dataesr/scientific-paragraphs-categorization huggingface dataset:
Figure14. Boxplot of the winter (November-April) trend values calculated over the 1968-2017 period (50 years) for each grid point of the entire Alps (grid points included within the 450-2550 m elevation range) using Theil-Sen linear regression for four variables (temperature, precipitation, snow depth and snow cover duration). For this length of the period, five datasets are available: observations (E-OBS v19. 0HOM for temperature, LAPrec for precipitation, and the set of in situ observations for snow depth and snow cover duration), MESCAN-SURFEX, ERA5, ERA5-Land and ERA5-Crocus. Precipitation and snow depth trends are expressed as relative (% per decade) decrease or increase compared to the mean of the period, while temperature and snow cover duration trends are provided as absolute rate.
• C at most are also found for the winter months but remain within the E-OBS uncertainty range. At high elevations, there are larger differences for all datasets. Similar to low and intermediate elevations, climate simulations (CNRM-ALADIN and CNRM-AROME) exhibit negative deviations with respect to E-OBS. The strongest differences are in winter, peaking from December through February with CNRM-ALADIN temperature values being 7 • C lower than E-OBS and CNRM-AROME 2. 5 • C lower. ERA5-based products show larger discrepancies compared to the intermediate elevation band, showing negative temperature differences around -5 • C with respect to E-OBS. At this elevation, MESCAN-SURFEX, MTMSI and CERRA also display negative temperature differences for the winter months, with MESCAN-SURFEX differences from E-OBS of around -2 • C and CERRA differences reaching -4 • C.
Acknowledgements. The authors gratefully acknowledge the WCRP-CORDEX-FPS on convective phenomena at high resolution over Europe and the Mediterranean (FPSCONV-ALP-3). This work is part of the  Med-CORDEX initiative  (http://www. medcordex. eu,last access:  16 August 2023 ). CNRM/CEN is a member of LabEX OSUG@2020. We acknowledge the E-OBS dataset from the  EU-FP6  project  UERRA  (https://www. uerra. eu,last access:  23 January 2023 ) and the  Copernicus Climate Change Service  and the data providers for the  ECA&D  project (https://www. ecad. eu,last access:  23 January 2023 ). We thank the following institutions for sharing with us daily snow depth records from station data: the  Piemonte Dipartimento Rischi Naturali , the  Ufficio neve e valanghe della Regione Autonoma Valle d'Aosta , the  Società Meteorologica Italiana  (http: //www.
nimbus. it,last access:  16 August 2023 ), the meteorological office of the  Slovenian Environmental Agency , the  ARPAV -Avalanche Centre of Arabba -Italy , and the  ARPA FVG -OSMER  eGRN. We would like to thank  Cécile Caillaud  ( CNRM ) for data curation of  CNRM-AROME  and providing support for its processing and  Antoine Verrelle  ( CNRM ) for providing information on UERRA and CERRA-Land products. We thank  Simon Gascoin  (CESBIO) and  Marie Dumont  ( CNRM/CEN ) for the enlightening discussions and advice on the use of MODIS satellite data. We thank  Raphaëlle Samacoïts  ( Météo-France ) and  Bertrand Decharme  ( CNRM ) for provision of and discussions about the MTMSI and ERA5-Crocus datasets, respectively The authors also thank the three anonymous reviewers for their careful revision, which helped improve the manuscript.
Introduction In virology, metagenomics tools performing taxonomic assignation from high throughput sequencing datasets are currently key for detecting unknown viruses and improving our knowledge of the virosphere (e. g. ,  [1] [2] [3] ). They are generally evaluated with datasets derived from biological samples or in silico spiked samples, which all contain known viral sequences present in public databases. This considerably limits the evaluation of their capability of finding distant viruses. Given the high degree of variability in viral protein sequences, simulating realistic evolutionary directions appears to be key to challenge tools in their capability to identify such distant viruses. Going further, expanding the current databases by realistic simulated sequences could improve the capacity of alignment-based searching strategies for finding distant viruses.
