dataesr/scientific-paragraphs-categorization huggingface dataset:
2 ). A large fuzzy cluster grouped ChIP-seq experiments with many peaks at promoter regions. This cluster included all RNA polymerase II (RNAPII) ChIP-seq experiments. Gene promoter coordinates were also included in this cluster (Fig. 2a-d , green lines). We noted that many transcription factors clustered according to the cell type rather than the factor. For example, Max and Myc clustered together in NB4 cell line, and also together in K562 cell line in the human ENCODE dataset. The same observation was true in mouse, where Max and Myc clustered together in MEL cell line and also together in CH12. LX cell line in the mouse ENCODE dataset. On the other hand, a very well defined cluster comprised of CTCF and cohesin ChIP-seq experiments from diverse cell types (including RAD21, SMC3, ZNF143) was present in all human and mouse datasets.
Other individual clusters had peaks with little or no overlap with promoter or CTCF clusters. Many of these experiments tended to form small clusters of either chromatin regulators or enhancerbinding transcription factors. Globally, but with exceptions, binding profiles of two distinct TFs in the same cell type tended to be more different that binding profiles of the same TF in two different tissues. The Heat*seq tool allows easy sub-selection of samples in each dataset based on factor or cell type. Figure  2e  contains all TF ChIP-seq experiments done in HUVEC (Human Umbilical Vein Endothelial Cells) in the human ENCODE dataset. It illustrates the three main groups of experiments: a tight CTCF cluster, a fuzzy promoterproximal cluster including RNAPII, MAX and CMYC ChIP-seq experiments, and a cluster with more distalbinding factors such as CJUN, CFOS, and GATA2.
HeatChIPSeq can also be used to explore the fraction of cell-specific binding sites for different factors. Figure  2f  includes selected experiments from the mouse ENCODE dataset in liver and heart. CTCF and RNAPII experiments from both tissues clustered together, reflecting mostly shared occupancy between tissues. EP300, on the other hand, formed two separated clusters, one for each tissue, reflecting a higher proportion of tissues specific binding sites. In Fig. 2g , we selected all experiments from mouse Embryonic stem cells from the mouse CODEX datasets. It allowed characterising further the factors that are neither mainly associated with RNAPII nor CTCF co-factors. We detected a well defined cluster of members from the polycomb complexes PRC1 and PRC2, as well as two clusters composed of pluripotency factors (and other TFs): the first cluster had experiments enriched with promoterproximal peaks and included factors involved in transcription initiation process or enriched at promoters including Myc and Gata4, while the second cluster, including Sox2, Nanog, Oct4 and Esrrb experiments, had mostly promoter distal peaks.
Genomic location preference of transcription factors The cluster analysis in the previous section indicated that some factors had a majority of their peaks close to gene transcription start sites (TSS) while others were mostly found at promoter-distal regions. We investigated this further by annotating each transcription factor peak (from the human an mouse ENCODE datasets) by its closest GENCODE gene TSS  [16, 17]  (see methods), and obtained for each factor the fraction of peaks overlapping a TSS, upstream to, or downstream to the nearest TSS (Fig. 3a  and b ). We divided experiments in three groups: Green labelled experiments (Fig. 3a  and b ) have more peaks located downstream of a TSS than upstream. This group includes ChIP sequencing experiments for the elongation specific pol II phosphorylated on serine 2, as well as ChIP-seq against ZNF274.
3e ) to factors with less than 20% of peaks at a TSS (including most CTCF or MAFK ChIP-seq experiments, Fig. 3d ). Most factors showed a conserved fraction of promoter proximal peaks across experiments and or cell types. Nonetheless, we noted a few inconsistent cases. For example, six of eight CFOS ChIP-seq in the human ENCODE datasets had less than 10% of the peaks overlapping a TSS (Fig. 3f ), while one experiment had around 30%, and another around 50% of their peaks overlapping a TSS. In the absence of biological replicates, it is difficult to conclude whether these differences reflect a real biological phenomenon or are simply due to technical biases  [20]. For each TF, we compared the fraction of peaks overlapping a TSS in mouse and human (Fig. 3h ). For most factors, their promoter proximal or distal preference was conserved, with an overall correlation of 0.
