dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements. This study is part of the  ANR  project  COCOA  (https://anr. fr/Projet- ANR-16-CE01-0007 ,last access:  10 May 2021 ). This work was granted access to the HPC resources of TGCC under an allocation made by  GENCI  ( Grand Équipement National de Calcul Intensif , grant  2019-A0040100239 ). It benefits from the development of the common modeling IPSL infrastructure coordinated by the IPSL climate modeling center (https://cmc. ipsl. fr, last access:  10 May 2021 ). Data files were prepared with  NCO  (NetCDF Operators; Zender, 2008, and http://nco. sourceforge. net,last access:  19 May 2021 ). Sketches are drawn with LibreOffice (https://www. libreoffice. org,last access:  10 May 2021 ). Plots and histograms are produced with Matplotlib (Hunter, 2007, and https://matplotlib.
org,last access:  10 May 2021 ) in Jupyter Python notebooks. Maps were drawn with pyFerret, a product of  NOAA's Pacific Marine Environmental Laboratory  (http://ferret. pmel. noaa. gov/Ferret, last access:  10 May 2021 ). Patrick Brockman  and  Jean-Yves Peterschmitt  brought invaluable help in the realization of the figures. We thank the reviewers for their kind yet helpful reviews: the original paper studied the IPSLCM6 legacy parallel algorithm, and the sequential ones were added from their suggestions. Financial support. This research has been supported by the  ANR  project  COCOA  (grant no. ANR-16-CE01-0007TS5 ) and by  Grand Équipement National de Calcul Intensif (GENCI , grant no. 2019-A0040100239 ). Review statement. This paper was edited by  Qiang Wang  and reviewed by  Li Liu  and  Jan Streffing.
2019; McGeary et al. 2019; Sheu-Gruttadauria et al. 2019). Changes in the sequences of the seed or of the 3'CR might lead to undetectable effects on regulatory efficiency or might cause dramatic alterations in the set of targeted transcripts  (Neilsen et al. 2012; Ameres and Zamore 2013; Tan et al. 2014). Across species, the seed and the 3'CR of orthologous miRNAs appear to be under selective pressure for sequence preservation  (Wheeler et al. 2009; Fromm et al. 2015) , arguing for conserved regulatory functions if miRNA binding sites in the 3'UTRs of targeted transcripts are also preserved. Sequence evolution of duplicated miRNAs, however, has never been studied following WGD. Furthermore, it remains unknown whether miRNA genes that are retained in two copies produce mature miRNAs that tend to have identical sequences or whether, following an initial period of neutral drift or selection, ScholarOne, 375 Greenbrier Drive, Charlottesville, VA, 22901 Support: (434) 964-4100 Downloaded from https://academic.
2020)  because genes unknown in any non-teleost species are unlikely to have been present in the pre-TGD common ancestor of teleosts and gar, which we refer to as the Teleost-Holostei last common ancestor or TH-LCA. Orthology relationships among miRNAomes were carefully assigned for each species by both sequence similarity and synteny conservation. These comparisons identified in medaka six additional orthologous miRNA gene loci that were not detected in expression datasets. The absence of expression data for these miRNA genes could be related to insufficient sequencing depth, the limited number of tissues and developmental stages studied, or to expression loss. Unfortunately, we cannot distinguish among these possibilities. To identify putative gene losses, we carefully checked for potential gaps in genome assemblies at genomic regions where the genes would have been expected from conserved synteny data, for absence of smallRNA reads originating from genes that would be missing in the genome assembly but potentially present in the biological genome, and the shared absence of genes in genome assemblies of related species, together suggesting true losses.
The mir430 gene family was also excluded from our analysis. Although mir430 genes have been well studied and are crucial for early embryonic development in zebrafish  (Giraldez et al. 2006) , the evolution of this family remains uncertain. miR-430-3p mature products show great sequence conservation across species, especially in their seed (Additional File 4A), their synteny is in contrast not conserved. We identified two clustered intragenic mir430 genes in the spotted gar genome assembly (Additional File 4B) and many intergenic mir430 genes and clusters in each of the genome assemblies of the four studied teleost species -at least 55 genes in zebrafish, 58 in medaka, 139 in stickleback and 157 in icefish -but none of these genomic regions displayed any conserved synteny with any other species in our dataset (Additional File 4B).