However, no tool provides a practical way to populate a tree with new branches at various positions. Here, we introduce Virus Pop, a pipeline for adding new branches to a protein phylogenetic tree. Virus Pop leverages the tools provided by the last version of IQ-TREE  [9]  and integrates them in a comprehensive process that simulates protein evolution in a realistic way in terms of site-dependent substitution rate in the input dataset. From collecting the input dataset of existing proteins to retro-translating simulated sequences, several options are available to simplify the construction of the starting dataset, and configurable steps allow the user to precisely constrain the locations of the generated sequences within the tree. The outcome is a set of simulated sequences that complete the initial tree with new branches connected at the desired topological position.
The evolutionary distances simulated, represented by the new branch lengths, can either be set automatically based on observed distances in the starting dataset, or manually specified. We evaluated the realism of the simulated sequences by examining the conservation of protein structure in generated sequences of the Sarbecovirus spike protein. We also illustrated two examples of how simulated sequences with varying parameters and increasing distances can serve as input data to evaluate Blast-based assignation. Finally, we showed that simulated sequences may be close to real sequences not yet indexed in databases and that the addition of such sequences in databases used for taxonomic assignment could help to reduce the number of unassigned reads in metagenomic data.
• Building the starting dataset. Many options are provided to offer convenient ways for the user to quickly build a dataset. At the end of this step, Virus Pop will have one or multiple fasta files, each representing a protein type and containing homologous sequences; • Finding the best evolutionary model and phylogenetic tree for each set of homologous proteins; • Choosing nodes (automatically or manually) in the phylogenetic tree and reconstructing ancestral sequences at these node positions; • Simulating the evolution of the reconstructed ancestral sequences (retro-translated nucleotide sequences are also provided). Figure  1  illustrates the full pipeline. Each step is detailed hereafter. Figure  1. The Virus Pop pipeline. This figure presents the complete pipeline, starting with only the input of a taxonomic group taken as an example: the Orthopneumovirus genus.
Building the Starting Homologous Protein Dataset The user can directly input protein sequence fasta files, however, Virus Pop provides an automated dataset construction tool for helping the user to gather sets of aligned homologous protein sequences. Each set must be large enough to be representative of the variability amongst the group of interest, while remaining small enough to allow reasonable time-efficiency. This tool constitutes a quick and easy solution for working on any virus group and protein type, without the need to manually select the sequences. The input parameter for automatic dataset construction is the name of a taxonomic group. The process can be decomposed in the following steps: 1. Parsing the NCBI taxonomy tree to extract all species belonging to the taxonomic group entered; 2.
4. Clustering proteins: running BLASTp  [16] , then extracting from the results clusters of homologous protein sequences with SILIX  [17]. SILIX is run with the following parameters: 20% minimum identity and 60% minimum overlap; and BLAST is run with a cost of 1 and 9 to open and close gaps, respectively; 5. Aligning the sequences in each cluster with MAFFT [18] (with default parameters); 6. Generating a descriptive protein name for each cluster, based on word frequency in the protein annotations. The result of the fetching tool is a ready-to-use dataset of aligned homologous protein clusters from the virus taxonomic group of interest with descriptive names that will help the user to know which proteins were extracted. Alternatively, the user can provide a list of taxa to download, a set of full genomes (complete nucleotide genomes and protein sequences in fasta files), or fasta files with homologous proteins.
• The Model Finder tool is used again, but constrained so that it will fit a 5-discretized gamma profile with the M model: M + 5G. A 5-discretized gamma profile is an evolutionary model that differentiates five substitution rate categories. Each locus in the protein considered is associated to one category. This is outputted in the form of a file which indicates, for each locus, the mean substitution rate corresponding to the category to which it belongs. Although the optimal number of bins is dependent on the dataset, we used five as it yields good results in most cases  [20].
