dataesr/scientific-paragraphs-categorization huggingface dataset:
, Database issue Here, we introduce the ReMap 2018 update, which includes the integration of 2829 quality controlled ChIPseq datasets for TFs, TCAs and CRFs. The new ChIP-seq datasets (n = 1763, defined as 'Public' for non-ENCODE) as well as the latest ENCODE ChIP-seq data (n = 1066) have been mapped to the GRCh38/hg38 human assembly, quality filtered and analyzed with a uniform pipeline. In this update, we propose a unified integration of all public ChIP-seq datasets producing a unique atlas of regulatory regions for 485 TRs across 346 cell types, for a total of 80M DNA binding regions. Each experiment introduced in this release has been assessed and manually curated to ensure correct meta-data annotation. Our ReMap database provides DNA-binding locations for each TR, either for each experiment, at cell line or primary cell level, or at the TR level in a non-redundant fashion across all collected experiments.
This update represents a 2-fold increase in the number of DNA-binding proteins, 7-fold in the number of processed datasets, 4-fold in the number of cell lines/tissue types and 6-fold in the number of identified ChIP-seq peaks. While the first version of the ReMap catalog covered 26% (793 Mb) of the human genome, the regulatory search space for ReMap 2018 covers 46% (1. 4Gb). Finally, we give the community access to various options to visualize and browse our catalog, allowing users to navigate and dissect their genomic loci of interest co-occupied by multiple TRs in various cell types. Browsing the ReMap 2018 catalog using the Public Track hub, IGV data sever, Ensembl or UCSC sessions clearly exposes the abundance and intricacy of combinatorial regulation in cellular contexts. This report presents the extensive data increase and regulatory catalog expansion of ReMap as a result of our largescale data integration and genome-wide analysis efforts.
Available datasets ChIP-seq datasets were extracted from the Gene Expression Omnibus (GEO) (2), ArrayExpress (AE) (3) and ENCODE (5) databases. For GEO, the query '('chip seq' OR 'chipseq' OR 'chip sequencing') AND 'Genome binding/occupancy profiling by high-throughput sequencing' AND 'homo sapiens'[organism] AND NOT 'ENCODE'[project]' was used to return a list of all potential datasets, which were then manually assessed and curated for further analyses. For ArrayExpress, we used the query (Filtered by organism 'Homo sapiens', experiment type 'dna assay', experiment type 'sequencing assay', AE only 'on') to return datasets not present in GEO. Contrary to other similar databases (chip-atlas http://chip-atlas. org, (6,7)), ReMap meta-data for each experiment are manually curated, annotated with the official gene name from the HUGO Gene Nomenclature Committee (8) (www.
genenames. org) and BRENDA Tissue Ontologies (9) for cell lines (www. ebi. ac. uk/ols/ontologies/ bto). Datasets involving polymerases (Pol2 and Pol3), and some mutated or fused TFs (e. g. KAP1 N/C terminal mutation, GSE27929) were filtered out. A dataset is defined as a ChIP-seq experiment in a given GEO/AE/ENCODE series (e. g. GSE37345), for a given TF (e. g. FOXA1), and in a particular biological condition (e. g. LNCaP). Datasets were labeled with the concatenation of these three pieces of information (e. g. GSE37345. FOXA1. LNCAP). A total of 3180 datasets were processed (Supplementary Table  S1 ). Specifically, we analyzed 2020 datasets from GEO (1862) and ArrayExpress (158) repositories (July 2008 to May 2017). We define these non-ENCODE datasets as the 'Public' set, in opposition to ENCODE datasets (1160) (full list of experiments in Supplementary Tables  S2  and 3 ).
ReMap 2015 contained the multi-cell peak calling processed from ENCODE release V3 (August 2013). For the ReMap 2018 update, we re-analyzed, starting from the raw data, all ENCODE ChIP-seq experiments for TFs, transcriptional and chromatin regulators, following the same processing pipeline as the Public set. We retrieved the list of ENCODE data as FASTQ files from the ENCODE portal (https://www. encodeproject. org/) using the following filters: Assay: 'ChIP-seq', Organism: 'Homo sapiens', Target of assay: 'TF', Available data: 'fastq' on 21 June 2016. Metadata information in JSON format and FASTQ files were retrieved using the Python requests module. We processed 1160 datasets associated to 161 TRs and 87 cell lines. We removed 2 TRs (POLR2A, POLR3G), and renamed TR aliases into official HGNC identifiers (e.
