dataesr/scientific-paragraphs-categorization huggingface dataset:
Causal Inference from Enhancer and Transcript/Gene Expression CAGE Data To test our hypothesis that causal inference using enhancer expression as a causal anchor predicts true TF targets, we used CAGE data generated by the FANTOM Consortium across hundreds of cell types and tissues in human and mouse  [6]. We used bi-directional expression in non-promoter regions as an indicator of likely enhancer activity in each CAGE sample  [1]. Specifically, non-promoter regions with a similar number of sequence tags in both the forward and reverse direction were predicted as putative enhancer regions. Moreover, these regions also showed a high overlap (over 90 percent) with H3K4me1 modification. Importantly, CAGE data offer a powerful resource as each sample contains both enhancer and gene activity information.
The ability to quantify enhancer expression (as a proxy for enhancer activity) and gene expression from the same sample is crucial for the ability to apply causal inference techniques. We first selected three mouse cell types (embryonic stem cells, macrophages and erythroblastic leukaemia) for systematic characterization, as these had more than 20 samples per cell type, with diverse treatments or time series. Furthermore, ChIP-seq and TF knock-out validation datasets were available for each of these cell types. In order to assign both promoter proximal, as well as promoter distal putative enhancers for each transcription factor, we selected predicted enhancers  [1]  within 50 kb of the transcription start site of each transcription factor in a cell type. This resulted in 109 enhancers for 48 transcription factors in ES cells, 55 enhancers for 8 transcription factors in macrophages and 5 enhancers for 4 transcription factors in erythroleukaemia, with an average of 3.
8 enhancers per transcription factor across cell types. We inferred causal transcription factor-target interactions for each transcription factor using the enhancer element most strongly linked to each TF in each cell type. We predicted targets for 48 transcription factors with two methods, one using continuous enhancer data ("Findr-C") and one using discretized, binary (on/off) enhancer data ("Findr-B") (see Methods). The Findr software outputs a score representing the putative probability of a causal interaction for each transcription factor-target pair (see Methods). For both methods, the targets with a predicted probability of a causal interaction greater than 0. 8 (see Methods) were validated using a compendium of ChIP-seq data  [20] , containing 78 factors in ES cells, 12 factors in macrophages and 17 factors in erythroblasts.
Of these factors, 18 in ES cells, 7 in macrophages and 4 in erythroblasts had enhancer expression in CAGE data. We noted that the suitability of Findr-B or Findr-C for causal inference was dependent on the factor, i. e. , using continuous enhancer data performed better for some factors (Figure  2 : Myc,Klf2, Figure  S1 : Fcgr3), while on/off data performed better for others (Figure  2 : Gata1, Fli1, Figure  S2 : Junb, Jarid2). Because the number of putative ChIP-seq targets for each factor varied widely across factors and cell types, from only 420 gene targets for JunD in erythroblasts to over 12,000 gene targets for ESRRB in ES cells, the background precision levels differed highly between factors (Figure  2 ). We further tested whether these results were sensitive to the target probability prediction threshold.
The enrichments of true positives were stable over a wide range around this threshold for both methods (Figure  3 , Figure  S2 ). Transcription factor binding inferred using ChIP-seq data is thought to be mostly opportunistic and therefore might not provide direct clues about the functional targets of the factor  [21]. We therefore collected perturbation data from publications, specifically expression data after knock-out or knock-down (KO) of a factor. We generated differentially-expressed gene lists for 85 factors in ES cells and 11 factors in macrophages (see Methods). Using these gene lists as known targets, we evaluated the predictions of both methods. This confirmed the factor-specific suitability of either the Findr-B or Findr-C method (Figures 4, Figure  S3 ). The evaluation so far used the default combination of likelihood-ratio tests in Findr (Figure  1B , Tests 2, 4 and 5; see the Methods for details), which was previously shown to perform better than the traditional causal inference method, which relies on a conditional independence test (Figure  1B , Test 3), by accounting for hidden confounding due to common upstream regulatory factors between a TF and its targets  [18].
Causal Inference Using CAGE Expression Data across Human Cell Types Finally, we inferred causal interactions between transcription regulators and targets using CAGE enhancer and TSS expression data in humans. Specifically, we inferred causal interactions for 20 transcription factors (with eRNA expression) using Findr-A (see Methods). We firstly validated the predicted interactions using a database of experimentally-validated regulatory interactions in human  [25]  and noted a statistically-significant overlap between the predicted and experimentally-validated gene sets (p < 10 -5 ). Figure  5  represents the hierarchical clustering of the predicted top 200 interactions for each factor. We noted that multiple enhancers of the same factor predicted highly overlapping targets for that factor.
