [47]  tree prior with 36 grid points, and a Cauchy relaxed random walk model  [7].
When recreating sampling bias along a north-south gradient, we find little impact from removing southern samples (S26 Fig), while directional sampling bias seems to have considerable effect in BEAST analyses when removing northern samples (S27 Fig) ; this bias is greatly reduced by introducing sequence-free samples. PhyREX inference seems, expectedly, mostly unaffected by sampling bias, and shows much broader posterior distributions for ancestral locations (S28 and S29 Figs). We also observed that many samples of this dataset were collected from few locations: six from Ladainha, five from Novo Cruzeiro, seven from Teofilo Otoni and five from Itambacuri. So, in a second alternative sub-sampling strategy, we reduced the maximum number of samples from any of these locations to two. As before, we aim to artificially recreate different sampling scenarios.
We confirm these results on real datasets from West Nile Virus and Yellow Fever Virus outbreaks by artificially recreating scenarios of sampling bias. As an alternative, we investigated the use of an inference model that is in theory not affected by sampling biases: the ΛFV implemented in PhyREX. Similarly to the structured coalescent, in fact, the ΛFV model conditions on sampling locations, and so should not be adversely affected when samples are not collected proportionally to prevalence; note however that the ΛFV might be affected by geographical biases in the case where the true geographical range of the organism being studied is not known, but we don't focus on this aspect here. We confirm that indeed this model is seemingly unaffected by different sampling strategies, but, more importantly, the model is also very different from the BMP, resulting in very different estimates.
The blue area on the map shows the inferred posterior distribution (kernel density estimate) of root location in the full analysis; the green area shows the posterior distribution of root location in the downsampled analysis. On the right is the maximum clade credibility phylogeny inferred from the analysis of the whole dataset. Orange tips are the ones that were removed in the downsampled dataset; the red triangle marks the root node. https://doi. org/10. 1371/journal. pcbi. 1008561. g006 some modifications, these two models would more substantially overlap in applicability. An example could be when restricting the allowed geographic range within the BMP to a limited space, that is, not allowing BMP migration outside of a confined area. In fact, we suspect that simulating migration under such a version of the BMP, and simulating a long phylogeny (in terms of distance traveled from the root before samples are collected) would lead to patterns very close to those simulated under the ΛFV.
Effects  of sampling bias on BMP inference with no phylogenetic uncertainty. Here a BMP model was used both for simulation and inference, and the phylogenetic tree is assumed to be known without uncertainty. Plots show inferred posterior distributions for the X dimension position of the tree root (plots A,D), the diffusion rate along the X dimension (plots B,E), and the correlation between the diffusion in the two dimensions (plots C,F). In each plot, the 100 distributions represent 100 independent replicates, and are vertically sorted based on the posterior median. Plots A-C are from simulations with non-biased samples. Plots D,E,F are respectively with "One-sided" sampling bias, "Central" sampling bias, and "Diagonal" sampling bias. To investigate the possible effects of sampling bias, we again consider the effects or restricting an analysis to a geographical subsample of an original dataset.
Here we compare the results of BMP analysis using all the data from  [46]  versus using only the northern samples (latitude above -19. 0, red squares in the phylogeny and on the map). On top is the maximum clade credibility phylogeny inferred from analysing the whole dataset. On the map (bottom) we show the location of the samples and the inferred location of the most recent common ancestor of all southern samples (red triangle in the phylogeny). The three, almost completely overlapping colored areas on the map show the inferred posterior distribution (kernel density estimate) of the location of this ancestor from three analyses: using only the northern samples (red area), using all samples (blue area) or using only the northern samples but adding the southern ones as sequence-free samples (orange areas).
The three small areas completely overlap, masking each other in the figure. A noticeable difference between the analyses is that when restricting to just the northern samples diffusion was inferred to be slower (95% HPD interval  [152, 1018]   geographical subsample of the YFV dataset. We compare the results of BMP analysis using all the data from  [46]  versus using only the southern samples (latitude below -19. 0, red squares in the phylogeny and on the map). On top is the maximum clade credibility phylogeny inferred from analysing the whole dataset. On the map (bottom) we show the location of the samples and the inferred location of the most recent common ancestor (red triangle in the phylogeny). The three colored areas on the map show the inferred posterior distribution (kernel density estimate) of the root location from three analyses: using only the southern samples (red area), using all samples (blue area) or using only the southern samples but adding the northern ones as sequence-free samples (orange areas).
