dataesr/scientific-paragraphs-categorization huggingface dataset:
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Inpactor was tested on the classification and annotation of LTR retrotransposons in pineapple, a recently-sequenced genome. The pineapple genome assembly comprises 44% of transposable elements, of which 23% were classified as LTR retrotransposons. Exceptionally, 16. 4% of the pineapple genome assembly corresponded to only one lineage of the Gypsy superfamily: Del, suggesting that this particular lineage has undergone a significant increase in its copy numbers. As demonstrated for the pineapple genome, Inpactor provides comprehensive data of LTR retrotransposons' classification and dynamics, allowing a fine understanding of their contribution to genome structure and evolution. Inpactor is available at https://github. com/simonorozcoarias/ Inpactor.
Implementation of Inpactor Inpactor is composed of four modules (Figure  1 ) and was developed using the Message Passing Interface (MPI) standard, in C language. It requires input parameters to be declared in a configuration file (Supplementary S1), where it is possible to define general information such as input file types (LTR_STRUC  [41]  output, REPET's TEdenovo output in FASTA format or a genome FASTA file), result directory, verbose mode and clean mode at the end of the execution. In addition, each module requires that different parameters be indicated in the configuration file. Step 1, preprocessing module. The objective is to sort information and features from the LTR_STRUC output or the FASTA files submitted to Inpactor, such as the full element sequence, LTR identity, length and sequences using tools from EMBOSS  [42]  and BLASTX results against six references domains (GAG, RT, INT, RNAse H, AP and ENV) available at the Gypsy Database Project  [15].
In addition, Inpactor creates an extra text file, which contains all LTR-RTs that are unclassified and thus named "no-class elements". Finally, the complete sequence of each classified and unclassified LTR retrotransposon (Figure  1 ) is extracted. A re-classification is performed following the 80-80-80 rule from Wicker et al. [8]  for unclassified elements (Step 2, classification module parameters). Unclassified LTR-RT elements are re-analyzed with previously classified elements by similarity using Censor  [43]. If the alignment covers a minimum of 80% of the unclassified element, with a minimum of 80% of nucleotide identity, and a minimum of 80 bases aligned (the 80/80/80 rule), the unclassified element is re-classified into the reference element  [8]. Step 3, domain extraction module. Reverse Transcriptase (RT) domain sequences are extracted from each autonomous-family element, because this domain is the most conserved and appropriate for phylogenetic analysis (Figure  1 ).
Other domains from the LTR-RT polyprotein might be used alternatively. BLASTX is executed using the FASTA file of all autonomous-family elements as the query and the reference RT domain database (the Gypsy Database Project  [15] ). Sequences that match with the database are extracted (extractseq, EMBOSS), and the domain is translated into amino acids using Genewise  [44]  with the option -pep. Only translated sequences larger than 200 amino acids are conserved for further analysis. Step 4, LTR-RT insertion times' analysis and phylogenetic analysis. Using the FASTA protein file from the RT domain extraction module, a multiple alignment is performed using Mafft  [45]  with the option -thread to indicate the number of cores. Then, a phylogenetic tree is created based on the maximum likelihood method with Mafft using -retree and -treeout options with the multiple alignment obtained previously (Figure  1 ).
The insertion times of full-length copies, as defined by a minimum of 80% of nucleotide identity over 100% of the reference element length, are dated  [19]. Timing of insertion is based on the divergence of the 5 and 3 -LTR sequences of each copy. The two LTRs are aligned using Stretcher (EMBOSS) and the divergence calculated using the Kimura 2-parameter method implemented in Distmat (EMBOSS)  [46]. The insertion dates are estimated using an average base substitution rate of 1. 3 × 10 -8 as the default parameter  [47]. This default parameter can be changed in the configuration file. Inpactor produces different types of files: a sequence file (FASTA), a global alignment matrix, a phylogenetic tree and tabular files with the insertion time analysis. In addition, the Preprocessing module produces one tabular file, which contains all information from the LTR_STRUC output; the Classification module creates one tabular file and one FASTA file for each LTR-RT type found, including the unclassified.
