dataesr/scientific-paragraphs-categorization huggingface dataset:
OeuAQPs are mentioned in red, and OleurAQP in bold. The name of each subfamily and subgroup is indicated next to the corresponding group. XIP* and XIP** correspond to XIP sequences from Rosids and Asterids, respectively. OeuAQP accession numbers and sequences are listed in and Figures  3  and S1. OleurAQP and FraexAQP accession numbers and sequences are listed in Figures  S2  and S3 , respectively. This Figure  1  is also presented in Supplemental Figure  S4  with high definition for ease of reading. The OeuSIP subfamily clusters the four OeuSIP homologues in two subgroups, OeuSIP1 and OeuSIP2, with two members each. (Figure  1 , Figures  S3  and S4 ). This modest number of OeuSIP members is comparable to the SIP number identified in other plant species. However, the OeuXIP1 subgroup features a pseudogenization of its SIP1;3-type, unlike the domesticated cv.
But whatever the variability level between counterparts, RNaseq analyses reveal a transcriptional expression for each, except for OleurTIP1;3c, OleurTIP7;1b, OleurXIP1;1b and OleurNIP5;1b, which show null expression. Every OleurAQP-type with its full-length duplicate sequence is presented here. However, we shall develop our arguments mainly on the expression data from the OleurAQP-type, and this for two reasons: (i) even if the paralogues for a OleurAQP-type show significant variabilities in their transcriptional expression, the kinetic of expression (i. e. , up or down profiles) are quite similar; (ii) we are analyzing the variations in OleurAQP expression only with respect to the potential functional roles the related proteins would play in different physiological processes in olive trees.
Genome-Wide Identification of Aquaporin Genes in Wild and Cultivated Olive Trees Aquaporin genes and deduced protein sequences of the wild olive tree O. europaea subsp. europaea var. sylvestris (v1. 0) were retrieved from the phytozome database, a Plant Comparative Genomics portal of the Department of Energy's Joint Genome Institute (https://phytozome. jgi. doe. gov/pz/portal. html#!info?alias=Org_Oeuropaea_er). The investigations were conducted using keyword queries ("Major Intrinsic Protein" and "Aquaporin"), complemented by tBLASTn searches with conservative criteria requiring a cut-off of E-value of 1. 0 -5 by using as queries the amino acid sequences of aquaporin sequences from A. thaliana (AtAQPs)  [35] , Populus trichocarpa (PoptrAQPs)  [30]  and Solanum lycopersicum (SlAQPs)  [19] , obtained from previous reports.
Every putative aquaporin sequence (OeuAQPs) was carefully scrutinized, including expected AQP motifs (NPA and ar/R selectivity filter) and the prediction of the transmembrane topology, with Interproscan from EMBL (http://www. ebi. ac. uk/Tools/pfa/iprscan/) and the NCBI Conserved Domain Database (https://www. ncbi. nlm. nih. gov/Structure/cdd/wrpsb. cgi). The identified sequences of OeuAQP were subsequently employed as queries to recover, using tBLASTn searches, their analogous OleurAQPs from the related cultivated olive tree O. europaea L. var. Picual available with the OliveTreeDB website (https://genomaolivar. dipujaen. es/db/index. php). This investigation was also complemented with a search by using annotation keyword queries ("Major Intrinsic Protein" and "Aquaporin"). Putative AQP sequences from the Oleaceae related species Fraxinus excelsior were retrieved from the Hardwood Genomics Project HWG website (DOI: https://doi.
org/10. 25504/FAIRsharing. srgkaf) by using SlAQP, OeuAQP and OleurAQP sequences as queries. F. excelsior AQP sequences were specifically annotated for this work according to the standardized gene nomenclature which obeys the following format: genus (three letter)-species (two letters)-gene name (locus) or MIP nomenclature (subfamily x;y). All AQPs from O. var. sylvestris (OeuAQPs), O. cv. Picual (OleurAQPs) and Fraxinus excelsior (FraexAQPs) are "roughly" named in the databases of origin. For this study, we renamed each sequence according to the standardized MIP nomenclature. In order to eliminate any ambiguity, each AQP appellation was phylogenetically established to the ones derived from A. thaliana, P. trichocarpa and L. esculentum. OeuAQP and OleurAQP protein names, accession numbers and sequences (genomic, CDS and deduced proteins) used in this work are listed in Figure  3  and Figures  S1  and S2 , respectively.
