dataesr/scientific-paragraphs-categorization huggingface dataset:
EPJ Web of Conferences An incremental approach (image by image) is developed within Matlab, the key steps of the algorithm are: • Isolation of each line within a sub-image (Fig. 4a ). • Detection of the contour of the line using a Sobel filtering method (Fig. 4b ). At this stage the line is described by a cloud of points. • Identification of the parameters of the analytical function by linear regression based on the cloud of points. The displacement of each point of the line is described by the following expression: U x (y, t) = U M M x (t) + U W H S x (y, t) + H (γ c1 ) U AS B x (y, t) + H (γ c2 ) U crack x (y, t). ( 1 ) Each components of the function in Eq. (  1 ) is linked to a specific area or stage of the deformation process (see Fig. 5 ), and each of them involved specific parameters: • The mater motion : U M M x (t) • The displacement field within the weakly heterogeneous strain area assumed in the form: • The displacement field within the ASB-related strongly heterogeneous strain area assumed in the form: U W H S x (y, t) = Ū W H S x (t).
, 2014]. The main advantage of this method is that it clearly decreases costs, as library preparation for bar-coding is nowadays roughly the same price as sequencing. The drawback is that genotypes cannot be derived anymore. Instead, we have access to the allele frequency in the population, a result known as the allelotype. In this work, we present a method for the de novo identification, differential analysis and annotation of variants from RNAseq data in non-model species. It takes as input RNA-seq reads from at least 2 conditions (e. g. , the modalities of the phenotype) with at least 2 replicates each, and outputs variants associated with the condition. The method does not require any reference genome, nor a database of SNPs. It can therefore be applied to any species for a very reasonable cost.
We first evaluated our method using RNA-seq data from the human Geuvadis project  [Lappalainen et al. , 2013b]. The great advantage of this dataset is that SNPs are well annotated, since the selected individuals were initially included in the 1000 genomes project  [Abecasis et al. , 2012]. This enables to clarify what is the precision and recall of our method, and how it compares to widely used methods like Gatk, which require a reference genome. We then applied our method in the context of non-model species. First we focused on Asobara tabida, an hymenoptera that exhibits contrasted phenotypes of dependance to its symbiont. Using RNA-seq data from two extreme modalities of the phenotype, we were able to establish a catalog of SNPs, stratify them by functional impact, and assess which SNPs had a significant change of allele frequency across modalities.
This list can be further mined for candidates to follow up experimentally. All the methods presented in this paper are implemented in software that are freely available at http://kissplice. prabi. fr/TWAS. In particular, the statistical procedure that we developed is available through an R package, kissDE, which is of general interest for researchers who have obtained read counts for pairs of variants in a set of conditions and wish to test if these counts reflect the specificity of the variant in a particular condition.
Overview We present here a collection of methods which can be used together to produce, from RNA-seq data alone, a list of condition-specific SNPs, stratified by their predicted impact We propose a statistical method, called kissDE to find condition-specific SNPs (even if they are not positioned) out of all SNPs found. Finally, we can also predict a functional impact for the positioned SNPs, and intersect these results with condition-specific SNPs using our package KisSplice2-RefTranscriptome (K2rt). on the protein. Figure  1  summarises the different steps. Trinity, Transdecoder and Blat are third-party software. KisSplice was published recently  [Sacomoto et al. , 2012] , kissDE and KisSplice2RefTranscriptome (K2rt) are methods we introduce in this paper.
Predicting the functional impact of SNPs KisSplice predicts SNPs, but outputs only a very local context around the SNP. In order to predict its functional impact, we need to place the SNP in a larger genomic context. For this, we relied on a widely used global transcriptome assembler: Trinity  [Grabherr et al. , 2011] , which takes as input RNA-seq reads and outputs contigs that correspond to either full-length transcripts (if the expression level of the transcript is sufficient) or to fragments of transcripts. The results of KisSplice were aligned onto the transcripts predicted by Trinity using Blat  [Kent, 2002]. Concurrently, we searched for coding potential in the transcripts using TransDecoder. Once we had the location of the SNP within the transcript and the location of the open reading frame (ORF), we could assess if the SNP was located within the CDS or not, and if so, if it was a synonymous or non synonymous SNP.