Discussion We explored the utility of eRNA expression as a causal anchor to predict transcription regulatory networks, by leveraging the observation that eRNAs mark the activity of regulatory regions. Previous studies support this notion, as eRNA expression has been shown to precede the expression of its effector gene temporally  [17]  and to correlate strongly with active regulatory regions across cell types  [15]. We therefore developed a novel statistical framework to infer causal gene networks (Findr-A), by extending the Findr software for causal inference using eQTL data  [18]. We demonstrated the applicability of Findr-A by predicting causal interactions from CAGE data generated through the FANTOM Consortium and validating them with ChIP-seq and perturbation data for three mouse cell types, as well as on the entire FANTOM5 data.
Notably, different factors were enriched for within cell type analysis as compared to across cell types. The causal regulatory network of cell type-specific factors (e. g. , Sox2, Esrrb in ES cells) could be inferred only using expression variation within a cell type and not across cell types. Due to the limited availability of validation data, a more comprehensive assessment was not possible. The current approach can be extended in several aspects in the future. Firstly, Findr assumes equal (or no) relations between all sample pairs  [18] , which hold for the majority of eQTL datasets. By accounting for heterogeneous sample relationships, such as biological and/or technical replicates, time series or population structure, we may be able to reconstruct more accurate networks. Secondly, the assumption that eRNAs act as causal anchors is only approximately true, because their activity ultimately is regulated by other regulatory factors, i.
This could be refined by using existing data generated by methods such as chromatin confirmation capture or by integrating multiple genomic features into a statistical predictor for associating enhancers with promoters  [5]. Finally, we used ChIP-seq and KO data for validation, because these datasets were available for the same cell types as the expression data in mouse. These data are far from ideal as ground-truth sets, as ChIP-seq data tend to be noisy and differential expression in transcription factor knock-outs could include indirect effects. Moreover, the targets predicted by ChIP-seq and KO experiments show a very poor overlap between them. For example, a large-scale study where 59 TFs were knocked down in a human lymphoblastoid cell line (GM12878) concluded that only a small subset of genes bound by a factor were differentially expressed following knock-down of that factor  [21].
It is therefore important to note that there is no universal agreement on ground-truth TF-target regulatory networks for validation, including networks obtained from literature mining. For example, there are only 151 common TF-target interactions among four literature-curated databases, which together comprise more than 10,000 interactions  [26]. As shown, our method predicts targets for many TFs w. r. t. to one ground-truth as well or better than the other ground-truth, reaffirming our hypothesis that eRNA data can be used to infer TF targets. In conclusion, we have demonstrated that enhancer activity can be used to infer causal gene regulatory networks. We foresee this approach to be of high value in the context of human medicine, by combining genetic, epigenetic and transcriptomic information across individuals to unravel causal disease networks.
• We used Cap Analysis of Gene Expression (CAGE) data (TPM expression values) from the FANTOM5 Consortium for enhancer and transcription start sites (TSS) in mouse embryonic stem (ES) cells (36 experiments), macrophages (224 experiments) and erythroblastic leukaemia (52 experiments)  [6, 17]. We also selected 1036 samples from all cell types and tissues in mouse.
Validation Methods For the purpose of evaluation, we calculated the Findr-B and Findr-C scores for all TF-gene combinations. Genes with scores exceeding a threshold of 0. 8 of Findr score were considered as predicted targets for each TF. Precision-recall curves were calculated using the the "PRROC" package. We used the FDR corrected (BH procedure) hyper-geometric test for enrichment analysis, where the overlap with respect to known targets from ChIP-seq and knock-out data in the same cell type was tested, and the resulting p-values were used to compare the performance of the two methods.
The nuclear pore complex (NPC) provides a paradigmatic example of an essential multisubunit complex whose homeostasis is crucial yet poorly understood. NPCs are megadalton-sized Fig. 1  The hnRNP K-like protein Hek2 specifically associates with a subset of NPC mRNAs. a Top, Representation of the yeast nuclear pore complex (NPC), showing subcomplexes as colored boxes. Bottom, mRNAs encoding NPC components are sorted by subcomplexes and the strength of their association to the different indicated RNA-binding proteins (RBP) is represented by a color code, as scored in distinct RIP, CLIP or CRAC datasets. Bright yellow indicates the preferred association of a given mRNA to the RBP of interest. For Sto1, Mtr4, Nab2, Mex67, Xrn1 and Ski2, multiple repetitions are displayed  27. For Hek2, the results from independent studies are represented: (1)  24  , (2)  26  , (3)  25  , (4)  27.
