dataesr/scientific-paragraphs-categorization huggingface dataset:
Helicase structures and models The rst SARS-CoV-2 helicase structure (PDB ID 6zsl) was deposited in July 2020 and the almost identical SARS-CoV helicase structure in 2019 (PDB ID 6jyt),  21  both resolved as crystallographic dimers (Fig. 2a  and b ). Interestingly, the dimerization interface is different in the two cases, leading to structurally dissimilar complexes. Recent works mainly focusing on the RdRp NSP12,  22, 23  which is expressed in the polyprotein sequence just before the helicase, also yielded structures of the replication machinery, including low resolution cryo-EM images of the helicase. In the cryo-EM structure of the RdRp complexed with the RNA helicase (and cofactors NSP7 and NSP8), the two helicase protomers mainly interact with NSP8 (Fig. 2c ). 4  The helicase chains were resolved using the apo helicase 6jyt as a template for the cryo-EM density maps and rened using soware algorithms.
23 Unfortunately, the 3. 7 Å resolution is too low in this structure to resolve the ATP pocket in a catalytically competent conformation. A more recent high resolution (2. 90 Å) cryo-EM work of Lou et al. , presents a partial structure of the replication and transcription complex (RTC) (Fig. 2d ). 24 The complex includes the RdRp, NSP7, NSP8 and two helicase NSP13 protein copies, with one of the helicases in complex with a single-stranded RNA (ssRNA) fragment. Considering these structure, we conclude that catalytically active form of the helicase is a monomer within the larger RTC complex, and the crystallized homodimeric forms are not the biologically functional unit. Even more recently, a structure (PDB ID 7NNO) was released as a monomer, binding an ATP analogue ANP. The active site agrees with our model, however, this structure does not host ssRNA.
Homology modelling Proteins with crystal structures were aligned with MUSTANG for a combined structural-sequence alignment. 43 The apo SARS-Fig. 2  Structural comparison of the deposited PDB structures of the helicase dimer in SARS-CoV-1 (PDBID: 6jyt, a), SARS-CoV-2 (PDBID: 6zsl, b), SARS-CoV-2 in complex with NSP7 NSP8 and NSP12 (PDBID: 6xez, c) and SARS-CoV-2 with a small fragment of ssRNA bound in complex with NSP7 NSP8 and NSP12 (PDBID: 7cxm, d). The interaction between the two helicase monomers differs depending on the experimental method used to resolve the structures. CoV-2 helicase structure was based on PBD ID 6jyt. 21 Missing residues were added and the I570V replacement were carried out in Pymol 2. 3. 0. 44 The positions of the Mg 2+ and ATP were determined using the coordinates of PDB ID 2xzo,  45  as a template.
Principal component analysis We used PCA to assess the conformational changes observed in the monomer molecular dynamics simulations. 50 The analysis was restricted to the a-carbon protein atoms to reduce the dimensionality of the dataset,  51  and the protein chain was truncated to limit the contribution of end effects. The data was grouped by the force-eld used (CHARMM/ Amber), and whether it was an apo or holo structure. The PCA was performed on these large groups using the scikit-learn library. 52, 53 Before the decomposition, each protomer in each frame from each simulation was aligned via RMSD minimization to a reference structure from the equilibrated holo model. Weighted RMSD modes N i were calculated to show the contribution of each of the m residues to the ith PCA mode using the following equation: 51 N i ¼ ffiffiffiffi l i p 0 B B B B B @ ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi ffi x 2 1 þ y 2 1 þ z 2 1 q « ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi x 2 m þ y 2 m þ z 2 m q 1 C C C C C A Where l i is the i th eigenvalue, and the vector rows correspond to the coordinates describing the positions of each of the m acarbon atoms.
The RecA domains, which are members of the AAA 30 and AAA 12 families, are more common in various ATP-binding structures and the MSA indeed sheds light upon important binding motifs such as the P-loop (Fig. 3b , grey squares) the DE motif (Fig. 3b , green squares) and the arginine ngers (Fig. 3b , black triangles). Furthermore, we identied several residues involved in RNA binding (Fig. 3b , red triangles) which are also conserved structurally in homologous PDB entries. Finally, the GDP(A)Q loop at position 400-404 with high consensus in the MSA features a glutamine potentially involved in the proton transfer during the ATP hydrolysis. This motif bridges the g-phosphate end of the ATP pocket and the RNA binding site; therefore we suggest that it may be involved in coupling the hydrolysis of ATP to the changes induced by the hydrolysis inducing the RNA translocation.
