dataesr/scientific-paragraphs-categorization huggingface dataset:
This collection of structural candidates thereby constitutes a "virtual chemical library" which can be used for structural similarity search against the HD-PBL set. The description of the extract set can be found in  Allard et al. (2023). All structures of the virtual library (in SMILES format) and their associated metadata (extract and taxonomical information) were imported in Datawarrior (DW)  (DataWarrior User Manual, 2015; Sander et al. , 2015). Each of the NP-like hits (A, B, C and D) from the HD-PBL were searched against all annotated compounds using the structural similarity filter of DW. The number of annotations displayed was limited to no more than 10 molecules most relevant structurally to the hit, by varying the similarity score using the FragFp descriptor (usually by placing it at a minimum score of 0.
marinum during infection compared to M. marinum in broth. Following the targeted isolations (see Section 3. 4), the antiinfective activity of the isolated structures was assessed in the D. discoideum-M. marinum system. Compounds (1-3, 5-10) were resuspended in 30% DMSO in PBS and tested at 25 μg/mL. Preliminary fluorescence and luminescence data indicated strong activity on M. marinum in infection, but also a growth inhibition of D. discoideum, hinting at a detrimental effect of these compounds on the host as well (data not shown). This non-specific effect was observed in a preliminary experiment for most of the isolated compounds from C. brandisiana (1, 2, 6, 7, 9). The notable exception to this however was onychine (3) which seemed most selective towards the pathogen over the host. As we reported in  Hanna et al.
(2020) , the reference hit compound D (ZINC00120276) was tested at 30 µM and inhibited growth of M. marinum in D. discoideum by 28% compared to the vehicle control. To validate the compound's activity and to disentangle a possible host inhibition effect, we used our dual readout of the D. discoideum-M. marinum system to determine the IC 50 of this compound on M. marinum in broth and in infection. The compound was tested in a serial dilution ranging from 12. 5 to 0. 05 µM with a dilution factor of 2. The obtained data confirmed our initial observation showing that the compound D (ZINC00120276) exerted an anti-infective activity. In addition, we calculated the IC 50 using a PL4 regression on M. marinum in infection and in broth which was 1. 8 and 1. 9 µM, respectively. On the other hand, we could not calculate the IC 50 on D.
Acknowledgments The authors are grateful to  Green Mission Pierre Fabre  and the  Pierre Fabre Research Institute, Toulouse, France  for establishing and sharing their unique libraries of plants and extracts. BioRender was used for the conception of most figures with a publication license provided by author TS. Graphs were made using GraphPad Prism (version 9. 2. 0) and chemical structures were drawn with ChemDraw (version 20. 1. 0. 110), both licensed to OK by the  University of Geneva. The Dictionary of Natural Products (DNP)  was used under the license acquired by the  Wolfender Lab.
Data availability statement The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/Supplementary Material. The raw data for the NMR analyses is available at doi: https://doi. org/10. 26037/yareta:4kk4gbotzzhqhppyp6hisefr7m The Molecular Network used in this study can be accessed through its GNPS Job ID = e038d15b1a9e49b7aec23e47f9562e32.
This restriction allows phyBWT2 to look into them to estimate phylogenetic signals. In Sect. "Experimental evaluation", we show that phyBWT2 produces phylogenic trees of quality comparable to the standard benchmarks by handling datasets of different types (short reads, contigs, or entire genomes). Furthermore, we experimentally provide evidence that phyBWT2 is more efficient than phyBWT in terms of the number of iterations performed to reconstruct a phylogenetic tree. Remarkably, phyBWT2 can keep the required data structures in external memory, thus alleviating the main memory usage. For these reasons, phyBWT2 can be considered as a replacement of our original tool phyBWT. State of the art The Burrows-Wheeler Transform (BWT)  [13]  of a string (and the eBWT of a set of strings  [14, 15] ) is a suitable permutation of the symbols of the string(s), whose output shows a local similarity, i.
e. symbols preceding similar contexts tend to occur in clusters. Both transformations have been intensively studied with important and successful applications in several areas. For instance, the eBWT has been used for defining alignment-free methods based on a pairwise distance matrix  [14, [16] [17] [18]  in order to build up a phylogenetic tree for mitochondrial DNA genomes. The positional clustering detects "interesting" blocks in the output of the eBWT  [14, 15] , so that the requirement on the fixed size k in k-mers is relaxed and becomes of variable-order, not fixed a priori, in an adaptive way according to the contexts. This framework has already been used in other bioinformatics tasks, such as for detecting SNPs and INDELs in short-read datasets  [19]  and for lossy compression of FASTQ datasets  [20].
