dataesr/scientific-paragraphs-categorization huggingface dataset:
If all sequences of a family lacked full-length homologs (i. e. , no mutual alignment coverage > 80%) but showed partial similarity with prokaryote sequences, the composite family was considered an S-gene family. These families were used to create HMM-profiles using hmmbuild (default parameters) from the HMMER  [74]  suite (version 3. 1b2) to search for distant homology. The corresponding HMM-profiles were used to screen the extended prokaryotic dataset using hmmsearch (E-value: 1 × 10 -5 ). When a full-length match (≥ 80% mutual coverage) was identified between an S-gene and a prokaryotic gene, the corresponding family was removed from the list of S-gene candidates. In theory, one S-gene could ultimately encode a non-S-protein, if the prokaryotic domain(s) of this S-gene were spliced out.
A more conservative taxonomic assignment was performed by considering the components carrying the following small and low complexity domains (according to the CDD database) as prokaryotic (LRR_4, WD40, LRR_RI, ANK, Kelch_1, Kelch_2, Kelch_3, Kelch_4, Kelch_5, Kelch_6, TPR, TPR_1, TPR_2, PPR, UBQ, MORN, FNI, Ube1_repeat1, Ubiquitin, TF_Zn_Ribbon, Zn-ribbon_TFIIS, UBL, RCC1, Ube1_repeat2, EFh, Kelch, FNIP, TPR_10, TPR_17, Zpr1, zf-ZPR1, UBA_EF-Ts, Ubox, S1, ZnF_C3H1, DnaJ, RING, UBA_PLICs, UBL, ZnF_C3H1). The presence of components exclusively found in eukaryotes (i. e. , without prokaryotic sequence hits) does not demonstrate that these domains are truly of eukaryotic origin. We cannot exclude the possibility that high divergence in eukaryotes and/or prokaryotes decreased sequence similarity to prokaryotic domains beyond recognition by BLAST.
Second, we verified whether only Diphoda were mixed in this/these partition(s), which is compatible (and even expected) in case of EGT and LGT or contamination. Based on these observations, we assigned a putative age to the gene family as old, if the S-gene distribution is primarily explained by vertical descent; likely old, when the mix between Opimoda and Diphoda was limited (for example, compatible with recent LGT that might explain the presence of the s-gene in some taxa but not in all of these taxa); and inconclusive, when LGT between Opimoda and Diphoda could not be ruled out. Regarding the phylogenetic reconstructions of the components of S-genes, HMM profiles of components were built and used to screen the prokaryotic database. For each profile, the 500 prokaryotic sequences having the best bitscores were retrieved (hmmsearch E-value: 1 × 10 -5 ).
Availability of data and materials Supporting data for this study, consisting of fasta sequences of S-genes, intron position alignments, components information and Phylogenetic trees have been made publically and freely accessible at http://www. evol-net. fr/ downloads/meheustEtAlSgeneEukaryogenesis. zip and in Additional file 21. Computer codes are freely accessible at https://github. com/raphael-upmc/ network/tree/master/other. Correspondence and requests for materials should be addressed to RM at raphael. meheust@gmail. com.
A screening of global databases of the barcode regions V4 and V9 of the 18S rDNA demonstrate the presence of these parasitoids beyond the North Atlantic coastal region. During a biweekly metabarcoding survey (Concarneau Bay, France), reads associated with one sequenced parasitoid coincided with the decline of Cerataulina pelagica bloom, whilst the other parasitoids co-occurred at low abundance with Pseudo-nitzschia. Our data highlight a complex and unexplored diversity of the oomycete parasitoids of diatoms and calls for the investigation of their phenology, evolution, and potential contribution in controlling their host spatial-temporal dynamics.
Sequence Analysis and Phylogenetic Reconstruction Sanger-sequences of the total or partial 18S rDNA of the specimens studied were assembled with Geneious 6. 1. 8 (Kearse et al. , 2012). Novel sequences were added to the alignment of  Gachon et al. (2017) , together with other oomycete sequences from GenBank, realigned with MAFFT  (Katoh et al. , 2002) , manually curated and trimmed, for a total of 81 sequences. Poorly aligned positions were removed with Gblocks 0. 91b  (Castresana, 2000 ; parameters in Supplementary Table  S2 ), which resulted in a 37% reduction in the length of the aligned sequences (from 1,896 bp to 1,206 bp). The dataset was then used to compute NJ (1,000 bootstraps) tree using the Tamura-Nei genetic distance model. A tree following the MP (100 bootstraps) criterion was computed using MEGA 7.
