dataesr/scientific-paragraphs-categorization huggingface dataset:
Fig. 7  provides comparisons between datasets generated by boolODE and scBoolSeq from one of the largest curated model of the benchmark of  [34] , a Boolean network of human gonadal sex determination (GSD)  [55]. It has two main fixed point attractors of biological interest, namely Sertoli cells and granulosa cells which correspond to male and female phenotypes. We notably compared the mean-variance and mean-dropout profiles of generated data with different dropout models, as proposed by both tools. Besides the dropout rate being constant, the mean-variance relationship of boolODE appears to be at very different scale than typical scRNA-Seq data (Fig. 2 ). It should be noted that when enforcing a constant dropout rate with scBoolSeq, the resulting dropout-mean profile is not constant as 0 values can still be sampled from learnt pseudocount distributions: gaussian distribution can give non-zero probabilities to negative values, which are corrected as 0.
Implementation and usage scBoolSeq has been implemented in Python on top of pandas  [56] , statsmodels  [57] , and scikit-learn  [58]  libraries. Fig. 8  shows basic usage of scBoolSeq to perform binarisation and synthetic data generation. Future engineering work will focus on leveraging the AnnData  [59]  Python package for handling large datasets that cannot be fit in RAM. Furthermore, using AnnData within scBoolSeq will allow its integration in the scverse  [60]  computational ecosystem for single-cell omics data analysis. scBoolSeq is distributed as a standard Python package, and is integrated in the CoLoMoTo Docker distribution  [61] , which facilitates the accessibility of tools related to Boolean and logical models, and the reproducibility of related computational analyses.
Discussion We introduced scBoolSeq, a novel method which provides a bidirectional link between scRNA-Seq data and Boolean Models. Our method builds on the classification of gene empirical pseudocount distributions into unimodal and bimodal distributions proposed by  [42] , that we extended with a probabilistic gene-dependent dropout model. We showed that the resulting characterization suffices to capture the main statistical features of real scRNA-Seq data. Then, scBoolSeq offers both the ability to binarise scRNA-Seq datasets and the ability to generate synthetic pseudocounts from binary states of genes. From pseudocounts to binary states We illustrated on a concrete application how the binarisation offered by scBoolSeq can be employed to process scRNA-Seq data in view of performing inference of Boolean networks, which are logical models of gene activity dynamics.
We believe that the fact that not all genes (and cells) cannot be classified with binarisation is good sign that the method enables discriminating cells in extreme state from cell in transient state, for which a fully binary view may not be adequate. One should note however that determining the activity of a gene based on its transcript level is a strong hypothesis. Methods such as VIPER  [62]  aim at adding information about each protein's regulon to better infer protein activity. Moreover, chromatin accessibility and other epigenetics information can also help to refine the binary classification. From binary states to pseudocounts Another major contribution of scBoolSeq is its method for generating synthetic scRNA-Seq data from Boolean gene activation states by biased sampling from learned pseudocount distributions on a reference dataset.
We showed that scBoolSeq provides a significant improvement over BoolODE as it produces synthetic scRNA-Seq data whose statistical characteristics (mean-variance and mean-dropout profiles) closely resemble those of real data. In addition to this, scBoolSeq allows simulating any arbitrary distribution of gene-wise dropout rates. This represents an unprecedented contribution as it allows measuring the sensitivity of inference methods to the dropout rate distributions of scRNA-Seq datasets. By offering the capability to generate synthetic scRNA-Seq datasets from ground-truth Boolean models with realistic statistical features, we believe that scBoolSeq is a clear asset for that generating benchmarks for the evaluation of various inference methods, such as GRN inference, trajectory reconstruction, and data-driven Boolean network inference.
Inference of Boolean networks from influence graph and dynamical properties From an influence graph G and a set of dynamical properties, the tool BoNesis  [51, 52] , available at github. com/bnediction/bonesis, allows inferring all the locally-monotone Boolean networks f having their influence graph enclosed by G, i. e. , G(f ) ⊆ G, and that posses the input dynamical properties. The dynamical properties supported by BoNesis include the existence of most permissive trajectories between partially specified configurations, and stable state properties of (partially specified) configurations. A partially specified configuration specify a Boolean state for a subset of nodes. In that case, BoNesis is free to complete the unspecified nodes with any Boolean state. BoNesis also allows specifying optimization objectives to filter solutions, notably to enumerate only sparser models, i.
