dataesr/scientific-paragraphs-categorization huggingface dataset:
By contrast, SOX17 binding regions appears slightly negatively correlated with RAR/RXR binding data set. As observed previously, POU5F1 binding sites overlap with a large fraction of RAR/RXR binding regions and the distance distribution in these regions shows that its binding sites are indeed closely associated with RAR/RXR binding. By contrast, SOX17 co-localizes with RAR/RXR in few instances and exhibits a slightly more uniform distribution suggesting that its binding occurs in a subset of RAR/RXR binding regions and that binding motifs are likely more distant. The absolute intersection between RAR/RXR regions and the positively correlated TFs (Figure  4B -D and Supplementary Figure  S7  panel A) shows a large number (typically >1000) of co-occupied regions. The fraction of co-occupied RAR/RXR regions should however be interpreted with caution, as it is strongly biased by each TF detection sensitivity.
RAR/RXR mediated transcriptional regulation in differentiating F9 EC cells RA exerts its biological effect primarily through gene transcription modulation  (67). Transcriptomic analyses were therefore performed in various biological systems using microarray-based approaches and several data sets are already available in public databases  (68). To obtain a more detailed and accurate transcriptomic profile of RA-treated F9 cells, we implemented a time course analysis of gene expression 0, 6, 12, 24 and 48 h after RA stimulation using high-throughput RNA sequencing (RNA-seq). We could unambiguously detect 23598 of the 36381 annotated loci in Ensembl mouse genome (v63, jun 2011), estimating at 65% the percentage of expressed genes at any time of the differentiation process. To identify the genes that are significantly modulated during RA-induced PrE differentiation process, we performed a differential gene expression analysis between vehicle treated cells and RA-treated cells using DESeq  (45).
CONCLUSION The data we collected in this work provide a picture of unprecedented detail of RAR/RXR location throughout primitive endoderm differentiation. Our data confirm the previously observed ligand-induced RAR subunit occupancy increase but highlight the occupancy of RAR/RXR binding sites primarily by non-RARs/RXRs dimers. Our study also demonstrates the redistribution of RAR/RXR binding sites during the RA-induced PrE differentiation process. This observation enables us to propose a coherent scenario that explains the transition between undifferentiated cell types (where RAR/RXR binds predominantly to DR0) and differentiated cell types, where RAR is known to bind to canonical (DR1, DR2, DR5) elements. Altogether, our data show that the selection of RAR/RXR binding sites and the control of their occupancy level integrate several mechanisms and are associated with the expression and binding of master transcription factors that define cell identity.
FUNDING P. V. was supported by a fellowship of the  Fondation pour la Recherche Médicale. Agence Nationale pour la Recherche  [ ANR-09-SYSC-009-02  to G. B. and F. A. B. ,  ANR-2011-BSV8-023-02  to G. B. ];  Institut National du Cancer  [ PLBIO08-083  to G. B. ). Funding for open access charge:  Centre National de la Recherche Scientifique.
ACKNOWLEDGEMENT The authors would like to thank B. Jost, C. Keime and the members of the IGBMC (Strasbourg, France) sequencing platform for ChIP sequencing library preparation and Solexa sequencing; J. Lachuer, F. Barbet and the members of the ProfileXpert sequencing platform (Lyon, France) for RNA-seq libraries sequencing and J. Moore (Fluidigm Corporation, South San Francisco, California 94080, USA) for technical help with DE genes RT-qPCR validation. We also would like to thank I. Masse for establishing RAR and RXR ChIP protocol and M. Paris, B. Boussau, O. Gandrillon, S. Gonin-Giraud, F. Flamant for critical reading of the manuscript and fruitful discussions. This work was performed using the computing facilities of the CC LBBE/PRABI. Conflict of interest statement. None declared.
We exploit the methodological similarity between ancestral genome reconstruction and extant genome scaffolding. We present a method, called ARt-DeCo that constructs neighborhood relationships between genes or contigs, in both ancestral and extant genomes, in a phylogenetic context. It is able to handle dozens of complete genomes, including genes with complex histories, by using gene phylogenies reconciled with a species tree, that is, annotated with speciation, duplication and loss events. Reconstructed ancestral or extant synteny comes with a support computed from an exhaustive exploration of the solution space. We compare our method with a previously published one that follows the same goal on a small number of genomes with universal unicopy genes. Then we test it on the whole Ensembl database, by proposing partial ancestral genome structures, as well as a more complete scaffolding for many partially assembled genomes on 69 eukaryote species.
