dataesr/scientific-paragraphs-categorization huggingface dataset:
Testing preservation and reproducibility of the UKBEC consensus and differential modules in other datasets Several independent brain gene-expression datasets were used to establish that the modules derived from the UKBEC samples were reproducible in the healthy human brain during development and at different ages across life, in different brain regions and tissues, and in the hippocampus of mice (see Additional file 2: Table  S2 ). To test spatio-temporal preservation of modules, we used transcriptome data from GSE25219  [32, 33]. These microarray expression data were generated using Affymetrix Human Exon 1. 0 ST array analysis of 16 brain regions from 1263 samples collected from 53 clinically unremarkable post-mortem human brains, spanning embryonic development to late adulthood. The quantile normalisation and filtering using expectation maximisation (EM) algorithm was done as previously described  [25].
The RNAseq expression data from the GTEx project was used to analyse the preservation of co-expression modules across different brain regions but also in other tissues. The fully processed, normalised and filtered gene level expression data were downloaded from http://www. gtexportal. org/ (GTEx_Analysis_V6). The raw data of the E-MTAB-3123 dataset of epileptic and normal human hippocampi were downloaded from https://www. ebi. ac. uk/arrayexpress/experiments/E-MTAB-3123/. Data normalisation, adjustment and filtering were applied as previously described  [87]. We also used the human post-mortem hippocampus microarray expression data from 63 healthy postmortem human brains publicly available from Pritzker Neuropsychiatric Disorders Research Consortium (http:// www. pritzkerneuropsych. org/?page_id=1196, GSE45642).
These data were processed as previously described  [25]. Finally, to assess whether the modules were preserved in another species we used the RNA-seq data from mouse hippocampi previously generated and processed  [24]. The datasets were annotated with human Ensembl gene ID using the biomaRt Bioconductor R package  [88, 89]  and selecting human genes that were 'one-to-one' orthologues with mouse genes. We used the function modulePreservation of WGCNA R package to calculate the Z summary value for each module  [31]. This function computes a permutation test to estimate the mean and variance of several preservation statistics under the null hypothesis of no relationship between the module assignments in the reference and test data. The Z summary value summarises density and connectivity based Z statistics generated through these permutations and therefore is indicative of the module robustness and reproducibility.
For each disorder, we included SNV DNM considering all missense, nonsense and splice-site SNV mutations. We adopted a FET (two-tail) to empirically compare the rates of DNMs overlapping the CCDS real estate of a module in case-and control cohorts. The code of the in-house R function to compute enrichment in DNMs is available at https://github. com/adelahay/ BrainCell (DNMFET function).
Protein-protein interaction network analysis First, to test the enrichment of PPI among protein products of M30 genes, we used the tool provided in the version 10. 0 of STRING database  [49]. The enrichment was calculated selecting known interactions (experimentally determined and from curated databases). Second, we tested M30 for enrichment in PPI using the DAPPLE module  [50, 51]  of the GenePattern platform  [52]. DAP-PLE looks for significant physical connectivity among proteins encoded for by genes according to proteinprotein interactions reported in the InWeb database  [103]. Then, we collated protein-protein interactions (PPI) from three different databases: GeneMANIA  [53] , Hippie  [54]  and iRefWeb  [55]. We created 100,000 random gene sets that each contained 320 different genes expressed in the UKBEC dataset (320 corresponding to the number of genes of M30).
GWAS-enrichment analysis To test for enrichment of genetic association in a geneset (i. e. co-expression module) we used versatile genebased association study (VEGAS2)  [104]  to generate a gene-based association statistic (P value) controlled for the number of SNPs in each gene and the LD between those SNPs. In all analyses gene-based P values were calculated using VEGAS2 and the top 10% option with 100,000 iterations and a gene window consisting of the transcriptional start and stop position of each gene. For the ILAE Consortium on Complex Epilepsies and the control GWAS datasets of waist-hip ratio, fasting insulin homeostasis, glucose challenge homeostasis, systolic blood pressure and diastolic blood pressure, the default 1000 Genomes European population was used to control for LD in the VEGAS2 analysis.
