dataesr/scientific-paragraphs-categorization huggingface dataset:
There are already many tools and pipelines available to perform differential expression analysis and mutation calling, yet too few tools that allow indepth data mining, such as mitoXplorer. To make prior data analysis as easy as possible for users, we provide a pipeline for differential expression analysis and mutant calling, which is available in our git-repository (https://gitlab. com/habermannlab/mitox rnaseq pipeline/). Nevertheless, we recognize that the potential heterogeneity of analyzed data from different studies has limitations, especially concerning comparative analysis between different projects. MitoXplorer is integrating, clustering and visualizing numerical data resulting from expression studies (transcrip-tome, proteome), as well as mutation data. Thus, it is currently limited to analyzing mito-genes without offering the ability to explore their embedding in a broader, cellular context and thus to learn about potential regulatory mechanisms of observed expression changes of mito-genes.
CONCLUSIONS mitoXplorer is a powerful, web-based visual data mining platform that allows users to in-depth analyze and visualize mutations and expression dynamics of mito-genes and mito-processes by integrating a manually curated mitochondrial interactome with -omics data in various tissues and conditions of four model species, including human. We used transcriptome and proteome data from cell lines with trisomy 21 to demonstrate the value of mitoXplorer in analyzing in detail the expression dynamics of mito-genes andprocesses. We have used mitoXplorer to integrate these data with publicly available datasets of patients with trisomy 21. Using mitoXplorer for data mining, we predicted failure of mitochondrial respiration in one of the trisomy 21 cell lines, which we verified experimentally.
The user interacts with mitoXplorer via several visual interfaces to analyze, integrate and visualize his private, as well as public data. Four interactive visualization interfaces are offered: (i) the Interactome View allows at-a-glance visualization of the entire mitochondrial interactome of a single dataset (see Figure2); (ii) comparative plots, consisting of a scatterplot and a sortable heatmap allows comparison of up to six datasets, whereby a single mito-process is analyzed at a time (see Figure3); (iii) hierarchical clustering allows comparison of a large number of datasets which are clustered according to their expression values. Hierarchical clustering plots are zoom-able and interactive (see Figure4); (iv) principal component analysis displays PCA-analyzed datasets in 3D, providing filtering and grouping functions.
(C) The heatmap is sortable by log 2 fold change (as indicated by the pointer in C), as well as by dataset. Clicking on one of the datasets will sort the heatmap according to the log 2 fold change of all genes in this dataset, as is illustrated here. Clicking on one of the genes will sort the heatmap according to its log 2 fold changes across different datasets. The time-series study of developing flight muscle (44)  was used to demonstrate the functionality of this visualization method.
In addition, the three PC components are shown. (C) Selecting color schemes on the right-hand side will change the coloring of the bubbles. In this case, only lung cancer is shown, and coloring is done according to Stage, Gender, Vital, and Skin color. This panel can also be used for selecting specific datasets. For instance, clicking on one of the stages will only display the chosen stage and omit datasets from other stages. As in the heatmap, datasets can be selected from the PCA for grouping. To do this, first a group name has to be defined; second, the datasets belonging to this group have to be selected by clicking on one of the dataset bubbles. This process can be repeated and the resulting groups can then be analyzed using Comparative Plots (see Supplementary FigureS2).
ACKNOWLEDGEMENTS We want to thank  Michael Volkmer  and  Edlira Nano  for help and advice in web-server management and development. We want to thank  Stephen Taylor  ( University of Manchester, UK ) for providing cell lines. We thank  Alice Carrier ,  Friedhelm Pfeiffer  and  Frank Schnorrer  for critical reading of the manuscript. We thank the  Max Planck Society , the  Max-Planck Institute for Biochemistry , the  Aix-Marseille University , the  CNRS  and the  Institute of Developmental Biology Marseille (IBDM)  for their generous support.
DATA AVAILABILITY The mitoXplorer web-server is freely available at http://mitoxplorer. ibdm. univ-mrs. fr/. The source code of mitoXplorer is available at https://gitlab. com/ habermannlab/mitox. The pipeline for differential expression analysis and mutation calling of RNA-seq data is available at https://gitlab. com/habermannlab/ mitox rnaseq pipeline. MitoMorph is freely available at https://github. com/giocard/mitoMorph. RNA-seq data published with this study are available via the Gene Expression Omnibus (GEO) database (accession number: GSE131249).