We implemented ARt-DeCo and tested it on several data sets. First we reproduced the experiment of  [29]  on 7 tetrapod genomes limited to universal unicopy genes, with comparable accuracy. Then we used all genes from 69 eukaryotic genomes from the Ensembl database  [30]. The program runs in about 18 h and is able to propose ancestral genome structures and thousands of extant scaffolding linkages. We examine in details one of them, chosen randomly on the panda genome, and show why it seems reasonable to propose it as an actual scaffolding adjacency. Then on a reduced data set of 39 whole mammalian genomes, we tested the precision and sensitivity of the scaffolding performed by ARt-DeCo by simulating artificial fragmentation of the human or horse genomes, removing up to 75% of the known gene neighborhoods of these well assembled genomes, and comparing the removed adjacencies with the ones proposed by ARt-DeCo.
Input The input of the method is • A species tree with all considered genomes and their descent pattern; We suppose the number of chromosomes of each extant genome is known. • A set of genes for all considered genomes, clustered into homologous families; for each family a rooted gene tree depicts the descent pattern of the genes. • A set of adjacencies, i. e. , pairwise relations between neighboring genes AB on extant chromosomes. Genes A and B are called the extremities of the adjacency AB. We consider as neighbors two genes that are not separated by another gene present in the dataset, but a relaxed definition can be used with no impact on the method itself. Internal nodes of the species tree are labeled with ancestral species (we always consider ancestral species at the moment of a speciation) and leaves are labeled with extant species.
Gene trees are reconciled with the species tree: all ancestral genes are labeled by the ancestral species they belong to, so the input yields a gene content for all ancestral species. Genes and species are partially ordered by the descent relation, so we may speak of a last, or lowest, or most recent common ancestor. Here, as in  [12] , we use a reconciliation minimizing the number of duplications and losses of genes. A module of ARt-DeCo is able to produce a suitable input from the raw Ensembl Compara  [30]  gene tree files and a species tree if needed. Once the input is given, two preliminaries are necessary: partitioning extant adjacencies and computing an a priori adjacency probability for each extant species. They are detailed in the two following subsections.
This allows to associate to a feature of a solution (here an ancestral adjacency) a support defined as the ratio between the sum of the probabilities of the solutions that contain this feature and the sum of the probabilities of all solutions. This approach has been implemented in the DeClone software  [31]. We integrated this possibility to ARt-DeCo and thus associate a support to both extant and ancestral adjacencies. Computations were run with a value of the kT constant equal to 0. 1 to ensure that the Boltzmann distribution is highly dominated by optimal and slightly sub-optimal solutions. This value was chosen after preliminary tests on a subset of instances that showed that scenarios sampled with this value of kT were in very large majority optimal scenarios.
Results We tested ARt-DeCo on three data sets. The first one is composed of 7 tetrapod species with only universal unicopy genes, and aims at comparing our method with the method of Aganezov et al. [29] ; on this data set, we obtain comparable results. Then we ran ARt-DeCo on the complete Ensembl Compara  [30]  database, including 69 eukaryotic species and 20,279 gene families with arbitrary numbers of duplications and losses. This shows that ⎡ ⎢ ⎢ ⎢ ⎢ ⎢ ⎢ ⎢ 1 -p(S) p(S) 1 Br ⎤ ⎥ ⎥ ⎥ ⎥ ⎥ ⎥ ⎥ , where Br = 1. As we can see on the graph, there is a phase change at 1. 0, meaning that from this value a good number of adjacencies can be proposed. Increasing the multiplicative factor does not qualitatively change the results. This experiment was repeated for different numbers of simulated fissions (see Additional file 2) and different species trees, and in all cases results exhibited the same profile.
