dataesr/scientific-paragraphs-categorization huggingface dataset:
Actually, this phenomenon has already been described previously for another CEBP family member. Indeed, Cai et al. have shown that CEBPA binds novel genomic sites when it dimerizes with AP-1 family members, which recognize the motif TCA/TGA  (37). More recently, by analyzing highthroughput sequencing datasets, Cohen et al. have extended this observation to CEBPB. In their study, they compared the CEBPB binding sites across six cell types and showed a higher occurrence of canonical CEBPB motif in the celltype shared regions compared to the cell-type specific loci  (15). TFregulomeR enabled the systematic validation of this phenomenon in a bigger cohort encompassing 16 cell types and, more importantly, provided a possible explanation for motif variations depending on context. After loading all binding sites present in our data compendium, we segregated the K562 CEBPB binding loci according to the number of cell types where they are enriched.
The CEBPB TFBS motif is currently described as a mixture of CEBPB and other cofactors in all main TF-binding profile databases, including JASPAR  (26) , HOCOMOCO  (27)  and MethMotif (19) (Figure  2E ). Here, we have il-lustrated how TFregulomeR can improve the accuracy of PWM through the deconvolution of TFBS motifs and the characterization of TF partners influencing DNA binding sites at the sequence and methylation levels.
Deconvolution of binding partners: the case of MAFF TFregulomeR compendium records MAFF ChIPseq experiments from three different cell types: K562, HeLa-S3 and HepG2, extracted from the MethMotif database (respective accession number: MM1 HSA K562 MAFF, MM1 HSA HeLa-S3 MAFF and MM1 HSA HepG2 MAFF). Surprisingly, MAFF displays distinct cell-specific DNA binding preferences in terms of DNA sequences and DNA methylation levels. The inconsistence of MAFF binding motifs is also present in other TF-binding profile databases (Figure  3A ). These binding sequences are composed by a common TCAGCA motif and a TGA trinucleotide, which is highly enriched in K562, moderately present in HeLa-S3, but absent in HepG2 (Figure  3A ). This observation was accentuated when we focused only on MAFF cell-specific peaks (Figure  3B ).
In contrast, NFE2L2 was not detected as MAFF's co-factor across HeLa-S3 and HepG2 cell lines (NFE2 ChIP-seq data are not available for the two cell lines), supporting the fact that the TGA trinucleotide enrichment in K562 MAFF binding loci coincides with the presence of NFE2 and NFE2L2 (Figure  3C ). Altogether, these observations suggest that the TGA sub-motif in K562 context actually correspond to NFE2 and NFE2L2 binding sites. To further validate this hypothesis, we generated the motif logo of MAFF K562-specific binding sites without the binding loci shared with NFE2 and NFE2L2, which encompass 517 regions. As expected, the resulting pattern displays a significant decrease in TGA trinucleotide enrichment (Figure  3D ). Although the MAFF-NFE2 binding complex has been already described in the JAPSAR database (accession number: MA0501.
In the middle plot, the overall methylated CG percentages across 16 K562 CEBPB sub-ensembles are further reported in black. (D) The MethMotif logos display sequence preferences together with DNA methylation states enriched in the K562 shared CEBPB peaks with/without CEBPD co-binding loci, and in the K562 exclusive CEBPB peaks with/without ATF4 co-binding loci. (E) CEBPB motif logos extracted from different TF-binding profile databases. The blue, orange and green bars stacked above motif logo denote the numbers of CGs homogenously unmethylated, homogenously methylated and heterogeneously methylated respectively. (C) MAFF co-binding factors in the cell-specific regions are reported in a heatmap for each cell type (shades of red). DNA methylation states for the co-factors with more than 10% co-binding percentage are also portrayed in the regions (±100 bp around peak summits) co-bound by MAFF with those factors as the heatmaps (shades of blue).
Therefore, it is appealing to study TF binding behaviors collectively instead of individually. In this respect, TFregulomeR enables combinatorial analyses of TF genomic binding propensities in an in vivo and cell-specific fashion. TFregulomeR is linked to a large and up-to-date pool of TF cistrome derived from ChIP-seq experiments, along with the methylome landscape inferred from WGBS. TFreg-ulomeR includes efficient and ready-to-use functionalities to ease the access, integration and analysis of large TFBS datasets. These manually curated datasets and designed functionalities together constitute a comprehensive toolbox to study CRM and TF interactions. We showed that the heterotypic dimerization of CEBPB with other bZIP family members leads to a degenerate (half-site) binding motif, which overall obscures the genome-wide motif pattern.
