dataesr/scientific-paragraphs-categorization huggingface dataset:
3b ). 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 (Fig. 3c ). 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 (Fig. 3d ). 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-centred way. While we here demonstrate the usability of AnnoMiner's peak annotation function with a TF ChIP-seq dataset, which usually comprises narrow peaks in the vicinity or within gene promoters, it can be also used for any type of genomic peak file.
In Supplementary Figure  S2  (together with Supplementary Table  S1 ), we show peak annotation for activating, as well as repressing histone modifications during early Drosophila development. Peak integration. The peak integration function performs the peak annotation analysis, but for up to five genomic regions files (representing peaks from independent TFs, TFs together with HMs or independent HMs), allowing the user to integrate peaks from different transcriptional regulators and identify gene features and their attributes that overlap with them (Fig. 2b ). The same algorithm as in peak annotation is used for the annotation of each individual peak file. With this function, genes co-regulated by TFs can be identified, or TF datasets can be integrated with chromatin structure data defined by histone modifications, or other epigenetic information derived by other experimental techniques.
The coverage plots of all chosen genomic region files are returned by AnnoMiner (Fig. 4a ), and the user chooses a target region for all and clicks on the 'Show Genes!' button (Fig. 4b ). An interactive, sortable and downloadable table is returned with all genes that have a peak of the transcriptional regulators or epigenetic marks in their selected target regions (Fig. 4c ). 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  48  ). Two subtypes of DLBCL are known, germinal centre B-cell-like (GCB) and activated B-cell-like (ABC).
The user can either annotate peaks (peak annotation), integrate peaks (peak integration) or annotate and analyse nearby genes (nearby genes annotation, long-range interactions) of a peak. AnnoMiner's fourth function allows searching for enriched transcription factor binding or histone modification events in the promoter regions of a list of co-regulated genes (TF & HM enrichment analysis). In the back-end, Java servlets are responsible for data upload, analysis, integration and ID conversion. User data, genome feature data from ENSEMBL, UCSC and NCBI, as well as ENCODE, modENCODE and modERN ChIP-seq data are stored in a MongoDB database. Currently, we host genome feature data from H. sapiens, M. musculus, D. melanogaster, C. elegans and S. cerevisiae. Data from ENCODE, modENCODE and modERN are available in AnnoMiner for human, mouse, Drosopihla and C.
elegans. to cancer, diffuse large B-cell lymphomas of the ABC type, IL10/STAT3 signalling and other relevant terms for the disease under study (Supplementary Figure  S3b, c ; Supplementary Table  S2 ). Moreover, AnnoMiner identified 28 additional direct targets of STAT3 compared to the ones already described in the original study (Supplementary Table  S2 ). To summarize, AnnoMiner's peak integration function was able to identify genes directly associated with ABC-type diffuse large B-cell lymphomas and potential direct targets for the transcription factor STAT3 by using a single user interface and a single AnnoMiner analysis step. To demonstrate the usability of AnnoMiner's peak integration function further, we used four different datasets, as well as different types of genomic data. The results are displayed in Supplemental Material: first, (Supplementary Figure  S4 , Supplementary Table  S1 ), we integrated H3K4me3 peak data during four stages of Drosophila MZT (maternal-to-zygotic transition) and identified its associated genes throughout MZT, which are thus constitutively transcribed from early to late MZT  49.
2d ). In this case, a range of base-pairs has to be chosen up-and downstream of the peak, which is then decorated with information on differential expression from the user-provided annotation file (Fig. 5d ). After selecting the up-or downstream region (Fig. 5e ), the user can retrieve the genes within that region of the peak together with their differential expression data (Fig. 5f ). The long-range interactions function will have its best use whenever Hi-C data are available, as these types of data will give insight about the genomic boundaries and thus help to choose the genomic neighbourhood of a peak. 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  51  (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 640 kb 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  51  ). 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 ).
6a ), 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 (Fig. 6b ) 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  53. When uploading the list of all DAF-16A/F targets provided in  53  to AnnoMiner's TF & HM enrichment analysis function, daf-16 was the 4th most significantly enriched transcription factor (Fig.