Acknowledgements ER, DB, MB, and SAM acknowledge funding from  ERC   Starting grant  (project  757850  BioNet). This project made use of time on ARCHER granted via the  UK High-End Computing Consortium for Biomolecular Simulation, HECBioSim  (http:// hecbiosim. ac. uk), supported by  EPSRC  (grant no. EP/R013012/ 1 ). We would like to thank  GENCI  for a generous allocation of computing time at CINES within the action " Project contre le COVID 19 " and the project  DARI A0080711431. We are also grateful for resourced provided by the  Leeds ARC HPC  service.
Overview of Sequenced Host-Virus Systems A total of 35 Illumina sequencing data sets were analyzed in this study (table 1). One data set was produced by sequencing genomes of the IIV6 purified from D. melanogaster S2 cells (fig. 1 ). IIV6 particles purified from S2 cells were then used to infect whole D. melanogaster flies, as well as D. hydei flies and two moth species, the spotted stalk borer (Chilo partellus) and the maize corn borer (Sesamia nonagrioides) (five data sets in total). In addition, six data sets were produced by sequencing genomes of the invertebrate iridescent virus 31 (IIV31) from two crustacean species (the terrestrial isopods Armadillidium vulgare and Porcellio dilatatus) (fig. 1 ). We also analyzed a population of the AcMNPV baculovirus initially replicated on T. ni  (Chateigner et al.
4% of viral genomes carrying  , Supplementary Material online). The three TEs were absent from the whole-genome sequence (WGS) of A. ipsilon (NCBI accession number: PNFC00000000. 1), the species from which the cell line used to passage AgseNPV-B was established. The WGS of the A. ipsilon cell line is not available. Thus, the origin of the TEs found in pp7 cannot be easily explained. In any case, the piggybac and Sola TEs were identified in our assembly of nonviral reads obtained from the AgseGV/A. segetum larvae system and the nonautonomous element was previously identified in the plutellid moth Plutella xylostella  (Han et al. 2016). The fact that we only retrieved these TEs in passage pp7 of the virus and not in earlier (pp0, pp1, pp3, pp5) or later (pp10) passages may be due to the stochasticity inherent to the detection of low frequency virus-borne TEs, which is likely pronounced given the relatively low depths at which these samples were sequenced.
melanogaster TE integrated in IIV6, that is, a piggybac transposon involved in seven TE-virus chimeras in the C. partellus data set (supplementary table  S1 , Supplementary Material online). There is one copy identical to this TE over its full length in the C. suppressalis WGS and we independently validated its presence in the C. partellus genome by polymerase chain reaction (PCR) and Sanger sequencing (piggyBac_Chilo_partellus in supplementary file S1, Supplementary Material online). Thus, this TE likely transposed from C. partellus to IIV6 during replication of the virus in the moth larvae. S4 , Supplementary Material online). We verified that none of these D. melanogaster TEs is present in the genomes of D. hydei, S. nonagrioides, or C. partellus using BlastN similarity searches. Thus, we conclude that D.
Here, we found D. melanogaster TEs integrated in IIV6 genomes purified from two moths (C. partellus and S. nonagrioides) and another fly (D. hydei). We also recovered TEs present in the AcMNPV G0 data set in AcMNPV genomes purified from S. nonagrioides larvae. This demonstrates that TE-bearing viral genomes can be encapsidated, as shown earlier for the TCp3. 2 and TCl4. 7 TE in CpGV mutants MCp4 and MCp5, respectively  (Jehle et al. 1995 (Jehle et al. , 1998)) , and that persistence of virus-borne TEs over at least few infection cycles may be common among large dsDNA viruses. Interestingly, we observe large differences in TE diversity and number of viral genomes bearing TE insertions after one replication cycle of the same starting viral population in different hosts. For example, although the frequency of TE-bearing genomes in the IIV6 population purified from D.
Detection of TEs Integrated in Viral Genomes The aim of this study was to characterize as comprehensively as possible the diversity of host TEs that can become integrated into viral genomes. To this end, we searched for TEvirus junctions in sequencing reads using the approach developed by  Gilbert et al. (2016). Reads were aligned separately on TE sequences and the viral genome using BlastN (-task megablast). Chimeric reads for which a portion aligned on a TE only and the other portion aligned on the viral genome only were then identified based on alignment coordinates. The R scripts used to detect and filter chimeric reads are provided in supplementary files S2 and S3, Supplementary Material online. Though artificial chimeras are certainly present in the various data sets analyzed here, several lines of evidence indicate that chimeras retained for counting and calculating the frequencies of TEs integrated into viral genomes are not artificial and result from bona fide integration that took place during replication of the virus.
