dataesr/scientific-paragraphs-categorization huggingface dataset:
The presence of such SLs in gene-coding loci indicates the potential for mRNAs to be reintegrated into the genome as intronless genes after reverse transcription  (25). We examined the A. ceratii genome for SLs and traces of such reintegration events. None of the predicted gene models was associated with a full-length SL motif; however, five gene models had truncated motifs (fig. S2A  and table  S3A ). The low frequency of SL motifs at the genomic level suggests that mRNA reintegration events are rare. Fifty-three orphan full-length SL motifs were identified across 50 scaffolds (table  S3B ), and another 713 truncated SL motifs with identities of 73 to 100% (table  S3C ) were found. In the transcriptome dataset, 70 transcripts with single SL motifs were observed (fig. S2B ). Only one contig contained a second truncated SL repeat (60% identity to the consensus sequence), and no third or fourth SL repeats were identified.
S6 ) and no other pathway necessitates plastid presence, the metabolism of A. ceratii poses no apparent barrier to plastid loss. To examine whether more endosymbiontderived genes are present in A. ceratii, we classified all of its predicted proteins by using an automated phylogenetic pipeline  (32). Proteins clustering with red algae or green plants in phylogenetic trees populated from a local database of representative eukaryotic and prokaryotic sequences were manually inspected for potential plastid functions (Materials and Methods). No putative endosymbiont-derived proteins were identified. Overall, there is no evidence for a plastid in A. ceratii, and we conclude that it has lost the organelle altogether, presumably in its common ancestor with Hematodinium.
The coxI fragments span positions 323-370 (g15932) and 390-444 (g833) of the Pfam domain PF00115 (cytochrome c and quinol oxidase polypeptide I), which constitutes the C-terminal moiety of coxI (Fig. 4 ). Both are transcribed and spliced to remove a single intron, as confirmed by transcriptomic read mapping and reverse transcription PCR (RT-PCR). We found no evidence for trans-splicing, leading to a fusion of the two mRNAs. The same coxI fragments with nuclear attributes were recovered from the reference genomes of two other Amoebophrya strains, and they clustered with Plasmodium and other dinoflagellates in a phylogeny (fig. S8 ). No sequences encoding for coxIII, cytb, or the N-terminal part of coxI could be identified by in-depth searches of reads or assemblies from the other two Amoebophrya strains.
Assembly Quality filtering (cutoff of Q20) and adaptor trimming (~0. 3% of the total) of raw data were performed in CLC Assembly Cell (Qiagen Bioinformatics), and any reads <40 bp were discarded. All orphan (not paired) reads were also discarded. A total of 85,139,449 (PE data) and 85,139,449 (MP data) PE reads were retained after quality control and processed further. First, PE data were assembled using the assembly pipeline in CLC Assembly Cell using standard parameters (--wordsize default=automatic; -p fb ss 250 520). To reduce the number of contigs from contaminants, we selected contigs fulfilling at least one of the following criteria: (i) coverage of 30 to 300 and a minimum length of 1 kb or (ii) contigs hit EST (expressed sequence tag) reference/ database  (16, 19)  using BLAST with an identity threshold of 98% to account for potential sequencing errors.
The resulting contigs were bridged with MP data using SSPACE2 (insert size, 2000 bp; size error, 0. 65; -m 50; -k 15; -a 0. 5; -x 0) forming 4630 scaffolds. From this, sequences from cocultured bacteria (e. g. , Loctanella, Oceanicaulis, and Chlamydia) were identified via BLASTn and removed. We also removed the sequences originating from the Alexandrium host by matching transcript data to the genome assembly and also identified a few small apparently oomycete-derived contigs. After an initial screen of the largest contigs against the complete National Center for Biotechnology Information (NCBI) nucleotide database, genus-specific databases were set up for each identified contaminant, and these were used to rescreen the entire assembly. The coverage combined with the GC content of the contigs (GC content lower than 48%) provided a further measure of contamination.
