dataesr/scientific-paragraphs-categorization huggingface dataset:
tgz within the data archive available from https://ir. library. msstate. edu/bitstream/ handle/11668/19731/Tice_etal. PhyloFisher. DATA. tar. gz. Fig V : Phylogenetic tree of ortholog EOG093403TH of the dataset from  [5]. Tree was inferred using the sgt_contructor. py as detailed in the main text. Tree is the output from parasorter. Leaf names in bold are identified by the fisher. py algorithm as suggested orthologs. Leaf names not bolded are from the sequences collected as potential paralogs. The leaves with a colored background are those sequences from the dataset from  [5]. Problematic paralogs are highlighted with red arrows, and the corrected replacement identified by PhyloFisher are highlighted by blue arrows. A downloadable figure and data associated with this figure are available in the directory archive FigS-X.
tgz within the data archive available from https://ir. library. msstate. edu/bitstream/ handle/11668/19731/Tice_etal. PhyloFisher. DATA. tar. gz. Fig W : Phylogenetic tree of ortholog EOG09340RBX of the dataset from  [5]. Tree was inferred using the sgt_contructor. py as detailed in the main text. Tree is the output from parasorter. Leaf names in bold are identified by the fisher. py algorithm as suggested orthologs. Leaf names not bolded are from the sequences collected as potential paralogs. The leaves with a colored background are those sequences from the dataset from  [5]. Problematic paralogs are highlighted with red arrow. A downloadable figure and data associated with this figure are available in the directory archive FigS-X. tgz within the data archive available from https://ir. library.
msstate. edu/bitstream/ handle/11668/19731/Tice_etal. PhyloFisher. DATA. tar. gz. Fig X : Phylogenetic tree of ortholog EOG093400WO of the dataset from  [5]. Tree was inferred using the sgt_contructor. py as detailed in the main text. Tree is the output from parasorter. Leaf names in bold are identified by the fisher. py algorithm as suggested orthologs. Leaf names not bolded are from the sequences collected as potential paralogs. The leaves with a colored background are those sequences from the dataset from  [5]. Problematic paralogs are highlighted with red arrows, and the corrected replacement identified by PhyloFisher are highlighted by blue arrows. A downloadable figure and data associated with this figure are available in the directory archive FigS-X. tgz within the data archive available from https://ir.
library. msstate. edu/bitstream/ handle/11668/19731/Tice_etal. PhyloFisher. DATA. tar. gz. Fig Y : Phylogenetic reconstruction of the tree of Saccharomycetaceae using the PhyloFisher 208 dataset. ML tree built using (LG +G4+F+C60-PMSF model, with an LG+G4+F+C20 ML tree as a PMSF guide input tree) in IQ-TREE v1. 6. 7. 1  [1]. Sub-clades that make up the Saccharomycetaceae are shown in dark blue, while the outgroup clades of the Saccharomycodaceae and the Phaffomycetaceae are shown in dark green and cyan. Nodes are maximally supported (100 MLBS) unless otherwise shown. Data associated with this figure are available in the directory archive FigY. tgz within the data archive available from https://ir. library. msstate. edu/bitstream/handle/11668/19731/ Tice_etal. PhyloFisher. DATA. tar. gz. ML, maximum likelihood; MLBS, maximum likelihood bootstrap support; PMSF, posterior mean site frequency; RTC, relative tree certainty.
Full genus names are written out to the right of the upper left ML tree. Nodes are maximally supported (100 MLBS or 1. 0 LPP) unless otherwise shown. The full tree of the PhyloFisher 208 dataset is available in the Supporting information (Fig Y in S1 Text). LPP, local posterior probability; ML, maximum likelihood; MLBS, maximum likelihood bootstrap support; PMSF, posterior mean site frequency.