Due to difficulties in convergence, and following results from the analysis with all samples, in the analysis with sequencesamples we added a normal distribution prior over root height with mean 0. Here we compare the results of using all the YFV dataset versus downsampling each location up to allowing a maximum of two samples per location, similarly to Fig 6. Here we also compare BEAST and PhyREX inference. The green area on the map shows the inferred posterior distribution (kernel density estimate) of root location in the full analysis in BEAST, while red is the same for PhyREX. The blue area shows the posterior distribution of root location in the downsampled analysis in BEAST, while cyan is the same for PhyREX. In PhyREX we defined a rectangular space with latitude interval [-23, -15] and longitude interval  [-48, -40].
(JPG) S8 Fig. Effects of extra samples on BMP inference of diffusion parameters. Similarly to S2 Fig, here we show BMP inference of diffusion parameters under BMP simulations, but this time we include 50 extra sequence-free samples (without genetic sequence but with correct of all samples; the orange area is the posterior from the analysis of the eastern samples and of sequence-free western samples (western samples included but without sequence data). (PDF) S23 Fig. PhyREX inference and artificial westward sampling bias in a West Nile Virus dataset, with a broader space. Same analysis as in Fig 5, but using a broader rectangular space (outlined in black) in PhyREX, latitude interval [6, 50] and longitude interval [-140, -35]. Posterior distribution of the diffusion rate inferred by PhyREX has a mean of 389.
2 (95% HPD interval [132. 4, 642. 3]) km/year in the full dataset and of 150. 5 ([16. 6, 287. 8]) km/year with only western samples. (PDF) S24 Fig. PhyREX inference and artificial eastward sampling bias in a West Nile Virus dataset. We re-analysed the dataset of S22 Fig, using PhyREX for both the full dataset and the one containing only eastern samples. In PhyREX we defined a rectangular space (outlined in black) with latitude interval [20, 45] and longitude interval [-130, -60]. The red area represents the posterior ancestral location from the analysis of only eastern samples in BEAST. the orange area is the same for PhyREX. The blue area is the posterior from the analysis of all samples in BEAST. The green area is the posterior from the analysis of all samples in PhyREX. Posterior distribution of the diffusion rate inferred by PhyREX has a mean of 329.
9 (95% HPD interval [149. 5, 497. 4]) km/year in the full dataset and of 219. 4 ([39. 3, 391. 5]) km/year with only eastern samples. (PDF) S25 Fig. PhyREX inference and artificial eastward sampling bias in a West Nile Virus dataset, with a broader space. Same analysis as in S24 Fig, but using a broader rectangular space (outlined in black) in PhyREX, latitude interval [6, 50] and longitude interval [-140, -35]. Posterior distribution of the diffusion rate inferred by PhyREX has a mean of 389. 2 (95% HPD interval [132. 4, 642. 3]) km/year in the full dataset and of 304. 7 ([36. 2, 580. 4]) km/year with only eastern samples. (PDF) S26 Fig. Recreating the effects of biased sampling over a Yellow Fever Virus outbreak investigation.
7 and standard deviation 0. 25. (PDF) S28 Fig. PhyREX inference and Northward biased sampling in a Yellow Fever Virus dataset. Similarly to S26 Fig, we consider the effects or restricting an analysis to a northern geographical subsample of the YFV dataset, and we compare PhyREX and BEAST inference. The colors on the map show the posterior distribution of the location of the considered ancestor for the analysis with BEAST and northern samples (red), PhyREX and northern samples (orange), BEAST and all samples (blue), and PhyREX and all samples (green). In PhyREX we defined a rectangular space with latitude interval [-23, -15] and longitude interval [-48, -40]. Posterior distribution of the diffusion rate inferred by PhyREX has a mean of 537. 0 (95% HPD interval [56. 8, 1030. 3]) km/year in the full dataset and of 1132.
