dataesr/scientific-paragraphs-categorization huggingface dataset:
Open ReMap pipeline A common issue with bioinformatics workflow is that it normally evolves at a different speed than data is published in the literature. We are making the code of our ReMap pipeline available to GitHub in the ReMap Github organisation (https://github. com/remap-cisreg). As the ReMap project expands dramatically and regulatory catalogs for other species are requested, it becomes essential to enable joint efforts between the ReMap team and external teams. This is essential for future collaborative production efforts. Briefly, our pipelines uses SnakeMake either in a Conda or Singularity environment depending on the HPC resources, where Torque and Slurm managers are both supported. Details of the pipeline are published on the repository.
Non-redundant peak sets and CRMs For each target, ReMap provides non-redundant binding regions, a specificity not found in other databases (Supplementary Table  S3 ). As the number of redundant peaks increases, and to improve the genomic accuracy of these nonredundant peaks, we updated our method. For a given TR, all peak lengths were truncated to the median size of all peaks for this TR. Then, to find clusters of redundant peaks, we used BedTools to intersect overlapping truncated peaks across different datasets (with at least 25% overlap, both ways). Once the clusters of overlapping peaks identified, non-redundant peaks are computed by averaging start, end and summits coordinates of all peaks in a cluster using original ReMap peaks lengths. For a given factor across all experiments, the non-redundant peak set consists of the computed non-redundant peaks plus singletons, and are available for download from the ReMap website.
HUMAN REGULATORY CATALOG EXPANSION This 2020 release of the ReMap human database exhibits significant growth in the number of datasets, the number of transcriptional regulators and overall in the number of binding regions integrated in our catalog. In this update, we curated processed and analyzed 3424 new human ChIPseq and 222 ChIP-exo datasets against TRs from GEO and ENCODE. Since the first ReMap release, we ensure consistency and comparability across datasets by processing from the raw data, through our uniform ReMap pipeline that includes read filtering, read mapping, peak calling and quality assessment (see 'Materials and Methods' section). Unlike other databases, the core foundation of ReMap lie in the manual curation of metadata, involving reading materials and methods as submitted experiments annotation is heterogeneous.
In addition, we run a critical data quality filtering step in our pipeline to address varying quality of DNAbinding experiments  (18, 19). After applying our quality filters we retained 2969 datasets (82%): 2767 ChIP-seq and 208 ChIP-exo datasets (Figure  1A  and Supplementary Figure  S1 ). This leads to an updated human regulatory atlas totalizing 5798 datasets. The uniform data processing contributes to a final ReMap 2020 human regulatory atlas of 164 732 372 peaks generated from 11 135 TRs (Figure  1B ). Our analyses produced 163 741 896 peaks set across 927 TRs for ChIP-seq and 990 476 peaks set across 208 TRs for ChIP-exo. This update shows a 2-fold increase in the number of TRs and number of peaks. The significant data growth is   1D ). The constant integration of a wide spectrum of cell lines and tissues will allow for a greater definition of the regulatory space across the genome.
Indeed, ∼34. 4% (106 Gb) of the human genome is covered by at least five features or more, and 17% (516 Mb) covered by 15 features or more (Figure  1E  and Supplementary Table  S4 ). As comparison, the ReMap 2018 catalog covered 19% (601 Mb) of the genome by at least five features, and 8% (257 Mb) with at least 15 or more features. In this update, we expand the regulatory panorama revealing dense co-localized regulatory regions at unprecedented depth (Figure  2A ). Indeed, the ReMap database shows an unprecedented landscape of the human regulatory abundance and complexity constituted by 165M binding regions forming 1. 7M CRMs. The genomic organization of our atlas reveals dense co-localizations of peaks forming tight clusters of heterogeneous binding regions with variable TRs complexity (Figure  2A , Supplementary Figure  S4 ).
Since 2015 we highlight this regulatory complexity by observing the vicinity of the human ELAC1 promoter illustrating ReMap 2015, 2018 and 2020 catalog growth (n = 229; 1037; 2546 peaks respectively). We observe three clusters of peaks, one large at the promoter followed by two clusters located at about +500 bp and +2 kb from the transcription start site. This third cluster has been detailed in our previous ReMap publications  (4, 8)  to illustrate how integrating data from different sources improves genome annotations. Indeed this cluster contains two ENCODE peaks for FOXA1 in the 2020 update, and only one in previous versions. This update further consolidate the binding location with 93 FOXA1 peaks (60 peaks in 2018, 15 in 2015) across different cells, antibodies, and laboratories (Supplementary Figure  S4 ).