Full genus names are written out to the right of the upper left ML tree. Nodes are maximally supported (100 MLBS or 1. 0 LPP) unless otherwise shown. The full tree of the PhyloFisher 208 dataset is available in the Supporting information (Fig Y in S1 Text). LPP, local posterior probability; ML, maximum likelihood; MLBS, maximum likelihood bootstrap support; PMSF, posterior mean site frequency. https://doi. org/10. 1371/journal. pbio. 3001365. g004.
tgz within the data archive available from https://ir. library. msstate. edu/bitstream/handle/11668/19731/Tice_etal. PhyloFisher. DATA. tar. gz. Fig Q:Cartoon tree of the tree generated by RTC sorted bins (top 75%, 180 orthologs, 63,750 sites) using rtc_binner. py. Input datasets for the matrix_constructor. pyconcatenationwas gt80trimal single ortholog files. This tree was inferred in IQ-TREE under LG+G4+F+C60+PMSF with an LG+G4+F+C20 input tree for generation of the PMSF site frequencies inferred in IQ-TREE with 1,000 ultrafast bootstrap replicates (UFBOOT). UFBOOT values of 100% are not shown; all other values are indicated at their respective node. Data associated with this figure are available in the directory archive FigQ. tgz within the data archive available from https://ir. library.
Problematic paralogs are highlighted with red arrows, and the corrected replacement identified by PhyloFisher are highlighted by blue arrows. A downloadable figure and data associated with this figure are available in the directory archive FigS-X. tgz within the data archive available from https://ir. library. msstate. edu/bitstream/handle/11668/19731/Tice_etal. PhyloFisher. DATA. tar. gz. Fig T: Phylogenetic tree of ortholog EOG093409ME of the dataset from.
Acknowledgments PhyloFisher was named during a fly fishing trip on the White River in Arkansas with AKT, TP, DZ ˇ, and MWB; we thank  Patrick Brown  for lending his cabin. We thank  Prof. Edward Susko  at  Dalhousie University  for allowing us to distribute the programs DIST_EST and MAMMaL with our PhyloFisher package. We would like to thank  Kristina Terpis  for her advice on fine-scale taxonomy of the Stramenopiles. We are also grateful to  Jolien van Hooff  and  Nicholas Fry  for acting as beta testers.
The software package is house on GitHub (https://github. com/ TheBrownLab/PhyloFisher). All data associated with the figures (Figs  3, 4,  and Figs A-Y the user to make more informed decisions regarding orthology assignments, leading to a more accurate final dataset. along with the accompanying PhyloFisher data are available from the archive https://ir. library. msstate. edu/handle/11668/19731. Additionally, the software package can be installed via Conda or PIP.
To the right of each Saccharomycetaceae clade is an abbreviation made up of the first letter of each genus included in the clade. Full genus names are written out to the right of the upper left ML tree. Nodes are maximally supported (100 MLBS or 1. 0 LPP) unless otherwise shown. The full tree of the PhyloFisher 208 dataset is available in the Supporting information (Fig Y in S1 Text). LPP, local posterior probability; ML, maximum likelihood; MLBS, maximum likelihood bootstrap support; PMSF, posterior mean site frequency.
Duplicated Genes and TE Number and Distribution Protein sequences from the human reference genome (Ensembl hg38) were used to define the duplication status of 20,213 protein-coding genes (see Materials and Methods). Three datasets with different levels of stringency for the definition of duplicated genes were generated. The criteria defining homologous gene sets were of medium stringency for dataset 1 and we will mainly focus on this dataset, while highlighting notable differences observed with the more stringent dataset 2 and the less stringent dataset 3. The distribution of duplicated and singleton genes for each dataset is shown in table  1. Before computing the TE environment of each gene, we sought to verify whether any structural bias between duplicated and singleton genes could confound our analyses.
Specifically, the intronic fraction was significantly larger for singleton genes compared with duplicated genes (Wilcoxon test, dataset 3, P value ¼ 0. 002 with Bonferonni correction, supplementary fig. S1C  and table  S1 , Supplementary Material online). The median number of exonic regions is therefore nine for both duplicated and singleton genes. However, for the less stringently defined gene set, the median number of exonic regions is eight for singleton genes, but nine for duplicated genes. The median gene length was longer for duplicated compared with singleton genes although not at a significant level (Wilcoxon test, P value ¼ 0. 2432, supplementary fig. S2  and table  S1 , Supplementary Material online) except for the least stringently defined gene set (Wilcoxon test, dataset 3, P value ¼ 1.