Finally, the domain extraction section generates only one FASTA file with all of the domains found. Inpactor requires using external bioinformatics software to perform specific functions such as sequences extraction and translation: NCBI-Blast (v. 2. 5. 0, https://www. ncbi. nlm. nih. gov/BLAST/), EMBOSS (v. 6. 6. 0, http://emboss. sourceforge. net), Wise 2 (v. 2. 4. 0, http://www. ebi. ac. uk/~birney/ wise2/), OpenMPI (v. 1. 8. 8, https://www. open-mpi. org/), Censor (v. 4. 2. 29, http://www. girinst. org/downloads/software/censor/), Mafft (v. 7. 305, http://mafft. cbrc. jp/alignment/software/) and LTR_FINDER (v. 1. 0. 5, https://code. google. com/archive/p/ltr-finder/).
Availability of Inpactor Inpactor's source code can be found at https://github. com/simonorozcoarias/Inpactor, under the GNU GPLv3 license and is composed of one source code in C language, two bash scripts and an example of the configuration file. All of these need to be in the same folder. Installation instructions and a user manual are available at https://github. com/simonorozcoarias/Inpactor/blob/master/ User%20manual%20Inpactor%20V%201. 0%20final. pdf. Sample data and results are also available.
Sequence Data Sources Inpactor was tested using five plant genomes with different genome sizes. Arabidopsis thaliana (117 Mb, http://plants. ensembl. org/Arabidopsis_thaliana/Info/Index) and maize (Zea mays, 2048 Mb; http://plants. ensembl. org/Zea_mays/Info/Index) were downloaded from the Ensembl genomes project  [49] ; rice (Oryza sativa, 362 Mb; http://ensembl. gramene. org/Oryza_sativa/Info/Index) was downloaded from the Gramene Project  [50] ; and Robusta coffee (Coffea canephora, 553 Mb; http://coffeegenome. org/) was downloaded from Coffee Genome Hub Project  [51]. The pineapple genome sequence (variety "F153") was generated from a combination of Illumina, Moleculo, PacBio, and 9400 Bacterial Artificial Chromosomes (BACs) and released  [40]. The genome sequences were deposited at the iPlant CoGe database, and they can be downloaded at https://genomevolution.
Identification of Repeated Elements REPET (TEdenovo package V. 2. 2-RC)  [52]  was used to find and classify repeated sequences in the pineapple genome sequences. In total, 3380 scaffolds accounting for 382,063,720 bp were processed. Consensus sequences obtained in REPET were annotated according to the REPBASE database (v. 19. 6, http://www. girinst. org/repbase/). They were named according to the acronym classification developed by Wicker and coworkers  [8]  (i. e. , DHX (Helitron), DMX (Maverick), DTX (TIR Transposon), DXX (MITE) for Class II elements and RIX (LINE), RLX (LTR retrotransposon), RSX (SINE), RXX (unclassified or non-autonomous retrotransposons), RYX (DIRS) for Class I elements). Consensus sequences were classified as chimeric if they showed characteristics of more than one classification, representing potential nested elements.
Annotation, Phylogenetic Analysis and Insertion Time Analysis of LTR Retrotransposons Consensus sequences from REPET that were identified, as "complete" (autonomous) or "incomplete" (non-autonomous) LTR retrotransposons were further classified into lineages and families using Inpactor. At the genome level, putative RT domains were identified using BLASTX  [53] , with an e-value cut-off of 1 × 10 -4 , and translated into amino acid sequences using Genewise  [44]. The resulting RT sequences (with a minimum length of 150 residues) and reference RT domains from the Gypsy Database 2. 0 were aligned, and a maximum likelihood tree was inferred and edited with Figtree (http://tree. bio. ed. ac. uk/software/figtree/). Insertion time analysis of LTR retrotransposons was performed as in Dupeyron et al.