In the case where no ORF was predicted for the transcript, we concluded that the SNP was within a non coding region. In practice, this can correspond to a non coding RNA, a UTR or an intron. Prediction of the functional impact of a SNP was included in a Python package, called KisSplice2RefTranscriptome (k2rt), which takes as input a set of predicted ORFs (bed format), the output of KisSplice (fasta format), and a mapping of the results of KisSplice to the transcripts (psl format). Importantly, Trinity, TransDecoder and Blat are third party software which can be replaced by others, provided the exchange formats are respected (bed and psl). In the case where a SNP mapped to several Trinity transcripts, we reported the functional impact of the SNP in each transcript. This happened in particular when a SNP was located in a constitutive exon of a gene that gave rise to multiple alternative transcripts through alternative splicing.
In the case where a SNP mapped to no transcript, then it could not be treated by k2rt and it was filtered out. Those SNPs were called orphan SNPs. They were mostly located in poorly expressed genes and/or highly repeated regions. Indeed, repeated regions are notoriously difficult to assemble. When repeated regions are located within genes, they may either generate chimeric transcripts in the assembly if the assembler is too permissive, or a series of truncated short contigs if the assembler is too conservative. By default, Trinity does not output contigs shorter than 200 nucleotides. Because these contigs are highly enriched in repeats and poorly expressed genes, it explains the origin of the majority of our orphan SNPs. As mentioned in the model section, the number of bubbles does not always correspond to the number of SNPs.
In the case of SNPs located less than k nucleotides apart, the number of bubbles corresponds to the number of pairs of haplotypes out of the total number of haplotypes. The same SNP may therefore be present in multiple bubbles. When mapping the bubbles to a reference transcriptome, it is possible to remove this redundancy and count the true number of SNPs. Indeed, if two bubbles map to the same transcript at the same location, then it means that they refer to the same SNP, and we count it only once. The software versions that we used were: Trinity r20140717, TransDecoder v2. 0. 1, BlatSuite36, KisSplice v2. 4, KisSplice2RefTranscriptome v1. 0. All were used with default parameters, except Blat where we set --minIdentity=80. We also set the minimum query coverage to 80% in k2rt. Changing both the minimum identity and the minimum query coverage from 70% to 90% only marginally affected our results.
Testing the association between a variant and a condition Given the number of SNPs (n) and the number of replicates (m), our dataset is a count matrix of size 2n × m, with 2 lines corresponding to 1 SNP (upper and lower path representing the two different alleles with one nucleotide differing between both paths). Before comparing the allele read counts from different libraries, the count data were normalised by library sizes as proposed in the DESeq package  [Anders, 2010]. This software has been shown to be the most efficient according to a recent normalisation comparison study  [Dillies et al. , 2013]. Some events were then filtered out based on their counts: if global counts (for all replicates and all conditions) for both variants were too low (< 10), we considered that we did not have enough power to conclude on this event and we did not test it.
Due to numerical instabilities associated with the estimation of Negative Binomial parameters, we adopted a model selection approach to determine the best model to handle the over-dispersion parameter (φ). Our strategy was first to estimate a model without over-dispersion using the glmnet package (model M(φ = 0)). We then considered two different estimation methods for the parameter φ, namely a global estimation approach using the package aod (model M(φ = φ global )), and a SNP-specific parameter using the DSS package (model M(φ = φ i DSS ). We finally used a BIC to choose the best model out of the three. Pseudo-counts (ie, systematic random allocation of ones) were considered for SNPs showing many zeros to avoid singular hessian matrices while fitting the generalised linear model.
The Geuvadis dataset Our method enables to find SNPs from RNA-seq data. In order to assess if the SNPs we find are correct, and if the list we output is exhaustive, we chose to test our method on RNA-seq data from the Geuvadis project. Indeed, the individuals whose transcriptome was sequenced in this project were already included in the 1000 genome project. Hence, their SNPs have been already well annotated. We downloaded fastq files from SRA (see Data access) and selected 10 Toscans and 10 Central Europeans. We sampled 10M reads for each individual and concatenated the fastq files in pools of 5 individuals.
