dataesr/scientific-paragraphs-categorization huggingface dataset:
These values are recovered from ãÃ and varðã Ã Þ, which were obtained from the fitting of the current model and (  4 ) and (5). By choosing the SNP with the maximum score statistic, we are selecting the SNP, which is in strongest association with the trait, from amongst those SNPs whose association is being modeled by the random effects. Step 2: model building and fitting. A new dimension-reduced model is built, according to (1) and (  3 ), from the trait data y, and known matrices X, Z, and M ÀS. Here, S is the set of indexes of the s previously selected SNP and the additional SNP found in the previous step. The model is fitted to the data and parameters estimated via maximum likelihood. Step 3: model selection. The importance of the ðs þ 1Þth selected SNP is determined via the extended Bayes information criteria (extBIC,  Chen and Chen 2008).
Overview Eagle is an R package for the genome-wide analysis of association data. It can handle data collected from inbred or outbred study populations. The populations can be of arbitrary and unknown complexity. The data can be larger than the memory capacity of the computer. Since Eagle is framed within a LMM paradigm, it is best suited to the analysis of data on continuous normally distributed traits. However, LMMs can also tolerate non-normal data  (Schielzeth et al. 2020). A flow chart of the analysis pipeline for Eagle is shown in Figure  1. The package contains functions for opening the GUI, inputting the data, performing genome-wide analyses, and for summarizing and visualizing the results. Non-R users need only be familiar with a single function OpenGUI that opens the GUI.
The map is specified via three columns of data. The first column contains the SNP labels. The second has the names of the chromosomes upon which the SNPs residue. The third column has their chromosomal positions. The data are space separated with the first row being the column names. The SNPs are in map order. Missing values are not allowed. The data are read with the function ReadMap. If the genotypic data are in variant call format, a separate marker map file is not needed. A variant call format file contains not only the SNP genotypes but also marker map information. The Z matrix is needed only if a trait has repeat observations. It is an incidence matrix. As such, it contains zeroes and ones only. The number of rows in the matrix equals the number of rows of phenotypic data. The number of columns equals the number of rows of genotypic data.
Association mapping analysis One of the most important functions in the package, from a user's perspective, is AM. This function implements the methodology presented above. It is the function that performs association mapping. The function has 12 arguments. The important arguments are as follows. The trait and fixed effects are specified via the arguments trait and fformula, respectively. The data are passed to AM through the arguments pheno, geno, map, and if required, Zmat. The number of threads, for parallel computation, is set with ncpu. The type 1 error rate is controlled with lambda. Its value is found by running FPR4AM. As an example, suppose the phenotypic data, SNP data, and marker map have been read with the functions described above and stored in data objects phenoObj, genoObj, and mapObj, respectively.
Improvements to Eagle The Eagle package has undergone continued development since it was first introduced in George et al. (2020). There have been significant improvements to its performance and utility. Eagle can analyze marker data larger than a computer's memory capacity. Previously, it was doing this by converting the raw SNP data into a packed binary file and storing it on disk. The packed file would then be unpacked and read into memory when needed. However, this unpacking process, when repeated many times, incurs a significant computational cost. Unpacking can be avoided by instead converting the raw marker data into a simple ASCII file containing the snp genotypes 0, 1, and 2. We have been able to reduce the run time of AM by 50% and FPR4AM by an order of magnitude. The latest version of Eagle can handle vcf data and comes with an interactive plotting facility (see Results).
Results Here, the steps for performing association mapping with Eagle are presented. Both modes of use are given. That is, via function statements issued at the R command line and via the GUI. For each function statement, a screenshot of its matching GUI page is shown where applicable. The example is for the analysis of a mouse data set. As stated previously, the goal of association mapping is to find the SNPs in strongest association with the genes underlying a heritable trait. The data are real. They were collected from a large GWAS in outbred mice  (Nicod et al. 2016). Many different traits were measured but our focus is on highdensity lipoprotein (Bioch. HDL). We chose this trait because from previous analyses, a number of genomic regions of interest across multiple chromosomes have been reported.
In the original study  (Nicod et al. 2016) , this trait was found to be influenced by the explanatory variables for sex (Sex), batch number (Batch), and average weight (Weight. Average). These same variables are treated as explanatory variables in our analysis. Even though large data sets are not a problem for Eagle, we wanted the example data to be easily accessible to R. A way of doing this is to host the data on GitHub (https://github. com/geo047/Example_Data). GitHub has a file size limit of 10 megabytes, which made it necessary to base the example on a subset of the original data.