The GWAS-enrichment statistic was calculated for the tested modules from the gene-based association P values (from VEGAS2) using the Z-test based bootstrapping method  [105]  (one-sided) where, for each network, 100,000 random gene sets of same size as the network were sampled from the list of all brain regions expressed genes (n = 15,199). We tested enrichment M30 module for association to several disease traits and used FDR method to account for the number of tests. Enrichment in differential expressed genes between cases and controls in epileptic patients and mice models of epilepsy Differential expression analysis was performed in three datasets (see Additional file 2: Table  S2 ). For human microarray hippocampus expression dataset E-MTAB-3123 (24 TLE patients/23 controls, https://www.
ebi. ac. uk/arrayexpress/experiments/E-MTAB-3123/  [90] ), the differential expression analysis of the adjusted dataset was performed using the limma package  [106]. The mouse datasets were annotated with human Ensembl gene ID using the biomaRt Bioconductor R package  [88, 89]  and selecting human genes that were 'one-toone' orthologues with mouse genes. The package EdgeR  [107]  was used for differential analysis of RNA-seq mouse hippocampus datasets for both models, the pilocarpine mouse model (100 cases/100 controls) and the Dravet syndrome mouse model (4 cases/ 4 controls). The pilocarpine mouse model is a mouse model of temporal lobe epilepsy (TLE), where the mice develop spontaneous recurrent seizures (SRS) (i. e. epilepsy) a few weeks following the induction of status epilepticus (SE) by an injection of pilocarpine  [24, 58, 108].
The Dravet syndrome mouse model is a Scn1a knock-in mouse model (Mouse Genome Informatics MGI:3713740) for which the hippocampi were collected at 14 days of age, prior to the onset of seizures  [109, 110]. For the Dravet syndrome mouse model experiments, only male mice were selected and healthy littermate mice were used as controls. We multiplied the adjusted P value (the -log10 of P value) by the log-transformed fold change to generate a gene-level score, which was used as a metric to 'rank' genes. This gene-level score reflects the significance and the intensity of differential expression. Gene set enrichment analysis (GSEA)  [57]  was then used to test if a group of genes (genes from respective co-expression module) occupy higher (or lower) positions in the ranked gene list than what it would be expected by chance.
Enrichment in transcripts anti-correlated with frequency of seizure in the pilocarpine mouse model To this aim, we used data obtained from 14 days of continuous video monitoring of behavioural seizures in the mouse model of TLE beginning 28 days following pilocarpine induced SE)  [24, 58]. For all genes, the Spearman correlation coefficient between the number of seizures and the level of its expression was calculated using the cor. test function in the stat R-package. We multiplied the -log10 of P values by the correlation coefficients of each Spearman's test to generate a gene-level score, which was used as a metric as described above to 'rank' genes. GSEA  [57]  was applied to the genome-wide ranked list of gene scores to test for enrichment of correlated genes or anti-correlated genes in the set of M30 genes.
Spatiotemporal analysis of module expression To determine the spatiotemporal expression dynamics of modules, we used quantile normalised gene level expression values (log 2 transformed) from GSE25219  [32]. These transcriptome data were generated using Affymetrix Human Exon 1. 0 ST array analysis of 16 brain regions comprising the cerebellar cortex, mediodorsal nucleus of the thalamus, striatum, amygdala, hippocampus and 11 areas of the neocortex. The data were generated from 1263 samples collected from 53 clinically unremarkable post-mortem human brains, spanning embryonic development to late adulthood (from 10 weeks of post-conception to 82 years of age, which corresponded to periods 3-15, as previously designated)  [32]. The log 2 -transformed gene expression data follow a bimodal distribution contributed by low (likely nonfunctional) and high expressed genes  [111].