Testing Inpactor on Reference Plant Genomes Genome annotation studies require the annotation and detailed classification of transposable elements, and more particularly LTR retrotransposons, representing the main part of plant genomes. Classification into main classes and lineages, insertion time analysis and phylogenetic analysis  [55, 56]  constitute basic information for understanding the impact, dynamics and evolution of LTR retrotransposons. Inpactor has been developed to combine automatic annotation, classification, insertion time and phylogenetic analyses into a limited time process, taking advantage of supercomputers. We first tested Inpactor using several numbers of cores  (1, 4, 8, 16 and 32) , with 10 repetitions for each experiment in order to calculate the speed-up and average run time per module (Table  1  and Supplementary S2).
Four different plant genomes (Arabidopsis thaliana, Oryza sativa, Coffea canephora and Zea mays) were used. Only the 32 cores' outputs were used for classifying predicted LTR-RTs into autonomous elements (Gypsy and Copia super-families and lineages) and for filtering putative non-autonomous element types (LARD, TRIM and TR_GAG; Figure  2  and Supplementary S3). Autonomous elements were sub-classified into lineages, and a phylogenetic tree per genome was constructed using the output files (Figure  2 ). Inpactor provided the insertion time analyses, indicating the insertion activity of LTR-RT elements over recent periods of time (Figure  2  and Supplementary S4). Executions were performed using one server (Supplementary S5-S8), with the 80-80-80 rule option disabled. Each Inpactor module was executed independently in the correct order (i.
e. , preprocessing, classification, domain extraction, insertion time and phylogenetic tree creation) to calculate the runtime of each module. The total runtime is the sum of each runtime module. Finally, Inpactor was run on the pineapple genome sequence similarly to the four reference genomes used in order to study its LTR retrotransposons diversity (Figure  3  and Supplementary S9). Inpactor can use different input files such as the LTR_STRUC output files and any FASTA files from other predictors of full-length elements LTR retrotransposons. LTR_STRUC is a relatively slow algorithm (running under a Windows-XP PC), compared to more recent prediction software  [57] , but it seems to offer a low percentage of false positives in plant genomes and an overall low number of putative elements. In the future, Inpactor will integrate more recent software used to predict LTR retrotransposons, such as LTRharvest  [57] , LTR-FINDER  [58] , and LTR_retriever  [59].
Additionally, we will also include Hidden Markov Models (HMM) to perform a more sensitive annotation of protein domains. Inpactor uses a Shell script gluing together other programs to construct analysis. To speed up the overall analysis, Inpactor will be implemented as a single C binary. As expected, Inpactor's results showed a different composition of LTR retrotransposon lineages in the reference genomes based on the detection of LTR_STRUC's full-length elements. Our results illustrate considerable variation in the classification of elements, despite that the quantitative detection of elements may be biased by the quality of the genome sequence and assembly. Insertion time and phylogenetic tree modules also provided evidence of different insertional activity of LTR retrotransposons during similar periods of time.
Using Inpactor on the Pineapple Genome To use Inpactor on the pineapple genome, we first identified repeated sequences with the REPET TEdenovo package. After clustering and cleaning, 2860 consensus sequences were obtained from the genomic scaffolds and classified according to their structural features and similarities with the REPBASE protein database  [60]. As a result, 75% of them were classified into Class I elements (retrotransposons) and 11% into Class II (DNA transposons), following the hierarchical classification proposed by Wicker and coworkers  [8]  (Supplementary S10). The remaining 14% of repeats were not identified at this step (Figure  4 ). Furthermore, 1402 LTR retrotransposon consensus sequences (RLX) were identified via TEdenovo, but 1148 sequences were classified as incomplete elements due to missing structural features detected by REPET  [52].
