dataesr/scientific-paragraphs-categorization huggingface dataset:
From transcriptome data to CRMs prediction and epigenetic context characterisation The goal of our study was to investigate the mechanism of zygotic genome activation. In order to do so, we (i) re-analysed published datasets to carefully define a list of ZGA-related genes, (ii) applied motif discovery approaches to uncover potential regulators of this process, and (iii) combined in-vivo datasets for various epigenetics factors to understand the interplay between the different regulators of the ZGA. In particular, using published transcriptome data, we proposed a novel method to cluster gene expression profiles in time-course experiments, which does not require any parameter in order to define co-expression clusters. Functional analysis (expression profiles, non-coding sequence analyses, functional classes enrichment) of the different clusters allowed us to delineate a comprehensive and coherent cluster of genes activated during ZGA.
The motifs discovered in the corresponding genes led us to propose several factors and co-factors potentially acting in trans, along with putative cis-regulatory modules. Analyses of specific associations of predicted CRMs and epigenetic marks led us to propose a model combining different factors (Zelda, TRL, CBP and other unknown factors), which presumably bind accessible and active chromatin regions. In particular, we highlighted to importance of a DNA-motif, AGATACA, which is not yet characterized, but might correspond to a structurally important element or a DNA-binding motif. From our results, we ranked the predicted CRMs combining TRL, CBP, DNAse1 accessibility, and H3K4me1 data to select the most relevant ones, which can be visualized in their genomic context using the UCSC genome browser.
High-throughput data Transcriptome and ChIP-seq data were retrieved from GEO database (http://www. ncbi. nlm. nih. gov/geo/). Transcriptome data: GEO Reference Series IDs GSE3955  [3] , GSE14287  [6]. ChIP-seq data: Zelda dataset (GSE30757), other datasets belong to the GSE23537 super series from ModENCODE project  [30]. DNAse1 accessibility data were retrieved from Berkeley Drosophila transcription Network Project (http://bdtnp. lbl. gov/Fly-Net/browseAccess. jsp). As the reads from these experiments were mapped on the dm2 assembly, genomic coordinates were converted from dm2 to dm3 assembly with liftOver (http://genome. ucsc. edu/cgi-bin/hgLiftOver).
Discrete transition profiles The analysis of microarray data was done with the R statistical package version 2. 15. 0  [37]  and Bioconductor libraries version 1. 4. 7  [38]. The original datasets extracted from GEO were normalized with the RMA method  [39]. RMA-normalized intensities were then converted into discrete transition profiles. We denote as T i,j the median value of the 3 replicates for gene i at time point j. The transition value X i,j of gene i from time point j -1 to time point j is computed as follows (Figure  2A ). X i,j = log 2 ( T i,j T i,j-1 ) (1) Transition values are converted to Z-scores using robust estimators to avoid the effects of outliers. Z i,j = X i,j -mj ŝj = X i,j -mj (IQR/1. 349) j ( 2 ) where m is the median, ŝj is the estimated standard deviation that corresponds to the observed inter-quantile range (IQR) of the transition values to time point j, standardized by the IQR of a standard normal distribution (mean = 0, s = 1) which equals 1.
Sequence retrieval We used the tool retrieve-ensembl-seq  [42]  to retrieve non-coding sequences associated to each Drosophila melanogaster gene (upstream, 5'UTR, 3'UTR, first intron). Upstream non-coding sequences were extracted up to the closest neighbor gene, with a maximal length of 5 kb. We activated the options to mask coding sequences and repeats, as well as options to retrieve non-coding sequences for all alternative transcripts and to merge overlapping ones.
Motif discovery To automatize motif discovery on the various non-coding sequence types for the different clusters defined during this study, we used the script gene-cluster-motifs, a task manager available in the stand-alone version of RSAT. Among the different motif discovery algorithms supported by this task manager, we ran oligo-analysis  [18]  and dyad-analysis  [43]. These algorithms are based on words and dyads counting respectively. The number of occurrences of each word (dyad) is compared to the expected frequencies observed in a reference sequence set. Specific background models were built for each sequence type (upstream, first intron, 5'UTR, 3'UTR) by computing oligonucleotide and dyad frequencies in the whole set of genomics sequences of the same type. Significance of over-representation is estimated using binomial distribution by computing a nominal p-value.