Both phyBWT2 and phyBWT exploit the underlying properties of the eBWT: (i) the clustering effect, i. e. the fact that the eBWT tends to group together equal symbols in the transformed string that occur in similar contexts in the input string collection; (ii) the fact that if a substring x occurs in one or more strings, then the suffixes of the input dataset starting with x-occurrence are close in the sorted list of suffixes. In other words, the greater the number of these substrings shared by two taxa is, the more they are similar. Although phyBWT2 and phyBWT do not use a distance matrix, they have some resemblance with split decomposition methods when reconstructing the tree from the information gathered through the eBWT. We recall that split decomposition relies on a solid mathematical ground  [21, 22] , and has been successfully applied to phylogeny  [23].
The recent alignment-free method SANS  [24, 25]  uses the notions of the split decomposition theory to greedily build a list of weakly compatible splits from which to infer phylogenies. In the list, each split has its own weight computed by counting k-mers that are stored in a colored de Bruijn graph  [24]  (this has been improved later by hashing  [25] , leaving the colored de Bruijn graph as input option). The calculated list of splits ordered by weight is then filtered according to two strategies that are described and implemented in the software tool SplitsTree  [26]. In our experimental study, we compare the trees obtained by SANS and phyBWT2. It should be noted that SANS is also able to reconstruct phylogenetic networks whereas phyBWT2 reconstructs phylogenetic trees only. As previously mentioned, a plethora of methods have been designed for phylogeny reconstruction (e.
Preliminaries In this section, we define the general terminology we will use throughout this paper. Let s be a string (also called sequence) of length n on the alphabet. We denote the i-th symbol of s by s  [i]. A substring of any s is denoted as s [i, j] = s[i] • • • s[j] , with s[1, j] being called a prefix and s[i, n + 1] a suffix of s. A k-mer is a string of length k. Let S = {s 1 , s 2 ,. , s ℓ } be a collection of ℓ strings. We assume that each string s i ∈ S has length n i and is fol- lowed by a special end-marker symbol S i [n i + 1] = $ i , 1 https:// github. com/ veron icagu errini/ phyBWT. which is lexicographically smaller than any other symbol in S , and does not appear in S elsewhere. 2.
Differently from phyBWT2, SANS is based on the construction of a list of splits obtained by computing all the k-mers of the dataset, which are either directly extracted or stored in a colored de Bruijn graph. Thus, it requires to fix a-priori the value k. Then, the list of splits is postprocessed according to filtering strategies (options proposed in SplitsTree  [26] ) that allow to limit the output splits in order to show phylogenetic networks or to calculate a subset of them representing a tree. For the sake of comparison, we applied to SANS the last filtering approach for drawing trees. In our experiments, in order to improve the sensitivity of our tool (note that SANS performs implicitly this step by adding the reverse-complement of the k-mers), we added the reverse-complement of the strings to each set of taxa.
Implementation Our tool has been implemented in C++. All tests were done on a DELL PowerEdge R750 machine, used in non exclusive mode. Our platform is a 24-core machine with 2 Intel(R) Xeon(R) Gold 5318Y 24C/48T CPUs at 2. 10 GHz, with 629 GB. The system is Ubuntu 22. 04. 1 LTS. Input and Output phyBWT2 takes as input ebwt ( S ), lcp ( S ) and cda ( S ), the parameter k m that is used to remove the noise during the construction of the positional clusters and the support threshold value τ in (0, 1] used for each positional cluster coloring. Such data structures can be computed via the bash script that we provide in the phyBWT2 repository. In the current implementation, the data structures are given in uncompressed form, but phyBWT2 can be adapted to directly take as input compressed data structures  [31].