Multiple Sequence Alignment, Phylogenetic Analysis and Subfamily Classification From the sixty-eight candidate AQP sequences recovered, fifty-two full-length deduced peptide sequences were aligned together with orthologous AtAQPs, SlAQPs and PoptrAQPs using ClustalW software (http://www. ebi. ac. uk/Tools/clustalx2/index. html). The results were viewed with Jalview (v2. 10. 5). A phylogenetic analysis based on the deduced peptide sequences of AQPs encoded by tomato, A. thaliana and poplar was used to assign the OeuAQP sequences to the five established AQP subfamilies (i. e. , PIPs, TIPs, NIPs, SIPs and XIPs). Functional annotations were initially attributed for most of the OeuAQP loci in the databases of origin and renamed as mentioned above. The phylogenic studies were done with the AQP-deduced peptide sequences.
A total of 323 amino acid sequences were aligned using the ClustalW progressive alignment method. Then, the phylogenetic tree of the sequence dataset was inferred using the maximum parsimony method. Maximum parsimony analysis was conducted using the subtree-pruning-regrafting (SPR) algorithm and was bootstrapped with 5000 replicates. All analyses were performed using the MEGA X program. A graphical representation and edition of the phylogenetic tree were performed with iTOL software (https://itol. embl. de/) [98].
Chromosomal Location, Gene Structure, Structural Features of OeuAQPs Values The phytozome assembly database was used to reveal the chromosomal location for each OeuAQP and to determine their related intron/exon structure. The exon-intron organization of OeuAQP genes were compared together using Gene Structure Display Server (GSDS) software (http://gsds. cbi. pku. edu. cn) [99]  based on the alignment of CDS sequences with the corresponding genomic sequences. Amino acid (AA) translations were obtained using Bioinformatics Resource Portal Expasy software (https://web. expasy. org/translate/). Protein features such as the theoretical isoelectric point (pI), the molecular weight (MW) and the grand average of hydropathicity (GRAVY) were calculated using the ProtParam program (https://web. expasy.
org/protparam/). The classical conserved domains, i. e. , NPA motifs, the ar/R filter, the Froger's positions and a panel of post-translational and regulatory positions (i. e. , phosphorylation, methylation, the leucine blocking residue and the histidine residue involved in gating) were manually identified based on multiple sequence alignments with heterologous SlAQPs and AtAQPs. The transmembrane regions were predicted using the TMHMM software tool (www. cbs. dtu. dk/services/TMHMM/) [100]  and SOSUI tools (http://harrier. nagahama-ibio. ac. jp/sosui/sosui_submit. html)  [101]  and, if necessary, manually corrected with heterologous SlAQP and AtAQP sequences. The subcellular localizations of OeuAQPs were predicted using the online tools WoLF PSORT (http://wolf-psort. hgc. jp) and Plant-mPLoc (http://www.
csbio. sjtu. edu. cn/bioinf/ plant-multi/)  [102]. The conserved motifs were detected using the multiple em for motif elicitation (MEME) software (http://meme-suite. org/tools/meme) [103] , with the maximum number of motifs set as 15. Specificity-determining positions (SDP1-9) were collected from multiple sequence alignments, which were performed using ClustalW as described in  [21]. Protein sequences corresponding to functional characterized AQPs transporting non-aqua substrates were obtained from Genbank according to related literature. To evaluate the evolution pressure of AQP genes, the Ka/Ks ratio was calculated. The non-synonymous (Ka) and synonymous (Ks) substitution ratio of AQP genes was calculated to test selection pressure. Transcript sequences and their deduced protein previously aligned with ClustalW (https://www.