The summit of each peak is represented by a vertical bar, which when aggregated closely gives information about the putative location of the DNA binding site. This FOXA1 clustering shows overlapping peaks, and does not reveal the discrete repertoire of binding regions in the human genome. Therefore, to address redundancy between datasets, we merged peaks for the same TR, resulting in a catalog of 76M non-redundant peaks. The genomic accuracy of these non-redundant peaks have been improved with a new method to reduce peaks redundancy (see 'Materials and Methods' for details). Non-redundant peaks and CRMs annotations are computed across all ReMap datasets and biotypes, thus representing a multi-cellular multi-tissue regulatory map. Taken together, the 2020 update of the ReMap human catalog provides a unique opportunity to identify complex regulatory architectures in our genome, each containing hundreds or thousands of bound regulators.
Arabidopsis transcriptional regulators atlas This ReMap release comes with the first large scale Arabidopsis regulatory atlas for transcription factors and general components of the transcriptional machinery. Indeed, the extent of the regulatory space in the Arabidopsis genome has not yet been fully apprehended with systematic integration of public DNA-binding assays. Thus, to enable genome-wide identification of Arabidopsis regulatory ele-ments we have collected, curated, uniformly processed and analysed 204 ChIP-seq and 848 DAP-seq datasets against TRs from GEO (Figure  1F  and Supplementary Figure  S2 ). We retained 509 datasets after quality control: 179 ChIPseq and 330 DAP-seq datasets leading to a final Arabidopsis regulatory atlas of 2 645 004 peaks for 372 TRs (Figure  1F  and G ).
Arabidopsis histone modifications atlas The ReMap Arabidopsis integration efforts have been complemented with the first release of histone modifications catalogue as a result of the uniform integration of ChIP-seq datasets. Histone modifications and variants regulate gene expression by remodelling the chromatin structure thus acting on chromatin accessibility  (21) (22) (23). The integration of the histone modifications assays can be used to attribute functional properties to genomic regions as histone modification positioning often correlate with genomic features  (6, 24). We have collected, curated, processed and analysed 358 ChIP-seq datasets against histone modifications (Figure  1F  and Supplementary Figure  S3 ). We retained 286 datasets after quality control leading to a final atlas of 4 528 203 peaks covered by 33 histone modifications and variants (Figures  1F, G , 2B, Supplementary Figure  S7 ).
A new web interface With this ReMap 2020 update, a new web interface has been designed to incorporate many of the features and char-acteristics present in contemporary websites. The new interactive website is designed with Laravel, a PHP web application framework for the development of web applications following the model-view-controller (MVC) architectural pattern. As template we used AdminLTE, a responsive HTML template based on the CSS framework Bootstrap 3. We used MySQL as a backend database to store ReMap datasets, targets, biotypes informations and external metadata. The site can be divided into six main sections: Search, Target and Biotype pages, Browsing data, the ReMapEnrich R Shiny tool, RESTful API and About pages. We have greatly improved the search functionalities of ReMap (Figure  3A ).
The search bar with advanced search options has been introduced into the main page for direct access to the data. The new search interface searches among all the main metadata, target synonyms, biotype synonyms to identify potential matches. Search results are presented in a paginated table which can be further filtered by a filter box. We have improved the Target and Biotype pages data visibility and usability by organising collections of blocks. Each Target and Biotype page contains an interactive paginated table for downloading separate dataset as narrow or broad peak files from macs2 (Figure  3B ). The 'Browsing' page and browsing vignettes assist in the visual exploration of the regulatory catalogues, allowing researchers to navigate data in genome browsers. Finally, the About pages provide detailed information for each species on the experiments, pre-processing steps, quality results, as well as basic statistics of the catalogues.