ChIP-seq processing Both ENCODE and Public datasets were uniformly processed and analyzed. Bowtie 2 (version 2. 2. 9)  (10)  with options -end-to-end -sensitive was used to align all reads on the human genome (GRCh38/hg38 assembly). For Public datasets, adapters were removed using TrimGalore (https://www. bioinformatics. babraham. ac. uk/projects/ trim galore/), trimming reads up to 30 bp. Polymerase chain reaction duplicates were removed from the alignments with samtools rmdup  (11). For the ENCODE data, the adapter trimming step was not employed, as this data already passed certain quality assessment steps (https:// www. encodeproject. org/data-standards/). TR binding regions were identified using the MACS2 peak-calling tool (version 2. 1. 1. 2) (12) in order to follow ENCODE ChIP-seq guidelines  (13) , with stringent thresholds (MACS2 default thresholds, P-value: 1e-5).
Input datasets were used when available. All peak-calling files are available to download. Among the 80M peaks identified, 99. 5% of peaks (79 753 407) were below 1. 5 kb in size (mean size: 286 bp, median size: 231 bp) and only 376 017 peaks were above 1. 5 kb in size (mean size: 2209 bp, median size: 1859 bp).
Quality assessment As raw data are obtained from various sources, under different experimental conditions and platforms, data quality differs across experiments. Since the ReMap 2015 release, our ChIP-seq pipeline assesses the quality of all datasets, unlike similar databases (chip-atlas http://chipatlas. org, (6,7)), (Supplementary Table  S4 ). We compute a score based on the cross-correlation and the FRiP (fraction of reads in peaks) metrics developed by the ENCODE consortium (13) (Supplementary Figure  S1 ). Descriptions of the ENCODE quality coefficients can be found on the UCSC Genome portal (http://genome. ucsc. edu/ENCODE/ qualityMetrics. html). Our pipeline computes the normalized strand cross-correlation coefficient (NSC) as a ratio between the maximal fragment-length cross-correlation value and the background cross-correlation value, and the relative strand cross-correlation coefficient (RSC), as a ratio between the fragment-length cross-correlation and the read-length cross-correlation.
DNA constraint scores We provide the conservation profiles at the nucleotide level for each of the 485 TRs present in our catalog. We assessed the DNA constraint for each base pair by considering ±1 kb around the summit of each non-redundant peak (see below). Genomic Evolutionary Rate Profiling scores (GERP) were used to calculate the conservation of each nucleotide in a multi-species alignment  (14). The computed GERP scores were obtained from the 24-way amniota vertebrates Pecan (15) multi-species alignment, and extracted from the Ensembl Compara database release v89  (16).
Genome coverage, non-redundant peak sets and CRMs Genome coverages were computed using the BedTools suite (17) (version 2. 17. 0) using the 'genomecov' function with the option -max 2 that combines all positions with a depth ≥2 binding locations. Full details of the ReMap 2015 and 2018 genome coverage are available in Supplementary Table  S5. ReMap also provides a catalog of discrete, nonredundant binding regions for each TR, a specificity not found in other databases (chip-atlas http://chip-atlas. org, (6, 18) ). We used BedTools to merge overlapping peaks (with at least 1 bp overlap) identified in different datasets for the same TR. The summit of the resulting peaks was defined as the average position of the summits of the merged peaks. Those peaks made of at least two or more peaks for a given factor are defined as non-redundant peaks.
Integration of data sources The 2018 release of the ReMap database reflects significant advances in the number of binding regions, the number of TFs, transcriptional co-activators, chromatin regulators and overall the total number of datasets integrated in our catalog. We initially selected, processed and analyzed 3180 ChIP-seq datasets against TRs from GEO, AE and ENCODE. To ensure consistency and comparability, all datasets were processed from raw data, through our uniform ChIP-seq workflow that included read filtering, read mapping, peak calling and quality assessment based on EN-CODE quality criterions. As the quality of ChIP-seq experiments vary significantly  (20, 21) , we incorporated a critical data quality filtering step in our pipeline--not implemented in other databases (chip-atlas http://chip-atlas.
org (6, 7, 18) ). Specifically, we considered four quality metrics, two metrics independent of peak calling for assessing signalto-noise ratios in a ChIP-seq experiment and two metrics based on peak properties. Following ENCODE ChIP-seq guidelines and practices  (13) , we used the NSC and the RSC (see 'Materials and Methods' section). Further, we used the FRiP and the number of peaks in the dataset (see 'Materials and Methods' section). After applying our quality filters based on these four ChIP-seq metrics we retained 2829 datasets (89%): 1763 datasets from GEO and ArrayExpress and 1066 from ENCODE (Figure  1A  and Supplementary Figure  S1 ). The significant increase of data is spread across almost all TFs when compared to ReMap 2015 (Figure  1B ). Nevertheless, we observe TFs (e. g. AR, ESR1, FOXA1) and CRFs (e.
