dataesr/scientific-paragraphs-categorization huggingface dataset:
RNA-Seq data analysis and molecular phenotype quantification In total, we gathered 11,323 publicly accessible raw RNA-Seq datasets (fastq files), representing 9,530 distinct samples (downloaded from NCBI SRA database by February 26 th , 2021), of which 98. 13% were generated using the Illumina platform. We removed 121 embargoed RNA-Seq samples, and then processed all the remaining RNA-Seq samples using a uniform pipeline. Briefly, we first trimmed adaptors and discarded reads with poor quality using Trimmomatic (v0. 39) with the following parameters: TruSeq3-PE. fa:2:30:10, LEADING:3, TRAILING:3, SLIDINGWINDOW:4:15 and MINLEN:36  45. We then aligned clean reads to the Sscrofa11. 1 (v100) pig reference genome using STAR (v2. 7. 0) with the following options: outFilterMismatchNmax 3, outFilterMultimapNmax 10 and outFilterScoreMinOverLread 0.
For PCG expression, we considered 21,280 PCGs from the Ensembl annotation of the porcine genome (Sscrofa11. 1 v100). For exon expression of PCGs, we extracted raw read counts of 290,536 exons by featureCounts (v1. 5. 2)  47  and then normalized them as TPM. To explore enhancer expression, we downloaded the previously predicted enhancers (strong active enhancers, EnhA) from 14 pig tissues  17. We merged these enhancer regions across tissues using bedtools (v2. 30. 0)  52  , resulting in 158,998 non-redundant enhancer regions. We obtained raw read counts of these non-redundant enhancer regions from all 7,095 RNA-Seq samples by featureCounts (v1. 5. 2)  47  , followed by TPM normalization. For lncRNA expression, we obtained 17,162 lncRNAs predicted from 33 Iso-Seq datasets, representing 10 tissues from 4 animals by using FEELnc  53.
We obtained the methylation level of cytosines using the bismark_methylation_extractor (--ignore_r2 6) routine and removed CpG sites represented by less than five reads. Ultimately, we retained 182 samples with more than 10 million clean reads and unique mapping rate > 40%, representing 10 tissues and cell lines. We then removed data from corpus luteum as it had less than five samples. We visualized the variance in DNA methylation among samples using t-SNE, and removed samples from two cell lines, fibroblast and iPSC, as they were not clustered together or had no corresponding cell types in the current RNA-Seq dataset. Finally, we kept 166 samples from seven tissues to detect tissue-specific hypomethylated regions (HMR) and allele-specific methylation (ASM) loci. To detect tissue-specific HMR, we used SMART2 (v2.
Bioinformatics analysis of Hi-C data We downloaded Hi-C data of five samples from five different pig tissues (i. e. , adipocyte, ear, embryo, liver and muscle) from NCBI SRA to identify TAD and Hi-C contacts (Table  S8 ). After trimming adapter sequences and low-quality reads using Trim Galore (v0. 6. 7) (https://www. bioinformatics. babraham. ac. uk/projects/trim_galore/) with parameters: --q 20 --paired --parameters. We then uploaded the gene expression (TPM) matrix of bulk RNA-Seq samples as the mixture file. We imputed cell fractions based on the signature and mixture files by running the Impute Cell Fractions analysis with the custom mode and used the permutation test (100 times) to determine the significance level.
Genotype imputation reference panel To develop a genotype imputation reference panel for imputing sequence variant genotypes into the RNA-Seq samples, we downloaded 1,307 public WGS samples from NCBI SRA by March 18 th , 2021, and newly generated an additional 510 WGS samples (Table  S2 ), representing five main pig populations worldwide, i. e. , Suidae but not Sus scrofa (SUI, n = 45), European wild boar (EUW, n = 54), European domestic pig (EUD, n = 855), Asian wild boar (ASW, n = 80), and Asian domestic pig (ASD, n = 783). We processed and analyzed the WGS data using a uniform pipeline as described in the following. Briefly, we filtered the raw sequence reads by Trimmomatic (v0. 39)  45  , and then mapped clean reads to Sscrofa11. 1 using BWA-MEM (v0. 7. 5a-r405) with default parameters  70.