Experimental set-up and data analysis Our experimental set-up is based on a custom built confocal microscope, controlled with Matlab (Mathworks). The output of a 561 nm-diode pumped solid state laser (Cobolt) is directed to galvanometric mirrors used for scanning (6215H, Cambridge technology), before reaching the microscope base (Olympus IX71) where it is focused by the objective (Zeiss, water-immersion, 63 × , NA = 1. 2). A dichroic mirror (Chroma ZT561rdc) and an emission filter (ET577. 5lp) filter out the excitation light from the fluorescence emission, which is detected by an avalanche photodiode (APD, SPCM-AQRH-13, Perkin Elmer) through a multimode fiber that acts as a pinhole with diameter 0. 7 times that of the diffraction-limited spot. The pulses delivered by the APD are sent to a data acquisition board (PCIe-6321, National Instrument) that counts events during periods of 1 µs.
Three time series of 20 s were recorded per FCS measurement, providing a mean value and an error of the autocorrelation curve, calculated using Matlab (Mathworks). The acquisitions were performed on a X,Y,Z grid to sample the FCS measurements above the substrate (Fig. 1 ). The X,Y-coordinates (typically 20), corresponding to different bead configurations, were chosen randomly within a squared surface of c. a. 100 × 100 µm 2. We took care to exclude positions right at the vertical of beads, because they lead to abnormal autocorrelation curves that cannot be fit to estimate reliable numbers of molecules. For each pair of X,Y-coordinates, a series of 20 measurements were performed from the surface up to 100 µm in depth, by steps increasing from 1 µm to 20 µm. For each sample, a mosaic confocal image of the beads covering a total surface of ca.
Computational details Most of the computations have been carried out using Matlab. The scalar angular spectrum model is implemented according to  [20]  on a 3000 × 3000 grid with a pitch of 0. 05 µm. The illumination field is calculated for a series of 81 planes spaced by 0. 08 µm. The MDF is then computed on a volume of 7. 5 × 7. 5 µm 2 (radial) by 6. 4 µm (axial). Integrals of MDF and MDF 2 are then computed to yield the FCS metrics N and CR. Computation time for a given distance Z between the scattering substrate and the focus is a few minutes on a standard laptop. Statistics over the scattering substrate bead configurations have been obtained from the numerical simulations of 15 model samples. Computation of Mie scattering cross sections have been carried out using the Fortran code "CALLBH" given by Bohren and Huffmann  [15].
Given this definition, we can describe the evolution of a weighted switch graph when some edge s ∈ W × W is crossed in the following way: I + (t) =      I(t), if (s, t, * ) / ∈ S ∨ I (s, t, * ) = , for any * ∈ {•, •} , if (s, t, •) ∈ S and I (s, t, •) = I (s, t, •) , otherwise. Although we introduced this general definition, we only consider a particular class of models for now. The current version of the package rPrism, version 1. 0, is suitable for one-level weighted switch graphs, which are defined as follows. Definition 3 A one-level weighted switch graph is a pair (W, S) with W = {} and S = S 0 ∪S 1 such that: • S 0 ⊆ W × W • S 1 ⊆ S 0 × S 0 along with an initial instantiation I 0 : S → Ω, where Ω is the set of weights. Note. In this context, ≡ 0, because our weights will be conceived as rates.
The entry "command1" determines the output of the program and must be filled according to the goal of the user, for example, can be "simpath 10" (meaning the simulation will make at least 10 steps) or "simtime 5. 7" (the simulation will run at least until the unit of time reaches the value 5. 7). The entry "command2" can be replaced as "all" in order to obtain the entire set of outputs or restricted to any combination of the commands: "odel", "simulation results", "simulation plot", "reachable sets", "transition matrix", "labels", separated by a space. Given a one-level switch graph, rPrism translates the introduced model into PRISM language in order to use it to study reactive models. For readers who are used to PRISM syntax, the process traduces nodes to variables and edges to actions. However, an additional module for rates is considered.