Our tool outputs an unrooted tree in newick format. The trees 5 reported in this paper are drawn by using the Interactive Tree Of Life (iTOL) tool  [32]. Datasets To show the effectiveness of our method, we have chosen six datasets with a diverse number of taxa, composition and different length of the strings (Table  1 ). More in details, we used six different types of datasets: i) Illumina sequencing data (short reads) for seven S. cerevisiae and five S. paradoxus strains from the study in  [33] ; ii) assemblies from 12 species of the genus Drosophila from the FlyBase database (largely accepted phylogeny shown in  [34] ) (also analyzed in  [24] ); iii) Illumina sequencing data (short reads) for 42 S. cerevisiae strains selected from the studies in  [35, 36]  and from the public repository under accession code PRJEB50706.
iv) 43 HIV-1 complete genomes used 4 https:// gitlab. ub. uni-biele feld. de/ gi/ sans, downloaded in January 2023. 5 Except the trees in Figs. 4, 5 , and 6 already in the conference paper  [11]. in the literature  [37] ; v) 20 sequences from Ebolavirus genus selected in  [38, 39]. vi) 27 genomes from E. coli and Shigella from the studies in  [40, 41].
Resource usage The new version phyBWT2 improves the performance of the former version phyBWT by reducing the internal memory usage and the running time on large datasets. Indeed, phyBWT2 does not load in main memory the data structures: it performs the clustering detection by reading portions of the input data structures computed for the whole dataset and by (possibly) reducing them to a subset of strings at the same time. In Table  2  we report the running times of both phyBWT2 and SANS on the tested datasets. We separate the running time required to build the data structures from the running time of phyBWT2 for several reasons. First, the input data structures eBWT, LCP and CDA are well-known structures in string algorithms and in bioinformatics, and efficiently building these data structures is a well-studied problem (  [15, [42] [43] [44] [45] [46] [47] [48] [49] ).
Thus, analyzing the best way to compute them efficiently does not fall within the goal of this paper. Second, the data structures do not depend on the k m -value used to remove the noise during the construction of the positional clusters. Therefore, they need to be built only once for each input dataset. One can try different parameters or techniques for inferring confidence values on phylogenetic trees (based on reconstructing many trees) without having to rebuild the data structures. The last feature does not hold for k-mers-based methods, such as SANS, whose data structures must be recomputed when varying the input parameter k. Nevertheless, we experimentally observed that the preprocessing step (see Table  2 ) is computationally more expensive than the phylogeny construction.
Experiments on 12 yeasts This dataset comprises 12 Illumina 151-bp paired-end sequencing experiments obtained from the study in  [33] , and deposited in the public repository SRA (Short Reads Archive) under accession code PRJNA340312. We selected seven sequencing data from the S. cerevisiae strains and five from the S. paradoxus strains. We performed adaptor-removing and quality-based trimming using trimmomatic  [50] , as described in  [33]. Then, for each sequencing experiment, we extracted 5 million of 151-bp paired-end reads as to form a dataset with 60× coverage on average per strain and a total FASTA file size of 26 GB.
Datasets Both phyBWT2 and SANS separate the S. cerevisiae and the S. paradoxus strains, which show an average whole-genome sequence divergence of ∼ 10%. As expected, by taking into account the relatively high divergence among S. paradoxus strains (0. 5% -4. 5%), also the same S. paradoxus partition is obtained. On the other hand, a few differences are shown in the S. cerevisiae partition which groups strains with a sequence divergence ∼ 0. 5%. Compared to SANS, phyBWT2 produces a tree which is closer to the benchmark although the differences with the benchmark shown by both SANS and our method can be explained considering the relatively low divergence among S. cereviasiae strains as well as the partially admixed genomes of some of the trains (e. g. S288C and DBVPG6044)  [51]. For a fair comparison, we also run SANS with different values of the input parameter k (i.
e. by varying k in the range  [15, 50] ). The unrooted phylogenetic tree obtained for k in  [15, 27]  is topologically equivalent to the one obtained from our approach depicted in Fig. 5a , but the UWOPS034614 strain is clustered with the S. paradoxus clade rather than S. cerevisiae clade. Instead, for k > 40 , either the strain UWOPS919171 is mis- placed or the stains Y12 and YPS128 are not grouped together, differently from the benchmark tree. Time and memory Given the necessary data structures for this dataset, the former version phyBWT runs in approximately 12 min with a memory peak of 81 GB by loading the whole data structures in the main memory. The new version phyBWT2 uses only 6. 5 MB of internal memory and fulfills the task in 3:12 min reducing the number of performed iterations from 6 to 1 (using the same parameter setting k m = 22 and τ = 0.
6 for both versions). On the same datasets, using default parameters, SANS runs in 30:29 min by using 3. 7 GB of internal memory. However, a direct time and memory comparison between phyBWT2 and SANS is not completely fair, as they take different inputs: if we do not assume availability of the data structures, computing them for this dataset takes over 3 hours, so SANS would be faster (see Table  2  for details).