We marked duplicated reads by Picard (v2. 21. 2) (http://broadinstitute. github. io/picard/). We removed 213 samples with low read depth (< 5x), one sample with low genome coverage (< 75%), and one sample with incomplete data. Finally, we kept 1,602 samples for jointly calling variants using the Genome Analysis Toolkit (GATK) (v4. 1. 4. 1) 80 with parameters: QD> 2, MQ < 40, FS > 60, SOR > 3, MQRankSum < -12. 5 and ReadPosRankSum < -8, yielding ~214 million SNPs. We removed SNPs with MAF < 0. 01 and/or missing rate > 0. 9 using bcftools (v1. 9)  81  and employed Beagle 5. 1 to phase the filtered variants and impute sporadically missing genotypes  82. Finally, a total of 42,523,218 SNPs were retained in the current version of PGRP (v1).
Genotyping and imputation of RNA-Seq samples We employed the GATK (v4. 0. 8. 1) to call SNPs at known loci in the dbSNP database (build 150), from 7,095 RNA-Seq samples, according to the recommended settings of the best practice guidelines  80. We filtered out low-quality SNPs using the filtering option: FS > 30. 0 & QD < 2. 0 & DP > 3. 0. We then imputed the filtered SNPs on autosomes to sequence level with Beagle (v5. 1)  82  using haplotypes from the PGRP as a reference. Finally, we obtained 7,008 samples that were genotyped and imputed successfully. We filtered out variants with MAF < 0. 05 and model-based imputation accuracy (DR 2 ) < 0. 85, resulting in 3,087,268 SNPs for the molQTL mapping. We obtained 12,207 linkage disequilibrium (LD) independent SNPs using PLINK (v1. 90)  83  with parameters: --indep pairwise 1000 5 0.
g. , Duroc),  ×  is the interaction term between genotype and ancestry proportion, and e is the residual error. We only considered SNPs within the cis-window (±1Mb of TSS) of each gene for the bieQTL mapping. We filtered out SNPs with MAF < 0. 1 in the top and/or bottom 50% of samples sorted by an ancestry proportion of interest (e. g. , Doruc), using TensorQTL (v1. 0. 3) with option: --maf_threshold_interaction 0. 1. We used eigenMT  100  in TensorQTL to correct for the multiple testing at the gene-level for the top nominal Pvalue of each gene. We then computed the genome-wide significance of genes using the Benjamini-Hochberg FDR correction on the eigenMT-corrected P-values. We defined genes that had at least one significant (i. e. , FDR-corrected P-value < 0. 01) bieQTL as bieGenes. We detected cell type interaction cis-eQTL (cieQTL) in seven bulk tissues using the same approach as described above, where we replaced the ancestry composition with the estimated cell type composition obtained from CIBERSORTx based on single-cell RNA-Seq data from brain, lung and PBMC  74.
| Species distributions across MPAs Spatial analyses were performed with QGIS v3. 16. 2 (QGIS Development Team, 2020), and computation and statistical analyses were performed with R v4. 0. 2 (R Core Team, 2020). Spatial information on protected areas was collected from the World Database on Protected Areas (WDPA; UNEP-WCMC & IUCN, 2020). This dataset was subsequently trimmed by excluding areas on land and those which did not include a coastal habitat-defined as those found outside of the continental shelf, i. e. >200-m depth  (Lucieer et al. , 2019)  and beyond the deepest reef corals  (Rouzé et al. , 2021). This yielded a database of MPAs (n = 3625; area = 942,568 km 2 ), which were then overlaid with the distributions of 805 coral species derived from Mouillot et al. (2016) to obtain presence/absence data within protected coral reefs.
The dataset was further trimmed to include only those MPAs that contained at least five species of scleractinian corals for which data for at least four traits of interest were available (see below), resulting in a final dataset of 215 MPAs covering 159,851 km 2. Species distribution data across 141 reef ecoregions defined by the Coral Geographic Database  (Veron et al. , 2009 (Veron et al. , , 2011) )  were also compiled.
Trait data were obtained from the Coral Trait Database compiled by  Madin, Anderson, et al. (2016). Eight traits were chosen for analysis representing the major trait categories of morphology, biomechanics, physiology and reproduction, all of which are known to be crucial for explaining phenotypic variation in reef corals  (Darling et al. , 2012 (Darling et al. , , 2013;; Hartmann et al. , 2017; Madin, Hoogenboom, et al. , 2016; Wong et al. , 2018)  (Table  1 ). The trait data were then used to compute the functional distances between species using Gower's dissimilarity index to summarize the trait space in fewer dimensions with principal coordinate analysis (PCoA). Sufficient PCoA axes were retained to capture 70% of total initial variability. Only 379 species with data available for at least four of the eight traits were considered.