These two lineages were founded by 3 females and were kept for 15 generations (three founders at each generation) before RNA extraction. The experimental design for RNA-seq sequencing aimed at describing the transcriptomic changes associated with the presence / absence of Wolbachia, and the variations observed in the two A. tabida lineages exhibiting an extreme phenotype. For this purpose, cDNA libraries were constructed from infected and non-infected ovaries in these two lineages. Because these RNA-seq data were issued from two distinct lineages from a non-model species, we exploited this dataset to validate the method developed here and to discover biologically relevant SNPs, using libraries obtained from infected ovaries. The samples used for RNA extraction were young female (0-1 day old) ovaries dissected in a drop of A-buffer (2 replicates of 30 ovaries per lineage).
RNA was extracted as described in  [Kremer et al. , 2009]. These RNA extracts were used to generate corresponding cDNA libraries, following the recommendations given by the manufacturer of the SMARTer PCR cDNA synthesis and BD Advantage 2 PCR kits (Clontech). These cDNA libraries were then purified with the Qiaquick kit (Qiagen) and their quality checked. Sequencing of cDNA was performed by the Genoscope (Evry), on an Illumina GA-IIx instrument, to obtain 1x75bp reads. These data were trimmed using the ShortRead package with default parameters and then used as input of the pipeline defined in Figure  1. Based on these results, 27 SNPS were chosen for verification. For each SNP, primers were designed on the corresponding transcript to amplify the surrounding genomic region. PCRs were performed from an aliquot of the purified cDNA libraries.
Virgin female flies were collected after hatching and isolated until they reached ten days. The RNA was extracted from a pool of 30 ovaries of 10-days-old flies for each line. The extractions were performed using the RNeasy kit (Qiagen) and samples were then treated with DNase (DNA-free Kit, Ambion) and stored at -80C. The samples were quantified by fluorescence in the Bioanalyser 2100 (Agilent), according to pre-established criteria by the sequencing platform. For each line, the extracted RNA was divided into two parts in order to generate two cDNA libraries (two replicates per condition). RNA was sequenced by Illumina Technology, in the IlluminaHiseq 2000. We sequenced 2x51bp paired-end reads and the medium size of the inserts was 300bp. We used UrQt  [Modolo and Lerat, 2015]  with the default parameters to remove the low quality bases and the polyA tail from the dataset before running the pipeline described in Figure  1.
Data access The human data used in this study can be found through the ArrayExpress database (http://www. ebi. ac. uk/arrayexpress/) under the accession number E-GEOD-29342 and we used the individuals named NA20808, NA20809, NA20810, NA20811, NA20812, NA20813, NA20814, NA20815, NA20819, NA20826, NA06984, NA11840, NA06986, NA06989, NA06994, NA07346, NA07357, NA10851, NA11829, and NA11832. The RNAseq libraries from D. mojavensis and D. arizonae are available through the NCBI Sequence Read Archive (SRA : http://www. ncbi. nlm. nih. gov/sra) under the accession no. SRX1272419 and SRX1277353. The A. tabida dataset will be uploaded in SRA in the coming weeks.
Identification of variants In order to evaluate the performance of our method, we needed to test it in the case where we knew which SNPs should be found. We thus focused on a dataset from humans in which SNPs were already annotated. We selected 2 populations (Toscans and Central Europeans) from the Geuvadis project  [Lappalainen et al. , 2013a] , and downloaded the RNA-seq data of 10 individuals in each population. We sampled 10M reads from each individual and pooled individuals 5 by 5, to obtain two replicates of 5 pooled individuals per population. We ran KisSplice and Trinity on these 4 read sets and we aligned the variants of KisSplice to the Trinity transcripts using Blat (with at least 90% query coverage and 80% identity). Out of the 66530 bubbles initially found by KisSplice, 55590 (83%) mapped to Trinity-assembled transcripts, in 52345 different transcriptomic positions, 5797 partially aligned, and 5143 did not align.
