dataesr/scientific-paragraphs-categorization huggingface dataset:
This whole scheme was designed to prevent regional effects, caused by the over-representation of particular regions in specific time intervals. The LOCATOR  20  program (version 1. 2) was run using a geolocated reference panel consisting of all non-DOM2 horses (n = 136), except the tarpan and the four Przewalski's horses present in our dataset, and considering nucleotide transversions covered at least in 75% of the samples, for a total of 3,194,008 SNPs. The geographic origin of each DOM2 horse was then estimated from the geographic structure defined by the populations present in the reference panel. Default parameters were used, except that the width of each neural layer was 512 (instead of 256). The best run was selected as the one showing the lowest validation error from a total of 50 independent runs.
Selection scans To pinpoint genetic changes potentially underlying biological adaptation within DOM2 horses, we contrasted the frequency of each nucleotide transversion in our dataset (n = 10,205,277) in DOM2 (n = 141) and non-DOM2 horses (n = 142). The extensive number of samples represented provided unprecedented resolution into patterns of allele frequency differentiation, and encompassed the largest diversity of non-DOM2 horses characterized to date. Weir and Cockerham F ST index values between both groups were calculated using Plink 76 (version 1. 9) and visualized using the GViz R package  86  (version 1. 36. 2), together with external genomic tracks provided by the gene models annotated for EquCab3 (Ensembl v0. 102) and the interrupted repeats precomputed for the same assembly and stored in the UCSC browser.
Data analysis The Struct-f4 software is available without restriction on Bitbucket at https://bitbucket. org/plibradosanz/structf4/src/master/, together with a companion manual providing installation and running instructions. All other analyses relied on available software, fully referenced in the manuscript, including: -AdapterRemoval2 (version 2. For manuscripts utilizing custom algorithms or software that are central to the research but not yet described in published literature, software must be made available to editors and reviewers. We strongly encourage code deposition in a community repository (e. g. GitHub). See the Nature Portfolio guidelines for submitting code & software for further information.
Acknowledgements We thank all members of the AGES group at CAGT. We are grateful for the  Museum of the Institute of Plant and Animal Ecology (UB RAS, Ekaterinburg)  for providing specimens. The work by  G. Boeskorov  is done on state assignment of DPMGI SB RAS. This project was supported by the  University Paul Sabatier IDEX Chaire d'Excellence (OURASI) ;  Villum Funden miGENEPI research programme ; the  CNRS 'Programme de Recherche Conjoint' (PRC) ; the  CNRS International Research Project  ( IRP AMADEUS ); the  France Génomique Appel à Grand Projet  ( ANR-10-INBS-09-08 ,  BUCEPHALE  project); IB10131 and  IB18060 , both funded by  Junta de Extremadura (Spain)  and  European Regional Development Fund ;  Czech Academy of Sciences  ( RVO:67985912 ); the  Zoological Institute ZIN RAS  ( АААА-А19-119032590102-7 ); and  King Saud University Researchers Supporting Project  ( NSRSP-2020/2 ).
Data availability All collapsed and paired-end sequence data for samples sequenced in this study are available in compressed fastq format through the European Nucleotide Archive under accession number PRJEB44430, together with rescaled and trimmed bam sequence alignments against both the nuclear and mitochondrial horse reference genomes. Previously published ancient data used in this study are available under accession numbers PRJEB7537, PRJEB10098, PRJEB10854, PRJEB22390 and PRJEB31613, and detailed in Supplementary Table  1. The genomes of ten modern horses, publicly available, were also accessed as indicated in their corresponding original publications  59, 63, [87] [88] [89].
Data Policy information about availability of data All manuscripts must include a data availability statement. This statement should provide the following information, where applicable: -Accession codes, unique identifiers, or web links for publicly available datasets -A description of any restrictions on data availability -For clinical datasets or third party data, please ensure that the statement adheres to our policy All collapsed and paired-end sequence data for samples sequenced in this study are available in compressed fastq format through the European Nucleotide Archive under accession number PRJEB44430, together with rescaled and trimmed bam sequence alignments against both the nuclear and mitochondrial horse reference genomes. Previously published ancient data used in this study are available under accession numbers PRJEB7537, PRJEB10098, PRJEB10854, PRJEB22390 and PRJEB31613, and detailed in Supplementary Table 1.
