dataesr/scientific-paragraphs-categorization huggingface dataset:
gipuzkoa. eus), were combined to obtain four output datasets. The comparison with observed data indicated the best input data setup for building the QSWATLitho project. These results are shown and discussed in section 3. 1. 1 Model's sensitivity to input data. Before applying the SWATLitho module, it is necessary to calibrate the water balance. The model setup and the calibration of hydrology are discussed in section 3. 1. 2 Case study. Once the model was built and hydrology was calibrated, a simulation was conducted (period: January 2014 to December 2017). The application of the SWATLitho module allowed the evaluation of the model's spatial and temporal performance. No calibration of the ICWR parameters has been done, and globally fitted values were used(Lechuga-Crespo et al. , 2020a). The model's spatial downscaling evaluation was done by contrasting simulated geochemical loadings at the subbasin outlet with observed loadings at the corresponding sampling location (see Figure3a).
Software availability • The Soil and Water Assessment Tool (SWAT) is freely available in https://swat. tamu. edu/software/ • The QGIS plugin for SWAT (QSWAT) is freely available in https://swat. tamu. edu/software/qswat/ • The modified QGIS plugin that is presented in this study (QSWATLitho) as well as the SWATLitho module written in Python are freely available in https://swat. tamu. edu/software/swat-litho/.
There are two groups among the Penelope elements, which could be considered as two superfamilies  [11, 12] , and three superfamilies have been characterised to date within LTR-retrotransposons  [13]. Depending on the publication considered, few superfamilies of YR-retrotransposons are also distinguished  [10, 14] , and about twenty for both DNA transposons and LINEs  [15] [16] [17] ). These subdivisions can be found, more or less common, in the widely used transposable element libraries (e. g. RepeatMasker  [18] ) or databases (e. g. Repbase  [19] ). In addition to the autonomous elements that encode all the machinery necessary for transposition, non-coding elements can exist that can still be able to transpose by hijacking this machinery, such as MITEs for DNA transposons, SINEs for LINEs, or LARDs and TRIMs for LTR-retrotransposons.
The four clades Flow, Tas, Suzu and Sinbad have been observed in diverse phyla and it can thus be assumed that they are widely distributed in metazoans. By adding mollusk elements, the formerly recognized Sinbad clade was further divided into three distinct clades, which can be grouped together in the Sailor lineage  [27]. The two new Bel/Pao clades, Sparrow and Surcouf, contain so far only elements from zebrafish and mollusks. The Gypsy superfamily is clearly the most diverse one with 34 clades listed in the Gypsy-Database  [31] , half of which observed in metazoans. In addition to the clades in this database two more clades have been reported in decapods and 16 in mollusks  [24, 27]. While most clades have a fairly limited distribution (insects, vertebrates, nematodes or tunicates), five clades are present in several phyla: Gmr1, CsRN1 and the three A-clade, B-clade, C-clade that formed the most widespread Mag lineage.
In Lamellibrachia luymesi  [35] , the opposite is observed; DNA transposons are as frequent (42%) as the LINEs (40%), whereas the LTR-retrotransposons are rare (7%), with 182 intact copies, of which a large majority were Gypsy (178) and two Copia and two Bel/ Pao (2)  [36]. Studies involving several types of TEs are most often carried out either on one or few complete assembled genomes, or in particular phyla for which many genomes are available (such as insects or vertebrates). However, for the majority of phyla, the number of partial or complete genomes remains very limited, often too limited to give full access to the diversity and distribution of TEs between species. The lack of genomic resources can present challenges for studying TEs in non-model organisms. Fortunately, low-coverage NGS sequencing analyses using assembly-free software such as DNApi-peTE  [37]  or RepARK  [38] , allow the detection and estimation of the proportion of TEs at reasonable cost.
As genomic data for polychaetes are currently very scarce, we carried out low-coverage genomic sequencing for 14 polychaetes in addition to 26 transcriptomes. Our goal is to perform comparative genomics and transcriptomics of TEs within divergent groups of annelids with good coverage. For this purpose, we targeted two very distinct groups of polychaetes: Canalipalpata and Aciculata (=Errantia). For the former, our dataset includes three families: Ampharetidae, Terebellidae, and Alvinellidae. For the latter, our sampling encompasses diverse lineages of Polynoidae, a family that comprises over 900 species. Thus, we took advantage of these original data to carry out the first wide-scale comparative analysis of TEs in annelids. Our analyses show that the composition and abundance of TEs are highly variable in polychaete genomes but LTR elements dominate in most species.