Creating the input files Three input files were created. These are the files phenoex. dat with the phenotypic data, genoex. dat with the genotypic data, and mapex. dat with the maker map. In the original study, data were collected from 1887 outbred mice on a large number of traits and SNPs. As such, this data set was too large to house on GitHub. So the study size was reduced to 800 randomly selected mice. Our focus was restricted to the analysis of a single trait, Bioch. HDL. The genotypic data were reduced to the genotypes from 70,484 SNPs. The SNPs were selected from every 5th locus of the original set where loci with a minor allele frequency of less than 1% have been removed along with loci on the sex chromosome. The phenotypic data in phenoex. dat are space separated and arranged into 801 rows and four columns.
The rows correspond to data on different mice. The columns contain data on Bioch. HDL and the explanatory variables Sex, Batch, and Weight. Average. The first row has the column names. The genotypic data in genoex. dat has 800 rows and 70,484 columns. The data are space separated. The columns are not named, nor are there missing values. Each row contains the genome-wide data for a mouse. Each column contains the genotypes for an SNP. Rows in the two files are assumed to be ordered such that the same row in each file corresponds to data collected on the same mouse. The columns are in marker map order. A numeric coding of 0, 1, and 2 was used for the SNP genotypes, AA, AB, and BB, respectively. The marker map in mapex. dat is space separated and has 70,485 rows and three columns. The first row is the column names.
Single-locus association mapping We begin by analyzing these data in the "usual" way, with single-locus association mapping. As stated previously, for a single-locus analysis, a separate LMM is fitted to the data for each SNP. The statistical significance of an SNP, when treated as a fixed effect, is a measure of the strength of association between the SNP and trait. The data were analyzed with the R package gaston  (Wang and Zhang 2018). The process was to read in the phenotypic and genotypic information with fread from the R package data. table. Scale the marker data. Convert the explanatory variables into a usable form with covObj¡-model. matrix(SexþBatchþWeight. Average, phenoObj). Calculate the genetic relationship matrix with the gaston function GRM and its eigenvalues/eigenvectors with the base function eigen.
Perform the analysis with the gaston function association. test with the arguments method¼"lmm" and test¼"wald" along with arguments for the trait data, the eigenvalues/eigenvectors of the scaled marker data, and covariate matrix. The R script for performing the analysis is available upon request. The single-locus analysis results are shown in the Manhattan plot in Figure  2. The positions of the SNPs on the genome are on the x-axis and the significance scores ðÀ log 10 ðp À valueÞÞ of the SNPs are on the y-axis. We would conclude from this analysis that there is a single region of interest on chromosome 5. In fact, from our Eagle analysis (see below), we know that there are five regions of interest for this trait. These are on chromosomes 1, 5, 6, and 10. The region on chromosome 5 is obvious.
Multi-locus analysis via Blink We thought that it would be informative to compare the performance of Eagle to another state-of-the-art multi-locus association mapping package. We chose Blink  (Huang et al. 2019)  as implemented in the R package GAPIT3  (Wang and Zhang 2020). Blink improves on the multi-locus method FarmCPU  (Liu et al. 2016). It is faster and more powerful  (Huang et al. 2019 ). An analysis was performed by reading in the phenotypic, genotypic, and map data into R. A covariate matrix was formed with covObj¡model. matrix(SexþBatchþWeight. Average, phenoObj). An analysis was then performed with the GAPIT3 function GAPIT with the argument model set to "Blink". Blink found four of the five SNPs found by Eagle. It found one of the two SNPs on chromosome 5 and the same SNPs as Eagle on chromosomes 1, 6, and 10.
Discussion The Eagle package has been created to make genome-wide multilocus association mapping easy. The package accepts marker data in different formats, has easy-to-use functions, comes with a user-friendly GUI, and has an interactive plotting function for visualizing the model building process. We welcome feedback via eaglehelp@csiro. au from users on how the functionality and usability of the package could be even further improved. As we saw in the example, Eagle brings clarity to situations where there are tightly linked SNPs in association with a trait. It can also uncover significant SNP-trait associations that are otherwise hidden to  single-locus association mapping. At the very least, Eagle complements single-locus association mapping. However, ultimately, with the aid of Eagle, our hope is that the genetics community will shift to multi-locus association mapping as the method-ofchoice for the genome-wide analysis of association data.
