dataesr/scientific-paragraphs-categorization huggingface dataset:
produce a custom database starting from COInr. The possibility of refining the taxonomic composition of the database, selection of the gene region and formatting the output to widely used database formats (blast, rdp, qiime) are filling the need for an easy way of creating customized COI databases. AUTH O R CO NTR I B UTI O N S EM designed the research, wrote the scripts, analysed the data and wrote the manuscript. ACK N O WLE D G E M ENTS I thank Francesco Mugnai for testing mkCOnr and making valuable comments on its use, documentation and the present paper and Gabriel Nève for language editing.
DATA AVA I L A B I L I T Y S TAT E M E N T Data Accessibility: The complete COI database can be downloaded from https://doi. org/10. 5281/zenodo. 6555985 (Meglécz, 2022). All scripts are available in https://github. com/megle cz/mkCOInr including full documentation and they are also archived in Zenodo at https://doi. org/10. 5281/zenodo. 6961340 (Meglécz, 2022). Benefits.
F Flowchart of mkcoinr. Double lines represent the different options for customizing the COInr database. These steps can also be consecutive | 5 MEGLÉCZ also allows sequences to be selected with a minimum taxonomic resolution. The select_region. pl script trims the sequences to a specific region of the COI gene. Using the usearch_global command of vsearch (Rognes et al. , 2016), sequences of the database are aligned to a small bait file, which contains a taxonomically diverse pool of sequences already trimmed to target region. The usearch_global program is similar to blast since it aligns each query sequence (sequences of the COInr) to the sequences of its database (the bait file in this case). Contrary to blast, usearch_global produces global alignments. The best alignment of each query sequence is used to trim the query sequence according to the alignment positions.
17550998, 0 , 0 Downloaded from https://onlinelibrary. wiley. com/doi/10. 1111/1755-0998. 13756 by Cochrane France, Wiley Online Library on [01/03/2023]. See the Terms and Conditions (https://onlinelibrary. wiley. com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons LicenseApart from sequences of animals, which comprised 99% of the database and corresponded 97% of the species, other Eukaryotes (plants, Fungi) and even some Bacteria and Archaea sequences were also present in the database (Table2). Within Metazoa, 83% of the sequences were from Arthropoda, which corresponds to 74% of the animal species of the database. Downloading the COI sequences from NCBI and all sequences from BOLD took approximately 24 and 40 h, respectively.
It is clearly the most time-consuming step. The rest of the pipeline until obtaining the final COInr database took approximately 1 h on a desktop computer (Intel Core i7-7700, CPU @ 3. 6 GHz, 32 Gb RAM). This compares to the few seconds of downloading the COInr database from Zenodo that clearly saves a tremendous amount of time for users. Creating a custom database from COInr varied in length as a function of the desired characteristics of the database. As an example, the creation of a custom database explained in the tutorial (Eukaryotes, noninsect sequences with known genera, enriched by custom sequences, limited to a 313-bp region, formatted to the rdp classifier) takes approximately 30 min. To evaluate the effect of using nonstandard taxon names, corresponding to open nomenclature (e. g.
, Allograpta aff. argentipila, Alona guttata group, Macrobiotus cf. hufelandi) or correct Latin names completed by arbitrary identifiers (e. g. , Macrobathra sp. ACL2485, Abablemma BioLep730, Abacarus sp. GD111), two databases were created: COInr, where only correct Latin names were used, and the all-names database created by the same pipeline, with the exception that all taxon names were accepted regardless of their format (e. g. , Lepidoptera sp. 096 PS-2011 was used instead of the taxID of.
The number of taxa and COI sequences of the input databases (NCBI-nt, BOLD), and in the COInr database (May 2022). COInr is the result of pooling and taxonomically aware dereplication of sequences in the input databases MEGLÉCZ | 7 No. of No. of taxIDs sequences After initial quality control NCBI 221,565 3,920,624 BOLD 231,425 7,590,488 After dereplication within input database NCBI 221,565 1,657,602 BOLD 231,425 2,843,248 After pooling and dereplication (COInr) Shared by BOLD and NCBI 184,552 2,944,524 Unique to NCBI 37,013 124,811 Unique to BOLD 46,873 190,319 Total 268,438 3,259,654 published and they were designed to fill specific needs such as hav- ing a database specific to a taxon, a geographical region or a taxo- nomic assignment tool. The Eukaryote CO1 Reference Set For The rdp classifier TA B L E 1.