Fourth, many TEs are known to duplicate a small target sequence motif upon integration called target site duplication (TSD). As in our earlier study  (Gilbert et al. 2016) , we were able to identify TSD motifs for several TEs found integrated into viral genomes (fig. 3 ). Viral and TE sequences on which sequencing reads were aligned derive from WGSs and TE library that are fully described below. All moth TEs found integrated into viral genomes are provided in supplementary data 1, Supplementary Material online.
Viral Genomes Used for BLAST Searches Genome sequences of four out of six viruses used in this study were retrieved from NCBI under accession number KR584663 (AgseGV-DA), KM102981 (AgseNPV-B), KM217575 (CpGV-M), and KM667940. 1 (AcMNPV strain E2). For IIV31, we used the genome provided in the supplementary data of  Loiseau et al. (2020). The IIV6 genome sequence available in NCBI was generated after replication on CF-124 cells of the C. fumiferana moth  (Fukaya and Nasu 1966). As moths are distantly related to Drosophila flies, we reasoned that adaptation of IIV6 to S2 cells may have resulted in changes in its genome. We thus assembled the IIV6 genome de novo with the tadpole program (https://jgi. doe. gov/data-and-tools/bbtools/bbtools-user-guide/tadpole-guide/, version of December 2018, options used: "k ¼ 17"; "k ¼ 31"; "k ¼ 60"; "k ¼ 90" with "mincov ¼ 100").
The different assemblies obtained with 17, 31, 60, and 90 mers with 500Â and 1,500Â depth coverage were then fused with Geneious version 11. 0. 2 (https://www. geneious. com, last accessed on july 2020, options: de novo assembly, Geneious assembler, high sensitivity). The final assembly was annotated based on the available IIV6 (NC_003038. 1) genome with the General Annotation Transfer Utility program  (Tcherepanov et al. 2006).
TE Library The TE library included all TE consensus sequences downloaded from Repbase in October 2017  (Bao et al. 2015) , all those identified in 195 insect genomes by  Peccoud et al. (2017)  as well as all TEs identified integrated in the AcMNPV genome after replication of the virus onto T. ni and S. exigua moths  (Gilbert et al. 2016). The library also Monitoring Insect TEs in dsDNA Viruses. doi:10. 1093/molbev/msab198 MBE included all D. melanogaster TE sequences downloaded from Flybase and all A. vulgare TE consensus sequences characterized in  Chebbi et al. (2019). In addition, our TE library was complemented with the TE consensus sequences constructed using RepeatModeler2  (Flynn et al. 2020 ) from the WGS of A. ipsilon (NCBI accession number: PNFC00000000. 1), C. suppressalis (PNFC00000000.
Inferring HTT between Insects To assess whether moth TEs found integrated into viral genomes underwent HT during insect evolution, we used them as queries to perform BlastN searches (option megablast) on all 352 insect WGS available in GenBank as of March 2020. BlastN hits showing >90% nucleotide identity to the TE over >200 bp and >80% of its length were considered further. To assess whether such high levels of TE identity were due to vertical or horizontal transmission, we compared TE synonymous distances (dS) with the distribution of dS expected to occur under vertical transmission between each species pair of interest. The dS between TEs were calculated over the longest open reading frame found in the two copies involved in the best alignment for each species pair of interest. The distribution of dS expected under vertical transmission was generated for each species pair of interest by calculating dS between all single-copy and complete genes extracted from the WGS of each species using BUSCO version 4.
0. 2 (Simão et al. 2015; Waterhouse et al. 2018). All genes showing homology to the Insecta gene set (i. e. , from 259 to 1,029 genes depending on species pairs) were used. MAFFT version 7. 310  (Katoh and Standley 2013)  was used to align nucleotide sequences of genes that are shared (orthologous) between species. Gene and TE alignments were analyzed in R version 3. 6. 1 (R Core Team 2019) with the "seqinr" package  (Charif et al. 2005)  to compute dS values.
Data Availability The data underlying this article are available in the GenBank Nucleotide Database at https://www. ncbi. nlm. nih. gov/genbank/ and can be accessed with BioProject accession number PRJNA692820. The library of TEs used in this study to search for TE copies integrated into viral genomes is provided in supplementary file S1, Supplementary Material online, available at https://doi. org/10. 6084/m9. figshare. 14776041. v1. The R scripts used to identify chimeric reads, make figure  4  and supplementary figure S2 as well as supplementary table  S1 , are provided as supplementary files S2 and S3, Supplementary Material online.