Over-represented words (oligos) and spaced word pairs (dyads) were assembled and converted to positionspecific scoring matrices with the tool matrix-frompatterns (RSAT). An important advantage of word-based approaches is their scalability: the computing time increases linearly with sequence size, in contast with machine-learning approaches such as MEME or Gibbs motif sampler, whose complexity is quadratic or worse (see  [27]  for a quantitative evaluation of time efficiency). Finally, discovered motifs were compared to motif databases (JASPAR: http://jaspar. genereg. net/ [44] , Fly-FactorSurvey: http://pgfe. umassmed. edu/TFDBS/ [45] ) with compare-matrices (RSAT).
Peak-motifs Peaks from genome-wise location studies were analysed with peak-motifs  [27, 28]  (RSATools). We ran all motif discovery algorithms available in the web site (oligo-, position-, local-word-and dyadanalysis). We searched for over-represented 6-and 7-mers(oligo-, position-, local-word-analysis) and for pairs or trinucleotides spaced by 0 to 20 nucleotides (dyad-analysis). Background was computed from input sequences using a markov model of k -2 with k representing the oligomer length (oligo-, dyad-analysis). We selected JASPAR Core Insects, DMMPMM and iDMMPMM motif databases for comparison of discovered motifs with known binding motifs.
Correlation between marks Following a previous publication  [32] , we used a complete partition of the Drosophila non-coding genome representing about 136 K regions, and scored these regions with the marks of interest (CBP, Trl, H3K4me1 and DNAse1 HS sites). All 136 K regions were ranked according to these four features. Next, we extracted the subset of regions overlapping the ZGA CRERs, and computed the Spearman correlation between the ranks of these regions for all pairs of features over 1000 subsamples of 80% of the regions. For sake of comparison, we have extracted the CRMs annotated with the terms Blastoderm (226 regions) or Central nervous system (397 regions) from the Redfly database and performed the same analysis. The barplots on Figure  6B  show the mean correlations over the 1000 subsamplings and the error bars indicate the standard deviations.
Acknowledgements ED salary was funded by the  Agence Nationale de la Recherche (ANR) , through the project  NeMo  ( ANR-07-BLAN-061 ). CH is supported by the  ANR Young Researchers  Grant "CardiHox". The TAGC laboratory acknowledge support from the  EU ERA-NET Plus  scheme in FP7, through the  ERASysBio+  project  ModHeart. The BiGRe and TAGC laboratories acknowledge support from the  EU -funded  COST action  [ BM1006  " Next Generation Sequencing Data Analysis Network "]. Collaboration between DT and JvH was initially supported by the  Belgian Federal Science Policy Office (Interuniversity Attraction Poles , project  P6/25 -BioMaGNet ) and further stimulated by a sabbatical year spent by JvH at the  TAGC lab , and by a two-months  Invited Professorship of JvH at ENS. We warmly thank  Nathalie Dostatni  and  Morgane Thomas-Chollier  for critical reading and thoughtful suggestions.
Identification of orthologous genes A consolidated list of known and candidate human orthologues of yeast chromosome stability genes (with demonstrated roles in sister chromatid cohesion) was identified through standard crossspecies approaches. Briefly, InParanoid 7 and HomoloGene databases were queried to identify known orthologues, while BLASTp was employed to identify the top-hit candidates (based on E-value) from the non-redundant protein sequences within the Homo sapiens database.
Nucleotide sequencing PCR products were subjected to bidirectional Sanger sequencing using M13 primers and the BigDye Terminator Version 3. 1 Cycle Sequencing Kit (Applied Biosystems). Sequencing reactions were run on ABI 3730xl DNA Analyzers (Applied Biosystems). Sequence trace quality was assessed with the base-calling program, Phred  [48] ,  [49]. All traces were included in the subsequent analysis, since deletion-insertion polymorphisms can mimic poor quality data from a Phred-quality measure, but may contain valid sequence data. All sequences for a given primer pair were assembled using Consed  [50] ; overlapping amplimers were assembled separately to allow independent cross-validation of calls in overlapping regions. Sequence variants, including singlenucleotide differences and short (,100 base pair) insertions and deletions, were identified using PolyPhred v6.
ESCO1, which encodes a lysine acetyltransferase that is essential for the establishment of sister chromatid cohesion in mammalian cells, was somatically mutated in 2. 2% (1 of 45) of serous ECs, 10% (2 of 20) of clear cell ECs, and 2. 4% (1 of 42) of endometrioid ECs. Two of the ESCO1 mutations are predicted to impact protein function. The ESCO1 R786C missense mutant, within the acetyltransferase domain, is predicted to impact protein function by both the SIFT and Polyphen algorithms (Table  2 ). We speculate that the ESCO1 E338X nonsense mutant, which we uncovered in a serous-EC, may be a loss-of function mutant since a protein produced by this allele would be prematurely truncated and fail to include the acetyltransferase domain. Alternatively, nonsensemediated decay of the ESCO1 E338X transcript might lead to haploinsufficiency.
