dataesr/scientific-paragraphs-categorization huggingface dataset:
Accuracy analysis. To analyze further the precision of our approach, we compared the result of NIQKI with the state of the art on a real dataset composed of one thousand bacterial genomes from Refseq. In this experiment, we computed the pairwise distances between all genomes with our approach and compared those results with Dashing using a very large number of fingerprints (1 million) as the ground truth. In a first experiment reported in Figure  3  we plot the correlation between NIQKI and Dashing estimations using different amounts of fingerprint (namely S = 65536,S = 4096 and S = 256) while using a constant fingerprint size (W = 12). As described in the theoretical analysis of the related studies, a smaller amount of fingerprints results in larger error bounds. We observe than NIQKI obtain a strong correlation with Dashing output even with a very low amount of fingerprint.
We fixed the default fingerprint size to 12 as it provides very low false positives and a reasonable memory overhead. Performance analysis. In this section, we display how our index can scale on synthetic and real datasets compared to the two most used tools of the state-of-the-art Mash and Dashing. All experiments were performed on a single cluster node running with Intel(R) Xeon(R) CPU E5-2420 @1. 90GHz with 192GB of RAM and Ubuntu 16. 04. with a timeout of 48hours. Bindash was not included in our benchmark because it is not able to compute a distance matrix from a file of files directly. All state of the art tools present a O(S. N ) query time (or O(S. log(S)). N ) if S minimum Minhash is used) as a query sketch have to be compared to each indexed sketch. In contrast, our index presents a query time linear with the number of hits.
It can be O(S. N ) in the worst case where all genomes are identical and can be O(S) in the best-case scenario where an alien entry is completely dissimilar with the indexed genomes (ignoring false positives). To show those two regimes we choose to perform a first "idealistic" benchmark with randomly generated genomes sharing no k-mers which display the best case of our index. In a second experiment, we compare those results to a real database composed of GenBank genomes. To build such an index we downloaded all Genbank bacterial assemblies (1,042,611) and randomly selected genomes from this pool. We want to point out that such a database is highly redundant, for instance, it contains 142,568 assemblies of the Escherichia coli organism. As such it constitutes a stress case for our index as each query associated with Escherichia coli should report a very large part of the indexed genomes among its matches.
In Figure  5    NIQKI used 65,536 32 bits genome identifiers and used the default fingerprint (W = 12). We observe that both Mash and Dashing runtime grow following a quadratic evolution which is expected as computing all pairwise distances needs Θ(S. N 2 ). The growth runtime of NIQKI is roughly linear and can become orders of magnitude faster than the state of the art. In Figure  6  we perform the same experiment on a realistic database constituted from bacterial GenBank genomes. We observe that Mash and Dashing deliver similar performances on synthetic and real datasets. The main difference is due to the fact that the mean size of bacterial genomes is around 5 megabases while the generated genomes were 10 megabases long. NIQKI results on small databases are similar to the synthetic cases, but a superlinear growth can be observed on the largest collections.
However, despite this observation NIQKI is still able to be orders of magnitude faster than state-of-the-art tools while using a comparable amount of memory. For example, the largest Mash experiment used 773 CPU hours on 50k genomes where NIQKI used only 15 CPU hours. The largest Dashing experiment used 645 CPU hours on 100k genomes where NIQKI used only 30 CPU hours. We extrapolate that Dashing or Mash would need more than ten thousand CPU hours to handle 500k genomes while NIQKI used 166 CPU hours. On the downside, because of its large overhead NIQKI uses more memory than the other approaches on small collections and is slightly more memory expensive than Dashing on larger databases. Indexing Genbank bacterial genomes. To access our approach scaling ability scalability we choose to index and compute pairwise distance on all bacterial genomes from GenBank.