Experiments on Drosophila Drosophila data are downloaded from the FlyBase database. 6  This dataset includes assemblies from 12 species of the genus Drosophila: D. melanogaster (mel), D. ananassae (ana), D. erecta (ere), D. grimshawi (gri), D. mojavensis (moj), D. persimilis (per), D. pseudoobscura (pse), D. sechellia (sec), D. simulans (sim), D. virilis (vir), D. willistoni (wil), and D. yakuba (yak). Nine of these species fall within the Sophophora subgenus, which includes members of the melanogaster, obscura and willistoni groups. The number of strings for each species varies: it ranges from 1, 870 for D. melanogaster to 17, 440 for D. grimshawi. The obtained dataset is a medium-sized input with a total number of symbols of more than 2, 161 Mbp. More details are reported in Additional file 1.
Validation and results As benchmark tree we used the accepted phylogeny  [34]  which we report in Fig. 6b. For this dataset, phyBWT2 produces the same tree as the one obtained in  [11]  by using phyBWT for any k m in  [23, 45]  and τ = 0. 5 (Fig. 6a ). The same parameter settings used for phyBWT also hold for phyBWT2. The Sophophora subgenus as well as the Drosophila subgenus are correctly detected, and inside the Sophophora subgenus, the melanogaster subgroup is correctly isolated. The only difference with respect to the benchmark tree by  [34]  is the taxon D. ananassae that represents the ananassae subgroup. Such subgroup is part of the melanogaster group together with D. melanogaster, D. sechellia, D. simulans, D. erecta and D. yakuba. However, our method places D. ananassae closer to the obscura group rather than the melanogaster subgroup.
Experiments on 42 yeasts In order to test the accuracy of phyBWT2 in discerning closely related populations (with sequence divergence varying between 0. 5% and 1%), we selected 42 representative S. cerevisiae strains and produced their phylogenetic tree. This dataset comprises 42 Illumina paired-end sequencing experiments obtained from the study in  [52]. More details about the public repositories to download them are provided in Additional file 1. For each sequencing experiment, we extracted paired-end reads yielding an average coverage of 30× per strain and a total FASTA file size of 38 GB. Validation and results As a benchmark we used IQ-TREE  [53]  with model selection  [54]  and ultrafast bootstrapping  [55]  (Fig. 7 ) and compared its results with those obtained from phyBWT2 and SANS.
8  The phylogenetic tree on the 42 yeasts sequences by phyBWT clustered with the Asian clade. On the other hand, the ale beer strains (AAC and ATV) are still incorrectly placed. The same holds for the outgroup (AMH_1a) that is not identified. Time and memory Given the necessary data structures for this dataset, phyBWT2 reconstructs the proposed phylogeny in approximately 1 h and half with a memory peak of 1. 8 GB by using k m = 25 and τ = 0. 6. phyBWT2 largely improves upon phyBWT not only but showing a better phylogeny reconstruction (by using similar parameter settings), but also in its performance. The former version runs in 5 h by using a large amount of memory (more than 175 GB) and performs 33 iterations against the only 5 carried out by phyBWT2. On the same dataset, SANS needs 13. 5 GB of internal memory and more than 32 h to reconstruct the phylogenetic tree for k = 32 , and even more time (around more than 45 h) for k = 25.
Although a direct time and memory comparison between phyBWT2 and SANS is not fair, since phyBWT2 requires data structures whose computation does not 9 The phylogenetic tree on the 42 yeasts sequences by SANS with default parameters depends on the choice of k, in this case even including the computation of the data structures phyBWT2 is more efficient than SANS: for this dataset, we can compute the eBWT and CDA array using the semi-external memory approach proposed by BCR  [15]  taking about 7 h and 4. 3 GB of internal memory, and deduce the LCP array from the eBWT using the tool bwt2lcp  [46]  taking around 1 h and 20 min and 45 GB of memory (see Table  2 ).
Experiments on HIV Clade classification is an important task also in the field of virology, as each clade (also termed subtype) corresponds to a cluster of genetic similarity. Thus, we studied the phylogeny of the Human immunodeficiency virus (HIV). There are two main types of HIV, and among them, HIV-1 is the most virulent and predominant. This dataset Fig. 10  The phylogenetic tree on the 42 yeasts sequences by SANS with option -k=25 is obtained by selecting 43 HIV-1 complete genomes used in the literature  [37]. In particular, it comprises thirty-five sequences from the major group (Group M) divide into subtypes A, B, C, D, F, G, H, J, K, seven sequences from the minor Groups N and O, and one CPZ sequence as an outgroup. Accession number, subtype, length (bp), and area of the HIV-1 sequences are reported in Table  S1.