Functional diversity for each MPA and ecoregion was estimated using the index of functional richness (FRic)  (Cornwell et al. , 2006; Villéger et al. , 2008) , which measures the convex hull volume defined by the species at its vertices in the multidimensional trait space based on the PCoA  (Cornwell et al. , 2006). FRic was computed using the R package 'FD'  (Laliberté & Legendre, 2010). Negative PCoA eigenvalues were resolved with the 'Cailliez' correction method  (Gower & Legendre, 1986). FD excess , the total FD within each assemblage relative to a random assemblage of species with the same species richness, was also computed for each MPA and ecoregion (analogous to PD excess above; similarly, generally negative). Linear regressions between PD F and FRic, and between PD excess and FD excess were performed for assemblages in all MPAs and ecoregions.
ACK N OWLED G EM ENTS This work was initiated as part of the sCAP working group supported by sDiv, the Synthesis Centre of the German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig (DFG FZT 118), and by the Canadian Institute for Ecology and Evolution. The research was funded by the National Research Foundation, Prime Minister's Office, Singapore, under its Marine Science R&D Programme (MSRDP-P03) and by the Mind the Gap-Sustainable Earth Fund. We thank the sCAP working group for discussion, Yuchen Zhang for help with spatial optimization, members of the Reef Ecology Lab for feedback and support, and Qiang Lin and two anonymous reviewers for constructive comments on the manuscript.
(Madin, Anderson, et al. , 2016)he Coral Trait Database(Madin, Anderson, et al. , 2016). Trait Growth form Coloniality Colony size Growth rate Skeletal density Reproductive mode Sexual system Symbiont transfer mode Note: | 1725 Description Data type References Branching, plating or domed form Categorical Darling et al. (2012) Solitary or colonial Categorical Darling et al. (2012) Largest recorded size of colony in cm Continuous Darling et al. (2012) Average monthly growth rate in mm month -1 Continuous Darling et al. (2012) Average density of CaCO 3 skeleton in g cm -3 Continuous Darling et al. (2012) Brooding or broadcast spawning Categorical Hartmann et al. (2017) Gonochoric or hermaphroditic Categorical Hartmann et al. (2017) Horizontal or vertical Categorical Hartmann et al. (2017) NG et al.
It is thought that this DNA has been captured in the plant genome either by non-homologous or microhomology-mediated endjoining during double-stranded DNA repair or by using hybrid integration mechanisms involving transposable elements (TEs)  [12, 22]. Many caulimovirid EVEs exist at very high copy numbers and this may result from repetitive integration of the same virus during one or more bouts of infection and/or amplification of this sequence by cycles of activation and reintegration  [31]. Using RT gene sequences of extant Caulimoviridae to scan plant genome and transcriptome databases for the presence of EVEs, a dozen operational taxonomic units (OTUs) have been identified that appear to exist only in the form of EVEs. These include the tentative genus Florendovirus, which is widespread in the genomes of flowering plants, as well as four different tentative Gymnendovirus and two Fernendovirus genera that are found in gymnosperm and fern genomes, respectively  [7, 12].
Repeat annotation programs like RepeatModeler  [34]  or the TEdenovo pipeline from the REPET package  [11]  can produce consensus sequences that represent caulimovirid EVEs but these programs inherently search for all interspersed repeats. The available tools to classify these consensus sequences, for example PASTEC  [16] , often misclassify caulimovirid EVEs as LTR retrotransposons. Lastly, low copy caulimovirid EVEs are not normally detected by repeat detection programs and this error can be consequential, as some EVEs are present at only single genome loci but are still capable of being reactivated to cause new infections  [6]. In this paper, we describe a new software pipeline called CAULIFINDER, which has been designed to annotate and classify caulimovirid EVEs in plant genomes using homology-driven approaches.
Step 1-construction of a genomic subset This first step of Branch A aims to identify putative endogenous caulimovirid EVE loci by doing a tBLASTX  [3]  search of a plant genome database using a reference library of caulimovirid genomes as the query sequences. This library comprises 11 genome sequences representing each exemplar virus isolate of the officially recognized genera in the Caulimoviridae, as well as reconstructed genome sequences of each of the 12 tentative genera of caulimovirid EVEs proposed by  [7]. The current library is described in Supplementary Table  1  and the latest version will be available in a dedicated dataverse (https:// doi. org/ 10. 57745/ ADFNMB). After tBLASTx against the plant genome, overlapping hits are merged and then extracted from the genome database as FASTA-formatted sequences.