This dataset represents more than one million genomes, counting more than 5 tera-nucleotides or more than 1 terabytes of gzipped fasta files. We choose to evaluate the cost of such an operation with varying the amount of indexed minimizer that linearly impact the memory cost and the running time of our approach. We choose to keep the memory overhead constant (O(2 W +log 2 (S) )) to ensure a fair memory comparison and raised the fingerprint size accordingly. We report the CPU time and Wall clock time along with the memory usage required for those experiments in Table  1. We observe that we can index and compute pairwise distance on such a database with medium-sized sketches in a couple of days with a reasonable memory footprint. If the indexing time is roughly constant and dominated by reading the input, we observe that the query time grows linearly with the sketch size (plus a "reading" time constant cost).
Conclusions and future work We showed that the use of inverted indexes on partitioned sketches leads to algorithmic improvement of fingerprint queries that can reduce running time by orders of magnitude with comparable precision. Theoretically, our proposed structure query is O(#hits) compared to O(S. N ) for the state of the art. This structure came with a memory cost as our index uses O(S(N logN + 2 W )) bits instead of O(S. N. W ). In practice, we showed that even a straightforward implementation could index small fingerprints in a very efficient manner while providing results comparable to the state of the art. We showed that on both idealistic synthetic databases and on real-world redundant databases that our approach could provide orders of magnitude faster queries on large instances with comparable memory footprint.
We also demonstrated our index capacity to index and query all bacterial genomes of Genbank (more than one million bacterial genomes) in a matter of days on a small cluster. While our index can index any kind of fingerprints (Minhash, Hyperloglog, Hyperminhash) we aimed to provide the best possible fingerprints of a given size to limit the number of false positives. To do so we generalized the concept of Hyperminhash to take into account different sizes of Hyperloglog and Minhash fingerprints dubbed (h, m)-HMH fingerprints. Interestingly Minhash, Hyperloglog, and Hyperminhash fingerprints can be seen as particular cases of (h, m)-HMH fingerprints. We show that given an expected sub-sampling we can select the (h, m)-HMH fingerprint with optimal parameter the provide the lowest false positive rate and confirmed this choice in practice.
Acknowledgements The authors thank  Sylvain Glémin ,  Florence Débarre ,  Vincent Castric  and  Denis Roze  for taking the time to discuss this work. This work was funded by the  European Research Council  ( NOVEL  project, grant # 648321 ). The authors also thank the  Région Hautsde-France , and the  Ministère de l'Enseignement Supérieur et de la Recherche  ( CPER Climibio ), and the  European Fund for Regional Economic Development for their financial support. Finally , the authors would like to thank the Editor and Associate Editor along with the both anonymous reviewers for the time and effort they devoted to the improvement of this article.
Rabies is a viral zoonosis that is transmissible to humans via domestic and wild animals. There are two epidemiological cycles for rabies, the urban and the sylvatic cycles. In an attempt to study the epidemiological role of wild canidae in rabies transmission, the present study aimed to analyze the genetic characteristics of virus isolates and confirm prior suggestions that rabies is maintained through a dog reservoir in Tunisia. Virus strains isolated from wild canidae were subject to viral sequencing, and Bayesian phylogenetic analysis was performed using Beast2 software. Essentially, the virus strains isolated from wild canidae belonged to the Africa-1 clade, which clearly diverges from fox-related strains. Our study also demonstrated that genetic characteristics of the virus isolates were not as distinct as could be expected if a wild reservoir had already existed.
Origin of Virus Isolates Animals suspected to be dead from rabies infection were collected by the veterinary services and sent to the IPT laboratory to undergo necropsy, brain extraction and rabies testing using a FAT test, as recommended by the OIE and the World Health Organization (WHO)  [18, 19]. All samples that tested positive for rabies were stored in the rabies laboratory biobank. In total, we selected 11 brain samples collected from wild animals, namely five foxes and six jackals, and 14 brain samples isolated from domestic dogs in the same location and during the same period of time (same delegation, same month), as shown in Table  1. These samples were characterized and integrated into the phylogenetic analysis together with additional sequences downloaded from GenBank.