Case Study: Early Born Retinal Neurons We performed the analyses on the scRNA-Seq dataset of lane 1 of GSE122466. The main steps hereafter denoted in paragraphs refer to the analyses performed in their homonymous Jupyter Notebooks provided in S1 Code. Highly Variable Gene Selection For this part we used the software STREAM  [15]. We took the count matrix of the first replicate (Identified with the prefix Lane_1 in their index). We performed standard quality control, with the same parameters as the analyses of the original article. Cells expressing less than 200 genes where discarded, as well as genes expressed in less than 3 cells. We selected the 1648 most highly variable genes and appended to them the two marker genes which were reported in the article but were not selected as being highly variable (Rbp4, Pou4f2 ).
Binarisation of scRNA-Seq data We binarised all HVGs across all cells and employed the metadata obtained from the previous trajectory inference step to retrieve cell groups. We defined meta-observations by aggregating each group, using the mode as summary statistic. We further selected genes having non-null variance, which reduced the original 1650 genes to only 1426. We only retained binarised genes present in the mouse regulon database DoRothEA  [40] , that is 1263. Boolean Model Inference : Having our binarised observations and selected genes, we defined our GRN using DoRothEA  [40]. DoRothEA gives a confidence score to each one of the interactions, based on the number of supporting evidence in different sources. In decreasing order, these levels are: A,B,C,D,E. We decided to exclude interactions with low supporting evidence, so we filtered out levels D,E and considered only levels A,B,C.
Supporting information S1 Fig. Example of distribution of rate parameters and dropout probabilities learnt by scBoolSeq. Left: Distribution of rate parameters λ October 22, 2023 18/24 estimated on dataset GSE122466. Right: Dropout probabilities computed between the minimum and maximum values of a sample from the parametric distributions corresponding to the same dataset. Each line corresponds to an individual gene. S2 Fig. Mean -Variance and Mean -DropOutRate relationships of HVGs using PROFILE parametric distibutions for bimodal and unimodel genes on selected scRNA-Seq datasets. Each green point represents the average of 100 independent replicates with the same sample size as the reference dataset. S3 Fig. Correlation between higher moments of real pseudocount data and from data generated from distributions and dropout model learnt by scBoolSeq on selected scRNA-Seq datasets S4 Fig.
). S5 Fig. Result of trajectory reconstruction using STREAM on early-born retinal neurons scRNA-Seq data. UMAP projection of the first 25 principal components to 3 dimensions (only 2 are shown). The cluster labels are determined by the majority label of unambiguous cell types identified via scBoolSeq binarisation. S6 Fig. Influence graph of sparser Boolean networks learnt using BoNesis from qualitative dynamics of case study obtained with scBoolSeq binarisation. This graph comprises 184 nodes forming Boolean networks that can reproduce the Boolean dynamics of early-born retinal neurons differentiation process. This graph is a subgraph of the input DoRothEA TF-TF interaction database. Green arrows indicate positive regulations, red arrows indicate negative regulations. Nodes without predecessors indicate nodes with constant function in the Boolean networks.
Funding Work of GML and LP was partly supported by the French Agence Nationale pour la Recherche (ANR) in the scope of the project "BNeDiction" (grant number ANR-20-CE45-0001). Work of GML was partly supported by the Talentos de Exportación -JuventudEsGto scholarship program of the Mexican State of Guanajuato. Work of LP was partly supported by the French government in the scope of France 2030 October 22, 2023 19/24 project "AI4scMED" operated by ANR (grand number ANR-22-PESN-0002). LC was party supporyted by ModICeD project from MIC ITMO 2020. AZ was supported by the French government under management of Agence Nationale de la Recherche as part of the "Investissements d'avenir" program, reference ANR-19-P3IA-0001 (Fig. Correlation between higher moments of real pseudocount data and from data generated from distributions and dropout model learnt by scBoolSeq on selected scRNA-Seq datasets.
Thus, the Boolean state of these nodes is identical in all stable states, and is in opposite state in the precursor state RPC. S7 Fig. Python code snippet showing usage of BoNesis for the inference of Boolean networks for the retinal differentiation case study. See S1 Code for full pipeline. S1 Code. Code and notebooks for reproducing binarisation case study and synthetic data generation scBoolSeq source code is available at github. com/bnediction/scBoolSeq. The Python package can be installed using conda or pip; see link for instructions. Notebooks for demonstrating scBoolSeq usage and reproducing the case studies presented in this paper can be visualised and downloaded at nbviewer. org/github/bnediction/scBoolSeq-supplementary.