Transposable elements in annelid transcriptomes Transcriptome analysis based on RNAseq datasets represents another tool to describe the diversity of active TEs without the need for reference genomes. But, as differences in expression levels may impact the coverage of any given sequence over several orders of magnitude, it remains unclear what is the ideal sequencing depth for our purpose. In order to study the influence of the sequencing effort on TEs detection, we computationally sub-sampled the original libraries into several sub-libraries, at regular increments of reads from 20 to 140 million, for which we compare trends from de novo assemblies using Trinity across 15 different annelid RNAseq (Fig. 2 ). The number of transcripts increases steadily with sequencing effort for all species (Fig.
fuligineum (24%). To compare the relative proportions for the different types of elements obtained for the two datasets (Fig. 6  and Additional file 6), we have considered weak (> 5%), moderate (ranging from 10 to 15%) and high variations (> 15%). For 9 annelids only limited differences are observed, restricted to weak variations that concern only one to three types of elements per species. Lepidonotopodium fimbriatum (Lepfim) shows slightly more differences with 10. 7% more transcriptomic DIRS sequences. P. unidentata (Punid) also appears a bit unusual because it exhibits a strong excess of genomic L2 sequences (difference of 15%). For the remaining four species (H. extenuata (Harex), H. fuligineum (Harful), P. furcosetosa (Pefu) and L. clava (Lepcla)), we observed high variations between the two methods linked to an excess of Gypsy in the low-coverage genomic data, and moderate excess of Crack and, to a lesser extent, of DIRS in the transcriptomes.
The other clades of Copia elements described in arthropods, and more specifically in insects (Copia, Tricopia, Mtanga, 1731, Humnum, as inferred in the GypsyDatabase) were not found here, as it was the case with the mollusks. The most striking result remains the fact that the CoMol clade, previously defined from only 4 mollusk elements, is the most represented in annelids, detected in half of the species. It could therefore also be present in other taxa that have yet to be identified, and thus constitute one of the major clades of Copia in metazoans. The GalEa clade, known to be the most widely distributed within metazoans, has also been found in most annelids. Finally, the presence of Hydra elements in several annelids supports the  ), but this grouping is not monophyletic using the RT/ RNaseH.
There are also in this Sailor group about ten isolated sequences, which cannot be attached to any clade. The tree obtained for the Gypsy superfamily reveals 17 clades in annelids (Fig. 9 ). A simplified representation of the Gypsy diversity is available and shows annelid elements of the same clade as compressed subtrees (Additional file 9). This better differentiates the reference Gypsy elements and therefore allows to determine whether a clade has been previously reported or not  [27]. Among the clades referenced in the GypsyDatabase only five are well recovered (clades A and B (again indistinguishable from each other), C-clade, Cigr and CsRN1). Concerning the MolGy clades previously defined solely from mollusk elements, 14 of the 16 clades are also present in annelids; while the MolGy7 and MolGy10 are missing.
We therefore compared the data obtained on TEs identified on 15 distinct annelid transcriptomes with an increasing sequencing effort: (i) The proportions of the different TE superfamilies show no clear influence of the number of reads. The only noteworthy point is the slightly more marked differences between the 20 million-read assemblage and the others. (ii) The superfamilies poorly represented in some species (between 0 and 3 elements depending on the assembly considered) were also checked (BLASTX on Repbase). Of a total of 258 sequences only 16 did not correspond to the predicted superfamily. These sequences were mostly found in transcriptomes obtained with 100 million or more reads. On the other hand, with only 20 million reads these rare superfamilies were no longer detected in one third of the cases (species/TE superfamilies association).
