dataesr/scientific-paragraphs-categorization huggingface dataset:
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 (Figure  7 a ). 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 72h APF, when myofibrils have matured (Figure  7  a).
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   S5 ); 21 TFs did not show a phenotype in this assay and the remaining 13 had not been tested (Figure  7 c ).
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)  (64)  (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 ChIPseq data from Yki (modENCODE dataset ENCSR422OTX) and Sd (modERN dataset ENCSR591PRH) done in fly embryos using AnnoMiner's peak integration function.
The nearby genes annotation function, which integrates 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. 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 TFgene 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 ReMap (67)  (66). 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.
Among the static files are the GeneIDs files used for the gene id converter and the ones for the DynamicRanges which is needed by the TFScan function to dynamically assess the upstream promoter boundaries for each TF. Both these files are easily accessible and in plain text format in order to allow easy update and/or modification by the user. The interface appearance is set by the CSS layer (mainly Bootstrap 4). Finally, test files are provided, which together with the tutorial page and video tutorials aim to quickly get the user started in using AnnoMiner. To make AnnoMiner even more user-friendly, we have created tutorial videos, explaining in detail its usage, available at: http://chimborazo. ibdm. univ-mrs. fr/AnnoMiner/tutorial. html (conservative IDR thresholded peaks taken from ENCODE data set ENCSR980EGJ, H.
sapiens HepG2 cells). TP53 has a broader distribution, ranging from the TSS to -4000 bp upstream, as well as shows significant overlap with the 3' downstream region of gene features. ). Again, the significant peak at the TSS was chosen for further analysis (main Figure  4 ). (a) STAT3 upregulated peaks were integrated with H3K4me3 peaks, as well as upregulated genes in ABC subtype (taken from GEO dataset GSE50721 from super-series GSE50724). S3.
and GEO dataset GSE52974, describing long-range enhancer elements regulating Myc expression for normal facial morphogenesis, mapped to mouse genome version mm9. For demonstration of the TF & HM enrichment function we used RNA-seq data from a longevity study focusing on daf-16/FoxO mutants in C. elegans ((57) and GEO dataset GSE72426) and C. elegans genome version ce11/WBcel245.
top 10 enriched datasets included Deaf1, Trl, Hr78 and cwo (Figure 7 b). Hence, these are potential transcriptional regulators of flight muscle development. To identify which of the 42 transcriptional regulators may have a function during flight muscle development we next integrated data from an RNAi-screen for muscle function (58), which had assayed for viability, flight muscle performance and body locomotion after muscle specific knock-down of individual TFs. From the 42 TFs identified by AnnoMiner, knock-down of two TFs resulted in flightless animals (CG14655, cwo), and seven were scored as lethal during development (Trl, Hr78, lola, Vsx2, Pif1B, salr, Hr51) (Supplementary Table.
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.
(57)e is returned, which contains the information on all TFs stored in the AnnoMiner database and their enrichments in the submitted gene list. The table is sortable and downloadable. Once it includes information on the experimental design, treatment, the target gene for each TF, as well as the score, FDR and combined score. Target gene lists can be copied to the clipboard for further analysis. For demonstrating the TF enrichment analysis function of AnnoMiner, we took advantage of a study identifying direct daf-16/FoxO targets from a longevity study ((57)and GEO dataset GSE72426). In this study, the authors looked for isoform-specific targets of daf-16. We combined the list of all DAF-16 isoform targets and submitted it to AnnoMiner for identifying enriched transcription factors. AnnoMiner displays the 10 most.
Acknowledgements We thank  Friedhelm Pfeiffer  and all team members of the  IBDM Computational Biology team  for critical reading of the manuscript. We thank  Aynur Kaya-Çopur  for sharing data before publication. We acknowledge the  France-BioImaging  infrastructure supported by the  French National Research Agency  ( ANR-10-INBS-04-01 , Investments for the future). Fly stocks obtained from the  Bloomington Drosophila Stock Center  ( NIH P40OD018537 ) and the  Vienna Drosophila Resource Center (VDRC)  were used in this study. This work was supported by the  Turing Center of Living Systems  ( CENTURI, A*MIDEX, Investments for the Future ) and the IBDM UMR 7288, by the  Max Planck Society , the  Centre National de la Recherche Scientifique (CNRS) , and  Aix-Marseille University.