Retro-Translation Because one of the main purposes of the development of Virus Pop is to provide datasets for characterizing, testing and improving taxonomic tools that often operate on metagenomic sequencing reads, the user may need nucleotide sequences. Virus Pop thus provides a retro-translation of the simulated sequences. The retro-translation is based on a probabilistic analysis of the codon usage in each amino-acid neighborhood, within the alignment. The principle is as follows: for each site i with amino acid AA k ← 0 while no corresponding codon usage is found look for coding of AA in sites [i -k : i + k] k ← k + 1 draw a codon amongst found codons This simple approach will provide retro-translations that preserve possible variations of the codon usage along the sequences. Indeed, if an amino acid at one site is always coded with the same codon, the retro-translation method proposed will maintain this characteristic.
Sarbecovirus Spike Protein Dataset The Sarbecovirus dataset was hand-built to be representative of the Sarbecovirus subgenus variability, with a special focus on the SARS-CoV-2 clade, including bat-related sarbecoviruses. It consists of 300 complete genomes, from two different sources: GISAID  [21]  and GenBank  [22]. We translated the viral genes to protein sequences and selected the spike protein sequences from each genome. As the spike protein includes a well-characterized receptor binding domain (RBD), which binds to the human ACE2 protein, it is an adequate protein for testing the simulation process: first, because the structure of the RBD-ACE2 complex is well-characterized at the amino-acid level since the emergence of SARS-CoV-2, and mainly because the study of its evolution and variation is a key example that illustrates the comprehension of human susceptibility to viruses from animal reservoirs.
The Circovirus Capsid Protein Dataset To test Virus Pop in the context of a protein sequence more variable than the spike protein of sarbecoviruses, we worked on the capsid protein of the members of the Circovirus genus (Circoviridae family) which are known to infect a large spectrum of animals, including a first case of human infection we recently discovered  [23]. The dataset was built with the automatic dataset construction tool. As we later tested the possibility to predict recently discovered Circovirus sequences based on this dataset, we manually checked that the database did not include the new genomes  [23, 24]. As the NCBI database includes a high number of species from the Circovirus genus, we used the -target_n_genome option to limit to 1 the number of genomes fetched within each species.
The -min_cluster_size option was set to 30 to limit the substitution model inferences to protein clusters containing at least 30 sequences. The complete command is as follows:. /run_virus_pop. py group_name Circovirus Circovirus_project -t 1 -s 30 The result is a dataset of 97 Circovirus complete genomes with annotated proteins. The clustering detects 4 homologous groups amongst which the two biggest are the replication (86 sequences) and the capsid protein (65 sequences). Based on the annotations, the automatic name construction respectively described them as "replication [associated] protein" and "capsid protein". The dataset is available in Supplementary Data (Folder S1). Figure  5  shows the phylogenetic tree constructed from the capsid Circovirus sequences.
(A). The Circovirus dataset phylogenetic tree. The tree was built with IQ-TREE. Solid colored circles indicate the internal nodes selected for ancestral sequence reconstruction and simulation. The red square indicates the ancestral node from which we generated the closest sequences to the two new human circoviruses studied in Section 3. 4. (B). Same tree with the simulated sequences. In this example, seven evolutionary distances of 0. 1, 0. 5, 1, 2, 3, 4 and 5 were simulated from each reconstructed ancestral sequence. The dark green ancestral node is not reported here as the new sequences changed the topology such that there is no equivalent node.
This means that once the model is inferred, the user can quickly test different simulation parameters. For the Circovirus dataset presented in Figure  5B , it ran in 22 min with 1. 5 min to build the dataset and only 8 s to generate the simulations. However, when required to simulate a high number of nodes, the time needed for the simulation part increases. For instance, to generate 100 ancestral sequences at each selected node presented in Figure  4A  and simulate 3 evolution distances from each reconstructed ancestral sequence, it takes 28 s.
2. We created models of the hACE2-RBD complex using a local installation of HAD-DOCK  [26]. hACE2 and RBD were first docked into a complex guided by intersubunits distance restraints extracted from the X-ray structure of the SARS-CoV-2 RBD bound to hACE2 (PDB code 6M0J; [27]). The models were then refined by short MD simulations in explicit solvent during the last step of the HADDOCK modelling pipeline. For each RBD sequence, we created 200 models of the hACE2-RBD complex.