We therefore excluded mir430 genes from our analysis because it was impossible to establish reliable orthology relationships between its members across species, to infer the organization of mir430 genes in the TH-LCA, and thus to decipher the evolutionary history of this outlier, highly duplicated and apparently mobile miRNA gene family. Together, our analysis led to sets of 233, 308, 283, 275, and 290 miRNA genes in gar, zebrafish, medaka, stickleback, and blackfin icefish, respectively, whose orthologs can also be found in non-teleost vertebrate species even if no expression could be measured in the studied expression datasets (Fig. 1A , Additional Table  1 ). Finally, to search for miRNA genes that might have been present in the TH-LCA but lost in the gar lineage, we searched for genes that are present in non-teleost vertebrates (e.
Orthology relationships (Fig. 1B , Additional File 5) revealed that most (76%) miRNA genes were present in all four studied teleost species (Fig. 1C ). In addition, 31. 3 ± 3. 0% of the TH-LCA miRNA genes were present in two ohnologous copies in the genome assemblies and/or smallRNA sequencing data of the studied teleost species, ranging from 35. 8% in zebrafish to 27. 9% in stickleback (Fig. 1A , Additional Table  1 ). This retention rate of miRNA ScholarOne, 375 Greenbrier Drive, Charlottesville, VA, 22901 Support: (434) 964-4100 genes in duplicated copies is significantly higher than the estimated 12-24% retention rate for protein coding genes  (Braasch and Postlethwait 2012; Pasquier et al. 2017 ) (p < 2. 22e-16 and p = 4. 5e-6 for protein-coding gene retention rates of 12% and 24%, respectively, Repeated G-tests of goodness-of-fit (RGT)).
Finally, these data revealed that the branch leading from the hypothesized Clupeocephala ancestor to the hypothesized Percomorphaceae ancestor experienced moderate gene loss with an average loss of 3. 92 genes per 10 MY over a period of 102 MY (Fig. 1C ). This rate could be related to genome evolution concomitant with the diversification of the enormous Acanthomorpha group, which counts about 16,000 marine species and represents about 85% of marine ray-finned fish species and about a third of all vertebrate species  (Wainwright and Longo 2017). The reconstruction of hypothetical ancestor miRNAomes does not account for possible convergent losses across lineages. Our dataset, however, revealed four examples of asymmetrical ohnolog retentions (e. g. , retention of the 'a' copy in one lineage and of the 'b' copy in another), ScholarOne, 375 Greenbrier Drive, Charlottesville, VA, 22901 Support: (434) 964-4100 representing 1.
1D, Additional File 6). These results represent the first genome-wide quantitative demonstration that clustered miRNA genes are more likely to be retained after whole genome duplication, and confirm previous observations made on subsets of teleost clusters  (Chan et al. 2012; Marco et al. 2013; Sun et al. 2013; Wang et al. 2016 ). Several possible hypotheses might help explain this finding. First, clustered miRNA genes are generally co-expressed in a polycistronic transcript  (Altuvia et al. 2005; Baskerville and Bartel 2005; Chan et al. 2012; Bartel 2018) ; therefore mutations that alter the regulation of cluster expression might simultaneously non-functionalize the entire cluster. Second, clustering of ScholarOne, 375 Greenbrier Drive, Charlottesville, VA, 22901 Support: (434) 964-4100 miRNA genes improves the processing of non-optimal hairpins within the cluster  (Fang and Bartel 2020; Shang et al.
2018). To analyze organ-specific enrichment of miRNAs in spotted gar, we followed the expression of mature miRNAs that displayed an overall expression of at least 5 Reads-per-Million (RPM) averaged over the entire dataset; 284 of the 329 gar miRNAs that Prost! detected (86. 3%) met this criterion. Organ pairwise differential expression analysis using DESeq2  (Love et al. 2014)  showed that the brain displayed the greatest number of differentially expressed (DE) miRNAs among the four studied organs; the gonads (testis and ovary) displayed the fewest DE miRNAs and showed the largest intragroup variability (Fig. 4A-B ), in agreement with previous findings for zebrafish and stickleback ScholarOne, 375 Greenbrier Drive, Charlottesville, VA, 22901 Support: (434) 964-4100  (Desvignes et al. 2019).