AnnoMiner's fourth function performs transcription factor or histone mark enrichment analysis for user-provided gene lists by utilizing hundreds of public, high-quality datasets from ENCODE for the model organisms human, mouse, Drosophila and C. elegans. Thus, AnnoMiner can predict transcriptional regulators for a studied process without the strict need for chromatin data from the same process. We compared AnnoMiner to existing tools and experimentally validated several transcriptional regulators predicted by AnnoMiner to indeed contribute to muscle morphogenesis in Drosophila.
Techniques such as ChIP-seq, ATAC-seq, Hi-C seq and others involve an NGS (Next Generation Sequencing) step, resulting typically in paired-end reads of isolated chromosomal fragments. Therefore, the first steps after sequencing consist of read mapping, which is usually done using a software such as BowTie2  (9)  followed by peak calling. There are a number of tools available for peak calling (reviewed e. g. in  (10) (11) (12) (13) ), which include MACS2  (14)  for standard peak calling, ChIPdiff  (15) , EpiCenter  (16)  or diffReps  (17)  for differential peak calling. The output of peak callers are the genomic coordinates of epigenetic marks or transcription factor binding sites under study. These coordinates are commonly stored in the Browser Extensible Data (BED) file format, a light-weight, standardized format to share genome coordinates.
These initiatives have also made it possible to explore available genomic data further and integrate them with each other as well as with usergenerated data. These standardized, high-quality data can be used to identify enriched transcription factor binding events in promoters of co-regulated genes, for example from a differential gene expression dataset (e. g. an RNA-seq dataset). This form of data integration predicts possible transcriptional regulators for biological processes under study, which is already widely used in the community. Enrichment analysis is commonly performed by testing for TF overrepresentation in the promoter regions of a user-provided gene list compared to a background list (e. g. considering the entire genome). Most available tools define the promoter region rigidly as a range of upstream and downstream base pairs from the annotated gene transcription starting site (TSS) and these parameters are kept fixed for all the TFs tested, without accounting for the differences between individual transcription factors.
g. two different TFs; two different HMs; or a TF in combination with a HM) with gene feature attributes; and third, nearby genes annotation, which helps to identify long range interaction effects on gene expression of a single genomic region. All three annotation features can optionally integrate user provided data, such as results from differential gene expression analysis, allowing to inspect for instance expression data and genomic peak data from multiple transcriptional regulators together. As a fourth function, AnnoMiner performs TF (Transcription factor) and HM (Histone Mark) enrichment analysis, using all high-quality filtered TF and HM data from ENCODE, modENCODE and modERN, which are stored in an internal database, and a user defined gene lists as input. AnnoMiner's TF enrichment function offers the DynamicRanges option, which uses promoter regions specific to each TF based on pre-calculated binding densities for each individual TF.
The AnnoMiner database The basic structure of the AnnoMiner web-server is shown in Supplementary Figure  S1. In the back-end, all genomic data (retrieved from UCSC), data from ENCODE  (33) , modENCODE  (36)  and modERN  (37) , as well as user uploads are stored in the document-oriented, non-relational database MongoDB. This type of database structure was chosen for its flexibility and its speed. For each model organism, the latest genome assembly is stored. For human, mouse and Drosophila, we also store the second latest release. We use Java Servlets and Java Connectors for analysis, data upload, and connection to MongoDB, respectively. The front-end is based on Javascript and HTML, whereby the Annominer. js performs all Post calls to the Java servlets in the back-end. To follow the Findability, Accessibility, Interoperability and Reusability (FAIR) principle  (48) , documentation and source code of the tool are available on GitLab: https://gitlab.