Funding This work was supported by  ANR  grant  ANR-18-CE45-0016-01  MITO-DYNAMICS awarded to BHH and FS, the  excellence initiative Aix-Marseille University A*MIDEX  ( ANR-11-IDEX-0001-02 , F. S. ), the  French National Research Agency  with  ANR-ACHN MUSCLE-FORCES  (F. S. ), the  European Research Council  under the European Union's  Seventh Framework Programme  (FP/2007-2013)/ ERC  Grant  310939  (F. S. ), the  Human Frontiers Science Program (HFSP ,  RGP0052/2018 , F. S. ).
table. 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.
(57)e is returned, which contains the information on all TFs stored in the AnnoMiner database and their enrichments in the submitted gene list. The table is sortable and downloadable. Once it includes information on the experimental design, treatment, the target gene for each TF, as well as the score, FDR and combined score. Target gene lists can be copied to the clipboard for further analysis. For demonstrating the TF enrichment analysis function of AnnoMiner, we took advantage of a study identifying direct daf-16/FoxO targets from a longevity study ((57)and GEO dataset GSE72426). In this study, the authors looked for isoform-specific targets of daf-16. We combined the list of all DAF-16 isoform targets and submitted it to AnnoMiner for identifying enriched transcription factors. AnnoMiner displays the 10 most enriched TFs, with their combined score indicated in the plot (a) as well as the table of all TFs stored for C.
INTRODUCTION 2] [3]  This vector also showed great promises for the treatment of muscular dystrophies, including Duchenne muscular dystrophy (DMD)  4, 5  or limb-girdle muscular dystrophy type 2D (LGM2D). 6, 7 At first, some of these clinical trials used the intramuscular (IM) route for vector delivery, but this mode of administration showed some limits. [10] [11] [12] [13] [14]  The intravenous (i. v. ) route is currently used for these disorders in several clinical trials (NCT03368742,  15  NCT03375164,  16  and NCT03362502  17  [DMD] and NCT03199469  18  [X-linked myotubular myopathy (XLMTM)]) but requires high vector doses (up to 3e14 viral genomes/kilogram [vg/kg]) to achieve a therapeutic effect that might lead to liver dysfunction, as recently described in nonhuman primates (NHPs)  19  but also in 3 XLMTM patients,  20  or to renal impairment, as observed in 1 DMD patient.
Cells were then washed twice with 1Â PBS and re-suspended in 1Â PBS supplemented with 0. 5% fetal bovine serum (FBS) and 2 mM EDTA. Cells were acquired using a BD FACS LSRII flow cytometer (BD Biosciences) and analyzed with FlowJo software (v. 10; BD Biosciences). IFN-g (MABTech) and IL-2 (Ucytech) ELISpot assays were performed according to the manufacturer's recommendations (Monkey IFN-g ELISpot PLUS kit; MABTech). Briefly, cells were stimulated in vitro for 48 h with overlapping peptides spanning the GFP sequence and divided into 5 pools (10 mg/mL; 15 mers overlapping by 10 amino acids [aas]; Sigma). A negative control consisted of unstimulated cells (medium only), whereas PMA (10 ng/mL)/ionomycin (250 ng/mL) stimulation was used as a positive control for cytokine secretion. Spot number was determined using an ELISpot reader ELR07 (AID) and analyzed with AID ELISpot Reader Software V7.
Quantification and analysis of inflammatory gene expression Frozen tissue was homogenized in TRIzol (Life Technologies), and total RNA was isolated according to the manufacturer's instructions. Total RNA was treated with RNase-Free DNase Set (QIAGEN) to eliminate DNA contamination. Then RNAs were concentrated using the RNeasy MinElute Cleanup kit (QIAGEN). Reverse-transcription reactions on 300 ng of total RNA from muscles was performed using the RT2 First Strand kit (QIAGEN). 84 genes involved in inflammatory response were analyzed using RT2 Profiler Rhesus Macaque Inflammatory Cytokines & Receptors PCR Array (QIAGEN). RQ values (2-DCt) were calculated using the dedicated Web-based software (https://geneglobe. qiagen. com/fr/products/analysis-type/ analysis-type-pcr/pcr-rna-lncrna/). Raw RQ values and open-source R script for heatmap and PCA are available at https://github.
