dataesr/scientific-paragraphs-categorization huggingface dataset:
Orthology detection and transposable element analysis Intergenic-expressed regions, according to the current pig genome assembly (Sscrofa10. 2), were extracted with Cuffcompare  [65]  and custom Python and R scripts. Putative coding transcripts were identified with Augustus  [29] , providing exon boundaries and allowing for complete protein translation. Functional annotation was performed by using BLASTP option from BLAST2GO, with the following parameters: E-value hit filter 1. 00E-6, annotation cutoff 55, gene ontology (GO) weight 5 and HSP-hit coverage cutoff 0  [67]. Additionally, an InterProScan tool implemented in the BLAST2GO software and the ANNEX data set was employed to refine the functional annotations. GO terms were summarized according to the three principal GO categories: cellular component, biological process and molecular functions.
Enzyme mapping of annotated sequences was performed using direct GO to enzyme mapping and used to query the Kyoto Encyclopedia of Genes and Genomes (KEGG) to define the main metabolic pathways involved  [68, 69]. Furthermore, the software RepeatMasker (http://www. repeatmasker. org/) version open-3. 30 was employed with the 'quick search' option and 'pig' species, in order to indentify repetitive and transposable elements in the adipose tissue transcriptome. The Search Engine used was NCBI/RMBLAST with the complete rm-20120418 database.
Gene expression quantification and correlation analysis with expression microarrays Qualimap v0. 5 software was employed to count the number of reads mapped for each gene, and the total number of counts was considered as expression values  [70]. Pearson correlations between the mean expression values of each group were calculated using the cor. test function of R. Five of the animals sequenced were also assayed with high-density oligonucleotide microarray chips (GeneChip® Porcine) from Affymetrix (Santa Clara, CA), containing a total of 23,937 probe sets  (23,256 transcripts) , representing 20,201 Sus scrofa genes. Microarrays were hybridized and scanned at the Institut de Recerca Hospital Universitari Vall d'Hebron (Barcelona, Spain) following Affymetrix standard protocols. The Gene-Chip Operating Software (GCOS) was used to generate expression data, and probes were normalized and adjusted for background noises with the GCRMA R package  [71].
Differential gene expression analysis The DESeq R package was employed to detect genes differentially expressed between groups  [72]. DESeq mediates a negative binomial distribution by modeling the biological and technical variance for testing DE genes in two experimental conditions. DESeq uses the unambiguous table of counts per gene obtained from QualiMap software using the comp-counts option as the input file  [70]. Before the analysis, some exploratory tests were performed to validate both the good data quality and the variance estimation. Per-gene estimates of the base variance against the base levels showed that the fit (red line) followed the single-gene estimates well (Additional file 8: Figure  S3 ). The residualEcdfPlot function used to check the uniformity of the cumulative probabilities revealed a similar curve pattern of the empirical cumulative density functions (ECDF) in both groups.
Gene functional classification, network and canonical pathways analyses A bioinformatics approach was used to elucidate the biological importance of differentially expressed genes in the adipose tissue transcriptome. Ingenuity Pathway Analysis software (IPA; Ingenuity Systems, www. ingenuity. com) was applied to identify functions and pathways represented and for generating biological networks. The IPA program consists of the Ingenuity Pathway Knowledge Base (IPKB), which is derived from known functions and interactions of genes published in the literature. IPA presents the top canonical pathways associated with the uploaded data with a p-value calculated using the righttailed Fisher's exact test. Functional analysis was used to identify the biological functions that are differentially represented between both groups (H and L).
Acknowledgments This work was funded by the  Ministerio de Economía y Competitividad  Project  AGL2011-29821-C02 , and by the  Innovation Consolider-Ingenio 2010 Program  ( CSD2007-00036 ,  Centre for Research in Agrigenomics). J. Corominas was funded by a  FPI  PhD grant from the  Spanish Ministerio de Educación  ( BES-2009-081223 ),  Y. Ramayo-Caldas  was funded by a  FPU  PhD grant from the  Spanish Ministerio de Educación  ( AP2008-01450 ) and  A. Puig-Oliveras  was funded by a  PIF  PhD grant from the  Universitat Autónoma de Barcelona  ( 458-01-1/2011 ).