ReMap (https://remap. univ-amu. fr) aims to provide manually curated, high-quality catalogs of regulatory regions resulting from a large-scale integrative analysis of DNA-binding experiments in Human, Mouse, Fly and Arabidopsis thaliana for hundreds of transcription factors and regulators. In this 2022 update, we have uniformly processed >11 000 DNA-binding sequencing datasets from public sources across four species. The updated Human regulatory atlas includes 8103 datasets covering a total of 1210 transcriptional regulators (TRs) with a catalog of 182 million (M) peaks, while the updated Arabidopsis atlas reaches 4. 8M peaks, 423 TRs across 694 datasets. Also, this ReMap release is enriched by two new regulatory catalogs for Mus musculus and Drosophila melanogaster. First, the Mouse regulatory catalog consists of 123M peaks across 648 TRs as a result of the integration and validation of 5503 ChIP-seq datasets.
Second, the Drosophila melanogaster catalog contains 16. 6M peaks across 550 TRs from the integration of 1205 datasets. The four regulatory catalogs are browsable through track hubs at UCSC, Ensembl and NCBI genome browsers. Finally, ReMap 2022 comes with a new Cis Regulatory Module identification method, improved quality controls, faster search results, and better user experience with an interactive tour and video tutorials on browsing and filtering ReMap catalogs.
INTRODUCTION Transcriptional regulators (TRs) such as transcription factors (TFs), transcriptional coactivators (TCAs) and chromatin-remodeling factors (CRFs), drive gene tran-scription and chromatin organization through DNA binding. Since the advent of chromatin immunoprecipitation followed by sequencing (ChIP-seq (1)), it has become possible to study the genome-wide occupancy of DNA-binding proteins. The popularity of ChIP-seq and other ChIP-based sequencing techniques has increased research in genome occupancy maps for various TRs, in various conditions, cells, tissues, and species. This led to an accumulation of functional genomics datasets stored in data repositories such as NCBI GEO  (2) , ENA EBI  (3)  or DDBJ (4) providing a unique resource for thousands of DNA-binding sequencing studies.
The manual curation and annotation of DNA-binding sequencing studies are at the foundation of the ReMap project. Each dataset introduced in ReMap is assessed manually to ensure correct target and biotype annotation, as well as experimental metadata curation, making it distinct from other integrative projects  (11) (12) (13) (14). The 2015 version of ReMap  (10)  introduced a Human regulatory catalog of 13 million (M) DNA binding regions for 237 TRs across 83 biotypes (cell lines and tissue types) by 2 Nucleic Acids Research, 2021 integrating 395 datasets from GEO and ENCODE. The 2018 ReMap version, followed by the 2020 version, released a Human regulatory catalog of 165M binding regions for 1135 TRs  (15, 16)  by processing ∼5800 datasets. Also, the 2020 ReMap database introduced the first Arabidopsis thaliana regulatory atlas as a result of a large-scale data integration of public data and analysis efforts.
Since 2018, the ReMap catalogs are used as one of the input sources for the computation of TF binding profiles for the JASPAR database  (17, 18). Here, we present the fourth release of ReMap ('ReMap2022'), which comes with a major expansion of the Human and Arabidopsis regulatory atlases. Moreover, we introduce two new regulatory atlases for Mus musculus and Drosophila melanogaster. These new catalogs are the results of our continuing efforts in large-scale data integration for these two model species. Faced with large catalogs, this update includes new quality controls to improve the repertoire of binding locations as well as a new method for Cis Regulatory Modules (CRMs) identification. Finally, our database update is backed up by new web functionalities for better community access. The web portal displays an interactive tour and genome track filters are available through track hubs on genome browsers.
