dataesr/scientific-paragraphs-categorization huggingface dataset:
89, ρ = 0. 87, ρ = 0. 89 for unfiltered, soft and hard filtered datasets, p < 0. 001 for the three; Table  2 ) but not significantly correlated with SNP density (Spearman's rank correlation test, ρ = -0. 26, p = 0. 42, ρ = -0. 20, p = 0. 51, ρ = -0. 24, p = 0. 46 for the three datasets, respectively), suggesting that the number of hotspots is more closely associated with differences of genome size than differences in data quality. These results were supported by the fact that recombination rates varied over many orders of magnitude at a fine scale and were concentrated in a short fraction of the genome (Fig. 1B , Fig. S2 ). For most species 80% of the total recombination was concentrated within 20% of the genome. The species-specific patterns were roughly the same between plants and human.
Our results for human, rice and Arabidopsis thaliana were globally Ubiquitous recombination gradients within plant genic regions shaped by recombination hotspots similar to state-of-the-art previous studies  [17, 20, 43, 60] , both for the shape of hotspots and the cumulative distribution function (Fig. 1A, B ) and the number of hotspots (Table  2 ). Three species (Arabidopsis thaliana, Malus sieversii and Populus tremula) showed slightly less heterogeneity than the rest of the dataset (Fig. 1B ). We noticed more variability between Oryza sativa chromosomes than for other species (Fig. 1B ). Overall our results showed that recombination was globally concentrated in a short fraction of the genome across eleven plant species, suggesting that recombination hotspots may be a general feature in plants, similarly to human.
1A ). For the unfiltered and soft filtered datasets, the bias was absent in three species and weak in eight other species, suggesting that we were not strongly limited to detect hotspots in regions experiencing extremely high or low recombination rates. On the contrary, in Phaseolus vulgaris the detection of hotspots was particularly biased towards lowly recombining regions. In Phaseolus vulgaris, Glycine max, Camellia sinensis and Malus sieversii the bias was more pronounced for the hard filtered dataset, probably because intense hotspots are more easily detected on a low recombining background. In general the soft filtered dataset seemed relatively more reliable for the exhaustive search of hotspot location along the genome since hard filtering was biased towards low recombining regions in a few species.
Diversity of gradients across species We applied the same analyses to our eleven plant species in order to identify common patterns across species. We also analysed the human dataset as a control species where hotspots are mainly located outside genic regions. We observed a diversity of patterns, more or less similar to the three species we detailed above. Recombination patterns were mostly determined by differences in hotspot location and intensity. Average 5'-3' recombination gradients were common across the eleven plant species and even in human (Fig. 5A , black lines), except in T. aestivum for which the power to detect gradients was limited (see methods for details). As for the three species we chose as an example, we also found three main different patterns. In Arabidopsis thaliana, Sorghum bicolor, Malus sieversii and Citrullus lanatus the detailed gradients per gene class were parallel to the average gradient and matched what was already known in plants  [17, 25].
In order to support the ubiquity of recombination gradients in our eleven plant species, we assessed if the diversity of recombination gradients we observed could be parallel to hotspot gradients (Fig. 6C , Fig. S12 ). Hotspot gradients were clearer and more consistent among species than recombination gradients, with a few exceptions (Fig. 6C ). Hotspot gradients were detected in all twelve species (Fig. S11 ), which suggested that with a sufficiently polymorphic dataset we should be able to detect recombination gradients in every species. Overall, filtered gradients and hotspot distribution suggested that recombination gradients could be a general pattern among plants. Ubiquitous recombination gradients within plant genic regions shaped by recombination hotspots Ubiquitous recombination gradients within plant genic regions shaped by recombination hotspots.
Materials and Methods To produce fine-scale recombination maps we analyzed previously published polymorphism datasets in eleven plant species and human (Table  1 ) starting from VCF files. We identified datasets by literature search with the keywords "resequencing", "high density SNP", "polymorphism data", "variant database", "Whole Genome Sequencing" or "WGS" in association with "plant", "angiosperms" and a set of plant species names. We also searched directly for public genomic databases (e. g. 1001 genomes). We used a previous study of 57 plant species to identify species with interesting and contrasting characteristics, such as monocots/eudicots, small/large genomes, low/high recombination rates, and homogeneous/heterogeneous recombination landscapes  [1]. Before downstream analyses, we kept only bi-allelic SNPs and filtered them with the same quality criteria for all datasets (minor allele frequency higher than 0.
