dataesr/scientific-paragraphs-categorization huggingface dataset:
On a positive note, the distribution of indels at the MDS junctions creates a signature that is, given the highly imprecise nature of IES excisions and the high number of IES, unique to each MAC genome developed during the juvenile stage following sexual reproduction. This 'barcoding' of MAC genomes allowed us to identify in our dataset strains issued from the same developed MAC genome (i. e. , all chromosomes had finished their phenotypic assortment), and that our D18 strain is in fact a recent amicronucleated descendent of SB210. We therefore believe that distribution of indels at MDS junctions could become a useful tool to characterise closely related strains of T. thermophila with high confidence. For instance, by sequencing at a low coverage (<10x) a few hundreds of cells sampled in a given environment, it should be possible to evaluate the number of cells issued from the same MIC, but also from the same developed MAC.
Recent history of strains We further explored the very surprising observation that strains isolated by P. Doerder at different locations in the US are virtually identical, given that fifteen out of these 22 strains were previously and independently Cox1-barcoded  [56, 63]. We found that only two out of the 15 common strains had the same Cox1 sequences (D10 and D11; Supplementary Material 8D). The majority of strains sharing the same MACs in our dataset did not seem representative of the natural sampling effort performed by P. Doerder. This might be due to wrong assignments and/or contaminations, at any stage of their ~20 years of lab maintenance since their initial field sampling. Cross contaminations might explain the existence of the three groups of strains sharing the exact same distribution of indels at MDS junctions (D2/D4/D9/D13/D15/D17/D19, D14/D21/D22, D3/D6/D12).
However, among the strains that were not barcoded by Doerder, which were all collected in the same geographic region (Supplementary Material 1), it is difficult to draw any conclusion on the potential cross-contaminations. For instance, the proximity between D3 and D6 could reflect a natural situation. To estimate approximately the divergence time among strains within the three groups with the same pattern of indels at MDS junctions, we looked at strain-specific mutations within each of them, which can only be MACde novo-SNPs (Supplementary Material 5D). Based on these observed numbers of strainspecific mutations, we suspect D21 and D22 on one hand, and D13 and D19 on the other hand to have split the more recently, and D3, D6 and D12 the more anciently. Comparing these numbers of MAC-de novo-SNPs (between 774 and 14,140 depending on the strain) with the few tens accumulated during the experiment of  [61]  that lasted ~1,000 asexual divisions (~200 days), we hypothesize that the putative cross contaminations in our dataset occurred several years ago.
Unfortunately, strains were not cryopreserved nor sequenced at any stage of their lab maintenance, meaning that we cannot precisely reconstruct their recent history. Nevertheless, the overdominance of SB210-like strains suggests a massive contamination of cells issued from this reference lab strain, generally leading to replacement of the native stains. In two independent cases, an SB210-like strain probably sexually reproduced with two divergent strains (not sampled here) leading to the formation of D5 and D16. Incongruence between our mitochondrial data and the barcoding performed in both  [56]  and  [63]  precludes any geographic assignment, and thus MAC-environment associations, from our dataset. To avoid confusion between the P. Doerder studies, Tetrahymena Stock Center references and our study, we hereafter only use our own denomination, D1 to D22, which matches the one we have used in all papers issued from our group since 2014 (e.
Mutational pattern To avoid the background noise in SNP calling created by imprecise IES excisions, we estimated polymorphism metrics of the MAC genome after excluding positions within 20 bp of the MDS junctions. Since we might have missed some MDS junctions, or some MDS junctions might be placed in different positions in distant strains, we also ignored in each strain all positions within 20 bp of any indel. These criteria eliminated on average 3. 7 Mb and 0. 4 Mb from the genomes of distant and SB210-like strains respectively. We first used this filtered dataset by keeping only positions for which the alternative variant as compared with SB210 had a minimum frequency of 30n (see Methods). This allowed to compare the transition/transversion ratio between a set a recently evolved strains (on which selection had less time to act) and a set of more divergent strains.