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 56 (GEO). 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 benchmarking set did depend 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.
elegans. Using AnnoMiner to identify important transcriptional regulators of Drosophila flight muscle morphogenesis and growth. The assess the performance of AnnoMiner further, we tested its TF & HM enrichment analysis function to predict unknown transcriptional regulators for a list of co-regulated genes. We chose a dataset quantifying gene expression dynamic during development of Drosophila melanogaster indirect flight muscles  46  , in which mRNA from indirect flight muscles had been isolated at several time-points correlating with key steps during muscle development (Fig. 7a ). We focused on the development of the contractile apparatus called myofibrillogenesis and compared gene expression at 30 h after puparium formation (APF), when myofibrils assemble, with 72 h APF, when myofibrils have matured (Fig.
7a ). This comparison revealed 2193 differentially expressed genes that were shown in the original study to be strongly enriched for genes relevant for myofibril and mitochondrial development. We submitted this differential gene list to AnnoMiner (Supplementary Table  S5 ) and searched all the 514 modERN and modENCODE TF ChIP-seq datasets from Drosophila stored in AnnoMiner for a potential enrichment of peaks. This identified 42 unique TFs significantly enriched with an FDR < 0. 05. (Supplementary Table  S5 ). The top 10 enriched datasets included Deaf1, Trl, Hr78 and cwo (Fig. 7b ). Hence, these are potential transcriptional regulators of flight muscle development. The peaks from a transcription factor (TF) track can be located in the promoter region or 5′ flanking region (peak 1) of gene A, in the coding region (peaks 2 and 3) or in the 3′ flanking region of gene A (peak 4).
Long-range interactions can also be determined by specific genomic boundaries defining so-called TAD-domains (topologically associating domains). Optionally, the user can upload a custom annotation file that contains for instance differential expression data (log2FC, FDR, etc. ). These data are then shown in the resulting table. The table can be sorted according to different values, downloaded, browsed online, and the list of genes can be copied to the clipboard for further analysis. The user can also select a new target region or start a completely new analysis. Significantly upregulated peaks from STAT3 (GEO dataset GSE50723 from super-series GSE50724), as well as H3K4me3 methylation data from a comparable cell line (GEO dataset GSE86718) were uploaded for producing this Figure; differential expression data were taken from the associated GEO dataset GSE50721 from super-series GSE50724.
Morphological analysis showed that CG14655 knock-down flight muscles displayed abnormal actin accumulations between their myofibrils, suggesting a role for CG14655 in myofibril development of flight muscle. Together, these findings demonstrate the predictive power of AnnoMiner to identify transcriptional regulators by combining chromatin binding and differential expression data. AnnoMiner helps identify targets co-regulated by Sd and Yki during flight muscle growth in Drosophila. Strikingly, two of the enriched transcriptional regulators in the above comparative flight muscle development dataset were the transcriptional effector of the Hippo pathway in Drosophila called Yorkie (Yki) and its essential Tead co-factor Scalloped (Sd)  58  (Supplementary Table  S5 ). Recently, an essential function for the Hippo pathway promoting flight muscle growth by transcriptional up-regulation of mRNAs coding for sarcomeric proteins, which built the myofibrils, was identified  47.
We wanted to know whether we could identify direct transcriptional targets of Yki/Sd during flight muscle growth. To this end, we integrated mRNA BRB-seq data from developing yki knock-down flight muscle (yki-IR), as well as from flight muscle expressing a constitutive active form of yki (yki-CA) compared to wild type controls (GEO accession GSE158957) with ChIP-seq data from Yki (mod-ENCODE dataset ENCSR422OTX) and Sd (modERN dataset ENCSR591PRH) obtained in fly embryos using AnnoMiner's peak integration function. Both proteins showed prominent base pair coverage of the TSS regions of their target genes (Fig. 8a ). We selected these peaks to retrieve associated genes and then integrated the BRB-seq data for 24 h APF yki knockdown (yki-IR 24 h), as well as 24 h and 32 h APF constitutively active yki, respectively (yki-CA 24 h, yki-CA 32 h; Supplementary Table  S7 ).
