dataesr/scientific-paragraphs-categorization huggingface dataset:
Dynamics of XY gene expression during spermatogenesis To compare PMSC features with gene expression profiles, we next re-analyzed RNASeq datasets performed on purified mouse germ cells at different stages of spermatogenesis (spermatogonia B, pachytene spermatocytes, round spermatids and elongating spermatids)  [47] , using the same mapping parameters we used to analyze ChIP-Seq datasets (i. e. , last version of the mouse genome and parameters allowing the mapping of reads from multicopy genes). Using these datasets, we compared the dynamics of expression of sex chromosome-encoded and autosomal genes, according to their RPKM value. The threshold RPKM value (0. 22) above which a gene was considered as expressed was calculated by comparing the expression levels of exons and intergenic regions, as previously described in Ramsköld et al.
The X chromosome is enriched in pachytene-repressed genes. Similar proportions of non-reactivated genes are found for all chromosomes. Values are indicated in the table. The level of statistical significance is marked with three asterisks (***) if p < 0. 001 or with (ns) if not significant. See Additional file 7 for detailed statistical analyses during spermatogenesis using RNA-Seq datasets from purified mouse germ cells.
It is important to note that the threshold value above which a gene is considered as expressed is a critical parameter. For the present study, this value was calculated using a method comparing expression levels of exons and intergenic regions, previously described in Ramsköld et al. [48] , and which is lower than the one chosen in other studies such as in  [54]. In spite of that, many Y-and X-encoded ampliconic genes which belong to families of genes specifically expressed after meiosis had expression values below that threshold and were considered as silent (~2/3rd of all Y-linked genes). Since the conservation between ampliconic genes of the same family sometimes reaches 99%, it is impossible to precisely determine which gene members are transcribed and which are not. Soh et al. have estimated transcription of Y ampliconic gene families in whole testicular RNA-Seq datasets based on the search of sequence variants that distinguish individual members of each gene family.
ChIP-Seq analyses Round spermatids ChIP-Seq datasets from  [39, 41, 57, 70, 71]  were analyzed as follows. Only those with a high number of reads and a good FastQC report were considered. All samples and input were single end and were aligned using Heng Li Burros-Wheeler aligner BWA  [72]  version 0. 7. 5a-r405 with "mem" command against last mouse genome release mm10 (see Additional file 12). By default with BWA, reads which do not map uniquely on the genome are distributed to one location picked randomly. Briefly, BWA-MEM algorithm works by seeding alignments with maximal exact matches (MEMs) and then extending seeds with the affine-gap Smith-Waterman algorithm (SW). In case of multiple primary alignments, -c INT discards a MEM if it has more than INT occurrence in the genome. The maximum is set to 10,000.
Expression analyses RNA-Seq datasets from  [47]  (GSE35005) were analyzed by GenoSplice (http://www. genosplice. com) using the following parameters: Reads were aligned using STAR 2. 4. 2a [75]  on Mouse Genome version GRCm38 (see also Additional file 12). All genes (including pseudogenes) were taken into account. Gene counts were computed using featureCounts from the Subread package version 1. 4. 6-p5 [76]. RPKM values were computed from Subread results. Ensembl 81 was used for gene annotation. Reads which mapped at different locations were assigned to the location with the best score or to one location picked randomly by the program (option-M, see line commands in Additional file 12). The limit is set to 10 (i. e. , reads which mapped to 10 or more locations with a similar score were excluded; this represents less than 0.
5% of all reads, cf. Additional file 12). The threshold RPKM value above which a gene was considered as expressed was calculated by comparing the expression levels of exons and intergenic regions (i. e. , intersection of density plots), as previously described in Ramsköld et al, see Additional file 13  [48]. Individual threshold RPKM values vary among datasets from 0. 15 to 0. 27, yielding to an average threshold RPKM value of 0. 22. Genes de novo expressed are genes found to be expressed (RPKM > 0. 22) at one particular stage (pachytene stage or in round spermatids) and not before. Pachytene-repressed genes are genes that are at least twofold downregulated compared to their RPKM value in spermatogonial B cells. Round spermatids non-reactivated genes are genes defined as repressed in pachytene stage (see above) which do not recover at least half of their spermagonia B RPKM value in round spermatids.