Transcription hotspots are enriched strongly both at promoter proximal and distal regions We further clustered ChIP-seq experiments using only the promoter proximal or the promoter distal peaks separately, in the six human and the two mouse for cell lines with more than 30 ChIP-seq experiments. In the human ENCODE dataset, six cell lines (A549, GM12878, H1-hESC, HeLa-S3, HepG2, and K562), with a total of 474 experiments were selected (Fig. 4a  and b ). From the 553,211 regions bound by at least one factor in the 474 experiments, 300,901 were promoter proximal ( <1 kb from the nearest TSS), and 144,514 were distal to a TSS ( >10 kb). In the mouse ENCODE dataset, two cell lines (CH12 and MEL), with a total of 88 experiments were selected (Fig. 4c  and d ). From the 221,772 regions bound by at least one factor in the 88 experiments, 33,171 were promoter proximal ( <1 kb from the nearest TSS), and 101,150 were distal to a TSS ( >10 kb from the nearest TSS).
Transcription factor hotspots are defined as genomic regions occupied by many factors in a given cellular context. They overlap with regulatory regions that are highly important for cell identity  [21]. We characterized transcription hotspots in human and mouse datasets by computing the TF binding density, i. e. the fraction of ChIP-seq experiments in each cell type with a peak in a given genomic region. The clustering of TF density for all genomic regions by k-means clustering (Fig. 4e  and f ) demonstrated that transcription factors hotspots are promoter proximal and distal. Transcription factor hotspots that are shared by all cell types are mostly found at the promoter regions, while transcription hotspots that are cell type specific are mostly found at non-promoter regions. This is in full agreement with our previous findings.
For each cell type, there were 2N-1 combinations of binding patterns possible. We evaluated likelihood of frequency of these combinatorial patterns to occur by chance by comparing to 100 random datasets generated such that the total number of binding events for each factor was preserved (i. e. the number of non-zeros in each column of M*N matrix were unaltered during randomizations). This analysis re-discovered the transcription factors of the same family (e. g. SMC family members in murine ES cells, CEBP family members in murine macrophages, Stat family members in murine dendritic cells) known to bind to overlapping genomic locations due to highly similar sequence motifs, or the components of known complexes or well studies interactions (Table  1 ). CTCF, RAD21 and ZNF143 form a part of cohesin complex and clustered together in the global clustering considering all peaks.
Conclusions As the next generation sequencing is becoming a preferred tool of the experimental groups world-wide, the genome wide data will be ever increasing in public domain. Understanding the potential of this data for novel discoveries, huge efforts are ongoing in storing and assembling data in public repositories and databases  [32] [33] [34]. One of the major challenges now is to generate methodologies and pipelines facilitating computational analysis of this large and under-exploited resource. This can be achieved in a plethora of ways, including integrating together with other datasets, to obtain new biological insights. To this end, the ENCODE consortium has taken a big initiative to provide a uniformly processed dataset to the scientific community for computational data integration and analysis  [12].
Similarly CODEX database  [11]  provides a uniformly processed and analysed data from blood and ES cells in human and mouse. The easy accessibility of pre-processed data is a major facilitator for generation of novel biological hypotheses from this resource. In this paper, we explored multiple resources and tools (Fig. 1 ), including integration with other data resources such as GWAS catalogue. We demonstrated the use of HeatChIPseq webtool to investigate global relations across ChIP-seq samples. We further analysed the genomic location preference of TFs across species. We showed that transcription hotspots are both promoter proximal and distal. We confirmed that promoter proximal binding of factors (including at transcription hotspots) is more shared across tissues, while the distal binding is more cell type specific.
We identified combinations of factors preferentially acting together. Finally, we integrated the data with disease-associated gene loci from GWAS studies to highlight the value of this data to associate novel regulators to disease. In summary, we demonstrate potential ways to develop new hypotheses about transcription control mechanisms. Importantly, the above results form only the tip of the iceberg of the potential insights from these resources. Our analysis has allowed us to reproduce and expand some observations previously made using these data. This shows that the in depth analysis of such data is still far from complete and must continue. Importantly, the analysis performed in this study can easily be extended to exploit ChIP-seq datasets in other species or in other cellular contexts, and therefore has the potential to significantly advance our understanding of a wide range of both normal and pathological cellular processes.