To address these questions and needs, we analyzed a genome dataset representing eight rhinoceros species (Figure  1 ), including all seven genera that survived into the Late Pleistocene  (Cerden ˜o, 1998). Our data include the five extant rhinoceros species represented by four de novo genome assemblies of black (Diceros bicornis), white (Ceratotherium simum), Sumatran (Dicerorhinus sumatrensis), and greater onehorned (Rhinoceros unicornis, also known as Indian) rhinoceroses and a resequenced genome of a Javan rhinoceros (R. sondaicus). The Javan rhinoceros genome was retrieved from a museum specimen dating to 1838 and resequenced to high coverage (253). In addition, we sequenced the genomes of three extinct rhinoceros species from Late Pleistocene fossils that are close to, or beyond, the radiocarbon dating limit of $50 thousand years ago (kya), specifically a Siberian unicorn, a Merck's rhinoceros, and a woolly rhinoceros, sequenced to 93, 123, and 353 coverage, respectively (Table  S1 ).
, 2021; Cappellini et al. , 2019; Kirillova et al. , 2017; Kosintsev et al. , 2019). To circumvent the problem of reference genome biases  (Gopalakrishnan et al. , 2017; Heintzman et al. , 2017; Figures S1A and S1B) , we conducted whole-genome alignments for the eight rhinoceros species using domestic horse (Equus caballus) as an outgroup. We then inferred a genome-wide species tree summarizing the phylogenetic signal from individual gene trees based on 22,066 100-kb genomic windows. Our phylogenomic analysis identified three major clades within the subfamily Rhinocerotinae and provided strong support for the geographical hypothesis of rhinoceros evolution. A clade comprising the two African species Diceros bicornis and Ceratotherium simum, the Diceroti, is the sister lineage to the remaining rhinoceroses in our dataset (with the exception of the Siberian unicorn).
The results corroborated the phylogenetic discordance discovered in the initial dataset (Figure  S1D ). We then simulated the gene tree The historical distribution range maps of the five extant species use information published previously  (Dinerstein, 2003; Havmøller et al. , 2016; Khan and van Strien, 1997; Rookmaaker and Antoine, 2012) , while the ranges of the three extinct species were drawn based on their fossil records  (Kahlke and Lacombat, 2008; Kosintsev et al. , 2019 Kosintsev et al. , , 2020;; Shpansky and Boeskorov, 2018). All ranges are approximations aimed at conveying each species' general, rather than detailed, distribution. As the greater one-horned rhinoceros sample derives from captive-born zoo stock, a geographic origin is not shown. distribution expected under a hypothesis of genetic drift with ILS and compared this to the empirical data.
4, p = 0. 04). On the other hand, our comparison with a broad range of animals shows that rhinoceroses in general display comparatively low levels of GWH, especially relative to not only the combined dataset of all animals but also ruminants and other large herbivores (Figure  3A ). The only mammalian family displaying lower average levels of GWH was the Felidae (Figure  S3B ), which is not unexpected, as carnivores/predators are generally less abundant than herbivores/prey  (Owen-Smith, 2015). These findings are robust to choice of reference genome used in our analyses (see Figure  S3C ). To better contextualize the observed levels of GWH, we characterized the inbreeding levels in our genomes through distributions of runs of homozygosity (ROH). To evaluate the robustness of our results, we first explored the effect of excluding transitions on ROH inference (Figure  S4A ).
DISCUSSION Our combined rhinoceros genome dataset represents a valuable resource for both the current and future study of the evolution and biology of these species, including characterization of the genetic basis of rhinoceros phenotypes (Table  S5 ). For example, we uncovered frameshift mutations in IFT43 (intraflagellar transport 43) that could contribute to rhinoceroses' poor eyesight. IFT43 is involved in the formation and maintenance of cilia, which are important for the development and function of the light-sensitive tissue at the back of the eye (the retina)  (Arts et al. , 2011). In the case of the Javan and greater one-horned rhinoceros, our genome sequences also provide a basis for further speciesspecific conservation genetic analyses. By analyzing this dataset, we resolved a long-standing debate related to the evolutionary history of living and recently extinct rhinoceroses and provided evidence that relatively low genomic heterozygosity and moderate inbreeding levels may represent their long-term natural state.
, 2017) , 0. 2 mM BGI reverse index-primer  (Mak et al. , 2017)  and 10 mL of library DNA template. Amplified libraries were sequenced on a BGISeq 500 platform at BGI Shenzhen with a SE100 sequencing strategy. We extracted DNA from the Merck's rhinoceros M1 tooth root, as previously reported by  Kirillova et al. (2017)  and generated data as described in  Cappellini et al. (2019). Briefly, in addition to the DNA extract obtained in Kirillova et al. , a second DNA extraction was performed following the previously reported methods  (Dabney et al. , 2013)  in the University of California Santa Cruz Paleogenomics laboratory. We constructed four libraries (single-indexed and double-stranded) for each DNA extract following the methods reported by  Meyer and Kircher (2010). The total of eight indexed libraries were pooled and sent to SciLifeLab (Stockholm, Sweden) for sequencing on two lanes of the Illumina HiSeq-X platform (150 PE).