Ensembl analysis Ensembl BLAST Web site (http://www. ensembl. org/ Multi/Tools/Blast) was used to search for genes related to takusan gene family. Using several gene accession IDs as queries, a total of 277 related genes (with >50% identity) were found; all mapped to chromosome 14. The analysis in which highly similar gene family members of the X and Y chromosome were treated as a single gene (Additional file 9) was performed as follows: Ensembl Biomart Web site (http://www. ensembl. org/biomart) was used to search for highly similar genes (with >80% identity) on the X and Y chromosomes. For the X chromosome, combination of the gene ID of identified members of multicopy gene families and the RNA-Seq data was used to analyze and collapse highly related genes into one single gene ID. For the Y chromosome, the multicopy gene-collapsed analysis was based on Soh et al.
Acknowledgements The authors wish to thank  James Turner  for critical reading of the manuscript,  Sophie Rousseaux  and  Saadi Khochbin  for sharing H4K8_hib ChIP-Seq data and for helpful discussions, and  Christophe Antoniewski  from the  ARTbio bioinformatics analysis facility (Sorbonne Universités, UPMC Université, CNRS, Institut de Biologie Paris Seine)  for help with Mississippi Galaxy Web site. The authors also thank the  Histology, Immunostaining and Laser Microdissection Facility , and the  Cellular Imaging Facility of the Cochin Institute  ( INSERM U1016 ,  CNRS UMR8104 ,  Université Paris Descartes ).
Funding This work was supported by  Inserm  ( Institut National de la Sante et de la Recherche Medicale ),  Cnrs  ( Centre National de la Recherche Scientifique ),  Université Paris Descartes , the  Agence Nationale de la Recherche  program  ANR-12-JSV2-0005-01  (to J. C. ) and a  Marie Curie  fellowship  FP7-PEOPLE-2010-IEF-273143  (to J. C. ). The funding bodies did not have any role in the design of the study, the collection, analysis, and interpretation of data nor in the writing of the manuscript. Received: 13 July 2016 Accepted: 17 October 2016.
nih. gov/) of the partial VP1 nucleotide sequences showed that the isolates from this study shared 96%-97% nt sequence identity with those of other EV-A71 isolates deposited in GenBank. The complete VP1 nucleotide sequences of the 4 EV-A71 isolates were determined by using reverse transcription PCR (GenBank accession nos. KT818793-KT818796; online Technical Appendix Table  2 ). Sequences were aligned by using ClustalW (http://www. clustal. org). Phylogenetic investigation indicated that the EV-A71 strains detected in 3 of the 4 isolates (from Senegal, Guinea, and Mauritania) clustered within subgenogroup C2. The most closely related EV-A71 strains are not those previously reported from other Africa countries (Madagascar, Cameroon, Nigeria, and Central African Republic) but are strains found in isolates from France, Finland, United Kingdom, Spain, Germany and Netherlands, all isolated during 2006-2010  (Figure).
Figure. Phylogenetic tree created with the complete VP1 nucleotide sequences (891 bp in length) of enterovirus A71 isolated from 4 patients with acute flaccid paralysis in West Africa, the most similar nucleotide sequences identified by a search in GenBank by using BLAST (http://www. ncbi. nlm. nih. gov/), and a representative global set of enterovirus A71 sequences belonging to different genogroups and subgenogroups. The coxsackievirus A16 prototype G-10 sequence was introduced as the outgroup. The tree was inferred with a neighborjoining method that used MEGA5 software (http://www. megasoftware. net/). Distances were computed by using the Kimura 2parameter model. The robustness of the nodes was tested by using Our findings highlight the presence of EV-A71 with a high degree of genetic diversity in patients with AFP in West Africa.