8c ). At 32 h AFP, AnnoMiner identified 177 unique genes and in total 541 transcripts as potential direct Yki/Sd targets. Using GO term enrichment analysis by modEnrichR  60  we found many processes and cellular compartments related to muscle development and function among the top enriched terms in these potential direct Yki/Sd targets (Fig. 8d , Supplementary Table  S7 ). To conclude, using AnnoMiner's peak integration function, we identified putative direct targets of the Yki/Sd transcriptional complex Figure  5. The nearby genes annotation function of AnnoMiner. (a) AnnoMiner's nearby gene annotation function shows the differential regulation of 5 up-and down-stream, as well as the overlapping gene of a selected genomic peak. Next to a BED file with a single genomic region (a peak, a SNP or a genomic deletion), the user must upload an annotation file containing information on differential gene expression.
This has two advantages: first, the user can visualize the distribution of peaks of the uploaded file with respect to all relevant attributes of annotated gene features in the genome. Second, AnnoMiner allows to interactively choose the target region(s) for which the associated genes are returned. While other tools provide statistics on the peak distribution relative to gene feature attributes (e. g. 28 ) in the output, to our knowledge, AnnoMiner is the only software that allows to easily retrieve specific gene-sets depending on the distribution of the peak coverage over gene feature attributes. The peak integration function offers the same flexibility. Moreover, both functions allow to directly integrate differential gene expression or other numerical data associated to genes with the genomic peak files.
The nearby genes annotation and long-range interaction functions, which integrate expression data with peak data, is novel and for the first time, users can in a web-based manner visualize and retrieve genes that are not the nearest neighbours of a peak. It could be useful to integrate genomic, non-coding variants causative for human genetic diseases with disease-associated gene expression data and explore transcriptional activity within TAD domains. AnnoMiner's TF enrichment analysis function offers to treat promoter regions dynamically for each specific TF with its DynamicRanges function. Finally, AnnoMiner is independent of the genomic assembly of the source data, as it on-the-fly translates submitted IDs and uses the ID compatible with the database chosen for gene centred peak annotation, as well as TF enrichment.
We compared AnnoMiner's TF enrichment analysis function to the best-performing software in the field, which includes ChEA3, TFEA. ChIP and EnrichR. AnnoMiner could not reach the accuracy of ChEA3. One of the reasons could be that the dataset of TF-gene association used by ChEA3 supersedes data we retrieve from ENCODE, modENCODE and modERN, as it includes several additional datasets which are at least partially manually curated or generated. This hypothesis is supported by the fact that EnrichR shows differing performance when using different source data, showing lower performance when using the ENCODE data alone compared to the ones from ChEA3. One possible solution could be to add curated data to AnnoMiner's TF enrichment analysis function, for instance from ChIP-Atlas 61 or ReMap  62. The disadvantage however is the higher cost in curation, as well as the fact that manually curated datasets are typically not available for model organisms such as Drosophila or C.
elegans, but are rather restricted to human or mouse, as is the ChEA3 tool. Yet, users can easily add their own data using the scripts provided to fill the mongoDB present in our gitlab repository, when running AnnoMiner locally (https:// gitlab. com/ haber mann_ lab/ AnnoM iner/-/ tree/ master/ scrip ts). Finally, we predicted and verified potential transcriptional regulators of muscle and myofibril morphogenesis as well as muscle growth using AnnoMiner's TF & HM enrichment analysis function. Amongst those is Trl, a  GAGA transcription factor which contains a BTB/POZ domain, as well as a C2H2 zinc-finger that binds to DNA in a sequence-specific manner. Previous studies suggest that Trl is required to keep promoters nucleosome-free, thus allowing Pol-II-access  63. We showed here that muscle-specific knock-down of Trl using four independent hairpins either leads to pupal lethality or flightlessness.
AnnoMiner identified two direct targets of Yki, which upon loss of yki were downregulated. Both code for important muscle structure proteins, constituents of the sarcomere or linking the sarcomere to the nucleus, and hence could contribute to the severe phenotype observed upon yki knock-down. Gain-of-function of yorkie results in muscle fiber hyper-compaction and premature expression of sarcomeric protein components  47. Consistently, AnnoMiner identified a number of direct Yki targets with functions related to muscle development and growth. This substantiates a role for Scalloped and its transcriptional co-factor Yorkie during flight muscle growth. To conclude, the new web-tool AnnoMiner is a user-friendly, intuitive, interactive and highly-flexible platform for genomic peak annotation and peak integration.