Numerical studies In this section we validate the effectiveness of our method on simulated data. All our numerical studies have been performed with the software R (see R Core Team (  2012 )). The method is available on the CRAN website (http://www. cran. r-project. org/web/packages/pacbpred/index. html), under the name pacbpred (see  Guedj (2012) ). Some comments are in order here about how to calibrate the constants C, σ 2 , δ and α. Clearly, a too small value for C will stuck the algorithm, preventing the chain to escape from the initial model. Indeed, most proposed models will be discarded since the acceptance ratio will frequently take the ). As the parameter σ 2 is the variance of the proposal distribution φ, the practioner should tune it in accordance with the noise level of the data.
net/projects/amen/; [27]. Raw data as well as a normalized expression file are available at the GeneOmnibus public data repository (http://www. ncbi. nlm. nih. gov/geo/query/ acc. cgi?acc = GSE16030). Statistical and cluster analyses. Non-informative clones for which too little cDNA was spotted (oligonucleotide signal ,3 times the background level in more than 20% of samples) were removed from the analysis. Androgen-responsive genes were then identified by comparing the control group to each of the treated groups (T1, ''T2, day7'', ''T2, day14'', 11KT) using the multi-class Limma statistical test (FDR 1%)  [28]. All differentially-expressed transcripts were submitted to the PAM algorithm (Euclidian distance measure) and grouped into 4 expression clusters. Three pairwise comparisons were also performed to identify genes differentially-regulated by testosterone and 11KT at a comparable time point (i.
e. at day 7) using the Limma statistical test: control vs ''T2, day7'', controls vs 11KT, 11KT vs ''T2, day7''. Here, a Bonferroni adjustment was used to obtain a global risk of type I error equals to 1%. Thus, for any one comparison to be considered significant, the p-value had to be less than 0. 0033, that is 1% divided by the number of pairwise comparisons. For heatmap representations, unlogged data were loaded into the MeV software (www. tm4. org/mev/) and normalized according to the following formula: Value = [(Value) -Mean(Row)]/[Standard deviation(Row)]. Metaanalysis. Expression data from testes at different developmental stages and isolated germ cells populations  [23]  were used to investigate the developmental pattern and the cell type in which androgen-responsive genes are expressed.
Briefly, samples in this dataset included -Testes in early stages containing slowly-dividing type A spermatogonia (Stage I) or growing numbers of actively-dividing type B spermatogonia (Stages IIa and IIb) -Maturing testes also containing meiotic spermatocytes (Stage IIIb) and post-meiotic spermatids (Stage V) -Spawning testes containing essentially mature spermatozoa (stage VIII) (see  [29, 30]  for a more precise description of all stages) -Fractions of isolated germ cells enriched in spermatogonia, spermatocytes or spermatids obtained as previously described  [31]. Genes differentially-expressed during testis development (F's statistic permutation test, FDR 5%) were compared to androgen-responsive genes.
Trout Microarray Annotation and Functional Data Mining Trout cDNAs spotted onto the microarray were annotated as previously described  [23]. GeneOntology (GO) terms associated to fish genes and their rat, mouse and human orthologs were extracted from the Ensembl database (version 52). Biological mining of expression clusters was then performed by searching for over-and under-represented functional GO terms (Gaussian hypergeometric test) as compared to all well-measured genes, using the AMEN software.
Prediction of Androgen Response Elements As there is yet no genome sequence available for the Oncorhynchus mykiss or any other salmonid we used that of Gasterosteus aculeatus (Stickleback) as a reference. Stickleback Ensembl genes were identified as previously described  [23]  and their Transcriptional Start Sites (TSS) were localized using the ensGene. txt file from UCSC (http://genome. ucsc. edu/) [32]. AREs were predicted within genomic regions from 210000 base pairs (bp) up to +2000 bp with respect to each TSS. Validated ARE motif matrices (M00447, M00481, M00953, M00956 and M01201) from the Transfac Professional database (release 2009. 1; http://www. biobase-international. com)  [33]  were predicted using the MATCH program  [34]. Core and matrix similarity cutoff values were set at 0.
1 web interface (http://eu. idtdna. com/analyzer/Applications/OligoAnalyzer/) to avoid self-and hetero-dimer formation as well as hairpin structures, and matched (BLAST algorithm) against the SI-GENAE trout contig collection (som. 8 version) to avoid nonspecific annealing to other transcripts (Table  S1 ). Aberrant values, as determined by Dixon test (p,5%), were removed and statistical analyses were performed using the Statistica software using the non-parametric test of Mann & Whitney (n individuals = 5 to 7).