ACKNOWLEDGMENTS The authors thank the  Oniris Boisbonne Centre (Nantes, France)  for NHP housing and care, the  Center for Production of Vectors  (CPVvector core from the  University Hospital of Nantes/French Institute of Health [INSERM]/University of Nantes ) for providing rAAV2/8 vectors (https://umr1089. univ-nantes. fr/en/facilities-cores),and  Lydie Guigand ,  Maëva Dutilleul , and  Hélicia Goubin  from the  UMR703 (Oniris, Nantes, France)  for their help with tissue preparation. The authors would also like to acknowledge the  Cellular and Tissular Imaging Core Facility  (MicroPICell; https://micropicell. univ-nantes. fr)and the  Cytometry Facility Cytocell  (Cytocell; https://cytocell. univ-nantes. fr/)at the  University of Nantes  for expert technical assistance. The study received funding from the  Inserm ; the  CHU de Nantes ; the " Fondation pour la Therapie Genique en Pays de Loire "; the  AFM-Téléthon  ( Association Francaise contre les Myopathies ); the  National Research Agency  ( ANR-09-BLAN-0265   GENE-TOL program ;  ANR-17-CE17-0007-01 TOLGEN program ); the  "Region Pays de La Loire " in the context of IMBIO-DC consortium; and the  IHU-Cesti  project that received French government financial support managed by the  National Research Agency  ( ANR-10-IBHU-005 program ).
In the worst case, this distracts the students from the educational purpose of the session. To improve the accessibility of such computational resources, some of the previously cited in silico approaches have been made accessible online thought web servers. Examples of web services for small molecule docking are as follows: Webina  (Kochnev et al. , 2020)  which allows the user to run AutoDock Vina; SwissDock  (Grosdidier et al. , 2011b)  a web interface based on EADock docking software  (Grosdidier et al. , 2011a) ; DockThor  (Guedes et al. , 2021)  a web server focused on SARS-CoV-2 therapeutic targets; and MedusaDock  (Wang and Dokholyan, 2019)  a docking methodology capable of incorporating structural constraints. Although well-adapted for research, current online docking facilities have generally one or more limitations, such as absence of collaborative working, limited interactivity (definition of the grid, visualization, and graphical analysis of the results), and navigation through multiple submission pages.
Particularly, in the context of education, group sessions using the same data usually imply a duplication of the runs, and sharing result analyses interactively is not possible. With the SeamDock web server, we propose a new approach to facilitate access to small-molecule docking for nonspecialists, including students. SeamDock can be seen as interfacing a docking library interactively through a web browser. Presently, the docking library embeds an interface with several docking engines, and interactive visualization is ensured using the NGL viewer  (Rose et al. , 2018). The design of the interactive web interface relies on the open source Seamless framework (https://github. com/sjdv1982/seamless) that brings innovation in terms of reproducibility, interactivity, and sharing. Each SeamDock run is in fact an interactive docking session.
As a result, SeamDock can be seen as a simple, didactic, evolving online docking resource best suited for education and training. The SeamDock web site is available online at https://bioserv. rpbs. univ-paris-diderot. fr/ services/SeamDock/. The SeamDock workflow (Figure  1 ) involves on the server side the use of the docking_py library for ligand/receptor preparation and docking computation, as on the web side, JavaScript in combination with the NGL viewer  (Rose et al. , 2018)  allows for a full visualization of ligand and the receptor structure as well as docking poses and their interaction with the receptor. Communication between the web page and the server is handled by the Seamless framework, which updates the workflow in response to changes in the web page inputs.
Docking_py Library The SeamDock application run on the server side through the in house developed Python library docking_py  (Tufféry and Murail, 2020). Docking_py is a python library allowing a simple and unified use of the docking software AutoDock 4  (Morris et al. , 2009) , AutoDock Vina  (Trott and Olson, 2010) , Qvina  (Hassan et al. , 2017) , and Smina  (Koes et al. , 2013). Docking_py is an open-source library which code is versioned and deposited on the GitHub platform at https://github. com/samuelmurail/docking_ py. The code development takes advantage of continuous integration through the Travis services (https://travis-ci. org), allowing a code testing at each code modification. Code documentation is available at https://docking-py. readthedocs. io. For simple user installation, the code has been deposited on the Bioconda channel (https://bioconda.
github. io/recipes/ docking_py/README. html) and on the Pypi repository (https://pypi. org/project/docking-py/). The docking_py library has been optimized to be used in Jupyter notebooks, with 3D docking results being displayed with the help of nglview  (Nguyen et al. , 2018 ) library (see as example https://docking-py. readthedocs. io/en/latest/notebook/Usage. html).