In addition, a subsample was processed in the ancient DNA laboratories at the GLOBE Institute, University of Copenhagen, following the same protocols as described with the same library construction method for the Javan rhinoceros, and sequenced on three lanes of PE100 data on a BGISeq 500 platform at BGI Shenzhen. A subsample was taken from a radius (IPAE 915/2804) of the Siberian unicorn and DNA was extracted at the Australian Centre for Ancient DNA (University of Adelaide) as previously reported by  Kosintsev et al. (2019). A new Illumina sequencing library was created for this study following the protocol described by  Meyer and Kircher (2010)  with an additional step added to excise deaminated cytosines with the USER enzyme mix (New England Biolabs) as described by  Briggs et al. , (2010).
Each PCR of 25 mL contained 1x AccuPrime reaction mix, 0. 3 mM IS4 amplification primer, 0. 3 mM P7 indexing primer, 7 U AccuPrime Pfx (Thermo Scientific) polymerase. The cycling conditions were 95 C for 2 min, 14 cycles at 95 C for 15 s, 60 C for 30 s and 68 C for 1 min. PCR replicates were pooled and purified using Agencourt AMPure XP beads (Beckman Coulter, Brea, CA, USA), eluted in 36 mL EB Buffer, and quantified using a high-sensitivity DNA chip on a Bioanalyzer 2100 (Agilent, Santa Clara, CA, USA). The library was subsequently sequenced on one Illumina HiSeqX lane with a 2 3 151 bp setup in the High Output mode at SciLifeLab (Stockholm, Sweden). For the historic/ancient rhinoceros samples, although multiple sequencing libraries were constructed and some were sequenced with different read length strategies, comparison tests of the different datasets found no bias differences (detailed in the Additional resources section) and thus we merged them for the subsequent analyses.
Genome assembly and annotation We merged the paired-end reads for all the ancient and historic rhino samples (Siberian unicorn, Javan, Merck's and woolly rhinoceros) using a modified version of AdapterRemoval  (Schubert et al. , 2016)  which masked the conflict bases that have identical sequencing qualities to Ns and removed collapsed reads < 30 bp. Then, we mapped the short reads from those rhinoceros species onto their corresponding genome references (Table  S2 ) and obtained their genomes (consensus sequences) using the doFasta module (-doFasta 2) in ANGSD (version 0. 924)  (Korneliussen et al. , 2014)  with a minimum requirement of mapping quality and base quality of 20. A minimum depth of 5 was set for the Javan and woolly rhinoceros, and the minimum depth was set as 3 for the Siberian unicorn and Merck's rhinoceros due to their relatively lower coverages ($303 for the former pair, versus $103 for the latter pair).
For the greater one-horned rhinoceros, we obtained its genome using a mate-pair assembly. Since assembly quality can vary for different datasets, we used three different assemblers and evaluated their performance. The following assemblers for short read sequence data were used: ALLPATHS-LG v. 52485 (HAPLOIDIFY = True)  (Gnerre et al. , 2011) , ABySS v. 2. 0. 2 (-k = 61)  (Simpson et al. , 2009)  and SOAPdenovo2 v. 2. 04 (-K 61)  (Luo et al. , 2015). Out of the three assemblers, ALLPATHS-LG was selected for downstream analyses as it produced the most gene-complete and the contiguous assembly (with a scaffold N50 of 27. 7 Mbp. Gene completeness was measured with BUSCO v. 5. 0. 0  (Seppey et al. , 2019)  using the ''mammalia_odb10'' ortholog dataset, which showed a low degree of missing, fragmented and duplicated genes: ''C:96.
, 2020)  for homolog prediction with its amino acid datasets including Sus scrofa, Tursiops truncatus, Bos taurus, Drosophila melanogaster, Vicugna pacos and Homo sapiens. De novo gene prediction was achieved with: a) Augustus (version 3. 2. 3) (Stanke et al. , 2008) , where ''human'' was used as the gene prediction species model for Augustus; b) and SNAP  (Korf, 2004)  using the provided mammal model (mam54). Finally, Maker (version 2. 31. 10)  (Holt and Yandell, 2011)  was used to weigh and merge different evidence, and obtain protein-coding-gene sets for the black, Sumatran and greater one-horned rhinoceros.