DNA Sequencing PCR products were purified with ExoSAP and sequenced using the BigDye Terminator v3. 1 cycle sequencing kit (Applied Biosystems, Waltham, MA, USA). Each amplicon was sequenced using one forward and one reverse primer to determine consensus sequences reaching up to 751 bp (N gene) and 1017 bp (P gene), using an ABI PRISM 3500 Genetic Analyzer (Applied Biosystems). Original sequences were trimmed and edited using BioEdit software. Then, they were aligned along with a set of sequences downloaded from GenBank using MAFFT online  [22]. All sequences were deposited in GenBank (accession numbers OK275663-OK275695).
Maximum Likelihood Phylogeny In order to determine taxonomy and clade membership for each virus isolate, we performed maximum likelihood (ML) phylogenetic analysis on the nucleoprotein dataset  [23]. We first determined the best-fit substitution model by Smart Model Selection (SMS)  [24] , and certain GTR model + Gamma 4 categories were selected as the best parameters based on the lower Akaike Information Criterion (AIC). We then implemented ML phylogeny in PhyML with 1000 bootstrap replicates, using PhyML online software available at " http://www. atgc-montpellier. fr/phyml/, accessed on 26 January 2021". The resulting tree was edited and annotated on FigTree 1. 7  [25].
Bayesian Phylogeny To determine the time to common ancestry among RABV clades circulating in Tunisia, we conducted a Bayesian MCMC phylogenetic analysis on the phosphoprotein dataset  [26]. Initial substitution rates were estimated for use as tree priors by using Tempest  [27]. A substitution model was averaged by using the bModelTest Package in Beast2  [28]. A Bayesian phylogenetic tree was constructed in Beast2 using a TN93 model with a discrete Gamma distribution among variations and a relaxed molecular clock model  [29]. A Bayesian skyline plot model was specified as a tree prior. The analysis was set to run for 30 million iterations. ESS values were examined using Tracer software. A Maximum Clade Credibility (MCC) tree was constructed after discarding the first 10% burn in using Tree Annotator V2.
Reservoir Species Analysis A pairwise genetic distance matrix was calculated using the 'Ape' package on R software and then visualized using the 'ggplot2 package on R software. The dataset included P sequences obtained from wild canids and dogs sharing similar sampling dates and locations. To identify the most likely animal species reservoir for each Clade, a second analysis was carried out on Beast2 using the animal host species as a discrete trait, as specified in a previous study  [16]. The analysis was set to run for 30 million MCMC iterations. An MCC tree with traits was set and annotated using TreeAnnotatorV2. 6. 2, then visualized and edited in Fig-tree 1. 7.
Comparison of Wild Canidae Virus Isolates with Tunisian Canine Variant Strains We compared viruses associated with foxes and jackals in Tunisia with variants previously identified in Tunisian dogs, which were downloaded from GenBank. The resulting ML tree is displayed in Figure  2 , showing that variants found in Tunisian wild canids cluster together with those identified in dogs. The phylogenetic tree was further divided in two sub-clades that were independent from the host species but related to the area of sampling, identified as northwest (NW) and northeast-center-south (NCS). All viruses isolated from wild canids clustered with high support within the northeast-centersouth (NCS) sub-clade, except for the isolate 8112, associated with a jackal found in 2006 in the El Kef governorate (northwest of Tunisia).
Another interesting result was that isolate 10582, associated with a jackal that originated from the Kasserine governorate (center west) in 2011, was mostly related with the isolate DogTN/Ks98 (isolated from Kasserine in 1998). Although they belonged to the NCS sub-clade, they did slightly deviate from other NCS isolates. This deviation was supported by a node with high bootstrap values (100%). The old Tunisian isolates, downloaded from GenBank (Tunisia 1986), also belonged to the NW sub-clade.
Reservoir Species Analysis To better understand virus transmission between dogs and wild canids, we performed a pairwise genetic distance analysis followed by a Bayesian analysis using a dataset composed of sequences of the phosphoprotein gene isolated from wild canids and dogs that shared the same sampling times and locations. Original P sequences were obtained for six jackals, three foxes and fourteen dogs. A pairwise distance matrix, calculated and visualized on R software, showed 100% similarity (0% dissimilarity) between viral sequences associated with animals that shared the same sampling location, irrespective of the animal species (Figure  3 ). Similarly, the MCC tree built using the host species as a discrete trait (Figure  4 ) confirmed that variants sharing similar sampling dates and locations cluster together independently of the animal species.