Regulatory catalog expansion With all ChIP-seq data uniformly processed, the ReMap 2018 catalog displays ENCODE data down to the cell line and dataset level rather than the simpler multi-cell analysis provided by ENCODE DCC used in ReMap 2015. Our analyses produced 48 693 300 peaks for the Public-only (non-ENCODE) set across 331 TRs and 31 436 124 peaks for the ENCODE set across 279 TRs, leading to a final ReMap regulatory atlas of 80 129 705 peaks generated from 485 TRs (Figure  1C ). We found 125 TRs common to the two sets, 154 proteins specific to ENCODE and 206 specific to the Public catalog (Figure  1C ). We also found that 839 400 CRMs are shared between both catalogs. Taken separately, the ENCODE peaks overlaps by 96% the Pub-lic regions, and 87% of the Public peaks overlap ENCODE regions (Figure  1C ).
It suggests that merging both Public and ENCODE sets complements the annotation of DNAbound regions, as it increases the number of regulatory regions in our atlas, hence improving the annotation of DNAbound elements in the human genome (Figures  1C  and 2 ). Indeed, about 13% (405 Mb) of the human genome is covered by at least one feature only from the entire ReMap catalog and 33% (1. 02 Gb) are covered by two or more features (Figure  1D  and Supplementary Table  S4 ). The Public-only and ENCODE-only sets cover the genome by two or more peaks by 28 and 15% respectively. The observed differences can be explained by the wide spectrum of cell lines and treatments included in the Public set (300 cell lines) compared to the ENCODE set (86 cell lines). As a comparison, the   2  and 3 ).
Large regulatory atlas The ReMap database provides a large view of a unique regulatory landscape constituted by 80M binding regions forming 1. 6M CRMs. The genomic organization of our occupancy map reveals dense co-localizations of sites forming tight clusters of heterogeneous binding sites with variable TRs complexity (Figure  2 ). For instance, the regulatory regions observed in the vicinity of the ELAC1 promoter illustrate the ReMap 2018 expansion (n = 1037 peaks). It highlights how the regulatory repertoire can be complemented by merging both Public and ENCODE sources. We observe a large cluster of peaks at the ELAC1 promoter followed by two clusters at +500 bp and 1 kb from the transcription start site. The third cluster exemplifies how integrating data from different sources improves genome annotations, as few peaks are available from ENCODE at this location.
Additionally, this cluster was detailed in our previous ReMap publication (4) and consisted of 15 FOXA1 ChIP-seq peaks from different cells, antibodies, and laboratories (Figure  3 ). In this update, we consolidate this FOXA1 binding location with 60 peaks. The summit of each peak is represented by vertical bars aggregated closely from each other, providing an information about the putative location of the DNA binding site. The clustering of FOXA1 peaks and summits illustrates our genome-wide repertoire. However, this FOXA1 example shows overlapping sites derived from various experimental conditions, and therefore does not reflect the total number of discrete binding regions across the genome. To address redundancy between datasets, we merged binding regions for the same TR, resulting in a catalog of 35.
Web display ReMap provides free public access to all data at http:// remap. cisreg. eu. The results presented here provide an informative annotation for 80M ChIP-seq peaks coming from public data sources, which are derived from 485 TRs across 346 diverse cell lines. This catalog provides an unparalleled resource for dissecting site-specific TF bindings (e. g. FOXA1 in Figures  2  and 3 ) or genome-wide binding analyses. The ReMap web interface displays informations about the integrated TRs (description, classification, external references to Ensembl gene IDs, UniProt, RefSeq, WikiGene, JASPAR, FactorBook, TF Encyclopedia and other resources), peaks, and datasets (quality assessment, read mapping and peak calling statistics, conservation score under peaks). The interface provides a simple 'Dynamic Search' available from the TRs, Cell lines and Download pages and is the entry point for users to search for specific data.
