dataesr/scientific-paragraphs-categorization huggingface dataset:
The first feelings resulting from the visualisation of these first renderings are rather positive, in particular, because they make it possible to better observe the superimposed layers of burials. The long term objective is to be able to represent in 3D a maximum of information contained in JSON, geolocation or spatially deduced. When the first procedurally generated 3D cemetery visualisations of cemeteries seem relevant to us, we plan to develop a more sophisticated tool in WebGL. This could also be combined with the aforementioned photogrammetry protocols  (Morgan et al. , 2019; Tchérémissinoff & Seguin, 2019). Integrations in virtual reality are envisaged. We want to organize these complex developments in an open-source logic and within a larger community than our small team, in particular with a GitHub.
Conclusion The search for large funeral ensembles, whatever the period considered, is interesting for the quantitative data, provided that it can be stored and queried easily. The available computer tools currently make it possible to process large volumes of statistical data, which are at the base of a potential renewal of knowledge. The archaeoanthropological data collected are varied, they are as much interested in the funeral practices, in the restitution of the grave as in the biological characterization of the samples (sanitary state, food, genomics. ). Currently, the challenge lies in the comparison of sites, and the constitution of increasingly important databases to write summaries and eventually encourage collaborative research to advance science and society.
Acknowledgements This application could not have existed without the help of the students Thierry Gaugry (in 2014) and  Guillaume Roy  (in 2015), who were undertaking the 4th year at the  National Institute of Applied Sciences at Rennes (INSA) ,  Johan Moreira  (in 2016), master in Computing Science at University of Rennes 1,  Pierre Fernandez  (in 2017), an associate's degree at  University Institutes of Technology at Lannion ,  Haoulatou Diallo ,  Mikael Le  and  Mouhyi Moutaraji  (in 2018), master's degree at University of Rennes 1. We are very grateful to  Patrice Georges  and  Sylvie Duchesne  for taking pictures in the field, and  Philip Miller  for editing the English.
By analyzing the partitioned pangenome graphs of isolate genomes from 439 species and metagenome-assembled genomes from 78 species, we demonstrate that our method is effective in estimating the persistent genome. Interestingly, it shows that the shell genome is a key element to understand genome dynamics, presumably because it reflects how genes present at intermediate frequencies drive adaptation of species, and its proportion in genomes is independent of genome size. The graph-based approach proposed by PPanGGOLiN is useful to depict the overall genomic diversity of thousands of strains in a compact structure and provides an effective basis for very large scale comparative genomics. The software is freely available at https://github. com/labgem/ PPanGGOLiN.
PPanGGOLiN uses a new statistical model to classify gene families into persistent, cloud, and one or several shell partitions. To the best of our knowledge three statistical methods are available to partition a pangenome. Two of them use probabilistic models that partition dichotomously the pangenome only into core and accessory components  [24, 25]. Conversely, the method proposed and implemented by Snipen et al. [26, 27]  (micropan R package) classifies a pangenome in K partitions using a Binomial Mixture Model relying on gene family frequencies. Unlike these three methods, PPanGGOLiN is not based on frequencies but combines both the patterns of occurrence of gene families and the pangenome graph topology to perform the classification. In the following sections we present an overview of the method, an illustration of a pangenome graph and then the partitioning of a large set of prokaryotic species from GenBank.
We evaluate the relevance of the persistent genome computed by PPanGGOLiN in comparison to the classical soft core genome. Next, we illustrate the importance of the shell structure and dynamics in the study of the evolution of microbial genomes. Finally, we compare GenBank results to the ones obtained with Metagenome-Assembled Genomes (MAGs) to validate the use of PPanGGOLiN for metagenomic applications.
Each gene family is then associated to its closest partition according to the BMM. This results in a partitioned pangenome graph made of nodes that are classified as either persistent, shell or cloud. The strength of the MRF constraints increases according to a parameter called β (if β = 0, the effect of the MRF is disabled and the partitioning only relies on the P/A matrix) and it depends on the weight of the edges of the pangenome graph which represents the number of gene pairs sharing the neighborhood. Another originality of our method is that, even if the number of partitions (K) is estimated to be equal to 3 (persistent, shell, cloud) in most cases (see 'Analyses of the most represented species in databanks' section), more partitions can be used if the pangenome matrix contains several contrasted patterns of P/A.
