dataesr/scientific-paragraphs-categorization huggingface dataset:
Similar concerns may be raised with TSSs identification. In some cases, a subset of the results is subject to further analysis, such as EMSA, footprinting, northern blotting and/or matrix analyses for site identification. Currently, we add a regulatory interaction in the database only for sites with strong evidence for TFBSs-validated sites  (22)  and where additional knowledge assigns the function of the TF on the regulated gene, such as ChIP-exo complemented with RNA-seq analysis  (24, 25). This illustrates the rationale of our approach to combine the data of different HT experiments to integrate these new data with existing knowledge in the database. There are more than 38 transcriptional regulators (TFs) whose sites have been identified by ChIP methodologies, and this number may increase to 200 TFs for which genomic SELEX screening has been done  (26) , with data available and published for 17 TFs.
200-300 nt). There are several HT-dedicated repositories and resources of microbial experimental results, such as TBDB  (27) , CollectTF  (28)  and RegTransBase  (29) , which hold curated motif data from HTs sources with links to GEO from NCBI  (30)  and to ArrayExpress from EMBL-EBI (31); databases with expression profiles, such as COLOM-BOS  (32) , which offers a variety of tools for analysis, M3D  (33)  and GenExpDB (http://genexpdb. ou. edu/main/). For a broader context of these resources and many more related to gene regulation, users can visit our link to additional resources (http://regulondb. ccg. unam. mx/menu/ about regulondb/additional resources/index. jsp). Our work does not duplicate those efforts, since our main goal is to detect evidence that can be added to either existing objects in RegulonDB and/or that can be combined to support knowledge of higher granularity.
HT data generated by gSELEX and ChIP-exo We curated in RegulonDB ChIP-chip data for the PurR regulon by using validation data  (22). For gSELEX, we generated two tables, one with raw data (data gSELEX peaks) and the other for cross-data comparisons, i. e. , gSELEX and microarrays data for H-NS and LeuO  (34) ; gSELEX and consensus sequences for the transcription factor CRP  (35). Only a few cases were uploaded to the database from ChIPexo plus RNA-seq analysis  (24, 25). The results generated for both methodologies are summarized in Supplementary Tables  S2  and S3.
HT dataset for TSSs under three conditions The dataset for 14 868 TSSs from the Storz lab has been curated. It includes 5495 TSSs corresponding to potential antisense RNAs (asRNAs). These data were generated from RNA-seq and prediction algorithms under three different biological conditions: the MG1655 wildtype strain grown to exponential phase or stationary phase in LB medium as well as the wild-type strain grown to exponential phase in M63 minimal glucose medium  (36)  (see also http://regulondb. ccg. unam. mx/menu/download/ high throughput datasets/index. jsp).
COEXPRESSION DISTANCE AROUND THE REGULA-TORY NETWORK One of the extensive uses of HT technologies is for the development of global expression profiles. As mentioned before, dedicated databases with information on E. coli include COLOMBOS  (32) , M3D  (33)  and GenExpDB (http: //genexpdb. ou. edu/main/). For years, RegulonDB has offered links that allow users to upload gene sets to search for their expression profiles in COLOMBOS (www. colombos. net). In addition to these links, we have implemented tools for a full comparison of expression of groups of genes across all conditions. The 'Coexpression' page can be reached directly from the search option. A single query gene or a group of genes are added either manually, based on the set of interest to the user, or are automatically uploaded as a collection of genes defining operons or regulons (from their corresponding pages).
For dual regulators, regulons are also separated into what we call 'strict regulons,' that is to say, D140 Nucleic Acids Research, 2016, Vol. 44, Database issue groups of target genes subject to the same effect (activator, repressor or dual effect) by a TF. For each group, i. e. each operon, regulon or strict regulon, we display a browser containing the following sections: the name of the group, the genes contained in the group, a 'coexpression matrix,' the 'coexpression distribution' of the group, and the 'top best coexpressed' genes. The 'coexpression matrix' section enables the user to see the coexpression values of genes with other genes within the group, and the 'coexpression distribution' section shows a plot of the probability density distribution of the coexpression values of the genes within the group, contrasted with a background.