Altogether, in vivo cistrome data empowers TFreg-ulomeR as a reliable tool to dissect different TFBSs from a generally characterized PWM matrix. Several strategies could be adopted to achieve motif deconvolution using TFregulomeR. Firstly, it has been shown that main leucine zipper factors usually directly dimerize with their partners. Therefore, motif deconvolution can be done by selecting the subsets of sequence binding sites exclusive to the TFs and their partners (Supplementary Figure  S5 ), or by removing overlapped peaks by co-factors (Figure  3D ). Secondly, given the fact that TF partners can be cell specific, motif deconvolution thus can be done by selecting the subsets of sequence binding sites from cell specific peaks of a TF of interest (Figures  2  and 3B ). In addition, the methylome datasets encompassed by TFregulomeR revealed distinct DNA methylation patterns in different deconvoluted motifs, suggesting an interesting correlation between binding partners and DNA methylation states for both CEBPB and MAFF (Figures  2  and 3 ).
10. 1. 1" following "GE1_1010_Sep10" protocol. Median raw expression values were further normalized by quantile method  [39]. Differential expression analysis between Rfx6 À/À vs controls and Rfx6 DEndo vs controls were performed using the R package limma version 3. 36. 5  [40]. Data have been deposited in GEO repository (GSE133038).
RNA sequencing Ilea from adult Rfx6 DAdInt mice was harvested at 8 days and 3 months after tamoxifen gavage. Total RNA was extracted using TRI-reagent or RNeasyÒ Midi Kit (Qiagen). Libraries were prepared using the total RNA-seq Ribo-zero protocol and sequenced on an Illumina HiSeq 2500 (single-end 50bp reads). Reads were identified and mapped onto the mm9 assembly of mouse genome using Tophat version 2. 0. 10 and the Bowtie2 version 2. 1. 0 aligner. Quantification of gene expression was performed using HTSeq version 0. 6. 1 and gene annotations from Ensembl release 67. Normalization of read counts and differential expression analysis between controls and Rfx6 DAdInt samples were performed using the method implemented in the DESeq2 Bioconductor library version 1. 0. 19  [41]. Data were deposited in GEO repository (GSE133038).
2. 12. Single-cell RNA-Seq; sample processing eYFP þ cells, from dissociated Neurog3 eYFP/þ intestinal organoids, were sorted directly into four 96 well plates « Precise WTA Single Cell Encoding Plate » (BDÔ Precise WTA Single Cell Kit, BD Genomics) (1 cell/well) using a FACSAria Fusion (BD). DAPI was used to sort only living cells. The library was prepared according to manufacturer's recommendations (910000014 Rev. 02 11/2016, BD Genomics) and sequenced on an Illumina HiSeq 4000 using paired-end 2 Â 100bp lanes sequencing. Data were deposited in GEO repository (GSE133038). 2. 13. Single-cell RNA-Seq analysis 2. 13. 1. Demultiplexing, alignment and quantification Fastq files were demultiplexed using the sample index encoded in reads 1. Reads 2 having a bad quality (-quality-cutoff 20,20) or corresponding to polyA sequences or to adapters were removed using Cutadapt version 1.
Normalization, dimensionality reduction, and clustering Data were then analyzed using the Seurat package version 2. 3. 4  [43]. Before creating the Seurat object, a gene filtering was performed. On the 29,161 genes expressed in the data, only 9,057 were retained as they met two criteria: they were coding genes or lincRNA and had at least 5 UMIs assigned in 5 samples. The normalization and scaling of the data were performed using the functions NormalizeData (method "LogNormalize") and ScaleData implemented in Seurat. Variable genes were identified using the FindVariableGenes function in the Seurat package: the 830 genes having a scaled dispersion superior or equal to 1 and a log-normalized average expression superior or equal to 0. 2 were selected as variable genes. Then, a PCA (Principal Component Analysis) was done with the function RunPCA of Seurat, based on these 830 variable genes.