Yet, eDNA sampling is often local or regional  (Valdivia-Carrillo et al. , 2021; West et al. , 2021)  while genetic reference databases are notoriously incomplete  (Marques, Milhau, et al. , 2020)  to assign eDNA sequences to known taxa preventing widespread biodiversity assessments. Here, we take advantage of a large-scale eDNA sampling of coastal marine ecosystems across all oceans, including tropical, temperate and polar areas, to model the distribution of fish biodiversity according to geographic, environmental and socio-economic factors. We used the diversity of Molecular Operational Taxonomic Units (MOTUs) and of genetic sequences as alternatives to taxonomic diversity, hereafter called MOTU and sequence diversity, respectively. We hypothesize that (i) fish MOTU diversity is mainly shaped by the environment following the well-known latitudinal diversity gradient with a peak in the Coral Triangle  (Bellwood & Hughes, 2001; Parravicini et al.
| eDNA extraction, amplification and sequencing Environmental DNA extractions were performed following the protocols by  Juhel et al. (2020)  and  Pont et al. (2018). As we analysed our data using MOTUs as a proxy for species to overcome genetic database limitations, we chose to amplify only one marker. The teleo barcode, on the 12S mitochondrial rRNA gene (forward primer-ACACC GCC CGT CAC TCT, reverse primer-CTTCC GGT ACA CTT ACCATG  [Valentini et al. , 2016] ) has been shown to be one of the most appropriate for fishes, owing to its high interspecific variability and its short size allowing the detection of rare and degraded DNA reliably  (Collins et al. , 2019; Kumar et al. , 2022; Zhang et al. , 2020). The primers were 5′ labelled with a unique eight-nucleotide tag (with at least three differences between tags) allowing the assignment of sequences to the respective samples during the sequence analysis.
nucleotides between two sequences. We then applied the unified framework based on the generalization of Hill number to measure sequence diversity. Hill numbers have been recommended to produce reliable diversity assessments from molecularly characterized samples  (Alberdi & Gilbert, 2019; Mächler et al. , 2021). We used the function alpha. fd. hill from package 'mFD'  (Magneville et al. , 2022) , with parameters q = 0, which gives equal weight to all sequences, and τ as equal to the mean genetic distance  (Chao et al. , 2019).
| Modelling MOTU and sequence α-diversity We investigated the relationship between fish MOTU and sequence diversity at each station and all explanatory factors with a generalized least square model (GLS) that considered the gaussian spatial autocorrelation between samples. A variance inflation factor (VIF) approach was used to identify and remove residual collinear factors Sensitivity analyses were performed on 10 subdatasets after randomly removing 20% of the stations, to assess the robustness of our models, and after removing samples from polar regions (Scotia Sea and Arctic), to control for the influence of these extreme regions.
Explanatory factor Definition Expectation Degree Heating Weeks (DHW) Daily temperature 1°C above the maximum monthly mean SST, over a 12 weeks period. vegdist from package 'vegan'. Similarly, we computed the dissimilarity in sequence β-diversity between each pair of stations using the Hill number framework. The sequence β-diversity was calculated with the function beta. fd. hill from the 'mFD' package, with parameter q = 0 and tau = 'mean'. We then performed a distance-based redundancy analysis.
| Functional and phylogenetic diversity In order to assess whether eDNA sequence diversity can be used as a robust proxy of functional and phylogenetic diversity, we first explored the relationship between the pairwise sequence, phylogenetic and functional distances, and then applied the α-and β-diversity Hill number framework on sequence, functional and phylogenetic distances. For that purpose, we selected only the MOTUs assigned to the species level in our dataset (n = 787). We computed genetic pairwise distances for these species with the function dist. gene from package 'ape'. We computed the pairwise functional Gower distance based on functional traits extracted from fishbase, available for 685 of our species using the function compute_dist_matrix from package 'funrar'. Selected traits describe species functional roles in the ecosystem, and encompass body size (shape and length), habitat use (preferred habitat and substrate, depth range), feeding ecology (trophic group and diet) and behaviour (water column position, gregariousness)  (Villéger et al.
, 2017). The pairwise phylogenetic distance between species was computed using the functions fishtree_phylogeny from the 'fishtree' package and cophenetic. phylo from the package 'ape', and the phylogeny from Rabosky et al. (2018). These distance matrices were compared using a mantel test, and by calculating the area under the curve (AUC) criterion, based on Somer's D statistic. AUC varies between 0 (no correlation) and 1 (identical matrices) and is computed with the functions coranking, R_NX and AUC_ln_K from package 'coRanking'. We applied the α-and β-diversity Hill number framework for sequence, functional and phylogenetic diversity (q = 0 and τ equal to the mean genetic, functional or phylogenetic distance), using the functions alpha. fd. hill and beta. fd. hill from 'mFD' package, for se-  Local or α-diversity ranged between 2 and 414 fish MOTUs per station (Supporting Information Figure  S6 ).