We have therefore precalculated the group values for operons and regulons. To quantify coexpression for all combinations of gene pairs, we implemented a rank-based approach, using data available in COLOMBOS version 2. 0, which contains expression profiles of 2470 different, contrasting conditions. The method and results will be described in detail in a paper to be submitted by Pannier et al. Gene coexpression is typically quantified by pairwise correlation analysis across large expression compendia. However, these analyses are difficult to interpret because of the highly variable distributions of such correlation coefficients. We use a rank-based approach that normalizes the differences between the range of correlation coefficients between genes, which allows comparisons of coexpression strengths among genes despite the large variability of expression values.
Each promoter is linked with the corresponding TU that it transcribes. Sigmulon page. We have included the sigma signal transduction map with a link showing the details of the reactions contained in the map. Datasets. In the submenu related to the datasets, included in downloads, we have integrated new information related to the TSSs experimentally determined in the laboratory of Dr Morett. The TSSs are included in the file named 'Highthroughput transcription initiation mapping. Illumina directional RNA-seq experiments where total RNA received different treatments to enrich for 5 -monophosphate or 5triphosphate ends. ' These objects are included in the new section 'Elements in the selected gene context region unrelated to any TU in RegulonDB,' previously described. Impact of RegulonDB. Figure  3  shows the accumulated citations for each RegulonDB paper by year and the concomitant expansion of new objects and properties related to the regulation of gene expression that we curate.
RegulonDB plays a central role in the development and testing of novel approaches of gene regulation in bioinformatics, comparative genomics, and systems biology, and it is the model to inspire similar approaches and studies for any other organism, including pathogenic bacteria  (37) (38) (39). Evidence of its usefulness is apparent from the more than 1200 citations in published articles, in addition to the many citations for the EcoCyc database, which incorporates our curation work. Within the 'Features' menu, we have added this type of information, showing the impact of RegulonDB, such as the number and type of journals for publications that have cited our RegulonDB-related publications. Releases. The release that corresponds to this paper is version 9. 0. Major changes to the overall navigation and structure of the main pages have been made, offering more structured access to the data, based on the two dominant types of users: biologists, usually conducting individual search queries, and those interested in data collections.
CONCLUSIONS RegulonDB is a complex evolving system. As mentioned in the 'Introduction' section, through the years we have gradually expanded both the content and level of detail of the biological data as well as the diverse types of experimental and bioinformatics sources of knowledge that nurture our understanding of gene regulation in E. coli K-12. A simplified diagram of our work is shown in Figure  4 , with a triangle representing integration of data, information and knowledge; we do not mean absolute definitions of what is data or information, but simply provide relative concepts of the hierarchical nature of a highly granular knowledge. In the following discussion, we try to locate the major advances reported in this paper in this context. For instance, the tsunami of HT-generated data is an effort that appears at the bottom, where decisions of what to represent where (datasets versus integrated sites in the database) reflect the tension of a classic paradigm of a reasonably well-organized genome, as opposed to one inundated by promoters and binding sites of unknown function.
ACKNOWLEDGEMENTS We acknowledge  Alfredo Hernández  for technical support,  Diego Z. Palomares Lagunas  for his contribution to the development of the GENSOR units webpage,  Gerardo Salgado  for administrative help,  Mishael Sánchez  for the processing of HT datasets,  Ingrid Keseler  for periodically sending us selected literature for curation,  José M. Camacho Zaragoza ,  María Cecilia Ishida Gutierrez  and  Sara B. Martínez Luna  for their contributions for curation of  GEN-SOR Units , and finally  Gustavo Engstrom ,  Víctor Bustamante ,  Yalbi Balderas ,  David A. Velázquez-Ramírez  and  Citlalli Mejía  for their participation in the prototype evaluation of coexpression.
The dbmss package for R provides an easy-to-use toolbox to characterize the spatial structure of point patterns. Our contribution presents the state of the art of distance-based methods employed in economic geography and which are also used in ecology. Topographic functions such as Ripley's K, absolute functions such as Duranton and Overman's K d and relative functions such as Marcon and Puech's M are implemented. Their confidence envelopes (including global ones) and tests against counterfactuals are included in the package.