Details of the amino acid constituting these motifs are listed in the legend, and residue occurrences are provided on page 2 of the file. Figure  S7. Comparison of protein structural features, evolution pressure and exon-intron structures of full-length OeuAQP and OleurAQP genes from the wild Olea europaea var. sylvestris and the cultivated Olea europaea cv. Picual, respectively. Figure  S8. SDP analysis of OeuAQPs from the wild olive species Olea europaea var. sylvestris from alignments with putative amino acid sequences of AQPs transporting non-aqua substrates. Multiple alignments were performed using ClustalW. Shown are the sequences containing the nine SD positions which are highlighted in yellow and the representative sequences are marked in blue. The Genbank accession numbers: AtPIP1;2 (Q06611), AtPIP2;1 (P43286), AtPIP2;4 (Q9FF53), AtTIP1;1 (P25818), AtTIP1;2 (Q41963), AtTIP1;3 (NP;192056), AtTIP2;1 (Q41951), AtTIP2;3 (Q9FGL2), AtTIP4;1 (O82316), AtTIP5;1 (NP;190328), AtNIP1;1 (CAA16760), AtNIP1;2 (Q8LFP7), AtNIP5;1 (NP;192776), AtNIP6;1 (NP;178191), CmNIP2;1 (BAK09176), CpNIP1 (CAD67694), GmNOD26 (P08995), GmNIP2;1 (XP;003534451), GmNIP2;2 (NP;001240190), HvPIP1;3 (BAA23745), HvPIP1;4 (BAF33068), HvPIP2;1 (BAA23744), HvNIP2;1 (BAH24163), NtAQP1 (O24662), NtTIPa (Q9XG70), NtXIP1;1α (ADO66667), NtXIP1;1β (ADO66666), OsNIP2;1 (Q6Z2T3), OsNIP2;2 (Q67WJ8), OsNIP3;2 (Q7EYH7), SlXIP1;1α (ADO66672), SlXIP1;1β (ADO66671), StXIP1;1α (ADO66669), StXIP1;1β (ADO66670), TaLsi1 (ADM47602), TaTIP2;1 (AAS19468), TaTIP2;2 (AAS19469), TgTIP1;1 (BAL41683), TgTIP1;2 (BAL41684), ZmPIP1;1 (Q41870), ZmPIP1;5 (Q9AR14), ZmNIP2;1 (Q19KC1), ZmNIP2;2 (Q9ATN2).
The name of each subfamily and subgroup is indicated next to the corresponding group. XIP* and XIP** correspond to XIP sequences from Rosids and Asterids, respectively. OeuAQP accession numbers and sequences are listed in and Figures3 and S1. OleurAQP and FraexAQP accession numbers and sequences are listed in FiguresS2 and S3, respectively. This Figure1is also presented in Supplemental FigureS4with high definition for ease of reading.
Organic carbon contents of sediment samples were analyzed using a vario MAX CNS analyzer (Elementar) and photosynthetic pigments by spectrophotometry. In total, carotenoids were measured for 44 lakes and organic carbon for 46 lakes. Technical details are given in Supplementary Methods. Molecular analyses. We applied strict laboratory protocols to ensure the validity of our molecular data (Supplementary Methods). For each sediment layer, two DNA extractions were performed on 0. 5 g of wet sediment using the NucleoSpin® soil kit, according to the manufacturer instructions (Macherey-Nagel, Düren, Germany). The V7 region of the 18S rRNA gene was PCR amplified on a 260-bplong fragment, from c. a. 25 ng of environmental DNA extracted from each sample and using the general eukaryotic primers 960 F (5′-GGCTTAATTTGACT-CAACRCG-3′)  63  and NSR1438 (5′-GGGCATCACAGACCTGTTAT-3′)  64.
The choice of the barcode region has been validated as reported by Capo et al. 17, 18. Details about the preparation of purified amplicons are given in Supplementary Methods. The purified amplicons for each sample were pooled at equimolar concentrations and sent to GeT-PlaGe (Plateforme Génomique 31326 CASTANET-TOLOSAN Cedex) for library preparation and paired-end (2 × 250 bp) sequencing on a MiSeq Illumina instrument (San Diego, CA, USA). Data processing. The MiSeq data were merged with VSEARCH  65  and the sequences were cleaned as follows: sequences were removed if they presented ambiguous bases "N", a length shorter than 200 bp, and had a mismatch in the forward or reverse primers. The putative chimaeras were detected by VSEARCH. The remaining rRNA 18S sequences were clustered into "molecular species" (OTU) at a 95% similarity threshold according to Mangot et al.
66  with VSEARCH (option cluster_small sorted by length). This process was implemented using the pipeline PANAM  67  (https://github. com/panammeb/PANAM2). Technical HTS replicates were merged after assessing their similarity (Supplementary Fig. 9 ). For each lake and core depth, reads of replicate samples were summed together and subsampled at their average sequencing depth. The taxonomic affiliation was performed using the RDP algorithm  68  on OTUs representative sequences with a confidence bootstrap threshold of 75%. The Protist Ribosomal Reference database PR2  69  was used as reference. Taxonomic names were harmonized following the revision to the classification of eukaryotes proposed by Adl et al. 25. OTUs were classified into taxonomic groups for each of the 12 ranks of this classification (Supplementary Data 1) but given the difficulty in classifying short DNA sequences into low taxonomic ranks, only the three highest ranks were used in the statistical analyses (see the list of taxa in Supplementary Table  5 ).