This work was supported by the  AGENAE program  (www. agenae. fr), INRA -CIPA-OFIMER European community IFOP grant. This research received funding from the  French National Research Agency  ( ANR-06-GANI-014  ''Spermgen'') and from the  European Community 's  Seventh Framework Programme  ( FP7/ 2007-2013)  under grant agreement  nu222719 , project  LIFECYCLE. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
, 2010; Assaf and Basser, 2005; Assaf et al. , 2004 Assaf et al. , , 2006)). Within the work of the CONNECT project, we aimed to further develop, generalize and extend the applicability of these methods and acquire a database to investigate the variance of these features over the healthy population. Among the methods that were used are: CHARMED, AxCaliber, ActiveAx and double PFG. 1. The CHARMED (composite hindered and restricted model of diffusion) framework  (Assaf and Basser, 2005; Assaf et al. , 2004 ) models white matter by combining two diffusion components: hindered and restricted. Within CHARMED it is assumed that diffusion in the axonal space is restricted while elsewhere it is only hindered. The analytical diffusion signal descriptions of each of these diffusion processes are different.
These issues were addressed and mostly resolved in the CONNECT project  (Alexander et al. , 2010; Ben-Amitay et al. , 2012; De Santis et al. , 2013b; Drobnjak et al. , 2010; Dyrby et al. , 2011 Dyrby et al. , , 2012;; Siow et al. , 2011). Following successful optimization of the micro-structural measurements, we first wished to demonstrate that acquiring the microstructural information provides an added value over conventional methods. In a database of ~20 healthy subjects (aged 20-30 years) that were scanned with both DTI and CHARMED protocols we set out to explore the benefits and potential of combining CHARMED information into tractography (detailed description of the experimental protocols and analysis procedures is given in Appendix A). Following transformation of the quantitative measures to MNI space further analysis was performed on both the single-subject and at the group level.
The group level MNI normalized data of each subject were combined to produce an average brain DTI data set with the corresponding restricted diffusion volume fraction map. Fig. 4  shows reconstruction of the corona radiata passing through the cerebral peduncles. When the reconstructed fibers are colored according to their FA values, the crossing fiber artifact of this fiber system with the superior longitudinal fasciculus is very apparent (see arrow on the left side of Fig. 4 ). When looking at the CHARMED data (right side of Fig. 4 ), this artifact is significantly reduced demonstrating the added value of incorporating micro-structural measures into tractography providing a more reliable and accurate micro-structural characterization of specific fiber systems. At the following step of combining tractography and micro-structural measures, we aimed to combine ADD measures with tractography to explore the micro-structural properties of neuronal fibers.
From this, a full AxCaliber data set can be re-sampled exactly perpendicular to the fitted fiber systems (independent of its original orientation). As a final step of the development of 3D AxCaliber, the axon diameter distribution of each voxel was then calculated from the re-sampled data  (Barazany et al. , 2011). Fig. 5  shows an example of the 3D AxCaliber analysis pipeline where the CHARMED analysis recognizes the two fiber systems at the crossing region (Fig. 5A ). Fig. 5B  shows spherical de-convolution analysis of the DTI data set. This analysis allows characterization of the diffusion properties of each of the fiber segments separately using the 3D framework described above. Following re-sampling of the data to create a full AxCaliber data set it was possible to calculate, for each image voxel, and for each fiber within it, the axon diameter distribution as shown in Fig.
Preliminary results in post-mortem monkey brains  (Sherbondy et al. , 2011)  give further supporting evidence for this. Towards the micro-connectome: atlas of brain micro-structure Once reliable indicators of brain microstructure and optimal approaches to incorporating these to provide robust estimates of brain connectivity were established, our next and final step of the project was to produce initial reference databases of brain connectivity and microstructure. There have been atlases produced in the pastderived from a single subject (19)and just using DTI data. Although useful, such atlases were built from the data of a single subject. By pooling data from multiple subjects, we were able to place confidence bounds, in each pixel in the image, on the quantitative metrics of microstructure and connectivity.