RESTful API We have developed a RESTful web service API to provide programmatic access to ReMap metadata as well as additional resources such as target, biotype informations, and BED files (Figure  3D ). The API has been implemented for querying the database with a batch list of TR, and/or biotypes. The RESTful API enables various bioinformatics tasks through a broad range of client software to access ReMap data programmatically. This web services is a convenient solution to integrate in an automated manner heterogeneous genomic information in complex work- flows without the need of local databases installations. The RESTful API is natively implemented in the Laravel Web Framework of ReMap, it returns the data in the convenient JSON format, and is accessible at http://remap. univ-amu. fr/rest page.
Data download and genome browsers Most users may prefer to download our atlas directly and query it locally, for instance to integrate with internal data or use in workflows. Each of the three ReMap database releases are available from the download page, where the entire catalogs are accessible in BED format in a variety of configurations: 'all' peaks, non-redundant peaks, CRM peaks. In addition, the download page is the entry point for searching and directly accessing BED files for specific targets, biotypes or datasets centric tables. Since the 2018 update we provided data navigation options for visual exploration of the ReMap regulatory catalogs combined with public or user-specific genome-wide annotations. As the ReMap catalogs expand in size and complexity, and researchers routinely generate large scale genome-wide genomic data, it became crucial to provide navigation flexibility to centralise these data.
CONCLUSION AND FUTURE DIRECTIONS The 2020 release of ReMap maintains the long-term focus of providing the research community with the largest catalogs of high-quality regulatory regions by integrating all available DNA-binding assays. The ReMap 2020 update comes with (i) a significant expansion of the human regulatory atlas; (ii) the first Arabidopsis regulatory atlas for transcriptional regulators; (iii) the first Arabidopsis catalog for histone modifications; (iv) a new website with improved user experience; (v) a new annotation tool with an R Shiny interface; (vi) a programmatic access to ReMap with a RESTful API; (vii) an updated genome browsing experience with Track Hubs data integration for different platforms. The quality of ReMap data is illustrated in the latest version of the JASPAR database  (28) , for which peaks were used to calculate TF link profiles.
We believe that our ReMap catalogs will help in better understanding the regulation processes in Human and Arabidopsis. In the future, as new datasets are constantly added to repositories, we would like to engage the scientific community in the curation process to increase our capacity to introduce new regulatory profiles for different species and different DNA-binding assays. As a long term goal, we would like to call the regulatory community with the curation and production processes to provide the best annotated regulatory repertoire in different species.
ACKNOWLEDGEMENTS We would like to thank  Lionel Spinelli  for in depth scientific discussions in pipeline implementation and his expertise on Docker and Singularity usage with the HPC resource. We would like to thank  Marius Gheorghe ,  Aziz Khan  and  Anthony Mathelier  from  NCMM Norway  for constant scientific feedback, the  UCSC Genome informatics groups  for help with tack hubs, the  Ensembl and Ensembl Plant group  for help with the Homo sapiens and Arabidopsis thaliana track hub. This work was granted access to the  HPC resources of Aix-Marseille Université  financed by the project  Equip@Meso  [ ANR-10-EQPX-29-01 ] of the  program 'Investissements d'Avenir ' supervised by the  Agence Nationale de la Recherche.
Background: Resting-state fMRI-based studies on functional connectivity in autism spectrum disorder (ASD) have generated inconsistent results. Interpretation of findings is further hampered by small samples and a focus on a limited number of networks, with networks underlying sensory processing largely under-examined. We aimed to comprehensively characterize ASD-related alterations within and between 20 well-characterized resting-state networks using baseline-data from the EU-AIMS Longitudinal European Autism Project. Methods: Resting-state fMRI data was available for 265 individuals with ASD (7. 5-30. 3 years; 73. 2% male) and 218 typically developing (TD) individuals (6. 9-29. 8 years; 64. 2% male), all with IQ>70. We compared functional connectivity within 20 networks -obtained using independent component analysis-between the ASD and TD group, and related functional connectivity within these networks to continuous (overall) autism trait severity scores derived from the Social Responsiveness Scale-2 across all participants.
In the present study, we investigated functional connectivity alterations in ASD across the entire brain in 478 participants of the EU-AIMS Longitudinal European Autism Project (LEAP;  25). Using this large, multicenter dataset including individuals with ASD and typically developing (TD) controls across a wide age range (6. 9-30. 3 years), we aimed to provide a comprehensive, data-driven characterization of ASD-related functional connectivity alterations both within and between 20 different resting-state networks (RSNs). RSNs were obtained using independent component analysis (ICA; 26) and covered the whole brain, including the sensory networks. Finally, to better capture the phenotypic heterogeneity among ASD and TD participants, we not only compared functional connectivity between the categorically-defined ASD and TD group, but also conducted dimensional analyses relating functional connectivity to a continuous measure of autism trait severity across all participants.