Introduction It is commonly accepted that protein's function arises from their structure, which itself is encoded in the sequence according to the Anfinsen's principle. 1  The sequence of proteins with tandem repeats is composed of a repetition of a short sequence, usually forming an elementary structural unit. [2] [3] [4]  They are particularly suitable for protein design, with their interactions it allow to conceive objects that can be tunable, leading to interesting properties regarding the design of repetitive units that are reminiscent of legos. 3, [5] [6] [7]  Some databases dedicated to Structural Repeats in Proteins have been developed to facilitate their identification and analysis. 4, [8] [9] [10]  Noteworthy, such protein folds can be used as domains to mediate oligomerization of chosen protein in a predictable way,  [11] [12] [13] [14]  allowing the design of higher order assemblies for example.
It has been performed only with sequences extracted from proteins of which the crystal structure in available in the Protein Data Bank (PDB), hence leaving no doubt on the position occupied in the LβH-I fold by each considered amino-acid. This analysis also helps identifying and understanding the role of specific motifs in initiating, stabilizing or terminating the fold. Overall, this study aims at (i) improving our understanding of the LβH-I's sequence-structure relationship, and at (ii) providing guidelines for the rational design of protein sequences able to adopt a stable LβH-I fold.
Materials and methods All the protein crystal structures analysed here (Table  S1 ) have been downloaded from the Protein Data Bank (PDB). The Uniprot code was used to search for structures and a similarity search has been performed to find similar other structures. The Database of Structural Repeats in Proteins (DbStRiP) developed by Chakrabarty and Parekh has also been used as a source of information. 4  PyMOL software was used to visualize all 78 structures and to generate pictures. 40  For each LβH-I, the labelling of the faces and the rung types as well as the position annotation for each aminoacid were performed manually, following the methodology illustrated in Figure  1. Excel 2016 (Microsoft) was used to process data regarding sequences.
Analyses Unless stated otherwise, all population genetic statistics were calculated in PLINK version 1. 9  [64] , and downstream analysis and plotting was carried out in R version 3. 3  [65]. In each population, we estimated LD (R 2 ) for each pair of markers within 50kb on the same chromosome, and compared this to physical distance between marker pairs. We calculated observed heterozygosity for each SNP and population using a reduced SNP dataset, which was pruned based on LD to remove all markers with R 2 > 0. 1, then thinned with a probability of retaining each variant of 0. 25. We calculated genome-wide (mean) F ST between each pair of populations using the pruned and thinned dataset described above. Pairwise F ST was compared to geographic distance between populations using Mantel tests, implemented in the Ecodist package in R  [66].
Acknowledgements This work was supported by grants from the  European Research Council  (grant  202487  to J. S. and grant  339092 -E-Response  to MEV) and  Natural Environment Research Council  (grant  NE/J012599/1  to J. S. and B. C. S). L. G. S was supported by fellowships from the  Edward Grey Institute for Ornithology  and the  BBSRC  ( BB/N011759/1 ). We thank  Claire Bloor ,  Geoff Scopes  and  Alessandro Davassi of Affymetrix  for their help during the chip design and genotyping calling processes. Richard Talbot  and  Alison Downing  of  Edinburgh Genomics  provided the genotyping service.
Acknowledgements The authors thank  J. Viaud  for kindly providing the PH-PLCd-Alexa647 probe. We thank  C. Le Clainche  ( Institute for Integrative Biology of the Cell, Gif-sur-Yvette, France ) for providing actin and A-S. Nicot for the design of BIN1 shRNA constructs. We also thank  C. Cazevieille  for assistance in TEM and  J. Mateos-Langerak  in SIM OMX imaging. The authors also thank  P. Sens  for scientific discussions. The authors acknowledge the  Nikon Imaging Center at Institut Curie-CNRS , the imaging facility MRI, the  PICT-IBiSA, members of the national infrastructure France-BioImaging infrastructure  supported by the  French National Research Agency  ( ANR10-INBS-04 ). L. P. acknowledges the  ATIP-Avenir program  ( AO-2016 ) for financial support. This project was also supported by grants from the  Agence Nationale de la Recherche (ANR)  ( ANR-13-BSV2-0004-01 ), the  Labex CelTisPhyBio  ( N° ANR-10-LBX-0038 ) part of the  IDEX PSL  ( N° ANR-10-IDEX-0001-02 PSL ), the  Association Francaise contre les Myopathies  ( 15352 ), the  CNRS , the  INSERM , the  University of Montpellier, University of Strasbourg , the  College de France  and the  Institut Curie.
