dataesr/scientific-paragraphs-categorization huggingface dataset:
Experimental Meta-data Tracking the origin of experimental evidence is essential for traceability and reproducibility. We store experimental meta-data in the Ensembl 'Funcgen' database, and these data can be accessed through the browser. Selecting an experimental peak reveals a link to the original data. This link is labelled with either a sequence archive ID (referring to the exact set of sequencing reads used in the Ensembl analysis) or a project name such as ENCODE if the data has not yet been submitted or deposited in an archive. Selecting this link leads to an Experiment view, containing a summary table of all available experiments, and a more detailed Sources table describing the sources of the selected feature. The summary table can be used to filter the content of the detailed Sources panel, allowing easy access and comparison of the meta-data for all the experiments incorporated into the Regulatory Build.
By clicking links in the summary table, filters on cell type, project name and evidence type can be added or removed. The system automatically composes the filters to display the desired datasets. In the Sources table, the 'source' and 'project' fields link out to the location of the source data (either in a sequence archive or on an associated project page) and the project website respectively. Other fields here include evidence type and name, links to the cell type EFO definition, PWMs and associated encoding genes for TF experiments. More fine-grained filtering is again enabled in the table header search box, allowing searches for more specific feature types.
BioMart Large-scale querying of Ensembl regulation data is possible through the Ensembl BioMart interface  (32) , which can output large numbers of features in several formats including tab-delimited text. Using the 'Ensembl Regulation' database (see Figure  8 ) as a starting point the user can select a regulation data set for a given species. The human and mouse data sets contain regulatory features, as well as regulatory evidence (experimental peaks), binding motifs (TFBS PWM mappings) and other regulatory regions (externally curated data). The Drosophila data set currently contains the latter only. Using filters, data for specific cell lines and/or specific experimental factors can be selected as can regulatory features overlapping specific genomic regions (e. g. upstream genes of interest).
Programmatic data access As with other Ensembl resources, regulation data can be extracted using a specific Perl Application Programming Interface (API) or by directly accessing the public funcgen MySQL databases at ensembldb. ensembl. org. The Ensembl website hosts specific funcgen tutorials and documentation describing this access (http://www. ensembl. org/info/docs/ funcgen/index. html). For other programming languages, key extraction functions are offered via the RESTful interface  (31).
Discussion The Ensembl regulation resources will continue to grow and evolve, accompanying the fields of genome regulation and epigenomics as new results are produced and new experimental techniques and analyses are developed. Firstly, in terms of scale, we expect the number of available cell types to climb rapidly to the hundreds, as more groups are contributing epigenomes to the global research community via the International Human Epigenome Consortium (IHEC) and other efforts. In particular, tissue samples will progressively replace the cell line models used thus far, which will enable us to detect regulatory features with greater sensitivity, since we are likely missing a considerable number of dynamic tissue-specific elements such as enhancers. Since our focus is to provide complete cell type specific annotations, we cover only chosen cell types, and do not attempt to include all publicly available epigenomic datasets.
These data can nonetheless be visualised in Ensembl using, for example, the IHEC data portal's track hub function (http://epigenomesportal. ca/ihec/). Our limited knowledge of TF binding motifs will need to be addressed. Current public databases such as JASPAR  (37)  only cover a fraction of existing TFs. Other techniques such as UniPROBE  (51)  or SELEX  (52)  will allow us to describe far richer binding interactions at enhancer and promoter sites. New technologies, such as chromatin conformation capture  (53) (54) (55)  will add a spatial component to our datasets. This might in turn affect analysis tools, such as segmentation, which could take into account spatial proximity when computing state likelihoods  (56). There will also be attempts to describe the cisinteractions of the regulatory elements thus uncovered.
Acknowledgements We thank all of the Ensembl users, especially those who have taken the time to contact us with suggestions and other helpful comments. We thank  Andy Jenkinson  for assistance with RRBS and other Methylation DAS data. We thank the other members of the wider Ensembl group at  EMBL-EBI  and the WTSI, along with  Guy Coates ,  Peter Clapham  and  Tim Cutts  for maintaining the Ensembl computer systems.