2 ([68. 4, 2373. 6]) km/year with only northern samples. (PDF) S29 Fig. PhyREX inference and Southward biased sampling in a Yellow Fever Virus dataset. Similarly to S27 Fig, we consider the effects or restricting an analysis to a southern geographical subsample of the YFV dataset, and we compare PhyREX and BEAST inference. The colors on the map show the posterior distribution of the location of the considered ancestor for the analysis with BEAST and southern samples (red), PhyREX and southern samples (orange), BEAST and all samples (blue), and PhyREX and all samples (green). In PhyREX we defined a rectangular space with latitude interval [-23, -15] and longitude interval [-48, -40]. Posterior distribution of the diffusion rate inferred by PhyREX has a mean of 537. 0 (95% HPD interval [56. 8, 1030.
Funding: AK and YS were supported by the  Cambridge Mathematics Placements  (CMP, https:// www. maths. cam. ac. uk/opportunities/careers-formathematicians/summer-research-mathematics/ summer-research-mathematics-cmp-andresearch-cms). GB was supported by the  Interne Fondsen KU Leuven / Internal Funds KU Leuven  (https://www. kuleuven. be/english/research/support/if) under grant agreement  C14/18/094 , and from the  Research Foundation -Flanders  (' Fonds voor Wetenschappelijk Onderzoek -Vlaanderen ', https://www. fwo. be/G0E1420N). SG was supported by the  Agence Nationale pour la Recherche  https://anr. fr/through the grant GENOSPACE. AK, UP, YS, NG and NDM were supported by the  European Molecular Biology Laboratory. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
The vast scale of SARS-CoV-2 sequencing data has made it increasingly challenging to comprehensively analyze all available data using existing tools and file formats. To address this, we present a database of SARS-CoV-2 phylogenetic trees inferred with unrestricted public sequences, which we update daily to incorporate new sequences. Our database uses the recently-proposed mutation-annotated tree (MAT) format to efficiently encode the tree with branches labeled with parsimony-inferred mutations as well as Nextstrain clade and Pango lineage labels at clade roots. As of June 9, 2021, our SARS-CoV-2 MAT consists of 834,521 sequences and provides a comprehensive view of the virus' evolutionary history using public data. We also present matUtils -a command-line utility for rapidly querying, interpreting and manipulating the MATs.
New Approaches In this work, we simultaneously address the issue of maintaining a comprehensive SARS-CoV-2 reference tree and its associated data processing, data sharing and computational analysis challenges. Specifically, we are maintaining and openly sharing a daily-updated database of mutation-annotated trees (MATs) containing global SARS-CoV-2 sequences from public databases, including annotations for Nextstrain clades  (Hadfield et al. 2018 ) and Pango lineages  (Rambaut et al. 2020 ) (Supplementary Figure  1 ). The MAT is an extremely efficient data format proposed recently  (Turakhia et al. 2021 ) which can facilitate the sharing of extremely large genome sequence datasets -an uncompressed MAT of 834,521 SARS-CoV-2 public sequences requires only 65 MB to store, and encodes more information than is contained in a 43 GB VCF and a 38 MB Newick file combined.
To accompany this database, we present matUtils -a toolkit for querying, interpreting and manipulating the MATs. Using matUtils, common operations in genomic surveillance and contact tracing efforts, including annotating a MAT with new clades, extracting subtrees of the most closely-related samples, or converting the MAT to standard Newick or VCF format can be performed in a matter of seconds to minutes even on a laptop. We also provide a web interface for matUtils through the UCSC SARS-CoV-2 Genome Browser  (Fernandes et al. 2020 ). Together, our SARS-CoV-2 database and matUtils toolkit can simultaneously democratize and accelerate pandemic-related research.