These genes include Gkn2, Tff1, and Muc5b, which are normally expressed in pit cells of the gastric glands, as well as Atp4b, which encodes a subunit of the gastric proton pump expressed in parietal cells of the fundic gland. These data suggest that heterotopic gastric mucosa forms in the small intestine of Rfx6deficient mice. We were able to confirm this hypothesis by RT-qPCR for Gkn2 and Tff1 and, at the protein level, for TFF1 (Figure  2C, D ). In agreement with the transcriptomic data, we found many patches of TFF1-positive cells in the small intestine of Rfx6-deficient newborn mice but never in controls. Next, we investigated gastric enteroendocrine cell differentiation. Tph1 expression was, similarly to the intestine, up-regulated, while the expression of Gast, Sst, and Ghrl was severely decreased in the stomach of Rfx6-deficient newborn mice (Figure  2E ).
Single cell transcriptomics revealed Rfx6 dependent genetic programs in the peptidergic enteroendocrine and enterochromaffin cell lineages To gain knowledge into the mechanisms controlling enteroendocrine cell differentiation and diversity as well as into the role of Rfx6, we performed single-cell RNA sequencing of sorted EECs. Enteroendocrine cells were captured at various stages of their development, from  Original Article endocrine progenitors to hormone expressing cells. Endocrine cells were purified from Neurog3 eYFP/þ mouse intestinal organoids. In this model, due to the stability of the eYFP protein, both crypt-based endocrine progenitors as well as differentiating hormone-expressing EECs express the fluorescent reporter (Figure  6A  and Suppl. Figure  7 ). After data filtering, 290 cells were analyzed.
ACKNOWLEDGEMENTS The authors thank the members of the Genomeast platform for RNA-Seq and  Doulaye Dembele  for initial analyses of the data, of the Cell sorting facility, of the  Mouse Clinical Institute Metabolic Exploration Plateform (MCI, Strasbourg, France)  for their help with the metabolic analyses. We thank  N. Messaddeq  (IGBMC), for Electron microscopy and  Martine Poulet  for technical assistance and  Tao Ye  ( IGBMC ) for submission of data to GEO. We thank  Miriam Ejarque  (IGBMC),  Catherine Tomasetto (IGBMC), Natalie Terry (Division of GI, Hepatology, and Nutrition Children's Hospital of Philadelphia ) and  Klaus Kaestner  for helpful discussions. This study was funded by the  Novo Nordisk Foundation  (Challenge Grant  NNF14OC0013655 ) and by Grants from the  Agence Nationale pour la Recherche  ( ANR Rfx-PancInt ) and from the  Fondation pour la Recherche Médicale (FRM).
IGBMC is supported by the grant  ANR-10-LABX-0030-INRT , a  French State  fund managed by the  Agence Nationale de la Recherche  under the  frame program Investissements d'Avenir   ANR-10-IDEX-0002-02. Sara Jimenez Correa  is an IGBMC international PhD programme fellow supported by the  LaxEx INRT.
Risk factors for PPR occurrence and spread, and their relative importance, were identified using literature review and expert-based knowledge. Corresponding geographic data were collected, standardized, and combined based on a weighted linear combination to obtain PPR suitability maps. The accuracy of the maps was assessed using outbreak data from the EMPRES database and a ROC curve analysis. Our model showed an excellent ability to distinguish between absence and presence of outbreaks in Eastern Africa (AUC = 0. 907; 95% CI [0. 820-0. 994]), and a very good performance in the Union of the Comoros (AUC = 0. 889, 95% CI: [0. 694-1]). These results highlight the efficiency of the GIS-MCE method, which can be applied at different geographic scales: continental, national and local. The resulting maps provide decision support tools for implementation of disease surveillance and control measures, thus contributing to the PPR eradication goal of OIE and FAO by 2030.
Identification of Environmental and Socio-Economic Factors for PPR Spread Searches were performed in two journal databases (PubMed/Medline and ISI Web of Knowledge) with the keyword "peste des petits ruminants. " Papers were screened and articles dealing with the identification of risk factors were taken into account (Supplementary Table  1 ). From this bibliographic review, the following factors were identified as potentially associated with the transmission and spread of PPR in livestock in the Union of the Comoros and Eastern Africa: • The density of small ruminants (goats, sheep): since PPRV is transmitted through direct contact between infected and susceptible animals, its spread is affected by host density; • The proximity to water bodies: water bodies such as rivers can be a gathering point for livestock, and thus increase the risk of contact between animals of different herds; • The movement of animals for trade and/or transhumance: animal movements are a major cause for long-distance spread of PPR; • The density of roads: this factor is used as a proxy for shortdistance animal movements for trade purpose; Additional risk factors were identified for Eastern Africa only: • The density of camels; • The density of railways: this factor is used as a proxy for animal movements for trade purpose.