Available datasets New ChIP-seq experiments were retrieved from the NCBI Gene Expression Omnibus (GEO) and EN-CODE databases  (2, 19). For GEO, the query '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 studies. The same query was used with 'arabidopsis thaliana'[organism], 'mus musculus'[organism] and 'drosophila melanogaster  '[organism]  to get all the potential datasets for each study. The selected experiments metadata are then manually curated and annotated with official nomenclatures for target names and biotypes. For incomplete metadatas, the materials and methods of associated and published papers are often examined to complete the curation. For Human we used the HUGO Gene Nomenclature Committee (20) (www.
genenames. org), the BRENDA Tissue Ontologies for cell lines  (21)  at the EBI Ontology Lookup Service  (22)  (www. ebi. ac. uk/ols/ontologies/bto) as well as the Cellosaurus database  (23)  to homogenize cell and tissue names (e. g. MCF-7 not MCF7, Hep-G2, not HepG2, Hepg2 etc. ). For Arabidopsis (A. thaliana) we used gene names from the Ensembl Plant genome annotation  (24). Ecotypes and biotypes descriptions were curated and homogenized when the information was available in the metadata or associated publication. For Mouse (M. musculus) we annotated gene names using the official MGI database (25) (http://www. informatics. jax. org/). For Drosophila (D. melanogaster) we used the Flybase database (26) (https://flybase. org/) for gene names. To improve automatic processing of files we removed parentheses and replaced them with hyphens to better handle these names in the pipeline (e.
g. E(z) to E-z, See Supplementary Table  S12 ). For Mouse and Drosophila the tissues or cell lines annotation were homogenized using BRENDA Tissue Ontologies as well as the Cellosaurus database. ChIP-seq studies involving RNA polymerases (RNA-Pol2 and RNA-Pol3) were filtered out. When multiple antibodies were pooled (e. g. RUNX1 and RUNX3, GSE17954) targets would be named as RUNX1-3. Also, when a 'global' antibody is used to pool a family of targets (eg: RAR, GSE35599) we would name the target as just RAR. We define a dataset as a DNA-binding experiment in a given GEO/AE/ENCODE series (e. g. GSE37345), for a given TR (e. g. FOXA1), and in a particular biotype (e. g. LNCaP, Larva, Leaf, Limb) in a given biological condition (e. g. 45min DMSO, 21d-wt-watered). Datasets are labeled with the concatenation of these informations (e.
g. GSE37345. FOXA1. LNCAP 45min-DMSO). For the 2022 update a total of 12 976 new datasets were processed across all four species (Supplementary Table  S1 ). Specifically, we analysed 4121 Human datasets deposited in public repositories from  11   S3 , S5, S7-S9 and S11). In the 2022 update, as well as in previous updates, the new ENCODE ChIP-seq experiments for TFs, transcriptional and chromatin regulators were re-analysed starting from raw data (FASTQ files) following the same processing pipeline. For Human, we processed data between 6 February 2019 to 18 March 12021, for Mouse, all data until 16 November 2020 and for Drosophila all data until 28 April 2021. We retrieved the list of ENCODE ChIPseq experiments as FASTQ files from the ENCODE portal  (8, 19)  (https://www. encodeproject. org/) using the following filters: Assay: 'ChIP-seq', Organism: 'Homo sapiens', Target of assay: 'TF', Available data: 'fastq' on 6 February 2019.
The same filters were used for the Organism: 'M. musculus' and 'D. melanogaster'. Metadata information in JSON format and FASTQ files were retrieved using the Python requests module. We processed 508 Human, 167 Mouse and 525 Drosophila ENCODE datasets, of whom 267 Human, 158 Mouse and 514 Drosophila passed our quality filters. We renamed TR ENCODE aliases using official HGNC and MGI identifiers (e. g. p65 into RELA, see Supplementary Tables  S4  and S10 ) for Human and Mouse respectively, and renamed cell lines to official BRENDA and Cellosaurus conventions (e. g. K562 into K-562, for curated names see Supplementary Tables S4, S6, S10 and S12).
ChIP-seq processing For each species, the GEO and ENCODE datasets were curated, processed and analysed in the same way. Bowtie 2 (version 2. 2. 9  (27) ) with options -end-to-end -sensitive was used to align all reads on the human genome GRCh38/hg38 assembly, the A. thaliana TAIR10 assembly, the D. melanogaster BDGP6. 32/dm6 assembly and the Nucleic Acids Research, 2021 3 M. musculus GRCm38/mm10 assembly. The GRCm39 assembly was released during the Mouse production. Trim Galore (https://github. com/FelixKrueger/TrimGalore) was used to remove adapters, trimming reads up to 30 bp. Trim Galore is a wrapper around Cutadapt and FastQC to consistently apply adapter and quality trimming to FastQ files. With samtools rmdup polymerase chain reaction duplicates were removed from the alignments. Following the EN-CODE ChIP-seq guidelines  (28)  we used the MACS2 peakcalling tool (29) (version 2.