These approaches define three families of functions: absolute (how many neighbors are there?), topographic (how many neighbors per unit of area?) and relative (what is the ratio of neighbors of interest?). The function values are not the main motivation. The purpose is rather to test the point pattern against the null hypothesis that it is a realization of a known point process which does not account for a property of interest. The basic purpose of Ripley's K is to test the observed point pattern against complete spatial randomness (CSR), i. e. , a homogeneous Poisson process, to detect dependence between point locations (the null hypothesis supposes independent points) assuming homogeneity (i. e. , the probability to find a point is the same everywhere). Ripley-like functions, available in the proposed R (R Core Team 2015) dbmss package  (Marcon, Lang, Traissac, and Puech 2015) , can be classified in three families: • Topographic measures such as K take space as their reference.
Topographic functions are implemented in the spatstat package  (Baddeley and Turner 2005)  for R but absolute and relative functions are missing. We fill this gap by proposing the dbmss package, which is available from the Comprehensive R Archive Network (CRAN) at http://CRAN. R-project. org/package=dbmss. It makes the computation of the whole set of distance-based methods simple for empirical researchers by introducing measures that are not available elsewhere and wrapping some topographic measures available in spatstat so that all can be used the same way. Estimated values of the functions must be tested against a null hypothesis. The usual empirical way to characterize a spatial structure consists in computing the appropriate function and comparing it to the quantiles of a large number of simulations of the null hypothesis to reject  (Kenkel 1988).
Package content The dbmss package contains a full (within the limits of the literature reviewed in Section 2) set of functions to characterize the spatial structure of a point pattern, including tools to compute the confidence interval of the counterfactual. It allows addressing big datasets thanks to C++ code used to calculate distances between pairs of points (using Rcpp infrastructure, Eddelbuettel and François 2011). Computational requirements actually are an issue starting from say 10,000 points (see  Ellison et al. 2010 , for instance). Memory requirement is O(n), i. e. , proportional to the number of points to store their location and type. We use loops to calculate distances and increment summary statistics rather than store a distance matrix which is O(n 2 ), following  Scholl and Brenner (2013).
wmppp(). Starting from a CSV file containing point coordinates, their type and their weight in four columns, a 'wmppp' object can be created by just reading the file with read. csv() and applying wmppp() to the result. Options are available to specify the observation window or guess it from the point coordinates and set default weights or types to points when they are not in the dataframe, see the package help for details. The simplest code to create a 'wmppp' object with 100 points is as follows. It draws point coordinates between 0 and 1, and creates a 'wmppp' object with a default window, all points are of the same type named "All" and their weight is 1. R> Pattern <-wmppp(data. frame(X = runif(100), Y = runif(  100  • A weighted, marked planar point pattern (a 'wmppp' class object). The window can be a polygon or a binary image, as in spatstat.
Rdata") R> Category <-cut(Emergencies$M, quantile(Emergencies$M, c(0, 0. 9, 1)), + labels = c("Other", "Biggest"), include. lowest = TRUE) R> X <-wmppp(data. frame(X = Emergencies$X, Y = Emergencies$Y, + PointType = Category), win = Region) R> KdE <-KdEnvelope(X, r = seq(0, 10000, 100), NumberOfSimulations = 1000, + ReferenceType = "Biggest", Global = TRUE) R> plot(KdE) The Emergencies data frame contains point coordinates (in meters) in columns X and Y and workload in column M. The second line of the code creates a vector containing a factor describing the workload to separate the 10% highest values. A 'wmppp' object is created then, containing the points and their mark. The KdEnvelope function is run from 0 to 10 km by steps of 100 m for the most serious emergencies. Figure  2  shows that the 10% most serious emergencies are more dispersed than the distribution of all emergencies.