2015 )) (Fig. 7A1 ); 2) Co-major strands (sometime called "co-dominant" strands), which are expressed at average levels less than two fold differently (Fig. 7A2 ); 3) Minor strands (sometime called "passenger" or "star" strands), which are strands that are expressed at average levels less than half the amount of their complementary strands (Fig. 7A1 ); and 4) Missing strands, which are strands that were not expressed in our datasets or any public annotation databases that we examined (Fig. 7A3 ). A few miRNA genes that had no expression for either strand were discarded because they might represent pseudogenized genes that would be under different selective pressure. Each pair of ohnologous strands within each species was then categorized as: 1) major strand pair when at least one of the corresponding strands from the two ohnologs is a major or co-major strand (Fig.
3 SNPs per pair (Fig. 7D , Additional Table  10 ) (p-adj < 1. 00e-7 and p-adj = 0. 0407 compared to major and minor strand pairs respectively, AOV). These results demonstrate that the most highly expressed strands of miRNA genes were under purifying selection for sequence conservation and that minor strands were under more relaxed purifying selection. In contrast, strands missing from our expression data experienced even more relaxed selection for sequence conservation, potentially only constrained to preserve folding of the precursor miRNA, which is crucial for biogenesis of the major strand  (Fromm et al. 2015). ScholarOne, 375 Greenbrier Drive, Charlottesville, VA, 22901 Support: (434) 964-4100.
2019). Third, strands that are missing expression in our dataset displayed evenly distributed SNP frequencies throughout their sequence and may thus be evolutionarily constrained only to preserve folding of the miRNA hairpin and thus biogenesis of their major complementary strand  (Fromm et al. 2015). Fourth, minor strands displayed an evolutionary pattern intermediate to that of major and missing strands with about a third of all minor strand pairs preserved as identical ohnologs, an average number of SNPs between ohnologous pairs intermediate between that of major and missing strands, and, similar to major strands, evidence of purifying selection in the seed and 3'CR sequences compared to other nucleotides of the mature miRNA. While the evolution of major strands would be under constraints to preserve mRNA regulation, and missing strands under constraints to preserve hairpins or duplex structures, the evolution of minor strand sequences reflects an interplay between purifying selection preserving miRNA biogenesis and existing regulatory functions and relaxed selection enabling the emergence of novel targets and thus novel roles  (Guo and Lu 2010; Fromm et al.
Fourth, our analysis of SNP frequency in post-TGD miRNA pairs revealed the importance of functional conservation and diversification in modulating miRNA sequence evolution. Specifically, we demonstrated that major strands are under stronger purifying selection than minor or missing strands and that the selective pressure for sequence conservation of major strands was the strongest in the seed and the 3'CR, as expected if major strands play a more important role in regulating specific messenger RNAs compared to minor strands and greater selective pressure for function conservation in line with the role of the seed and 3'CR in target recognition. In contrast, strands that were missing expression in our dataset may be evolutionarily constrained only to preserve the folding of the miRNA hairpin and thus the biogenesis of their major complementary strand.
Origin of smallRNA sequencing data Small RNA sequencing data analyzed in this study are publicly available under accession numbers SRP063942, SRP039502, SRP151190, SRP157992, and SRP069031 for spotted gar, zebrafish (AB strain), medaka (Cab strain), stickleback (Boot Lake fresh water strain), and blackfin icefish (West Antarctic Peninsula), respectively  (Desvignes et al. 2014; Braasch et al. 2016; Desvignes et al. 2016; Gay et al. 2018; Desvignes et al. 2019). Small RNA read analysis using Prost! Raw Illumina sequencing reads were processed as in  (Desvignes et al. 2019). For each species, libraries were simultaneously analyzed using Prost! (Desvignes et al. 2019)  selecting for read length 17 to 25 nucleotides and aligning reads to the species' reference genome using bbmapskimmer. sh version 37.
2019) , the published Blackfin Icefish annotation  (Kim et al. 2019) , the extended zebrafish miRNA annotation  (Desvignes et al. 2014) , and the published gar miRNA annotation  (Braasch et al. 2016). All annotation datasets used in this study are available on the Prost! Github page (https://github. com/uoregonpostlethwait/prost). The miRNA and isomiR nomenclature used follows the rules established for zebrafish  (Desvignes et al. 2015; Ruzicka et al. 2019; Desvignes et al. 2020). For differential expression analysis in gar, zebrafish, medaka, and stickleback, only sequences with a minimum of 30 reads were retained. From Prost! output, we used the non-normalized read counts of annotated miRNA reads to perform differential expression analyses using DESeq2  (Love et al. 2014 ) with a cutoff adjusted p-value of 0.