Due to the conserved nature of the RT domain in particular, many hits are distantly related Copia and Gypsy-like LTR retrotransposons (Metaviridae and Pseudoviridae), which are abundant in plant genomes. To filter out these sequences, extracted sequences are compared by BLASTX alignment (BLAST+ package  [3]  to a library called "baits. fa", which contains a comprehensive set of caulimovirid protein sequences as well as the collection of RT and RNaseH protein sequences from Copia-and Gypsy-like LTR retrotransposons from the Gypsy database (https:// gydb. org/)  [23]. Those sequences with best hits to the retrotransposon proteins are discarded. The remaining sequences that map to approximately the same location in the plant genome (separated by 5 kbp or less) are then merged, since these loci probably derive from single integration events.
This merging process consolidates different hits obtained for the same locus. The loci are then extended in both directions (option -X: "extension value", 2 kbp by default) to capture any remaining ORFs and non-coding sequences and identify the junctions of the EVE with the plant DNA. Finally, the extended loci are extracted from the plant genome database as FASTAformatted sequences and combined into a single file to build a genomic subset enriched for caulimovirid EVE loci (hereafter referred to as "sub-genome"). This enrichment process allows more sensitive search parameters to be applied in the next step without a cost in computational time compared to whole genome repeat identification.
Step 2-building a consensus library In the second step of Branch A, the sub-genome is used as input for the TEdenovo pipeline from the REPET package  [11]. TEdenovo identifies high-scoring pairs (HSPs) using Blaster and these are used by three sequence clustering algorithms, Grouper, Piler and Recon  [10, 29, 30] , to identify groups of HSPs sharing high sequence similarity. By default, CAULIFINDER Branch A only considers the groups containing at least five HSPs (option -hsp: "HSP number"), meaning that at least five caulimovirid EVE sequences must group together. A maximum of 20 sequences from each group are then aligned to produce respective consensus sequences. Apart from reducing computational time, use of an enriched sub-genome dataset allows TEdenovo sensitivity to be increased as an all-by-all BLAST identity threshold of 85% can be used, which is not recommended for use on a whole genome.
Step 3-characterization of consensus sequences In the third step of Branch A, each consensus sequence is scanned for conserved protein domains using RPS-BLAST against the CDD database [26] and those containing a TE-specific protein domain, for example, an integrase domain, are discarded (option -ch: "filter_chimeras" set to TRUE by default). Consensus sequences are also compared using BLASTx to the library "baits. fa" in order to discard potential false positives and to provide a preliminary classification to genus rank in the Caulimoviridae. In addition, consensus sequences are clustered on the basis of pairwise similarity and coverage using BLASTCLUST (ftp:// ftp. ncbi. nih. gov/ blast/ docum ents/ blast clust. html) to establish groups that share high sequence homology (options -S: "identity" and -L: "coverage", set to 90% and 0.
Step 3-phylogenetic analysis In the last step, the representative caulimovirid EVE RT sequences are combined to form a library that contains reference RT sequences from all Caulimoviridae genera (established from the work described in  [7] ) and outgroup sequences from retroviruses and LTR retrotransposons. This library is used to produce a multiple sequence alignment (MSA) using MUSCLE and sequences having high alignment quality are selected using trimAl. Four additional rounds of alignment with MUSCLE followed by filtering using trimAl are carried out (Fig. 2 ). This iterative process is meant to ensure high quality protein datasets necessary for automated phylogenetic reconstruction. The last MSA is curated using GBLOCKS  [5]  to remove poorly aligned sequence columns and then deliver this new file to PhyML  [15]  for phylogenetic reconstruction.
Development and distribution CAULIFINDER pipelines are coded in bash and python scripts (2. 7) that are versioned as gitlab project saved in forgemia (https:// forge mia. inra. fr/ urgi-anagen/ event_ cauli finder), one of the forge provided by INRAE. The fasta libraries and the documentation to launch Branch A and Branch B are also available in this gitlab project. The fasta libraries are also published in a dataverse (https:// doi. org/ 10. 57745/ ADFNMB) where they will be updated and versioned. As FAIR principles (Findable, Accessible, Interoperable and Reusable)  [40]  apply to research pipelines, we have decided to deploy the CAULIFINDER pipelines in the Docker open access container image delivery system  [28]  to meet the Open Science goals. Hence, CAU-LIFINDER can be used in various environments (cloud, serverless and operating systems like linux) and always under the same conditions (all dependencies in the same soft version) to ensure repeatability.
