dataesr/scientific-paragraphs-categorization huggingface dataset:
1g  and Supplementary Data 2, dataset 6). To enable broader evolutionary comparisons, we reconstructed orthologous gene families for multiple vertebrate and non-vertebrate species (Supplementary Data 2, dataset 7), generated several equivalent datasets for zebrafish and medaka (Extended Data Fig. 2a ), and built a dedicated server for synteny comparisons (Extended Data Fig. 1h ). A comprehensive functional annotation of the B. lanceolatum genome identified 88,391 putative cis-regulatory elements of DNA as defined by assay for transposase-accessible chromatin using sequencing (ATAC-seq) (these elements are hereafter referred to as APREs), as well as 20,569 protein-coding genes supported by orthology. We divided the APREs into promoters-around transcription start sites (TSSs), which were highly supported by cap analysis gene-expression sequencing (CAGE-seq) data, Extended Data Fig.
Discussion By applying functional genomics approaches to the cephalochordate amphioxus, we have deepened our understanding of the origin and evolution of chordate genomes. We identified APREs in amphioxus, the activation of which is tightly associated with differential DNA demethylation in adult tissues-a mechanism previously thought to be specific to vertebrates. Additional cases may be subsequently found in other non-vertebrate species when similar multi-omics datasets are analysed. In amphioxus, APREs of this type usually fall within gene bodies of widely expressed genes, which suggests that gene regulation by demethylation could have originated as a mechanism to allow better definition of enhancers in a hyper-methylated intragenic context. If so, this mechanism could have been co-opted into new genomic contexts-that is, distal intergenic enhancers-later in the evolution of vertebrate genomes, which are characterized by their pervasive, genome-wide hypermethylation.
De novo gene models were built using Augustus  38  and subsequently refined with EVM 39 using PASA assemblies and aligned proteins from other species. In parallel, all strand-specific RNA-seq reads were mapped to the genome using Tophat2 40 , assembled using Cufflinks  41  and open reading frames were predicted using Trans-decoder  42. Models obtained using both these approaches were reconciled yielding a total 218,070 transcripts from 90,927 unified loci, of which 20,569 were protein-coding and had homologues in at least one of the other studied species (see 'Comparative genomics'). Gene Ontology (GO) terms were assigned to amphioxus proteins based on their PFAM and Interpro domains, as well as blastp hits against human proteins (1 × 10 -6 ). Repeats were annotated and filtered with RepeatMasker using a custom library generated with RepeatModeller.
Long non-coding RNAs were identified by filtering all transcripts for protein-coding potential using CPAT  43  trained with zebrafish transcripts, and further discarding those that had a positive hit in a HMM search against the NR and PFAM databases (Extended Data Fig. 1g ). Comparative genomics. We used OMA  44  to reconstruct gene families and infer homology relationships based on well-established phylogenetic relationships between species  45  , and further merged families sharing Ensembl paralogues with 'Euteleostomi' or 'Vertebrata' ancestry. To define the set of high-confidence ohnologue families (Supplementary Data 2, dataset 9), we retained families with two to four copies in three out of five vertebrates (excluding teleosts) and subjected them to phylogenetic reconciliation. To assess genome sequence conservation, reciprocal whole-genome alignments of Branchiostoma floridae, Branchiostoma belcheri and B.
lanceolatum were performed using LASTZ and processed with phastCons  46  to produce conservation scores. The distribution of phastCons scores in APREs was determined using 'dynamic' ATAC-seq peaks that showed no temporal discontinuity in activity. Comparative transcriptomics. To investigate the evolutionary conservation of chordate development at the molecular level, newly generated data from zebrafish, medaka and amphioxus, as well as available data from the SRA (frog and chicken), were compared (Supplementary Data 2, dataset 3 and Supplementary Table  3 ). Gene expression was estimated with Kallisto 47 using Ensembl transcriptome annotations (Supplementary Table  4 ), and summing up transcripts per million (TPMs) from all transcript isoforms to obtain one individual gene-expression estimate per sample.
Reads were mapped to an in silico, bisulfite-converted B. lanceolatum reference genome  7, 58. Differentially methylated regions in the CpG context were identified as previously described  7. Differential transcription-factor motif enrichment was obtained with DiffBind from Bioconductor. CAGE-seq. Libraries were constructed using the non-amplifying non-tagging Illumina CAGE protocol  59. Mouse CAGE-seq data were obtained from FANTOM5  60. Reads were aligned using Bowtie. Nearby individual CAGE TSSs were combined using the distance-based clustering method in CAGEr 61 to produce tag clusters, which summarize expression at individual promoters. Tag clusters were clustered across samples to produce comparable promoter regions, referred to as 'consensus clusters'. The consensus clusters were then grouped by expression patterns using a self-organizing map  62.