eukaryotes -a proof of scaling up We ran ARt-DeCo on the full Ensembl Compara database (1,222,543 protein coding extant genes in 69 extant species) in about 18 h. The input contains 1,023,492 adjacencies in the extant genomes, showing that many genomes assemblies are highly fragmented, from 11 chromosomes for the perfectly assembled opossum genome to 12,704 contigs for the wallaby genome, an order of magnitude comparable to the number of genes. In Figure  3 , the black bars show the proportion of genes with 0, 1 or 2 syntenic neighbors in the extant input genomes. Around 30% of genes have at most one neighbor, while we would expect less than 1% for perfectly assembled genomes. ARt-DeCo predicts 36,445 new extant adjacencies. As shown in Figure  3  (red bars) there is a significant increase in extant genes with 2 syntenic neighbors, as in a bona fide scaffolding, at the expense of a very small number of genes with more than two neighbors, corresponding to syntenic conflicts.
The adjacency we chose is randomly taken from the predicted ones between a contig and a chromosome in the panda genome (Ailuropoda melanoleuca). For this adjacency between genes RCSD1 and CREG1, we analyze gene neighborhoods around homologous adjacencies in others species. On Figure  7 , we represent the species tree with information on evolutionary events that occurred along species tree (adjacency loss, duplication and gain, and gene loss and duplication) and adjacency status with color code on species name (red for species without RCSD1-CREG1 adjacency, blue for species with RCSD1-CREG1 adjacency in Ensembl database and green for species for which ARt-DeCo infers an adjacency between RCSD1 and CREG1 while not present in Ensembl). To illustrate the validity of new adjacencies inferred by ARt-DeCo we analyze the gene neighborhood around RCSD1-CREG1 adjacency.
mammals -validity We switched to a smaller dataset to measure the validity of the method, because the computing time don't allow too many replicates in the entire database. We selected all protein coding gene families from the 39 eutherian mammal genomes stored in the Ensembl database  [30]. ARt-DeCo proposes 1,056,418 ancestral adjacencies and 22,675 new adjacencies in extant genomes. A proportion of 95% of these adjacencies have a >0. 9 support, meaning that they are present in over 90% of parsimonious solutions, computed as described in  [31]  for a kT value equal to 0. 1 (chosen to ensure that the probability distribution over the solution space is highly dominated by optimal solutions). Figure  8A  shows the shape of extant genomes through the number or cumulative support of adjacencies incident to one gene.
This was already remarked in  [12] , and is likely due to errors in gene trees in the Ensembl database  [35, 36]. Additional computations show that ancestral species indeed contain a larger proportion of nonlinear contigs: only 91. 2% contigs are linear for those species, among which contigs hosting only one gene are more represented than in extant genomes. Thus, a small part of the inferred adjacencies are incorrect, leading to some artificially treelike or cyclic contigs. The bars with supports are more dispersed, as expected, because they take their values from non integer numbers. It puts the conflicts into perspective: when a gene has more than two neighbors, usually one adjacency is less supported. We also performed experiments with artificial adjacency breaks as in the 7 tetrapods experiment.
Discussion and Conclusion Ancestral gene order reconstruction, when ancestral genes are given, can be seen as a scaffolding problem. Indeed ancestral genes may be seen as contigs, and finding an order between contigs is a similar problem in both extant and ancestral genomes. If this similarity had already been remarked and exploited in some way  [24, 26, 37] , a fully integrated approach has only recently been achieved by Aganezov et al. [29] , with a method which was limited to universal unicopy genes and a small number of genomes. Extending DeCo  [12] , a software aimed at reconstructing ancestral genomes and scaling up to dozens of genomes with possibly complex histories, Figure  7  Evolutionary history of the adjacency between RCSD1 (turquoise) and CREG1 (light green). ARt-DeCo infers the creation of this adjacency at the root of Amniots.
We integrated the different evolutionary events concerning the RCSD1-CREG1 adjacency along the species tree. Empty red crosses represents an adjacency losses (i. e. , cases where both adjacent genes are lost at the same time); each full red cross represents a gene loss (only one of the genes is lost); each empty green square indicates an adjacency duplication (places where the two adjacent genes are duplicated together); a full green square indicates a gene duplication and an orange triangle represents an adjacency gain. Color code for species name gives information on adjacency status. In red species, RCSD1 and CREG1 genes are not adjacent, while blue species host the RCSD1-CREG1 adjacency as described by Ensembl, and green species have the RCSD1-CREG1 adjacency inferred by ARt-DeCo (though it is absent from Ensembl).