0. 14 (Tamura et al. , 2007)  default parameters. IQ-TREE 1. 5. 5  (Nguyen et al. , 2015)  was used to calculate a ML (1,000 bootstraps) phylogeny after assessment of the best fitting model of molecular evolution via ModelFinder  (Kalyaanamoorthy et al. , 2017) , resulting in TIM2+R4. As a control the manually curated alignment before automated Gblock trimming (1,344 positions) was used to estimate a phylogeny with ML and NJ methods as described above (Supplementary Figure  S1  and Supplementary Data Sheet S1). To further support the 18S based phylogeny, the amino acid sequences of the mitochondrial markers cox1 (260 aa) and cox2 (191 aa) for the parasitoid of Melosira cf. nummuloides were concatenated and aligned with a dataset of 25 oomycetes cox1 and cox2. Four diatoms were selected as an outgroup.
Assessment of OOM_1 and OOM_2 Global Distribution and Diversity The 18S rDNA sequences from samples 12-150, 13-374, 10-044, and 10-045 were used as a query to investigate the metabarcode datasets from BioMarKs  (Logares et al. , 2014) , Ocean Sampling Day  (Kopf et al. , 2015)  and TARA Ocean 1   (de Vargas et al. , 2015). Pseudo-nitzschia australis parasitoids 10-044 and 10-045 were merged into a single query, because their 18S rDNA V4 -V9 hypervariable regions are identical. 467 runs of the Ocean Sampling Day project (PRJEB8682, 162 biosamples) were screened using EDirect and SRA Toolkit utilities combined with in-house scripts referred to as MOULINETTE  (Badis et al. , subm. ). MOULINETTE extracts from metabarcoding projects deposited in the Sequence Read Archive all reads (if any), that match the given query sequence, and their GPS coordinates.
For the TARA Ocean and BioMarKs datasets, a blastn of, respectively, the V9 and the V4 hypervariable region was conducted  (Altschul et al. , 1990). Stations were considered positive and GPS points retained and mapped if they contained 10 or more merged paired reads with over 99% identity to the V4 -V9 hypervariable region of the query sequence of our diatom parasitoids. Maps were generated using the open source software GPS Visualizer 2  (Adam Schneider). The global distribution and diversity of the OOM_1 and OOM_2 clades were also investigated. Reads related to all the sequences of our parasitoids were further clustered into OTUs and their presence in metabarcode databases screened. For this purpose, SRA was screened using MOULINETTE for 1127 publicly available marine metabarcoding datasets using the hypervariable region V4 of the 18S rDNA.
The resulting alignment was used to compute a ML tree in MEGA 7. 0. 14 with bootstrap test of phylogeny set to 1000, using the best fitting model Tamura 3 parameters and gamma distribution (four categories) and 80% coverage cut-off. A second screening of SRA deposited metabarcoding databases was done to exclude the presence of the studied parasitoids in freshwater environments. Metabarcode datasets were selected using the keywords "Freshwater, " "Lake, " "Wastewater, " and "Aquatic" (see Badis et al. , subm. for details) resulting in the screening of 19,000 datasets (data not shown).
, 2014 (Mahé et al. , , 2015)) , with a default clustering threshold of d = 1 (according to  Mahé et al. , 2015). OTU taxonomic assignation was performed comparing the representative read of each OTU to the V4 reference database. Metabarcodes matching the 18S rDNA sequences obtained by single cell with a similarity above 97% and showing abundance above 10 reads in at least one sample were selected for downstream analysis. This resulted in one barcode matching the sequences 10-044/10-045 (97. 92% identity), five barcodes matching the sequence 13-374 (identity between 96. 87% and 97. 13%) and one barcode matching 12-150 (100% identity). The dynamics of these three groups were studied as a proxy for the presence of the subclades OOM_1_1 (seq. 10-045 related barcode), OOM_1_2 (seq. 13-374 related barcodes) and clade OOM_2 (12-150 related barcode), within the barcode dataset in relation to diatoms and Pseudo-nitzschia OTUs.
Global Distribution, Diversity, and Local Ecological Patterns The V4 and V9 hypervariable regions of the 18S rDNA sequences obtained from our SC isolates have been used to perform a stringent screening of public databases. Reads matching our reference sequences of parasitoids of P. fraudulenta, P. australis and P. cf. plurisecta were found in diverse marine zones, such as semi-enclosed, temperate high salinity regions (Mediterranean and Red Sea) and the three major oceans (Indian, Atlantic and Pacific, Figure  5 ). In publicly available databases, reads matching the parasitoid of P. australis 10-044/10-045 (red) within the OOM_1 clade were not found in the Mediterranean Sea, but in the Black Sea. In the Indian Ocean, only reads matching the parasitoid of P. cf. plurisecta 12-150 (green) within the OOM_2 clade were found.