Methods Peaks bed files where downloaded from ENCODE at UCSC (human data, hgdownload. cse. ucsc. edu/goldenPath/hg19/ encodeDCC/wgEncodeAwgTfbsUniform/) or from the ENCODE data portal (mouse, encodeproject. org) for a total of 690 experiments in human and 156 experiments in mouse. Human and mouse peak files from ChIP-seq experiments were also downloaded from CODEX  [11]  (codex. stemcells. cam. ac. uk), for a total of 238 experiments in human and 651 experiments in mouse. For each of four datasets, all peaks lists were merged using bedtools merge  [35]  to find non-overlapping peak-containing regions. The merged peaks where then used to create a genomic regions x experiments binary matrix where each matrix cell indicates the presence or absence of a peak at a given region in a given experiment.
This binary peak matrix was correlated and clustered, using Euclidean distance of the correlation values and complete hierarchical clustering from the relevant R functions. Clustering branches were re-ordered using the order. optimal function from the cba package  [36]. Processed data and detailed dataset processing instructions are available through our web application Heat*seq  [14]  and the related GitHub repository github. com/gdevailly/HeatStarSeq_gh. The mouse ENCODE dataset was implemented specifically for this study. TSS lists where obtained from GEN-CODE  [16, 17] , using the median TSS for genes with several alternative transcription start sites. CpG islands coordinates where obtained as a bed files from the UCSC table browser  [37]. Individual peak files and merged peak lists from the human and mouse ENCODE datasets were annotated with the closest GENCODE  [16, 17]  TSS (v21 for human, after using lift-over to convert it back to hg19, vM14 for mouse) using Bedtools  [35]  closest command with the -D parameter.
Annotated peak lists were used to analyse the distribution of peaks upstream to, at, or downstream to the nearest TSS. Annotated merged peak lists were used to create binary genomic regions x experiments matrices of only the regions at less than 1 kb from the closest TSS or only the regions at more than 10 kb from the closest TSS. Promoter proximal and promoter distal matrices were then clustered as described before, using only experiments in the 6 cell lines (humans) or the 2 cell lines (mouse) with the most experiments (ENCODE data). The binary matrices were used to compute the TF density of each merged peaks for each cell line, defined as the number of experiment with a peak at a given location divided by the total number of experiments from the cell line in a dataset. TF density matrices of merged peaks x cell line were clustered using k-mean clustering to identify clusters of high TF density that were either ubiquitous or cell-line specific.
The random binding sets maintained the same number of targets for each TF. The sequence motif enrichment for each ChIP-seq sample was performed using HOMER  [27]  with all peak locations as an input. A list of significantly disease associated genomic regions were downloaded from www. ebi. ac. uk/gwas/. P values were calculated using hypergeometric test, and were adjusted using Bonferroni correction. The enriched combinatorial patterns were calculated by generating random binding data keeping the same number of peaks for each factor, and the significance was estimated compared to 100 randomizations.
Funding AJ is a Chancellor's fellow and AJ lab is supported by institute strategic funding from  Biotechnology and Biological Sciences Research Council (BBSRC ,  BB/J004235/1 ). GD is funded by the  People Programme (Marie Curie  Actions FP7/2007-2013) under REA grant agreement No  PCOFUND-GA-2012-600181. The publication costs were funded by the  University of Edinburgh.
Digital Soil Mapping (DSM)  (McBratney et al. , 2003; Lagacherie et al. , 2006; Arrouays et al. , 2017a , Rossiter, 2018)  has been developed as a solution to generate highresolution maps of soil properties over large areas. Two projects, Glob-alSoilMap  (Arrouays et al. , 2014b)  and SoilGrids  (Hengl et al. , 2014 (Hengl et al. , , 2017)) , presently aim at delivering the first generation of global, highresolution soil property fine grids. The former relies in a bottom-up approach (from country to globe), while the latter uses a top-down approach (a global model that predicts properties for every country). As acquiring new soil data is laborious and expensive, both projects promote the use of existing legacy and heritage soil survey data available across the world. These data are being rescued, compiled and processed into a common, consistent and harmonized dataset of relevant soil properties covering the planet's land surface  (Arrouays et al.