Fig. 7  shows an example for atlas reconstruction for one specific fiber systemthe arcuate (superior longitudinal) fasciculus. For this tract (as well as for the others studied although not shown in this paper), mean values, standard deviations and correlations between the parametric maps (FA, MD and Fr) are shown. Moreover, the distribution of the mean and the standard deviation of the parameters are projected along the tract. (c) Atlas of mean axon diameter 10 volunteers from the ARCHI database were also scanned with the ActiveAx-optimized protocol (Based on protocol in Appendix A) in order to provide mean axon properties for the entire brain and specific fiber systems. Fig. 8  shows the resultant estimates of axon diameter for an entire hemisphere of one of the volunteers. This is the first demonstration of the orientation-independent mapping of microstructure in the majority of the white matter in vivo.
AxCaliber; and 3. ActiveAx. The general experimental conditions are summarized in the following table: In addition to micro-structural protocol, a HARDI data set was also acquired consisting of 60 gradient directions measured at b = 1000 s/mm 2 and whole brain coverage at a cubic resolution of 2. 1 mm 3. The analysis procedure included the following frameworks: Connectivity analysis: 1. Open Walnut: www. openwalnut. org 2. BrainVisa: http://brainvisa. info/ 3. ExploreDTI: http://www. exploredti. com/. Micro-structure analysis: 1. CHARMED/AxCaliber: http://neuroimaging. tau. ac. il/yawiki/index. php/CHARMED_code 2. ActiveAx: http://cmic. cs. ucl. ac. uk/camino/. Data sets for Connectivity and Micro-structure analysis: http:// www. brain-connect. eu. ActiveAx and Tractography: http://dig. drcmr.
Results: We developed an original gene clustering method based on discretized transition profiles, and applied it to datasets from three landmark early embryonic transcriptome studies. We identified 417 genes significantly up-regulated during ZGA. De novo motif discovery returned nine motifs over-represented in their non-coding sequences (upstream, introns, UTR), three of which correspond to previously known transcription factors: Zelda, Tramtrack and Trithorax-like (Trl). The nine discovered motifs were combined to scan ZGA-associated regions and predict about 1300 putative cis-regulatory modules. The fact that Trl is known to act as chromatin remodelling factor suggests that epigenetic regulation might play an important role in zygotic genome activation. We thus systematically compared the locations of predicted CRMs with ChIP-seq profiles for various transcription factors, 38 epigenetic marks from ModENCODE, and DNAse1 accessibility profiles.
We were mostly interested in zygotic activation; the coherent clustering shown by purely zygotic clusters, and the fact that we did not find any specific motif to NC dependent and independent genes led us to merge the ten uXXX and zygotic clusters (Pilot "usss", "udss", "uuss", "ussd", Lu "us D us H ", "us D ss H ", "uu D us H ", "uu D uu H ", De Renzis purely zygotic, early zygotic) into a single cluster totalizing 417 genes, hereafter denoted as "ZGA cluster", for further analysis (Additional file 5: Figure  S4B ). We evaluated the relevance of this newly defined ZGA cluster and analysed the enrichment of these clusters in gene ontology terms (GO biological process, molecular function and cellular component), using the software tool compare-classes of RSAT suite  [19]. We found 184 significantly enriched terms (E-value < 0.
Thus, after having merged the CRERs detected in the different types of regulatory regions, we obtained a final set of 1394 non-overlapping CRERs, hereafter denoted as "predicted CRMs". In addition to de novo motif discovery, we analysed the enrichment of the ZGA cluster for known motifs, using the program cisTargetX  [23]. This tool reveals enriched regulatory features (e. g. motifs or in-vivo datasets) in a set of regions, and ranks these features using a Z-score like enrichements score. Consistently, the results reveal a high enrichment for Zelda (score 12. 8) and TRL (score 3. 5) binding motifs (Additional file 8: Figure  S5 ). In this analysis, binding motifs for Dorsal (DL), Krüpple (KR) and Bicoid (BCD) were also reported as significantly enriched, which is not unexpected, given the high level of correlation between the binding of these TFs and Zelda.
Indeed, a first study of in-vivo binding of BCD, CAD and KR showed that by far the most over-represented motif under the binding peaks was CAGGTAG, hence the binding motif for Zelda  [24]. However, many of the CRER do not contain any binding sites for these factors: of the 1394 predicted CRMs, only 765 (54%) contain a predicted binding site for BCD, KR, DL or CAD, using a threshold of p = 0. 0001 on the binding site. Hence, the observed enrichment for these transcription factors is restricted to a subset of predicted CRMs. This can be confirmed using in-vivo datasets for these factors including Zelda; we restricted our analysis to the CRER containing a predicted Zelda binding site (780 CRER), of which 599 overlap with an in-vivo Zelda binding event, using the ZLD-ChIP datasets published in  [7].