Polymorphism metrics overall MAC genome Across the 22 strains, the MAC nucleotide diversity measured in non-coding RNAs was found to be the lowest, followed by protein-coding sequences and finally introns and intergenic regions (Table  1 ). Estimations of polymorphism metrics are dependent upon the sampling, and we suspected the presence of cross-contaminations likely biasing our sampling (see section on the recent history of strains). We thus also calculated diversity metrics considering only one strain for each of the seven clusters differing in their MICs. Nucleotide diversities logically increased ('reduced' column in Table  1 ; strains D1, D3, D5, D7, D10, D11 and D16 on the basis of Figure  2 ). The fact that introns and intergenic regions displayed similar nucleotide diversities, whatever the dataset considered, suggests that introns are not enriched in conserved regulatory elements as it is the case in the compact genome of Paramecium tetraurelia  [36].
Untranslated regions (UTRs), which approximately correspond in T. thermophila to the 200bp surrounding the genes  [68] , showed asymmetrical levels of polymorphism: while the 3' UTRs displayed levels of nucleotide diversity similar to the rest of intergenic regions, we observed a marked decrease of nucleotide diversity in the 5' UTRs (Figure  4A ), suggesting that regulatory elements (e. g. , promoters, enhancers, silencers) are predominantly located in 5' UTRs. Within coding regions, the nucleotide diversity was of 1. 01, 0. 67 and 0. 23% for 4, 2, and 0-fold degenerated sites respectively for the full dataset, and 1. 76, 1. 17 and 0. 43% for the 'reduced' dataset. In Paramecium, where species are generally distributed worldwide, these diversity values vary between 0. 6-13, 0. 3-8 and 0. 1-1.
Polymorphism of protein-coding sequences Considering the proportion of non-synonymous (pN) and synonymous (pS) differences in protein-coding sequences using the reduced dataset, we found that more than 85% of the protein-coding genes had a pN/pS ratio below 0. 5 (Figure  4C  and Supplementary Material 5G), suggesting that T. thermophila genes are primarily under purifying selection. We then clustered the genes by their annotation using the slim Gene Ontology (GO), and reported in Supplementary Material 13 the distribution of pN/pS values for each slim GO term. Focusing on the 'cellular component' GOs, the 78 genes encoding for 'proteins of the cilium' had the lowest median pN/pS value (0. 02). We hypothesize the constraints on these genes are due to the fundamental role of cilia in feeding and locomotion processes in ciliates, and the general stability of cilia organization within the 'morphospecies' of the genus Tetrahymena  [69, 70].
Conclusion We provide here the first intraspecific genomic dataset in the model ciliate T. thermophila. While we could describe several interesting patterns, this work should be seen as a first step. Increasing the number of complete T. thermophila MAC, but also MIC, genomes with long read methods should help to obtain more precise assemblages. This would allow to tackle questions that are still open in both mechanistic biology and ecology. Especially, one strong limitation to our study is the impossibility to assign strains to their geographic origin, which prevented us to explore genome/environment associations in this model species. Another limitation is our small number of diverging strains (seven). While we could provide a first assessment of MAC intraspecific diversity in T. thermophila, we clearly underestimate its true diversity.
Funding information This work is part of the  Agence Nationale de la Recherche (ANR)  project  POLLUCLIM  ( ANR-19-CE02-0021-01 ) attributed to D. L. that employed R. D. This work is part of  TULIP (Laboratory of Excellence  grant no. ANR-10 LABX-41 ), and was financially supported by a senior package attributed to H. P. that employed R. V.
In order to understand whether a particular network of three key gene regulators (BCL6, IRF4, BLIMP1), influenced by two external stimuli signals (surface receptors BCR and CD40), is able to describe GC B cell di erentiation, we used a stochastic model to fit SC transcriptomic data from a human lymphoid organ dataset. The model is defined mathematically as a piecewise-deterministic Markov process. We showed that after parameter tuning, the model qualitatively recapitulates mRNA distributions corresponding to GC and plasmablast stages of B cell di erentiation. Thus, the model can assist in validating the GRN structure and, in the future, could lead to better understanding of the di erent types of dysfunction of the regulatory mechanisms.