OBJECTIVES OF GLOBALSOILMAP The GlobalSoilMap initiative aims at developing and transferring methods to improve the prediction accuracy of soil properties and their associated uncertainty, by using legacy soil data and ancillary spatial information. The objective of the GlobalSoilMap project is to provide a scientific framework to deliver fine grids of soil attributes as a consistent, spatially explicit and continuous database freely available for all. The fine grids of soil attributes will be delivered at high spatial resolution, thus applicable to both global and local applications. The specifications of the GlobalSoilMap products have been discussed collegially by a scientific consortium, and written into a detailed document based on peer-review. The specified product includes predicted values of selected key soil properties at 6 standard depth intervals (0-5, 5-15, 15-30, 30-60, 60-100 and 100-200 cm), at a global scale on a 3 arc-second support grid (approximately 90 × 90 m) along with their uncertainties.
The key primary soil properties include the clay, silt and sand contents, coarse fragments, pH in water, soil organic carbon (SOC) content, effective cation exchange capacity (ECEC), soil depth to bedrock and effective root zone depth. Additional key properties, mainly derived using pedotransfer functions (PTF, see a review from, Minasny and Hartemink, 2011), include bulk density and plant-available water holding capacity. This minimal dataset may be supplemented by other soil attributes on an ad-hoc basis (e. g. , P, N and S contents, electric conductivity, soil type, presence of diagnosis horizons, trace elements contents). Predictions are generated using state-of-the-art Digital Soil Mapping (DSM) techniques  (McBratney et al. , 2003; Grunwald et al. , 2001; Minasny and McBratney, 2016; Arrouays et al.
EXAMPLES FROM FRANCE Here, we briefly describe the development and delivery of Glob-alSoilMap products for mainland France and in some specific French regions. The years 2013 and 2014 were mainly dedicated to rescuing and harmonizing all soil point data available for the country, and to gathering and resampling all the co-variates that were meaningful and exhaustively available over the territory to the 90-m resolution needed for the final products. Then, the soil profile properties were harmonized according to the GlobalSoilMap specifications (e. g. , by using spline functions, Bishop et al. , 1999)), and various prediction models were evaluated. The first available products were SOC (Mulder el al. , 2016a) and total soil depth  (Lacoste et al. , 2016). A much more complete set of 'primary' data was then produced leading to a 2 nd version of GlobalSoilMap France  (Mulder et al.
, 2016b). We also assessed how the relief (and mainly the slope) could affect the assessment of stocks on a regular square grid (Chen, Arrouays, 2018). As many secondary data (e. g. , bulk density, available water capacity) had very scarce in situ georeferenced measurements, we then focused on developing pedo-transfer functions to predict them using more easily available data (e. g. , for bulk density, Chen et al. , 2018a; for available water capacity, Román  Dobarco et al. , 2019)  We also used the GlobalSoilMap products to derive new products (e. g. , Chen et al, 2018b; Román Dobarco et al. submitted) which are more directly usable by end users. We are now finalizing the AWC and BD products, and we are beginning to test new approaches for prediction of censored soil properties such e. g.
soil depth. At more regional or even local scales, we tested if the use of other methods could also be used to predict other soil attributes, such as, for instance, the presence of diagnosis horizons (Richer-de-Forges et al. , 2017). The use of DSMART  (Odgers et al. , 2014)  as a tool for disaggregating/downscaling soil maps was tested in Brittany  (Vincent et al. , 2016) , and new methods, such as Quantile Random Forest (QRF) were tested to use point data for GlobalSoilMap products in the south of France (Vaysse and Lagacherie, 2017). In the western Paris croplands, a bootstrap resampling procedure provided estimations for the uncertainty regionally and for each sample location, using a criterion of pointwise RMSE  (Zaouche et al. 2017). We also explored the potential of new airborne gamma-ray measurements in some regions, and the potential of a large range of spatial and spectral resolutions of Visible and Near Infrared (400-2500 nm) imagery data  (Vaudour et al.