Using in-vivo datasets to investigate epigenetic mechanisms The motif discovery analysis described in the previous section, and in particular the presence of Trl-related motifs, suggests a possible involvement of epigenetic factors in the activation of zygotic gene expression. In order to complement the previous motif analysis, we thus decided to make use of recently published in-vivo datasets and to investigate epigenetic regulation by analyzing ChIP-seq and DNAse1 accessibility data, using read densities as well as peaks locations. We focused on the factors CBP (0-4 h), Trl (0-8 h) and Zelda (extracted at 1 h, 2 h and 3 h after fertilisation), modified histones (H3K4me1 0-4 h, H3K4me3, H3K9Ac, H3K27Ac, H3K27me3) and open chromatin (DNAse1 accessibility, stage 5).
Differential motif analysis reveals ZGA specific associations After having shown that ZGA CRERs reveal specific associations between motifs, we wanted to investigate whether these associations were general, or ZGA specific. In order to do so, we systematically performed a differential motif analysis with the program peakmotifs (RSAT)  [27, 28]  between the ChIP peaks located in non-coding regions associated (hereafter denoted "ZGApeaks") and not associated ("non-ZGA peaks") with genes of the ZGA cluster (Additional file 10: Figure  S7  and Additional file 11: Figure  S8 ). The Zelda binding motif is over-represented in CBP, TRL and DNAse1 ZGA-peaks vs. non-ZGA peaks, confirming the importance of this factor for the control of zygotic genome activation (Additional file 10: Figure  S7 ). The unknown motif AGATACA appears also to be systematically enriched in ZGA vs.
non-ZGA datasets, confirming its relevance to ZGA specific processes. The differential analysis of Zelda-bound regions at different time points shows that the TRL-related motif and AGATACA are highly differentially enriched, underlying the ZGA-specific association between these three motifs. As expected, and as a control of the differential analysis, the Zelda motif does not appear, being present in ZGA as well as non-ZGA peaks. CBP does not establish direct interactions with DNA, but interacts with a large diversity of DNA-binding transcription factors. In a recent study, the importance of Dorsal for the recruitment of CBP has been shown  [29]. Interestingly, in this study, a strong correlation between CBP and TRL binding had also been shown. Here, we do not find Dorsal binding sites over-represented in ZGA vs.
For each of these datasets, we computed the density of reads under CRMs for various marks of transcriptional and epigenetic regulation: Zelda (global transcription factor), CBP (non-DNA binding cofactor) and TRL (chromatin remodelling factor), histone marks, and DNA accessibility profiles, and compared it with the density of reads under randomly selected regions of similar sizes and types (upstream, intron,. ). We also computed a p-value using the Wilcoxon rank test in order to evaluate the difference of enrichment between ZGA CRMs and controls (Additional file 12: Table  S4 ). The results are displayed as ROC curves (Figure  6A ), indicating the proportion of CRMs reaching a given density score (ordinate) versus random regions reaching the same score (abscissa). The area under the curve (AUC) was computed to quantify the relative enrichment of different datasets (Additional file 13: Figure  S9A ).
This seems consistent with a general activation of many genes in the whole embryo. Figure  7  shows the ROC curves for CRM occupancy by CBP, DNAse1 and H3K27me3 at successive stages of embryonic development. For both ZGA predicted and blastoderm-specific curated CRMs, CBP occupancy and DNAse1 accessibility are clearly restricted to very early stage (0-4 h) corresponding to the two waves of ZGA (1 h and 3 h, respectively), and rapidly decay at later stages. The same trends are observed for Trl (see AUC distributions for all data sets in Additional file 13: Figure  S9B ). In contrast, the strong enrichment of repressive mark H3K27me3 in curated blastoderm-specific CRMs is constant during all the studied period (0-16 h). On the downside, comparing the right and left panels reveals that enrichments curves are more pronounced for experimentally validated blastoderm CRMs than for ZGA predicted CRMs, which likely reflects the generation of false positive among the latter.