This opens the way to discuss on the optimal location of fire stations. The second example uses the paracou16 point pattern (Figure  3 ) provided in the package. It represents the distribution of trees in a 4. 1-ha tropical forest plot in the Paracou field station in French Guiana (Gourlet-Fleury, Guehl, and Laroussinie 2004). It contains 2426 trees, where the species is either Qualea rosea, Vouacapoua americana or Other (one of more than 300 species). Weights are basal areas (the area of the stems virtually cut 1. 3 meter above ground), measured in square centimeters. R> data("paracou16", package = "dbmss") R> plot(paracou16) The question to test is dependence between the distributions of the two species of interest. Bivariate M (r) is calculated for r between 0 and 30 meters. 1000 simulations are run to build the global confidence envelope.
R> Envelope <-MEnvelope(paracou16, r = seq(0, 30, 2), + NumberOfSimulations = 1000, Alpha = 0. 05, + ReferenceType = "V. Americana", NeighborType = "Q. Rosea", + SimulationType = "RandomLabeling", Global = TRUE) R> plot(Envelope) The calculated function (Figure  4 ) is M, showing the repulsion between V. Americana and Q. rosea up to 30 m. Significance is unclear, since the observed values of the function are very close to the lower bound of the envelope. The complete study, with a larger dataset giving significant results, can be found in  Marcon et al. (2012).
Ktest The Ktest has been developed by  Lang and Marcon (Lang and Marcon 2013; Marcon et al. 2013). It does not rely on simulations and returns the p value to erroneously reject CSR given the values of K. It relies on the exact variance of K calculated with edge-effect corrections. It only works in a rectangular window. The following example tests a 1. 5-ha subset of paracou16 (100 m by 150 m, origin at the South Western corner). It rejects CSR (p = 0. 0027). R> data("paracou16", package = "dbmss") R> RectWindow <-owin(c(300, 400), c(0, 150)) R> X <-paracou16[RectWindow] R> plot(X) R> Ktest(X, seq(5, 50, 5)) [1] 0. 002682576.
Conclusion We built this package to provide an easy-to-use toolbox for users of spatial statistics mainly in economic geography and ecology. We wrapped up some spatstat functions to allow using them similarly to our original functions to build a rather complete set of tools, including topographic, absolute and relative functions. The analysis is limited to testing a point pattern against an appropriate null hypothesis, according to the framework developed in the economic literature  (Combes et al. 2008)  but we believe dbmss is a useful extension of spatstat for researchers who are motivated by empirical results more than by the tools themselves, regardless of their scientific field. Full features for point pattern analysis can be found in spatstat for those who wants to go further, including the simulation of many point processes as alternate null hypotheses and model fitting beyond exploratory statistics.
COMPUTING TIME Experimental results presented below are obtained with an NVidia Quadro 4000 and an Intel Xeon QuadCore 2. 8Ghz on a 512x512x512 -32 bits data volume (see Fig. 7 ). We compared the computation times of optimized versions of the separable flowing bilateral filters with Tukey's biweight functions kernels implemented on GPU and CPU multi-threads. CPU code is implemented using the openMP API. In addition to pre-compute filter's spatial coefficients, the inner loop is parallelized on all cores for each of the passes of the filter, achieving in our case a quasi linear efficiency compared to a single core implementation. Fig. 6  illustrates the computation times for the different implementations described in the previous section. Firstly, it may be observed that the GPU implementations of the separable bilateral filter (cf.
The impact of incomplete taxonomic sampling No matter how complex our models of genome evolution, our inferences depend on the sampling of our data set (Fig. 1 ). Although progress in sequencing methods is moving at a fast pace, and genome sequences keep accumulating in databases, we will always be missing a clade or species that will prevent our data sets from being complete. It is unclear how such missing data impacts our inferences. Fig. 1  shows that missing species can lead to incorrectly interpret transfer events as duplication events, both for gene content and gene tree-aware approaches, but the magnitude of this effect is unknown. Worse, if our sampling of a clade misses a group of species with idiosyncratic characteristics (e. g. larger genomes, larger rates of gene transfer. ), then our estimate of the parameters of genome evolution for this group will be biased.