Transcription factor enrichment analysis Transcription factor enrichment analysis in AnnoMiner uses publicly available TF ChIPseq data from the ENCODE, modENCODE and modERN databases. In order to keep only high-quality data, we undertook a strict selection of available TF ChIP-seq datasets. First, we kept only experiments, for which data had already been pre-analysed following the ENCODE processing pipeline. Second, only experiments with replicates and optimal IDR peaks files (narrowPeak) were retained. Third, we manually inspected each narrowPeak file, evaluating the peak distribution around the gene's TSS and discarded the ones with irregular peak distributions in relation to promoter regions of genes, as we considered those as outliers (Supplementary Figure  S2 c, d ; all outliers csv files are available in our gitlab repository at https://gitlab.
com/habermann_lab/AnnoMiner-paper). The number of used TF ChIP-seq datasets for each of the model organisms and assemblies is available in Supplementary Tables  S1, S2. Over-representation analysis. For each TF ChIP-seq dataset stored in the AnnoMiner database, we first compute the contingency table of potential targets in both the uploaded gene list (signature gene-set) and in the background list of all genes of the chosen organism. We then apply a one-tailed hypergeometric test followed by Benjamini-Hochberg correction  (49). Furthermore, we calculate the enrichment score as follows:.
Benchmarking dataset Benchmarking data-sets for testing AnnoMiner's enrichment function in the form of TF regulons were obtained from human single TF perturbation experiments (knock-downs, knockouts, over-expression and chemical inhibition) followed by RNA-seq from Gene Expression Omnibus (GEO  (52) , 160 experiments), manually curated and made available from Keenan et al. (43). We retained only experiments targeting TFs for which AnnoMiner is storing at least one high quality TF ChIP-seq dataset for the assembly GRCh38 (hg38), leading to a total of 75 RNA-seq experiments left for benchmarking (benchmarkingSet).
Benchmarking metrics Precision-Recall (PR) and Receiver-Operating Characteristic (ROC) areas under the curves (AUC) for the benchmarkingSet were computed using the R package PRROC  (53) , following the benchmarking procedure proposed and applied by  (43)  and  (54). In brief, for each TF, signature gene-sets were derived based on differentially expressed genes of the perturbed TF versus its control and used for TF enrichment analysis. For each dataset, the rank of the perturbed TF was assigned to the positive (foreground (fg)) class, while all other ranks of TFs retrieved for the dataset were assigned to the negative (background (bg)) class; using the PPROC package, precision-recall (PR) and Receiver-Operator Curves (ROC) using continuous interpolation were computed and areas under curve (AUCs) for both were calculated (PR-AUC and ROC-AUC).
Epigenetic and RNA-seq data used For analysing STAT3 in acute B-cell lymphomas, we used data from super-series GSE50724  (55). Only peaks which were significantly upregulated in ABC type B-cell lymphoma were selected for AnnoMiner's peak integration (FDR cut-off <= 0. 05; FC >= 1. 25). We used H3K4me3 data from an ABC cell line stored in the ENCODE database (GSE86718_ENCFF763KFL, replicated peaks BED file) for peak integration with significantly upregulated STAT3 peaks. Human genome version hg19 was used for analysis. RNA-seq data (GSE50721) from the same super-series was used for integration with STAT3 and H3K4me3 peaks, whereby we used statistical results provided by the authors of the study. For demonstrating the nearby genes annotation function, we used data from  (56)   Analysed RNA-seq data from developing indirect flight muscle (IFM) in Drosophila melanogaster were taken from GSE107247  (46).
We used the DESeq2 comparisons between time-points 72 h and 30 h after puparium formation (APF) provided by the authors. Genes with log2FC in absolute value greater than 2 and FDR smaller than 0. 01 were considered as differentially expressed. For integration with phenotypic data, we made use of data published on muscle morphogenesis and function in Drosophila  (58)  and flight muscle growth in Drosophila  (47). For integrating gene expression changes of yki knocked-down flight muscles and flight muscles expressing a constitutively active yki compared to wild type, with Yki and Sd protein genome occupancy data, we used BRB-seq data from yki knock-down and yki constitutively active fly strains (47) available from GEO (GSE158957). Optimal IDR threshold narrow peak files for Yorkie (Yki, ENCSR422OTX) and Scalloped (Sd, ENCSR591PRH) ChIP-seq were downloaded from the ENCODE/modERN resource, both for Drosophila genome version dm6.