These reference sequences have been carefully selected in  [56]  according to several criteria, and can be downloaded from the Los Alamos National Laboratory HIV Sequence Database. 7  Validation and results For this experiment, we use as benchmark the phylogeny depicted in [37, Fig. 2] , which is the Neighbor-Joining phylogenetic tree on the 43 reference sequences where the CPZ sequence (CIV strain AF447763) is used as an outgroup. We run phyBWT2 and SANS on this dataset by using different parameter settings. We compared the reconstructed phylogenetic trees by using the functions ClusteringInfoDistance() and SharedPhylogeneticInfo() provided in the R package TreeDist  [57] , which implements a suite of metrics to quantify the topological distance between pairs of unweighted phylogenetic trees.
Figures  11  and 12  depict the trees that obtained the best scores according to the above measures based on the amount of phylogenetic or clustering information that two trees hold incommon. More in details, we set k m = 16 and τ = 0. 6 in phyBWT2 and k = 16 in SANS. For both tools, subtypes are distinctly grouped together in different branches. The phylogeny produced by phyBWT2 is consistent with the one in  [37]. The relationships among the subtypes are well demonstrated, for instance subtypes B and D (resp. C and H) are closer to each other than to the others, and subtype F (resp. A) contains two distinguishable sub-subtypes F1 and F2 (resp. A1 and A2) that are close related to subtypes K and J (resp. G). Time and memory Given the necessary data structures for this dataset, both phyBWT and phyBWT2 reconstruct the proposed phylogeny very quickly (less than 1 second).
Experiments on Ebolavirus For this experiment, we used the 20 published sequences from  [38]  selected in  [39]. The Ebolavirus genus includes five viral species: Ebola virus (Zaire ebolavirus, EBOV), Sudan virus (SUDV), Tai Forest virus (TAFV), Bundibugyo virus (BDBV), and Reston virus (RESTV). Ebola viruses are single-stranded RNA whose genomes consist of about 19 kilobases. Details for each sequence in Additional file 1. Validation and results For this experiment, we use as benchmark the phylogeny trees depicted in  [39, Fig. 4 ]. Figures  13  and 14  depict the trees that obtained the best scores according to the measures based on the shared amount of phylogenetic or clustering information provided in the R package TreeDist  [57]  and described for the HIV-1 dataset. More in details, we setk m = 16 and τ = 0.
6 in phyBWT2 and k = 21 in SANS. Both phyBWT2 and SANS exactly separated the five species. According to the four trees in  [39, Fig. 4 ], the EBOV sequences are clustered into a monophyletic clade, and BDBV and TAFV viruses are positioned close and then clustered with the EBOV branch. These trees also show the phylogenetic uncertainty in the placement of the SUDV clade (red). Our method (Fig. 13 ) places the SUDV clade as sister to the EBOV, TAFV and BDBV clade, in according with [39, Fig. 4E ], whereas SANS (Fig. 14 ) places it as sister to RESTV clade in according with [39, Fig. 4A ]. Differently from the study in  [39, Fig. 4 ], the phylogeny reconstructed by SANS keeps the EBOV branch and the TAFV-BDBV clade separated (Fig. 14 ). Time and memory Also in this second dataset of viral genomes, both phyBWT and phyBWT2 reconstruct the proposed phylogeny very quickly (less than 1 second).
Experiments on E. coli-Shigella For this experiment, we used a real-world dataset collected in the study  [41]  to assess the accuracy of the alignment-free methods in phylogenetic reconstruction of sequences that underwent horizontal gene trasfer events and genome rearrangements. It comprises 27 genomes of E. coli and Shigella whose reference supertree  [40]  was generated based on thousands of single-copy protein trees. Details for each sequence in Additional file 1. Validation and results As benchmark we use the phylogeny tree depicted in [41, Additional file 2: Figure  S8 ] where E. coli reference groups and Shigella (S) are indicated. Indeed, the 27 taxa are attributed to six distinct groups (the E. coli reference, or ECOR, strains)  [40]. Figures  15a  and b  depict the trees we selected among them obtaining the higher scores according to the clustering information measure described above and computed by using the R package TreeDist  [57].