Collection of Geographical Data and Creation of Spatial, Standardized Suitability Indices Geographical data were collected for each country (Figure  1 ) from different sources (Table  1 ), imported into a Geographic Information System (GIS), and processed to produce standardized spatial suitability indices with values ranging from 0 (completely unsuitable for occurrence and spread) to 1 (completely suitable) (GIS software: ESRI ArcGIS TM , Spatial Analyst). The standardized spatial suitability indices for all countries were: sheep density, goat density, animal mobility index, road density, and proximity to water bodies. The animal mobility index was derived from the proximity to markets for eastern African countries, and from results from a mobility study for the Union of the Comoros  (43).
Additional standardized spatial suitability indices for eastern African countries were: camel density, railways density, proximity to wildlife national parks and proximity to dry areas. The calculation methods of the standardized geographical layers are provided in Table  2. At the end of the process, standardized spatial suitability indices were raster layers with pixel dimensions of 300 × 300 m, a good compromise between computational limitations due to the size of the study area (2,874,667 km²) and the spatial resolutions of the different datasets. The correlation between the different suitability indices was assessed (Supplementary Table  2 ). The resulting maps for the standardized spatial suitability indices are presented in Supplementary Figures  1, 2.
Assessment of the PPR Suitability Map PPR outbreaks reported and geo-located between 2007 and 2018 were used to assess the consistency of PPR risk maps (Source: EMPRES-i database). In total, 23 PPR outbreaks, so called "presence, " were recorded, including 13 outbreaks in Kenya, 3 in United Republic of Tanzania, 1 in Uganda, and 6 in Union of the Comoros (Figure  1 , Supplementary Table  3 ). As no outbreaks were reported in Ethiopia, the assessment of the PPR suitability map could not be performed for this country. One hundred locations of disease "pseudo absence" were randomly generated in the countries where PPR outbreaks occurred in continental Africa (Uganda, Kenya, and Tanzania), under the condition of being 25 km distant from other "absence" or "presence" locations (Supplementary Figure  3 ).
In the Union of the Comoros, 6 locations of PPR "pseudo absence" were randomly generated in the Grande Comore Island, under the condition of being 5 km distant from other "absence" or "presence" locations, taking into account the size of the Comoros islands (Figure  1 ). Then, the value of the quantitative suitability estimates for PPR occurrence and spread was extracted for each "presence" or "pseudo absence" location. The AUC (area under curve) of the ROC curve  (44)  was calculated to evaluate the capacity of the model to distinguish "presence" from "absence" locations with good predictive accuracy. The suitability maps were evaluated separately for (i) continental countries where outbreaks were reported (i. e. , Kenya, Tanzania and Uganda) and (ii) the Union of the Comoros (pROC package, R software, https://cran.
DISCUSSION The identification of areas at-risk for PPR is essential for implementing risk-based surveillance and control measures. To our knowledge, this is the first study aiming to produce regional suitability maps for PPR using GIS-MCE method combined with outbreak dataset validation. As a whole, there is a good consistency between areas identified at risk by the model and what is known about PPR circulation in the five countries of interest. In the next paragraphs, we discuss in detail the comparison of the obtained suitability maps and results of previous epidemiological studies, for each studied country. Regarding the eastern African countries, one should keep in mind that the number of outbreaks used for validation is very low compared to the surface of the territories studied. In addition, PPR outbreaks may be underreported in the four eastern African countries, where the disease is endemic and thus declaration non-mandatory.
The use of more extensive serological data from future studies, national surveillance datasets, or information extracted from published reports (56) could be easily integrated in the framework of the model validation and would make our results stronger. In our study, the GIS-MCE method was applied to territories with different sizes, including small islands and continental countries. Our results demonstrated the effectiveness of the GIS-MCE method, which has been successfully applied at different geographic scales and settings. Indeed, the validation of the suitability maps using reported PPR outbreaks produced good results according to the ROC AUC method. These results suggest that the output regional-scale suitability maps have a reasonable predictive accuracy and could be used for riskbased surveillance and control purposes.