Please note, that in all the results, we refer to "2-kb (or 10-kb) flanking region" as the contiguous sequence going from 2 kb (or 10 kb) upstream of a gene to 2 kb (or 10 kb) downstream of the gene including the gene itself. The exonic sequences within these regions were not considered (see Materials and Methods). The total numbers of TEs found in the environment of genes correspond to 826,444 and 967,135 insertions for 2and 10-kb flanking regions respectively (see Materials and Methods and supplementary table  S2 , Supplementary Material online). Among the 20,213 genes included in the datasets, 833 and 76 genes did not contain any TEs inside and in their 2-and 10-kb flanking regions respectively. Table  2  shows how the four TE categories are distributed in and around genes according to the different sizes of flanking regions.
S6 , Supplementary Material online and Materials and Methods). We did not find any differences in recombination rates for duplicated genes compared with singleton genes after Bonferonni correction for multiple tests (Wilcoxon tests, dataset 1, raw P value ¼ 0. 03834; dataset 2, raw P value ¼ 0. 08513; dataset 3, raw P value ¼ 0. 02856). It is known that TEs are not randomly inserted according to GC content  (Lander et al. 2001; Grover et al. 2004 ). The higher numbers of TEs in the environment of singleton genes compared with those of duplicated genes might be partly explained by the GC content of genes and their vicinity  (Vinogradov 2005; Jjingo et al. 2011). TE gene fractions have been highly correlated with human gene length (GL)  (Jjingo et al. 2011 ). To study the specific relationship between the duplication status and the TE density in the vicinity of genes independently from selection pressure and the GC content, we considered a linear model with TE density as the dependent variable, the duplication status as the explanatory variable, and the K a /K s ratios, GC content, recombination rate, and GL as covariables.
On the contrary, LINE densities where higher around duplicated genes compared with singleton genes. LTR retrotransposons followed the same tendency but not to a significant extent for all datasets (table 3). We observed significant relationships between the GC content and the TE density of gene contexts for all TE categories with negative relationships for LINEs, DNA transposons, and LTR retrotransposons for both flanking region sizes. SINEs have also accumulated significantly in the vicinity of genes with a lower GC content in 2-kb flanking regions and in 10-kb flanking regions when considering the interactions of GC content with recombination rates and K a =K s ratio (table  3  and supplementary tables S7 and S8, Supplementary Material online). According to our linear model analyses, K a =K s ratio was significantly negatively associated with the overall TE densities for 2-and 10-kb flanking regions and for all datasets (supplementary tables S7 and S8, Supplementary Material online).
It should be noted that we detected a negative relationship between GL and TE density when considering all TEs and DNA, SINE, and LTR elements separately. However, a positive relationship with GL was found for LINEs for 2-kb flanking region size. According to the linear models, a positive relationship was found between TE densities and recombination rates for all TEs and DNA, SINE, LINE, and LTR elements separately for 2and 10-kb flanking regions and all datasets. These relationships were significant with interactions taken into account, especially those with GL and GC. An exception was that no significant relationship was found for LINE density for 2-kb flanking regions. A negative relationship between recombination rates and TE distribution would be expected, but this expectation is based on considering intergenic regions too.
Gene Functions Are Associated to TE Environment and Duplication Status To decipher whether the functions of the human genes could explain the relationship between the duplication status of a gene and its TE environment, we compared the functions of the human genes with different densities of TE. Gene functions were assigned by the PANTHER V14 software  (Mi et al. 2019)  to GO-slim annotations for the three GO ontologies Biological Process (BP), Molecular Function (MF), and Cellular Component (CC). In total, for the 2-kb flanking regions, 4,752 TE-poor genes, 9,821 TE-medium genes, and 4,922 TE-rich genes were assigned GO-slim annotations, and for the 10-kb flanking regions respectively 4,731, 9,826, and 4,937 genes (supplementary table S11, Supplementary Material online). We compared the functions of the duplicated genes and the singleton genes for each TE density category (see Materials and Methods).
TE Density Is Related to Gene Essentiality To further explore the links between the duplication status of a gene and its TE context, we considered whether not only the function but the essentiality of a gene could be important. Essential genes can be defined as those indispensable for reproductive success of a living system or those required to support cellular life, for example. Rather than being a static binary property, recent studies suggest that gene essentiality is both context dependent and can evolve  (Liu et al. 2015; Wang et al. 2015; Chen et al. 2017; Rancati et al. 2018). We retrieved human gene essentiality data from OGEE,  (Chen et al. 2017 ) which is composed of 18 datasets, all but one corresponding to cancer cell line experiments. We observed that duplicated genes are less likely to be essential than singleton genes (v 2 ¼ 237.