-Advances should be made and research efforts are needed on estimating the uncertainties, and even on estimating uncertainties on uncertainties  (Lagacherie et al. , 2019). -Attempts to merge different model strategies and products (for instance deriving from different covariates, spatial extents, soil data sources, and models) should be made in order to get the most useful information from each of these predictions, and to identify how controlling factors may change depending on scales (Román  Dobarco et al. , 2017 Dobarco et al. , , 2019;; Caubet et al. , 2019). From a 'policy and scientific' point of view: We are now in the situation where there is a global consensus about the specifications for gridded soil information products. We have a very advanced science and technology. The technology to support the development of high resolution grids, such as IT solutions to deal with large datasets and to support fast calculation, is quickly increasing.
ACKNOWLEDGMENTS We would like to thank the  IUSS Council  for endorsing the Glob-alSoilMap WG and to thank the Global Soil Partnership plenary assembly to have endorsed the GlobalSoilMap specifications for delivering fine grids if soil properties. The GlobalSoilMap activities of the  INRA InfoSol Unit  are partly funded by the  Environment & Agronomy department of the National Institute for Agronomic Research (INRA). Testing the added value of remote sensing data is partly worked in the CES Theia Digital Soil mapping project funded by the  French national center of space studies  ( CNES 160102  agreement). Most of the French point soil data has been collected or rescued thanks to funding from national ' Groupement d'Intérêt Scientifique Sol ' involving the  French Ministry of Ecology , the  French Ministry of Agriculture , the  French Environment and Energy Management Agency (ADEME) , the  French Institute for Research and Development (IRD) , the  Institute for National geographic and Forest Information (IGN)  and the  INRA.
In most cases, these national funding were complemented by French Regions or more local agencies. We thank all the people involved in sampling the sites and populating the database. Songchao Chen  received the support of  China Scholarship Council  for three years' Ph. D. study in INRA and  Agrocampus Ouest  (under grant agreement no. 201606320211 ). We also thank the  Russian Science Foundation  (Grant  N°15-16-30007 ) for his support to the Glob-alSoilMap Moscow's conference. Бюллетень Почвенного института им. В. В. Докучаева. 2018. Вып. 95. Dokuchaev Soil Bulletin , 2018, 95.
However, studies using remote sensing to map irrigated the metrics derived from the SAR backscatter time series, irrigated and non-irrigated fields can be separated. Furthermore, the SAR characteristics of backscatter show a difference for different irrigation types; based on this aspect, the irrigated trees and irrigated crops can be separated as well. In this paper, we introduce four metrics, including the mean value, the signal variance, the correlation length, and the fractal dimension derived from Sentinel-1 multi-temporal data. By combining different metrics, the irrigated trees, irrigated crops, and non-irrigated fields are separated. In Section 2, the studied site, the database, and the land use are presented. Section 3 describes the methodologies and metrics that were studied. Section 4 shows the results of the classification mapping and comparison with ground truth.
Fractal Dimension To quantitatively characterize backscatter dynamics, fractal geometry can be used to extract robust features hidden in the fluctuations. Fractals have the property of self-similarity  [49] [50] [51]. Our fractal characterization is based on the power spectrum dependence of a fractional Brownian motion. We determine the power spectrum of the backscatter time series by computing its Fourier transform. The scaling behavior of the data is revealed by a power-law dependence of the spectrum as a function of frequency  [52, 53] : S(f) ≈ 1/f β The fractal dimension is derived from the slope β of a least-squares regression linear fit to the data points in the log-log plot of the power spectrum, leading to the dimension  [50] : D = 7/2β/2. Over irrigated fields, the fractal dimensions are expected to be higher in principle, since irrigation should bring faster change in the irrigation period to SAR signals.
Support Vector Machine A Support Vector Machine (SVM) is a typical learning machine for two-group classification problems  [54]. SVM is a discriminative classifier that is formally defined by a separating hyperplane. In other words, given labeled training data (supervised learning), the algorithm builds an optimal hyperplane, which can be used to categorize a new dataset. In a two-dimensional space, the hyperplane becomes a line dividing a plane into two classes, which lay in each part of the plane. Our method of separating non-irrigated fields, irrigated crops, and irrigated trees is based on the linear Support Vector Machine. In our process, first, we separate irrigated and non-irrigated fields using mean value and variance, then, the irrigated fields are further separated into irrigated trees and irrigated crops using variance and correlation length.