dataesr/scientific-paragraphs-categorization huggingface dataset:
6 nM. For thermophoresis analysis, each ligand dilution was mixed with one labelled sensor volume, leading to a final concentration of VicK (WalK) of 3. 75 nM and final ligand concentrations at half of the ranges above. Approximately 4 µL of each solution was filled into Monolith NT standard treated capillaries (NanoTemper Technologies GmbH). Thermophoresis was measured using a Monolith NT. 115 pico instrument (NanoTemper Technologies GmbH) at room temperature with 5-s/30-s/5-s laser off/on/off times, respectively. Instrument parameters were adjusted with 50% LED power and 60% MST power. Data from three independently pipetted measurements were analyzed (NT. Analysis software version 1. 5. 41; NanoTemper Technologies). Following data analysis, the thermophoresis signals were fitted to the formula for K D from the law of mass action: f(c) = unbound + (bound -unbound) × ([VicK]+c + K D -(( [VicK]+c + K D ) 2 -(4 × [VicK] × c) 2 × [VicK] where f(c) is the observed thermophoresis signal, "unbound" is the signal in the absence of ligand (normalized to 0), "bound" is the signal at infinite ligand concentration, c is the concentration of ligand in the same units as VicK (WalK) concentration, and K D is the dissociation constant.
Through MODELLER for basic modeling, we aligned the FASTA code of VicKex (WalKex) with the PDB database, and we found a significant alignment with the extracellular domain of sensor histidine kinase YycG from Staphylococcus aureus (PDB code: 5IS1) and with the extracellular receptor domain of the sensor kinase WalK from Staphylococcus aureus (PDB code: 4YWZ). We then predicted the 3D structure of VicKex (WalKex) by the homology modeling servers RaptorX, GalaxyWeb, and MODELLER through the alignment with YycG and WalK from S. aureus. The models thus obtained were then analyzed to determine the optimum predictive model for molecular docking.
DNA barcodes can be used to delineate Operational Taxonomic Units (OTUs), which are increasingly used to support integrative taxonomy approaches, or in ecology, where they can be used as species proxies to estimate community diversity and describe their spatial distribution  [3] [4] [5]. Different methods based on single-locus data are available to delineate OTUs in DNA barcode data. These methods can be classified into two main families, which differ in the bioinformatics approach used. Some methods such as the General Mixed Yule-Coalescent model (GMYC)  [6] , or the Poisson Tree Process model (PTP)  [7] , use previously constructed phylogenetic trees. Other methods are based on the analysis of distance matrices of nucleotide sequences. This last category includes the Refined Single Linkage Algorithm (RESL)  [8] , Automatic Barcode Gap Discovery (ABGD)  [9]  and Assemble Species by Automatic Partitioning (ASAP)  [10].
They are free of phylogenetic assumptions and allow faster computation times and efficient processing of larger datasets. However, they all rely on a threshold value of genetic divergence that must be established before delimitation. For this, Herbert et al. (2003)  proposed the 'empiric 10x rule', which assumes that the level of genetic variation between individuals of two different species must be at least 10 times greater than the level of intraspecific variation within each species for the gene to be considered an effective barcode. Specifically, this gap between intraspecific and interspecific variation, referred to as the 'barcode gap', is a key feature of the COI barcode. However, this threshold can vary substantially among taxa due to differences in the nucleotide substitution rates, the effects of bottlenecks resulting from strong population reduction  [11]  or because of selective sweeps  [12].
Indeed, the barcode gap for most insects including Lepidoptera lies at approximatively 2%  [13, 14] , whereas 14% are usually observed for earthworms  [3, 15]. For all these reasons, OTU delimitation is a delicate procedure, and the choice of the most appropriate method for a given set of sequences and taxa is therefore critical. For our study we compare five popular methods for delineating single-locus OTUs (i. e. , GMYC, PTP, RESL, ABGD and ASAP) in order to determine their strengths and limitations while processing sets of earthworm DNA barcodes of increasing size. We considered the ease of use of the different software, the computational time required to generate OTUs, the efficiency of the output results, and how well candidate OTUs matched putative species designations obtained through an integrative taxonomy approach.