Web interface of AnnoMiner (front-end). The web interface of AnnoMiner allows the user to upload, integrate and visualize the results. AnnoMiner's web interface is intuitive and fully interactive relying on the JavaScript library JQuery and Bootstrap 4. Data upload, parameter settings and results are shown on the same page. To exploit the tool's multiple functions, the front-end is sending JQuery's POST requests to the back-end's Java Servlets. Back-end of AnnoMiner. The back-end of AnnoMiner is developed in Java and is running as a Java web server application deployed on Tomcat 8 on a Debian operating system. Separate Java Servlets were developed for each functionality, in order to be modular and easily implementable. The data used for the analysis are stored in a non-relational MongoDB database.
Genomic peak annotation functions Annominer's three annotation functions (peak annotation, peak integration and nearby genes annotation, Figure  1 ) take as input one or more files containing genomic coordinates (in BED format), for example from ChIP-seq, with the aim of finding their associations with annotated gene features. This is done by determining the overlap between the genomic coordinates of a peak and the attributes of annotated gene features. The user can set the following parameters for the search: the minimum required overlap among the gene feature attributes and peak features (in bp or %); whether only the longest isoform of a gene or all its isoforms will be considered; the gene's directionality; the organism and the genome resource. Optionally, a user-provided annotation file can be uploaded, containing for instance differential expression data which will be integrated with the gene lists generated from AnnoMiner annotation.
An interactive, sortable and downloadable table of all genes which overlap with the selected target region with peaks from the BED file is returned to the user (Figure  3 c ). If the user provides a gene-based, custom annotation file, for instance containing differential expression data, these data will be integrated and displayed in the resulting table (Figure  3 d ). While an annotation file will in most cases contain differential expression data based on RNA-seq analysis, it can contain any numerical or even text data. In summary, peak annotation in AnnoMiner annotates genomic regions provided by the user with gene features and their attributes in a flexible and user-centric way.
Peak integration The peak integration function performs the peak annotation analysis, but for two genomic regions files (representing peaks from two independent TFs, a TF together with an HM or two independent HMs), allowing the user to integrate peaks from two different transcriptional regulators and identify gene features and their attributes that overlap with both (Figure  2 b ). The same algorithm as in peak annotation is used for the annotation of each individual peak. With this function, genes co-regulated by two TFs can be identified, or a TF dataset can be integrated with chromatin structure data defined by histone marks, or other epigenetic information derived by other experimental techniques. Two coverage plots are returned by AnnoMiner (Figure  4  a), and the user chooses a target region for both and clicks on the 'Show Genes!' button (Figure  4 b ).
An interactive, sortable and downloadable table is returned with all genes that have a peak of both transcriptional regulators in their selected target regions (Figure  4 c ). Custom gene annotation, such as differential expression data can again be provided, which is then integrated and displayed in the results table. To show that AnnoMiner's peak integration function can be used to integrate three different datasets in one analysis step, we re-analysed a data series published on STAT3 function in different forms of diffuse large B-cell lymphomas (DLBCL, GEO super-series GSE50724, (  55 )). Two subtypes of DLBCL are known, germinal centre B-cell-like (GCB) and activated B-cell-like (ABC). The ABC type responds only poorly to available therapies and can be often associated with an overexpression of STAT3  (55).
The authors had compared STAT3 binding by ChIP-seq analysis between 8 patient-derived cell lines from GCB-and ABC type. They had performed RNA-seq analysis of the same cell lines to retrieve differentially expressed genes between the two subtypes. We made use of these data to identify genes with increased expression levels in the ABC type together with increased STAT3 binding events. We only considered STAT3 peaks that were significantly upregulated (FDR 0. 05, fold change 1. 25) in ABC-type DLBCL. To demonstrate the added value of Annominer's peak integration function, we used H3K4me3 data from ENCODE from one of the ABC cell lines, OCI-Ly3 (accession: ENCFF763KFL), to limit the search to active promoters. Both peak files showed highest coverage with the direct promoter region of associated genes (Figure  4 a, b ).