01 to draw conclusions on differential expression as previously described  (Desvignes et al. 2019). Heat maps and similarity matrices were generated using the Broad Institute Morpheus webserver (https://software. broadinstitute. org/morpheus/) and log2-transformed normalized counts from annotated miRNAs that displayed a minimum normalized average expression of 5 Reads-per-Million (RPM) over the entire dataset. Hierarchical clustering on rows and columns was performed using the "one minus Pearson's correlation" model and the "average" linkage method.
Each hit was manually checked for potentially being an already annotated paralogous gene or the missing miRNA gene, and small RNA sequencing data were searched for expression of miRNAs from the identified locus. If no BLASTN alignments were returned, an analysis of conserved syntenies was performed to determine the genomic region where the miRNA gene "should" have been compared to the outgroup (e. g. , between two protein-coding genes or intragenic within a certain protein-coding gene). This genomic region was then searched for expressed reads in the small RNA sequencing dataset and if an expressed read displayed any features suggesting that it could originate from the searched miRNA gene. Second, it is possible that a miRNA gene is missing from a genome assembly due to assembly gaps and errors.
Acknowledgments This work was supported by the  National Institutes of Health  (grant numbers  NIH   R24 OD011199 ,  NIH   5R01 OD011116 , and  NIH   R01 GM085318  to JHP), the  National Science Foundation Office of Polar Program (NSF   OPP-1543383  to JHP and TD), and  Agence Nationale de la Recherche  ( ANR-18-CE20-0004  to JB). This work benefited from access to the  University of Oregon  high performance.
Sequence Retrieval The results of the amino acid sequence search showed that 14 protein sequences from nine C. vulgaris strains of the UTEX259 UTEX259 culture collection (taxid 3077)-scaffolds met determined criteria. The accession numbers of Transcriptome Shotgun Assembly (TSA) and Whole Genome Shotgun (WGS) sequences are given in Table  1andTable  2, respectively. As can be seen from the Table  1 , all found lipase sequences belong to AB_hydrolase family (Interpro number IPR029058) and display Acyl hydrolase motif GXSXG. Nine of them show high sequence identity to Lipase_3 domain-containing protein (Chlorella variabilis) from the ESTHER database. Two sequences, namely Lip_5800 and Lip_5999, present high identity with sn1-specific diacylglycerol lipases alpha from Auxenochlorella protothecoides and Micractinium conductrix, respectively.
Discussion TAG lipases responsible for the degradation of the lipids accumulated in oil bodies are attractive knockdown targets for the enhancement of the lipid productivity and storage in microalgae. Nonetheless, considering the numerous data available on bacterial, terrestrial plant and animal lipases those from algae and more especially microalgae have been relatively neglected. Therefore, more emphasize has to be given to the characterization of algal lipases, and hence, further work is needed in these aspects. Future approaches to maximize the enzymatic potential of microalgae are likely to focus on three different strategies: (i) the use of ever-increasing amounts of available omics data to optimize microalgal strains for the production of valuable products, through the overexpression of one or more enzymes by the use of genome editing tools; (ii) the identification and subsequent characterization of metabolic pathways involving the production of specific enzymes, such as lipases which are still poorly characterized; (iii) the search for genes with direct biotechnological applications in microalgal genomes and transcriptomes datasets.
The feasibility of employing any of the aforementioned approaches or a combination of them will be directly influenced by progress in growth and genetic manipulation of microalgae. In this study, we have used computational approach to identify lipase genes and to classify the respective lipases from a C. vulgaris strain. Lipases operate usually at the interface between lipid and water. An important feature of many lipases that is used for lipase classification is the presence of a mobile subdomain lid or flap located over the active site  [22]. Among the 14 putative TAG lipases identified after C. vulgaris genome analysis, 10 have high identity in ESTHER database with Lipase_3 domain-containing protein. Family 3 of lipolytic enzymes are widely distributed in animals, plants and prokaryotes and possess the conserved consensus sequence GXSXG.
Sequence Retrieval BlastP search was performed using amino acid sequences of functionally characterized lipases from terrestrial plants (Trifolium pretense and Diplocarpon rosae), fungi (Colletotrichum chlorophyti), microalga (Scenedesmus sp. and Symbiodinium microadriaticum) and bacteria (Pseudomonas fluorescens and B. subtilis) available in the NCBI database (http://ncbi. nlm. gov/protein/). The FASTA sequences were searched using tblastn modality against Transcriptome Shotgun Assembly database (TSA) of C. vulgaris strain UTEX259 UTEX259 (taxid 3077) and every hit with an E-value < 10 -5 was identified as putative Lipase transcript. The open reading frames (ORFs) were searched using the ORF finder program  [42]  and the longer ones were blasted a second time against non-redundant protein database to ensure that the respective TSA corresponds to a putative Lipase ORF.