Datasets We used three sets of sequences of increasing size obtained from earthworm samples from Guyana (Table  1 ). The first dataset (dataset 1)  [15] , contained 651 COI sequences from specimens collected at two localities about 10 km apart: the Inselberg (lat: 4. 0883; long: -52. 6800) and Pararé (lat: 4. 0379; long: -52. 6726) stations of the Nouragues Nature Reserve. The samples in this dataset were originally studied using an integrative taxonomy approach, resulting in 48 putative species. The second dataset (dataset 2)  [16]  contained 2304 sequences including those of dataset 1 plus others obtained from samples collected at four additional localities: the Trinité Nature Reserve (lat: 4. 6024; long: -53. 4132), the Mont Galbao (lat: 3. 6023; long: -53. 2748), the Mont Itoupé (lat: 3.
0231; long: -53. 0953) and the Mitaraka Range (lat: 2. 2340; long: -54. 4503). Study authors used AGBD resulting in 119 OTUs. The third dataset (dataset 3) contained 4773 sequences including the sequences from dataset 2 plus others obtained from samples collected at five additional localities: the Paracou Research Station (lat: 5. 2740; long: -52. 9250), the vicinity of the village of Cacao (lat: 4. 5806; long: -52. 3994), the Kaw Montain (lat: 4. 5688; long: -52. 2161), the Laussat area (lat: 5. 4782; long: -53. 5964) and the Limonade River south of Saul (lat: 3. 5650; long: -53. 2040). These nested datasets allowed us to test each method and dataset by determining mismatch between proposed OTUs and the putative species available for dataset 1  [15].
Number of Haplotypes Number of Putative Species (1)  or OTUs (2) All sequences were obtained from individuals fixed in absolute ethanol. For datasets 1 and 2, DNA extraction, PCR reactions and sequencing of the barcode region of the COI gene were performed at the Centre for Biodiversity Genomics (Guelph, ON, Canada) following standard protocols. A primer cocktail combining the M13-tail primer pairs LCO1490/HCO2198  [17]  and LepF1/LepR1  [18]  was used. Samples that failed after this first run were amplified using the internal primers MLepR1 and MLepF1 and the LCO/HCO pair, respectively  [19]. The remaining sequences were obtained at the Laboratoire d'Ecologie Alpine (Grenoble, France) with two Illumina MiSeq runs. The M13-tail primer pairs LCO1490/HCO2198  [17]  were used with a tag on the 5' side to allow identification of the samples during the bioinformatics analysis of the results, each sample being characterized by its tags on the forward and reverse primers.
The PCR products were sequenced on MiSeq (paired-end sequencing, 2 × 250 bp), and the bioinformatics analysis of the sequences was performed with OBITools (www. grenoble. prabi. fr/trac/OBITools, accessed on 1 June 2015). Approximately 220 bp were obtained for each end of the DNA barcode. All sequences are available in the BOLD dataset DS-EWFG2022 (dx. doi. org/10. 5883/DS-EWFG2022). Each dataset was analysed either by keeping all the sequences (referred to as 'full datasets') or by keeping only a single sequence per haplotype (referred to as 'reduced datasets'). Some methods such as GMYC can only handle reduced datasets. This also allowed us to assess whether OTU delimitation methods perform similar with full or reduced datasets. To obtain reduced datasets, we merged sequences by haplotypes using FaBox software  [20]  and then aligned them on MEGA 11  [21].
For dataset 1, the 651 sequences totalled 242 haplotypes, for dataset 2, the 2304 sequences totalled 862 haplotypes, and for dataset 3 the 4773 sequences totalled 2015 haplotypes (Table  1 ). Both PTP and GMYC require the prior creation of a tree in Newick format. For PTP we used ultrametric trees (UPGMA) obtained using the MEGA software. For GMYC, the ultrametric tree from MEGA did not allow the model to be run correctly. We therefore produced an ultrametric tree by maximum likelihood on RAxML and then applied a molecular clock via chronos function in the R package ape 5. 5  [22]. Moreover, the GMYC model does not allow the analysis of trees with branch lengths of 0, which is the reason why we had to use this method only for datasets reduced to a single sequence per haplotype.