The search form allows users to narrow their searches based on gene aliases, dataset names or IDs, cell line names or ontology. For example, entering 'Oct' as search term in the 'Dynamic Search' returns three TFs POU2F2, POU2F1, POU5F1 having various 'OCT' aliases. Additionally, one could use the search box in the Cell or Download page to search for specific cell types containing the 'Colo' term for instance, or 'GSE66218' for a precise experiment from the Download page. Moreover, we provide a tool that allows the annotation of genomic regions provided by users. Those regions are compared against the ReMap catalog returning statistical enrichments of TR bindings present within userprovided input regions compared to random expectations. It allows for the study of over-represented TR binding regions.
Browsing and downloading data Updates made in ReMap 2018 reflect significant improvement in the variety of genome navigation options. As the ReMap 2015 UCSC session was popular, we now provide more data navigation alternatives. The content of the ReMap database can be browsed through four options: (i) across two mirror sites of the UCSC Genome Browser  (22)  where a public session has been created (Figure  2  and Supplementary Figure  S3 ), (ii) across three Ensembl Genome Browser mirrors (16) (Supplementary Figure  S4 ), (iii) using the ReMap public track hub  (23)  or (iv) using the IGV data server (24) (Supplementary Figure  S5 ). For each option, we provide four tracks, the full ReMap catalog containing all peaks, the Public-only peaks, the ENCODE-only peaks and a track containing only peaks above 1.
FUTURE DIRECTIONS Next-generation sequencing technologies are playing a key role in improving our understanding of regulatory genomics. As ChIP-seq technology is applied to a broader set of cell lines, tissues and conditions, we will continuously maintain and update the database. In the near future, we propose on adding to the ReMap portfolio different peak-caller analyses to further consolidate the peak repertoire. Also, we aim to provide direct access to aligned reads through a FTP server, allowing users to upload and navigate aligned raw data of their choice. We plan on releasing a Bioconductor R-package for genomic region enrichment analyses for large genomic catalogs such as ReMap, which will be replacing our current web enrichment tool. In D274 Nucleic Acids Research, 2018, Vol. 46, Database issue the coming year, we would like to provide a Bioconductor R-package to search and download ReMap data for a specific study, to get genomic range objects, raw counts and/or metadata used for a specific study.
CONCLUSION The 2018 release of ReMap maintains the long-term focus of providing the research community with the largest catalog of high-quality regulatory regions by integrating all available ChIP-seq data from DNA-binding assays. The usefulness of ReMap is exemplified by the last release of the JASPAR database  (25) , for which ReMap ChIP-seq peaks were used to derive 45 new TF binding profiles that were incorporated in the 2018 release of the vertebrate CORE collection  (Khan et al. 2018) , providing a 9% increase from JASPAR 2016  (26)  by solely relying on the ReMap 2018 catalog. Although new datasets are constantly added to repositories, we believe that our ReMap atlas will help in better understanding the regulation processes in human. In this update, we have (i) widely expanded the collection of datasets curated and analyzed from public sources with now 485 TFs, transcriptional co-activators and chromatin regulators; (ii) uniformly processed and integrated the EN-CODE ChIP-seq data; (iii) enhanced the website usability by allowing dynamic search of TRs, aliases, cell lines and experiments, (iv) expanded the genome browsing experience by integrating ReMap in all UCSC and Ensembl Genome Browsers mirror sites and provided a Track Hub for data integration in other platforms; (v) improved the capacity to download all ReMap files in bulk or individually.
ReMap ChIP-seq binding pattern of 2829 datasets. A genome browser example of the ChIP-seq binding peak depth of the ReMap 2018 catalog compared to ReMap 2015 at the vicinity of the ELAC1 promoter (chr18:50,967,094-50,970,983). The tracks and peaks displayed are compacted to thin lines so the depth of ReMap 2018 bindings can be compared to ReMap 2015. A full and un-compacted screenshot is available as Supplementary FiguresS2 and 3. On this location the ReMap 2018 catalog contains 1307 peaks, whereas the ReMap 2015 contains 229 peaks (ReMap 2015 lifted to GRCh38/hg38 assembly). The following genome tracks correspond to the GENCODE v24 Comprehensive Transcript Set and the 100 vertebrates base-wise conservation showing sites predicted to be conserved (positive scores in blue), and sites predicted to be fast-evolving (negative scores in red).