, 2021). The Arctic also contains some reefs during the past quaternary glaciation  (Bender et al. , 2017; Pellissier et al. , 2014). Our study reveals a low sequence β-diversity between stations of tropical regions, suggesting that the high fish MOTU dissimilarity between these regions is due to close relative species. In other words, most fish species in the Caribbean and other tropical regions belong to the same evolutionary lineages. The pairwise species genetic distances, computed only for the MOTUs assigned at the species level (~30% of the dataset), were positively but weakly correlated to the phylogenetic and functional pairwise distances for these same species pairs. On such a short barcode (~60 bp), several species can have the same sequence  (Polanco et al. , 2021)  so a null pairwise genetic distance while they diverge in terms of phylogeny and traits.
However, seasonal variation in the sampling of the different regions can also influence the level of α-and β-diversity, as eDNA is more detectable during summer, when species are more active  (Troth et al. , 2020). Due to the incompleteness of available reference databases at the global scale  (Marques, Milhau, et al. , 2020) , the assignment to the species level remained impossible for more than 70% of sequences. For this reason, we used MOTUs curated by a conservative bioinformatic pipeline  (Marques, Guérin, et al. , 2020). A conservative curation of MOTUs better reflects the true level of fish diversity  (Sales et al. , 2021)  by decreasing the number of MOTUs representing the same taxa. Our conservative MOTU pipeline may, however, underestimate fish diversity of some cryptobenthic or rare fish groups that are more poorly represented in public databases, and of families with low taxonomic resolution (Supporting Information Figure  S22 ).
Combining eDNA with other sampling methods could provide a more exhaustive diversity inventory, as cameras or divers may detect some species not detected by eDNA  (Juhel et al. , 2022; Valdivia-Carrillo et al. , 2021). Using multiple markers could be an alternative to overcome the incompleteness of genetic reference databases and the lack of primer resolution  (Ruppert et al. , 2019) , but this approach would be much more expensive. Improving the accuracy of taxonomic assignment and completing genetic reference databases are thus urgently needed to improve estimates of largescale biodiversity patterns  (Marques, Milhau, et al. , 2020)  and local monitoring  (Dalongeville et al. , 2022). From an extensive eDNA survey from the tropics to the poles, our study associates the distribution of eDNA sequences released by coastal fishes to environmental, geographic and human factors.
and the European Nucleotide Archive (ENA) as a reference database (release 143, March 2020), supplemented by our custom reference database, F I G U R E 1 Sampling locations and patterns of α-diversity across coastal regions. (a) Map of the sampling sites, with the number of stations per site and (b) relationship between fish (α-diversity (number of molecular operational taxonomic or MOTUs per station) and sequence α-diversity (expressed as Hill number with genetic relatedness between MOTUs). The Spearman correlation between fish MOTU and sequence α-diversity is ρ = 0. 92 (p < 0. 001).
, SM and LM conceived the study. DM, LP, SM and LM contributed to the study design. LM, VM, CA, MA, FB, GBP, RH, JBJ, K, EM, GM, AP, HYS, LV, EB, JD, FH, NF, NG, PL, NL, KT, LV, IV XZ and WZ contributed to the acquisition of data. AV, TD, XZ and WZ supervised/conducted the eDNA extraction, amplification and sequencing. VA, EB, RH, LM, NF and LP contributed to the laboratory analyses for the reference database. PEG, VM and LM contributed to the bioinformatic analysis. LM and DM contributed to the statistical analysis and the creation of the figures. LM, DM, LP and SM contributed to the interpretation of the results. LM, DM and SM wrote the manuscript. All co-authors edited the manuscript. RH, SM, DM, LV, LP, KT, XZ and LP acquired funding to conduct the study. ACK N O WLE D G E M ENTS We acknowledge SPYGEN staff for the technical support in the eDNA laboratory.