Recently, an extension to multiple rates (bPTP) was designed, allowing for lineage-specific distributions of intra-specific branch lengths  [28]. We used this extension on the PTP web interface (https://species. h-its. org/ptp, accessed on 1 April 2022)  [7]. RESL was developed as a stepwise clustering process that uses single link clustering as a tool for preliminary assignment of records to an OTU (with a seed distance threshold of 2. 2%) and a subsequent finishing step that uses Markov clustering (MCL), a graph analytic approach  [8]. This method is used for the barcode identification numbers (BINs) system, which provides taxonomic numbering for the set of barcodes implemented in the Barcode of Life Data systems (BOLD)  [29]. We used this method, and the range of associated tools, using the 'Cluster Sequences' tool available on the BOLD platform (https://www.
boldsystems. org, accessed on 1 April 2022). ABGD aims to identify a unique barcode gap, i. e. , a threshold of genetic distance that makes it possible to decide whether two individuals belong to the same species  [9]. The model uses a pairwise distance matrix to identify the threshold value that distinguishes between intra-and inter-specific divergence levels. It then compares the sequences in the dataset in pairs and places them in the same OTU if their nucleotide divergence is lower than this threshold. Sets of sequences are created grouping together all sequences with a nucleotide divergence below the threshold. Unlike the approach of Herbert et al. (  2003 )  [2] , on which the PTP and GMYC models are based, ABGD is based on a coalescence model that does not set an arbitrary threshold defining the limit between intra-and inter-specific divergence.
Comparison of Methods In order to compare these methods, we considered four criteria: the computational time needed to delineate OTUs, the prior knowledge needed to use the method, the operationality of the output results and the reliability of these results. The time needed to finalise the analysis of the data is crucial. Indeed, given the everincreasing volume of sequences produced (e. g. , in the case of NGS approaches), processing large datasets is an important consideration for methodological choices. Thus, we reported for each method the computation time required for the analysis of the different datasets, or eventually we noted when a method failed to process a particular dataset due to its size. In order to determine the most efficient method, we arbitrarily determined that a 'reasonable' computation time could not exceed one day.
Prior Knowledge Needed PTP and GMYC require phylogenetic reconstruction beforehand, which is a major constraint compared to methods using distance matrices, which only require a simple fasta file of sequences. For GMYC, several software packages are required to even produce a tree that can be used by the software (RaxML-NG and the ape function chronos). This step can be time consuming, especially when dealing with large sequence sets, and requires some experience in the use of tree building software. GMYC does not require any additional prior knowledge to set up the model, but for PTP it is necessary to first specify if one wishes to work from a rooted tree or not. In the case of a rooted tree, the software launches the search for the maximum likelihood boundary at the root of the input phylogeny, so rooting must be correctly performed to obtain accurate estimates  [7].
In our case, we took the Kimura K80 ts/tv 2. 0, which allowed us to obtain with dataset 1 the same delineation in 48 OTUs as obtained by  Decaëns et al. (2016)   [15]. In addition, for ABGD, the values of Pmin, Pmax and the 'step', which defines a range of values of interspecific divergence must be set. These values go from Pmin to Pmax by proportionally cutting the interval into as many values as steps. These parameters therefore require either a priori knowledge of the value of the barcode gap for the taxon under study, or the making of assumptions. In our case, we have kept the same values as those used in Maggia et al. (2021) (i. e. , Pmin = 0. 05, Pmax = 0. 2, Steps = 30)  [16].
Computational Time The computation time required to obtain OTU delineations by the different software is given in Table  2. In general, the fastest methods are those using distance matrices. The RESL method needs from 15 s for the reduced dataset 1 to 1. 5 min for the full dataset 3 to produce delineations. ABGD and ASAP are also relatively fast, taking only a few seconds to analyse dataset 1 reduced to a few minutes for the full dataset 2. However, dataset 3 takes longer to process, ranging from around 10 min when reduced to one sequence per haplotype to over 1 h when full. Methods using phylogenetic trees are comparatively slower. GMYC already requires 10 min for the upstream creation of an ultrametric tree from dataset 1 and 22 min for the delineation of OTUs. For PTP, the time needed to create the ultrametric tree (from a few seconds for dataset 1 to just under a minute for dataset 2) is negligible compared to the time necessary for the model to produce OTU delineations: from 1 min and 30 s for reduced dataset 1 to more than 40 min for reduced dataset 2.
