dataesr/scientific-paragraphs-categorization huggingface dataset:
Datasets of 3 UTRs and polyadenylation sites Ensembl v90 spliced 3 UTR sequences for all human transcripts were downloaded from the Ensembl BioMart service  (24). The maximum 3 UTR length was calculated for each protein in the human proteome (UniProt AC) by selecting the longest 3 UTR among all transcripts encoding for a certain protein. Genome-wide polyadenylation sites for human were downloaded from APADB v2  (25)  as well as PolyASite version r1. 0  (26) , on December 2017. APADB polyadenylation sites per kb were calculated for proteins produced from transcripts with 3 UTRs longer than 1000 nt, considering the length of the longest 3 UTR. For PolyASite, polyadenylation sites on the terminal-exon 'TE' category were considered. Gene names and Ensembl transcript IDs were converted to UniProt AC using the UniProt ID mapping tool  (23).
RBP-3 UTR interaction network Interactions between RBPs and 3 UTRs were retrieved from the Atlas of UTR Regulatory Activity (AURA) v2. 4. 3 database (AURAlight dataset) on January 2018  (27). The AURA database contains interactions between 3 UTRs and RBPs collected and mapped from various experiments, including several types of cross-linking and immunoprecipitation (CLIP) methods. Gene and coding-transcript identifiers were mapped to reviewed UniProt ACs using UniProt ID cross-referencing files (HUMAN 9606 idmapping. dat) (23). Only interactions involving proteins present in the PPI network were used. The network contains 163,490 interactions between 201 RBPs and the 3 UTR of the mRNAs of 10893 protein-coding genes.
Prediction of 3 UTR-protein complexes 3 UTR-protein complexes were predicted with in-house Python v2. 7 scripts using the protein-protein and RBP-3 UTR interaction networks described above. Each 3 UTRprotein complex includes: (i) an interaction between the RBP and the 3 UTR, (ii) an interaction between the intermediate protein (i. e. the protein which interacts with both the RBP and the nascent protein) and the nascent protein, (iii) an interaction between the intermediate protein and the RBP. We only considered the presence of one intermediate protein, and complexes formed without any intermediate protein were not examined (i. e. RBP interacting directly with the nascent protein). Since the PPI network used does not contain self-interactions, the intermediate protein must be different from the nascent and the RBP.
Protein tissue presence filter Tissue protein presence from Human Protein Atlas version 18 (January 2018) (28) was used to filter 3 UTR-protein complexes. This dataset contains data on 58 normal tissues. Information on cell type associated with tissue names was not used in this study. Proteins with reliability score (level of reliability of the protein expression pattern) indicated as 'uncertain' and proteins with presence level 'not detected' were excluded. We only considered 3 UTR-protein complexes where all proteins of the complex are present in at least one of the 58 tissues. Gene names and Ensembl Gene IDs were converted to 13044 reviewed UniProt AC using the UniProt ID mapping tool  (23).
UTR usage in predicted complexes Sequences and hg19 genomic coordinates of 3 UTRs interacting with RBPs were taken from the AURA database (file: UTR hg19. fasta, 65 285 sequences) on December 2018. As AURA does not provide cross-references to Ensembl identifiers, we used BEDTools  (29)  intersect with parameters 's -r -f 1. 0' (to ensure strendness and a perfect match) to map AURA UTR coordinates on GENCODE release 19 (GRCh37. p13) gene annotation file. In doing so, we were able to associate 62,898 UTR sequences (96% of the total) to the corresponding protein-coding genes. UTR sequence redundancy was reduced at 100% identity using the CD-HIT algorithm  (30).
Proteins localized in the plasma membrane Plasma membrane proteins were retrieved from two datasets: (i) UniProt  (23) , querying reviewed Homo sapiens proteins with the GO term 'plasma membrane' (GO:0005886) annotated by 'any manual assertion' method (4602 proteins) and (ii) plasma membrane proteins experimentally detected by HPA version 18  (31) , querying for the subcellular locations 'plasma membrane' and 'cell junctions' (1734 genes mapped to 1776 UniProt ACs using the UniProt ID mapping tool). Note that both datasets include proteins that are integral to the plasma membrane (e. g. receptors) as well as peripheral membrane proteins that may attach to integral membrane proteins or penetrate the peripheral regions of the membrane (e. g. receptor-interacting proteins). Information on the presence or absence of signal peptide, lipidation sites, and transmembrane domains was obtained from UniProt on January 2019  (23).