Single-cell data We used the B cell dataset from human lymphoid organs published by Milpied et al. [30]. The authors studied normal B cell subsets from germinal centers of the human spleen and tonsil and performed integrative SC analysis of gene expression. They used an adapted version of the integrative single-cell analysis protocol  [31]. In short, the authors prepared cells for flow cytometry cell sorting. Then in every 96-well plate the authors sorted three to six ten-cell samples of the same phenotype as a single-cell. They performed multiplex qPCR analysis using the Biomark system (Fluidigm) with 96x96 microfluidic chips (fluidigm) and Taqmann assays (Thermofisher)  [30]. They obtained results in the form of fixed fluorescence threshold to derive Ct values. We used Ct values to derive Expression threshold (Et) values: Et = 30 ≠ Ct.
When there was an unreliably low or undetected expression (Ct > 30), Et was set to zero  [30]. Using SC gene expression analysis of a panel of 91 preselected genes and pseudotime analysis (based on the cartesian coordinates of SC on the first and second principal components of the PCA), the authors separated GC DZ cells, GC LZ cells, memory cells and PB_PC cells. Here we focused on three genes, BCL6, IRF4 and BLIMP1. We selected the SC gene expression values for BCL6, IRF4 and BLIMP1 for GC DZ cells (317 SC) and for PB_PC (104 SC) (see Figure  5 ). The experimental dataset includes at the GC B cell stage 30 cells with zero BCL6 mRNA amount, 292 cells with zero IRF4 mRNA amount and 292 cells with zero BLIMP1 mRNA amount. For the end of the B cell di erentiation (PB_PC), there were 25 cells with zero BCL6 mRNA amount, 79 cells with zero IRF4 and 5 cells with zero BLIMP1 mRNA amount.
Assessing the variability of the stochastic model Due to the stochastic nature of the stochastic system (  4 )-(  6 ), it is important to first evaluate the variability of the model-generated SC data, that is of model's outputs. Indeed, when one repeatedly simulates a finite number of cells from the stochastic system (  4 )-(  6 ) for the same parameter value set (Tables  2 3 4 5 , version I), the resulting model-derived empirical distributions are slightly di erent between each run due to the stochasticity of the model. We investigated how strongly shapes of distributions of simulated SC mRNA molecules vary for di erent executions of model (  4 )-  (6). We evaluated the level of variability of model (  4 )-(  6 ) using the Kantorovich distance (KD, see Section 2. 7). We simulated 200 datasets, each containing 200 single cells, of System (  4 )-(  6 ) with a fixed parameter set (see Tables  2 3 4 5 , version I).
We estimated the KD between pairs of simulated datasets (mRNA counts for three genes at GC and PB_PC stages for 200 simulated cells), and obtained a distribution of all KD that we call the model-to-model (m-t-m) distribution (Figure  3 ). Shapes of m-t-m distributions are di erent for each gene and stage of di erentiation. For instance, for BLIMP1, long tails are observed. We can also notice that the mean value of IRF4 at GC stage is low compared to other genes. In order to get a more accurate evaluation of the variability in model's outputs, we plotted distributions of the number of mRNA molecules (model's outputs) for each node of the GRN with the highest m-t-m distribution at both GC and PB_PC stages (Figure  4 ). Qualitatively, no di erence is detected in the shapes of model-generated distributions.
The e ect of mutual repression between BCL6 and BLIMP1 could be evaluated by performing a more extensive parameter value search. The current parameter value set (Tables  2 3 4 5 , version III) makes model (  4 )-(  6  to positive regulation of BLIMP1 and consecutive repression of BCL6  [5, 34]. Another important transcription factor in GC development is MYC, which regulates B cell proliferation  [42]  and the DZ B cell phenotype  [43]. MYC indirectly activates the histone methyltransferase enhancer of zeste homologue 2 (EZH2), which is responsible for the repression of IRF4 and BLIMP1  [44, 45, 46, 47]. The transcription factors mentioned above are present in the SC RT-qPCR dataset from Milpied et al. [30]  that we used and could be used to extend the current GRN. Inclusion of additional transcription factors may have both positive and negative e ects on the application of model (  4 )-  (6).