In fact, a one-level weighted switch graph for such model is presented in Fig. 4. rPrism was used to simulate the evolution of this system. The plot of the output is presented in Fig. 5. Below, the code introduced in rPrism is presented. We note that this model shows a cyclic behavior as would be expected for a circadian rhythm system. Thus, this kind of model seems suitable to represent by higher-level edges the effect of non linear or even unknown mechanisms of a cell and still obtain coherent simulations and results. Through all examples we can find a common pattern: reactivity and, in particular, higherlevel edges appear to describe the dynamics caused by a component/variable which is not considered. Indeed, note that, for instance, in the hemoglobin example we could consider a model with five states where each node is fully described by the number of oxygen molecules bound.
In this way, we could obtain a model as the one shown in Fig. 6. Compared to the this one, the reactive model in Fig. 3  ignores a variable: the number of oxygen molecules already bound by the hemoglobin protein. We note that the proposed software -rPrism -is able to, in general, construct a reactive model which contains less states than a non-reactive model. Nevertheless, rPrism internally introduces additional state variables when translating the introduced model into PRISM language, in order to retrieve the hidden information about the system. Using simple words, rPrism considers additional variables which determine what weights must be considered for each edge, at each time. Therefore, we cannot think about one-level switch graphs as reduced models but as a different description of the same model with, in general, the same "computational size".
Conclusions and future work In this paper we briefly introduce the software rPrism to study one-level weighted switch graphs. The proposed software has a proper syntax in order to be a friendly software, translating the rPrism input language to a suitable input for simulations in Prism. Also, we present an example of a biological system which can be modeled and studied using this approach. As future work, we intend to extend the rPrism language with the aim of exploring as much as possible the rich set of features of PRISM, namely, adding some model checking capabilities for reactive graphs. In fact, we choose to use PRISM to be the basis of our work based on its model checking capacities. It has a temporal logic language embedded and it would allow us to compute the validity of properties as well of probabilities for some events to occur, which would be quite relevant for model analysis and predictions for biological systems.
Acknowledgments. This work was supported by  ERDF -The European Regional Development Fund  through the  Operational Programme for Competitiveness and Internationalisation -COMPETE 2020 Programme  and by  National Funds through the Portuguese funding agency, FCT -Fundação para a Ciência e a Tecnologia , within project  POCI-01-0145-FEDER-030947  and project with reference  UID/MAT/04106/2019  at  CIDMA. The authors acknowledge the support given by a France-Portugal partnership PHC PESSOA 2018 between  M. Chaves  ( Campus France  # 40823SD ) and  M. A. Martins. D. Figueiredo  also acknowledges the support given by  FCT  via the PhD scholarship  PD/BD/114186/2016.
Here we present the first comprehensive analysis of gene expression changes (RNA-seq) and DNA regulatory element accessibility (ATAC-seq) in zebrafish RGCs at specific time points along the axon regeneration continuum from early growth to target re-innervation. Our analysis reveals that successful CNS axon regeneration is regulated by stage-specific gene regulatory modules, and punctuated by regeneration-associated changes in chromatin accessibility at stages corresponding to axonogenesis and synaptogenesis. Together, these data suggest candidates for gene regulatory targets for promoting successful vertebrate CNS axon regeneration.
Given the temporal dynamics in gene expression associated with different stages of regeneration, we queried the data for evidence of stage-specific processes that drive successful regeneration. To identify canonical pathways represented by differentially expressed transcripts in each cluster, we conducted Ingenuity Pathway Analysis (Qiagen, Redwood City, CA). Enriched pathways, based on high stringency criteria (-log(p-value) > 4), were detected for six out of the seven temporal clusters (Fig. 1C , Enriched pathways; Table  S3 ). We found little overlap in enriched pathways between the first three clusters, consistent with the idea of distinct processes active at different stages of regeneration. Many of the enriched pathways at each time point were consistent with pathways previously associated with axon regeneration and/or developmental wiring of the nervous system.