05, less than 10% of missing data per site, quality score ≥ 30 when the information was available). When the quality score was not available in the published VCF, we checked in the original study that the dataset we re-used was already filtered by a similar quality score. The genome assemblies and corresponding annotations (GFF files) on which SNPs were originally mapped were retrieved from public databases, mostly NCBI (Table  S1 ). GFFs with gene, mRNA, CDS and exon features were parsed using custom Python scripts to infer introns, 5' and 3' UTRs, flanking sequences (1-3 kb upstream/downstream, in consecutive windows of 1 kb) and intergenic sequences according to the GFF3 specifications. Scripts are freely available as a Python package (https://github. com/ThomasBrazier/PiSlice. git v1. 0).
Only protein-coding genes were retained when this information was annotated. For datasets without information about gene biotype, we considered all genes as protein coding. In order to avoid low sample size for longer genes, we removed genes with more than 14 exons as in  Ressayre et al. (2015). When more than one splicing variant has been annotated, the splicing variant with the longest CDS sequence was retained, as it is often also the most expressed sequence. We considered only the CDS part of exons. All statistical analyses were performed with R version 4. 0. 4  [77]. Genomic coordinates were manipulated with the R package GenomicRanges  [78]. We used a custom pipeline for data processing, SNP filtering, statistical phasing, population structure and re-sampling, recombination rates estimates with LDhat 2.
Population sampling Population-scaled recombination rates and hotspot detection are based on the assumption of a population without genetic structure and absence of gene flow from other populations  [79, 80]. For each dataset, we inferred population structure and sampled one single genetic population as homogeneous as possible and well distinct from other possible genetic clusters. As much as possible, we tried to sample a single large population (> 40 diploid individuals or 80 haploid genomes for selfing species) of wild/landrace individuals with the highest possible polymorphism level. Results and metadata of the original publications were used for a presampling. The genetic structure within the pre-sampled population was assessed with FastSTRUCTURE for K = 1-7  [81]. FastSTRUCTURE was run on a random subset of 100,000 SNPs for computation issues.
Population-scaled recombination rates After population sampling, the VCF was phased with Shapeit2, with a phasing window size of 2 Mb  [82]. Selfing species were treated as haploid individuals, by randomly sampling only one phased haplotype per individual. When the sample size was sufficient, at most 40 diploid individuals (or 80 haploid sequences for selfing species) were randomly subset for LDhat 2. 2 for computational issues. The population-scaled recombination rates ρ were estimated with LDhat 2. 2  [40, 42]. For the estimate of genetic diversity required by LDhat we used the genome averaged θπ estimated with VCFtools on the population dataset  [83]. The per-base mutation rate was set to 1. 28. 10 -8 for all species. When θπ was in the same order of magnitude as a pre-generated look-up table (θ = 0.
Hotspot detection Recombination hotspots were inferred with LDhot using default settings  [43]. We tested the significance of putative hotspots in 3 kb windows, with a step size of 1 kb along the genome. The background window was ± 50 kb around the hotspot centre. The hotspots inferred were then summarised using the LDhot summary program with a significance cutoff of 0. 001 for calling a hotspot and 0. 01 for merging adjacent hotspots. In order to control for hotspot quality and reduce false positive hotspots, we compared the raw hotspot dataset to filtered datasets after soft and hard filtering strategies. As hotspots are generally defined as narrow genomic regions, the soft filtering approach removed every hotspot larger than 10 kb. The intensity of a hotspot was estimated by dividing the recombination peak rate by the background recombination rate (± 50 kb around the hotspot centre).
As a consequence, a lack of SNPs at the beginning of genes where recombination hotspots are supposed to be located could reduce our resolution to accurately position hotspots in this region, thus leading to an artificial leakage of high ρ/kb values at a larger scale than the true biological size of the hotspot. To evaluate this, we re-calculated the gradient of ρ/kb as a function of the rank, but this time we kept only ρ/kb estimates beginning in the genomic interval (exon/intron). Thus, we were sure to measure only the actual recombination within the interval. We observed the same patterns using this restricted set of ρ values confirming that the observed gradients were a true biological signal (Fig S13A  , B ). Secondly, we assessed the power to detect a true gradient, if there was one, for a given empirical dataset.