Derivation of 20 resting-state networks Scans were obtained using 3T MRI scanners at the five different sites. Acquisition parameters of the multi-echo R-fMRI scan and structural scan as well as the preprocessing procedure are detailed in the Supplemental material. All analyses described below were conducted in MNI152 standard space. To investigate functional connectivity alterations, we first extracted 20 spatially independent components by applying ICA (with dimensionality 20) as implemented in FSL melodic  (26)  to R-fMRI data of 75 TD participants. This TD sample included participants from each site and consisted of 25 children (6. 9-11 years), 25 adolescents (12-17 years), and 25 adults (18-30 years) to obtain resting-state components representative for all sites and the full age-range of our sample.
M A N U S C R I P T A C C E P T E D ACCEPTED MANUSCRIPT 8 inspection showed that the obtained components did not contain components representing noise, but instead all represented well-known and reproducible resting-state networks (RSNs). This is likely related to the application of careful ICA-based correction for head motion effects (using ICA-AROMA; 34) in our preprocessing pipeline. Accordingly, all 20 components were selected for further analyses. Figure  1  shows the spatial configuration of all RSNs, including sensory, motor, DMN, and task-related networks. The TD subjects used for the derivation of these RSNs were excluded from further analyses. For each of the remaining 138 TD and 265 ASD participants, we applied dual regression as implemented in FSL  (35, 36)  to obtain the subject-specific spatial maps and mean timeseries (across all voxels in the spatial map) corresponding to the 20 RSNs.
M A N U S C R I P T A C C E P T E D ACCEPTED MANUSCRIPT Preprocessing included removal of the first five volumes to allow for signal equilibration, primary head motion correction via realignment to the middle volume (MCFLIRT; 3), grand mean scaling and spatial smoothing with a 6mm FWHM Gaussian kernel. Next, we thoroughly corrected for secondary head-motion related artifacts, by applying ICA-AROMA, a novel ICA-based method, which automatically detects and removes motion-related components from the data (4). ICA-AROMA has been demonstrated to remove head motion-related artifacts with high accuracy while preserving signal of interest  (4, 5). Finally, we applied nuisance regression to remove signal from white matter and cerebrospinal fluid, and a high-pass filter (0. 01 Hz). The R-fMRI images of each participant were co-registered to the participants' anatomical images via boundary-based registration implemented in FSL FLIRT  (6).
The T1 images of each participant were registered to MNI152 standard space using 12- M A N U S C R I P T A C C E P T E D ACCEPTED MANUSCRIPT Oldehinkel et al. Supplement 3 parameter affine transformation and refined using non-linear registration with FSL FNIRT (10mm warp, 2mm resampling resolution; 3). Finally, we brought all participant-level R-fMRI images to 2mm MNI152 standard space by applying the R-fMRI to T1 and T1 to MNI152 transformations. All further analyses were conducted in MNI152 standard space.
Acknowledgements We thank all participants and their families for participating in this study. This work was supported  EU-AIMS  (European Autism Interventions), which receives support from the  Innovative Medicines Initiative Joint Undertaking  under grant agreement no. 115300 , the resources of which are composed of financial contributions from the  European Union's Seventh Framework Programme  (grant FP7/2007-2013), from the  European Federation of Pharmaceutical Industries and Associations  companies' in-kind contributions and from  Autism Speaks. We further gratefully acknowledge support from the  Netherlands Organization for Scientific Research (NWO)  through  VIDI grants  to  CFB  ( 864. 12. 003 ) and  AFM  (Grant No. 016. 156. 415 ). JKB received funding from the  FP7  under Grant Nos.