We thank V. Arnal, F. Holon, N. Guellati, P. Lenfant, I. B. Vimono and G. Borrero-Perez for their help with sampling and genetic analyses. FU N D I N G I N FO R M ATI O N The sampling in the Caribbean, Indian Ocean and Polynesia and the sequencing were funded by Monaco Explorations. Data acquisition in Indonesia were supported by the Lengguru 2017 Project (www. lengg uru. org), conducted by the French National Research Institute for Sustainable Development (IRD), National Research and Innovation Agency (BRIN) with the Research Center for Oceanography (RCO), the Politeknik Kelautan dan Perikanan Sorong, the University of Papua (UNIPA) with the help of the Institut Français in Indonesia (IFI), funding from Monaco Explorations and corporate sponsorship from the Total Foundation and TIPCO company.
Data acquisition in New-Caledonia was supported by projects ANR SEAMOUNTS and CIFRE REEF 3. 0 conducted by the IRD and GINGER-BURGEAP-SOPRONER company. Data acquisition in Colombia was supported by Monaco explorations, ETH Global grant and the project Reefish, conducted in collaboration with the Instituto de Investigaciones Marinas y Costeras-INVEMAR. The sampling in the Mediterranean Sea was provided by the Region Occitanie, the Agence de l'Eau RMC and the RESERVEBENEFIT project through the 2015-2016 BiodivERsA COFUND call with the ANR. Sampling and sequencing for Antarctica was funded by Umweltstiftung Greenpeace. Sampling in Atlantic was supported by Ifremer (FisheDNA project), H2020 Environment (PANDORA project n°773713) and the Pays de la Loire region ('étoile montante' fellowship n° 2020_10792).
DATA AVA I L A B I L I T Y S TAT E M E N T All eDNA data (except from New-Caledonia) are available in open access in Zenodo: https://doi. org/10. 5281/zenodo. 7805935. New-Caledonia eDNA data are available from Zenodo upon request at: https://doi. org/10. 5281/zenodo. 6381130. The code used for the analyses is publicly available at: https://github. com/lmath on/Pole2.
1C ). A genomic region (chr22:44,850,001-45,630,709 in hg19) appears frequently deleted in individuals with PMS carrying interstitial deletions and contains seven genes (Supplemental Fig. 1 ). In the following sections, we describe specific genes located within the 22q13 region that could contribute to the neurological symptoms of individuals with PMS. For this purpose, we used GeneTrek (https://g enetrek. pasteur. fr/, Leblond et al. , 2021) , which gathers different databases related to NDD. Genes are listed in three non-overlapping categories depending on their association level with NDD. The high confidence NDD (HC-NDD) genes correspond to 1,724 genes, which are considered as robustly associated with NDD by at least one of five major sources: SPARK gene list (https://sparkforautism. org/), SFARI (https ://www.
Intolerance to LoF mutations is based on pLI (probability of being LoF intolerant) and LOEUF (LoF observed/expected upper bound fraction). Both metrics were extracted from gnomAD. The LOEUF is a recent metric that considers the length of the gene and the number of samples. It allows estimating the tolerance to LoF mutations with more continuous values than the pLI. A gene with a LOEUF<0. 35 is considered intolerant to LoF  (Karczewski et al. , 2020). For brain expression analyses, we used data from BrainSpan and Gtex (https://www. brainspan. org, https://www. gtexportal. org/home/). 3. The 22q13 region and the HC-NDD and candidate NDD genes Among 110 protein-coding genes in the 22q13 region, 12 (10. 9%) are HC-NDD genes, including four with a dominant inheritance: ACO2, SCO2, TCF20, and SHANK3.
Minimum genomic coordinates were all converted in hg19 using the liftOver package of R (version 1. 20. 0). Only patients with array data and both start and end genomic coordinates available are shown. We excluded duplicated individuals from France and individuals with multiple events in the region. For patients of our cohort (n = 57), genomic coordinates were validated by CRAM visualisation obtained after whole genome sequencing. A scale representing the number of individuals deleted for the region is shown at the top of the figure. C. Distribution of individuals with a 22q13 deletion in function to the number of deleted genes. (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article. ) Fig. 2. Characterisation of HC-NDD and candidate NDD genes in the 22q13 region.
, 2012). All carriers were heterozygous for an S112R missense variant. Recently, a database gathering extensive clinical information on 123 individuals with ACO2 dominant or recessive mutations was developed (https ://www. lovd. nl/ACO2). Among 21 patients affected by a recessive neurodegenerative syndrome, 13 (62%) also suffered from seizures  (Guehlouz et al. , 2021). While they are listed in the HPO for epilepsy, there is, to our knowledge, no report of epilepsy in carriers of variants affecting FBLN1 and TUBGC6P in the literature.