We used the expectation maximisation (EM) algorithm to model gene expression levels as a mixture of normal distributions and identify the underlying distributions of low and high expressed genes. Only the genes, with mean of log 2 -transformed expression values over the 95 % percentile of distribution of low-expressed genes (here > 5. 61) were considered for further analysis (n = 8704). The EM algorithm was implemented using normalMixEM function from the mixtools R package. Spatio-temporal dynamics of co-expression modules M1 and M3 across 16 brain regions and 13 developmental time points were illustrated as a heatmap, as previously described  [33]. Module expression for each region and developmental time point was calculated by averaging the scaled expression across all genes in a module.
Cell-type analysis A publicly available set of cell-type marker genes was used to determine whether the network is enriched for genes specific to a particular cell type  [41]. Marker genes were defined as preferentially expressed in a particular cell type, using single cell RNA-seq from mouse cortex and hippocampus for nine cell types  [41]. All expressed background genes used to build these sets of marker genes were annotated with human ENSG gene ID using the biomaRt Bioconductor R package  [88, 89]  selecting 'one-to-one' orthologues. Module enrichment for each cell type was performed using FET to empirically compare the rate of genes overlapping the cell-type marker genes within and outside the module. The code of the in-house R function to compute enrichment in cell-type marker genes is available at https://github.
com/adelahay/ BrainCell (CellFET function). To evaluate the pattern of expression of the M30 genes across cell types, we also used another single-cell RNA-seq mouse brain dataset  [42]. The processed and normalised gene level expression values (reads per kilobase par million mapped reads (RPKM)) from GSE71585  [42]. We selected the 1424 single-cell data that were classified consistently into one of the 49 cell types (core cells). To display the changes of expression across cell types, the expression in one cell type was calculated by averaging the expression of all cells of this cell type. The M30 genes average expression values were then scaled and plotted as a heatmap to show the change of gradients across cell types. The code of the in-house R function to build the cell-type heatmap is available at https://github.
Samples and sequencing A total of 234 genome sequences were obtained from the 1000 bull genomes project, run II  (Daetwyler et al. 2014). These included 129 Holsteins (125 Black and 4 Red), 43 Fleckviehs, 47 Angus, and 15 Jerseys. We considered all these sequences except the 4 Red Holsteins. We based our analyses on the phased and corrected autosomal data produced by the 1000 bull genomes project  (Daetwyler et al. 2014). These data included 27,535,425 biallelic single nucleotide polymorphisms (SNPs) and 1,507,728 biallelic indels.
Choice of unrelated animals To remove potential biases arising from sample size heterogeneity between breeds and inbreeding within breeds, we selected a subset of 25 unrelated animals in Holstein, Fleckvieh, and Angus breeds, while keeping all 15 Jersey animals. Within each breed, we computed the genetic relationship matrix (GRM) of all available animals based on SNPs from chromosome 1 with minor allele frequency (MAF). 10%, using the GCTA 1. 04 software  (Yang et al. 2011 ). We then selected unrelated animals as follows. First, we removed all animals with inbreeding value (the diagonal term of the GRM). 1. 5 (this threshold was chosen because inbreeding values. 1. 5 clearly appeared as outliers compared to the rest of the distribution; Supplemental Material, Figure  S1 ). Second, we considered all animal pairs with genetic relationship.
In subsequent reanalysis of small genomic regions, we kept all variants in the analysis. hapFLK makes use of the local clustering approach in  Scheet and Stephens (2006)  to model haplotype diversity. This model requires specifying a number of haplotype clusters as the input parameter. Using the cross-validation procedure implemented in the fastPHASE software, we found that 15 clusters provided the smallest imputation error rate. hapFLK can be computed on unphased or phased genotype data. Genotype calling made used of imputation approaches, and data were therefore already phased  (Daetwyler et al. 2014). We computed hapFLK both on the haplotype data and on the genotype (but imputed) data and found the two analyses provided similar results (not shown). The distribution of hapFLK is not known, but, from theoretical arguments hapFLK is a deviance statistic.