In total, these screens identified 16 Mb that were removed as contaminants, leaving 87. 7 Mb (2351 scaffolds) defined as A. ceratii-specific sequences. The completeness of the genome was assessed via CEGMA using default settings  (24). Transriptomic data used in the study were obtained from two previously published studies  (16, 19). Gene prediction was performed using Augustus (http://augustus. gobics. de/). A training set that contained 16 genes previously identified with BLAST was constructed. After adjusting Augustus parameters, the previously generated EST sequences were used to detect bona fide splice sites. In total, 19,925 protein-coding genes were predicted, of which 4856 had transcript support. The Augustus predictions were evaluated using the self-training prediction tool genemarkES (http://exon.
gatech. edu/GeneMark/). The number of genes predicted with this tool was similar to that predicted by Augustus, and the overlap between the predicted gene sets was extensive. Translated amino acid sequences from the predicted gene models were annotated using the Trinotate pipeline and compared with the NCBI nucleotide database using the BLAST tool, with a cutoff e value of 10 -11 (http://trinotate. github. io/). Annotations were further supplemented by Kyoto Encyclopedia of Genes and Genomes mapping (https://www. genome. jp/kegg/) and were finally manually curated. Raw and assembled sequence data were submitted to GenBank under BioProject number PRJNA306142. Contigs (data file S1), scaffolds (data file S2), gff file (data file S3), predicted protein sequences (data file S4), assembly statistics (data file S5), and the annotation table (data file S6) generated by Trinotate were uploaded as auxiliary supplementary data with the article.
Phylogeny Predicted proteins from the A. ceratii genome were added into an alignment of 100 conserved protein sequences previously used in a multiprotein phylogeny of dinoflagellates  (31). Sequences were aligned by the local pair algorithm in MAFFT v7. 215 and stripped of hypervariable sites by using the -b 4 -g 0. 4 settings in BMGE v1. 1. Sequence orthology within single protein alignments was verified by maximum likelihood phylogenies (LG + Gamma 4 + F model) computed in RAxML v. 8 (https://sco. h-its. org/exelixis/web/software/ raxml/), and divergent paralogs and contaminant sequences were removed. Single protein alignments were concatenated in SCaFoS v1. 25 by using the o=gclv gamma=yes l=1 m=1 setting. Chimeric sequences were created for species where overlapping sequence fragments or nonoverlapping fragments of a congruent phylogenetic position were present.
Metabolic analysis of plastid dependency To determine whether A. ceratii contains a plastid organelle, we searched for plastidial and cytosolic pathways for the biosynthesis of isoprenoids, tetrapyrroles and fatty acids, and Fe-S cluster assembly. The predicted proteome of A. ceratii was searched by HMMER v3. 2. 1 at https://www. ebi. ac. uk/Tools/hmmer/ and BLASTP by using query sequences from Hematodinium, Oxyrrhis, apicomplexans, and chromerids. Hits at the e-value threshold of 10 -5 were collected and validated for specificity by reverse BLASTP analysis against the NCBI nr database. Most tetrapyrrole biosynthesis enzymes were also host-derived (ALAS, HemB, HemC, and HemE), but one (HemD) was apparently plastid-derived. The origin of all five heme enzymes was confirmed by using single-protein phylogenies (computed in IQ-TREE as described in species phylogeny above).
Acknowledgments: We thank  M. Sengco  for providing the parasite strain,  E. Bigeard  for maintaining the parasite in Roscoff,  S. Lepanse  ( MerImage platform in Roscoff ) for electron microscopic investigations,  I. Goerlich  for skillful and diligent assistance in the Illumina sequencing, and  Nancy Kühne  for support in the laboratory. Funding: Financial support was provided by the  PACES Research Program  of the  Alfred-Wegener-Institut, Helmholtz-Zentrum für Polar-und Meeresforschung , and the  China Scholarship Council (CSC). This work was also financially supported by  French ANR  projects  HAPAR  ( 2014-DEFI 1 ), the European Project  Micro B3  (contract  287589 ),  UCL Excellence Fellowship  (to J. J. ), and the  Natural Sciences and Engineering Research Council  ( RGPIN-2014-03994  to P.
Differential expression analysis. Pairwise comparison of each stage with the other four stages (loop-design) resulted in 10 datasets (one for each comparison). The number of transcripts after each comparison for different padj and log2 fold change ≥|2| is shown in Table  1. In the present study the most stringent parameters (transcripts with padj <0. 005 and with log2 fold change ≥|2|) were considered as differentially expressed. Hierarchical clustering of all differentially expressed transcripts grouped early developmental stages under one sub-branch, the next two under a second sub-branch and the last studied stage (24 hph) was placed on its own in a separate branch (Supplementary Fig. S2 ). Differentially expressed transcript abundance between the four stages and the two extreme stages studied is shown in Fig.