In addition, functional trophic groups were attributed to OTUs based on their estimated taxonomy. Most of the information on trophic classification of microeukaryotes was obtained from Adl et al. 25  completed by specialized literature available for each taxon (see Adl et al. 25  for references). We divided OTUs into five trophic functional groups: consumers (bacterivores, algivores, and omnivores), saprotrophs, parasites, photosynthetics (exclusively autotrophs), and mixotrophs (consumers with photosynthetic capacity). The taxonomic and functional classification of each OTU is given in Supplementary Data 1. Rare OTUs with less than ten reads in the complete dataset and OTUs identified as multicellular animals (Metazoa) and terrestrial plants (Embryophyta) were removed. OTUs identified as unclassified Eukaryota were considered unreliable and were excluded from the analyses.
The complete tracking of the number of DNA reads and OTUs during the filtering and data pre-processing steps is available as Supplementary Information (Supplementary Fig. 2 ). All statistical analyses were conducted with R 3. 5. 3. Scripts to reproduce the results are available at https://doi. org/10. 5281/zenodo. 3662243. Community analyses. To control for differences in sequencing depths between samples, count data were converted into relative proportions. This approach was preferred over within-sample variance normalization methods as it produces more accurate and reliable comparisons at the community level  70. Multivariate analyses were conducted with R and the vegan package  71. Nonmetric Multidimensional Scaling analysis (NMDS) on Bray-Curtis dissimilarities was used to visualize dissimilarities in OTUs composition among communities in a reduced two-dimensional space.
Finally, lakes-to-center distances were compared between top and bottom using a paired Wilcoxon test. In addition, multivariate analyses were conducted on the OTU matrix using the Jaccard dissimilarity index and on the taxa matrix using the Bray-Curtis dissimilarity index (Supplementary Figs. 5  and 6 ). We used a regression tree model to investigate the effect of lake elevations on the magnitude of lake community changes, which was measured for each lake by the Bray-Curtis dissimilarities between the top and bottom communities. As two groups of lakes could be clearly identified, a decision tree was preferred over a classical regression to better capture the non-linearity in the data. Splits were determined by variance reduction and the optimal number of splits was determined via cross-validation using the tree fitting algorithm of the R package rpart  74.
Acknowledgements We thank  Cecile Chardon  ( INRAE ) for technical support, and  Paul MacKeigan ,  Valentin Vasselon , and  Cécilia Barouillet  for perceptive comments on this manuscript. This work was supported by  Suez Lyonnaise des eaux Project  " Outils innovants pour la diagnose écologique et la gestion des lacs ". F. K. was supported by the  Pole Research & Development on Lacustrine Ecosystems (ECLA)  of the  French Biodiversity Agency (OFB). We thank  OLA (Observatory on LAkes) , the information system OLA Data (https://doi. org/10. 15454/VBWYWG), the research infrastructure  AnaEE-France  ( Analysis and Experimentation on Ecosystems) , and the " Observatoire Hommes-Milieux Pyrénées Haut Vicdessos -Labex DRIIHM ANR " for providing limnological data.
Data availability All raw reads are available through ENA (https://www. ebi. ac. uk/ena) using the study accession number PRJEB35411. The taxonomic affiliation was performed using the Protist Ribosomal Reference database (PR2) available at https://pr2-database. org/. Any additional data needed to reproduce the results are available at https://doi. org/10. 5281/ zenodo. 3662243.
The technical issue concerns the sequencing technology: insert size, read size and sequencing coverage have been shown to impact SV discovery. The second issue concerns SV caller algorithms and their ability to decipher and translate the biological signal from the alignments. Thus, SVs located in repeated regions or containing repeats larger than the read size are difficult to detect  [9]. In particular, insertions are one of the most difficult SV types to call  [7, 8]. Because the inserted sequence is absent from the reference genome, or at least at the given locus of insertion, calling such variants and resolving the exact inserted sequence requires finely tuned approaches such as de novo or local assembly  [10, 11]. This increased difficulty is well exemplified by the dramatic under-representation of such SV type in usual reference databases or standard variant callsets.
For instance, dbVar at present references only 28% of insertions or duplications among the SVs larger than 50 bp. On the opposite, deletions represent more than 70% of the database, although both types are expected to be roughly equally abundant in human populations  [12]. Moreover, only 1. 5% of the reported insertions are sequence-resolved, that is with an inserted sequence fully characterized. One explanation is that the size of the reads is small compared to the target event size and the detection is mainly based on alignments which may produce artefacts  [13]. Another source of difficulty for insertion detection is the presence of repeated patterns at the precise rearrangement breakpoints. Several molecular mechanisms involved in rearrangement genesis are known to produce such repeated sequences, referred as junctional homology  [14] [15] [16].