AVAILABILITY ReMap 2018 can be accessed through a web interface at http://remap. cisreg. eu. Downloads are available in BED format for the entire ReMap catalog, the Public-only peaks, the ENCODE-only peaks, and in FASTA and BED formats for each TR. In addition, UCSC and Ensembl Genome Browsers users can navigate ReMap across their mirror sites, use ReMap in UCSC public sessions, or use the public track hub. Finally, Integrative Genome Browser (IGV) users have the option of loading an IGV optimized dataset directly in the application.
Inverse probability of treatment weighting (IPTW) is a popular propensity score (PS)-based approach to estimate marginal treatment effects in observational studies at risk of confounding bias. A major issue when estimating the PS is the presence of partially observed covariates. Multiple imputation (MI) is a natural approach to handle missing data on covariates: covariates are imputed and a PS analysis is performed in each imputed dataset to estimate the treatment effect. The treatment effect estimates from each imputed dataset are then combined to obtain an overall estimate. We call this method MIte. However, an alternative approach has been proposed, in which the PSs are combined across the imputed datasets (MIps). Therefore, there are remaining uncertainties about how to implement MI for PS analysis: (i) should we apply Rubin's rules to the IPTW treatment effect estimates or to the PS estimates themselves? (ii) does the outcome have to be included in the imputation model? (iii) how should we estimate the variance of the IPTW estimator after MI? We studied the consistency and balancing properties of the MIte and MIps estimators and performed a simulation study to empirically assess their performance for the analysis of a binary outcome.
This approach, known as inverse probability of treatment weighting (IPTW)  [5]  aims to emulate the sample that would have been observed in a randomised trial. In practice, a major issue when estimating the PS is the presence of partially observed covariates, as the PS cannot be estimated for individuals with at least one missing covariate value. Standard analyses include complete case analysis  [6]  or the missingness pattern approach  [7] , but a popular alternative to handle missing data is multiple imputation (MI)  [8]. However, there remain four important unresolved questions about how to implement MI for PS analysis. First, two approaches to combine information from the imputed datasets have been proposed in the context of PS analyses: combining the treatment effects estimated on each imputed dataset (method called MIte hereafter) or combining for each individual their PS value across the imputed datasets (method called MIps hereafter)  [9, 10].
MIte intuitively seems to adhere best to the MI philosophy by applying the full analysis strategy on each imputed dataset. Furthermore, Seaman and White  [11]  proved that for an infinite number of imputations, the MIte estimator is consistent. However, Mitra and Reiter  [9]  published recommendations on the use of MI for PS analysis, and they advocated using MIps, rather than MIte, for PS matching. The second question is whether or not to include the outcome in the imputation model for the missing covariates. Mitra and Reiter did not include it in their study  [9] , which could explain the bias they observed for both MIte and MIps. One of the advantages claimed for the PS approach in general is that it allows the investigator to use the data on the treatment and covariates to develop a well-fitting PS model without needing to look at the data on the outcome  [9].
For MIps, there is, to our knowledge, no variance estimator that takes into account both the uncertainty due to the estimation of the PS given the complete data and that due to the imputation of the missing data. Finally, the fourth question is how well MI performs in comparison to other popular approaches to handle missing data, namely complete case (CC) analysis or the missingness pattern (MP) approach. Qu and Lipkovitch  [13]  and Seaman and White  [11]  assessed the performance of MIte additionally including the missingness pattern indicator in the PS model, but they did not compare this approach to other combination rules after MI or to the MP approach alone. In this article, we show that the best method when using MI with IPTW is to include the outcome in the PS model and use Rubin's rules to combine the treatment effect estimates from the imputed datasets (the method MIte).
Motivating example: effect of statin use on short-term mortality after pneumonia To illustrate the importance of an adequate handling of missing covariates for PS analysis, we focused on a published study of the effect of statin use on short term mortality after pneumonia  [22]. We utilised the THIN database, which consists of anonymised patient records from general practitioners (GPs) in the UK. As of the end of 2015, the database represented 3. 5 million unique active patients, or approximately 6% of the UK population. The database has been found to be broadly representative of the UK population, and the validity of recorded information has been established in previous studies  [23, 24]. Douglas et al carried out an analysis of 9073 patients who had a pneumonia episode, of whom 1398 were under statin treatment when pneumonia was diagnosed.