Funding Ensembl receives majority funding from the  Wellcome Trust  (grant numbers  WT095908  and  WT098051 ) with additional funding for specific project components from the  National Human Genome Research Institute  ( U41HG007234 ,  1U01 HG004695 ), the  Biotechnology and Biological Sciences Research Council  ( BB/L024225/1 ), and the  European Molecular Biology Laboratory. The research leading to these results has received funding from the  European Union 's  Seventh Framework Programme  ( FP7/2007-2013)  under grant agreement  282510  (BLUEPRINT). The research leading to these results has received funding from the  European Union 's  Seventh Framework Programme  ( FP7/2007-2013)  under grant agreement  223210  (CISSTEM). The research leading to these results has received funding from the  European Union 's  Seventh Framework Programme  ( FP7/ 2007-2013)  under grant agreement  222664  (Quantomics).
The research leading to these results has received funding from the  European Union 's  Seventh Framework Programme  ( FP7/2007-2013)  under grant agreement  HEALTH-F4-2010-241504  (EURATRANS). This work was also supported by the  European Commission  through grant agreement  018883  (HEROIC), an  Integrated  Project funded under the 6th Framework Program; and by the  European Commission  under grant agreement  226073  SLING, an  Integrating Activity within Research Infrastructures  of the  FP7 Capacities Specific Programme. Funding for open access charge: The  Wellcome Trust.
Availability All Ensembl data and source code are freely available and may be downloaded in their entirety from the Ensembl website. Each Ensembl release is maintained as an archive web site for at least 3 years after the date of initial release. Ensembl is updated approximately five times each year with new data, genome assemblies, and sequenced genomes. Additionally, the data is available through a programmatic interface and through the web-based Ensembl Biomart.
Lateral variations along the Himalayan arc are suggested by an increasing number of studies and carry important information about the orogen's segmentation. Here we compile the hitherto most complete land gravity dataset in the region which enables the currently highest resolution plausible analysis. To study lateral variations in collisional structure we compute arc-parallel gravity anomalies (APaGA) by subtracting the average arc-perpendicular profile from our dataset; we compute likewise for topography (APaTA). We find no direct correlation between APaGA, APaTA and background seismicity, as suggested in oceanic subduction context. In the Himalayas APaTA mainly reflect relief and erosional effects, whereas APaGA reflect the deep structure of the orogen with clear lateral boundaries. Four segments are outlined and have disparate flexural geometry: NE India, Bhutan, Nepal & India until Dehradun, and NW India.
Data and New Measurements Gravity Data Compilation. Song and Simons 7 used ETOPO-5 digital elevation model for bathymetry/ topography and marine satellite gravity data-derived free-air anomalies  8  to perform their study. Today, bathymetry as well as marine gravity field derived from satellite altimetry are available at a resolution of ~10 km and an accuracy of ~2 mGal (ref. 9). In a collisional setting high-resolution topography data is readily available from the SRTM mission  10  , but contrary to subduction zones continental satellite gravity data remains to be low-resolution. Therefore the challenge of constituting a comprehensive dataset hinges on gravity data. Satellite gravity datasets from missions such as GRACE and GOCE have a resolution of ca. 100 km (ref. 11), which is similar to the width of the Himalayas (ca.
250 km) and the width of the seismically locked zone (ca. 100 km). Global gravity models such as EGM2008 (refs 12 and 13) claiming higher resolution are created using interpolation, therefore studies based on such models (e. g. ref. 14, with application to the Himalaya) cannot resolve features smaller than 100 km wavelength. We hence aim to build an as complete as possible land gravity dataset to carry out the highest resolution plausible analysis. We start from our previous compilation of gravity data  15  , based on data from the International Gravimetric Bureau (BGI, http://bgi. omp. obs-mip. fr/), and refs 16-18. This dataset has a good coverage on either side of the Himalayas, but only a few profiles across the orogen. Therefore we carried out targeted field campaigns to complete this dataset with new field measurements in Nepal  19  and Bhutan  20.
Finally, we include gravity anomaly data from further three sources. Data by ref. 21 were digitized from projected profiles of the publication. Data reported in ref. 22 were kindly made available by the authors. Unpublished data by G. Poretti was also made available for our study (personal communication). The distribution of the source of gravity data is shown in Fig. 1a. The consistency of the eight different datasets was verified by producing a series of profiles across the orogen. The relative gravity anomaly dataset of ref. 21 was shifted by -210 mGal to fit the earlier compilation (see details in ref. 15), and two out of five profiles were discarded as found inconsistent with neighbouring datasets. The gravity data from ref. 22 and Poretti (pers. comm. ) have been reprocessed in the same manner as our own new data in Bhutan and Nepal.