Results A unique mRNP composition for a subset of NPC mRNAs. In order to unravel novel mechanisms regulating NPC biogenesis, we systematically analyzed the association of Nup-encoding (NPC) mRNAs with different RNA-binding proteins (RBPs) in budding yeast. For this purpose, we took advantage of previously published large-scale datasets obtained through RNA immunoprecipitation (RIP)  [22] [23] [24] [25]  , crosslinking immunoprecipitation (CLIP)  26  or crosslinking and analysis of complementary DNA (CRAC)  27. We collected the association data for 39 NPC mRNAs (encoding Nups and NPC-associated proteins, Fig. 1a  and Supplementary Fig. 1a ) with a panel of 10 mRNA-associated factors involved in different stages of mRNA metabolism, including assembly into mRNP (Sto1), processing (Npl3, Nab4/Hrp1), nuclear export (Yra1, Nab2, Mex67), degradation (Xrn1, Ski2, Mtr4) or mRNA localization/translation (Hek2) (Fig.
1a ). This analysis revealed that NPC mRNAs have generally the same typical features of expressed, protein-coding RNAs, e. g. , they readily associate with mRNA export factors (Mex67, Nab2), but not with the non-coding RNA degradation machinery (Mtr4) (Fig. 1a , bottom right panel). Strikingly however, a small subset of NPC mRNAs (namely NUP170, NUP59, NUP188, NUP116, NUP100, NSP1 and NUP1) appeared to specifically bind the conserved Heterogeneous nuclear ribonucleoprotein K-like factor Hek2 (a. k. a. Khd1  28, 29  ), a feature detected in four independent datasets (Fig. 1a , bottom left panel). The enrichment of certain NPC mRNAs among Hek2-bound targets appeared significant in a Gene Set Enrichment Analysis (P = 0. 02) and was neither a mere consequence of the different expression levels of these particular transcripts (Supplementary Fig.
In contrast, immunoprecipitation of Hpr1, a subunit of the mRNP packaging THO complex, did not reveal any similar preferred association to a subset of NPC mRNAs (Supplementary Fig. 1d ). We then asked whether Hek2 was directly associating to this subset of NPC mRNAs (i. e. , NUP59, NUP116, NUP100, NSP1 and NUP1), as expected from CLIP/CRAC studies  26, 27. To this aim, we first delineated Hek2-binding sites on these mRNAs by mining CLIP/CRAC data (Fig. 1c ) and by searching their sequences for common motifs using the MEME software (Fig. 1c, d ). This in silico approach revealed that these mRNAs share a common CA-rich motif (Fig. 1d ), similar to the two previously reported Hek2-binding sites, i. e. (CNN) 6  24  and CAUCAUCA  26. As anticipated from a previous study  26  , this motif was overlapping some but not all in vivo Hek2-binding peaks as defined by CLIP or CRAC, allowing us to define putative minimal bound domains in NSP1 and NUP116 mRNAs (Fig.
Methods Yeast strains and plasmids. Unless otherwise indicated, all the strains used in this study (listed in Supplementary Table  1 ) are isogenic to BY4742/BY4741 and were grown in standard culture conditions. Experiments using the ulp1 allele were performed at semi-permissive temperature (30 °C) as previously described  35. Experiments with the ubc9 thermosensitive mutant were performed following 2 h of shift at 37 °C. When indicated, cycloheximide (0. 1 mg per ml, Sigma), MG132 (100 µM, Sigma) or ethanol (10% v/v) were added to the medium for the indicated time. Drug-sensitive erg6Δ strains were used for MG132 treatment  16. Construction of plasmids (listed in Supplementary Table  2 ) was performed using standard PCRbased molecular cloning techniques and was checked by sequencing. Bioinformatic analysis of RNA immunoprecipitation datasets.