Reagents Natural and synthetic phospholipids, including POPS, POPC, POPE and TopFluor-TMR-PI(4,5)P2 are from Avanti Polar Lipids, Inc. Alexa Fluor 647 and 488 Maleimide labelling kits are from Invitrogen. The following antibodies were used in this study: monoclonal mouse antibody to BIN1 (clone C99D against exon 7) from Millipore, polyclonal rabbit antibody to IRSp53 (BAIAP2) from Atlas Antibodies (HPA023310), HRP-conjugated beta actin monoclonal antibody from Proteintech (HRP-60008), ezrin antibody from M. Arpin laboratory  (Algrain et al. , 1993) , monoclonal rabbit antibody to phospho-Ezrin (Thr567)/Radixin (Thr564)/Moesin (Thr558) Cell Signaling (Cat. 3726). Atto390 488 phalloidin was from Sigma.
, 2022) , can occur also through contaminated food or water. Despite these observations, few tests for metabolic disruptions have been accepted or are currently under development by the Organization for Economic Cooperation and Development (OECD) or the US Environmental Protection Agency (EPA). This regulation is particularly deficient as to the identification and the risk assessment of chemicals that can induce liver steatosis. The European OBERON project (https://oberon-4eu. com) has as its goal the development of an integrated testing strategy (ITS) to detect EDC-related metabolic disorders by developing or improving and validating a battery of test systems. It is based on the concept of an integrated approach for testing and assessment (IATA)  (Audouze et al. , 2020). On the basis of epidemiological studies supported by human biomonitoring data which suggest an association with metabolic disorders, 10 EDCs were selected for study: bisphenol A (BPA), bisphenol F (BPF), bisphenol S (BPS), butylparaben (BP), cadmium chloride (CdCl 2 ), 1,1-dichloro-2,2-bis(4-chlorophenyl)ethene (p,p'-DDE), dibutyl phthalate (DBP), bis(2-ethylhexyl) phthalate (DEHP), perfluorooctanoic acid (PFOA) and perfluorooctanesulfonic acid (PFOS).
In Hepoid-HepaRG, lipid droplets were measured after fixation with 4 % paraformaldehyde containing 5 % sucrose for 10 min at room temperature. Hoechst 33342 and LipidTOX™ HCS (ThermoFisher, 1/ 1000) staining were performed simultaneously to stain nuclei and lipid droplets, respectively. Images were acquired with a Leica SP8 confocal microscope with a 40x oil objective. Images (40 μm stack) were then analyzed using ImageJ software using a specific macro that detects lipid droplets and nuclei. The results are expressed as the number of lipid droplets per cell.
Statistical analysis The results are expressed as the mean ± SEM of at least three independent experiments. The number of replicates is given in the figure legends. Statistical analysis and graphing were carried out using GraphPad Prism (10. 0. 2 version). A Shapiro-Wilk normality test was performed to evaluate the Gaussian distribution of each dataset. In the case of a normal distribution, data were analyzed by one-way or twoway ANOVA followed by a Dunnett (all conditions compared to control) or Bonferroni (all conditions compared to their respective control) post hoc test to assess differences between specific conditions. For datasets that did not follow a normal distribution, data were analyzed using Kruskal-Wallis test followed by a Dunn's correction. A p-value was used to determine sample significance with *, p < 0.
In order to estimate the capability of pollutants to affect lipid metabolism in the 3D human hepatic spheroid culture, we analyzed the impact on droplet formation of 4 prototypical representatives of the different EDC families by measuring the lipids in 3D cultures. Hepoid-HepaRG cells were treated with 10 μM BPA, p,p'-DDE, DEHP or PFOA for 14 days. An accumulation of lipid droplets in Hepoid-HepaRG was observed after 14 days of treatment with all 4 compounds tested with a statistically significant increase of 7. 6-fold after exposure to p,p'-DDE as compared to the control (Fig. 3A ). A 6. 3-fold increase was observed after exposure to DEHP (p = 0. 08). In this model, our findings also demonstrated that both the mixtures of PA:OA or OA alone similarly increased TG content after 14 days (data not shown).