(iii) The third analysis deals with the increase of the number of elements, and thus the nature of the new sequences detected when the number of reads increases. To address this issue, the 152 Copia sequences obtained with 40 million reads (assembly-40) were compared with the 217 sequences obtained with the maximum number of reads (assembly-Max, i. e. 140 million for 13 species and 100 or 120 million for six others). Only 103 sequences are common between these two datasets, of which ten are separated into two fragments in assembly-40. Curiously, in both cases we found nearly 20% of specific sequences (25 and 41 sequences respectively), as well as several other unshared sequences (6 and 24, respectively) but too short (< 400 bp) to know whether or not they are isolated fragments of an element already described.
This number is in agreement with those suggested to study gene expression. Indeed, a comparative transcriptomic study across 10 invertebrates was used to generate a tractable catalog of annotated genes  [41]. Through a saturation analysis the authors concluded that the sequencing efforts (16. 4 to 51. 7 million with a mean of 39. 5) were sufficient to accurately estimate the completeness of their transcriptome datasets. A second study looked for an optimal sequencing depth for de novo transcriptome assembly in order to gather information about genes and their expression  [42]. Using mainly marine organisms, including the polynoid Harmothoe imbricata, they concluded that representative assemblies may be generated with as few as 20 million reads or 30 million reads for RNA-level coverage. On the other hand, it cannot be taken for granted that the largest set of reads will produce the best contigs; and using conserved genes as a metric, there appears to be limited benefit of sequencing beyond 60 million reads as the discovery of new genes is low and sequencing errors of highly-expressed genes are likely to accumulate.
5 for Copia and BEL/Pao, and by a factor of 4 for Gypsy. Several factors may interact to explain such difference; i) there are indeed more TE families in annelids, because annelids have for example larger genomes  [56] , ii) the number of TE families may be slightly overestimated if the transcript of an element is broken into several fragments, and iii) the number of studied species is different. Here we analyzed the entire set of transcribed sequences larger than 150 bp in 26 annelid transcriptomes compared to our previous study on only 9 molluscan genomes in which only the complete, and therefore probably recent, copies of TEs have been taken into account. Elements from 46 additional species of mollusks were however obtained from the analysis of the databases, but for these we had no information on the nature of the data made available (number of transcripts deposited in the various databases), and again only families identifiable by an intact RT/RNaseH were taken into account.
While it was difficult to extrapolate the existence of these two new clades outside mollusks, their presence in annelids confirms that they are not limited to a single phylum. On the contrary, the characterization of the Sinbad clade appears more complex. Either we can consider that this Sinbad clade is indeed maintained but that the dataset here is not sufficient to support it. Or it is possible that this clade, already poorly supported in mollusks, is artifactual and sometimes gathers various isolated elements (11 Sailor elements of annelids could not be attached to any clade). In the latter case, the historic name Sinbad could be used for the entire lineage instead of Sailor. When studying mollusks, only 6 of the Gypsy clades referenced in the GypsyDatabase  [31]  were found and no less than 16 new potential clades MolGy were characterized.
Dry DNA pellets were suspended in DNAse-free water and the remaining contaminants were eliminated by adding 1% of PVPP. RNA-seq and DNA-seq libraries were produced at Genome Québec and sequenced accordingly on a HiSeq 2000 to obtain 150 bp paired-end reads using Illumina TruSeq kit for paired-end reads  [58] , following mRNA stranded purification and Covaris fragmentation, respectively. According to the species, the Illumina RNAseq sequencing effort varied from a quarter of a lane (40 million reads) to one full lane (160 million reads). Raw genomic data have been deposited in NCBI and are available under the Bioproject PRJNA766809. For 15 species, random subsets of paired-end fastq reads were produced by down sampling a fraction of about 20, 40, 60, 80, 100, 120 and 140 millions of reads (seqtk sample -s100 readlibrary1.
fq. gz 0. 1 > subset1. fq). Transcriptomes were de novo assembled with the Trinity 2. 8. 4 software (https:// github. com/ trini tyrna seq/ trini tyrna seq/ relea ses/ tag/ Trini ty-v2. 8. 4) where raw reads were subjected to a screening process using the trimming and normalization options following the bioinformatic parameters: Trinity --seqType fq --left X --right X --trimmomatic --quality_trimming_params "ILLUMINACLIP:illumina. fa:2:30:10 LEADING:5 TRAILING:5 SLIDINGWINDOW:4:15 MINLEN:36" --normalize_reads --max_memory 50G --CPU 8 --output trinity_dir_X.