6, df ¼ 1, P value < 2. 2 Â 10 À16 table 4 and supplementary table S12, Supplementary Material online). We then defined five categories of essentiality for these genes, "Essential" (E) genes are defined as those that are essential in all the test datasets, "Conditional Restricted Essential" (CRE) genes as those that are essential in fewer than 25% of the test datasets, "Conditional Medium Essential" (CME) genes as those that are essential in between 25% and 75% of the test datasets and "Conditional Largely Essential" (CLE) genes as essential in more than 75% of the test datasets. The remaining genes were defined as "Non essential" (NoE) (see Materials and Methods and supplementary table  S13 ; SINE, Kruskal-Wallis v 2 ¼ 804. 12, df ¼ 4, P value < 2. 2 Â 10 À16 ; LTR retrotransposons, Kruskal-Wallis v 2 ¼ 172.
31, df ¼ 4, P value < 2. 2 Â 10 À16 ). Results were similar for the 10-kb flanking regions, and when TE density was replaced by TE coverage in the comparisons (supplementary table  S14 , Supplementary Material online). Thus, to better study the relationship between the duplication status and the TE density of genes, we reverted to the linear models with TE density as the dependent variable, the duplication status as the explanatory variable, and GL, the K a /K s ratio, GC content, and recombination rate as covariables, and added a categorial covariable of gene essentiality (Ess), expressed as the proportion of OGEE datasets in which each gene was essential relative to the number of OGEE test datasets listing the gene. Genes were considered as non-essential when the essentiality value was below a threshold of 0.
The relationship between TE density and essentiality for total TEs, DNA transposons, and SINEs was significant for 2and 10-kb flanking regions, but this was not true for LINEs. For LTR retrotransposons this relationship was also significant for all datasets, but sometimes only through interactions with GL or GC (table 5 and supplementary tables S15 and S16, Supplementary Material online). When all TEs are considered along with the interactions with GC, recombination rates, K a =K s and GL (table 5), the positive relationship indicates that higher TE densities are found in the vicinity of essential genes compared with non-essential genes. This relationship is more nuanced when each TE type is considered separately. Higher densities of SINEs and DNA transposons are found around essential genes compared with non-essential genes, when interactions with GC, recombination rates, K a =K s and GL are taken into account (table 5 and supplementary tables S15 and S16, Supplementary Material online).
The exceptions often relate to the most stringent dataset 2, the 2-kb flanking regions, and some interactions. Our results thus indicate the tendency for DNA transposons and SINEs to be more dense in the environment of duplicated genes than in the environment of singleton genes. However, when all TEs are considered together the relationship between duplication status and TE density varies from one dataset to another and between the two flanking region sizes (table 5 and supplementary tables S15 and S16, Supplementary Material online). When TE coverage was used instead of TE density, this relationship was significant for all analyses (supplementary tables S17 and S18, Supplementary Material online).
The SINE Alu superfamily was present at significantly higher densities around singleton genes compared with duplicated genes (also for 10-kb flanking regions for datasets 2 and 3) and around highly essential genes compared with less-essential genes. The SINE MIR superfamily did not show a significant relationship between TE densities and duplication status. MIR densities were however significantly higher around highly essential genes compared with lessessential genes for all datasets and flanking regions sizes with the exception of dataset 1 for 2-kb flanking region (table 6 and supplementary tables S20-S23, Supplementary Material online). As for all LINE elements, the L1 superfamily showed significantly higher densities in duplicated genes compared with singleton genes, and a tendency, significant in most cases, to be denser in less-essential genes compared with highly essential genes.
This could explain some of the observed differences between our datasets. For example, it has been shown that chimeric duplicated gene copies may be longer with more exons than the original genes  (Courseaux and   Nahon 2001). In Populus trichocarpa, duplicated genes originating from WGD are longer than other genes, whereas tandem duplicated genes are significantly shorter  (Rodgers-Melnick et al. 2012). According to  Nakatani et al. (2007)  and  Makino and McLysaght (2010) , about 20-30% of the protein-coding genes in the human genome are derived from WGD, relatively ancient events  (McLysaght et al. 2002; Dehal and Boore 2005). Moreover, it has been estimated that 25-40% of the recent gene duplications are generated by tandem duplications in mouse and human  (Pan and Zhang 2008) , accounting for 10-18% of genes in the latter  (Shoja and Zhang 2006).
