dataesr/scientific-paragraphs-categorization huggingface dataset:
transposition. Moreover, ONT allows detecting very recent TEIs that are sequenced as singleton reads. resources and support for our research project. T. -M. N. D. was supported by France Excellence. D. S. acknowledges financial support from France Génomique National infrastructure, funded as part of "Investissement d'avenir" program managed by Agence Nationale pour la Recherche (contract ANR-10-INBS-09). ".
Acknowledgments This work was supported in part by the  Fondation pour la Recherche Médicale , grant number " DEQ20180339167 " to S. C, by the  ANR Exhyb  to C. V. , by the  CNRS. We thank  J. Gonzalez  and  C. Goubert  for the discussion,  C. Jourdan  and  B. Barckmann  for  G0-F100 ,  G73  and  G0  DNA extraction,  C. Brun  for the polytene mapping and  D. Gourion  for the modelization and statistics in section 3. 10. We thank  Ndomassi Tando  and the  IRD itrop "Plantes Santé" bioinformatic platform for providing HPC.
Data Availability Long reads sequencing data used for this study have been deposited at ENA () under the accession numbers PRJEB39340 and ERP122844. The small RNA-seq datasets and the Illumina DNA-seq datasets were deposited in NCBI SRA (https://www. ncbi. nlm. nih. gov/sra) under the accession numbers PRJNA644327 and PRJNA644748, respectively. Release 6. 23 of the D. melanogaster genome and Release 2. 2 of the D. Simulans used in this study are available on FlyBase (http://www. flybase. org). Bioinformatic scripts and pipelines used for long reads analyses are available at https://github. com/DrosophilaGenomeEvolution/TrEMOLO and for small reads Illumina insertion at https://gitlab. in2p3. fr/pveber/te-insertion-detector/.
3D imaging of a trout ovary sample after C-Eci and 3D analysis of follicular content The C-Eci procedure for 3D ovarian imaging was applied to the rainbow trout that has much larger ovaries and oocytes (up to 4-5 mm) than medaka. Pieces of an ovary (about 8x6x2mm) collected from an adult female fish were processed through the C-Eci protocol and MG staining. Opacity was sufficiently reduced to see through the samples (Fig. 4A ). A sample was used for 3D imaging by confocal microscopy and the whole volume was reconstructed in 3D (Fig. 4B ). No major loss of resolution was observed in XY planes at increasing depths (Fig 4C, left panels). Follicles were segmented from the 3D reconstruction data using a computational semi-automatic 3D segmentation procedure (Fig. 4C , right panels). Follicles located at the border of the volume displayed could not be segmented and few others were not detected, likely due to a discontinuous MG staining (Fig.
3D image reconstruction and segmentation Whole specimens imaged by confocal microscopy were treated with the Amira 2019. 2 software (Thermo Fisher Scientific) on a 64-bit Windows 10 Pro computer equipped with a 2x Intel Xeon Silver 4110 (8 Core, 3. 0GHz) processor, a Nvidia Geforce GTX 1080 graphic card and 384 Go of RAM. To minimize the data volume, raw data initial size (spatial resolution 1,8 x 1,8 x 6 µm) were scaled to 0. 3 (1:3) in x and y. 3D segmentation of follicles was performed semi-automatically in the project editor of Amira using a combination of different operation with systematic 3D interpretation. To binarize the nuclear signal, the image gradient (canny deriche) was calculated and a Top-Hat was applied on resulting images to threshold nuclei. Images were inverted to visualize the internal part of follicles and connective tissues.
The extinction of species can destabilize ecological processes. A way to assess the ecological consequences of species loss is by examining changes in functional diversity. The preservation of functional diversity depends on the range of ecological roles performed by species, or functional richness, and the number of species per role, or functional redundancy. However, current knowledge is based on short timescales and an understanding of how functional diversity responds to long-term biodiversity dynamics has been limited by the availability of deep-time, trait-based data. Here, we compile an exceptional trait dataset of fossil molluscs from a 23-million-year interval in the Caribbean Sea (34 011 records, 4422 species) and develop a novel Bayesian model of multi-trait-dependent diversification to reconstruct mollusc (i) diversity dynamics, (ii) changes in functional diversity, and (iii) extinction selectivity over the last 23 Myr.