For each gene, the ranks of exons were sorted by descending ρ/kb values, to simulate the maximal negative 5'-3' gradient that could be observed from empirical observations, if there is one. Ten out of eleven datasets had the necessary power to detect true gradients of recombination, except for Triticum aestivum lacking resolution (Fig S13B ). Ubiquitous recombination gradients within plant genic regions shaped by recombination hotspots Ubiquitous recombination gradients within plant genic regions shaped by recombination hotspots estimated from Marey maps in  [1]. The grey ribbon is the 95% confidence interval estimated by 1,000 bootstraps on SNP markers.
Acknowledgments We wish to thank  Laurent Duret ,  Pierre-Alexandre Gagnaire ,  Marie Raynaud ,  Julien Joseph ,  Nicolas Lartillot  and all the other members of the  HotRec ANR  project, as well as members of the lab and people met outside for the discussion of the results. We used the GENOUEST computing facility for analyses. Funding:  Agence Nationale de la Recherche , Grant  ANR-19-CE12472 0019 /  HotRec. Author contributions: Original idea: S. G. ; Data analyses: T. B. ; First draft: T. B. ; Editing and revisions: T. B. , S. G. Competing interests: The authors declare no conflicts of interest. Data and materials availability: Analysis scripts and documentation are available at https://github. com/ThomasBrazier/landrec-gradients. Data produced in this study are available at https://osf.
The dashed horizontal line is the mean genome-wide recombination rate. (B) The cumulative ordered recombination fraction as a function of the cumulative genome fraction for each chromosomes and species. We represented dashed vertical lines at 20% of the genome and dashed horizontal lines at 80% of the total recombination. (C) Normalized recombination rates (centered-reduced recombination rates, i. e. the hotspot recombination rate minus the species genome-wide recombination rate, all divided by the genome-wide standard deviation) as a function of the distance to the hotspot centre for the soft filtered dataset. Species in the legend ordered by descending peak height. Human is in black.
CFSE stably binds to the amine groups present in cytoplasmic proteins, conferring stable fluorescence intensity to the cell. As total protein content is supposed to be relatively equally distributed between sister-cells during cell division, so is the fluorescence intensity  [29, 30]. We used this specification to validate that the two cells isolated were actually sister-cells. We evaluated the CFSE intensity correlation between pairs of sister-cells, and compared it to intensity correlation values of randomly paired cells from the same dataset (Figure  S1  E-F for self-renewing cells and G-H for differentiating cells). Outstandingly, CFSE correlation values be-tween self-renewing sister-cells and differentiating sister-cells were extremely high (0. 91 and 0. 95 Figure  S1 E  and G , respectively), whereas for randomly paired-cells, CFSE correlation values dropped between -0.
Generation 2 cells transcriptomes continue to diverge during differentiation We generated a second dataset consisting of generation 2 T2EC sisters and cousin-cells (after two cell divisions) using the methodology described above. As scRNA-seq requires the lysis of the cell under investigation, generation 1 data and generation 2 data consist of different cell families and thus cannot be compared to each other so both dataset were treated and analyzed separately (see Methods). The second generation dataset was composed of 4 cousin-cells per family (8 families of cells in self-renewing and 5 families of cells in differentiation condition), and within the 4 cousins, they consisted of two couples of sister-cells. After data filtering and normalization, we retained 983 genes for subsequent analysis.
One should note that LDHA was previoulsy found to also be involved in the erythroid differentiation process  [25]. We computed again and compared the Manhattan distances for the T2EC cells between sisters and non related cells using as a vector only the 55 memory genes (Figure  7 ). As a result, the difference in within-distance between sister-cells and non related cells, in both biological conditions (self-renewing and differentiating), was even more pronounce than when computing the Manhattan distances using all 1177 genes of the scRNA-seq dataset (see above), further confirming that the identified genes are the ones imprinted by the transcriptional memory. To validate our findings, we also checked if these memory genes were not only genes associated high mRNA half-life. We crossed our gene list to a previously published dataset which evaluated half-life duration of genes during T2EC differentiation using RT-qPCR  [35].