Expression heatmaps produced from Z-scores (calculated using transcripts per million [TPM] estimates) of 4,614 differentially-expressed transcripts with adjusted p values < 0. 01. Mean normalized transcript counts were generated for each transcript across all samples at all time points. Each row represents a single transcript where each biological sample was compared to the mean, with red and blue indicating standard deviation above and below the mean, respectively. Each column represents one of the three biological replicates for each time point. Genes within each cluster were analyzed by Ingenuity Pathway Analysis (IPA) using -log(p-value) > 4, to identify pathways expressed in a regeneration stage-specific manner. NA, not applicable, indicates that no pathways were detected that met our significance threshold.
We discovered 265 definitive transcription factor encoding genes associated with 339 transcripts that were differentially expressed at one or more post-injury time points (Fig. S2 , Table  S5 ). We further refined this list to 205 transcripts corresponding to 159 transcription factor encoding genes with defined DNA recognition motifs available in the JASPAR  28  or CIS-BP  29  databases. As predicted, we found that differentially expressed transcription factors display temporal clustering similar to that of the regeneration-associated differentially expressed transcripts at large (Fig. 2C ; Table  S6 ). Thirty transcription factor families are represented among the transcription factors that were differentially expressed during regeneration. Over half of the differentially expressed transcription factors fall into four families (basic leucine zipper, basic helix-loop-helix, 2-Cys-2-His zinc finger, and homeodomain).
Three replicates were obtained for each time point. Whole retinas were used for transcriptomic studies because the quality of the RNA was much higher from whole retina. cDNA libraries were generated for each RNA sample using Tru-Seq Stranded Total & mRNA Sample Prep Kits, (Illumina 20020595). Each cDNA library was indexed for multiplexing and subsequently sequenced on four lanes of the Illumina Hiseq2000. Libraries were sequenced at 50 bp, 30-40 million paired-end reads/sample. After merging technical replicates of RNA-seq samples across lanes, adapter sequences were trimmed (TrimGalore, v0. 4. 4), sequence quality was validated (FastQC, v0. 11. 5), and transcript abundance was estimated using 500 bootstrap samples (Kallisto (v0. 42. 4)  45. ATAC-seq data generation. Chromatin was extracted from nuclei isolated from purified populations of RGCs.
Motif enrichment and discovery was carried out within the MEME Suite of motif-based sequence analysis tools (version 5. 0. 3)  51. Statistical analysis of RNA-seq and ATAC-seq data. Following pseudoalignment and quantification of transcripts, differentially expressed transcripts were identified using Sleuth (v0. 29. 0)  52  , either comparing each time point post injury (2, 4, 7, 12 dpi) to the initial time point (0 dpi) using a Wald test statistic or using a likelihood ratio test (LRT) to compare the full model with a time factor versus the null model, controlling the false discovery rate (FDR) at 5% or 1%  53. Expression heatmaps (based on Z-scores calculated using log transcripts per million [TPM] estimates) were produced using ComplexHeatmap 54 (v1. 17. 1), where transcript clusters were identified using the K-means algorithm, and hierarchical clustering (Euclidean distance, complete linkage) was used to cluster rows.
The Ingenuity Pathway Analysis tool (Qiagen, Redwood City, CA, USA) was used to analyze enrichment of molecular and functional gene networks within the differentially expressed gene sets (FDR < 0. 05) at each time point after injury (2, 4, 7, 12 dpi) compared with the initial time point (0 dpi). After quantifying peaklet accessibility, DESeq2 (v1. 18. 1)  55  was used to identify differentially accessible peaklets in an analogous manner to the RNA-seq analysis described above. Peaklets with FDR-controlled p-values < 0. 05 in one of the four comparisons were considered to be differentially accessible. ChIPpeakAnno (v3. 12. 7)  56  , the danRer10. refGene UCSC annotation package (v3. 4. 2), and AnnotationHub (v2. 10. 1) were used to annotate peaklets with genes. Specifically, non-exonic (i. e.
01. Mean normalized transcript counts were generated for each transcript across all samples at all time points. Each row represents a single transcript where each biological sample was compared to the mean, with red and blue indicating standard deviation above and below the mean, respectively. Each column represents one of the three biological replicates for each time point. Genes within each cluster were analyzed by Ingenuity Pathway Analysis (IPA) using -log(p-value) > 4, to identify pathways expressed in a regeneration stage-specific manner. NA, not applicable, indicates that no pathways were detected that met our significance threshold.