Illustration of a partitioned pangenome graph depicting the Acinetobacter baumannii species We computed the pangenome of 3 117 Acinetobacter baumannii genomes from GenBank using PPanGGOLiN. For the persistent, shell and cloud genomes, we obtained 3 084, 1 529 and 64 833 gene families, respectively. If we compare our results with those of Chan et al. study  [18] , the size of the persistent genome predicted by PPanGGOLiN is included in their soft core estimation ranging from 2 833 (95% of presence) to 3 126 (75% of presence) gene families using 249 A. baumannii genomes. On the partitioned pangenome graph built with PPanGGOLiN (Fig 2 ), the gene families classified as persistent (orange nodes) correspond to the conserved paths that are interrupted by many islands composed of shell (green nodes) and cloud genomes (blue nodes).
Over one hundred distinct capsule types and their corresponding genomic organization have been reported in A. baumannii  [31]. A zoom on this region of the graph shows a wide variety of combinations of genes for the synthesis of capsular polysaccharides. Based on the 3 117 A. baumannii genomes available in GenBank, we detected 229 different paths, sharing many common portions, but only a few are conserved in the species (only 24 paths are covered by more than 10 genomes). Among them, two alternative shell Edges correspond to genomic colocalization and nodes correspond to gene families. The thickness of the edges is proportional to the number of genomes sharing that link. The size of the nodes is proportional to the total number of genes in each family. The edges between persistent, shell and cloud nodes are colored in orange, green and blue, respectively.
Analyses of the most represented species in databanks We used PPanGGOLiN to analyze all prokaryotic species of GenBank for which at least 15 genomes were available. This is the minimal number of genomes we recommend to ensure a relevant partitioning. The quality of the genomes was evaluated before their integration in the graph to avoid taxonomic assignation errors and contamination that can have a major impact on the analysis of pangenomes (see Materials and methods). This resulted in a dataset of 439 species pangenomes, whose metrics are available in S1 File. We focused our analysis on the 88 species containing at least 100 genomes  (Fig 3). This data was used for in-depth analysis of persistent and shell genomes (see the two next sections). Proteobacteria, Firmicutes and Actinobacteria are the most represented phyla in this dataset and comprise a variety of species, genome sizes and environments.
The gene family frequencies in each of the 88 pangenomes are available in S1 Fig. For four species, Pseudomonas stutzeri, Clostridium perfringens, Clostridium botulinum and Colinsella sp. , the size of the soft core genome is unexpectedly small and represents less than 55% of the genomes whereas it is above 75% for the PPanGGOLiN persistent. For the first three species, this could be due to sampling effects and species heterogeneity. For the last one (Colinsella sp. ), this could be explained by the fact that the species is made of incomplete genomes from metagenomes (i. e. MAGs) that were submitted as complete genomes in GenBank. For an in-depth comparison of these approaches, we performed multiple resamplings of the genome dataset for each species in order to measure the variability of the pangenome metrics and the impact of genome sampling according to an increasing number of genomes considered in the analyses (hereafter called rarefaction curves, see Materials and methods for more details and S2 Fig as an example for Lactobacillus plantarum).
This is illustrated on S2 Fig showing a step every 20 genomes (i. e. corresponding to 20 ¼ 100 100À 95 where 95% is the threshold of presence used) on the soft core curve of L. plantarum. We found a total of 20 species having atypical values of γ-tendency (absolute value above 0. 05) and/or IQR area (above 15 000) for the soft core and only 2 species for the persistent genome of PPanGGOLiN, which are Bacillus anthracis and Burkholderia cenocepacia. For B. cenocepacia, it could be explained by the high heterogeneity of its shell (see next section), which is made of several partitions and complicates its distinction from the persistent genome during the process of partitioning. For Bacillus anthracis, the source of variability to define the persistent genome is a result of an incorrect taxonomic assignation in GenBank of about 17% of the genomes that are, according to the Genome Taxonomy DataBase (GTDB)  [36] , actually B.