The selected TSA sequences were submitted to a blastn search against the whole Genome Shotgun contigs (WGS) database of the same C. vulgaris strain (taxid 3077) and single hits with E-value < 10 -100 were identified as scaffolds with putative Lipase genes. Gene predictions from the selected WGS scaffolds were performed using ab initio gene models through Augustus  [43]. The application was trained on the gene structures of Chlamydomonas reinhardtii and the TSA sequences were used in cDNA uploaded option. The final output ORF and protein sequences were saved for further in silico analysis.
Physicochemical Characterization of Protein Sequences Basic physicochemical properties such as molecular weight, extinction coefficient, isoelectric point, aliphatic index, grand average of hydropathicity and instability index were estimated by ProtParam tool (http://web. expasy. org/protparam/) [35]. Extinction coefficients were calculated assuming all pairs of Cys residues form cystines or assuming all Cys residues are reduced. Sequence analysis and lipase motifs search were performed with InterPro  [45]  and the Expasy my hits search tool (https://myhits. isb-sib. ch/cgi-bin/ motif_scan), respectively. These sequences were also compared in the ESTHER database to check higher sequence identity  [19]. For predicting subcellular localization Deepmito  [46] , Mitoprot v1. 101  [47] , HECTAR v1.
Tertiary Structure Prediction, Structure Validation and Quality Prediction Three-dimensional models of the selected putative enzymes were generated using different approaches. For sequences with acceptable homology in the template of the programs, UCSF Chimera (https://www. rbvi. ucsf. edu/chimera/) and the automated protein homology modeling server SWISS-MODEL (http://swissmodel. expasy. org/) were used. For sequences with low homology with the structures in the database, multiple-threading alignments using the I-TASSER approach (zhanglab. ccmb. med. umich. edu/I-TASSER/) was used. I-TASSER is an automated bioinformatics tool for predicting protein structures from an amino acid sequence followed by iterative structural assembly simulations and atomic-level structure refinement. The predicted structures were evaluated to ensure correctness of the model stereochemistry, as checked by a Ramachandran plot (http://mordred.
bioc. cam. ac. uk/~rapper/ rampage. php) (Lovell et al. , 2003)  and Verify 3D  [52]. The Ramachandran plot scores of the predicted structures showed more than 90% of the amino acids were in favorable regions. ProSA-web Z-score plot (https://prosa. services. came. sbg. ac. at/prosa. php) [53]  was used to check whether the Z-score of the input structures is within the range of typically found for the native proteins of a similar size. The Z-score values of all protein structures checked in this study were highlighted as a black dot, which indicates being in the range of native conformations. The final modeled structures were further energetically minimized and molecular dynamics simulation was performed with CABS-flex 2. 0 (http://212. 87. 3. 12/CABSflex2). The latter program is an efficient simulation engine that allows modeling of the large-scale conformational change related to protein flexibility  [54].
variabilis) E1ZMR0 GHLX01002999. 1 IPR029058, AB_hydrolase IPR002921, Fungal_lipase-like PF01764, Lipase_3 GHSLG 56. 8% Lipase_3 domain-containing protein (C. variabilis) E1Z559 GHLX01001704. 1 IPR029058, AB_hydrolase IPR002921, Fungal_lipase-like PF01764, Lipase_3 GHSLG 56. 1%Lipase_3 domain-containing protein (C. variabilis) E1ZAU0 GHLX01004551. 1 IPR029058, AB_hydrolase IPR002921, Fungal_lipase-like PF01764, Lipase_3 GHSLG 53. 5% Lipase_3 domain-containing protein (C. variabilis) E1Z6D5 GHLX01003928. 1 IPR029058, AB_hydrolase IPR002921, Fungal_lipase-like PF01764, Lipase_3 GHSLG 54. 5% Lipase_3 domain-containing protein (C. variabilis) E1ZMR0 GHLX01006297. 1 IPR029058, AB_hydrolase IPR002921, Fungal_lipase-like PF01764, Lipase_3 GHSLG 61. 7% Lipase_3 domain-containing protein (C. variabilis) E1Z6D6 GHLX01001795.