To obtain the true allelic frequency within a pool, we took advantage of the availability of the genotypes of each individual from the Geuvadis dataset, and we simply summed up the number of alternative alleles over the total number of alleles within the pool. The expressed allele frequencies were obtained from KisSplice calls, summing the alternative allele counts of each individual over all allele counts of the pool. We found that the distortion highly depends on the expression levels (Supplementary Figure  4 ). While the correlation was weak (0. 65) for poorly expressed loci (less than 3 reads), it increased steadily with the expression level up to a plateau of 0. 98. When we restricted to loci with at least 10 reads, the correlation reached 0. 95. Similar results were obtained for every other pool and when we mixed up all 20 individuals within one pool (see Supplementary Figure  5 ).
Functional impact When no reference genome is available, it is not possible to obtain a genomic location for each SNP and therefore to apply SNPeff  [Cingolani et al. , 2012] , or PolyPhen  [Adzhubei et al. , 2010] , which are widely used softwares for assessing the impact of a SNP on the protein. In absence of any reference genome, a reference transcriptome can nevertheless be obtained, using a full-length transcriptome assembler like Trinity  [Grabherr et al. , 2011]. Based on this transcriptome, it is possible to assess the coding potential of each transcript using TransDecoder, to position the predicted SNPs onto the assembled transcripts using Blat  [Kent, 2002] , and finally to assess the impact of each SNP on its transcript(s). In the end, each positioned SNP is classified as coding or non coding.
In the case where the SNP is located in the coding region, it is then classified as synonymous or non synonymous (See Methods). Out of 52345 positioned SNPs (those which aligned to Trinity transcripts), 15582 cases (29%) fell in the CDSs and the other 71% fell in non-coding regions (including UTRs). Among the ones falling in the CDSs, we found that 51% (8063) were synonymous, while the other half (7519) were non synonymous. To validate our predictions, we then intersected the genomic positions of our predicted SNPs with the genomic positions of SNPs in dbSNP, for which the functional impact is known. Out of the 52345 SNPs we predict, 41252 could be assigned a genomic position which matched a SNP annotated in dbSNP. Out of those 41252 cases, 39615 had a correct functional prediction. A thorough examination of the 1637 cases wrongly predicted reveals that in most cases, the transcript predicted by Trinity was very partial and was overlapping an intron (this happens when pre-mRNA is sampled together with mRNA at the RNA extraction step, despite selection of polyA+RNAs).
Application to Intraspecific Polymorphism: the case of Asobara tabida We first applied our method to Asobara tabida, for which RNA-seq data from two lineages (SFR2 and SFR3) were available. These lineages come from the same population, but they differ by their phenotype of dependence to their symbiont Wolbachia. In the absence of Wolbachia, SFR2 individuals produce no eggs, while SFR3 produce some. Consequently, we suspect a low but significant genetic differentiation between lineages that could be associated with the phenotypes, or to genetic drift associated with maintenance in the laboratory. While the experimental design, with a single lineage for each phenotype, does not enable us to separate between these two effects, we think that this dataset is still well tailored for a validation of our method because: (a) no reference genome is available for this species; (b) individuals were pooled for RNA extraction; and (c) replicates are available for each lineage.
Application to Interspecific Divergence: the case of Drosophila mojavensis and Drosophila arizonae Similarly to the Asobara dataset, the drosophila dataset corresponds to non-model species, where individuals had to be pooled prior to RNA sequencing. In this case however, the two modalities of the phenotypes are not two populations of the same species, but two recently diverged species. This therefore enabled us to assess if our method also applies to a very different evolutionary scale, where differences of one nucleotide are no longer SNPs, but divergences. Additionally, the availability of the reference genome for D. mojavensis (and not D. arizonae) enabled us to study in depth the case of condition-specific inexact repeats. D. mojavensis and D. arizonae are two closely related species that diverged 1MYA.
We sequenced through RNA-seq the ovarian transcriptomes of two replicates of pools of 30 individuals for each species. We obtained 55M paired-end reads per replicate. We ran our pipeline on the data and obtained 51730 positioned SNPs, and most of them (51135) were condition-specific. The condition-specific SNPs were mostly in coding regions (60%, i. e. , 40674 SNPs). We could classify 34382 of them as synonymous, and the other 6292 SNPs as nonsynonymous. We selected 11 cases for experimental validation, 6 of which were divergent sites, and 5 were cases where the site was polymorphic in one species and fixed in the other. We were able to validate that the variation was condition-specific for all the divergent sites, and for 4 cases out of 5 for the polymorphic cases. Additionally, for 2 cases out of these 4, we were able to amplify the two alleles in the species where the site was predicted to be polymorphic.