Quality control and data filtering All analysis were carried out using R software (version 4. 1. 2;  [49] ) and are available on the following git repository https://gitbio. ens-lyon. fr/LBMC/sbdm/sistercells. For the sister-cells dataset, cells were filtered based on several criteria: reads number, genes number, counts number and ERCC content. For each criteria the cut off values were determined based on SCONE  [50]  pipeline and were calculated as follows: Mean(parameter) -3*sd(parameter) We then removed orphan cells, meaning cells which sister was not present in the dataset. After filtering, we kept 60 undifferentiated cells  (30 couples)  and 64 differentiating cells (32 couples). For the cousin-cells dataset we performed the same filtering strategy and kept only cell groups which contained the 4 cousin-cells.
Normalization Filtered matrix were normalized using SCTransform from Seurat package (version 1. 6  [52]  -https://gitbio. ens-lyon. fr/LBMC/sbdm/sister-cells commit 945aaca7 and 94f13467) and were corrected for batch effect, day of isolation effect, medium effect and sequencing depth effect. Both datasets (sister-cells and cousin-cells) were processed independently.
Acknowledgements We gratefully thank all members of  SBDM  team for very fruitfull discussions, suggestions and commentaries on our project. We thank the computational center of  IN2P3 (Villeurbanne/France)  and  Pôle Scientifique de Modélisation Numérique (PSMN, Ecole Normale Supérieure de Lyon)  where computations were performed. We thank the  BioSyL Federation  and the  LabEx Ecofect  ( ANR-11-LABX-0048 ) of the  University of Lyon  for inspiring scientific events. We acknowledge the contributions of the CELPHEDIA Infrastructure (http://www. celphedia. eu/), especially the center  AniRA in Lyon.
Availability of data and materials Data tables are supplied as supplements files. scRNA-seq data are available in SRA repository under the BioProject accession PRJNA882056 and BioSample accessions SAMN30926136 and SAMN30926137. Embargo will be released upon publication. R codes are available at : https://gitbio. ens-lyon. fr/LBMC/sbdm/sister-cells Embargo will be released upon publication.
Acknowledgments FP was supported by  ANR (Agence Nationale de la Recherche)  grant  N° 19-CE12-0025-01  and the  Centre National de la Recherche Scientifique. MAS was funded through grants from the  Ministerio de Ciencia, Innovación y Universidades , the  Agencia Estatal de Investigación (AEI)   PID 2019-104145GB-I00. GM laboratory receives institutional funding from the  Centre National De La Recherche Scientifique  and the  Université de Rennes. TEM imaging was performed at the  Microscopy Rennes imaging Center (Biosit, Rennes, France) , a member of the national infrastructure France-BioImaging supported by the  French National Research Agency  ( ANR-10-INBS-04 ).
Detección del ADN de virus de hepatitis B La detección del ADN de VHB en los tejidos fue realizada como se describió previamente  19. Brevemente, el ADN genómico se extrajo mediante el método de fenolcloroformo y la cuantificación se realizó utilizando el kit dsDNA en la plataforma Qubit ® (Invitrogen, Waltham, MA, EE. UU. ). Todas las muestras se examinaron para detectar la presencia de ADN del VHB utilizando el sistema de PCR QX200 TM Droplet Digital TM (Bio-Rad, Hercules, CA, EE. UU. ). La secuenciación de VHB se realizó utilizando el kit de secuenciación BigDye TM Terminator v3. 1 (Applied Biosystems, Waltham, MA, EE. UU. ). La filogenia del VHB se determinó utilizando el modelo de dos parámetros de Kimura y el método de neighbor joining en la versión 7 del software Molecular Evolutionary Genetics Analysis (MEGA, University Park, PEN, EE.
Análisis del transcriptoma El perfil transcriptómico se analizó a partir de un grupo de muestras de CHC peruanos obtenidos de la base de datos Gene Expression Omnibus (GEO, Bethesda, MA, EE. UU. ), bajo los códigos GSE136247 y GSE111580  15. El transcriptoma de estas muestras se obtuvo utilizando los microarreglos GeneChip TM Human Transcriptome Array 2. 0 y el GeneChip TM WT PLUS Reagent Kit (Applied Biosystems, Waltham, MA, EE. UU. ). Los datos transcriptómicos fueron normalizados mediante Robust Multichip Average (RMA), mientras que el efecto de lote fue corregido utilizando el algoritmo ComBat de la librería limma (lenguaje R)  20. La matriz (genes x muestras) fue posteriormente colapsada mediante el algoritmo collapse genes-09 (Fred's Softwares, Toulouse, Francia). Los genes diferencialmente expresados (GDE) se obtuvieron utilizando la librería de R, limma, con un valor p corregido (valor q) de 0.