Stratification of gene sets by GREAT or intergenic-region size between amphioxus and zebrafish was done using the function stratify from the matt suite  63  , with a range of ±500 bp. The DNA-binding specificity of each transcription factor was predicted on the basis of the binding domain similarity to other transcription-factor family members, as previously performed  64. Transcription-factor motifs from CIS-BP version 1. 02  64  were downloaded and clustered using GimmeMotifs  65  (P ≤ 0. 0001). Two hundred and forty-two clusters of motifs were assigned to one or more orthologous groups in both amphioxus and zebrafish and used for all analyses (Supplementary Data 2, dataset 10). These motifs were detected in APREs using the tools gimme threshold and gimme scan from GimmeMotifs  65. Effect of WGDs on gene expression.
Article reSeArcH Extended Data Fig. 2  | Dynamics of chromatin marks on APREs and reporter assays. a, Summary of the zebrafish and medaka RNA-seq and ATAC-seq datasets generated for this study. Dashed lines indicate equivalent developmental stages in the two species, based on aprevious study  70. The number of biological replicates is indicated for each experiment. Zebrafish 24-hpf ATAC-seq data are from a previous study  67. b, Cumulative distribution of the distance between CAGE-seq peaks and the closest annotated TSSs for genes with expression cRPKM > 5 in any of the samples covered by CAGE-seq (see Fig. 1a ). Only CAGE-seq peaks within 1 kbp of an annotated TSS were tested (amphioxus: 10,435 peaks; zebrafish, 23,326 peaks; and mouse, 23,443 peaks). c, Cumulative distribution of distances between each APRE and the closest annotated TSS normalized by the average intergenic distance of the species (amphioxus, 83,471; zebrafish, 252,774; medaka, 174,139; and mouse, 216,857 APREs, as per Fig.
Average H3K27ac profiles are represented in the top panels. The number of APREs per cluster and stage are provided in Supplementary Data 2, dataset 8. f, Alluvial plot that shows the dynamics of each APRE among the clusters described in e. APREs that remained active (cluster 1 in all stages) along the three developmental stages are represented in blue, constitutively inactive APREs (cluster 3 in all stages) in dark grey and dynamic APREs in red or orange (if inactivated or activated, respectively, during development). Five groups of APREs of special interest are highlighted with stronger colours and named GR1-GR5. g, Representative enriched DNA motifs found in each of the groups described in f. GR1 APREs were enriched in early motifs (for example, Smad3 and Oct4, Sox2 and Nanog); GR3 APREs in motifs of transcription factors involved in the generation of the three germ layers (for example, Foxo3, Sox6 and Sox17); GR4 APREs in tissue-specific transcription factors (for example, Foxa2, Otx2 and Crx); and GR5 APREs in CTCF and CTCFlike (BORIS) motifs.
Article reSeArcH Extended Data Fig. 8  | Comparison of temporal gene expression profiles in amphioxus and zebrafish. a, Heat map showing the significance of orthologous gene overlap between Mfuzz clusters across eight matched developmental stages in amphioxus and zebrafish as derived from an upper-tail hypergeometric test. Some clusters with highly significant overlap are highlighted, and their corresponding temporal expression profiles are shown. The profiles of all clusters for the two species are included in Supplementary Figs. 3, 4. Exact P values and sample sizes are provided in Supplementary Data 2, dataset 8. b, Distributions of NACC values for orthologous genes (in red) or random orthology assignments (blue) for each species against human. Lower NACC values imply higher conservation of relative expression.