Dissimilar cellular component GO term analysis The PrOnto method  (21)  was used to identify 'cellular component' (CC) GO term pairs that are unlikely to occur in the same protein (association probability) or in interacting proteins (interaction probability). PrOnto probabilities were recalculated for CC GO term annotations collected from the Gene Ontology Consortium  (22)  on December 2017, including terms 'Inferred from Electronic Annotation' (IEA). GO term pairs with PrOnto association and interaction probabilities <0. 05 were considered dissimilar. EMF protein dissimilar GO terms can be consulted on the related MoonDB 2. 0 database. We tested whether certain protein groups have more proteins with dissimilar terms than expected. Since the ability to find dissimilar GO term pairs depends on the number of GO terms annotated to a protein, we control this by sampling (10,000 times) sets of proteins from the human proteome of the same size of the protein group in question and with the same distribution of numbers of CC GO terms annotated.
Gene Ontology enrichment analysis The Gene Ontology (GO) term enrichment analysis was performed using the g:Profiler R package  (32). We only considered enrichments where the minimum query/term intersection size was = >5, and where the P-value was <0. 05 after correction using the Benjamini-Hochberg procedure. In all g:Profiler analysis, electronic GO annotations (i. e. with the evidence code IEA) were considered.
EMF proteins as a model to study the regulation of protein localization and multifunctionality by alternative 3 UTRs The usage of alternative 3 UTRs has been found to regulate the subcellular localization of proteins  (8, 33) , which in turn could regulate their functions. Therefore, we hypothesize that 3 UTRs may play a role in moonlighting protein function regulation. To investigate this possibility, we explore a comprehensive set of human moonlighting protein candidates, i. e. 238 'extreme multifunctional' (EMF) proteins (Supplementary File 1: Table  S1 ) that we identified using our MoonGO approach  (14)  and that are available in our recently updated MoonDB 2. 0 database  (18)  (see Methods). Numerous moonlighting proteins have been found to perform different functions when localized in different cellular compartments.
Coherently, we found that EMF proteins are annotated with significantly more 'Cellular Component' (CC) GO terms than other groups of proteins (see Materials and Methods, Figure  1A ). Indeed, on average an EMF protein is associated with 7. 8 CC GO terms, whereas the average in the PPI network--from which the EMF proteins are identified--is 4. 6 CC GO terms (Mann-Whitney U test P-value = 1. 1 × 10 -22 ). We also observed that 111 EMF proteins (46. 6% of the total) belong to the multilocalizing proteome, defined by immunofluorescence-based approaches in the Human Protein Atlas (HPA) (31), a significantly higher fraction than in the PPI network (36. 7%, twosided Fisher's Exact Test, odds ratio (OR) = 1. 52, P-value = 1. 7 × 10 -3 ) (Figure  1B ). Moreover, using a dataset of 1233 known and machine learning-predicted translocating proteins from the Translocatome database (34), we found that 41.
8%, OR = 1. 46, P-value = 0. 18, Figure  1b ). Altogether, this suggests that part of the EMF proteins is not localized at the membrane via the classical protein transport pathway. In order to estimate the potential of EMF proteins to be regulated by the 3 UTR mechanism, we investigated the features of their 3 UTR sequences. Using 3 UTR models from the Ensembl database  (24) , we found that mRNAs encoding EMF proteins have significantly longer 3 UTRs than mRNAs encoding all other human proteins of the proteome, and more stringently, of the interactome (see Materials and Methods, mean 'EMF' (1987 nt) versus 'Proteome' (1643 nt), Mann-Whitney U test P-value = 1. 5 × 10 -3 ; mean EMF (1987 nt) versus 'Interactome' (1739 nt), Mann-Whitney U test P-value = 3. 0 × 10 -2 ) (Figure  1C ). In addition, using polyadenylation sites from APADB  (25)  and PolyASite databases  (26) , we showed that mRNAs encoding EMF proteins bear a higher number of alternative polyadenylation (APA) sites in 3 UTRs than mRNAs encoding the other groups of proteins (Figure  1D ; mean EMF (2) versus 'Interactome' (1.
By searching for sets of co-interacting 3 UTRs, RBPs, nascent and intermediate proteins (Figure  2 ; see Materials and Methods) from two large-scale experimental datasets forming a RBP-3 UTR and a protein-protein interaction networks (from the AURA  (27)  and the MoonDB 2. 0 (18) databases, respectively; see Materials and Methods), we identified possible 3 UTR-protein complexes. To increase the biological relevance of the predictions, complexes should contain only proteins co-detected in at least one of the 58 HPA normal tissues  (28).