Detection of TE sequences in genomes Estimation of the abundance and the respective proportion of each LTR-retrotransposon family using reads were carried out using the DnaPipeTE software with default parameters  [37]. TE abundance have been calculated as the read fraction corresponding to 1X genome coverage that align against each TE consensuses. For each species, reads that map on the corresponding mitochondrial genomes using the BWA software  [61]  were first discarded. DnaPipeTE were run on read subsamples ranging between coverage of 0. 01x and 0. 5x in intervals of 0. 05x (11 runs). For each of the 11 runs per species, we selected the subsample yielding the highest contig N50 in the assembly step of dnaPipeTE, as a measure of optimized read subsampling. TE families were annotated using BLAST against RepBase 10/10/2017 version  [62].
Detection of TE sequences in transcriptomes A python script was written to optimize the detection of TEs in the newly assembled transcriptomes, which includes 5 major steps (http:// gofile. me/ 2ppPR/ sY5fR oTUA): (1) Transcripts that possess a putative TE sequence were detected by BLASTX similarity-search on a custom database, LAC28, based on the 18,011 amino-acid sequences of the Repeatpeps library (Nov 2018, http:// repea tmask er. org/ libra ries/) appended with published RT/RnaseH sequences from mollusk BEL/Pao elements and from various GalEa retrotransposons  [21, 27]. As the library contains only a few elements from annelids, we also enriched our database with 410 annelid sequences newly identified. For this purpose, we manage to represent all types of TEs with between 3 to 19 sequences mainly coming from six species (three Alvinellidae and three Polynoidae, see Additional file 13 for details and Additional file 14 for amino acid sequences).
These sequences, previously detected by BLASTX searches, were manually checked to fully correspond to TEs by comparison using CENSOR on Repbase (https:// www. girin st. org/). Moreover, in a preliminary test, we also used CENSOR to confirm 5873 putative TE sequences revealed by the first version of our database (between 22 to 286 sequences for each TE type). This allows us to discard from our LAC28 database sequences giving unreliable results; so that thereafter more than 95% of the sequences revealed by BLASTX searches actually correspond to an element of the expected type. (2) BLASTX results were filtered to eliminate a maximum of false positives. Indeed, according to the earlier experience, analyses of 200 putative TE sequences reveal that most hits which have an identity match < 25%, an alignment length < 50, a number of gap > 15, or an e-value > e -22 do not correspond in fact to identifiable TEs.
(3) Remaining transcripts were then checked by crossmatching using a tBLASTn search of TE sequences of LAC28 on a database corresponding to selected transcripts that possess a putative TE sequence. Transcripts that were not recovered or that did not correspond to the same element type in both BLASTX and tBLASTn searches were discarded. (4) All transcripts that passed the filters and possess a TE sequence were grouped into different fasta files according to the type of element. Here we use the term superfamily/type since none of these terms is universally accepted. Overall, we considered 40 types of elements in annelids (20 DNA transposons including Helitron elements, 14 LINEs, Penelope elements, and 5 retrotransposons), which correspond to the usual groups defined in RepeatMasker and Repbase libraries (see Additional file 15 for details).
Cladistic analyses Phylogenetic analyses were performed as in  [21]  on amino acid sequences corresponding to the RT/RNaseH or Integrase domains of the newly characterised sequences, reference elements from Repbase or Gypsy-Database, and previously identified Copia and BEL/Pao retrotransposons. Boundaries of RT/RNaseH domains have been determined by BLASTX searches according to those defined for RT 5′ part and RNaseH 3′ part of Copia, BEL/Pao and Gypsy multiple alignments defined in the GypsyDatabase. DNA sequences were translated using a custom-made script and the longest representative of each family was selected. Multiple alignments of protein sequences were performed using MAFFT  [63]  and are freely available at http:// gofile. me/ 2ppPR/ 1XcV5 4vF2. After a manual curation of the alignments, phylogenetic analyses were conducted using Neighbor Joining  [64]  and the pairwise deletion option of the MEGA5.