Case no T3 is also known as OM-1323. n/a Not applicable. 1 Transcript accession numbers: ESCO1 (NM_052911. 1), CHTF18 (NM_022092. 1), MRE11A (NM_005591). " Protein accession numbers: ESCO1 (NP_443143. 2), CHTF18 (NP_071375. 1), MRE11A (NP_005582. 1). doi:10. 1371/journal. pone. 0063313. t002.
{ Case no T3 is also known as OM-1323. n/a Not applicable. 1 Transcript accession numbers: ESCO1 (NM_052911. 1), CHTF18 (NM_022092. 1), MRE11A (NM_005591). " Protein accession numbers: ESCO1 (NP_443143. 2), CHTF18 (NP_071375. 1), MRE11A (NP_005582. 1). doi:10. 1371/journal. pone. 0063313. t002.
Geometry The direction r9 of the realized foraging trail was assessed every morning by taking a digital picture. Only data from the last three days of each trial were analyzed, assuming that trails required a few days to form a path close to the optimum. When a trail was diffuse, i. e. the ants were scattered around a straight line, we measured the two outermost edges and calculated the average. In two cases the trails were overly diffuse and we could only reliably use data from a single day, in both cases the last day. The angle a (denoting the shortest path) deviated slightly from 30u and 60u both within and among trials, because of minor variation in food position due to handling. The angle measurement was also subject to small optical distortions in the digital pictures. Thus, we daily measured the colony-specific angle a from those pictures the same way we measured r9 and calculated the mean angle of a over the course of each trial (mean 6 SD for n = 9 trials: a 30 = 28.
The thickness of the optical section is ∼1. 6 μm with a 20X objective and ∼6 μm with a 10X objective. GFP labeled neurons were pseudo-colored green and other markers immunoreactive neurons were pseudo-colored magenta, red or blue. From the overlap of green and magenta, double-labeled neurons appeared white. The analysis was performed by counting nuclear GFP fluorescence (for assessment of D2R-positive cells) and Cy3-positive cells. Quantification of immunoreactive cells was performed using image analyzer software (Image-J). Striatopallidal neurons (D2R-and A2aR-expressing MSNs) and classes of interneurons (ChAT-, parvalbumin-, NPY-and calretinin-expressing interneurons) were quantified throughout the extension of the caudal striatal territory that lacks D2R/A2aR-containing MSNs either in Drd2-EGFP or Adora2a-Cre mice.
IMAGES PROCESSING AND 3D RECONSTRUCTION MODEL All image processing, registration and 3D reconstruction were performed using IMOD package software, a set of open source image processing, modeling and display programs freely distributable developed at the Boulder Laboratory for 3-D Electron Microscopy of Cells  (Kremer et al. , 1996). IMOD package is freely available for Linux, Windows and Mac OS X systems, the different versions can be downloaded at http://bio3d. colorado. edu/ imod/. The procedures to obtain 3D models have been described before  (Mailly et al. , 2010). Briefly, an image stack was built from stained Drd2-EGFP sections acquisitions. Sections were then aligned and on each section, contours of anatomical structures and Drd2-EGFP negative areas were manually delineated. For each delineated structure, an independent 3D object was calculated.
ACKNOWLEDGMENTS This work was supported by  Inserm  and grants from  ATIP-Avenir (Inserm) ,  Sanofi-Aventis R&D  and from the  Agence Nationale de la Recherche  ( ANR-2010-JCJC-1412 ) to Emmanuel Valjent. Research in Jean- Antoine Girault  and  Denis Hervé lab  was supported by grants from the  Fondation pour la recherche médicale (FRM) , the  Agence nationale de la recherche  ( ANR-BLAN08-1_346422 ),  European Union   Framework program  7 ( FP7, SynSys ), and the  European research council (ERC). Alban de Kerchove d'Exaerde  is a Research Associate of the  FRS-FNRS (Belgium)  and is supported by  FRS-FNRS (Belgium) ,  FER  from  ULB ,  Action de Recherche Concertée  from the  CFWB. Julie Espallergues  was a recipient of a postdoctoral fellowship from  UM1. The authors would like to thank  Drs JR  Crittenden We are grateful to  Laurent Fagni  and  Julie Perroy  ( Institut de Génomique Fonctionnelle ) for providing transgenic mice used in this study.