05, y siguiendo el flujo de trabajo recomendado  20. Adicionalmente, los 5,000 genes más variables en el tejido CHC fueron identificados utilizando la desviación estándar. Los mapas de calor basados en la agrupación jerárquica no supervisada se generaron mediante la librería de R, pheatmap. La identificación de vías metabólicas de interés se realizó utilizando el algoritmo Sample Enrichment Score (SES) y las bases de datos KEGG, Reactome (reactome 2018803), Gene Ontology-Cellular Component (C5 CC) y MSigDB Hallmarks (H) compiladas por Fred's Softwares  21. Los códigos fuente y las bases de datos de Fred's Software están disponibles en: https://sites. google. com/site/fredsoftwares/products.
Análisis del metiloma El perfil de metilación de las muestras de CHC peruanos con código GSE136319 y GSE136380 fueron obtenidos a partir de la base de datos GEO. El metiloma de estas muestras se obtuvo utilizando los microarreglos e Infinium Human-Methylation450K MethylationEPIC BeadChip (Illumina, San Diego, CA, EE. UU. ). Las posiciones diferencialmente metiladas (PDM) fueron identificadas mediante la librería R minfi utilizando un valor q a 0. 05  15. Los criterios considerados para el análisis de metilación fueron: ␤ promedio entre CHC y HNT > 5% (para las posiciones hipermetiladas) o ≤ 5% (para las posiciones hipometiladas). Mediante el cálculo de la desviación estándar se identificaron igualmente las 5,000 posiciones con metilación más variable. La clasificación según asociación a «islas CpG» y «estructura genética» se realizó según lo definido por Illumina.
Análisis estadísticos Los análisis de factor múltiple (AFM) y de componentes principales (ACP) se realizaron en R utilizando la librería FactoMineR  22. Las variables con datos incompletos fueron tratados con la función imputeMFA de la librería R missMDA. Brevemente, como se recomienda en la guía de FactoMi-neR, se aplicó un AFM, puesto que nuestros datos contaban con variables numéricas y categóricas. El AFM fue realizado según las recomendaciones establecidas de la librería, y todos los grupos fueron considerados como variables relevantes, y ningún dato suplementario fue incorporado en el análisis. Adicionalmente, los t-test y 2 fueron realizados en R. La significancia estadística fue evaluada uni o bilateralmente de acuerdo a las circunstancias. Todas las figuras del presente artículo fueron generadas en R y GraphPad Prism v8 (GraphPad Software, Boston, MA, EE.
DNA isolation, WGS, and data analysis Genomic DNA of the obtained V. cholerae ∆SI strain (A101) was extracted using the DNeasy® Blood and Tissue Kit (QIAGEN) following the manufacturer's protocol. The DNA quantity was determined using a BioSpectrometer (Eppendorf). DNA sequencing was performed at Institut Pasteur using Illumina paired-end reads, at MiGS (Pittsburgh, US), now SeqCoast Genomics (Portsmouth, US) and inhouse at MBA (Madrid) using MinIon. Data analysis was accomplished with Geneious Prime software and the genetic variants were identified by mapping the generated reads to the V. cholerae N16961 reference genome (Accession number: chromosome 1 CP028827. 1; chromosome 2 CP028828. 1).
RNA-seq data analysis Raw Illumina reads were trimmed and adapter-removed using Trim Galore v0. 6. 6 (https://github. com/FelixKrueger/TrimGalore) with a quality threshold of 20 and removing reads shorter than 50 bp. Trimmed paired reads were mapped to the V. cholerae N16961 reference genome (Accession number: chromosome 1 CP028827. 1; chromosome 2 CP028828. 1) using BWA-MEM v0. 7. 17  69. featureCounts from the Rsubread v2. 10. 2 package 70 was used to obtain read counts per annotated feature, including CDS, ncRNA, tmRNA, RNase P, tRNA, antisense RNA and SRP RNA. Differential expression analysis was performed from raw counts using DESeq2 v1. 36. 0 71 , by comparing the expression data of the deletion mutant against the WT strain. Differentially expressed genes (DEGs) of CDSs were annotated with Gene Ontology (GO) terms retrieved from UniProt on July 7, 2023  72.