602805  (AGGRESSOTYPE), 603016 (MATRICS), and 278948 (TACTICS) and from the  European Community 's  Horizon 2020 Programme  ( H2020/2014-2020 ) under Grant Nos. 643051 (MiND) and 642996 (BRAINVIEW). We also gratefully acknowledge funding from the  Wellcome Trust   UK Strategic Award  ( 098369/Z/12/Z ) and funding to DM from the  NIHR Maudsley Biomedical Research Centre. We also acknowledge the contributions of all members of the  EU-AIMS LEAP group :  Jumana Ahmad ,  Sara Ambrosino ,  Bonnie Auyeung ,  Tobias Banaschewski ,  Simon Baron-Cohen ,  Sarah Baumeister ,  Christian F. Beckmann ,  Sven Bölte ,  Thomas Bourgeron ,  Carsten Bours ,  Michael Brammer ,  Daniel Brandeis ,  Claudia Brogna ,  Yvette de Bruijn ,  Jan K. Buitelaar ,  Bhismadev Chakrabarti ,  Tony Charman ,  Ineke Cornelissen ,  Daisy Crawley ,  Flavio Dell'Acqua ,  Guillaume Dumas ,  Sarah Durston ,  Christine Ecker ,  Jessica Faulkner ,  Vincent Frouin ,  Pilar Garcés ,  David Goyard ,  Lindsay Ham ,  Hannah Hayward ,  Joerg Hipp ,  Rosemary Holt ,  Mark H.
DISORT The Discrete Ordinate Radiative Transfer (DISORT) solver  (Stamnes et al. , 1988 (Stamnes et al. , , 2000) )  computes a net shortwave radiation flux at the surface for direct and diffuse radiation for atmospheric conditions that are prescribed using the libRadtran software package  (Mayer and Kylling, 2005). DISORT considers a plane-parallel, scattering and absorbing atmosphere for a monochromatic and unpolarized beam of light at a given angle. The pseudo-spherical variant SDISORT  (Dahlback and Stamnes, 1991)  also accounts for atmospheric curvature, which is particularly relevant for high SZA. SDISORT is used in this paper and is called DISORT from now on unless stated otherwise. A total of 32 streams, i. e. , computational polar angles, are used to solve the radiative transfer equation  (Stamnes et al.
However, RACMO2 does not compute sub-band energy fluxes. Hence, even with an efficiently derived fully spectral snow albedo or with smaller spectral bands, it would not be possible to estimate the snow albedo accurately within RACMO2 as subband energy fluxes are essential but unavailable. Therefore, we discard this approach. For the computation of RWs, a weighted mean of the subband albedo is computed using the solar energy distribution within a band provided by DISORT. This weighted mean of a band then corresponds with a certain representative wavelength. For this wavelength, TARTES would produce exactly the weighted mean albedo. This coupling framework forms the Spectral-to-NarrOWBand ALbedo module (SNOWBAL, version 1. 2). In many bands, e. g. , bands 6 or 8, the albedo is not a continuously decreasing function (Fig.
Discussion We have presented the SNOWBAL module to couple a narrowband regional climate shortwave radiation model with a spectral albedo model by using representative wavelengths. After calculating a RW, which depends on the SZA for clearsky conditions and on LWP and IWP for overcast conditions, a narrowband albedo can be computed for that RW. We have shown that the albedo errors that arise due to the use of this method are relatively small (< 0. 01). The uncertainties in-duced by other modeled physical properties that influence the albedo, like SSA and impurity content, are often larger. Impurities are shown to have a limited effect on the RW and have been neglected in the time series analysis. Still, impurities will impact the albedo. However, RACMO2 lacks a sophisticated impurity scheme, and impurities are parameterized differently compared to RACMO2 and PKM, thus making it difficult to assess the effect of impurities on the albedo using TARTES.
C. T. van Dalum et al. :  Spectral to narrowband snow albedo by using TARTES in RACMO2 will be evaluated using in situ measurements and remote sensing observations. Code and data availability. SNOWBAL module v1. 2, written in Python, alongside a code to produce a time series of RACMO2 using SNOWBAL, is supplemented along with this paper. TARTES is freely available at http://pp. ige-grenoble. fr/pageperso/picardgh/ tartes/ (last access; 4 December 2019). The libRadtran software package, including DISORT, is also freely available at http: //www. libradtran. org/doku. php (last access; 4 December 2019). RACMO2. 3p2 model data used in this paper can be found here: https://doi. org/10. 5281/zenodo. 1468647 (van Dalum and van de Berg, 2018).