007, P = 0. 95,  Fig 5). The results remain qualitatively similar when analyzing the shell or the cloud separately (see  S5 and S6 Figs). During this analysis, we noticed that, among host-associated bacteria with relatively small genomes (between �2000 and �3000 genes), three species (Bifidobacterium longum, Enterococcus faecium and Streptococcus suis) have a high fraction of shell genes (> 28%) but low shell γ-tendency. Two of them (B. longum and E. faecium) are found in the gut of mammals and the third (S. suis) in the upper respiratory tract of pigs. They differ from other host-associated species in our dataset that are mainly human pathogens (e. g. bacteria of the genus Corynebacterium, Neisseria, Streptococcus, Staphylococcus) and have a low fraction of shell genes (< 20%). It is possible that these three species have specialized in their ecological niches while maintaining a large and stable pool of shell genes for their adaptation to environmental stress.
Analysis of Metagenome-Assembled Genomes in comparison with isolate genomes The graph approach should make our tool robust to gaps in genome data, making it a useful tool to analyze pangenomes obtained from MAGs. To test this hypothesis, we built the pangenomes of the Species-level Genome Bins (SGBs, clusters of MAGs that span a 5% genetic diversity and are assumed to belong to the same species) from the recent paper of Pasolli et al. [41]. This study agglomerated and consistently built 4 930 SGBs (154 723 MAGs) from 13 studies focussed on the composition of the human microbiome. We skipped the quality control step (already performed by the authors), and computed the pangenomes following the procedure we used for the GenBank species. The only parameter which differs is the K value which is set to 3 as the detection of several shell partitions is difficult for MAGs because of their incompleteness.
To make the comparison with GenBank species, SGBs were grouped according to their estimated species taxonomy (provided by the supplementary table S4 of  [41] ). In this table, we noticed potential errors in the taxonomic assignation of two species (Blautia obeum and Chlamydia trachomatis corresponding to SGBs 4844 and 6877, respectively) and thus excluded them from the analysis. Keeping the same constraint as previously, only species with at least 15 genomes in both MAGs and GenBank were used for the comparison. A total of just 78 species (corresponding to 151 SGBs) could be analyzed as a lot of microbiome species are laborious to cultivate and thus less represented in databanks (see S2 File). Then, we compared the MAG pangenome partitions predicted by PPanGGOLiN with those obtained with Gen-Bank genomes.
To perform this, we aligned MAG and GenBank families for each species and computed the percentage of common families for each partition (see Materials and methods for details and S2 File for detailed results). We observed that the size of the estimated persistent genome of MAGs is similar to the one of GenBank genomes for most species (  Fig 7). In 55 out of the 78 species, the absolute fold change of persistent size is less than 1. 2 and 90% (SD = 5%) of its content is common between MAGs and GenBank genomes. The 23 other species with more important differences showed smaller persistent genomes with only 60% (SD = 15%) of the persistent genome of GenBank being found in MAGs. For these species, the PPanGGOLiN method missed a fraction of the persistent genome due to the incompleteness of MAGs.
Indeed, in such cases, the missing gene families are mostly classified in the shell of the MAGs which contains 32% (SD = 11%) of the GenBank persistent families. Nevertheless, 89% (SD = 9%) of the MAG persistent families match the GenBank meaning that PPanGGOLiN correctly assigned persistent families for MAGs even if the persistent genome of these 23 species is incomplete. However, two species, Bifidobacterium longum and Faecalibacterium prausnitzii, have less than 75% of their MAG persistent families in common with GenBank ones. For B. longum, this could be explained by the fact that the MAGs were obtained mostly from human adult samples while this species in databanks are from a broader host range (infants and pigs). It means that the MAG persistent might contain additional genes related to host-specificity.
As a matter of fact, 412 gene families from the MAG persistent (25% of the total MAG persistent) are found in the GenBank shell which supports our hypothesis. For F. prausnitzii, the differences might be explained by a poor estimation of the persistent using GenBank data due to the low number of considered genomes (17 genomes versus 4232 MAGs). As expected, the soft core (based on the usual threshold of 95% presence) is unrealistically low in the MAG species with only �98 gene families on average and only 4 species out of 78 having more than 500 families classified in the soft core (see S2 File). Hence, the soft core approach is not well adapted to the analysis of MAGs. Furthermore, using lower thresholds of presence is not adequate because defining a unique threshold for all the families misses the heterogeneity of gene family presence in MAGs.