In the latter case, the colour of the box reflects the direction of differential expression (either blue for up-, red for downregulated, green if an equal number of genes are up-and downregulated or grey if unchanged, Figure  5 a ). By selecting one or more boxes (Figure  5  b), the selected genes, as well as their log2FC and FDR as uploaded by the user will be returned in a table (Figure  5 c ). As a proof of concept for the nearby genes annotation function, we used a study that had shown the requirement of long-range enhancers regulating Myc expression for normal facial morphogenesis ((56), GEO dataset GSE52974). In humans, cleft lip or cleft palate (CL/P) is a frequent congenital malformation. This malformation has been associated with risk factors located at a 640kb noncoding region on chromosome 8.
The corresponding region in mouse was studied by Uslu and colleagues and refined to a more specific enhancer region, the medionasal enhancer (MNE, 56). Deletions within the MNE in mouse led to smaller snouts and abnormalities of nasal and frontal bones amongst other defects. Myc was the only gene observed to be differentially expressed in the vicinity of this deletion. We used the CL/P deletion 8-17 (chr15:62668548 -63550550) from Uslu et al. to create a single-peak BED file and uploaded it together with the significantly differentially expressed genes from the re-processed RNA-sequencing data from the same strain compared against control (GEO dataset GSE52974) to test the AnnoMiner nearby genes annotation function (Supplementary Table  S4 ). Indeed, only a single gene is significantly differentially expressed (pink box, Figure  5  a), which is Myc (Figure  5 c ).
The results of the enrichment analysis are visualized as an interactive bar plot (for the first 10 hits, Figure  6  a), as well as an interactive table. In the table, all available TF ChIP-seq datasets in the AnnoMiner database for the species of interest are ranked according to their Combined Score. Along with this value, AnnoMiner also reports information about the experimental condition, cell line or developmental stage, contingency table values, p-value, enrichment score, FDR and the list of potential targets of the TF (Figure  6.
b) in the downloadable version of the table. As a proof of principle for predicting transcriptional regulators we selected a differential expression dataset from daf-16/FoxO mutants in C. elegans  (57). When uploading the list of all DAF-16A/F targets provided in (57) to AnnoMiner's TF & HM enrichment analysis function, daf-16 was the 3 rd most significantly enriched transcription factor (Figure  6 a ). Interestingly, pqm-1, which has been shown to bind to daf-16 response elements  (63) , was found at 1 st and 2 nd position by AnnoMiner. To conclude, AnnoMiner's TF & HM enrichment function is a powerful tool for predicting relevant transcription factors co-regulating sets of genes with similar expression patterns.
Performance evaluation of AnnoMiner's TF & HM enrichment analysis function We wanted to compare the performance of AnnoMiner's TF & HM enrichment analysis function with other web-tools for TF enrichment analysis. We followed in principle the evaluation protocol proposed by Keenan et al. , which used PR-AUCs and ROC-AUCs calculated from the PPROC R-package for estimating performance ((43); for details see also Methods). In brief, we took manually curated datasets provided by  (43)  containing single TF perturbation experiments followed by RNA-seq from Gene Expression Omnibus (GEO (  52 )). Gene expression data used for benchmarking were restricted to experiments targeting TFs for which AnnoMiner is storing at least one high quality TF ChIP-seq dataset for the human assembly GRCh38, resulting in a total of 75 datasets that we could use for benchmarking.
We chose the following web-tools for comparison: ChEA3, TFEA. ChIP and EnrichR (Table  1 ). ChEA3 offers the user 2 different methods to rank predicted TFs (meanRank and topRank) and we evaluated both ranking methods. EnrichR offers different resources for enrichment analysis and we used the resources ARCHS4, ChEA 2016, ENCODE 2015, ENCODE and ChEA Consensus and TRRUST 2019 for enrichment analysis, respectively. The number of datasets included in the benchmarkingSet depended on the resource tested (see Methods). The Anderson-Darling test returned significant results for all web-tools tested (ADtest in Table  1 ), except for EnrichR in combination with the ENCODE_and_ChEA_Consensus resource. This highlights the ability of all tools to rank the perturbed TF among the top candidates of the results.