Acknowledgements and fundings We want to thank all families concerned by  Phelan-McDermid  syndrome and the  Phelan-McDermid Foundation  for collecting, storing, and sharing the data used in this review. This study has been supported by the European Reference Network on Rare Congenital Malformations and  Rare Intellectual Disability (ERN-ITHACA ). ERN-ITHACA is partly cofunded by the  Health Programme of the European Union. Funding was also obtained from the  European Union 's  Horizon 2020 research and innovation programme  under the  EJP RD COFUND-EJP N • 825575. This work was also funded by  Institut Pasteur , the  Bettencourt-Schueller Foundation ,  Université de Paris Cité , the  Eranet-Neuron (ALTRUISM) , the  Association Française du Syndrome Phelan-mcDermid  and the  Association Téhani et les enfants Phelan-McDermid , the GenMed Labex, AIMS-2-TRIALS which received support from the  Innovative Medicines Initiative 2 Joint Undertaking  under grant agreement  N • 777394  and the  INCEPTION program  ( Investissement d'Avenir  grant  ANR-16-CONV-0005 ).
Together, these data support the hypothesis that sfRNA delivery enhances DENV2 infection by inhibiting IFN induction and subsequent signaling. Limitations of the work. Given the challenges of working with mosquito saliva we felt it was important to include this section where we describe limitations of the work presented. In order to quantify sfRNA levels in very small volumes of mosquito saliva we used RT-qPCR. We developed a method where primers targeting the coding region detects gRNA and primers targeting the 3'UTR detect both gRNA and sfRNA  [8, 13]. sfRNA copies are then calculated by subtracting RNA copies quantified with gRNA primers from RNA copies quantified with 3'UTRtargeting primers. This method also detects gRNA decay intermediates and thus overestimates levels of completely processed sfRNAs.
Oligopaints Library Design (Fig 3) Using the Oligominer pipeline  [27] , the Oligopaints RNA FISH probe library was designed to bind DENV2 NGC genome. Briefly, the blockParse script was used to select oligos between 35-41 nt long with a T m of 42-47˚C. The selected oligos were aligned to the DENV2 NGC, Homo sapiens (hg38, GenBank Accession #: GCA_000001405. 28) and Ae. aegypti (AaegL5. 0, GenBank Accession #: GCA_002204515. 1) genomes using bowtie2  [43]. Oligos aligning with H. sapiens or Ae. aegypti genomes or having target site at 42˚C in these genomes were identified using the outputClean script and manually removed from the library  [27]. Candidate oligos were further filtered using the kmerFilter script from JellyFish algorithm  [44]  with the following parameters: '-k 5 -m 18' and oligos with 18-mers occurring more than five times in the DENV2 NGC genome were removed.
Lastly, oligos binding on reverse complements to the DENV2 genome were generated with probeRC script  [27]. Overall, the library consists of 240 oligos with an average density of 22 oligos/kb of DENV2 genome. Main-and Back street sequences were appended to the 5' and 3' end (respectively) of the DENV2 Oligopaints library using the OligoLego appending tool  [45]. 233 oligos binding nucleotides 1-10,293 on the DENV2 NGC genome were barcoded as gRNA, while 7 oligos binding nucleotides 10,294-10,717 were barcoded as 3'UTR. Sequences of MainStreet barcodes, bridge oligonucleotides, and fluorophore conjugated secondary oligonucleotides are detailed in S7 Fig.
This was done with the "segment_and_mea-sure_cells. ijm" ImageJ macro (https://github. com/Garcia-BlancoGroup/Yehetal2022). In the case of 3'UTR/sfRNA images with nuclear autofluorescence signal (S8A Fig), we used the cytoplasmic integrated densities of gRNA and 3'UTR/sfRNA signals for analysis. Using ImageJ, we manually segmented the nuclear area using the DAPI images and emptied the area occupied by the nucleus in both 3'UTR/sfRNA and gRNA image stacks  [47]. Once removed, the 3'UTR/sfRNA: gRNA ratios were computed as described above, using the "seg-ment_and_measure_cells. ijm" ImageJ macro (https://github. com/Garcia-BlancoGroup/ Yehetal2022). Extracellular vesicles (EVs). To segment each EV, we identified the best in-focus z-slice in wide-field image stacks and manually segmented the vesicle area using ImageJ  [47].
This was PLOS PATHOGENS done with the "segment_vesicles_best_focus. ijm" ImageJ macro (https://github. com/Garcia-BlancoGroup/Yehetal2022). 3'UTR/sfRNA: gRNA ratio was calculated as for cells using the "measure_vesicles_best_focus. ijm" ImageJ macro (https://github. com/Garcia-BlancoGroup/ Yehetal2022).