Phylogeny of Simulated Sequences To verify that the constraints on the topology and evolutionary distances of simulated sequences were respected, we compared the phylogenetic tree before and after adding the simulated sequences. We performed this test on the Sarbecovirus spike (Figure  4 ) and Circovirus capsid protein datasets (Figure  5 ). The complete sequences and Newick trees are available in Supplementary Data (Folder S1). For the Sarbecovirus spike protein, ten ancestral nodes were selected for sequence reconstruction. Then, evolution was simulated on each ancestral sequence with default parameters for the number and distances of simulation. As a result, three sequences were generated from each ancestral node with a distance varying from 0. 5 to 2 times the median distance in the ancestral node subtree.
Biological Significance of Simulations: Structural Evaluation of the Sarbecovirus Spike Dataset We completed the phylogeny visual inspection presented in Section 3. 1 with an evaluation of the affinity of the simulated spike protein receptor binding domain (RBD) for the human ACE2 (hACE2) receptor. For this calculation, we used a computational pipeline that combines structure prediction from sequence with AlphaFold2  [25] , assembly of the hACE2-RBD complex with HADDOCK, and binding free-energy estimation with FoldX (see Section 2. 5). We consider that a well-predicted affinity is an indicator that the structure of the RBD is preserved enough for binding to hACE2 to be possible. This would mean that the simulated evolution went in a realistic direction and can then be under positive selection pressure but does not inform on the conservation of the whole spike protein structure, which can also influence the binding of the RBD.
Virus Pop for Tools and Pipeline Testing To illustrate how Virus Pop can be used for challenging and comparing taxonomic assignation tools, we tested the alignment results of BLASTp  [16]  and DIAMOND  [30]  with our simulated sequences against our initial set of real sequences. The purpose was to illustrate how the local alignment performance varies with simulated distances and how different tools yield different performances. We chose BLASTp and DIAMOND as they are widely used for local sequence alignment of protein sequences and are often integrated within taxonomic assignation pipelines (e. g. ,  [1] [2] [3] ). Depending on the software used within each taxonomic assignation pipeline, their parameters and cut-off values, their performances will be affected in a different way. The results presented here were obtained on our two datasets.
Beyond that, the alignment coverage drops quickly, with DIAMOND performing worse than BLAST. On the Sarbecovirus spike protein, because many sites are almost invariant, the performances of the two tools are similar. The only slight alignment number decrease is visible before and at the very beginning of the NTD region (red arrow in Figure  8 ). However, this does not constitute a formal benchmarking of those tools as we present results here on a limited amount of data, and as they could both be tested with different parameters that include computing times. For instance, the "ultra sensitive" mode of DIAMOND would produce alignment results almost similar to those of BLAST. The purpose here is to illustrate how Virus Pop can help in assessing the performance of a tool or a full pipeline. Depending on the input data type, time performances or other constraints, it can participate in making development choices.
Improving Taxonomic Tool Sensibility by Expanding Reference Databases Beyond testing and evaluating taxonomic assignation tools, an application of Virus Pop that we put to the test was the interest of using the simulations to expand databases. We hypothesized that if the simulations represent realistic evolution directions, then they may be closer to yet unobserved viruses than any of the indexed viruses. Thus, integrating simulations to databases may increase the range of the detection tool. We tested this hypothesis on two recently discovered Circovirus capsid proteins  [23, 24]  that were not part of our initial dataset. To mimic in a simple and deterministic way the read alignment issue, we generated all 50-amino-acid-long segments of the two proteins. We obtained a total of 330 fake "perfect" reads (165 for each of the two 214 amino-acid long proteins).
For the simulation part, as we were in a context where we wanted to expand the tree at all possible positions and in many evolutionary directions, we generated 200 ancestral sequences for each of the 130 internal nodes. We used the default parameters for the distances which means that three sequences were created from each ancestral sequence. As a result, we obtained 600 simulations under each internal node which corresponds to a total of 78,000 simulated sequences. The sequences are available in Supplementary Data (Folder S4) Finally, we compared the result of blasting the protein segments against the initial database and against the augmented database. We discarded all alignments with a bit score below 40. Note that, in this protocol, the initial dataset was generated randomly with each species of the taxonomic group on NCBI being represented by only one sequence.