Seamless The SeamDock web server is implemented with the open source Seamless framework (https://github. com/sjdv1982/seamless). A Seamless workflow is a graph of connected cells that can contain data or code. A cell can be shared and synchronized over HTTP, and mounted to the local file system. Cells can contain scientific data, code, results, or web content (html, css, js). Between cells, transformations can be defined that connect a code cell and input cells to a cell that will contain the result. The code in a code cell can be written in any programming language: currently, Seamless supports Python, bash, Cython, Fortran, C, and C++. Transformations may be run in a Docker container. For SeamDock, transformations are forwarded as Singularity jobs to our HPC cluster. Seamless describes cell contents as checksums, not values.
All ligand torsions are kept active, with the exception of peptidic ligand for which the backbone dihedral angles will be freeze. The output is a pdbqt file that will be used as input for all docking software. To prepare the receptor structure, first the receptor center of mass is centered on position (0, 0, 0), and a filter is applied on amino and nucleic acid residues to remove water or ligand from the receptor and keep only protein, DNA, and RNA atoms. The receptor coordinate extensions are extracted and used to define FIGURE 2 | Overview of the ligand input interface. SeamDock provides different options for ligand input. Users can provide ligand files of type pdb, mol2, or sdf, a SMILES specification can also be provided. Once the ligand structure has been prepared, its 3D structure will be displayed in a NGL stage.
the maximum value of the docking box size. The protein residue protonation is computed with the pdb2pqr python library  (Dolinsky et al. , 2007) , using the Propka method  (Olsson et al. , 2011)  at a pH of 7. 0. The prepare_receptor4. py script from AutoDock Tools  (Morris et al. , 2009)  is then executed to compute the atom types and charges of the receptor and to add hydrogen atoms if missing.
Docking Procedure Once the user has defined a ligand structure, a receptor structure, the docking box, and the docking parameters, the user can launch the docking using the dedicated web form button. The docking procedure differs depending on the selected docking software. For AutoDock docking, two successive steps are executed to compute the docking grid (using MGLTools preprare_gpf4. py to prepare the grid parameter file and autogrid4 to compute the grid). The prepare_dpf4. py from MGLTools is then launched to prepare the docking parameter file, which is then used as input for autodock4. For Vina, Smina, and Qvina, the procedure is simple, as no grid or input parameter file needs to be precomputed. The three docking software are directly launched with the receptor and ligand pdbqt files defined as input.
Input/Output Visualization To allow the user an interactive and complete 3D visualization of the structures of the different molecules in play, we have fully integrated the 3D viewer NGL Viewer  (Rose et al. , 2018)  in the web page. For both ligand and receptor input, a 3D visualization stage allows the user to inspect the computed 3D structures. Similarly the different docking pose 3D structures can be inspected interactively. NGL Viewer is a key feature of SeamDock, as it allows the user to interactively visualize and modify the docking box over the receptor structure. Moreover NGL viewer is used in the docking outputs to compute all ligand-receptor interactions. JavaScript functions allow to display interactively, in the NGL stage, selected docking pose from the results table, or to highlight a molecular contact from the interaction tables.
RESULTS Users can easily proceed to a molecular docking without extensive computing or biophysics knowledge. Our web server does not necessitate any software installation but only a simple web browser. The user needs to provide a ligand and a receptor file or ID. The user can then choose among the four proposed docking software; AutoDock 4  (Morris et al. , 2009) , Vina  (Trott and Olson, 2010) , Qvina  (Hassan et al. , 2017) , and Smina  (Koes et al. , 2013) , and can choose over a limited number of docking options. After few minutes, docking results will be updated on the user web page, giving access to a full 3D FIGURE 3 | Overview of the receptor input interface. SeamDock provides two options for receptor input. Users can provide a receptor structure file of type pdb, or enter a PDB ID, with or without a selection of chains.
Ligand Input In the ligand input part (see Figure  2 ), the user can specify the ligand structure either by providing a file in the mol2, sdf, or PDB format or enter a SMILES in the text field. One great advantage of the SMILES format is that most chemical databases provide compound description in this format, which can further be edited interactively by the user, for example, to add or change a chemical group, for instance, to demonstrate the impact of a modification on the docking results. Besides, very few web sites make it possible to interactively compute the 3D structure of a compound. Usually, the compound library is preprocessed prior to the calculations (e. g. , https://molview. org/). After input completion (enter or mouse focus change), ligand preparation usually requires few seconds depending on the size and complexity of the ligand.