The numbers of 3 UTR-protein complexes predicted are 2. 8-12 times higher than expected by chance for interac-tome and EMF proteins, respectively (Supplementary File 2: Table  S6 ), therefore supporting our predictions. Only 42% of the RBPs known to interact with a 3 UTR in AURA database, and present in the PPI network (87 out of 204, Table  1 ) are found in EMF-containing complexes, despite the general propensity of RBPs to bind a large number of RNAs  (35). This, therefore, increases our confidence in the specificity of the predicted 3 UTR complexes. EMF proteins are often highly connected in the PPI network  (14). Indeed, 80% of them are hubs, defined here as nodes whose degree is at least twice the network average (≥32). They are thus potentially more likely to form 3 UTR-protein complexes, as the number of partners of a protein in the network could impact the number of 3 UTR complexes that could form.
This proportion is remarkably higher than in the PPI network, where it drops to 47. 5% (OR = 4. 56, two-sided Fisher's Exact Test, P-value = 1. 2 × 10 -5 ) (see Materials and Methods). Furthermore, we observed a similar trend using an independent set of plasma membrane proteins from the HPA database (31) (see Methods; Supplementary File 2: Table  S7 ). As the lack of addressing signal has been remarkably observed for dozens of cell surface moonlighting proteins  (7)  performing alternate functions in other subcellular locations, we speculate that the formation of 3 UTR-protein complex could play a role in the unconventional plasma membrane translocation of the EMF proteins and more generally in the regulation of the localization of the EMF proteins. To investigate the potential role of 3 UTR-protein complex formation in protein localization, we assessed whether EMF proteins found at the plasma membrane have been also observed in other 'unexpected' cellular locations.
e. the different intermediate proteins found in these complexes should belong to different network modules involved in dissimilar biological processes (Figure  4A ). First, we found that 80% of the EMF proteins present in 3 UTR complexes fulfill Figure  3. Preferential isoform usage in multiple UTR ('mUTR') EMF-containing predicted complexes and percentage of proteins with GO term annotations to dissimilar cellular components. (A) Nascent mUTR EMF proteins were classified according to the length of the 3 UTR(s) bound to RBPs: the longest or the longer isoforms are bound to RBPs ("long"); the shortest or shorter isoforms are bound to RBPs ("short"); RBPs bind all isoforms, or a substantial fraction of them, without a clear preference for long or short isoforms ("other"). (B) Dissimilar CC GO terms were determined with the PrOnto method.
Archetypal moonlighting proteins are predicted to form 3 UTR-protein complexes As our analysis has been performed on a dataset of candidate proteins to extreme multifunctionality, we aimed at confirming that experimentally verified moonlighting proteins could participate in 3 UTR-protein complexes. For this, we first verified that proteins of the reference dataset available in the MoonDB 2. 0 (18) and the MoonProt (39) databases (47 and 65 literature-curated moonlighting proteins, respectively), form such complexes. Indeed, up to 52% of the known moonlighting proteins do so, a proportion that is similar to the one obtained for the 238 predicted EMF proteins (54%, Table  1 ). Moreover, the number of predicted complexes is significantly higher than expected by chance for both moonlighting proteins datasets (Supplementary File 2: Table  S8 ).
For this, we took advantage of the availability and the combination of two network data types--3 UTR-protein and PPI networks --, covering a large part of the human transcriptome and proteome. Our inferences allowed us to both provide a detailed analysis of the characteristics of the 3 UTR-protein complexes as a group and evaluate the prevalence of their formation at the interactome level. We indeed discovered that as much as 54% of the extreme multifunctional proteins and up to 17% of the whole interactome proteins could be involved in such complexes, thereby underlining their potential high extent and cellular importance. However, our inferences are notably sensitive to the comprehensiveness of the analyzed data since cellular 3 UTR-protein complexes that are formed by molecular interactions not present in the analyzed datasets cannot be found.
Indeed, while experimental human PPI networks cover most interacting proteins  (13) , public interaction databases may not include all the interactions known in the literature or recently discovered. In addition, when building our PPI network to be analyzed, we select binary interactions to increase the accuracy and the confidence of our predictions. For instance, the ELAVL1-SET-CD47 protein complex is found by our approach only when we extend our large-scale PPI network with interactions identified by coimmunoprecipitation techniques (data not shown). Moreover, given that current RBP-3 UTR interaction datasets contain data for only a subset (less than 400 RBPs (  27 )) of an increasingly growing number of proteins interacting with RNAs (which may amount to as many as 2000 RBPs  (45, 46) ), we may have underestimated the number of existing 3 UTR-protein complexes in human cells.