Other indexed sequences that were not fetch in this run may be closer to the two recently discovered Circovirus. Out of the 330 simulated reads, 170 were aligned at least once against the initial real database. This figure goes up to 202 against the augmented database, meaning that we aligned 10% more reads. In addition, with 6000 and 60,000 simulations at each internal node, we increase this score to 16% and 33% more reads detected, respectively. To complete this approach and visualize how close to the targets the simulated sequences are in the scenario with 600 simulations at each internal node, we present a zoom of the augmented tree (Figure  9 ). It contains the two closest real sequences in the starting dataset, the 600 simulations created from their common ancestor (this internal node is highlighted in Figure  5 ) and the two new protein sequences.
Database We ran Virus Pop on 80 virus genera infecting humans and 110 genera affecting other vertebrates. These virus lists were obtained from ViralZone  [32]. In the first group, 57 genera had a sufficient number of full genome sequences available with protein clusters detected by Virus Pop. In the second group, only 36 genera met these two conditions, allowing the pipeline to run through to completion. With the increasing availability of NGS sequencing data, simulating additional genera with Virus Pop will be possible. For each genus, up to 15 full genomes of each species within were fetched. Then, for each homologous protein group found, 300 simulations (100 ancestral sequences times 3 automatically computed distances) were generated at each internal node of the inferred phylogenetic tree.
To gather enough sequences within 3 genera with few species (Sapovirus, Parechovirus and Hepatovirus), the number of fetched genomes was increased to 25. The result is a total of 995 simulated homologous protein groups with a grand total of 24,138,277 sequences in both amino acid and nucleotide. The number of homologous protein groups found and simulated within one genus ranges from 1 (for 21 genera) to 138 for Orthopoxvirus. The resulting database is available at https://doi. org/10. 5281/zenodo. 7712690.
• Generating simulations with substitution rate variations depending on sites and inferred from the input dataset. Existing tools do allow to simulate substitution rate variations. For instance, IQ-TREE and Seq-gen both give the possibility to generate rate heterogeneity based on a gamma-model  [34]. However, they are implemented without the possibility for the user to constrain which site will be in which gammarate category. As a result, for instance, if 20% of the amino acid sites evolve slowly, simulations will statistically reproduce this parameter. However, the 20% of slowly evolving sites will be randomly spread over the alignment and conserved segments may be lost. Other methods for rate heterogeneity simulations exist but, so far, no tool provides an easy way to analyze the rate heterogeneity in real dataset and to reproduce it.
Indeed, overall structure is partly the result of internal interactions within monomers or between multimers, with dependencies between sites that will co-evolve to preserve their interaction. Integrating the interactions within the mathematical model would amount to simulate evolution in a "structure-aware" way. However, this would mean going beyond the initial assumption of site independence on which the evolution models are based. But this drastic simplification is currently necessary as evolutionary models are inherently very complex mathematical objects. For instance, determining an empirical amino-acid substitution model, as those used in Virus Pop, consists in finding 210 parameters (see Appendix A. 1). Having a dataset in which each type of substitution is represented in a quantity sufficient to derive representative rates is already a challenge.
Thus, even if we could define models with dependencies amongst sites and if we had the computational power to solve them, we would be limited by the availability of data. There have been, however, some published efforts put into trying to incorporate protein structure constraints to account for site dependencies. However, they were defined on case-by-case observations of some amino acid interactions  [36, 37]. To illustrate the usefulness of Virus Pop, we then presented how precisely constrained new sequences constitute a well-defined dataset convenient for testing and comparing the performances and limits of taxonomic assignation tools. Our two examples (see Section 3. 3) show how different the results are, depending on the ratio of highly preserved loci. On the Sarbecovirus spike protein, simulated substitutions are stacked on a few loci that, compared to the preserved areas, have a substitution rate that is more than 1000 times faster.