The resulting hapFLK distribution was different from the one observed on real data; in particular it showed a depletion of low hapFLK values compared to real data; i. e. , on real data some regions look more similar between breeds than on simulated data. The reasons for this are not clear, but we note that (i) fastsimcoal2 estimation does not use haplotype or linkage disequilibrium information so the haplotype patterns simulated are not expected to necessarily fit the data; (ii) simulations assume homogeneous recombination and mutation rates, which does not hold on real data; and (iii) common background/purifying selection between breeds might reduce differentiation in some genome regions. Despite the lesser hapFLK variance in simulations, scaling the hapFLK distribution to a x 2 with 14 d.
f. provided a very good fit (see Figure  S6 ). Applying the  Storey and Tibshirani (2003)  approach to estimate the proportion of alternative hypotheses in the resulting P-value distribution led to an estimate of 0 (i. e. , p0 ¼ 1). A python script to perform the scaling of hapFLK to x 2 distributions is now available on the hapFLK webpage: https://forge-dga. jouy. inra. fr/projects/hapflk/documents.
1 Mb with a median length of 20 kb (Figure  S9 ). The median size of detected regions was thus considerably smaller than that obtained in previous studies where hapFLK was applied to 60K data in sheep  (Fariello et al. 2014; Kijas 2014). Nineteen of the regions encompassed at least one gene while 38 contained no gene. In total, 82 genes were included in hapFLK regions, which represents 0. 4% of the total number of genes in the genome, corresponding to a small enrichment in protein-coding genes in hapFLK signatures. To investigate whether sequencing improves detection power with hapFLK, we thinned the data set by considering only SNPs present on the Illumina BovineHD BeadChip. We found (Figure  5 ) that the excess of small P-values was much larger when applying hapFLK to all sites identified in the 1000 bull genomes project than when applying it only to the SNPs that are included in the SNP chip.
Identifying causal adaptive polymorphisms As demonstrated above, genomic scans for selection based on sequencing data have a higher detection power than those based on genotyping chip data and locate the selection signatures with higher precision. This is an expected outcome of the higher marker density, and the same could be said when comparing high-density to medium-density chip data. But a more fundamental difference between dense SNP chip data and sequencing data is that, with the latter, one can reasonably expect the causal polymorphism under selection to be included in the observed data. Clearly, not all selection signatures can be related to a single polymorphism, and even in this case this polymorphism might be absent in our data due to insufficient coverage or remaining calling issues.
We expect the 1000 bull genomes data set to grow by inclusion of new populations, which will most likely increase power of differentiation approaches such as FLK and hapFLK by adding information on breeds more closely related to each other than on the initial release. On the other hand, among the 57 selection signatures detected by hapFLK, 49 were not detected by the withinpopulation approach. This comes from the fact that this approach is specifically designed to detect hard-sweep signals, while hapFLK also detects incomplete or soft sweeps  (Fariello et al. 2013 (Fariello et al. , 2014)). For the 49 regions detected by hapFLK and not by the within-breed approach, the evidence of a hard sweep, measured by the statistic T OR ; was always very low in all breeds, indicating that the signal detected by hapFLK had nothing to do with a hard sweep in one of the breeds.
For example, in the ARL15 signature, the swept haplotype clearly segregates at moderate frequency (85%) in the Jersey population (Figure  S12 ). This ability of hapFLK to detect incomplete and soft sweeps is extremely interesting for the study of livestock species. Indeed, recent intensive selection in modern livestock breeds has mainly targeted polygenic traits such as milk or meat production, which is much more likely to produce soft-and incomplete-sweep patterns than hard-sweep patterns  (Pritchard et al. 2010; Hernandez et al. 2011). In conclusion, our study illustrates how sequencing data offer great advantages for the detection of adaptive loci: higher power and better precision in localizing adaptive genes and even in some rare cases causal mutations. However, we found that many of our strongest signals and mutations lie in noncoding regions of the genome, hinting that a majority of Figure  9  Allele frequencies in the sweep region including SLC45A2 and RXFP3.