During the calculation of absolute gravity, the recorded data have been corrected for polar motion, pressure variations, Earth tides (ETGTAB  25  ) and ocean load (FES2004  26  ). The FG5 instrument provides an absolute gravity value at about 120 cm height above ground. We transfer this to the benchmark on the floor by estimating the vertical gravity gradient in a series of relative gravity (Scintrex CG5 instrument) measurement loops between the floor and 120 cm height  27. A similar protocol has been used to connect the absolute gravity value with the relative gravity network: gravity ties have been done between the absolute benchmarks and the reference stations of the relative gravity surveys  23. Our final new gravity dataset in Nepal and Bhutan is available from the authors and will be available through BGI (http://bgi.
88°E, sharp in the foreland and little more gradual in the orogen; • Bhutan is again positive-to-negative from foreland to orogen; • There is a change around 93°E, visible in the foreland (there is no data within the Himalayas, but there is also a change further north near 94°E); • APaGA is negative in the Assam foreland, with a few positive data points north of the Himalayas. At the current level of data coverage, the foreland-to-orogen variation of APaGA is negative-to-positive at the area of Nepal and NE India, and is positive-to-negative near Bhutan and NW India. This is also represented on Fig. 3a  as an anti-correlation of APaGA values south and north of the topographic front along the arc. Despite some spread in the gravity data in Nepal due to merging various datasets, the APaGA averages with 1σ deviation are distinct on either side of the orogen front.
Conclusions We have compiled the to-date most comprehensive field-based gravity anomaly database for the Himalayan orogen and investigated arc-parallel topography and gravity anomalies (APaTA and APaGA). While APaTA mainly reflect relief and erosional effects within the Himalayas, APaGA reflect the deep structure of the orogen and its clear lateral segmentation. Four blocks are outlined: NE India, Bhutan, Nepal & India until Dehradun, and NW India. The segment boundaries in the India plate are related to inherited structure (Mahendragar-Dehradun Fault, Munger-Saharsa Ridge), while in the Eastern Himalayas the Shillong block boundaries are highlighted by focused seismic activity. The segmentation of the orogen into these blocks is also reflected in the mean features of the sedimentary foreland basin.
We found that the cumulated seismic moment of instrumental seismicity does not highlight   [42] [43] [44] [45]. Circles are estimates from receiver functions  [46] [47] [48] [49]. Map created with GMT software 39 version 4 (http://gmt. soest. hawaii. edu/). the boundaries found by APaGA, likely because the observation time is not representative of the full seismic cycle. However, large earthquakes of the past millennium do not propagate across the segment boundaries defined by lower plate inherited structure. Therefore the here identified segments are not only disparate in terms of flexural behaviour, but also seem to set an upper limit for the lateral extent of potential megathrust earthquakes. Further geophysical and paleoseismological measurements are needed to constrain the rupture extent of past events along the Himalayas, and to better characterize the nature of transition between the here defined segments.
The thus defined curved area extends between azimuths 144° and 239° (clockwise from North), spanning from the state of Assam in NE India to NW India, respectively (Fig. 1 ). Topography data was taken from the SRTM mission 10 at 1 arc-minute resolution. This is averaged using 8 km-wide radial bins to compute the mean arc-perpendicular topography profile. For gravity anomaly profile a similar averaging is performed with further binning into 1°-wide azimuthal elements, to avoid artefacts from the uneven distribution of data points. The thus obtained average arc-perpendicular profiles are then subtracted from the original datasets to result in Arc-Parallel Topography and Gravity Anomalies, APaTA and APaGa (Fig. 2a, c ). APaTA is also presented in a smoothed version with spatial averaging within a 30 km radius circle (Fig.
35 and GANSSER project data 36. Star indicates that magnitude scales are not homogeneous across the three catalogues. Earthquakes with magnitude larger than 7 are highlighted with yellow contour. The 1905 Kangra earthquake rupture contour 40 is shown in yellow. MDF: Mahendragar-Dehradun Fault. MSR: Munger-Saharsa Ridge. (c) Foreland basin depth. Isopach contours are from ref. 32. Inverted T symbols denote borehole data [42] [43] [44] [45]. Circles are estimates from receiver functions [46] [47] [48] [49]. Map created with GMT software 39 version 4 (http://gmt. soest. hawaii. edu/).