RIP, CLIP or CRAC data were collected for the following RNA-binding proteins: Yra1 (RIP followed by microarray analysis, one replicate  22  ), Nab2 (CRAC, three replicates  27  ), Npl3 (RIP followed by microarray analysis, one replicate  23  ), Nab4/Hrp1 (RIP followed by microarray analysis, one replicate  23  ), Mex67 (CRAC, three replicates  27  ), Sto1 (CRAC, three replicates  27  ), Xrn1 (CRAC, two replicates  27  ), Ski2 (CRAC, four replicates  27  ), Mtr4 (CRAC, three replicates  27  ) and Hek2 (CRAC, one replicate  27  ; CLIP, one replicate  26  ; RIP followed by microarray analysis, one replicate in two distinct studies  24, 25  ). For each dataset, all protein-coding RNAs were ranked and given a color according to their relative binding to the corresponding RBP. Scores available from microarray or sequencing analyses  [22] [23] [24] [25]  were used to split the RNAs in four equally sized groups corresponding respectively to "high" (light yellow), "medium" (dark yellow), "low" (dark blue) and "very low/no" (light blue) binding.
CLIP data were used to define bound (light yellow) and unbound (light blue) mRNAs according to the published peak calling analysis  26. CRAC hits were first normalized by hits per million within each RBP CRAC dataset, then for each mRNA (∑i 2 = 1) to account for differences in mRNA abundances, and scaled to occupy the 0-1 range. Colors ranging from light blue (0) to light yellow (1) were used to depict the binding of a given mRNA to a RBP. Binding categories were further displayed for NPC mRNAs (Fig. 1a ) or proteasome/exosome RNAs (Supplementary Fig. 1c ). Gene set enrichment analyses were performed as previously described  64. The MEME software (v4. 11. 3)  65  was applied to the sequences of NUP59, NUP116, NUP1, NSP1 and NUP100 mRNAs. Out of 6 retrieved motifs, 5 corresponded to FG-coding sequences, while one, found with an e-value of 4.
Total RNAs were extracted from yeast cultures using Nucleospin RNAII (Macherey Nagel). Reverse transcription and cDNA quantification were performed as described above for RNA immunoprecipitation. Transcriptome analysis was achieved using microarrays as previously reported  35  : the hek2Δ versus wt comparison was performed twice using independent samples and dye swap. The averaged log2 of the mutant/wild-type ratios and the standard deviation between the two replicates were calculated for each gene. The genes showing a standard deviation of >0. 5 were removed from the dataset. Comparisons of hek2Δ transcriptome with Hek2 and Nab2 binding profiles were realized using published datasets  23, 24. Transcripts were split in four equally sized groups corresponding respectively to "strong", "medium", "low" and "very low/no" binding.
Images were scaled equivalently and 3Dprojected using ImageJ, and further processed with Photoshop CS6 13. 0 ×64 software (Adobe). Nuclear envelope intensities were determined with ImageJ following subtraction of the cytoplasmic background. Statistics. The experiments were not randomized and the investigators were not blinded to allocation during experiments and outcome assessment. No statistical methods were used to predetermine sample sizes; (n) values were chosen in accordance with standard practices in RNA analyses in yeast, correspond to the number of biological replicates (e. g. , independent yeast cultures) and are indicated in the corresponding figure legends. Error bars correspond to standard deviations. The two-tailed Welch's t-test, which allows unequal variance, was used to compare RNA-binding efficiencies in vitro or in vivo (Figs.
Acknowledgements We thank  P. Chartrand ,  C. Dargemont ,  V. Doye ,  V. Géli ,  M. Hochstrasser ,  E. Johnson ,  S. Wente  and  X. Zhao  for reagents;  H. Bretes ,  L. Fusée  and  G. Lelandais  for help with strain construction, in vitro experiments and data normalization, respectively;  D. Tollervey  for CRAC analyses;  A. Babour ,  V. Doye ,  T. H. Jensen  and  R. Rothstein  for discussion and critical reading of the manuscript. This work was supported by:  CNRS  (to B. P. );  Fondation ARC pour la Recherche sur le Cancer  (to B. P. );  Ligue Nationale contre le Cancer  (to B. P. ; fellowship to J. O. R. );  Ecole Doctorale  " Structure et Dynamique des Systèmes Vivants " (# 576 ),  Université Paris-Sud ,  Université Paris-Saclay  (fellowship to J. O. R. ); and the  "Who am I?" laboratory of excellence  (grant numbers  ANR-11-LABX-0071 ,  ANR-11-IDEX-0005-02 , fellowship to J.
Data availability. The complete microarray data are available in the ArrayExpress database under accession number E-MTAB-6065 (https://www. ebi. ac. uk/ arrayexpress/experiments/E-MTAB-6065/). The uncropped scans of the blot images shown in Figures are provided in the Supplementary Fig. 5. All the other data supporting the findings of this study are available within the paper and its supplementary information files, or from the corresponding author upon request.