Detection of Argonaute sequences in transcriptomes Argonaut protein sequences were searched in the transcriptomes by BLASTX (e-value < e -70 ) using as query 10 reference sequences from Capitella teleta (Piwi1_Ct ELT87139, Piwi2_Ct ELU02261), Drosophila melanogaster (AUB_Dm AGA18939, Piwi_Dm AAD08705, AGO1_Dm NP_725341. 1, AGO2_Dm NP_648775, AGO3_Dm ABO27430), Trypanosoma brucei (Piwi-like_Tb AAR10811, argonaute-like1_Tb AAR10810, and Arabidopsis thaliana AGO_AT CAA0278680 (some of them also include in the phylogeny). Sequences were then translated and, if long enough, the largest of each transcript cluster was included in a phylogenic tree following the method previously used for TEs and including also the reference proteins Piwi1_Av KM406471, Piwi2_Av KM406472, AUB_Bm NP_001098066, AGO3_Bm NP_001098067, Piwi1_Dr NP_899181, Piwi2_Dr ACF35261, AGO1_Mm NP_700452, Piwi1_ Mm NP_067286, Piwi2_Mm NP_067283, Piwi1_Nv XP_001641994.
We present LEVIATHAN, a new structural variant calling tool that aims to address these issues, and especially better scale and apply to a wide variety of organisms. Our method relies on a barcode index, that allows to quickly compare the similarity of all possible pairs of regions in terms of amount of common barcodes. Region pairs sharing a sufficient number of barcodes are then considered as potential structural variants, and complementary, classical short reads methods are applied to further refine the breakpoint coordinates. Our experiments on simulated data underline that our method compares well to the state-of-the-art, both in terms of recall and precision, and also in terms of resource consumption. Moreover, LEVIATHAN was successfully applied to a real dataset from a non-model organism, while all other tools either failed to run or required unreasonable amounts of resources.
However, these methods do not make use of efficient barcode indexing strategies. As a result, they either need to store the barcodes of each region, which can be extremely memory consuming, or extract the barcodes from the same region multiple times, which can be highly time consuming. Moreover, all of the aforementioned methods were mainly designed for human data, and especially, all of them were tested exclusively on human datasets in their respective publications. As a result, this focus on human data, and the lack of indexing strategies lead to scalability issues and to a poor applicability to non-model organisms, for which reference genomes are highly fragmented or sequencing data display high levels of heterozygosity. For example, Long Ranger displays an error message and cannot run on reference genomes composed of more than 1,000 contigs, while tools such as LinkedSV and VALOR2 can require up to more than 1 TB of RAM, and others such as GROC-SVs and NAIBR sometimes undergo an indefinite sleep after running for a few days.
Using default parameters, our method can detect large SVs of at least 1,000 bp, including deletions, duplications, inversions and translocations. However, it has no support for novel insertions yet. Our experiments on simulated data show that it compares well to the state-of-the-art in terms of recall and precision, and also in terms on resource consumption. Moreover, experiments on real data also show that it manages to run on non-model organisms on which other tools either fail to run or require unreasonable amounts of resources. LEVIATHAN thus allows to analyze a wider range of datasets than the state-of-the-art, and opens doors to broader analysis of SVs in a large variety of organisms.
Index construction The index construction step relies on LRez  [15] , a tool and library designed to process Linked-Reads barcodes, which, among other functionalities, provides indexing features. LEVIATHAN thus uses LRez to build an index containing the occurrence positions of each barcode in the BAM file. This index is stored as a map, associating each barcode (in binary representation, 2 bits per nucleotide) to its list of occurrence positions in the reference genome, in format chromosome:position.
Computing the number of common barcodes between region pairs Once the barcode index is built, the reference genome is divided in non-overlapping regions of size L (L = 1, 000 by default, although this can be user-defined). As a result, LEVIATHAN only considers SVs whose breakpoints are located more than L bp apart on the reference genome. Iterating through the index then allows to easily identify region pairs that share at least one common barcode, as well as the exact number of common barcodes between such region pairs. This information is stored in a map, where the key is a region pair, and where the value is the number of common barcodes these regions share. This indexing and querying strategy allows to avoid the explicit comparison between every possible region pairs, and thus allows a massive speed-up.