Time Scale of Rabies Virus Evolution in Tunisia In order to estimate the time scale of divergence of the Tunisian rabies variants, we performed a Bayesian analysis using the phosphoprotein dataset, including sequences from the study, plus two sequences from Algeria, and eight additional sequences from Tunisia (Table  3 ) that were downloaded from GenBank. Molecular clock analyses implemented in Tempest confirmed a positive correlation between time and substitution events and estimated a slope rate of 5. 4 × 10 -4 substitution/site/year that was thus set as a prior for the mean clock rate. The MCC tree resulting from evolutionary analyses is displayed in Figure  5. The overall tree structure confirms that Tunisian isolates are divided into two phylogenetic groups, NW and NCS, that diverged a few decades ago; the most recent common ancestor (tMRCA) between these sub-clades was estimated to occur in the year 1957 (Figure  5 ).
Each viral variant has its specificity for a host reservoir encrypted in its genetic settings  [11, 31]. Our results also suggest that there is not any independent spillover of rabies virus in wild canids in Tunisia. This is not the case for the neighboring Middle Eastern region (Iran, Irak, Syria, Jordan) where both urban and wild rabies cycles exist in the same country  [16]. When aligning the Tunisian wild canids sequences with those of the Tunisian dog strains downloaded from GenBank, the ML phylogeny showed that all isolates that have similar sampling dates and locations strongly cluster together independently of the animal species. Specifically, the current genetic diversity of the Tunisian isolates is represented by two major and geographically distinct phylogenetic sub-clades, previously defined by  Amouri et al.
On both sides of the dorsal ridge, on the plains of the northwest and the center-east, scattered farms are widely spread. Domestic animal and dog presence is also influenced by such landscapes  [39]. Thus, the dorsal ridge may act as a natural barrier, responsible for the geographical dispersion of both branches of the NCS variants. In addition to the latter hypothesis, if a spillover existed in wildlife, the viruses isolated from wild animals living on both sides of the same mountain would have had close genetic characteristics. On the contrary, mountain chains seem to act as a natural barriers to virus spread. In the present study, we performed molecular clock analysis using sequences of phosphoprotein but not nucleoprotein, because R squared analysis only showed positive correlation on the P dataset.
These age estimates are consistent with previous analyses of RABV in North Africa, where the most recent common ancestor for all North African RABV was estimated to have existed during the period 1878-1945  [17]. Thus, the diversification of the two Tunisian sub-clades (NCS/NW) is more recent than that of all the North African clades. Our main limitation was the inability to access recent North African sequences in GenBank. This might have affected our accuracy in tMRCA estimation. In addition, because of the unavailability of recent Algerian sequences from the northeast (near the Tunisian border), it was not possible to reliably distinguish whether the virus isolates were the result of introduction of the Tunisian variants into Algeria.
In the first SBGN bricks paper  [15] , we showed that these occurrences could be generalised into generic templates called SBGN bricks, which could then be used as building blocks when building SBGN diagrams. We defined several of such templates in the three SBGN languages and showed how the building blocks were applied for educational purposes. Furthermore, SBGN bricks were used to speed-up the creation of SBGN diagrams via template-based construction  [15]. The template-based construction was implemented for SBGN-ED  [8]  and PathVisio  [35, 18]  (sbgnbricks. sourceforge. net) and is also supported by more recent editors such as Newt  [27]  (newteditor. org) and Krayon (github. com/draeger-lab/krayon4sbgn). In this manuscript, we introduce a comprehensive set of SBGN bricks that largely extends the ones previously introduced in  [15] , as well as a novel approach to defining a semantic layer which structures the set of SBGN bricks.