However, little is known at present about the prevalence of these homologies and their sizes for human insertion variants due to their poor referencing in databases. More recently, novel long reads sequencing technologies have overcome these limitations and allowed the generation of more accurate datasets, finally referencing sequence-resolved insertion variants in the human genome  [8, 17]. Thanks to several international efforts, some gold standard callsets have been produced in 2019, referencing tens of thousands of insertions in several human individuals  [18, 19]. Among the reported insertions by Chaisson et al, a great majority (83%) could not be discovered by any of the tested short-read based SV callers. This result of recall below 17% is drastically different from the announced performances of insertion callers when evaluated on simulated datasets  [20].
In-depth analysis of an exhaustive insertion variant callset In this work, we first aimed at precisely characterizing an exhaustive set of insertion variants present in a given human individual. We based our study on a recently published SV callset published by Chaisson and colleagues in 2019  [18]. Using extensive sequencing datasets, combining different sequencing technologies and methodological approaches (short, linked and long reads, mappingbased and assembly-based SV calling), three human trios were thoroughly analysed to establish exhaustive and gold standard SV callsets (Supplementary Table  S1 ). We first focused our study on the individual NA19240, son of the so-called Yoruban (YRI) Nigerian trio, whose SV callset contained 15,693 insertions greater than 50 bp.
Among tandem duplications, tandem repeats are characterized by multiple tandem repetitions of a seed motif within the inserted sequence. Mobile element insertions are a very specific sub-type whose sequences are known and referenced in families. They are notably characterized by very high copy numbers in the genome (typically greater than 500). Other dispersed duplication types were then required to have a copy number lower than 50, in order not to be confounded with potential mobile element insertions. We did not define segmental duplications and CNVs as additional sub-types of dispersed duplications, as they are defined in the literature by their size (above 1 Kb), the size being another independant level of characterization. In order to classify the insertion callset, all inserted sequences were aligned against the human reference genome, a mobile element database and were scanned for tandem repeats (see Methods).
Using simulations to investigate the factors impacting the insertion calling recall In real insertion callsets, most of the previously identified factors impacting SV discovery are intertwined. In order to quantify the impact of each factor independently, we produced various simulated datasets of 2x150 bp reads at 40x coverage, containing each 200 homozygous insertion variants on the human chromosome 3. As a baseline, we simulated 250 bp novel sequences taken from Saccharomyces cerevisiae exonic sequences inserted inside human exons. This is meant to represent the easiest type of insertions to detect. Then, we considered four scenarios of simulations, where only one of the four previously studied factors is changed at a time with respect to the baseline simulation. Four insertion variant callers were evaluated on these datasets.
Factors impacting insertion site recall Recalls of insertion sites for all four methods are presented for the different simulated datasets in Table  1. On the baseline simulation, all tools succeeded to detect 100% of simulated insertions, except for GRIDSS with 81% of recall. The size of the inserted sequence impacted the recall of the insertion sites for most tools, except MindTheGap. GRIDSS was challenged by small insertions (50 bp) whereas Manta and SvABA had more issues with large insertions. The most extreme behavior was observed for SvABA which was not able to find the insertion sites of any of the simulated novel sequences larger than 500 bp. When simulating various insertion types, GRIDSS was the only tool whose recall was not negatively impacted. Manta could not find any type of dispersed duplications and showed a lower recall to detect tandem repeats with 25 bp size seeds.
Concerning the impact of the genomic context of insertions, no loss of recall was observed in non repeated locations. Alignment-based SV callers showed no change in recall in small simple repeat (<300 bp), SINE and LINE locations. Manta and SvABA recalls lost 5 to 6% of recall in simple repeat regions larger than the insert size (>300 bp). MindTheGap lost 42 and 47% of recall in large simple repeat and SINE location simulations. Simulating insertions close to each other on the genome, at less than 150 bp, reduced the recall of SvABA (-98%), MindTheGap (-33%) and Manta (-15%). Table  1  Insertion site recall of several short-read insertion callers according to different simulation scenarios. Cells of the table are colored according to the variation of the recall value of the given tool with respect to the recall obtained with the baseline simulation (first line, colored in blue): cells in red show a loss of recall >10%, cells in grey show no difference compared to baseline recall at +/-10% Finally, when simulating the 889 insertions of NA19240 callset located on chromosome 3, with their reported inserted sequence at their real locations as described in the variant calling file (scenario 5), the recall of all tools dropped to less than 44%, reaching for many tools their lowest values among the different simulated datasets.