In Sect. 2 we summarize the stellar parameters of HD 110432. In Sect. 3 we present the VLTI/AMBER observations and the data reduction process. A first analysis using an axi-symmetrical geometrical models is presented in Sects. 4 and 5, and a more advanced modeling of the differential data using a kinematic model is discussed in Sect. 6. Finally, we summarize our results in Sect. 7.
2 R. Using the distance derived from van Leeuven (2007) Hipparcos parallaxes, i. e. d = 373 ± 41 pc, we infer a photosphere angular size of 0. 16 mas. The parameters are summarized in Table  1. Using this set of stellar parameters and photometric measurements from the SIMBAD 1  database, we tried to determine the relative flux of the circumstellar environment within the VLTI/AMBER spectral domain, i. e. the H and K bands. To determine the stellar flux in these bands we used Kurucz models reddened using the law of extinction from  Cardelli et al. (1989). However, we found that the influence of the reddening in the near-IR fluxes is negligible compared to the uncertainties on the stellar parameters and distance. Finally we found that the emission in the H and K bands are both dominated by the circumstellar environment with a relative circumstellar flux (F disk ) of 71 ± 7% and 79 ± 5%, respectively.
2009 , for detailed information on the AMBER data reduction). We selected individual exposures with the standard selection criteria  (Millour et al. 2007 ). We rejected the 80% of frames with the lowest signal-to-noise ratio (S/N). For observations in LR mode we also rejected the frames with a piston larger than 10 μm, as well as frames with a flux ratio between the beams higher than three. The interferometric observables (visibility, differential phase, and closure phase) were then averaged and calibrated. For this last step we used scripts described in  Millour et al. (2007)  that are now part of the standard amdlib package. The calibration process includes an estimation of the calibrators' size and their uncertainties from various catalogs, a determination of the transfer functions and their evolution during the whole night, and a computation of the calibrated visibilities and phases.
Extension of HD 110 432 in the continuum Even if the spatial resolution of the VLTI/AMBER instrument is about 4 mas, it is not necessary to fully resolve the target, i. e. to reach the first zero of the visibility function to estimate its size. It is always possible to use a partial resolution of the interferometer to estimate the size of the target with a model that fits the data and thus determines the angular size of targets smaller than 4 mas. See for instance the review of the interferometric observations of rapidly rotating stars by van Belle (2012). Thus, to estimate the disk extension in the near-IR, we model the central star as a uniform disk + an extended elliptical Gaussian distribution. This model then has four free parameters: the full width at half maximum (FWHM) of the major axis of the Gaussian distribution (θ disk ), the relative environment continuum flux (F disk ), the flattening ratio of the major to the minor Gaussian distribution axis ( f ), and the position angle of the major axis of the disk measured eastward from the north (PA).
Since we have VLTI/AMBER data in two different spectral domains, namely the H band between 1. 54-1. 87 μm and the K band within 1. 94-2. 37 μm, we tried to constrain both data sets separately. HD 110 432 is a variable Be star, and the data used to estimate the circumstellar contribution to the total near-IR continuum flux is not contemporaneous to our interferometric measurements. Nevertheless, using the values of F disk obtained in Sect. 2, i. e. 71 ± 7% and 79 ± 5% in the H and K bands respectively, we can infer the disk dimension in the continuum. The best LITpro models and the corresponding extensions are given in Table  3. We obtain a major Gaussian distribution axis of 0. 92 mas, a flattening ratio of 1. 56 in the H band that, assuming a flat disk, corresponds to 50 ± 5 • with a reduced χ 2  r of 4.
Finally, the better agreement of the asymmetric Gaussian disk with the interferometric data is mostly because this model is able to produce stronger asymmetries in the visibility with almost symmetric S-shaped phases. Moreover, this model also reproduces the visibility and phases observed along the polar orientation better. Nevertheless, the quantity and quality of our data are not sufficient to fully constrain the intensity distribution in the emission lines. Regarding the possibility of a companion, we have no evidence of binarity from our interferometric measurements, neither from a modulation of the visibility modulus as a function of time or spatial frequency, nor from a phase modulation of the fringes. The question of an unseen companion orbiting HD 110 432 becomes an issue when studying the origin of its hard X-ray flux, if indeed the X-rays are formed somehow from infall in the form of gas stream or blobs originating in the Be star.