1,000 bootstrap replications. Bootstrap support values >80 are shown in nodes. Black triangles indicate the 4 strains from this study. Open square indicates a partial sequence. Scale bar represents nucleotide substitutions per site. Abbreviations of virus names indicate genogroups or subgenogroups/GenBank accession number/origin/year of isolation, respectively. A color version of this figure is available online (http://wwwnc. cdc. gov/EID/ article/22/4/15-1588-F1. htm).
While these techniques apply to "objective" representations of kinship and alliance networks, we also present two methods that take into account the generally lacunar and biased character of empirical kinship datasets. The first method we propose to deal with this problem (Section 4) is a generalized version of White's (1999) "reshuffling" approach, which consists in redistributing marriage or descent links between individuals or groups while keeping the numbers of links constant. (For alliance networks, the question can be dealt with analytically by straightforward calculation of expected marriage circuit frequencies. ) The second method (Section 5) consists in simulating the processes of network exploration by a virtual fieldworker navigating through kinship or alliance networks according to given behavioral constraints.
However, the abundance of structural features which have thus become accessible raises a new question: to what extent can they be taken as indicators of sociological phenomena (such as marriage preferences or avoidances), rather than as effects of chance or of observer bias? This paper presents a series of recently developed simulation techniques that deal with this issue. Starting from a new approach to "classical" agent-based modeling of kinship and alliance (group) networks (Section 2), we then present an automatic model discovery technique which, instead of constructing alliance networks from given matrimonial rules, reconstructs plausible matrimonial rules underlying given alliance networks  (Section 3). While these techniques apply to "objective" representations of kinship and alliance networks, we also present two methods that take into account the generally lacunar and biased character of empirical kinship datasets.
2006 , Geller et al. 2011 ) are still relatively rare. One reason for the scarcity of agent-based simulations based on realistic models of kinship networks is the highly complex nature of the genealogical datasets used by social scientists. Not only do numerous demographic and sociological factors (fertility and mortality rates, marriage and residence preferences, spatial distribution and migration, etc. ) jointly affect the form of real-world kinship networks, the datasets collected by social scientists are generally subject to (often substantial) observer bias and thus cannot be taken as miniature images faithfully reproducing the morphology of the actual networks from which they are obtained. In this survey article, we discuss several recently developed simulation techniques designed to deal with the problem posed by the multiplicity of (agent-and observer-related) factors affecting the generation of kinship and marriage links.
All result from the research project "Simulations de la Parenté-Kinship Simulations. " 1  After presenting a new technique for doing "classical" agent-based modeling of kinship and alliance (group) networks (Section 2), we will then introduce an automatic model discovery technique that, instead of constructing alliance networks from matrimonial rules, reconstructs matrimonial rules underlying the empirical alliance networks (Section 3). While these techniques (both in their "direct" and in their "reverse" form) apply to "objective" representations of kinship and alliance networks, we then introduce methods for taking into account the generally lacunar and biased character of empirical kinship datasets. The first method (Section 4) is a generalized version of  White's (1999)  "reshuffling" approach and consists in redistributing marriage or descent links between individuals or groups, while keeping the number of links constant.
The Example Data Set As far as possible, we shall use empirical data to illustrate our methods. Our example dataset, created by Isabelle Daillant, stems from the Chimane of Bolivian Amazonia, who numbered about 7000 people at the time of data collection (see  Daillant 2003). The dataset, which comprises 2642 individuals and 753 marriages (see Fig. 2  for a graph of the genealogical network), was deliberately constructed for the purpose of studying matrimonial behavior. The data were collected in a way that allowed for a largely symmetric and unbiased representation of male and female links (facilitated by the absence of unilineal groups and an ambilocal residence rule). The Chimane have a Dravidian kinship system, implying prescriptive cross-cousin marriage and prohibited parallel-cousin marriage rules that, contrary to many other groups with marriage rules, are rather strictly adhered to, as can be seen from the matrimonial census: 30% of all unions are between real or classificatory cross cousins (in the Dravidian sense), and among these, 22% are between direct first-degree cross-cousins (94 marriages with the patrilateral, 95 with the matrilateral cross-cousin, which coincide in 25 cases).