, 2010). A formal evaluation of model performance when using a larger number of grain size classes and sediment density is thus still missing. Here, we further develop an existing hydromorphodynamic model based on the dynamic coupling of TELEMAC-3D and SYSIPHE in order to consider an enhanced sediment grain-size distribution with distributed sediment density. Moreover, this study aims to evaluate and discuss the added value of the new development for improving sediment transport and riverbed evolution predictions. This paper is organized as follows: First, we present the hydromorphodynamic model and the developments that were made. Second, we describe the study area, the available observation dataset and the experimental design. Next, we present and discuss the results. Finally, we summarize the findings of this study and propose perspectives for future developments in hydromorphodynamic modelling.
Modelling framework The proposed modelling framework is based on TELEMAC-MASCARET  (Hervouet, 2007). The fluid hydrodynamics are simulated using the TELEMAC-3D model, which solves the Navier-Stokes equations in a hydrostatic mode. The morphodynamic and sediment transport modelling is carried out using the SISYPHE  (Villaret, 2010; 2013)  model, an additional module of TELEMAC-MASCARET. This modelling framework has the following interests: (i) the two aforementioned models are based on a finite element of unstructured mesh, which is particularly suitable for river and coastal area modelling as it allows the simulation of complex geometry, and (ii) they can be dynamically coupled. The dynamic coupling of the two models is especially relevant for sediment transport and morphodynamic modelling as it allows, at each simulation time step, to take into account the effect of the riverbed changes on the flow and vice versa.
Available data Since January 2014, monitoring efforts have been concentrated on continuously recording streamflow and water turbidity as a proxy of suspended sediment concentration. Moreover, bathymetry (i. e. riverbed elevation) and sediment deposition were measured more episodically at selected locations on the riverbanks and the riverbed. The continuous data used in this study were acquired during a moderate-magnitude flood event that occurred in March 2017. During this event, a peak discharge of 45 m 3. s -  was recorded and the turbidity did not exceed 150 NTU (Fig. 2 ).
Suspended Sediment Concentration (SSC) SSC is generally measured punctually whereas models require continuous input data time series. In this context, turbidity data is often recognized as a good proxy for estimating continuous the time series of SSC  (Martínez-Carreras et al. , 2016). In this study, the turbidity is monitored every 5 minutes at the downstream boundary using an YSI 600 OMS turbidimeter. During the flood event, turbidity values ranged from 0 to 150 NTU. These measurements are used to calibrate the relationship between turbidity and SSC. The polynomial regression between the two datasets (e. g. Versini et al. , 2015)  exhibits a Pearson's correlation coefficient of 0. 968 and a residual mean of 1. 44 mg•L -1 (Fig. 2 ). The calculated SSC is compared to the observation in Fig.
Acknowledgments This study is part of the  MOBISED  project co-funded by the  Luxembourg National Research Fund (FNR)  and the  French National Research Agency (ANR)  in the framework of the  FNR/INTER-ANR research programme  (Contract No. INTER/ANR/13/9441502 ). We would like to thank  Jean-François Iffly ,  Jérôme Juilleret ,  Luc Manceau  and  Cyrille Taillez  for the maintenance of field equipment and the accurate field data acquisition, and  Claire Delus  and  Benoît Losson  for the constructive scientific discussions related to hydrological and sedimentary issues in the Orne River basin. Hydrol. Earth Syst. Sci. Discuss. , https://doi. org/10. 5194/hess-2018-511Manuscript under review for journal Hydrol. Earth Syst. Sci. Discussion started: 6 November 2018 c Author(s) 2018. CC BY 4. 0 License.
The dataset and toolbox presented in this article are related to the research article entitled "Modeling potential tree belt functions in rural landscapes using a new GIS tool" (Nowak and Pędziwiatr, 2018)  [1]. The dataset (spatially referenced) is used as input for all the five analysis modules described in the related article. The dataset contains vector and raster data, which cover north-west part of the Gen. Dezydery Chłapowski Landscape Park in Poland. Moreover, the present work shows the result (a vector layer) of using the TBM toolbox within ArcGIS (ESRI) software. The Tree Belt Modeling (TBM toolbox) code and internal structure can be fully accessed, and are available for further modification and incorporation of tools. The toolbox is available for download as supplementary material to this online article.