It is suitable for identification of nearby genes or long-range interactions of a genomic peak, as well as to perform Transcription Factor and Histone Modification enrichment analysis for a list of genes. This manuscript details all AnnoMiner functions and shows its usefulness for annotating and integrating peaks from two different ChIP-seq experiments together with transcriptomics data. Finally, AnnoMiner helped identify several key regulators of indirect flight muscle development and growth in Drosophila, some of which were confirmed experimentally.
Methods The AnnoMiner software and database. AnnoMiner is a modular software consisting of a library of java classes for retrieval, storage and analysis of annotated genomic peak data together with a web application providing a graphical user interface for a number of predefined analyses. The AnnoMiner web-sever is a JavaEE web application implemented in the front-end as a single page application using Javascript, jQuery and Bootstrap 4. Queries for the provided analyses are processed by individual servlets that send a response back to the front-end in JSON format (Supplementary Figure  S1 ). The java library used in the back-end manages the retrieval of data from remote sources at UCSC and modENCODE  36  or alternatively from files in BED or gff format. Retrieved datasets are stored in a MongoDB database.
This document-oriented NoSQL database system has been chosen for its flexible data model and runtime speed benefits benchmarked against an SQL database solution. For optimal performance, a custom database connectivity layer has been developed based on the MongoDB java driver. The overall structure of the application is shown in Fig. 1  and Supplementary Figure  S1. AnnoMiner's database currently holds genomic data from ENCODE  33  , modENCODE 36 and modERN  37. For each model organism, the latest genome assembly is stored. For human, mouse and Drosophila, we also provide the second latest release. Following the Findability, Accessibility, Interoperability and Reusability (FAIR) principle  64  , documentation and source code of the tool are available on GitLab: https:// gitlab. com/ haber mann_ lab/ annom iner.
Using the java library classes from the code repository developers will be able to define custom analyses on annotated genomic ranges datasets and extend the database towards new data sources. The repository also provides executable java classes and python scripts for local maintenance such as to populate a local database with ENCODE, modEN-CODE or modERN data or to reproduce our benchmark analysis. Comparing 30 h to 72 h APF revealed a list of 2193 differentially expressed genes (taken from GSE107247), which was uploaded to AnnoMiner. The TF enrichment analysis function of AnnoMiner identified 42 unique candidate TFs. (b) Output of the top-ten enriched TFs from AnnoMiner. Trl was identified from 2 different ChIP-seq datasets and is highlighted in red. (c) Phenotypic integration with muscle-specific RNAi knock-down data revealed candidate TFs with a potential function in muscle (flightless or lethal).
The user can choose whether or not the directionality of the deregulation (up-or downregulation) is shown in the box plot. In the first case, hovering on the box of interest, the user can extract information regarding the overall number of deregulated genes (coverage). In the second case, the user will extract separate information about the number of up-and down-regulated genes. Depending on the user input, the long-range interactions function returns neighbouring genes of a single peak, or of many peaks within the user-defined genomic window. Transcription factor enrichment analysis. Transcription factor enrichment analysis in AnnoMiner uses publicly available TF ChIP-seq 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  S8c ,d; all outliers csv files that were excluded are available in our gitlab repository at https:// gitlab. com/ haber mann_ lab/ AnnoM iner-paper/-/ tree/ master/ ChIP-seq_ exper iment_ exclu ded). The number of used TF ChIP-seq datasets for each of the model organisms and assemblies is available in Supplementary Tables  S8, S9.
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  65. Furthermore, we calculate the enrichment score as follows: And a combined score based on the p-value and enrichment score according to 66 : Promoter definition. AnnoMiner allows the user to fully customize the promoter region considered for TF enrichment analysis. The user can choose both the upstream and downstream borders to consider without constraints. DynamicRanges option. The user can also choose dynamic ranges that we pre-calculated for each individual TF based on available ChIP-seq data stored in AnnoMiner.