Example: the Chimane alliance network morphology as a result of marriage exchange and main component pull We have applied this method to the alliance network formed by the agnatic components of the Chimane dataset (see Fig. 3 ). Here we simply select the shortest program of the five, given that the quality of the approximations found are very similar for all runs: if (exp (targetInDegree) > targetOutStrength) return reverseStrength else return 8 The first aspect to notice is that the identity number of nodes does not enter the equations, which means that the explanation offered by the generator does not assume behavioral heterogeneity. The generator defines two modes of operation, which are selected by a comparison of the exponential of the target input degree (the number of the wife-taker's wife-givers) and the target output strength (the number of the wives given by the wife-taker).
Example: Testing the significance of Chimane consanguineous marriages Let us apply this procedure to the Chimane dataset. As we are only interested in marriages, we first eliminate all unmarried individuals from the genealogy, thus reducing the number of parent-child links and, as a consequence, the constraints restricting the freedom of permutation. We then transform the Chimane-network into P-graph format and then repeatedly switch arcs (subject to the specified constraints) until 100% of all arcs have been reshuffled and 1000 iterations have been run without improving the distance from the original network. Permutations of marriages shall only take place within a generation. The maximal distance thus achieved is about 43%. Fig. 7  shows the reshuffeled network. Table  6  gives an overview of the changes the reshuffling process has brought about in the numbers of consanguine marriage circuits (up to degree 3).
Example: distorting the balance of agnatic and uterine ancestor chains As an example, let us look at the effects that an agnatically biased data collection would have on the Chimane dataset. Let us, for the purpose of illustration, assume that our network represents in miniature form the true morphology of the Chimane genealogy. As has been said, the Chimane have a cognatic kinship reckoning and an ambilocal residence rule, which renders Daillant's network largely balanced (although, even in this network, there is a moderate bias in favor of agnatic over uterine linear chains). Now assume that another observer explores this network, choosing his or her informants from the generational level G-2 (fortunately the Chimane dataset includes information on generational levels in emic terms as ethnographic data, so we do not have to compute them).
This work has been partially supported by the  French National Agency of Research (ANR)  through grant " SIMPA "  ANR-09-SYSC-013-02. The paper has benefited from regular interactions with the members of the research group  TIP  ( Traitement Informatique de la Parenté-Kinship  and Computing, www. kintip. net). We are particularly grateful to  Isabelle Daillant , who has not only generously allowed us to use her Chimane dataset, but also much helped to improve the paper by her careful critical reading.
New experimental techniques in epigenomics allow researchers to assay a diversity of highly dynamic features such as histone marks, DNA modifications or chromatin structure. The study of their fluctuations should provide insights into gene expression regulation, cell differentiation and disease. The Ensembl project collects and maintains the Ensembl regulation data resources on epigenetic marks, transcription factor binding and DNA methylation for human and mouse, as well as microarray probe mappings and annotations for a variety of chordate genomes. From this data, we produce a functional annotation of the regulatory elements along the human and mouse genomes with plans to expand to other species as data becomes available. Starting from well-studied cell lines, we will progressively expand our library of measurements to a greater variety of samples.
These measurements can be used to identify regulatory elements  (20) (21) (22) , but also characterise disease  (23). To detect any signal, it is crucial to survey many of these biochemical features, often running many assays on a considerable number of samples. For this reason, large consortia have already produced vast reference datasets  (24) (25) (26). To make sense of these large datasets, the Ensembl Regulation resources provide a rich and powerful framework to browse or query these data and enable cell types comparison. In addition to cell-type specific measurements, we provide a number of summaries, as well as mapping microarray probes to the current reference sequences. Alongside all other Ensembl resources  (27) (28) (29) (30) , this data can be browsed on the web, but also accessed programmatically through MySQL, Perl or REST  (31)  for intensive queries.