Solid lines show the median, and the dashed lines mark the interquartile range. The number of orthologue pairs were as follows: mouse, 15,109; zebrafish, 16,480; and amphioxus, 8,633. c, Differentially enriched GO terms among pairs of zebrafish and amphioxus Mfuzz clusters with significant orthologue overlap (P < 10 -10 upper-tail hypergeometric test) with homochronic (48 pairs) and heterochronic (35 pairs) patterns. The GO enrichment of a group was calculated as the number of cluster pairs with significant enrichment for that given term (Supplementary Data 2, dataset 12). d, Top, per cent of zebrafish genes from each developmental pathway we studied, based on the temporal similarity of their corresponding Mfuzz cluster (homochronic, heterochronic or intermediate). Only genes belonging to clusters with significant orthologue overlap were analysed; the number of genes is provided in parenthesis below the pathway name.
h, Distributions of intergenic-region sizes (left) and number of APREs per gene (right) for a subset of 13,941 pairs of genes with matched intergenic-region size distributions (±500 bp) in amphioxus and zebrafish. P values correspond to Mann-Whitney U tests: *0. 05 > P value ≥ 0. 01, **0. 01 > P value ≥ 0. 001, ***P value < 0. 001. In a and d, all comparisons between each distribution of a vertebrate species and the equivalent distribution in amphioxus produced significant P values (P value < 0. 001); for simplicity, in these panels asterisks are not shown. Exact P values and sample sizes are provided in Supplementary Data 2, dataset 8. Extended Data Fig. 10  | Regulatory evolution after vertebrate WGD. a, b, For each mouse (a) or frog (b) gene, the number of positiveexpression domains across nine equivalent samples is subtracted from the number of domains in which the single amphioxus orthologue is expressed.
f, g, Number of ohnologues with strong specialization (≤2 remaining expression domains) in mouse (f) or frog (g) expressed in each tissue or developmental stage. h, i, Representative in situ hybridization assays in zebrafish embryos for different members of specialized families (right) and for the single amphioxus orthologue (left) (Chordc1 and Itgb1bp2 (h) and Rab11 (i)). Zebrafish image data for this paper were retrieved from the Zebrafish Information Network (ZFIN), University of Oregon, Eugene, OR 97403-5274; (http://zfin. org/, accessed May 2018) and are used with the permission of B. Thisse. Amphioxus in situ hybridization was performed once using 10 embryos per probe, all of which showed the same expression pattern. j, Distribution of the dN/dS ratio between human and mouse for different classes of ohnologues based on their fate after WGD.
nature research | reporting summary April 2018 the Histone Antibody Specificity Database (http://www. histoneantibodies. com, PMID : 26212453) and the Antibody Validation Database (http://compbio. med. harvard. edu/antibodies/). Furthermore, these antibodies have been validated by ChIP-seq in previous publications in a wide range of animal species, all of which have 100% amino acid identity in their Histone 3 sequences (PMIDs: 24642862, 22593555, 22196729).
Field-collected samples Most of the next-generation sequencing data generated in this study is from the amphioxus Branchiostoma lanceolatum, a marine, non-cephalopod invertebrate. As indicated in the Methods section, adult specimens were collected from the wild at the Racou beach near Argelès-sur-Mer, France, (latitude 42° 32' 53" N and longitude 3° 03' 27" E) with a specific permission delivered by the Prefect of Region Provence Alpes Côte d'Azur. Branchiostoma lanceolatum is not a protected species. Gametes are collected by heat stimulation. Embryos are collected from in vitro fertilization after induced spawning, and often batches of several dozens or hundreds of mixed embryos are used for the experiments (detailed in Methods). Adult tissues are collected from several individuals of both genders, except for male and female gonads.
Data access links May remain private before publication. https://www. ncbi. nlm. nih. gov/geo/query/acc. cgi?acc=GSE106372   SE 32,457,194   Antibodies -Rabbit polyclonal to Histone H3 (tri methyl K4) -ChIP Grade (#ab8580, Abcam) -Rabbit polyclonal to Histone H3 (acetyl K27) -ChIP Grade (#ab4729, Abcam) -Mouse monoclonal to Histone H3 (tri methyl K27) -ChIP Grade (#ab6002, Abcam).
Data availability Next-generation sequencing data have been deposited in Gene Expression Omnibus (GEO) under the following accession numbers: GSE106372 (ChIPseq), GSE106428 (ATAC-seq), GSE106429 (CAGE-seq), GSE106430 (RNA-seq), GSE102144 (MethylC-seq and RRBS) and GSE115945 (4C-seq). Raw genome sequencing data and the genome assembly have been submitted to European Nucleotide Archive (ENA) under the accession number PRJEB13665. UCSC hub and annotation files are available at http://amphiencode. github. io/.