As such, questions on the mechanisms underlying this extinction event remain open. Caribbean molluscs provide an ideal model to understand the ecological responses of communities to extinction over long timescales  [3, 5, 6]. They have an unusually rich fossil record and have persisted through major environmental changes. Despite the widespread extinction event experienced by Caribbean faunas in the recent geological past, today the Caribbean Sea is a biogeographic zone categorized as a biodiversity hotspot  [35, 36]. Nevertheless, the entire region is vulnerable to anthropogenic threats such as acidification, climate change, invasive species, and overfishing  [37]. Here, we studied a 23-million-year fossil sequence of Caribbean molluscs based on a novel dataset comprising 34 011 records representing 4422 species (electronic supplementary material, figure  S1 ).
Consistent with our current knowledge on the habitat loss and sea-level oscillations that took place as Caribbean molluscs entered the extinction phase  [7, 20, 40, 42] , our models found statistical support for an environmental effect on extinction rates (figure  2a ). Within the neritic zone (i. e. above 200 m of depth, the zone in which all our samples lived; also see electronic supplementary material, dataset), species living in shore-face environments (less than 5 m of depth; also called intertidal; electronic supplementary material, table  S1 ) had the lowest extinction risk. We suggest that these species were better equipped to tolerate the dramatic eustatic changes that took place from the late Pliocene onwards, as they would constantly face large sea-level changes during their lifetime  [54]  in comparison to species living in other environments (e.
Material and methods (a) Dataset We compiled a dataset consisting of 34 011 fossil occurrences, representing 960 genera and 4422 species of Caribbean molluscs ranging from the Neogene to the Pleistocene/Holocene boundary (23. 03-0. 01 Ma; electronic supplementary material, dataset). The data was collected at the species level from the published literature and unpublished field observations available from the Paleobiology Database (https://paleobiodb. orgl) and museumdeposited species available from iDigBio (https://www. idigbio. org; both downloaded 23/12/2018). While each data portal maintains a taxonomic backbone, the nomenclature of these independent datasets was standardized using the World Register of Marine Species taxon match service for extant species and genera. This eliminated many synonyms, misspelling, and consistently enforced taxonomic ranks.
Extinct genera and species were accommodated within this taxonomic framework, with care taken to eliminate spelling variations. We defined the study area of the Caribbean following the marine ecoregions of the world  [56]  (as available at www. worldwildlife. org). Accordingly, our dataset only includes fossils from the 'Tropical Northwest Atlantic' province (between 5. 7°N and 35. 7°S). To identify relevant records, we used the speciesgeocodeR package v. 1. 8 in R  [57]. In a few cases  ([11] ; figure  1 ), we assigned sites that are located outside any of the provinces and are terrestrial today, to the geographically closest province, using the 'dist2Line' function of the geosphere package v. 1. 5-10 [58]. The age range of each occurrence was further refined using published references to place them into the finest stratigraphic resolution possible.
(b) Speciation and extinction rates We estimated speciation and extinction rates of Caribbean molluscs through a Bayesian analysis of all Neogene and Quaternary fossil occurrences identified to species level, while accounting for incomplete sampling due to fossilization and preservation processes and dating uncertainties. The stratigraphic ranges assigned to each occurrence were treated as dating uncertainty  [61]. We resampled the age of each occurrence randomly from a uniform distribution spanning its minimum and maximum age and repeated this procedure 10 times to generate 10 resampled datasets. All analyses described below were repeated on the 10 replicates and their results combined in a joint posterior sample to incorporate dating uncertainty in the results  [61]. Three main sets of parameters were simultaneously estimated using PyRate v.
(c) Functional diversity analyses We selected five traits (i. e. body size, life habit, locomotion, environment, and diet; electronic supplementary material, table  S1 ) that characterize the ecological roles of Caribbean molluscs  [63] [64] [65]. Traits were assigned to species using authoritative taxon-specific texts, online databases, and expert assessments based on both extant relatives and the fossil record (electronic supplementary material, dataset). There were limited differences in the number of species occurrences per trait category (electronic supplementary material, figure  S6 ). Based on the trait assignments, we calculated a series of functional indices in order to evaluate changes in functional composition and structure across the Neogene and Quaternary (23. 03-0. 01 Ma). To make this comparison, we used the longevity of each species estimated in PyRate (i.