We built a comprehensive set of 476 template bricks that substantially extends the one introduced by Junker et al. in 2012  [15]. To better structure this set and to precisely describe the complex relationships the bricks share with the concepts they represent, we coupled them with a new ontology, the BricKs Ontology (BKO), that gathers and organizes all bricks and terms. We then evaluated the completeness of our set of template bricks by annotating all SBGN PD maps of the ACSN  [17]  and PANTHER  [32, 22]  databases.
Extending to the ER and AF languages The PD language allows depicting biochemical processes under a mechanistic and temporal point of view. It is widely used by pathway databases and modelling tools to represent molecular networks under the form of detailed maps  [34]. Hence we primarily focused on building a complete set of template bricks expressed in PD. However we also built template bricks expressed in the other two SBGN languages, AF and ER. AF permits representing a flow of activities that influence each other, ER is used to represent relationships between biochemical entities without any temporal aspect. Although the three SBGN languages are orthogonal, some molecular networks may be depicted using one or the other, depending on the point of view one wants to adopt. Figure  5  and Figure  6  give excerpts of the AF and ER maps matching the PD map of Figure  1  and annotated with terms using AF and ER template bricks, respectively.
Evaluating the completeness of the ontology To evaluate the completeness of our set of terms and template bricks with respect to the description of the main concepts found in biomolecular networks, we identified the set of instance bricks making up the PD maps of two databases: the Atlas of Cancer Signalling Network  [17]  (13 maps) and the PANTHER database  [32, 22]  (174 maps). To this end, for all maps, we identified all instances matching each PD template brick (see Methods for more details), resulting in zero or more instances for each template brick. Overall, we obtained 15708 distinct instances of BKO bricks for the ACSN, and 6339 for the PANTHER database. All processes and activities from both databases were matched by at least one template brick. This was to be expected since our set of template bricks covers all ways to represent the general kind of processes (irreversible/reversible processes) and activities (modulatory activity and binding activity) in PD.
The number of instances matched by each term of the ontology for each of the two databases is given in Supplementary Table  S2. To further evaluate the completeness of our ontology, we investigated those instance bricks that were matched by at least one of the 30 template bricks representing generic processes or activities (e. g. an irreversible process, a catalytic activity), without being matched by template bricks representing more specific processes or activities, and that are subclasses of the former in the ontology (e. g. a protein phosphorylation, a protein kinase activity). Those instances could reveal that a particular term or template brick is lacking in the ontology. The list of the 30 template bricks representing generic concepts is given in Supplementary Table  S3. Across the two databases, there were 6396 of such instances, representing 28% of the total number of instances.
Mix of two processes of unknown nature: two processes are represented using only one SBGN process (e. g. a dissociation and a dephosphorylation). Process not in the ontology: the process represented in the brick is not an instance of any template brick of the ontology. because these processes are not clearly identified in regard to biology (non standard values in state variables such as "?" or "*" (2. 5%); production from an empty set (0. 08%); methylation of a nucleic acid feature of unspecified nature (0. 06%); creation of asRNAs (1. 6%)) or because their bio-molecular mechanisms involves a sequence of more elementary processes (GTP/GDP exchange process (1. 8%)). Overall, our analysis showed that BKO is complete enough to accurately represent all types of processes and activities found in the maps of the two databases.
pd2af. org) designed for the same type of transformation. The notion of bricks also contributed to the development of the conversion rules of the STON tool that allows storing and querying PD maps in a Neo4j Graph Database  [33] , of the conversion rules of the cd2sbgnml bidirectional converter  [2]  between the CellDesigner format  [11]  and PD maps expressed in the SBGN-ML format  [3] , and of the conversion rules of ySBGN (github. com/sbgn/ySBGN), a bidirectional converter between yEd (www. yworks. com/products/yed) GraphML format and the SBGN-ML format. Recently this approach was also used as a basis for the development of the ModelBricks project  [7]  (modelbricks. org), which enables the on demand generation of executable models from PD maps. We expect that the large extension of the set of template bricks and its formal representation in BKO will lead to the development of new methods and tools.