In our cells, a limited number of RNA binding proteins (RBPs) are responsible for all aspects of RNA metabolism across the entire transcriptome. To accomplish this, RBPs form regulatory units that act on specific target regulons. However, the landscape of RBP combinatorial interactions remains poorly explored. Here, we performed a systematic annotation of RBP combinatorial interactions via multimodal data integration. We built a large-scale map of RBP protein neighborhoods by generating in vivo proximity-dependent biotinylation datasets of 50 human RBPs. In parallel, we used CRISPR interference with single-cell readout to capture transcriptomic changes upon RBP knockdowns. By combining these physical and functional interaction readouts, along with the atlas of RBP mRNA targets from eCLIP assays, we generated an integrated map of functional RBP interactions.
In order to broadly and systematically annotate regulatory interactions between RBPs, we combined data from three independent modalities, namely (i) BioID2-mediated proximity protein labeling, (ii) RBP Perturb-seq, and (iii) eCLIP RNA ENCODE dataset (Fig. 1 , Fig. S1 ). First, to capture the protein neighborhood of each RBP, we used BioID labeling-based pulldown followed by mass spectrometry (D. I. Kim et al. 2016). We fused BioID2 to 50 RBPs (see Methods), and generated stable cell lines expressing each fusion construct in a human leukemia cell line K562. We then performed, in biological triplicates, streptavidin pull-down experiments followed by mass spectrometry. For each RBP, we also included matched controls by processing the same lines without a biotin pulse. This allowed us to generate a high-quality protein neighborhood dataset and to systematically identify co-localized RBPs.
Integrated RBP interaction maps to reveal regulatory modules The abovementioned data modalities capture complementary aspects of regulatory interactions between RBPs. Therefore, integrating these sources of information is a critical step toward generating a more comprehensive and generalizable map of regulatory interactions (Fig 1, Fig. S1 ). To accomplish this, we first generated RBP-target interaction maps for each individual modality, where 'target' can be either neighboring protein (BioID), downstream gene (Perturb-seq), or target RNA (eCLIP; Fig. S2A-C ). In order to make the values comparable between datasets, we z-score transformed them across the target features. RBPs that fall close to each other in this feature space, which reflects physical and functional proximity, likely function as part of the same regulatory modules.
And as shown in Fig. 2A , our map similarly captures a number of canonical RBP modules involved in key post-transcriptional regulatory programs such as 'cytoplasmic translation' and 'mRNA splicing'. However, as stated above, RBPs carry out multiple independent functions, which are not captured by this "one RBP-one cluster" scheme. This is clearly highlighted by the many significant off-diagonal interactions that are observed in our clustered RBP map. And more importantly, our Integrated RBP Regulatory Map captures substantially more regulatory modules than an analogous map built based on the current state-of-the-art protein-protein interaction database, STRING-DB  (Szklarczyk et al. 2018 ) (Fig. S2D ).
While eCLIP data brings together the rRNA-binding RBPs, proximity labeling data clearly distinguishes mitochondrial RBPs (TBRG4, FASTKD2, and SUPV3L1) from others (Fig. 2A ). RBPs form more functional interactions than captured by traditional PPI methods. We tested this hypothesis by comparing the interaction map built using protein-protein interaction data from STRING-DB database with the map built using our multi-modal integrated data from the current study. The integrated map, generally corresponding to the STRING-DB, showed larger regulatory modules, demonstrating the utility of a multi-modal data integration approach in revealing more functional interactions (Fig  S2 D, E ).
2020). Similarly, interactions between PPIL4, NPM1, and SAFB suggest that PPIL4 also takes part in transcriptional regulation. Consistent with this observation, PPIL4 was recently shown to interact with JMJD6, an annotated histone demethylase with known roles in transcriptional control  (Barak et al. 2021). Finally, the interaction between LIN28B and RPS3, METAP2, and DDX6 similarly recapitulates its known role in mRNA translation  (Basak et al. 2020 ). As mentioned above, the use of complementary data types allows our Integrated RBP Regulatory Map to better capture this multifunctionality of RBPs. As a demonstration, for each RBP of interest, we tallied the annotated functions of its closest neighbors across each of our three modalities (Fig. 2D ). For instance, when considering ZNF800, we found that UCHL5 and GRWD1, which are among its closest neighbors in the eCLIP dataset, are known chromatin remodeling proteins.