Similarly, expression levels of another group of genes, those involved in body pigmentation, and of their paralogs when present, were also searched in the annotated dataset. The list of genes that participate in body pigmentation (Supplementary Table  S8 ), as well as their categorization depending on the pigmentation pathway (melanin-, pteridine-and iridophore-related genes), was based on the study of Braasch et al. 21. Figure  7  illustrates the expression pattern of pigmentation genes found to be duplicated in the present study. Eight of the genes studied had similar expression patterns with their paralog (hdacI, en1, sox9, erbb3, silv, mcoln3, mitf, and csf1r). Notably, decreased expression of one paralog concurred with increased levels of the other paralog in the case of tyr, rab38, gja5 and itgb1 of the melanin-pigment related genes and spr of the pteridin-pigment related genes.
Discussion The present study investigated the expression patterns of five key developmental stages of the three-spined stickleback with a focus on paralogous genes. To primarily establish defined stage-specific "expression fingerprints", the transcriptome patterns during early embryogenesis were explored. The generated transcriptome profiles were used to evaluate the sampling and sequencing procedures. First of all, the three biological replicates of each stage as illustrated in Fig. 1a  were grouped together and separated from the other developmental stages validating effective staging and distinct transcriptomic profiles for each stage. Furthermore, a similar percentage of null counts and numbers of reads for each sample indicated comparable sequencing data between replicates of each stage and between stages (Supplementary Fig.
5)  42. For adaptor sequences as well as low-quality nucleotide reads removal, Trimmomatic (v. 0. 32)  43  was used. Cleaned sequencing reads were mapped to the three-spined stickleback genome (Gasterosteus_aculeatus. BROADS1. dna. toplevel. fa, http://ftp. ensembl. org/pub/release-76/fasta/gasterosteus_aculeatus/dna/) applying CRAC (v. 2. 5. 0)  44 , a software for mapping RNA sequencing reads to the genome with high precision in splice junctions. Genome-guided assembly was performed using Stringtie (v. 1. 2. 2) assembler  45  in two steps: the first step assembled the mapped reads of each RNA-Seq sample separately. The resulting assemblies were then merged, at the second step, into one transcriptome containing unique sequences. Annotation. Annotation of the assembled transcriptome was performed by i) mapping reads to the genome of the three-spined stickleback with default parameters, ii) submission to blastx search against the nr database of NCBI with E-value < 1 10 -8 and iii) using Blast2GO platform (v 4.
Differential expression. Trimmed reads of each stage were mapped to the constructed transcriptome with the RSEM estimation method (with align and estimate abundance. pl script of Trinity, r20140717); (parameters used: "RF" library type, "fq" sequence type, bowtie as aligning method)  47. The generated count matrix served as an input file for differential expression analysis, following the DESeq2 pipeline  48  integrated into SARTools  49. After pair-wise comparison, transcripts with padj <0. 005 and log2fold change ≥|2| between two stages were considered as differentially expressed. Data evaluation. Principal component analysis (PCA) was used to evaluate the quality of the whole data matrix using plot3D function in R. Furthermore, sample clustering was performed with WGCNA (Weighted Correlation Network Analysis) to detect outliers and group all differentially expressed transcripts according to their expression pattern, using the "block-wise network construction and module detection" R script which is appropriate for large datasets  50.
Heat maps were constructed with heatmap. 2 function in R, using normalized count data of differentially expressed transcripts. Enrichment analysis. Two-tailed enrichment analysis with default parameters (filter value: 0. 05 filter mode: FDR, two-tailed) was performed applying Blast2GO (v 4. 1. 9) with padj ≤0. 05 filter  46. All the annotated transcripts were used as a reference dataset and modules 1, 2, 3 and 4 that counted the majority of the transcripts (Fig. 3 ) were used as test set. Paralog identification. To detect paralogous genes, extra gene copies were examined in all differentially expressed transcripts between different developmental stages of the three-spined stickleback's developmental stages. In the present study, two genes were considered as putative paralogs if they combined the following: i) they shared identical or similar (-like) annotation terms and ii) they mapped in different chromosomes or linkage groups.