ACKNOWLEDGEMENTS We would like to thank  Lionel Spinelli  ( TAGC, Inserm/Aix-Marseille Univ ) for helping in generating the result reports and for valuable advices with statistical analyses. We are also grateful to  Lionel Spinelli  and  Anaïs Baudot  ( MMG, Inserm / Aix-Marseille Univ ) for critically reading the manuscript, and  Philippe Pierre  ( CIML, CNRS / Inserm / Aix-Marseille Univ ) for fruitful scientific discussions. Author contributions: Conceptualization: A. Z. and C. B. , with inputs from DR along the project. Methodology: D. R. , A. T. and A. Z. Formal Analysis: D. R. , A. P. and A. Z. Investigation: D. R. , A. P. , A. Z. and C. B. Writing: D. R. , A. Z. and C. B. Visualization: D. R. , A. Z. and C. B. Supervision: A. Z. and C. B. Project Administration: A. Z. and C. B. Funding Acquisition: C.
, 2019). Comparatively to offshore canyon sediment, coastal sediment had the highest biological Hg methylation rates whereas ambient mercury compounds concentrations were lower  (Azaroff et al. , 2019). Currently, no data exist about the microbial populations carrying hgcA in submarine canyons or even in marine sediments. Knowledge of the relationship between community composition and MeHg production may help to elucidate the origins of the large variability in MeHg/Hg ratios in oceanic ecosystem. Building on these earlier discoveries and recognizing the implication of microbial communities on Hg methylation in Capbreton submarine canyon sediments, this study aimed to provide a mechanistic understanding of MeHg formation in sediments along the canyon transect. For this purpose, we linked sediment geochemistry and Hg speciation with parallel analysis of the global prokaryotic diversity (16S rDNA) and microorganisms carrying hgcA.
Amplicons were sequenced by the Get-PlaGe sequencing service (INRA, Toulouse, France) using MiSeq 250 pb paired-end technology and reagent kit V3. Data were preprocessed using the Galaxy FROGS pipeline  (Escudié et al. , 2018). Chimeraic and PhiX reads were removed, Operational Taxonomic Units (OTUs) clustering, after a de-noising step allows building fine clusters with minimal differences, with an aggregation distance equal or above 3. Data were normalized to the minimum number of reads. Taxonomic assignments were performed using the Silva database v. 128  (Pruesse et al. , 2007). Sequences data have been deposited in GenBank under the accession number PRJNA608532. Hg methylation community composition: hgcA gene cloning and sequencing hgcA gene diversity was studied in four surface sediments along the canyon gradient from the coast to the offshore (G03, G10, G14 and G24).
Forward sequences were only used and converted to protein with ExPaSy tool (Swiss Institute of Bioinformatics, Lausanne, Switzerland) then, trimmed with Chromas Pro (Technelysium software). The obtained protein sequences were aligned with MUSCLE  (Edgar, 2004). The alignment was trimmed to the amplicon, and a tree was generated using MEGAX  (Kumar et al. , 2018) , with the Maximum Likelihood method based on the Poisson correction model  (Zuckerkandl and Pauling, 1965)  (with n replication bootstraps = 500). Phylogenetic analyses were processed using HgcA proteins homemade database including HgcA sequences from isolated strains' genomes and from metagenomes obtained from NCBI (https://www. ncbi. nlm. nih. gov/) and Integrated Microbial Genomes and Microbiomes platform (IMG/M, developed by the Joint Genome Institute, CA, USA, http://img.
jgi. doe. gov/) (Markowitz et al. , 2012)  and previous studies (Supporting information, Table  S3 ). HgcA sequences were also collected from Pfam database  (Finn et al. , 2016)  using custom Pfam models (corrinoid-iron sulfur proteins (CFesP) classified under Pfam3599) according to  Podar et al. (Podar et al. , 2015). From these references, we selected only sequences with the conservative cysteine in HgcA  (Smith et al. , 2015)  within the CWA motif. All reference sequences are compiled in Supporting information, Table  S3. Sequences are archived in GenBank under submission number. 2316824.