To further continue our study, we could also use SC RNA-seq dataset from Milpied et. al  [30]. The authors have produced SC RNA-seq dataset from GC B cells and analysed the similarities between SC RNA-seq and SC RT-qPCR dataset. Even though the gene-gene correlation levels were lower in SC RNA-seq compared to SC RT-qPCR, SC RNA-seq analysis confirmed the observation obtained by SC RT-qPCR  [30]. From the stochastic modeling perspective, combining the data from SC RT-qPCR and SC RNA-seq should improve our understanding of the SC dataset variability and the quality of the fitting. To summarise, the stochastic model (  4 )-(  6 ) is capable of qualitatively simulating and depicting the stochasticity of experimental SC gene expression data of human B cell di erentiation at the GC and PB_PC stages using a GRN made of three-key genes (BCL6, IRF4, BLIMP1).
Acknowledgments We thank  Arnaud Bonna oux  and  Matteo Bouvier  for their help with the WASABI framework and their critical reading of the manuscript. We thank the computational center of  IN2P3 (Villeurbanne/France) , especially  Gino Marchetti  and  Renaud Vernet. We also thank  Aurelien Pélissier  and  Elias Ventre  for their scientific discussions.
A global genome database of all of Earth's species diversity could be a treasure trove of scientific discoveries. However, regardless of the major advances in genome sequencing technologies, only a tiny fraction of species have genomic information available. To contribute to a more complete planetary genomic database, scientists and institutions across the world have united under the Earth BioGenome Project (EBP), which plans to sequence and assemble high-quality reference genomes for all ~1. 5 million recognized eukaryotic species through a stepwise phased approach. As the initiative transitions into Phase II, where 150,000 species are to be sequenced in just four years, worldwide participation in the project will be fundamental to success. As the European node of the EBP, the European Reference Genome Atlas (ERGA) seeks to implement a new decentralised, accessible, equitable and inclusive model for producing high-quality reference genomes, which will inform EBP as it scales.
As genomic sequencing has become increasingly cost effective and the platforms and computational algorithms become more technically efficient, many biodiversity genomics tools have become available to expedite the investigation of both known and unknown species e. g. , DNA barcoding, genome skimming, reduced representation sequencing, transcriptome sequencing, and whole genome sequencing for reference genome production  10. Reference genomes (see glossary) are one such tool that offer an unparalleled, scalable, and increasingly cost-effective high resolution insight into species, and their accessibility has made the construction of a planetary-wide genomic database of all eukaryotic life a more realistic endeavor  11. To date, reference genomes do not exist for most of eukaryotic life. For instance, the largest genomics data repository, the International Nucleotide Sequence Database Collaboration (INSDC), has genome-wide DNA sequence information for just 6,480 eukaryotic species (about 0.
The state of reference genome production today After two decades of uncoordinated and unstandardised biodiversity genomics sequencing data production (e. g. with little coordination among individual research laboratories or projects), the Earth BioGenome Project (EBP)  11  was established. The goal of the EBP is to create a global network of biodiversity genomics researchers that share a mission to produce a database of openly accessible, standardised, and complete reference resources that span the whole eukaryotic phylogenetic tree. The project has a three-phase approach and to date (Phase I) has produced ~1,213 reference genomes for species across ~1,010 genera  16. However the rate of production is fast increasing, for instance in 2022 over 316 reference genomes were produced and in the coming years the rate is estimated to increase by at least 10 fold.
3: Sampling and metadata acquisition During sample collection important metadata concerning the species collection event were expected to be documented by the sample collector. To standardise this process a robust metadata schema was developed, using the DToL metadata schema as a foundation  23. The tailored ERGA schema, including unique ERGA specimen identifiers as well as ToLID (https://id. tol. sanger. ac. uk/), was codified into a. csv formatted 'manifest' and made publicly available (https://github. com/ERGA-consortium/ERGA-sample-manifest). In tandem, a standard operating procedure document  24  was developed to provide details on how to complete all of the 81 validatable manifest fields. Unique to ERGA, fields were developed to mandate important information disclosure e. g. , permanent unique identifiers (PUID) associated with ex-situ specimens, permits, and Indigenous rights and interests (TK and BC Labels and Notices)  22, [25] [26] [27].
After this, the assembly and the associated metrics underwent an internal round of peer-review from assembly experts (ERGA Sequencing and Assembly Committee). After feedback integration, each genome team uploaded the pre-curation assembly to the internal ERGA data repository along with details of the assembly construction (https://gitlab. com/wtsi-grit/documentation/-/blob/main/yaml_format. md) and each team was provided with the opportunity to submit their reference genome to an internal panel of expert curators who conducted a final manual curation  35. Due to the decentralised nature of the infrastructure, all 98 species progressed through the steps at different rates, depending on the number and complexity of permits    From the 30 reference genome assemblies with a 'Curated' or 'Pre-curation' status, we found 14 cases where the assemblies do not meet the quality standard 6.