Data Availability All reads resulting from Illumina sequencing were submitted to the public database of SRA (Sequence Read Archive) of NCBI under the PRJNA395155 Bioproject, comprising 15 biosamples (one biosample for each library) coded from SAMN07374938 (early morula, replicate 1) to SAMN7374952 (24 hph, replicate 3). This Transcriptome Shotgun Assembly project has been deposited at DDBJ/EMBL/GenBank under the accession GHCM00000000. The version described in this paper is the first version, GHCM01000000.
In silico analysis of TcSBP The TcSBP cDNA was identified from a library of T. cacao meristem (genotype TSH1188) infected by M. perniciosa  [11]. The complete sequence of the entire gene and of the corresponding proteins was obtained from the CocoaGenDB database (http://cocoa-genome-hub. southgreen. fr/). Open reading frame (ORF) detection was performed using the ORFinder software (http://www. ncbi. nlm. nih. gov/gorf/gorf. html). Sequence homology search was made with BLAST  [12]  on the National Center for Biotechnology Information (NCBI). Multiple sequence alignment was performed with the ClustalW2 software  [13]. The prediction of theoretical isoelectric point (pI) and molecular weight (MW) was obtained using the Expasy Molecular Biology Server (www. expasy. org)ProtParam tool (https://web.
The tree was constructed using the neighbor-joining method of ClustalW, with 1000 bootstraps. The search for regulatory sequences of TcSBP was performed in the CocoaGenDB database. A region of 1500 bp upstream the UTR was identified and selected for cis-element analysis. The presence of the cis-regulatory elements in the promoter region of TcSBP was analyzed using the plantCARE (sphinx. rug. ac. be:8080/PlantCARE/cgi/index. html) software  [20].
Molecular modeling of TcSBP and docking analysis The prediction of the three-dimensional (3-D) model of the TcSBP protein was obtained using the Swiss-Model server and the Automated Protein Homologymodeling, which relies on the high similarity of target-template  [21]. The crystal structure of the Sulfolobus tokodaii SBP was used as template (Protein Data Bank code: 2ECE) to build the structural model of TcSBP. The stereochemical quality of the models was evaluated using the Procheck  [22]  and Anolea programs  [23]. The 3-D model visualization was obtained using the PyMol (The PyMOL Molecular Graphics System, Version 1. 5. 0. 4 Schrödinger, LLC. ) and Discovery Studio 4. 0. For docking analysis of TcSBP 3-D model (receptor) with inorganic selenium compounds (ligands), selenite and sodium selenite were search by keyword in the PubChem databases (http://pubchem.
ncbi. nlm. nih. gov). The 2D structure of these compounds were copied in Similes and saved in PDB format using the Marvin program, and then used for molecular docking. The ligand and receptor molecules were prepared using the AutoDockTools 1. 5. 6  [24]. The grid definition, adjusted to active site, was set up manually according to the recommendations of the program manual  [24, 25]. The ligand and receptor structures were then saved in pqbqt format to be used for docking calculations. AutoDock Vina was used to perform Docking Scoring for each ligandreceptor complex  [25]. Before running each Docking calculation, a configuration file was generated with information about grid size and coordinates, and indicating the ligand and receptor files. The reports for each calculation were analyzed to obtain affinity energy (kCal/Mol) values for each ligand conformation in its respective complex.
Moreover, the presence of M. perniciosa in the plant material was checked by semi-quantitative RT-PCR using specific M. perniciosa actin primers  [26] ; both genotypes presented fungus incidence (data not shown) coherent with previous data obtained in the same conditions of plant culture and inoculation  [4, 26]. Apical meristems were harvested at 24, 48 and 72 hours after inoculation (hai), and 8, 15, 30, 45, 60 and 90 dai. Non-inoculated plants (controls) were kept and harvested under the same conditions at 72 hai, 60 dai and 90 dai. For each genotype and at each harvesting time (for inoculated and non-inoculated plants), 20 samples were collected (1 sample = 1 apical meristem of 1 cacao plantlet). The 20 samples collected from one genotype at one harvesting time were pooled; thus 9 inoculated and 5 non inoculated (control) samples were immediately frozen in liquid nitrogen and stored at -80°C until use.