This work was supported by the Agence Nationale de la Recherche [ANR-12-BS02-0008, ANR-11-BINF-0001-06, ANR-2010-BLAN-170101]; by the São Paulo Research Foundation -FAPESP/Brazil [2010/10731-4 to C. M. Carareto]; and the European Research Council under the European Communitys Seventh Framework Programme (FP7 /20072013)/ERC Grant Agreement No. [247073]10.
Acknowledgments The authors would like to thank  Marie-Christine Charpentier  and  Delphine Charif  for help on the analysis of the Asobara dataset ;  Gustavo Sacomoto  for help on the analysis of the Geuvadis dataset ;  Sebastien Deraison  for help on experimental validation of the candidates of Drosophila 6. 0. 3Conflict of interest statement. None declared.
FRAP and photoactivation experiments. Cultured striatal neurons were transfected with a plasmid coding for DARPP-32 fused with carboxy terminal of EGFP (2. 5 mg per 35-mm diameter dish) or PKA-GFP (2. 5 mg) or EGFP (2. 5 mg). FRAP and all imaging experiments were carried out at 37 °C at least 24 h later on a confocal SP5 II upright microscope (Leica Microsystems) with a 40X HCX APO (0. 80 NA) water objective at the Institut du Fer aMoulin Cell and Tissue Imaging facility. FRAP and photoactivation experiments were performed with the FRAP Wizard software (Leica Microsystems). During acquisition, the temperature of the sample and the objective was maintained at 37 °C in a heated chamber. The pinhole aperture was opened in such a way that the full width at half the maximal intensity was larger than the depth of the cell.
Before photobleaching, 30 images were taken at 0. 116-s intervals. After photobleaching of a circular region for 500 ms, 500 images were taken at 0. 116-s intervals. FRAP data analysis and calculation of diffusion coefficients. In experiments, a circular region within the cytoplasm was photobleached. The average fluorescence density of the bleached region in each image was corrected by deducing the average fluorescence density of the background taken at the same time. The diffusion coefficients were calculated by using a published method  52  , taking into account the radius of the bleached area, the half recovery time and a correction constant calculated based on the laser intensity. The bleached area was analysed on the first post-bleaching image. The radius (o) of the bleached area was measured within a region where fluorescent intensity was o1e À 2 of the intensity before bleaching and this was measured for each individual experiment.
Computational modelling. Model structure and validation. PKA activation was modelled as a sequential binding of four cAMP molecules to PKA whole enzyme, followed by the release of two active catalytic subunits. The parameters for these processes were validated against published experimental data 30. PDE10A was the only phosphodiesterase considered in the model, as it is the predominant isoform in D1R-expressing MSNs  56. The parameters concerning its hydrolysis ability were validated against the published values 57. Two phosphorylation sites of DARPP-32 were considered in the model: Thr34 phosphorylated by PKA and Ser97 phosphorylated by CK2. Thr34-phospho-DARPP-32 competitively inhibits PP1 (ref. 58). This site is dephosphorylated by calcineurin  27. Basal phosphatase activity of calcineurin was included.
A fully implicit finite volume, regular grid (variable time step) method  66  was used in all simulations. The core of the model can be found in the Virtual Cell public database via VCell software under user luli with model name: Li2015_geometry CreatesNonlinearNuclearPKA. Computational model validation. To validate the parameters involved in PKA activation, a compartmental model that contains only PKA activation and its activity, was set up in COPASI  67. To start the simulation, 10 nM PKA whole enzyme were mixed with 200 mM kemptide and cAMP with varying concentrations. The enzyme activity was calculated as the concentration of phospho-kemptide after a 2-min simulation in agreement with experimental procedures  30. Additional parameters, used to enable this fitting, were the binding, dissociation of PKA to kemptide and the catalytic constant of PKA phosphorylates kemptide (Supplementary Table  1 ).