A daily-updated mutation-annotated tree database of global SARS-CoV-2 sequences To aid the scientific community studying the mutational and transmission dynamics of the SARS-CoV-2 virus and its different variants, we are maintaining a daily-updated database of SARS-CoV-2 mutation-annotated trees (MATs) composed of public data. Starting with the final Newick tree release dated November 13, 2020, of Rob Lanfear's sarscov2phylo (https://github. com/roblanf/sarscov2phylo) that is re-rooted to Wuhan/Hu-1 (GenBank MN908947. 3, RefSeq NC_045512. 2), we have set up an automated pipeline to aggregate public sequences available through GenBank  (Clark et al. 2007 ), COG-UK  (Nicholls et al. 2020) , and the China National Center for Bioinformation on a daily basis and incorporate them into our MAT using UShER (see Supplementary Methods).
matUtils provides a wide range of functions to analyze and manipulate mutation-annotated trees We have created a high-performance command line utility called matUtils for performing a wide range of operations on MATs for rapid interpretation and analysis in genomic surveillance and contact tracing efforts. matUtils is distributed with the UShER package  (Turakhia et al. 2021)  and uses the same mutation-annotated tree (MAT) format as UShER. matUtils is organized into five different subcommands: annotate, summary, extract, uncertainty and introduce (Figure  1 ), described briefly below. We provide detailed instructions for the usage of each module on our wiki (https://usher-wiki. readthedocs. io/en/latest/matUtils. html).
Both ways of annotating ensure that the clades remain monophyletic, but we use the second approach to label Nextstrain clades and Pango lineages in our SARS-CoV-2 MAT database since it can be automated using available data (see Supplementary Methods). matUtils annotate has high congruence with Nextstrain clades and Pango lineage annotations (Supplementary Table  S1 ). Once clades are annotated on a MAT, the UShER placement tool  (Turakhia et al. 2021 ) can assign each newly placed sequence to its corresponding Pango lineage, and this being used as a feature in Pangolin 3. 0 (https://github. com/cov-lineages/pangolin/releases/tag/v3. 0) to perform clade assignments in a fully phylogenetic framework.
Uncertainty: A fundamental concern in SARS-CoV-2 phylogenetics is topological uncertainty  (Hodcroft et al. 2021). This is especially true for public health, where sample level uncertainty statistics convey the reliability of genomic contract tracing. matUtils provides such a statistic through its uncertainty function, which computes the number of equally parsimonious placements  (Turakhia et al. 2020 ) that exist for each specified sample in the input MAT. Importantly, matUtils also allows the user to calculate equally parsimonious positions for already placed samples. This is accomplished by pruning the sample from the tree and placing the sample back to the tree using the placement module of UShER  (Turakhia et al. 2021 ) (see Supplementary Methods). matUtils uncertainty additionally records the number of mutations separating the two most distant equally parsimonious placements, reflecting the distribution of placements across the tree (see Supplementary Methods).
Since matUtils is primarily designed to work with the newly-proposed and information-rich MAT format, it does not have direct counterparts in other bioinformatic software packages currently, but its efficiency is similar or better than state-of-the-art tools that offer comparable functionality (Supplementary Tables  S2-S9 ). For example, matUtils is able to resolve polytomies in a 834,521 sample tree in 9 seconds, a task which takes over 37 minutes using ape (Paradis and Schliep 2019) (Supplementary Table  S3 ). matUtils is also very memory-efficient, requiring less than 1. 4 GB of main memory for most tasks, making it possible to run even on laptop devices. Certain functions of matUtils (such as extracting subtrees of provided sample names or identifiers) have also been ported to UCSC SARS-CoV-2 Genome Browser  (Fernandes et al.
2020)  and are available from https://genome. ucsc. edu/cgi-bin/hgPhyloPlace. This provides a user-friendly web interface to public health officials and researchers working on combating the pandemic. Our database and utility fill a critical need for open, public, rapid analysis of the global SARS-CoV-2 phylogeny by health departments and research groups across the world, with highly-efficient file formats that do not require high speed internet connectivity or large storage devices, and tools capable of rapidly performing large-scale analyses on laptops. (F): matUtils introduce takes as input a list of samples of interest, and outputs their predicted introduction nodes and paths, along with confidence scores for both the introduction and parent node (not shown). Using the -a flag, the user can also determine the largest monophyletic clade and regional association index associated with the input population.