Acknowledgements This work and its publication is funded by the  Agence Nationale pour la Recherche ,  Ancestrome  project  ANR-10-BINF-01-01. ARt-DeCo analyses benefited from the  Montpellier Bioinformatics Biodiversity platform services. We thank  Yann Ponty  for technical help for patching DeClone to integrate the scaffolding and the exploration of the solution space. This article has been published as part of  BMC Genomics Volume 16 Supplement 10 , 2015: Proceedings of the 13th Annual Research in  Computational Molecular Biology (RECOMB) Satellite Workshop on Comparative Genomics: Genomics. The full contents of the supplement are available online at http://www. biomedcentral. com/bmcgenomics/supplements/ 16/S10.
With the white beam provided at a synchrotron source, a reasonably large amount of data can be collected in a single shot. The acquisition time is fast, and only a few patterns recorded on stationary randomly oriented crystallites are necessary to get a full dataset  (Cornaby et al. , 2010; Dejoie, McCusker, Baerlocher, Kunz & Tamura, 2013). This makes the method attractive for the structural characterization of beam sensitive crystals  (Cornaby et al. , 2010)  and for in situ  (Perry et al. , 2014)  or time-resolved studies  (Yorke et al. , 2014). One of the drawbacks of the method is the complexity of the data analysis, caused by the energy dependence of correction factors, and the overlap of harmonic reflections  (Helliwell et al. , 1989). Furthermore, even though more data can be collected in a single shot without rotating the crystal, the duration of the exposure to a white synchrotron beam is sometimes still long enough for radiation damage to occur.
Another way of collecting microcrystal diffraction data is by using a suitably equipped electron microscope. This allows extremely small crystallites (~100 nm on an edge) to be examined, because electrons interact with matter much more strongly than do X-rays. Unfortunately, precisely because of this strong interaction, the electron diffraction intensities can be severely distorted by the effects of dynamical and multiple scattering, and this complicates their use in structure analysis. This problem has been reduced with the introduction of the precession electron diffraction technique  (Vincent & Midgley, 1994; Midgley & Eggeman, 2015) , and three-dimensional electron diffraction datasets can now be collected, using either the method of automated diffraction tomography  (Kolb et al. , 2007)  or that of rotation electron diffraction  (Zhang et al.
, 2013; White et al. , 2013). The broader range of energies puts more reflections in diffracting condition and simultaneously allows the full width of most reflections to be measured, making integration easier. This provokes the question as to whether or not the SFC approach using a broad bandpass beam could be applied to materials with smaller unit cells (up to ca 25000 A ˚3). Computer simulations indicate that with such a setup, a full dataset can be obtained with just a few hundred crystals, instead of tens or hundreds of thousands  (Dejoie, McCusker, Baerlocher, Abela et al. , 2013). Here we describe the application of this approach to experimental data collected on three inorganic materials at a synchrotron source under conditions mimicking the SwissFEL beam. We have developed new algorithms to index the experimentally measured single-shot patterns of randomly oriented crystals of such materials, and show that the measured intensities are accurate and suitable for structure analysis.
e. 360 different frames or crystal orientations). In order to simulate the case of crystals with platelike morphology which would probably display a preferential orientation on a flat surface, a second series of measurements was carried out by rotating around a single face of one of the crystals ( scan). A conventional monochromatic ' scan (0. 25 steps) was also performed on the same test crystals to obtain reference datasets. A two-dimensional Dectris Pilatus 2M detector was positioned at a distance of 144 mm from the sample for the ' scans, and at 224 mm for the scans. Geometry calibration (sample-detector distance, normal incidence position of the detector, tilt angle of the detector) was carried out with the XMAS program  (Tamura, 2014) , using an LaB 6 reference powder pattern. Three test samples were used: the zeolite ZSM-5  (Olson et al.