In such a map, the diversification events of the parasites are linked to their host phylogenetic history and four types of events are considered: cospeciation events, host switches, sorting events and duplication events  (Page 1994a; Page 1994b ) (see material and method for a description of each event). When graphically displayed, reconciliation maps greatly ease our understanding of the evolution of biotic interactions. Algorithms to optimize reconciliations are numerous. One of the first reconciliation software, TreeMap 2, uses an algorithm called "Jungles"  (Charleston 1998)  where each event is assigned a cost: the chosen reconciliations are the ones that have minimum costs. However it generates in the process an exponential number of scenarios. Recent methods have proposed algorithms that are more efficient and can also just search for an optimal reconciliation: e.
g. Tarzan,  (Merkle & Middendorf, 2005) ; Jane,  (Conow et al. 2010)  ; Core-PA  (Merkle et al. , 2010) , Mowgli,  (Doyon et al. 2010) , COALA  (Baudet et al. 2015) , ecceTERA  (Jacox et al. 2016) , Notung  (Stolzer et al. 2012) , EUCALYPT  (Donati et al. 2015)  and  ILPEACE (van Iersel et al. 2014). Recently, the RASCAL software proposed to infer suboptimal scenarios to reduce computing times  (Drinkwater & Charleston 2016). Cospeciation is witnessed on a reconciliation map whenever a speciation node in the parasite phylogeny is mapped onto a speciation on the host phylogeny. Another requirement for demonstrating that two interacting lineages have cospeciated is to provide evidence of the temporal congruence of the cospeciation event in the host and parasite phylogenies  (Page 1991). Though reconciliation algorithms do not strictly enforce the simultaneity of cospeciation events, they can enforce time consistency in the sequence of.
2010). We also updated the SylvX reconciliation viewer  (Chevenet et al. 2016) , in order to integrate and visualize annotations (e. g. geographic areas) at ancestral nodes for the host and parasite phylogenies and highlight inconsistent zones in the reconciliation. We then test these new developments on a mock dataset and on a 'textbook' example of cospeciation, namely the interaction between figs (Ficus) and their pollinating fig wasps  (Cruaud et al. 2012; Rønsted et al. 2005; Wiebes 1979 ).
SylvX takes a host tree in Newick format with node id numbers and a reconciliation (with symbiont tree node id. ). The host tree (outputSpeciesTree. mpr) and reconciliation obtained with Mowgli can be directly imported into SylvX. The latter also supports input files from other reconciliation. CC-BY-NC 4. 0 International license a certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under The copyright holder for this preprint (which was not this version posted January 16, 2018. ; https://doi. org/10. 1101/166215 doi: bioRxiv preprint software, e. g. , ecceTERA  (Jacox et al. 2016). Annotation files for the host and parasite phylogenies giving node information can be imported in a CSV format. As many annotations as needed can be added in the annotation files and it is up to the user to choose which ones to plot onto the species tree and the reconciliation map through SylvX's interface.
In order to seamlessly pass a user annotation file in csv format into both Mowgli and SylvX, we provide a Perl script that can be run through the command line in order to: 1) obtain tree node identifiers that will be used by both programs and 2) merge input trees and corresponding annotations files into Mowgli's input format. Files can be generated so that a single (most likely) ancestral range can be specified or alternative geographic areas can be assigned to all nodes (see Supplementary Material 1 for a description of the full procedure to generate files, set a threshold value above which to keep alternative areas and perform a complete analysis).
Datasets To demonstrate the method and its utility, we tested it on two datasets. We first created a mock dataset: two phylogenetic trees with nine tips for a hypothetical host/parasite interaction in which extant and ancient geographic areas for each lineage are informed. The dataset was generated by hand so that: 1) present-day geographic areas of associated taxa are consistent (i. e. , hosts and associated parasites live in the same area); 2) the two phylogenies are not perfectly parallel but show some cospeciation events; 3) some geographic locations at nodes that we would like to cospeciate do not coincide in the parasite and host phylogenies. We ran Mowgli on this dataset successively with and without enforcing geographic constraints using in both cases the default parameters (cost 0 for a cospeciation, 1 for a loss and 1 for a host switch, 1 for duplication, not enforcing the root of the parasite tree to be mapped on the root of the species tree).