Using all our Sanger sequences as queries (Table  1 ) of public datasets, we identified 11 and 4 additional OTUs that fall within OOM_1 and OOM_2, respectively. Within OOM1, two OTUs fall into the subclade defined by the sequences of the P. australis and M. cf. nummuloides parasitoids, with 99. 9% bootstrap support; four OTUs group with the sequence of the P. fraudulenta parasitoid, and define a sub-clade supported with 97. 5% bootstrap; five OTUs cluster with the parasitoid of P. pungens and form another sub-clade supported with 98. 9% bootstrap. Therefore, we named these three well-supported subclades as OOM_1_1, OOM_1_2, and OOM_1_3, respectively (Figure  6A ). These 15 OTUs further highlight a wide geographic distribution of the taxa within the OOM_1 and OOM_2 clades, at a global scale (Figure  6B ).
Bearing in mind that the type species for the genus of diatom parasitoids Ectrogella was first described infecting a freshwater diatom species of Synedra  (Zopf, 1884) , we attempted to detect sequences related to OOM_1 and OOM_2 in a custom selection of 19,000 public freshwater metagenomic datasets. Some sequences of soil-and freshwater-dwelling oomycetes were also included as a positive control. As expected, this screen yielded OTUs closely related to "crown" oomycetes (e. g. , Aphanomyces, Saprolegnia, Phytophthora) and early-diverging terrestrial oomycetes (e. g. , Haptoglossa). However, none of the OTUs retrieved from these non-marine environments could be confidently assigned to OOM_1 or OOM_2 clades as per their best blast hit against the n/r Database and 18S phylogenetic reconstruction (not shown).
cf. pungens/multiseries. Additionally, the clear polyphyly of the parasitoids observed here casts serious doubt as to whether all species that have tentatively been synonymised with extant Ectrogella taxa really correspond to identical, or even related, species. Finally, bearing in mind that the type species for Ectrogella (E. bacillariacearum) was described in a freshwater diatom (Synedra sp. ), a screening of 19,000 metabarcoding datasets from freshwater environments was conducted (totalling 487 Gb of 18S amplicons). Despite Synedra being present, no sequence affiliated to either OOM_1 or OOM_2 was retrieved, when in comparison, 15 OTUS affiliated to OOM_1 and OOM_2 were retrieved from a smaller marine dataset (totalling 43 Gb of 18S amplicons). Therefore, a hypothesis that should be fully considered is that OOM_1 and OOM_2 may be exclusively marine and that the type species for Ectrogella might be distinct from both clades.
A second implication of this potential lack of host specificity is that it would be speculative to match the ultrastructural data acquired by  Hanic et al. (2009)  on a parasitoid of P. pungens with either OOM_1_1 (as described by  Buaya et al. , 2017) , OOM_1_3 (our data, Figure  6a ) or perhaps even any other of our novel (sub)clade(s). OOM_2 forms a discrete and strongly supported clade sister to the [OOM_1 and Anisolpidium] group (90% in ML, 50% in MP, unsupported in NJ), that contains the parasitoid isolated from P. cf. plurisecta together with two uncultured eukaryotes sequenced in the context of marine stramenopiles diversity surveys  (Massana et al. , 2004; Lin et al. , 2012) , and the recently defined Olpidiopsis drebesii  (Buaya et al. , 2017). According to the extensive dataset developed here, OOM_2 is only weakly related to other Olpidiopsis species, despite it containing Olpidiopsis drebesii  (Buaya et al.
The highest abundance of parasitoid reads was recorded on June the 6th and belonged to the subclade OOM_1_2, (ca. 97% similar to the V4 18S rDNA of P. fraudulenta parasitoid). The most likely host of this putative parasitoid was the centric planktonic diatom Cerataulina pelagica, which accounted for 92. 14% of all diatom reads, thus forming a nearly monospecific bloom. On that date, the abundance of Pseudo-nitzschia was very low compared to the parasitoid read abundance (Supplementary Figure  S2 ), and direct cell count confirmed this finding (data not shown). Furthermore, while a crash in C. pelagica abundance coincided simultaneously to a peak in OOM_1_2, the abundance of Pseudo-nitzschia OTUs grew steadily until June the 29th, to reach the highest abundance in the dataset, further supporting the idea of Cerataulina as OOM_1_2 host.