Output Results Each method provides some visualisation of the results usually in its own format, which constrains the possibilities of interpretation for the user. For the PTP method, the software returns a text file with the distribution of sequences in the different OTUs obtained (Figure  1A ). It also provides a phylogenetic tree with a colour code on the branches to visualise the delimitations (blue and red). Indeed, all sequences after a red branch are gathered in the same OTU. The terminal branch remains blue in the case of singletons (i. e. , OTU represented by a single sequence in the dataset) (Figure  1B ). GMYC only provides a phylogenetic tree with a color-coded system on the terminal branches to visualise OTUs; only singletons appear in black in this tree (Figure  2A ). Note that the graphical quality is not optimized, and it is therefore necessary to return to the input tree, which has the same topology, in order to interpret the groupings of sequences into OTUs.
Note that the graphical quality is not optimized, and it is therefore necessary to return to the input tree, which has the same topology, in order to interpret the groupings of sequences into OTUs. As no text output is provided, it is necessary to manually transfer the OTU assignments into a spreadsheet so that they are available for further analysis. For the RESL method, the 'clustering sequences' tool implemented on BOLD allows the delimitation of OTUs from the sequences of a particular dataset. The software returns a table listing the sequences assigned to OTUs (Figure  3A ). There is no graphical output, but the delimitation can then be completed by a neighbor joining tree that allows direct visualisation of the BINs (Figure  3B ). In the case of ABGD, the software gives a histogram showing the distribution of distance per pair of sequences (Figure  4A ), which thus allows users to efficiently visualise the 'barcode gap'.
ABGD also provides an output showing the results of several delineations (Figure  4B ). One of the input parameters ('steps') is the number of delineations that one wishes to obtain. For each delimitation, the software returns a text file that gives the distribution of sequences within the OTUs (Figure  4C ). The ASAP software offers a similar but improved version of the ABGD output. It first produces a histogram showing the distribution of the intra-and inter-specific genetic distances and allowing the visualisation of the barcode-gap (Figure  5A ). It also produces a summary table of the 10 best partitions with the highest ASAP scores (Figure  5B ). For each of the partition, the number of OTUs is given, as well as the p-value and W-values that determine the ASAP score and the 'threshold distance', which is the limit value of genetic divergence for which two sequences are considered to belong to different OTUs.
For each of the delimitations, a text file gives the distribution of sequences in the different OTUs (Figure  5C ). Finally, the software provides a graphical output that allows the 10 proposed partitions to be visualised on an ultrametric clustering tree (Figure  5D ). of the input tree of the dataset which allows a more efficient visualisation of the identity of the leaves on the GMYC output tree. For the RESL method, the 'clustering sequences' tool implemented on BOLD allows the delimitation of OTUs from the sequences of a particular dataset. The software returns a table listing the sequences assigned to OTUs (Figure  3A ). There is no graphical output, but the delimitation can then be completed by a neighbor joining tree that allows direct visualisation of the BINs (Figure  3B ). In the case of ABGD, the software gives a histogram showing the distribution of distance per pair of sequences (Figure  4A ), which thus allows users to efficiently visualise the 'barcode gap'.
Result Reliability The number of OTUs obtained from the three datasets and with the different methods is shown in Figure  6. With the exception of the PTP method for the reduced dataset 2, the phylogenetic tree methods (GMYC and PTP) failed to successfully process datasets 2 and 3. The distance matrix methods (ABGD and ASAP) produced broadly similar results in terms of OTU number, and also produced a generally lower number of OTUs than the other methods.
Result Reliability The number of OTUs obtained from the three datasets and with the different methods is shown in Figure  6. With the exception of the PTP method for the reduced dataset 2, the phylogenetic tree methods (GMYC and PTP) failed to successfully process datasets 2 and 3. The distance matrix methods (ABGD and ASAP) produced broadly similar results in terms of OTU number, and also produced a generally lower number of OTUs than the other methods. The mismatch rates obtained with the different methods for processing dataset 1 are shown in Figure  7  (for split mismatches) and Figure  8  (for merge mismatches). In general, the results obtained are quite similar whether the dataset is used in its full version or reduced to a single sequence per haplotype. We can also observe that merge mismatches are rather rare (only a few isolated cases with the ASAP and GMYC methods), and that the majority of mismatches correspond to splits.