Comparing gene tree-aware and unaware approaches Although reconstructing genome evolution is a widely pursued endeavour, there have been few assessments of the inference methods used to reconstruct gene histories along the species tree. In this article we compare gene-content approaches with gene tree-aware approaches by using publicly-available software on two well-known clades in the tree of life. We use a state-of-the-art probabilistic genecontent approach, Count  [7, 28] , and a probabilistic gene tree-aware approach, ALEml undated (available at https://github. com/ssolo/ALE), adapted to handle undated species trees. We address the impact of incomplete taxonomic sampling by performing rarefaction studies, whereby species are pruned from our species trees and DTL rates are compared across samples.
Genome evolution in Fungi Fungi are characterized by two life forms: one, yeast-like, is unicellular. The other is multicellular and includes fungi with macroscopic fruiting bodies as well as filamentous fungi. In this study, we focus on the clade Dikarya, a subkingdom of fungi that account for roughly 98% of described species. This clade is composed of two well-characterized phyla, Basidiomycota and Ascomycota. We use the genome sequences included in the HOGENOM database  [30]. These two clades display a wide variety in genome sizes (from 5,200 to 10,000 protein coding genes, approximately), and have a phylogeny that can be unambiguously rooted between Basidiomycota and Ascomycota. Studies of genome evolution in these clades have focused for instance on the impact of whole genome duplications  [31] , on the evolution of the yeast (unicellular) form  [32] , or on the evolution of pathways for the decomposition of plant material  [33, 34].
Fungi First, we selected all the species belonging to Fungi in the HOGENOM6 database  [30] , yielding 32 species. We retrieved the protein sequences clustered into homologous gene families (21701 families, discarding the very large families HOG100000000, HOG200000000 or HOG300000000, for which no alignment is available in the database). We discarded 8662 families containing only 2 or 1 genes from Fungi. Gene trees were constructed for 1791 families containing only three genes (triplets), for which a single topology is possible. We aligned all families with 4 sequences or more using MUSCLE  [42]  with default parameters and selected reliably aligned sites using GBLOCKS  [43]. The parameters employed were "minimum number of sequences for a conserved position" b1 = 50, "minimum number of sequences for a flank position" b2 = 50 and "allowed gap positions" b5 = a (all).
To estimate computing time per family, we measured the time PhyloBayes took  [21]  to compute 10 trees based on each alignment. We discarded the decile of the slowest families. For each remaining alignment we ran 2 chains using Phy-loBayes, calculating 5500 gene trees (discarding the first 500 as burn-in), using the LG model of evolution  [44]. In the end we were able to compute at least one chain for 9596 gene families. Combined with the 1791 triplets, our data set contains in total 11387 gene families, totaling 135346 genes, while 24327 genes were discarded during our selection process (not counting the three HOGENOM families without alignments). Given that the tree of Fungi is still unresolved with Microsporidia branching in an undefined place, we decided to use a smaller dataset, comprising the clade of Dikarya (28 species).
Cyanobacteria We selected all the species belonging to Cyanobacteria in the HOGENOM6 database  [30] , yielding 40 species. We reconstructed an unrooted species phylogeny using PhyML  [17]  using the LG model of evolution  [44]  and a gamma distribution to account for rate variation  [45]  from a concatenate of 470 near universal single-copy gene family alignments (38 or more species represented out of 40). In total the alignment contained 126 180 amino-acid sites including 67 646 without missing data. The resulting tree agreed with our genome-scale reconstruction  [10]  and other previous phylogenomic results (see discussion in  [10] , available in the supplementary material). We rooted the species tree according to  [10]. For 7415 gene families with 3 or more genes we employed the alignment procedure and sampling procedure described above.
Analyses Highways were identified between pairs of species that exchange large numbers of genes. The number of genes exchanged was averaged over 100 reconciliations drawn from ALEml undatedusing the program ALEsample, and summed across all gene families in our datasets. Synteny information was extracted from gene positions in the genomes. Pairwise comparisons of genomes between species were performed. Synteny was found to be conserved if a gene had as a neighbor a gene whose ortholog was also its own ortholog's neighbor. For simplicity, only gene families with one gene per species were considered in the synteny analyses. For a given pairwise comparison, a gene was declared as non-transferred if, along the path between two species, no transfer event had affected the gene of each species, and declared as transferred otherwise.