Moving from a regional to a global perspective, this analysis suggests a broad distribution of the sequenced parasitoids, with occurrences of reads spanning water bodies as diverse as the Atlantic, Indian and Pacific Oceans, the Mediterranean, Black, Red, North and Caribbean Seas (Figure  5 ). This widespread distribution is even broader when OTUs (>97%) related to OOM sequences are considered (Figure  6b ), although the metabarcoding datasets analysed are not time series and accordingly, do not allow the inference of clear biogeographical patterns. A high sampling frequency will be required to resolve the seasonal and biogeographical patterns of such dynamic -putatively rare -protists that interact with other organisms. Therefore, the ecological relevance of these organisms needs to be addressed through directly targeted plankton surveys.
ACKNOWLEDGMENTS We are grateful to  Stéphane Audic ,  Nicolas Chomérat ,  Anne Doner ,  Audrey Duval ,  Chantal Le Gac-Abernot ,  Julien Quéré , and REPHY national monitoring network for technical support and stimulating discussions. All colleagues from the laboratories of  Ifremer Brest  and  Ifremer LER BO  who contributed to the DYNAPSE campaign are gratefully acknowledged. The objectives of this work tie in to the scope of the  French GDR (Groupement de Recherche)  on harmful microalgae Phycotox (https://www. phycotox. fr).
FUNDING This project has received funding from the  European Union 's  Horizon 2020 research and innovation programme  under the  Marie Skłodowska-Curie  grant agreement No. 642575 , the  FP7 Marie Curie Actions   PEOPLE-2007-2-2. ERG No. 230865 , and from the  United Kingdom NERC  under the grants GlobalSeaweed ( NE/L013223/1 ) and  MultiMARCAPP  ( NE/L013029/1 ). The  Loire-Brittany Water Agency (Agence de l'Eau Loire-Bretagne)  is acknowledged for funding the  DYNAPSE  project. The  "Laboratoire d'Excellence" LabexMER (Agence Nationale de la Recherche  - ANR -10-LABX-19 ) is acknowledged for supporting researcher exchanges between laboratories coauthoring this work.
This rather strict selection brought to a 55% reduction of the dataset (from 9,562,085 to 472,810 distinct sequences). Run sequence data are available here upon request 4. Before OTUs clustering, all sequences with percentage of identity (ID) to the reference database PR2 (Guillou et al. , 2013) ≤80% were removed, considering that values under this threshold lead to unreliable taxonomic assignation (Stoeck et al. , 2010; de Vargas et al. , 2015; Mahé et al. , 2017). The remaining reads were clustered by the agglomerative, unsupervised single-linkage- clustering algorithm Swarm 2 Finally, purified products were diluted to obtain equimolar concentration before being sequenced by Illumina R MISeq (2×250) at Genotoul. Samples tagging and library pools were (Edgar et al. , 2011)l. Quality check of the sequence data was performed via removal of reads with biased nucleotide (i.
Sampling and genomic datasets We sequenced the genomes of ten L. granatensis specimens sampled over the species distribution range in Iberia, five in the southern region not affected by mitochondrial introgression and five along the gradient of mitochondrial introgression in the northern half of the Peninsula (Fig. 1a ). Three L. timidus genomes, two from the Alps and one from Fennoscandia, were also sequenced (Fig. 1b ), and one L. americanus genome was used as outgroup for some analyses. All sequenced specimens were females. Using an iterative mapping approach  [32] , we built a hare pseudo-reference genome using the rabbit genome as template. This procedure increased average read mapping proportions from 92. 3 to 93. 6%. The median sequencing depth was 25. 9X, with a range between 22. 8X and 37.
b Empirical  [45]  and simulated mitochondrial DNA introgression frequencies in the ten sampled localities, ordered from Southwest (ALT) to Northeast (NAV). For the simulated data, dots depict the median introgression frequency value per population based on 1000 simulations and vertical lines represent 1. 5 × interquartile range (IQR) extensions (hereafter "mitonuc"). Such genes with high frequencies of introgression, paralleling that for mtDNA, would be of particular interest, so we used here the results of the RND test, more amenable to detect high frequency introgression. Of the 1211 mitonuc genes reported in databases  [49, 50] , 1178 were covered by at least one RND window passing our threshold of information content (see "Methods"). Among the 3312 genes overlapping introgressed regions (in at least one individual), 166 were mitonuc genes, which does not reflect an enrichment (Pearson's Chi-squared test p value = 0.