Despite this, for all the completed assemblies, Ns accounted for less than 0. 05% of the genome, with the exception of Mustela lutreola (mMusLut1). For this genome assembly, which has yet to undergo final curation (the only large ONT-based assembly evaluated >2 Gbp), 0. 55% of its sequence was composed of Ns (Figure  4b ). Besides EBP metrics, when estimating completeness using single-copy orthologs, Phakellia ventilabrum (odPhaVent1) and Gordionus montsenyensis (tfGorSpeb1) assemblies had lower values than recommended. tfGorSpeb1 is one of the first of its phylum to be sequenced  36  , and so is therefore underrepresented in the BUSCO database (Figure  4c )  37. Two species, Trifolium dubium (drTriDubi1) and the Salvelinus alpinus (fSalAlp1), both have higher ploidy levels (tetraploid and partial tetraploid, respectively) and had much higher BUSCO duplicate values than the recommended 5% (Supplementary Table  1 ).
io/GTN:T00039; https://workflowhub. eu/workflows/325?version=1) was not appropriate as this approach would not cater for polyploid genomes nor those assemblies produced that were ONT-based. A further challenge was the provision of a centralised system for the storage and transfer of raw and final genomic and transcriptomic data. This was particularly challenging in cases where data production spanned two or more locations (e. g. , PacBio sequenced at one site, Hi-C at a second, and RNA at a third) and was subsequently assembled at another site. While the Nextcloud instance created by BSC was an elegant solution for transferring vast quantities of data between parties, it required a vast amount of personnel hours to manage, in addition to its baseline system-wide maintenance requirements. Moving forward, a key goal for ERGA is the production of standardised and reusable pipelines that are: responsive to all sequencing "recipes" (PacBio, ONT, or other future technologies); written for Galaxy, Snakemake, and/or Nextflow workflow managers; made publicly available (https://github.
4) Genome annotation The first hurdle in annotation is the availability of sufficient evidence (transcriptomic and protein sequence data) from focal species, databases and predictive models of repeats. Secondly, even with appropriate data, the most accurate genome annotation pipelines require advanced skills to both install and run which reduces their accessibility and ultimately their utility. Finally, robust annotation quality assessment tools are lacking particularly for species with underrepresented genomic resources, for instance gene content assessment tools such as BUSCO  51  remain unable to account for species within taxonomic groups that have incomplete gene sets available leading to unreliable quality assessments. Obtaining, and equitably distributing, financial resources will be required to equip researchers, labs, and regions for annotation in a manner that responds to their varying resource realities.
Glossary Biodiversity genomics -The application of genomic methods to research biodiversity. BUSCO -A bioinformatic method (Benchmarking Universal Single-Copy Orthologues) used to estimate the completeness of the coding fraction of an organism's genome based on the proportion of (lineage specific) single copy orthologous genes that are found in a genome assembly  51. INSDC -International Nucleotide Sequence Database Collaboration (https://www. insdc. org/) is an initiative between the DDBJ, EMBL-EBI and NCBI that together act as a global repository of sequence data and associated metadata, and provide tools and services that allow access to genomic resources. Reference genome -An accepted standard representation of an organism's DNA sequence. High-quality reference genomes typically have high completeness (chromosome-level with few gaps in sequence), few errors, and are annotated and accessible.
CARE Principles - The CARE principles for Indigenous data governance (https://www. gida-global. org/care) provide a governance framework that supports the recognition of rights and interests Indigenous Peoples' to their physical and digital data as well as their Indigenous Knowledges  64. Metadata -A collection of data that provides contextual information about multiple characteristics of other, corresponding original data. Voucher -A voucher specimen is a permanently preserved object (either whole or in part, and/or physical or digital) of an identified organism (verified by a recognised expert) and which is deposited in an accessible facility or database. A voucher provides physical evidence about any specimen's taxonomic identity  14. Voucher deposition is a best practice for conducting biodiversity genomics research.