In order to estimate an appropriate starting value, the brute force algorithm can be run with large angle increments (17 , 0. 3 rad) to probe a representative distribution of approximately 1500 orientations. The mean and standard deviation (sd) of all corresponding values of nfit are calculated. In this way, the approximate number of reflections that can be indexed with incorrect orientations can be estimated for a particular frame (i. e. its 'noise' level). A meaningful formula for nmin1 was found empirically to be mean + 3 Â sd. By applying this formula, the indexing algorithm can adjust to arbitrary variations in the data automatically, and this improves the effectiveness of the routine considerably. The mono1 and mono2 algorithms are implemented in Python2. 7, using the pandas, numpy and scipy libraries.
Indexing results The three algorithms were evaluated using data collected on three test samples. Indexing results are reported in Table  2. For ZSM-5, with the largest unit cell, all algorithms work well, but the monochromatic approaches are faster. The a and b axes of ZSM-5 differ by only 0. 5%, but all three algorithms could assign the axes correctly in almost all cases, despite the fact that there is a 4% range of energies. The unit cell of Cs 2 [Pt(CN) 4 ]ÁH 2 O is much smaller, so the number of reflections per frame decreased. This made very little difference to the laue approach, except to make it faster, but did affect the mono1 algorithm, where only 205 of the 360 frames could be indexed. Of these, however, 99% were correct. For sanidine, with the smallest unit cell, only 29% of the frames could be indexed using the mono1 method, but all of these were indexed correctly.
It is perhaps important to note that fine tuning is possible in all algorithms, but we were interested in having a fully automated robust procedure that would be able to process a large amount of data without intervention. The main point is that reflections on most of the single snapshot images can indeed be indexed, despite the differences in unit cell, symmetry and number of crystals, and this means that a useful dataset can be extracted reliably for different materials. We also tested the monochromatic based algorithms on pure monochromatic data (' scan) collected on all 3 samples. Although the algorithms were optimized to deal with a 4% bandpass, they still performed satisfactorily in indexing these data. We therefore reason that single frames collected with any bandpass between 0 and 5% can be indexed successfully.
To investigate the effect of flat or needle-shaped crystals dispersed on a grid, 4% bandpass data were collected on a single-crystal of ZSM-5 in a -scan mode (rotation around one face of the crystal), and results were compared with data collected with a more conventional ' scan. The geometry of the experimental setup ( -scan mode) only allowed a 90 rotation. Nonetheless, the effect on the completeness of the data (number of unique reflections measured versus total number expected), calculated in the 1. 0-5. 0 A ˚resolution range (where it is maximal), was not as dramatic as might be expected. The values of 54% and 60% were obtained for the -scan and '-scan datasets, respectively. For a real SFC experiment, of course, a sample mount that reduces preferred orientation of the crystals should be used.
Structure solution and refinement To test the data analysis procedures beyond the indexing step and to evaluate the quality of the measured data, reflection intensities for the indexed patterns were integrated using the seed-skewness method  (Bolotovsky et al. , 1995). The patterns indexed with the laue approach were used for this purpose. Reflection intensities were corrected for various factors using techniques similar to those used for Laue microdiffraction data  (Dejoie et al. , 2011). In particular, each frame was corrected for the decay of the intensity of the incident flux with time, and for the variation of the incident flux with energy. All frames with less than 20 indexed reflections for ZSM-5 and 6 indexed reflections for sanidine and Cs 2 [Pt(CN) 4 ]ÁH 2 O were discarded. Six incorrectly indexed patterns remained in the final dataset for ZSM-5, as a result of the a and b axes being reversed.
On the other hand, all frames for sanidine and Cs 2 [Pt(CN) 4 ]ÁH 2 O in the final dataset were correctly indexed. As the volume of the crystal was constant in this experiment, no scaling between the individual frames was applied. The situation will be different at SwissFEL, where each crystal can have a different volume and where the variation of the incident flux from shot to shot will be significant. These problems will be addressed in a future publication. In order to identify the fully measured reflections in each dataset, the wavelength associated with each reflection (determined during the indexing process) was examined. Those lying close to the wavelength boundaries are most likely to be partially measured  (Dejoie, McCusker, Baerlocher, Abela et al. , 2013). A simple schematic drawing is shown in Fig.