Statistical analysis Pearson correlations were calculated between chemical parameters using the R software environment version (http://www. r-prorject. org/). To remove redundancy of the chemical parameters data in order to avoid collinearity, we used the variance inflation factor (VIF) with the mctest package  (Imdadullah et al. , n. d. )  in R using the Farrar-Glauber test. Multivariate analysis (Principal Component Analysis (PCA) were performed with FactomineR and Vegan package in R  (Lê et al. , 2008; Oksanen, 2007). PCA for 16S rDNA diversity analysis was performed at the phylum level because of PCA constrains, with relative abundance above > 0. 05%. We used the factoextra package  (Kassambara and Mundt, 2017)  for extracting and visualizing the results. Hierarchical Clustering on Principal Components (HCPC) were performed using FactomineR package in R.
Acknowledgements This work was supported by the  European Union  and the  Adour Garonne Water Agency. Europe commits the New Aquitaine with the  European Regional Development Fund. We thank the crew of  INSU-CNRS research vessel (Côte de la Manche ). We gratefully thank  Carl Lamborg  ( University of California, Santa Cruz ) for his proofreading and anonymous reviewers for helpful criticize of the paper Compliance with ethical standards policy implications. Environ. Res. , Mercury in Marine Ecosystems: Sources to Seafood Consumers  119 ,  101-117. https://doi. org/10. 1016/j. envres. 2012. 03. 013Mazières, A. , Gillet, H. , Castelle, B. , Mulder, T. , Guyot, C. , Garlan, T. , Mallet, C. , 2014. Highresolution morphobathymetric analysis and evolution of Capbreton submarine canyon head ( Southeast Bay of Biscay-French Atlantic Coast ) over the last decade using descriptive and numerical modeling.
Methods Species occurrence data. Occurrences of the small Indian mongoose U. auropunctata were compiled from the online databases VertNet and GBIF, in addition to the databases of the Guadeloupe National Park, Martinique Regional Park, Saint-Martin Natural Reserve, French national wildlife organizations, recent literature, museum specimens and personal observations. Only recent observations (from 1950 to the present day) with complete and precise locations were selected, giving a total of 971 occurrences (Table  S1 ). All occurrence records were aggregated into 0. 08° cells corresponding to the resolution of environmental variables, resulting in a total of 403 records (Fig. 1 ). environmental data. Current and future favourable environmental envelopes for the small Indian mongoose were calculated using 19 bioclimatic variables (derived from temperature and precipitation measures), averaged for the period 1950-2000 from the WorldClim 1.
4 database  70. As is apparent from its current distribution and the known ecology of the species, the small Indian mongoose is able to cope with, and found in, a great variety of natural and anthropogenic habitats. As such, bioclimatic variables represent appropriate and reliable parameters to portrait the current and future areas favourable for the species at a global scale. A 5 arc-minute spatial resolution (approximately 9 kilometers at the equator) was selected for all bioclimatic variables. Future favourable envelopes for the small Indian mongoose were modelled using climate projections from global climate models (GCM) based on the Coupled Model Inter comparison Project Phase 5 (CMIP5) averaged for the period 2041-2060. Three GCMs were used (CNRM-CM5; GISS-E2-R; MIROC-ESM-CHEM; Table  1 ), for two Representative greenhouse gas concentration pathways scenarios (RCP): the most optimistic RCP2.
Conversely, unfilling refers to environmental conditions in the native area that are absent in the invaded area. Both indices vary between 0 and 1. All niche conservatism tests were performed using the package ecospat v2. 1. 1  72  implemented in the R software  73. Data preparation and modelling process. Data preparation and pseudo-absence selection were realised following the methodology developed and assessed by Louppe et al. 69. The preparation of presence-only data for ENMs included two important steps. The first one consisted of filtering the presence data to reduce autocorrelation and sampling bias (e. g. refs. 67, 74 ). The second one consisted of selecting pseudo-absence data outside the environmental space resulting from the set of occurrences (i. e. the environmental space occupied by the species) to calibrate models.
3-7  89. Because our procedure is a presence/pseudo-absence procedure, we did not calculate discrimination capacity metrics (e. g. the area under the receiver operating characteristic curve or the true skill statistics) because (1) these metrics are designed to be calculated on real absences, and (2) they are dependent on prevalence, which can lead to spurious evaluations of ENMs  90, 91. Rather, we evaluated our models with the Boyce index, specifically developed for such data  [92] [93] [94]. The Boyce index assesses how much model predictions match the observed distribution of species occurrences. Values of the Boyce index vary between -1 and +1. Positive values indicate a model with predictions that are consistent with the distribution of occurrences in the evaluation dataset whereas negative values indicate a model with predictions that are not consistent with the distribution of occurrences.