In order to measure the impact of cost settings on the reconciliation scenarios, we ran this dataset using alternative costs for host switches and losses. As a second dataset, we used a subset of the data from the latest phylogenetic investigation of figs (Ficus) and their pollinating wasps  (Cruaud et al. 2012). For both partners of the association, biogeographic scenarios were available for phylogenies of 200 taxa. From the complete phylogenetic trees (available in http://datadryad. org, doi: 10. 5061/dryad. hr620), we derived two trees of 23 taxa each, that included a couple of representative species for each Ficus main taxonomic subdivision. We excluded one of the fig subgenera (Pharmacosycea) and its associated pollinators (Tetrapus spp. ) whose phylogenetic positions are still debated.
We have not tested our method on the total dataset presented in  Cruaud et al. (2012)  as some uncertainties remain concerning the root of the phylogenetic trees, which could lead to spurious interpretations. The most likely ancestral geographic areas of each node were directly derived from the biogeographic reconstructions of  Cruaud et al. 2012, obtained  with Maximum Likelihood Optimization in Mesquite  (Maddison & Maddison 2006 ). We ran Mowgli on this dataset with and without enforcing geographic constraints (using default event costs and not enforcing the root of the parasite tree to be mapped on the root of the species tree), and explored how these reconstructions shed light on the biogeographic history of the association. In order, to investigate how incertitude on ancestral geographic ranges impacts the reconciliation, we ran the reconciliation on the dataset including alternative ancestral areas for both Ficus and their associated pollinators.
Results Figure  2  represents the reconstruction obtained on the mock dataset. When not taking geographic constraints into account (Fig. 2A ), a cospeciation event at a node where the two associates do not live in the same area was retrieved (node S1 of the host tree in Fig. 2A ). The transfer T1 preceding this cospeciation event is also geographically impossible as it suggests a dispersal (the donor host lives in Asia or Africa, and the receiving host lives in America) while the parasite actually stays in Africa. The scenario obtained when enforcing geographic constraints is more costly (Fig. 2B ): it entails one more transfer and consequently one less cospeciation event but is biologically more realistic. When using different cost vectors (i. e. using a cost of 3 for losses), the reconciliation where geographic constraints are not taken into account includes additional transfers to avoid losses (Fig.
On the fig/fig wasp dataset (Fig. 3 ), not accounting for geographical constraints leads to geographic inconsistency in one node (cospeciation S1 in the host tree of Figure  3A ). The transfer that precedes it (T5) is impossible and the mapping of the fig wasp phylogeny onto the fig phylogeny from node S1 to event T6 is geographically inconsistent ((Fig. 3A ). Enforcing geographic constraints when a single (most likely) area is specified for each node generates a reconciliation scenario that is more costly (Fig. 3B , one more transfer is necessary to reconcile the two phylogenies) but coherent with the   Cruaud et al. 2012). The annotation of ancestral geographic areas on the reconciliation map also shows that host switches occurred both in "sympatric" settings (within the same geographic areas as broadly defined in our dataset) and allopatric settings (i.
DISCUSSION We provide here significant extensions for a reconciliation tool (Mowgli) and a visualization tool (Sylvx) to infer co-diversification scenarios that, for the first time, can take the historical biogeographies of the associated lineages into account. The extension of the Mowgli software precludes geographic inconsistency during the reconciliation process. The resulting reconciliations can then be visualized and edited in the SylvX updated graphical interface that now integrates annotations of ancestral nodes. Mowgli is already one of the few time-consistent efficient methods that build optimal reconciliations. With the integration of geographic constraints in its algorithm, this tool now provides more realistic codiversification scenarios than other reconciliation methods. Producing.
Perspectives The tools developed in this study can be applied to all interspecific interactions for which biogeographic scenarios are available for both partners. Fast developments in sequencing technologies generate more accurate and more exhaustive phylogenies and methods in historical biogeography have also improved. Therefore, we can hope that numerous datasets will be available in the near future and that cospeciation could be tested on more systems  (Cruaud & Rasplus 2016). For instance, robust phylogenies and biogeographic scenarios are now available for groups of lice that have been model systems in coevolutionary studies  (Boyd et al. 2017). Once comprehensive phylogenies of the hosts are available, our method could be used to better understand the geographic context of host switches in this model system.