Quality assessment and filters Because the analysed data comes from a variety of sources and is produced under varied experimental circumstances and platforms, the data quality differs from study to study. Since the first release of ReMap 2015, and unlike similar databases (Supplementary Tables S3, S5, S9 and S11), our pipeline has assessed the quality of each processed dataset. The same quality pipeline and cutoffs were used for ReMap 2022 as they were for ReMap 2020, we named these quality assessments as QC1 in Figure  1. Briefly, we derived a score based on the ENCODE consortium's cross-correlation and FRiP (percent of reads in peaks) metrics (Supplementary Figures S1-S4, ENCODE quality coefficients http: //genome. ucsc. edu/ENCODE/qualityMetrics. html) for all species and ChIP-seq datasets for this release.
The normalized strand cross-correlation coefficient (NSC), which is a ratio of the maximal fragment-length cross-correlation value to the background cross-correlation value, and the relative strand cross-correlation coefficient (RSC), which is a ratio of the fragment-length cross-correlation to the readlength cross-correlation, are then computed by our pipeline. Datasets that failed QC1 were not included in the catalogs or the BED files available for download. Rejected datasets are listed in (Supplementary Tables  S3, S5 , S9 and S11). In 2022, two new sets of filters were added in our assessment steps, named QC2 in Figure  1. These filters were applied to the new catalogs, and retroactively to previous data for Human and Arabidopsis. We discarded datasets having less than 100 peaks or more than two times the number of annotated genes according to the Ensembl gene annotation statistics.
As of late 2021, about 20 000 coding and noncoding genes are identified for Drosophila giving a cutoff of a maximum of 40 000 peaks, about 30 000 annotated genes for Arabidopsis giving a cutoff of 60 000 peaks, and about 40 000 annotated genes for Human or Mouse giving a cutoff of 80 000 peaks (Supplementary Figure  S5 ). For the second filter, within each dataset we discarded peaks that fall outside a base pair length range. The range is defined as 50bp minimum and an upper cutoff in which we have 99% of catalogue (Supplementary Figure  S6A ). These upper cutoffs are rounded to 1. 5 kb for Human and Mouse, 2kb for Drosophila and Arabidopsis (see Supplementary Figure  S6B ). Full data with rejected peaks are made available in the download table as 'Permissive peaks' in the Supplementary tab.
Open ReMap pipeline The code of the ReMap pipeline is available on Github in the ReMap Github organisation (https://github. com/ remap-cisreg). The pipeline uses Snakemake  (30)  in a Conda (https://conda. io) or Singularity (https://sylabs. io/) environment, depending on the High-Performance Computing (HPC) resources available, and both Torque and Slurm managers are supported. You can also find multiple python and shell scripts used to format the datasets. The repository contains information in the Github wiki on the utilisation of the pipeline.
Non-redundant peak sets ReMap provides non-redundant binding regions (NR peaks) for each target, a unique feature not seen in other databases (Supplementary Table  S2 ). This gives an accurate genomic location of peaks regardless of biotypes, in a multicell manner. All peak lengths for a TR were trimmed to the median size of all peaks in that TR. Then, we used Bed-Tools to intersect overlapping truncated peaks across multiple datasets to discover clusters of duplicate peaks (with at least 25% overlap, both ways). After the clusters of overlapping peaks have been identified, non-redundant peaks are computed by averaging start, end and summits coordinates of all peaks in a cluster using original ReMap peaks lengths. The non-redundant peak set across all experiments for a particular factor consists of calculated non-redundant peaks plus singletons and is available for download from the ReMap website.
Cis regulatory modules To accurately delineate CRMs in the 2022 release, we have developed a new methodology that relies on detecting peak density along the genome. Briefly, we delineate CRMs at each local minimum of NR peak density function (See Supplementary Figure  S7B ). A NR peak is assigned to a CRM if its peak summit falls in between the identified flanking local minimas. Finally, the CRM boundaries are reduced using the 5 and 3 coordinates of the flanking NR peaks. The 'peakMerge' code is available at https://github. com/remapcisreg/peakMerge.