To explore the diversity within the pangenome of each species, we compared the shell of GenBank genomes and MAGs for the 55 ones with similar persistent genomes. Interestingly, we observed for all the 55 species only a partial overlap between the MAGs and GenBank shells (see S9 Fig). Indeed, as the MAGs are obtained only from a specific environment (i. e. the human microbiome), the diversity of GenBank is not fully captured by MAGs. It is especially the case for most of the Firmicutes and Proteobacteria. Conversely, most of the MAGs of Bacteroidetes phylum cover more than half of GenBank diversity while containing a large fraction of shell genes that are lacking in the shell of isolate genomes (i. e. less than 45% of the families are represented in the shell of GenBank). As already reported by Pasolli et al.
It should be stressed that genome sampling is one of the main limitations of pangenome studies and can therefore influence PPanGGOLiN partitioning especially for the shell genome. An improvement in the method could be to normalize the data to remove sampling bias. But as suggested by Brockhurst et al. [42] , this issue should first be examined from a biological perspective by collecting and analyzing genomes from ecologically coherent microbial populations or ecotypes. Future applications of PPanGGOLiN could include the prediction of genomics islands within the shell and cloud genomes. A first version of this application (Bazin et al. , in preparation) is already integrated in the MicroScope genome analysis platform  [43]. Next, it would be interesting to determine the architecture of these variable regions by predicting conserved gene modules using information on the occurrence of families and their genomic neighborhood in the pangenome graph.
Analysis of isolate genomes and Metagenome-Assembled Genomes To obtain the set of isolate genomes to be analyzed, we downloaded all archaeal and bacterial genomes (220 561 genomes) of the GenBank database at the date of the 17th of April 2019. We removed genome assemblies that do not respect quality control criteria defined by GenBank. They correspond to entries with an assembly status flag different from "status = latest" in the "assembly_status. txt" files. In addition, genomes were discarded if they had more than 1000 contigs or a L90 > 100. These filters allowed us to exclude poor quality assemblies, some of which may correspond to contaminated genomes and others to incomplete ones. For each species (identified by its NCBI species taxid), a pairwise genomic distance matrix was computed using Mash (version 2.
0)  [37]. To avoid redundancy, if several genomes are at a Mash distance < 0. 0001, only one was kept (the one having the lowest number of contigs). A single linkage clustering using SiLiX (version 1. 2. 11)  [53]  was then performed on the adjacency graph of the Mash distance matrix considering only distances below or equal to 0. 06. This Mash distance corresponds to a 94% Average Nucleotide Identity (ANI) cutoff which is a usual value to define species  [54]. Genomes that were not in the largest connected component were discarded to remove potential taxonomic assignation errors. Only species having at least 15 remaining genomes were then considered for the analysis. The list of all the GenBank assembly accessions used after filtering is available in S3 File. This dataset consists of 439 species encompassing 136 287 genomes (see S1 File).
MAGs from the Pasolli et al. study  [41]  were downloaded from https://opendata. lifebit. ai/table/SGB. In this dataset, the genomes are already grouped in Species Genome Bins. These SGBs do not exactly match the GenBank taxonomy. Thus, SGBs assigned with the same species name (column "estimated taxonomy" in the supplementary table S4 of  [41] ) were merged to allow comparison with GenBank. SGBs that do not have a taxonomy assigned at the species level were not considered. A total of 583 species encompassing 698 SGBs and 71 766 MAGs were analyzed but only MAGs from 78 species were finally compared to GenBank genomes. To avoid introducing a bias in our analysis due to heterogeneous gene calling, GenBank annotations were not considered as they were obtained using a variety of annotation workflows.