We can now state our first theorem (for details and full proof, refer to the Supplementary Appendix available on Dryad at https://dx. doi. org/10. 5061/dryad. 6p55sp3). Theorem 1: For any phylogenetic tree with any number of leaves, I () is a monotone decreasing function of (with limit 0 as →∞). In contrast, I () is bounded above by a monotone increasing function ϕ(), which agrees with I () at = 0 and as →∞. The latter limit corresponds to the highest possible information that can be obtained with n samples drawn from , corresponding to a multinomial distribution (given by a tree with very long edges). A brief outline of the Proof of Theorem 1 follows. The proof of both parts applies the classical data processing inequality (DPI) from information theory  (Cover and Thomas 1991) , but in different ways.
Statement and Sketch Proof of Theorem 2 Notice that the estimation error curves in the simulations (Fig. 2 ) appear to come down as n increases. However, it is not at all clear whether they would continue to decrease towards zero or would instead converge to some nonzero value. We show that Yule trees with fixed heights allow for asymptotically precise estimation of both the root state and the relative rates as the number of leaves become large. To simplify the calculations, we increase the speciation rate (as this grows, the number n of leaves is a random variable that tends to infinity). In Theorem 2, we allow more general models than the equal input model (as assumed in Theorem 1), encompassing most models used in phylogenetics, including the HKY model used in our simulations. We state Theorem 2 as follows (for details and full proof, refer to the Supplementary Appendix available on Dryad).
We show that Rij is a statistically consistent estimator of R ij as grows. The proof uses the distribution of the number and height  (Rosenberg 2006; Stadler and Steel 2012)  of 3-leaf subtrees in a Yule tree, together with further asymptotic arguments to show that Rij converges in probability to R ij. For the second part of Theorem 2, first suppose that T n is a star tree. Then neither the ancestral root state, nor the equilibrium vector can be estimated accurately, even as n →∞. More precisely, the following nonidentifiability result holds. One can switch the root state to a different state, and adjust the parameters and to give an identical probability distribution on the data that such a tree generates regardless of how large n is (details are provided in the Supplementary Appendix available on Dryad).
As expected, RootMultin ≈ RootML ≈ 0. 65 with = 16, and FreqMultin ≈ FreqML ≈ FreqTips with = 16 and Yule trees. Coalescent trees have much shorter pending branches, and the convergence of FreqML and FreqTips toward FreqMultin is slower. All software programs (except PhyML) used to perform the simulation study were implemented in Common Lisp and are available on request. We used the version 3. 3. 20170530 of PhyML available from https://github. com/stephaneguindon/phyml.
As before f : Ω h \Θ h → R represents the Laplacian data, and g : Θ h → R the values of the solution on Θ h. Again, Neumann boundary conditions on the edges of the image are automatically enforced due to the chosen definition for the discrete Laplacian. Equation (  6 ) is linear in the set of unknowns u(i, j), and therefore, it can be expressed as A h u h = b h. (7) Abusing of notation, let u h now denote a HW × 1 column vector obtained by stacking column wise the image u(i, j), b h is a column vector containing a mixture of data terms f and g, and the matrix A h is a HW × HW sparse matrix obtained from ∆ h and Θ h. Equation (  7 ) can be expressed in different ways. In the following we describe three alternatives to define this linear problem: the Reduced Formulation, the Extended Formulation, and a Self-Adjoint Extended Formulation.
in the bright areas (d) (Dirichlet conditions). The second row of Figure  6  illustrates the solution obtained for the Self-Adjoint Extended (e-g) and the Extended formulation (f-h). Matlab backslash is used as the numerical solver in the first two examples (e-f). On the other hand, images (g) and (h) illustrate the solution to the same system of sparse linear equations but when a multigrid Vscheme iterative solver is used. The solutions (e), (f) and (g) are equivalent (we numerically checked that these images are identical). This was expected as the Self-Adjoint and the Extended discrete formulations are mathematically equivalent to each other. Therefore given an arbitrary numerical solver, if it converges to the solution there will be no difference between the Self-Adjoint and the Extended formulations.
To that end, we can define the set Θ as the set of pixels x i for which the gradient is higher than a certain threshold t. The Dirichlet data term g may be set as the values of the image I at the given positions x i , and finally, a null Laplacian data term f = 0 may be used for interpolation. Figure  7  shows an example of an input image, the pixels kept and the corresponding data term g. Numerically this problem can be stated as the Poisson-like system of equations  (13)  (with the data terms specified above). As described in previous sections, the discrete formulation of the problem leads to a sparse linear system of equations Au = b. The structure of A depends on the shape of the set Θ, which determines the eigenvectors of A. For example, Figure  8  illustrates four different sets Θ and the corresponding eigenvectors associated to the smaller eigenvalues of the matrix A.