The expression analysis of TcSBP was performed using standard settings of the ABI PRISM 7500 and Sequence Detection System (SDS) software, version 1. 6. 3 (Applied Biosystems). The qPCR reaction consisted of 10 ng/μl of cDNA, 1 μM of each primer from reference or targets genes (Supplementary material 1) and 11 μl of Power SYBR Green Master Mix (Applied Biosystems) in a total volume of 22 μl. Cycling conditions were: 50°C for 2 min then 95°C for 10 min, followed by 40 cycles at 95°C for 15 s, 60°C for 30s and 60°C for 1 min. To verify that each primer pair produced only a single PCR product, a dissociation analysis was carried out from 60°C to 95°C and analyzed with the Dissociation Curve 1. 0 program (Applied Biosystems). The expression analysis of TcSBP in meristems of cacao plantlets inoculated or not with M.
perniciosa, was obtained by RT-qPCR using 6 experimental technical replicates. The relative expression was analyzed with the comparative Ct method (2 -ΔΔCt ) using MDH and ACT as endogenous reference genes and non-inoculated plants (control) as calibrator. Statistical analysis was made using the SASM-Agri software which tested the experiments as a completely randomized design. t-test and F-test (ANOVA) were applied with a critical value of 0. 01. The Scott-Knott (P≤0. 01) test was employed for mean separation when Fvalues were significant.
In silico analysis of TcSBP In silico analysis on CocoaGenDB revealed the presence of a unique sequence encoding TcSBP located on the chromosome 4 (Fig. 1 ). The TcSBP gene is 4774 bp in length, contains 7 exons and 6 introns and has an ORF of 1431 bp (Fig. 1 ). TcSBP encodes a protein of 476 amino acids, with a MW of 52732. 7 Da and a putative pI of 5. 74. Twenty-one putative phosphorylation and no N-glycosylation sites were observed in the TcSBP protein sequence (Fig. 2A ). TcSBP does not contain any signal peptide, and its subcellular location is unknown (data not shown). The alignment of TcSBP with SBPs from other organisms revealed high identity (>70%) with plant species and human and allowed the identification of the CSSC, DELHH and HGD conserved region (Fig. 2A ). The full-length amino acid sequences of plants and mammalian SBPs available in the databases were used to build an unrooted dendrogram (Fig.
Acknowledgements The work of AMMA was supported by the  Fundação de Amparo à Pesquisa do Estado da Bahia (FAPESB). The work of SPM, EML and MMF was supported by the  Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES). FM, KPG and CPP were supported by a  Productivity grant  from  Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq). This research was supported by the  FAPESB  project ( DTE0038/2013 ) coordinated by FM. The authors thank  Francisca Feitosa Jucá  ( UESC/Ceplac ) and  Louise Araújo Sousa (Ceplac)  for technical help in plant inoculation experiments,  Dr. Gesilvado Santos  ( UESB ) for advices and discussion about SBP role. This research was made in the frame of the  International Consortium in Advanced Biology (CIBA).
Little auk occurrence data. Current occurrence data were direct observations drawn from three open access databases, the Global Biodiversity Information Facility (https://www. gbif. org/), the Ocean Biogeographic Information System-Spatial Ecological Analysis of Megavertebrate Population (http://seamap. env. duke. edu/) and the North Pacific Pelagic Seabird Database (https://www. usgs. gov/centers/asc/science/ north-pacific-pelagic-seabird-database?qt-science_center_objects=0#qt-science_center_objects), and complemented with information on breeding locations from the literature  [47] [48] [49]  and from the Norwegian Polar Institute 50 (Russian data excluded). Only dated and located data for which environmental variables were available (see below), were conserved and duplicates deleted. Museum data weren't considered.
Overall, we used respectively 67, 68 and 580 occurrences to model nesting, summer foraging and wintering habitats. Environmental data. To predict nest site distributions, we used monthly mean air surface temperatures from 1948 to 2018 retrieved from National Oceanic and Atmospheric Administration (NOAA)'s 0. 5° Global Historical Climatology Network version 2/Climate Anomaly Monitoring System. Since little auks breed underground and are limited by snow cover in their access to nest cavities, we calculated the percentage of time during which the ground was covered by snow two months before, and during the breeding period using the National Snow and Ice Data Center (NSIDC)'s IMS 4 km Daily Northern Hemisphere Snow and Ice Analysis between 2006 and 2017. Also, since little auks are central-place foragers during the breeding season, and have a constrained foraging range during that period, we created a discrete variable to deal with the distance from the coast (<10 km, <20 km, <50 km, <100 km, <200 km or > =200 km).