Furthermore, this deployment strategy circumvents problems associated with the installation of dependencies. Users only need to install the container engine and launch genome analysis in a project directory. The CAULIFINDER container is available on dockerhub (https:// hub. docker. com/r/ urgi/ docker_ cauli finder) and its code is deposited in the public gitlab repository "Caulifinder_docker" (https:// forge mia. inra. fr/ urgi-anagen/ cauli finder_ docker) together with a content description and usage instructions. The image is designed with: • the Centos7 operating system • Slurm as jobs scheduler • MariaDB as database manager through the free docker image docker-centos7-slurm (https:// hub. docker. com/r/ giovt orres/ docker-cento s7-slurm) from dockerhub • REPET instance v2. 5 • other free softwares listed in a README file (https:// forge mia.
The main output files of this run are available in the supplementary dataset. As a proxy for sensitivity, we next investigated the similarity of the CAULIFINDER output sequences with the 17 reference florendovirus and single Vitis endovirus sequences by combining all together in a single file and clustering alike sequences using 85% nt identity and 85% target sequence coverage parameters. All 17 reference sequences grouped in clusters containing at least one CAULIFINDER output sequence. In one sequence cluster, VvinAV and VvinBV were grouped together, a result that reflects their high sequence similarity  [12] , while VvinCV and VvinDV each formed separate sequence clusters. The two components of the bipartite genomes always grouped in the same sequence cluster. Additionally, reference sequences for each of the tentative virus species accurately sorted into the sequence cluster that contained its closest relatives.
2 ). The construction of concatemers is not surprising since caulimovirid EVEs are often present as tandem repeats or cluster in genome hotspots  [2, 12, 14, 32]. It is likely that the extension step performed on the merged hits favored sequences that are redundant to each merged hit. Suspected concatemeric sequences can be interrogated using dot plot sequence alignment tools, and duplicated sequences can then be manually removed to obtain a monomer. This manual curation is not necessary to run the RepeatMasker annotation (step Fig. 3  Overview of the multiple sequence alignments obtained for the VvinAV-VvinBV cluster using CAULIFINDER Branch A. The alignments were obtained using MAFFT with the ginsi and leave gappy regions 0. 8 settings and visualized in the overview window of the Jalview program  [39]  with the following nucleotide colours: A (green), T (blue), G (red), C (orange).
We obtained results similar to those from the first run for VvinAV, VvinBV_compA and VvinDV compA. However, we obtained only truncated versions of VvinBV_ compB, VinDV_compB and VvinCV_sc1 (Fig. 3B  and Supplementary Fig. 1 ), and no sequence clustered with VvinCV_sc2. VvinCV is less abundant than other EVEs in the V. vinifera genome and there are fewer full length genome copies: average fragment size is only 593 bp  [12]. Hence, manual methods to reconstruct the EVE sequences produce much better results than TEdenovo, especially when the proper probe is absent from the reference virus genome library. The consensus sequences obtained for the two B genomic components (VvinBV_compB and VinDV_ compB) were very truncated at their 3′ end when compared with the reference sequences (Fig. 3 ). Conserved MP and AP domains and a zinc finger motif were found in the VvinBV-compB-sc1 sequence following a search of the CDD database, but a truncated RH1 domain was also identified when this component sequence was manually reassembled and annotated  [12].
In VvinDV_compB, only the MP and zinc finger motif were detected. Furthermore, when compared to the florendovirus-filtered reference genome library using tBLASTx, we found that significant hits were only found against the MP domain (Supplementary Fig. 3 ), reflecting substantial divergence of the other component B protein domains. As a result, in the initial tBLASTx comparison of the caulimovirid genome library to the V. vinifera genome, the component B copies were only detected in positions corresponding to the MP domain, which is located at the 5′ end of the conceptually linearized component sequence. Subsequent extension of the MP sequence in both directions by 2 kbp, which is the default value for the pipeline, was not sufficient to retrieve the entire component B sequences, resulting in the presence of only truncated copies in the sub-genome database.
Run 3: chasing for component B Considering the results obtained in "run 2", we hypothesized that increasing the extension size around merged hits in step 1 would result in a better representation of full length component B sequences in the sub-genome database. CAULIFINDER Branch A was repeated with the same parameters and libraries as in "run 2" except that a 5 kbp extension on the merged hit loci was applied instead of the default of 2 kbp. As hypothesized, this modification resulted in improved lengths of component B consensus sequences (Fig. 3C  and Supplementary Fig. 1 ). In parallel, we noticed an increase in the number of sequences generated and a greater genome coverage (Table  1 ).