ACKNOWLEDGMENTS We gratefully thank for their contribution to the study  Nicolas Gaidet-Drapier ,  Marie Gely  and  Floriane Boucher ,  Matthieu Roger ,  Marie-Astrid Viel ,  François Roger  (CIRAD), and  Artadji Attoumane  ( Institut de Recherche pour le Développement). The authors thank for their help and fruitful discussions the many diverse staff members from the  Ministry of Agriculture, Union of the Comoros, FAO ,  Royal Veterinary College (UK) , the  International Livestock Research Institute , the  Interafrican Bureau for Animal Resources , the  Uganda Veterinary Services , the  Uganda Ministry of Agriculture Animal Industry and Fisheries , the  Uganda Bureau of Statistics , and the  Ethiopian National Animal Health Diagnostics and Investigation Center , the  AMCC-project.
Bioinformatic treatment We first demultiplexed raw reads, based on internal barcodes, using demultadapt (https://github. com/Maillol/demultadapt). We removed adapters and low-quality bases using cutadapt 1. 2. 1  (32). We then discarded reads with a mean quality lower than 30 using a Perl script (https://github. com/SouthGreenPlatform/arcad-hts/blob/ master/scripts/arcad_hts_2_Filter_Fastq_On_Mean_Quality. pl). We mapped reads onto the D. rotundata reference genome (genome, BDMI01000001. 1 to BDMI01000021. 1; mitochondrial genome, LC219374. 1; plastidal genome, KJ490011) using BWA-MEM software 0. 7. 12-r1039  (33). The yam genome assembly we used was 594 Mb, with an N50 (a measure of assembly quality) of 2. 12 Mb  (34). We performed SNP calling using GATK UnifiedGenotyper 3. 6-0-g89b7209  (35).
Genetic origin of cultivated yam We used fastsimcoal 2. 5. 2. 21  (12)  to evaluate from which wild relative the cultivated yam likely originated. We used a subset of 100,000 randomly sampled SNPs with observed heterozygosity lower than 0. 8 for this analysis. We estimated a joint multidimensional SFS (site frequency spectrum) for all pairs of populations using a customized R script. To reduce potential bias due to missing data, we first computed realized allele frequencies, i. e. , allele frequencies for each SNP were calculated excluding missing data. We then rescaled the realized allele frequencies to (1 -X)N to get the final SFS, with X as the mean frequency of missing data and N as total number of genotypes. Models implemented in fastsimcoal  (12)  included three or four populations, with size ranging between 100 and 1,000,000 haploid individuals, time of divergence ranging from 100 to 1,000,000 generations, and mutation rate ranging from 1 × 10 -7 to 1 × 10 -9.
The first group was derived from the number of singletons per individual, a statistic representing a local measure of genetic diversity, useful for the inference of the geographical origin of an expansion  (11). The second group of statistics was derived from the SFS of the genetic sample, a statistic related to the demographic history of the population. We estimated the relative proportion of singletons for 11 groups (fig. S4 ) defined by using the k-means clustering method of the R stats package  (42). We summarized the SFS by using eight bins: (i) SNPs found only once (singletons), (ii) SNPs found twice, (iii) SNPs found three to four times, (iv) SNPs found 5 to 11 times, (v) SNPs found 12 to 21 times, (vi) SNPs found 22 to 35 times, (vii) SNPs found 36 to 55 times, and (viii) SNPs found more than 55 times.
We compared observed and simulated summary statistics by using an ABC analysis. We performed ABC using the ABC (13) R package with 500 independent neural networks and a tolerance rate of 0. 001 (1000 simulations retained). To assess the goodness of fit of our model, we performed posterior predictive checks, i. e. , we used the parameters of the 1000 retained simulations to perform 1000 extra simulations. We then checked whether these new simulations were able to recover the values of the summary statistics observed in the data.
Temporal dynamics of effective population size We estimated past changes in effective size, Ne, using SMC++  (15). We used MSMC tools (https://github. com/stschiff/msmc-tools) to create masks for mappable positions (https://github. com/popgenmethods/smcpp/blob/master/README. rst). To get more reliable estimates of effective population size, we reestimated the frequencies of derived alleles five times, using five randomly chosen individuals as distinct lineages. We set the maximum number of generations to 100,000 and let SMC++  (15)  define the lower bound based on a heuristic approach. We fixed the mutation rate to 6. 5 × 10 -9 , corresponding to the mutation rate estimated for monocotyledons (rice, maize, and barley)  (43) , and the generation time to 1 year.