Acknowledgments Data analyzes were performed on the computer cluster of the  Genotoul bioinformatics platform Toulouse Midi-Pyrénées  (www. bioinfo. genotoul. fr). The cattle genomes were obtained from the 1,000 bull genomes project (www. 1000bullgenomes. com). We would like to thank  Didier Boichard  for useful discussions on this project and two anonymous reviewers for constructive comments on the manuscript. File S1: Hard sweep regions detected by the within-breed HMM approach. (. txt, 38 KB) Available for download as a. txtfile at: http://www. genetics. org/cgi/data/genetics. 115. 181594/DC1/24File S2: Breed-specific hard sweep regions, as defined by the 2 following criteria. (. txt, 5 KB) Available for download as a. txtfile at: http://www. genetics. org/cgi/data/genetics. 115. 181594/DC1/25.
This situation where SGI1 cannot excise and thus cannot be lost at least through integrase-mediated excision, provides further experimental evidence of incompatibility between these genetic elements. The sgiAT system is widely distributed. While 412 counterparts of the SgiA antitoxin are found in GenBank, only about half, 187, are detected for the SgiT toxin. Furthermore, all the SgiT homologous proteins found in GenBank are always located next to an homolog of the SgiA antitoxin, thereby confirming a potential coupled action between the two proteins and supporting the hypothesis of the impossibility of harboring a gene encoding the SgiT toxin without the presence of its corresponding antidote. While a few pair encoding genes are located on some plasmids (15%), all the others are located on the chromosome in the vicinity of genomic island-related genes suggesting that these sgiAT homologs may participate in their stabilization in the genome.
Distribution of sgiAT. Protein sequences of S025 and S026, with GenBank accession numbers ACS32052 and ACS32053 respectively, where used as tblastn queries to search for similar sequences in GenBank. Homologous protein sequences where retrieved with genome id, coordinates and taxonomic unit. Homologous proteins from a same genome and distant of less than 1 kb were conserved for further analysis. To classify the genomic region encoding couples of SgiAT homologous proteins, sequence header was analyzed for the presence of the word "plasmid" and 30 kb surrounding regions were analyzed for the presence of genomic island related genes. Sequences were aligned with clustalo and maximum-likelihood phylogenetic trees were constructed by phyML under a LG model with optimal tree structure from the best of NNI and SPR.
Acknowledgements We would like to thank  Professor Laurence Van Melderen  for her kind assistance and suggestions in the characterization of the toxin-antitoxin system and for kindly providing the vectors and strains used in this study. We also thank  Karine Praud  and  Camille Riffaud  for expert technical assistance. This work was supported by public funds from the  French National Institute of Agricultural Research. K. T. H. is supported by a PhD fellowship of the French ministry of higher education and research. M. G. was supported by funding from the  French research agency  (grant  Microtype  project under grant agreement no. ANR-14-ASMA-0002-01 ).
2013a ). -"Double-dipping," i. e. , using the same data for inference and for confirmatory purposes, a problem that often plagues brain-imaging research  (Kriegeskorte et al. 2009) , can be largely circumvented in computer-based inference by leaving a subset of the data out of the training database and using it specifically to independently test the classifier. -Hundreds of brain sensors are summarized into a single time curve that "projects" the data back onto a psychological space of interest. By identifying a nearoptimal spatial filter, this aspect of the method simultaneously bypasses the complex problems of source reconstruction and of statistical correction for multiple comparisons across hundreds of sensors and provides cognitive scientists with immediately interpretable signals. -Finally, because distinct classifiers are trained for different subjects, and only the projections back to psychological space are averaged across subjects, the method naturally takes into account inter-individual variability in brain topography.
Conclusion Determining the sequence of processing stages through which a stimulus passes is an essential goal for cognitive neuroscience. Furthermore, if the GNW theory is correct, assessing whether a brief stimulus reaches a late stage of information processing in which the sensory information is stabilized and is made available to further processors can provide an efficient signature of consciousness. Both of these goals can now be achieved through the use of temporal decoding and of the temporal generalization method. Multivariate decoding of temporal signals provides a sensitive method to probe the time course of information processing. The code is freely available as part of the open-source MNE-Python software  (Gramfort et al. 2014 ; http://martinos. org/mne/). Thus, all of the above techniques can now be readily applied to novel problems.