A second interesting case is when the value on a single pixel is set (i. e. Θ = {x 1 }). In this case, the solution is unique, while the eigenvectors of A associated to the smaller eigenvalues are smooth. This implies that setting a single pixel of the solution guarantees that the solution of the problem is unique while leading to the numerically more challenging problem. Evaluating multigrid convergence rate. In a third round of experiments, we compared multigrid iterative method with: Gauss-Seidel, Conjugate Gradient, Preconditioned Conjugate Gradient  [1] , Bi-Conjugate Gradient, Symmetric LQ  [12] , Generalized minimum residual  [15] , Transposefree Quasi-minimal Residual  [1] , and Bi-Conjugate Gradient Stabilized  [18]  methods. We tested our own implementation of Multigrid, Gauss-Seidel and Conjugate-Gradient methods and used implementations available in Matlab 2  in the rest of the cases.
Acknowledgment The authors would like to thank  Jean-Michel Morel  for fruitful discussions,  Juan Francisco Garamendi  for pointing us to useful references, and  Enric Meinhardt-Llopis  for his interesting suggestions and ideas which substantially improved this work. Work partly founded by  CSIC  and  PEDECIBA (Uruguay) , the Office of Naval research by grant  N00014-17-1-2552  and  ANR- DGA  project  ANR-12-ASTR-0035.
Second, we used individual lentiviruses, each carrying a reprogramming factor. With an MOI of R5, the CEFs were efficiently infected as determined by the GFP reporter lentivirus (data not shown). Morphological changes were observed during the selection process, and colonies emerged with AP-positive cells (Figures  1C  and 1D ). However, individually picked resistant colonies did not Primary chicken embryonic fibroblasts (CEFs) were infected with the pLent polycistronic lentivirus. Colonies emerged (A) and expressed GFP (B), indicating that they were infected, although they failed to be amplified. More than 50 colonies were isolated in several independent experiments. Similarly, CEFs were infected with a cocktail of individual lentiviruses carrying mouse Oct4, Sox2, Klf4, and c-Myc cDNAs. Compact colonies with AP-positive cells were obtained (C and D) but could not be established long term.
None of the 50 picked clones and the initially infected pooled cells could be established long term, in sharp contrast to the MEFs that gave rise to iPSCs with high efficiency using the same constructs (data not shown). Fourth, we evaluated the effect of non-integrative Sendai viruses carrying human cDNAs. Infection of CEFs, as validated by the detection of the viruses as recommended (data not shown), led to morphological changes characterized by the appearance of round yellowish cells clustered in small colonies at 7-10 days after infection (Figure  1H ). These phenotypic changes did not evolve into an ES-like morphology or long-term establishment, and cells progressively reverted to a fibroblast morphology. Under similar conditions, human embryonic fibroblasts were fully and efficiently reprogrammed (data not shown).
, 2015) , and all oligonucleotides (provided by Eurogentec) were designed with Primer 3 (Table  S5 ) from sequences extracted from the Gallus_gallus-5. 0 chicken genome. For each analysis, the chosen figure is representative of several independant experiments. The transcriptomic profile was analyzed by RNA-seq analysis (Helixio, http://helixio. com), as described previously  (Vautherot et al. , 2017) , using CEFs and DEFs as starting material for the reprogramming and four independently established reprogrammed clones (1A, 1D, 3E, and 3F) for chicken and two (R71-2 and R71-5) for duck. Previously generated data  (Vautherot et al. , 2017)  from cESCs, PGCs, and BCs as chicken stem cells were used for comparison. Data are available at the GEO under accession number GEO: GSE109970 as SuperSeries to group the chicken (GEO: GSE102353) and duck (GEO: GSE109969) data.
5 and L * = 5. An example of    2014 ), these correlation factors are multiplied with the NOAA-POES data in order to obtain mean electron fluxes between > 0. 1 and > 3 MeV along all magnetic field lines between L * = 2. 5 and L * = 5 (Fig. 5 ). An equatorial pitch angle distribution shape based on a sine function is assumed and constrained by data all along the magnetic field lines between L * = 2. 5 and L * = 5 (Fig. 5 ). The most significant change in this new version of the SLOT model is the dependence of fluxes on the solar cycle. In order to have dependence on the solar cycle in GREEN-e, we have to study in detail the dynamics of the measurements from all NOAA-POES spacecraft. As an example of this solar cycle dependence, > 300 keV electron fluxes vs. time from all NOAA-POES data is plotted in Fig.