(d) Multi-trait-dependent extinction model To assess whether extinction rates varied among lineages as a function of one or multiple discrete traits, we developed a novel multi-trait-dependent extinction (MTE) model, which was included in the PyRate software. We express the likelihood of the estimated longevity of species i, here indicated with t i , based on the birth-death model described by Silvestro et al. [61]  and assuming a lineage-specific extinction rate μ i. Thus, the likelihood of an extinct lineage i is computed as Pðt i jm i Þ / m i Â expðÀm i t i Þ, where t i is the lineage duration until extinction, while the likelihood of an extant lineage j is computed as Pðt j jm j Þ / expðÀm j t j Þ, where t j is the lineage duration until the present, or until the most recent boundary of the time window of interest.
Foundation and the  Federal Ministry for Education and Research (Germany). C. D. B was funded by the  Swedish Research Council  ( 2017-04980 ). D. S. received funding from the  Swiss National Science Foundation  ( PCEFP3_187012 ;  FN-1749 ) and from the  Swedish Research Council  (VR:  2019-04739 ). X. M. was supported by the  Swiss National Science Foundation  ( P2GEP2-178032 ). A. A. was supported by the  Swedish Foundation for Strategic Research , the  Swedish Research Council , the  Knut and Alice Wallenberg Foundation , and the  Royal Botanic Gardens, Kew. This research has received funding from the  European Union 's  Horizon 2020 research and innovation programme  under the  Marie Skłodowska-Curie  grant agreement no. 663830. Acknowledgements. We thank the curators, staff, and field collectors of the data presented in the electronic supplementary dataset, particularly of those institutions funded through the  National Science Foundation Advancing Digitization of Biological Collections Paleo-Niches Thematic Collections Network  accessed from iDigBio (NSF#  1206757 ,  1206769 ).
This study approximately doubles the number of published genomes of mycorrhizal fungi, and it samples major groups, for which there were previously no symbiotic genomes available, including Russulales, Thelephorales, Phallomycetidae, and Cantharellales. These groups are important, because they (1)  are often ecologically dominant (Russulales and Thelephorales), (2) represent early diverging clades for which no ectomycorrhizal genomes were previously available (Phallomycetidae and Cantharellales), and (3) include groups that arose before (Cantharellales) or after the origin of ligninolytic peroxidases (class II POD) implicated in white rot  26. This dataset presents an opportunity to carry out a broader analysis of the evolution of saprotrophic capabilities than that we previously attempted in our large-scale comparative analysis of Agaricomycetidae  12.
4a ), respectively. We estimated the MRCA of Pezizomycotina at 326 Mya (Fig. 4b ), while Floudas et al. 26  obtained a mean age of 344 Mya. We inferred gene families from the predicted proteomes using gene family clustering. A total of 68,923 and 129,258 gene families were identified in Ascomycota and Basidiomycota, respectively, and were used to infer orthology and paralogy (Supplementary Data 5). The species in our datasets contained substantial novelty in gene content. Species-specific genes ranged from 994 in T. melanosporum to 39,410 in Mycena galopus, for a total of 251,392 and 606,378 taxon-specific genes in Ascomycota and Basidiomycota, respectively. The large number of species-specific genes in M. galopus is the result of a series of striking expansion of gene families. Functional gene categories encoded by mycorrhizal genomes.
8% in the Boletales (two species), to 10% in Laccaria, and 14% in the Tuberaceae (Fig. 6 ). To test our phylostratigraphic results, we assessed the evolutionary conservation of 5917 ectomycorrhiza-induced transcripts identified in ten different ectomycorrhizal interactions among the 135 studied fungal genomes (Fig. 7a ). We found that 15. 2 % of the 5917 symbiosis-induced genes are shared by all species in our dataset (clusters IV and V). Most encode proteins involved in core metabolic or signaling functions. Genes from cluster III are conserved within Basidiomycota only, while genes from cluster VII are shared by Ascomycota only. Most of them have no known function (i. e. , no KOG domain). Altogether, 31. 2% of these symbiosis-induced genes are species specific (cluster VI). Most of these genes encode proteins with unknown KOG functions or mycorrhiza-induced small secreted proteins (MiSSPs).
Discussion After the origin of Pinaceae (ca. 200 Mya), ectomycorrhizal symbiosis arose repeatedly, perhaps 80 times or more, across multiple lineages of Mucoromycotina (Endogonales), Ascomycota, and especially Basidiomycota  [2] [3] [4] 8, 34, 35. The anc stors of ectomycorrhizal fungi are genetically and ecologically diverse, making this a superb example of convergent evolution  3, 7, 9, 12. To assess the general shared properties of the lifestyle evolution and functional biology of ectomycorrhizal symbioses, we conducted a comparative analysis of 62 mycorrhizal and 73 non-mycorrhizal fungal species. Our dataset, the most comprehensive so far, includes several major fungal clades that have not been sampled previously for ectomycorrhizal genomes, i. e. , Cantharellales, Phallomycetidae, Thelephorales, and Russulales.