The tendency of the different methods to separate putative species into several OTUs is also extremely variable: it is null for ABGD, low for ASAP and RESL, but much higher for PTP and GMYC. The mismatch rates obtained with the different methods for processing dataset 1 are shown in Figure  7  (for split mismatches) and Figure  8  (for merge mismatches). In general, the results obtained are quite similar whether the dataset is used in its full version or reduced to a single sequence per haplotype. We can also observe that merge mismatches are rather rare (only a few isolated cases with the ASAP and GMYC methods), and that the majority of mismatches correspond to splits. The tendency of the different methods to separate putative species into several OTUs is also extremely variable: it is null for ABGD, low for ASAP and RESL, but much higher for PTP and GMYC.
Regardless of the delimitation method used, the majority of split mismatches correspond to cases where a putative species is split into two distinct OTUs. However, PTP and generate over-split cases where a putative species can be split into more than three and up to 15 OTUs (in the case of an Ocnerodrilidae species with the PTP method). The PTP method generates the highest number of splits overall (Figure  7 ), with 52. 1% mismatch for the full dataset and 43. 8% mismatch for the reduced dataset. It is noteworthy that almost 50% of these mismatches correspond to splits in three or more OTUs. With the GMYC method, we also find this pattern with 37. 5% of putative species split, 66. 7% of which are in three or more OTUs. We also find one species split into 12 OTUs (Martiodrilus tenkatei) and four merges including 1 OTU grouping together five putative species that are taxonomically well separated from each other (Atatina sp.
TD, Pontoscolex corethrurus, Pontoscolex sp. TD01, Pontoscolex sp. TD02, Wegeneriona sp. TD04). Regardless of the delimitation method used, the majority of split mismatches correspond to cases where a putative species is split into two distinct OTUs. However, PTP and GMYC also generate over-split cases where a putative species can be split into more than three and up to 15 OTUs (in the case of an Ocnerodrilidae species with the PTP method). The PTP method generates the highest number of splits overall (Figure  7 ), with 52. 1% mismatch for the full dataset and 43. 8% mismatch for the reduced dataset. It is noteworthy that almost 50% of these mismatches correspond to splits in three or more OTUs. With the GMYC method, we also find this pattern with 37. 5% of putative species split, 66. 7% of which are in three or more OTUs.
We also find one species split into 12 OTUs (Martiodrilus tenkatei) and four merges including 1 OTU grouping together five putative species that are taxonomically well separated from each other (Atatina sp. TD, Pontoscolex corethrurus, Pontoscolex sp. TD01, Pontoscolex sp. TD02, Wegeneriona sp. TD04). For ASAP and RESL the number of mismatches generated by the processing of the reduced dataset is equal to or lower than that obtained with the full dataset. However, the identity of the putative species affected by these mismatches remains the same whichever dataset considered. On the other hand, a certain number of putative species are split after analysis of the complete dataset, whereas this is not the case with the reduced dataset. This is especially true for the RESL and ASAP models. With PTP, a higher proportion of mismatches is also observed when analysing the full dataset, but some putative species are also split after analysis of the reduced dataset whereas they are not with the full dataset (e.
g. , for Pontoscolex corethrurus and HaplotaxidaGEN sp. TD02). Finally, there are also differences in the magnitude of the mismatches, i. e. , the number of OTUs resulting from the split of a given putative species. For example, the putative species OcnerodrilidaeGEN_sp. TD01 is split into 4 OTUs by the PTP method applied to the reduced dataset, whereas it is split into 15 OTUs with the full dataset. In general, the analysis of dataset 1 shows ABGD and ASAP to be the two most reliable methods when it comes to analyzing less than one thousand sequences. In a second step, we compared the reliability of the results obtained by these two methods with datasets 2 and 3 which contain a larger number of sequences (Figures  9  and 10 ). With dataset 2, no split is observed using the ABGD method, and a few merge mismatches appear with the full dataset, whereas with ASAP a single split is observed for the putative species Neogaster sp.