Acknowledgements. This work has benefited from funding from the  French Centre National de la Recherche Scientifique (CNRS)  through the  Institut National des Sciences de l'Univers (INSU)  and its  Programmes Nationaux  (ASHRA, PNPS). This research made use of SIMBAD and VIZIER databases, operated at the  CDS, Strasbourg, France. We thank  Yamina Touhami  for an interesting discussion about the continuum disk size and the referee for his extensive and extremely helpful criticisms of the original manuscript.
DNA extraction and sequencing Total genomic DNA was extracted by the phenolchloroform method  [83]. We applied kDNA depletion methods to minimize the presence of this type of molecule, as previously described  [58] , which result in less than about 5% of remaining kDNA in the sample. After kDNA depletion, about 5 μg of DNA were submitted to each Roche 454 shotgun sequencing run, according to the manufacturer's protocols. Different genomes have so far been sequenced to different levels of draft quality, with estimated coverages of 15X to 23X (considering a genome of ~30 Mbp). Sequences were assembled using the assembler version 2. 3, provided by Roche. Resulting assemblies are available from GenBank, as detailed in "Availability of Supporting Data" below. Endosymbiont genomes were finished to a closed circle as previously described  [25].
Gene discovery and annotation Endosymbiont genes were used as previously published  [25]. In an initial scan of the genome, trypanosomatid genes were discovered and mapped to metabolic pathways using ASGARD  [84] , employing as reference the UniRef100  [85]  and the Kyoto Encyclopedia of Genes and Genomes, KEGG  [86]  databases. The identified segments of DNA were then extracted from the genome and manually curated for completion and proper location of start and stop codons by using the GBrowse genome browser  [87]. Putative sequence functions were confirmed by domain searches against NCBI's Conserved Domain Database  [88]. Genes and annotations from other trypanosomatids were used when needed and as available at KEGG. All trypanosomatid genes characterized in this study have been submitted to NCBI's GenBank and accession numbers are available from Additional file 22.
Phylogenetic analyses For phylogenetic analysis of each enzyme characterized in this work, corresponding putative orthologous genes from all domains of life were collected from the public databases by BLAST search (E-value cutoff of 1e-10, maximum of 10,000 matches accepted) against the full NCBI NR protein database, collecting sequences from as widespread taxonomic groups as possible and keeping one from each species (except for alignments with more than ~1,500 sequences, in which case one organism per genus was kept). Only sequences that were complete and aligned along at least 75% of the length of the query were selected. All analyses were performed at the protein sequence level. Sequences were aligned by Muscle v. 3. 8. 31 [89]. Phylogenetic inferences were performed by the maximum likelihood method, using RAxML v.
7. 2. 8  [90]  and employing the WAG amino acid substitution model  [91] , with four gamma-distributed substitution rate heterogeneity categories and empirically determined residue frequencies (model PROTGAMMAWAGF). Each alignment was submitted to bootstrap analysis with 100 pseudoreplicates. Trees were initially drawn and formatted using TreeGraph2  [92]  and Dendroscope  [93] , with subsequent cosmetic adjustments performed with the Inkscape vector image editor (http://inkscape. org). Phylogenetic conclusions have been displayed as strong in the summary table for phylogenetic results (Additional file 2) if BSV was 80 or greater, and moderate if BSV was between 50 and 80with one exception, EC:2. 1. 1. 37, described in the results. Resulting phylogenetic trees are available from TreeBase (accession number S14564), as detailed in "Availability of Supporting Data" below.
Acknowledgments We would like to thank  Marta Campaner  and  Carmen C. Takata (USP) ,  Vladimir Lee ,  Andrey Matveyev , and  Yingping Wang  ( VCU ) for technical support, and  Carlisle G. Childress Jr. and  J. Michael Davis  ( VCU Center for High-Performance Computing ). Sequencing was performed in the Nucleic Acids Research Facilities, and analyses were performed in the  Bioinformatics Computational Core Lab  and the  Center for High Performance Computing at VCU. The research leading to these results was funded by: the  European Research Council  under the European Community's  Seventh Framework Programme  ( FP7/2007-2013) / ERC  grant agreement n°[247073]10; the  French  project  ANR MIRI BLAN08-1335497 ;  FAPERJ grant  coordinated by  Dr. Cristina Motta  and the  FAPERJ-INRIA  project  RAMPA ; by the  Laboratoire International Associé (LIA) LIRIO co-coordinated by Ana Tereza R.