Acknowledgements We thank the  UWM High Performance Computing (HPC) Service  for computing resources used in this work. We are grateful to  Heather Leskinen  and  Sarah Sarich  for comments on the manuscript and edits to the material and methods. We wish to thank the  University of Wisconsin Biotechnology Center DNA Sequencing Facility, Madison  for providing RNA and ATAC sample sequencing facilities. We gratefully acknowledge support from the  Research Growth Initiative Grant RGI  (to A. J. U. and P. L. A. ),  University of Wisconsin-Milwaukee. A. R. was supported by the  AgreenSkills + fellowship program , which received funding from the  EU 's  Seventh Framework Program  under grant agreement number  FP7-609398  (AgreenSkills + contract). S. D. was supported by the  Clifford H. Mortimer Award, University of Wisconsin-Milwaukee.
Data Availability Raw sequencing files for the RNA-seq and ATAC-seq data will be submitted upon publication to the Sequence Read Archive (SRA). All scripts used to process and analyze the RNA-seq and ATAC-seq data may be found at https://github. com/andreamrau/OpticRegen_2019. The Regeneration Rosetta app may be accessed at http://lsshiny-prod. uwm. edu/rosetta/, and all associated source files for creating the app may be found at https://github. com/andreamrau/rosetta or https://doi. org/10. 5281/zenodo. 3407277.
Introduction RNA molecules play fundamental roles in many biological processes, such as regulation of gene expression  [1] [2] [3] [4] , RNA splicing  [5] [6] [7] , and protein synthesis  [8] [9] [10]. The understanding of these processes improves as new structures of RNA molecules and their complexes with various molecular partners are determined, and the molecular details of interactions analyzed  [11] [12] [13] [14] [15] [16] [17]. As of 20 September 2018, 1345 RNA structures (and 2284 RNA complexes with other molecules) were available in the Protein Data Bank (PDB)  [18] , including 807 (1680) solved by X-ray crystallography, 504  (120)  by NMR spectroscopy, 29 (478) by cryo-EM, and 5  (6)  by other methods. However, the experimental determination of RNA structures at high resolution, which allows the description of individual atomic features as well as atomic interactions, is time-consuming, and in many cases not possible.
As a result, complete high-resolution structures are available for an extremely small fraction of all the known RNA molecules that are crucial for numerous fundamental cellular processes; for instance, only 87 out of 2791 RNA families in the Rfam database have at least one experimentally determined structure. It will be very challenging to obtain a comprehensive overview of structures for all RNA molecules that have a biochemical function (beyond protein-coding). Such efforts have been undertaken toward elucidating protein structures, e. g. , through the Protein Structural Genomics initiative and related efforts around the world  [19, 20]. The flexible nature of biomacromolecules has proven to be a challenge to overcome when it comes to sample preparation for biophysical techniques that have a scope for their structural determination  [21] [22] [23].
For the development and testing of computational methods for RNA 3D structure modeling, datasets of experimentally determined macromolecular 3D structures are fundamental. In particular, datasets of high-resolution structures determined by MX and NMR, available in the PDB, were used to derive statistical parameters that describe the structural and dynamical features of RNA molecules. These parameters describe the frequency of different molecular features and can be encoded in the form of scoring functions used in computational folding simulations, e. g. , to estimate the energy of different conformations, or to assess the models according to their similarity to structures observed previously. For instance, statistical information on torsion angles and sugar puckers can be used as prior knowledge in structure prediction algorithms that rely on conditional probabilities  [137, 138].
Thus, a 'template' molecule can be used as a structural framework, in which the original sequence is 'mutated' to become the sequence of the 'target' molecule while retaining most of the local and global structural features. The identification of a template structure (one or more) and aligning the target and template sequences (to determine residues that are spatially equivalent) is the first and the most crucial step of this kind of modeling. Thus, the success of comparative modeling is limited by the availability of experimentally solved RNA 3D structures that can serve as templates, and hence this approach is heavily dependent on macromolecular structure databases. Typically, comparative modeling can produce highly accurate models for target molecules that exhibit high sequence similarity to the templates, indicative of a close evolutionary relationship.