The distribution of transcripts belonging to Ascomycota and Basidiomycota in each cluster is shown as pie charts. Data were visualized and clustered using R (package HeatPlus  97  ). The hierarchical clustering was done by using a Euclidian distance and Ward clustering method. Color scale on the left (white to red) shows the % of sequence identity according to BLASTP. The symbiosis-induced genes were retrieved from the ectomycorrhizal transcriptomes of ten species: A. macrosclerotiorum EW76-UTF0540, A. muscaria Koide, C. geophilum 1. 58, H. cylindrosporum h7, L. bicolor S238N, P. involutus ATCC 200175, P. croceum F1598, P. microcarpus 441, T. matsutake 945, and T. magnatum (see Supplementary Data 10, 13 and 14). GEO accession codes are provided in the "Methods/Phylostratigraphy" section. Source data are provided as a Source data Fig.
Mitochondrial genomes were assembled separately. All transcriptome sequencing was done with Illumina only, and subsequently assembled into putative transcripts using either Rnnotator 11 or Trinity  62. Each genome was annotated using the JGI Annotation Pipeline  63, 64  , aided by the transcriptome when available. Detailed methods are described in the Supplementary Methods. Organismal phylogeny. We assembled two datasets, one of Ascomycota that included 51 genomes and one of Basidiomycota that included 62 genomes. We opted for analyzing Ascomycota and Basidiomycota genomes separately, and only fungal genomes to improve computational efficiency and because the origins of the ectomycorrhizal symbioses are shallow evolutionary events, so we reasoned that additional resolution cannot be added by including non-fungal outgroup taxa.
Protein sequences were downloaded from MycoCosm (mycocosm. jgi. doe. gov). Clusters with single-copy genes were identified and aligned with MAFFT 7. 221 (ref. 73 ), ambiguous regions (containing gaps and poorly aligned) were eliminated, and single-gene alignments were concatenated with Gblocks 0. 91b (ref. 74 ). A phylogenetic tree was constructed with RAxML 7. 7. 2 (ref. 75 ), the standard algorithm, the PROTGAMMAWAG model of sequence evolution, and 1000 bootstrap replicates. Molecular dating. In order to time calibrate the phylogenies, we used the penalized likelihood algorithm as implemented in r8s (ref. 76 ) with the POWELL optimization. To identify the appropriate smoothing parameters, we performed a cross-validation analysis for each dataset. For the Basidiomycota phylogeny, the fossil Archaeomarasmius legetti from the mid-Cretaceous (90-94 Ma)  77  was used to calibrate the node containing the suborder Marasmiineae, and the fossil of a suilloid ectomycorrhiza from the middle Eocene (50 Mya)  78  was used to calibrate the node containing the suborders Boletineae, Paxillineae, Sclerodermatineae, and Suillineae.
For the Ascomycota phylogeny, the fossil Paleopyrenomycites devonicus from the early Devonian (~400 Ma)  79  was used to calibrate the split between Pezizomycotina and Saccharomycotina. The Basidiomycota supermatrix consisted of 940,059 sites from 1042 genes. All nodes were recovered with a bootstrap support of 96% or higher. The Ascomycota supermatrix consisted of 1,474,883 sites from 1432 genes. All nodes were recovered with bootstrap support of 100%. Analysis of PCWDE evolution. A comprehensive list of CAZyme annotations can be found in Supplementary Data 11 and at the Mycorrhizal Fungi page at the JGI MycoCosm database by following this link: https://mycocosm. jgi. doe. gov/ mycocosm/annotations/browser/cazy/summary;-6jBQE?p=Mycorrhizal_fungi. We investigated the evolution of gene families encoding CAZymes with a particular interest in PCWDEs, which include enzymes active on cellulose (various GH families, AA3_1, LPMO, and CBM1), lignin (AA1_1, AA1_3, AA2_class II POD, AA5_1, DyP, HTP, and OXO), and pectin (CE8, PL, and GH families; see also Supplementary Data 8).