It should be noted that in this study we retrieved human gene essentiality data from OGEE  (Chen et al. 2017 ) which is based on 18 datasets, all but one corresponding to experiments in cancer cell lines. We can hypothesize that an essential gene for a tumor cell line may be non-essential in a healthy cell. If the presence of SINEs and DNA transposons deregulates these genes in tumor conditions, it is possible that this deregulation confers an essentiality status on the genes in the cell line. It would be thus interesting to further study the deregulation of essential genes according to their TE environment. Cases of exonization have been observed for the Alu sequences in humans  (Amit et al. 2007; Sela et al. 2007). Such events seem to happen more often for the Alus than for the other TEs probably because of their structure  (Sela et al.
When the merging was complete, we computed the percentage of the protein length aligned and the average percentage of identity over the aligned regions. After merging BLAST results, we constructed three gene family datasets according to the level of identity between putative gene family members. For this, protein pairs with 30% identity covering respectively 70%, 80%, and 50% of the protein length were retained as potential paralogous gene pairs forming, respectively, datasets 1-3. For each candidate gene pair, the highest bit score among all hits between their isoforms was included in the dataset. We used the Walktrap method developed by  Pons and Latapy (2005)  to gather gene pairs into families for each dataset. To do so, a density parameter of a gene in a family was defined as the proportion of the other genes of the family it is linked to.
Starting from the set of all genes, families were then recursively split by the Walktrap algorithm until each gene was either isolated or had a density parameter greater than a given threshold. Single-linked nodes were notably avoided by such a procedure. We constructed datasets of families according to density thresholds of 50%, 70%, and 30% respectively for the datasets 1-3. The chosen values for identity coverage and density levels are arbitrary, allowing us to verify that the analyses are relevant for more or less stringent definitions of homologous gene sets. According to our procedure, dataset 2 corresponds to the most stringent definitions of duplicated genes and dataset 3 the least stringent, whereas dataset 1 can be considered as a trade-off between the two. Results for dataset 1 alone are presented except when results are notably different for the other two datasets.
Computation of the Density and Coverage of TEs in the Vicinity of Genes Positions of TE sequences were determined using RepeatMasker annotations (reference of the human genome UCSC hg38) and parsed with the perl tool "One code to find them all" (Bailly-Bechet et al. 2014) using the -strict option. We first estimated the number of TEs within and around genes using each TE position to allocate it to a gene vicinity. To define the vicinity of a gene, we considered either the 2and 10-kb flanking regions located upstream and downstream of the gene as described by  Mortada et al. (2010). Indeed, it has been demonstrated that the promoter regions of human genes can be located as much as 10 kb upstream of the gene start, although the majority of promoters are within a 2-kb region upstream  (Kim et al.
Between the two BLAST processes, only the BRH was retained. We obtained 16,645 putative human-chimpanzee orthologous gene pairs in this way. Multiple sequence alignments of coding sequence (CDS) and corresponding proteins (in FASTA format) were performed using CLUSTALW  (Thompson et al. 1994 ) using default options. Protein sequence alignments were subsequently imposed on the coding region of nucleotide sequences using a PERL script based on Bioperl functions. For all pairwise alignments, the number of nonsynonymous substitutions per nonsynonymous site (K a ), the number of synonymous substitutions per synonymous site (K s ), and the K a /K s ratio were calculated using the PAML package  (Yang 1997 (Yang , 2007) )  according to the  Yang and Nielsen (2000)  model.
Estimation of the Recombination Rate Both physical and genetic distances were used to estimate recombination rates (Recomb, measured in cM/Mb) along human chromosomes. The average genetic and physical position of 5,076 deCODE markers  (Kong et al. 2002)  were downloaded from UCSC (https://genome. ucsc. edu/cgi-bin/ hgTables) in June 2020. For each chromosome, we calculated the best-fitting LOESS curve between physical and genetic distance data using the MareyMap Online tool (http://lbbeshiny. univ-lyon1. fr/MareyMapOnline/) (Siberchicot et al. 2017). The recombination rate (Recomb) was estimated as the derivative of the LOESS curve using the MareyMap Online tool. Negative Recomb estimates were removed from the analysis.