Genomes from GenBank and Pasolli et al were consistently annotated using the procedure implemented in PPanGGOLiN. Prodigal (version 2. 6. 2)  [55]  is used to detect the coding genes (CDS). tRNA and tmRNA genes are predicted using Aragorn (version 1. 2. 38)  [56]  whereas the rRNA are detected using Infernal (version 1. 1. 2) [57]  with HMM models from Rfam  [58]. In the case of overlaps between a RNA and a CDS, the overlapping CDS are discarded. Homologous gene families were determined using MMseqs2 (version 8-fac81)  [59]  with the following parameters: coverage = 80% with cov-mode = 0, minimal amino acid sequence identity = 80% and cluster-mode = 0 corresponding to the Greedy Set Cover clustering mode. PPanGGOLiN partitioning was executed on each species using the NEM approach with a parameter β = 2.
5. The nodes having a degree above 10 (which is the default parameter) were not considered to smooth the partitioning via the MRF. The number of partitions (K) was determined automatically for each NCBI species using a δ ICL = 0. 05 and iterating between 3 and 20 for the possible values of K. K was fixed at 3 for the MAG analysis. The partitioning was done using chunks of 500 genomes when there were more than 500 genomes in a species. To compare PPanGGOLiN results between MAGs and GenBank genomes for each species, the representative sequences of each MAG gene family (extracted using the mmseqs2 subcommand: "result2repseq") were aligned (using mmseqs2 "search") on those of GenBank genomes. If the best hit of the query had a sequence identity > 80% and a coverage > 80% of the target, the 2 corresponding gene families of each dataset were associated.
PPanGGOLiN software implementation PPanGGOLiN was designed to be a software suite performing the annotation of the genomic sequences, building the gene families and the pangenome graph before partitioning it. Users can also provide their own annotations (GFF3 or GBFF format) and gene families. The application stores its data in a compressed HDF5 file but can also return the graph in GEXF or JSON formats and the P/A matrix with the partitioning in CSV or Rtab files (similarly to the ones provided by Roary  [34] ). It also generates several illustrative figures, some of which are presented in the article. PPanGGOLiN was developed in the Python 3 and C languages and is intended to be easily installable on Linux and Mac OS systems via a BioConda package  [60]  (see https://bioconda. github. io/recipes/ppanggolin/README.
Acknowledgments We acknowledge  Alexandre Renaux  and  Jonathan Mercier  for their preliminary insights on pangenome graphs. We thank  Melanie Buy  for drawing the PPanGGOLiN logo. Finally, we thank  Guilhem Royer ,  Valentin Sabatet ,  Johan Rollin ,  Mohammed-Amin Madoui ,  Tom Delmont ,  Nicolas Pons  and  Pierre Peterlongo  for all their advice along this work.
This research was supported in part by the  IRTELIS  and  Phare PhD  programs of the  French Alternative Energies and Atomic Energy Commission  ( CEA ) for GG and AB respectively, the  French Government "Investissements d'Avenir" programs  (namely  FRANCE GENOMIQUE  [ ANR-10-INBS-09-08 ], the  INSTITUT FRANC ¸AIS DE BIOINFORMATIQUE  [ ANR-11-INBS-0013 ], and the  Agence Nationale de la Recherche  [Projet  ANR-16-CE12-29  for EPCR]). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
were downloaded from the NCBI FTP server (ftp://ftp. ncbi. nlm. nih. gov/ genomes/genbank) 17 April 2019. Metagenome//opendata. lifebit. ai/table/SGB. All analyses described here were run using PPanGGOLiN software (version 1PPanGGOLiN source code is freely available from https://github. com/labgem/ PPanGGOLiN under a CeCILL license. All relevant data are within the manuscript and its Supporting Information files.
The Atlantic rainforest of Brazil is one of the global terrestrial hotspots of biodiversity. Despite having undergone large scale deforestation, forest cover has shown signs of increases in the last decades. Here, to understand the degradation and regeneration history of Atlantic rainforest remnants near São Paulo, we combine a unique dataset of very high resolution images from Worldview-2 and Worldview-3 (0. 5 and 0. 3m spatial resolution, respectively), georeferenced aerial photographs from 1962 and use a deep learning method called U-net to map (i) the forest cover and changes and (ii) two pioneer tree species, Cecropia hololeuca and Tibouchina pulchra. For Tibouchina pulchra, all the individuals were mapped in February, when the trees undergo mass-flowering with purple and pink blossoms.