Adaptive introgression between the two species Independently of mitochondrial introgression, we searched for evidence of adaptive introgression in our system, in the frame of our demographic model. Evidence of adaptive introgression has now been suggested from the analyses of genomic datasets in several animal species, for instance, in humans  [88] , mice  [14, 89, 90] , butterflies  [8, 91, 92] , mosquitoes  [57, 93, 94] , or hares  [15]. The interrogation of the functions of 123 genes for which introgression frequencies could not be predicted by our simulations (Fig. 4b  and Table  1 ) revealed, in addition to spermatogenesis, enrichment in innate immune response functions. Adaptive introgression of immune-related genes has been inferred, for instance, in humans  [4, [95] [96] [97] [98] [99] [100] , mosquitoes  [101] , the Alpine Ibex  [102] , and house mice  [103, 104].
3  [115]. Soft clipped bases were further removed using NGSutils version 0. 5. 7  [116]. Reads were then realigned around INDELs using the Genome Analysis Toolkit (GATK v3. 2-2  [117, 118] ). Finally, Picard Markduplicates (http://broadinstitute. github. io/picard/) was used to remove read duplicates. Multi-sample SNP/genotype calling was carried out using the algorithm implemented in Samtools v1. 3 for each species independently, requiring minimum base and mapping qualities of 20. Species VCF files were then merged and genotypes filtered using a minimum site quality (QUAL) of 20, RMS minimum mapping quality (MQ) of 20, minimum individual coverage (FMT/DP) of 8X, and maximum overall coverage (DP) of 430X. For variable sites, a minimum genotype quality (FMT/GQ) of 20 was required. All sites failing any of the filtering criteria were coded as missing data.
Inference of introgression-Efficient Local Ancestry Inference (ELAI) In order to infer genomic segments of L. timidus origin introgressed in L. granatensis we used the Efficient Local Ancestry Inference (ELAI) method  [38]. This method implements a two-layer HMM (hidden Markov model) to infer local ancestry of admixed individuals without prior definition of window sizes, by looking at two layers of linkage-disequilibrium-within and among defined groups. It returns at each variable position in the genome the most likely proportions of ancestries (true values being expected to take values 0, 1, or 2 in two-way admixture). We ran ELAI on the unphased dataset and two population samples: L. granatensis defined as the admixed population, and L. timidus defined as one of the donors in the admixture.
To evaluate the power to detect introgression using ELAI we artificially introgressed random portions of chromosome 1 from L. timidus into L. granatensis using our phased data. Several introgression fragment sizes-10, 30, and 50 kb-were used. For each introgression tract length, we artificially "introgressed" 200 non-overlapping sequence tracts taken from one L. timidus haplotype (from the Alps), replacing the orthologous tract in a randomly chosen L. granatensis haplotype. The "introgressed" fragments had a minimum of 100 informative sites and did not span the centromere. Five artificially introgressed datasets were generated for each fragment length (i. e. , 1000 fragments per length) and ELAI was run as described above for the real data. We expressed the power of ELAI to detect introgression for each fragment length as the proportion of artificially introgressed fragments for which the average ancestry of informative SNPs within the fragment was at least 0.
Dating introgression To infer the age of introgression we used an approach based on identical by state (IBS) tracts of DNA shared within and between populations  [43]. We used the phased dataset for the ten L. granatensis individuals and the two L. timidus individuals sampled in the Alps to minimize potential effects of substructure within our geographically widespread L. timidus sample (Fig. 1b ). Only sites segregating in this subset were considered. Furthermore, sites with missing genotypes in L. timidus or more than 40% missing genotypes in L. granatensis were removed. We generated sets of IBS tracts shared within L. granatensis, within L. timidus, and between the species for the 21 autosomes. We excluded regions of low SNP density (centromeric regions, regions with more than 10,000 consecutive 'N' bases in the reference genome, or regions between SNPs that are 5000 bp or more apart) in order to avoid erroneously inferring large IBS tracts that span these regions.
IBS tracts shared between haplotypes from the same species are informative about the species demographic history while IBS tracts shared between species are informative about their divergence times and the fraction and timing of past genetic exchanges. We inferred demographic parameters under several demographic models, considering one or four pulses of introgression, and either constant or variable population size (Additional file 1: Table  S3 ). IBS tract length distributions within species and between species were computed and jointly fit to the observed data. In order to improve computation time and numeric stability, we binned the IBS tract length data by computing the expected abundance of tracts between (3/2) n and (3/2) n + 1 bp. We further excluded IBS tracts shorter than 300 bp (following Liu et al.