Acknowledgements We are grateful to people and institutions who have made fieldwork possible in Nepal and Bhutan, namely the  Department of Mines and Geology (Kathmandu) ,  Department of Geology and Mines (Thimphu) ,  HELVETAS Swiss Intercooperation Thimphu Office , and all the local drivers. We greatly acknowledge the  RESIF-Gmob  facilities for making gravimeter Micro-g LaCoste FG5 # 228  and Scintrex CG5 instruments available. We are indebted to colleagues who have provided gravity data:  Giorgio Poretti  (Trieste),  Paramesh Banerjee  (Singapore), as well as the  Bureau Gravimétrique International. We thank a large number of colleagues for help and discussions over the past 10 years at  ENS Paris , at  Géosciences Montpellier , at  ETH Zürich , and in Nepal and Bhutan. This study was financed by  North-South Centre at ETH Zürich, ETH Zürich ,  CNES TOSCA, CNRS-INSU ,  ANR , and  Swiss National Science Foundation  (Grant  PP00P2_157627 ).
Mice Wild M. m. domesticus was trapped at a large number of sites across Europe and the Americas (fig. 1A  [upper panel] and supplementary table S1, Supplementary Material online). A set of 29 Mus musculus castaneus mice trapped in northern India and Taiwan (fig. 1A , lower panel) were included as an outgroup  (Yang et al. 2011). Trapping was carried out in concordance with local laws and either did not require approval or was carried out with the approval of the relevant regulatory bodies (depending on the locality and institution). All DO mice were bred at The Jackson Laboratory. Litter sizes were counted within 24 h of birth. Individual investigators purchased mice for unrelated studies and contributed either tissue samples or genotype data to this study (supple mentary table S2, Supplementary Material online).
Chromatograms were analyzed with the Sequencher software package (Gene Codes Corporation, Ann Arbor, MI). For Primer Set B, products were visualized and scored on 2% agarose gels. Assignment to haplotypes was validated by comparing the results to quantitative PCR (qPCR) assays for the single protein-coding gene within R2d2, Cwc22 (see "Copynumber assays" below). For generation þ61, haplotypes were assigned based on MegaMUGA genotypes and validated by the normalized per-base read depth from whole-genome sequencing (see below), calculated with samtools mpileup  (Li et al. 2009). The concordance between qPCR, read depth, and haplotypes assigned by MegaMUGA or Sanger sequencing is shown in supplementary fig. S5 , Supplementary Material online.
Copy-Number Assays and Assignment of R2d2 Status Copy number at R2d2 was determined by qPCR for Cwc22, the single protein-coding gene in the R2d repeat unit, as described in detail in  Didion et al. (2015). Briefly, we used commercially available TaqMan kits (Life Technologies assay numbers Mm00644079_cn and Mm00053048_cn) to measure the copy number of Cwc22 relative to the reference genes Tfrc (cat. no. 4458366, for target Mm00053048_cn) or Tert (cat. no. 4458368, for target Mm00644079_cn). Cycle thresholds (C t ) were determined for each target using ABI CopyCaller v2. 0 software with default settings, and relative cycle threshold was calculated as DC t ¼ C reference t À C target t We normalized the DC t across batches by fitting a linear mixed model with batch and target-reference pair as random effects.
The C allele is associated with high copy number and is therefore presumed to be the derived allele. We computed the EHH statistic  (Sabeti et al. 2002)  in the phased data set over a 1 Mb window on each side of the index SNP using selscan (selscan -ehhehh-win 1000000). The result is presented in figure  2B. Decay of haplotypes away from the index SNP was visualized as a bifurcation diagram (figure  2C ) using code adapted from the R package rehh (https://cran. r-project. org/package¼rehh, last accessed February 24, 2016).
Inference of Local Phylogeny at R2d2 To determine whether the R2d2 HC haplotype(s) shared among wild mice have a single origin, we constructed a phylogenetic tree from the 39 MegaMUGA SNPs in the region flanking R2d2 (Chromosome 2: 82-85 Mb). We first excluded individuals heterozygous in the region and then constructed a matrix of pairwise distances from the proportion of alleles shared identical-by-state between samples. A tree was inferred from the distance matrix using the neighbor-joining method implemented in the R package ape (http://cran. rproject. org/package¼ape, last accessed February 24, 2016).