We first performed all-vs-all Blast using mpiBlast 1. 6. 0 (ref. 80 ) for 51 Ascomycota and 61 Basidiomycota proteomes, and clustered proteins into gene families using the Hifix 1. 0. 5 clustering method, with default parameters resulting in 243,784 and 408,904 clusters, respectively. Identification of PCWDE families was performed based on InterPro (IPR) domains in the similarity-based clustering (see above). IPR data from the JGI website were used to annotate proteins with IPR domains for all species. We considered clusters as PCWDE families if at least 50% of the proteins had the appropriate IPR domain. Further classification of PCWDE families was supported by BLASTP 2. 6. 0+ search  80  , and validation by the CAZy annotation pipeline  81. Multiple sequence alignments were inferred using PRANK 140,603 with default settings, and trim-Al 1.
Class I and class II PODs were also curated by the RedoxiBase database (http:// peroxibase. toulouse. inra. fr) to distinguish between LiP, versatile PODs, MnP, or atypical Class II PODs. The latter PODs have been annotated as basidiomycete sub-class B (CIIBB) or sub-class C (CIIBC)  83. Phylostratigraphy. Phylogenetic ages of ectomycorrhiza-induced genes were assigned based on the most phylogenetically distant orthologous gene in the containing gene family, using a phylostratigraphic approach  84. Ectomycorrhizainduced transcripts were retrieved from RNA-seq-based transcriptome profilings: Amanita muscaria, GSE63867; Cenococcum geophilum, GSE83909; Hebeloma cylindrosporum, GSE63868; P. involutus, GSE63924; Piloderma croceum, GSE63925; T. magnatum, GSE116692; A. macrosclerotiorum, SRP130276 and SRP130279-82; L.
bicolor, SRP164436-38, SRP164526, SRP164559, and SRP164564; Pisolithus microcarpus, SRP122806, SRP122812, SRP122818, SRP122826, SRP122829, and SRP122850; and T. matsutake, SRP103258). Details of the RNA-seq experiments are described on Gene Expression Omnibus (GEO), NCBI. Note that this pooled set of ectomycorrhiza-induced genes represent a heterogeneous set of symbiosis-induced transcripts from ectomycorrhizal roots from different fungal-tree associations sampled at different stages of development, from early to late stage of symbiosis establishment. We selected those having an expression fold change (FC) > 5 and FC > 2 (Supplementary Data 9). The phylogenetic trees of the Asco-and Basidiomycota were divided into phylostrata, based on the phylogenetic distance in terms of internal nodes from the species being examined.
0. 2, with odb9 using default parameters  85. Secretomes were predicted as described in Pellegrin et al. We compared the different categories of secreted proteins (e. g. , CAZymes, lipases, and proteases) according to their lifestyle. Note that the comparisons of genomes are based on analyses of haploid genomes. P values were estimated from nonparametric multiple comparisons based on the generalized Campbell and Skillings' procedure with the function gao_cs in R package nparcomp  86. Distributions with the mean of the total count and statistically significant ecological groups (lifestyles; P < 0. 05) were determined. Genomic features (e. g. , genome size and TE coverage) were statistically compared between fungal lifestyles based on the GLS procedure with the Brownian motion model for random evolution, using R package nlme with the function gls  87.
Output files generated were combined and visualized with a series of custom R scripts, Proteomic Information Navigated Genomic Outlook (PRINGO), incorporating R graphic packages ggplot2, ggtree, egg, and ggpubr  [88] [89] [90] [91]. The coverage of TEs in genomes was calculated and visualized, using a custom pipeline named Transposon Identification Nominative Genome Overview (TINGO)  27. We determined the proportion of variances in genomic features explained by fungal ecological groups, and phylogenetic distances of 61 and 51 species from Basidiomycota and Ascomycota, respectively. Phylogenetic trees were converted into evolutionary distances of species with R package ape  92. Genomic features (i. e. , TE coverage, genome size, and predicted secreted proteins) were converted into numerical distances for PERMANOVA according to the workflow using R package vegan  93, 94.
Significantly different variables were examined (P value <0. 05; PERMANOVA; variables ~phylogeny + ecology). Significant differences among the fungal ecological groups were tested using pair-wise PERMANOVA with R package RVAideMemoire  95. Since the 5′ and 3′ LTR of LTR retrotransposons are identical upon insertion, we estimated their time since insertion using the number of substitutions that occur between the two LTRs as described in Castanera et al. 96. The list of softwares and R packages is provided in Supplementary Dataset 15.