Uniform processing of epigenomic data We first select cell types for which we have sufficient data to produce a segmentation (see below), and download all the epigenomic datasets associated with those cell types in the form of sequencing reads. Since we are aggregating data from diverse sources, it is vital to remove artefacts due to differences in analysis pipelines. Moreover, scientific consortia generally have neither the remit nor the resources to update their analysis results each time the reference assembly or other genome annotation is updated. We therefore remap all of the original data onto the current reference genome, call peaks and normalise the signal with our uniform pipeline  (33).
Motif features Chromatin immunoprecipitation experiments locate genomic fragments bound by a protein. Nonetheless, they do not directly locate the exact TFBS at nucleotide resolution. We therefore annotate TF binding peaks with overlapping motif features, i. e. putative TFBS based on existing publicly TFBS Position Weight Matrices (PWM) from JASPAR  (37). We start by associating TFs used in ChIP experiments to Ensembl gene IDs based on the antibody used in the ChIP experiment. We then associate each JASPAR matrix to Ensembl genes. If the JASPAR protein identifier is from the correct species, this is done directly using Ensembl's database of cross-linked identifiers across bioinformatics resources. Otherwise, the matrix is associated to the Ensembl human gene with the best Blastp  (38)  alignment to the JASPAR protein (blastp version: 2.
DNA methylation Both Reduced Representation and Whole Genome Bisulfite Sequencing (RRBS/WGBS) data  (16, 25, 26, 40)  are provided via the database, characterising 44 cell lines. This data was processed with Bismark (41) with default parameters, and replicate counts were merged. The number of converted reads at each position was fitted to a mixture model of two beta-binomial distributions and a uniform discrete distribution conditioned on the total number of reads at that position. This model assumes that a base pair is fully methylated, fully unmethylated or in an undetermined third state. For each dataset, the parameters of these distributions were set by Expectation Maximization (EM) across all the data points. See Figure  2  for an example fit. Using these parameters, we evaluated the posterior probability of all three states at each observed position.
Experimental metadata Normalised annotation of experimental metadata (such as the cell type and experimental factor) is essential for data integration. Large-scale projects such as ENCODE have led the way by using internal stable nomenclatures. To improve traceability and data integration, we use the The 'Open chromatin & TFBS' item has been selected to reveal a configuration matrix. To assist first time users, a tutorial is presented, this tutorial can be hidden or shown as desired. Experimental Factor Ontology (EFO)  (47) , and actively work with the EFO curation team to correct or submit new entries as required.
Data visualisation and access Ensembl location view Ensembl regulation data can be visualised in the Ensembl genome browser  (48). Figure  1  shows an example of the main Location view, where regulatory features can be observed alongside gene models and other user-selected data. Clicking on a regulatory feature reveals a pop-up menu with basic details: feature coordinates, stable ID, its classification and mapped TF binding motifs. By default, only the MultiCell regulatory features are displayed in this view, but the display can configured to show additional cell-specific annotations and regulatory evidence using the configuration panel linked on the left hand side of the page. Experimental evidence for the MultiCell regulatory features can be displayed as regions, generally ChIP-seq peak calls.
Clicking on a peak displays its type (e. g. DNase1 or the specific TF name); the parameters that defined the region from the data including score and peak summit representing the position of highest sequencing read coverage; and a data source such as an ENA accession. Additionally, the read coverage density functions underlying the peaks, measured in Reads Per Kilobase per Million mapped reads (RPKM), can be displayed as curves. Motif features (PWM alignments) are displayed as black boxes within regulatory features and ChIP-seq peaks of the corresponding TF. Clicking on a motif feature reveals a floating menu where the TF name, JASPAR PWM ID and the ratio of the motif's binding score divided by the PWM's optimal binding affinity are all highlighted within a Motif Information table. The regulatory segmentation data is shown as a continuous line across the genome whose colour changes with the local classification.