Analyses of Fitness Effects of R2d2 HC in the Diversity Outbred To assess the consequences of R2d2 HC for organismal fitness, we treated litter size as a proxy for absolute fitness. Using breeding data from 475 females from DO generations 13, 16, 18 and 19, we estimated mean litter size in four genotype groups: R2d2 LC/LC homozygous females; R2d2 HC/LC heterozygous females with transmission ratio distortion (TRD) in favor of the R2d2 HC allele; R2d2 HC/LC heterozygous females without TRD; and R2d2 HC/HC homozygous females. The 126 heterozygous females were originally reported in  Didion et al. (2015). Group means were estimated using a linear mixed model with parity and genotype as fixed effects and a random effect for each female using the lme4 package for R. Confidence intervals were obtained by likelihood profiling and post hoc comparisons were performed via F-tests, using the Kenward-Roger approximation for the effective degrees of freedom.
Null Simulations of Closed Breeding Populations Widespread fixation of alleles due to drift is expected in small, closed populations such as the HR lines or the HR8xC57BL/6J advanced intercross line. But even in these scenarios, an allele under positive selection is expected to fix 1) more often than expected by drift alone in repeated breeding experiments using the same genetic backgrounds and 2) more rapidly than expected by drift alone. We used the R package simcross (https://github. com/kbroman/simcross, last accessed February 26, 2016) to obtain the null distribution of fixation times and fixation probabilities for an HR line under Mendelian transmission. We assume that the artificial selection applied for voluntary exercise in the HR lines (described in  Swallow et al. 1998)  was independent of R2d2 genotype.
Among females heterozygous for the driving allele, transmission ratio (m) was calculated according to genotype at the modifier loci (if any). For males and homozygous females, m ¼ 0. 5. Individuals were assigned a relative fitness of 1 if m ¼ 0. 5 and 0. 8 if m > 0. 5. Recombination was simulated under the Haldane model (i. e. , a Poisson process along chromosomes with no crossover interference). Finally, for each individual in the next generation, one chromosome was randomly chosen from each parent with probability m. Simulation runs were restarted when the driving allele was fixed or lost, until 100 fixation events were observed in each condition of interest. Probability of fixation was estimated using the waiting time before each fixation event, assuming a geometric distribution of waiting times, using the fitdistr() function in the R package MASS.
Acknowledgments We wish to thank all the scientists and research personnel who collected and processed the samples used in this study. In particular we acknowledge  Luanne Peters  and  Alex Hong-Tsen Yu  for providing critical samples;  Ryan Buus  and  T. Justin Gooch  for isolating DNA for high-density genotyping of wild-caught mice; and  Vicki Cappa ,  A. Cerveira ,  Guila Ganem ,  Ron  and  Annabelle Lesher ,  K. Sa€  ıd,  Toni Schelts ,  Dan Small , and  J. Tapisso  for aiding in mouse trapping. We thank  Muriel Davisson  at the  Jackson Laboratory  for maintaining, for several decades, tissue samples from breeding colonies used to generate wild-derived inbred strains. We also thank  Francis Collins ,  Jim Evans ,  Matthew Hahn , and  Corbin Jones  for comments on an earlier version of this manuscript.
8, 1. 2 and 0. 45 μm ultrafilters under vacuum. The filtered supernatant was extracted with ethyl acetate (v/v). Ethyl acetate was rotary evaporated to dryness at 30 °C, and the extract resuspended in 100 % acetonitrile prior to UPLC MS/MS analysis. UPLC MS/MS was performed on a Waters Aquity UPLC-TQD apparatus driven by a MassLynx 4. 1 software, and fitted with an Acquity HSS C18 column (2. 1 mm × 50 mm; 1. 8 μm). The temperature was set at 20 °C and the solvent flow at 0. 6 mL/min. The solvent composition is provided in Additional file 2. Mass spectrometry was performed by electrospay ionization. The triple quadrupole analyzer was used in both MS and MS/MS modes with a m/z mass ranging from 50 to 800. The most sensitive analytical conditions were first determined using synthetic N-(octanoyl)-L-homoserine lactone (C8-HSL) and 3O,C8-HSL.