dataesr/scientific-paragraphs-categorization huggingface dataset:
To support this capability, we provide analysis tools for predicting whether an RBP can bind RG4s, RG4 enrichment in a gene set, and de novo RG4 prediction. Genome-browser and table views allow exploring, filtering, and downloading the data quickly for individual genes and in batch. QUADRatlas is a significant step forward in our ability to understand the biology of RG4s, offering unmatched data content and enabling the integrated analysis of RG4s and their interactions with RBPs.
from the >200 eCLIP assays in the ENCODE dataset  (17) ) or associations to cellular phenotypes and diseases are also generally missing. These limits hamper our possibility of reconstructing the interaction network of RG4s and its cellular context, making the discovery of cellular RG4mediated mechanisms to be exploited as therapeutic strategies difficult. To address this need and provide a platform allowing the transcriptome-wide, integrated exploration of RG4s, RBPs, and their interactions, we present here QUADRatlas, the RG4 and RG4-binding proteins (RG4BPs) database. QUADRatlas includes thousands of experimentally determined and predicted RG4 elements in the coding human transcriptome, coupled with a curated catalog of RG4binding proteins and their association with RG4s. Furthermore, we provide a wide collection of RBP binding sites and involvement in functions in RNA biology, biological processes, and pathologies.
Data collection Human genomic and transcriptomic sequences and annotations were obtained from GENCODE, version 36 (assembly GRCh38. p13) (18). Links to external databases are composed on the fly wherever possible to ensure future compatibility with these resources. Gene-disease associations were retrieved from OMIM  (19)  and DisGeNET  (20). OMIM data was filtered to remove entries with phenotype mapping key = 1 and empty phenotype classification. From DisGeNET, only disease entries were considered, removing diseases falling into classes C21, C22, C24 and C26. Binding sites for human RNA-binding proteins (RBPs) were obtained from ENCODE  (17) , selecting replicatemerged, IDR-filtered (threshold = 0. 05) peaks from eCLIP assays. The obtained sites were annotated to the gene and transcript containing them with ctk  (21).
Experimentally determined RG4s Coordinates of experimentally determined RG4 elements were retrieved from two published datasets obtained with the RT-stop profiling and rG4-seq techniques  (7, 22, 23). Regions from the rG4-seq dataset were remapped to the hg38 human genome assembly and the sequences verified to ensure correspondence with the original hg19 version. Single-nucleotide RG4s derived by RT-stop profiling were extended by 30 nts upstream and downstream to obtain the most likely region occupied by the whole element. For regions in which the 30 nts extended outside the exon containing the central RG4 nucleotide the coordinates were extended into the previous/next exon. The BSgenome. Hsapiens. NCBI. GRCh38 v. 1. 3. 1000 and Biostrings v2. 54. 0 R packages were used to extract the sequences of the RG4 elements.
Predicted RG4s RG4 elements were independently predicted on human 5'UTR, CDS, and 3'UTR sequences derived from UCSC (GENCODE v36, GRCh38. p13 assembly). We used three tools, namely QGRS mapper (score threshold = 19, (  24 )), pqsfinder (v2. 2. 0, score threshold = 47 (25,26)), and G4Hunter (score threshold = 1. 2, window size = 25,  (27, 28) ). Parameters were selected by considering the respective tool authors' indications and assessing the most commonly used thresholds for similar types of sequences, and looking at inflection points in the number of predicted elements, likely to indicate a decrease in the number of false positive predictions. To allow comparing scores between the three tools, scores were normalized in the 0-100% range by considering the minimum and maximum scores that could be output by each algorithm.
[((Protein Name)) AND ((G4) OR (g-tract) OR (quadruplex) OR (quartet) OR (guanine rich) OR (g rich) OR (g-quadruplex) OR (g-quartet) OR (g-rich) OR (guaninerich) OR (poly(G)) OR (GGG) OR (GGGG*)) ]. This second search aimed to identify the RG4s bound by these proteins, the localization (e. g. 5'UTRs, CDS, etc. ) of the RG4 in the transcript, and the role of the RG4-protein interaction in RNA biology, biological processes, and diseases. The identified RNA biology functions and biological processes were annotated to Gene Ontology terms with the GO. db v. 3. 10. 0 R package. Finally, each RG4BP was mapped to external databases providing information on the protein structure, modifications, and interactions (Uniprot  (37) , SMART  (38) , Phosphosite  (39) , STRING  (40)  and Biogrid (  41 )).
Database and web interface implementation QUADRatlas runs on an Ubuntu Linux server 20. 04. 3 LTS machine. The data is stored and managed by a PostgreSQL v12. 11 database, whose schema can be found in Supplementary Figure  S1. The back-end runs on the Django v3. 2. 9 web framework, while the Angular v13. 0. 3 platform and the Angular Material UI component library v14. 0. 2 were used to build the graphical user interface. Finally, the genome browsers were implemented with the JBrowse 2 v1. 7. 10 component  (42). The QGRS mapper  (24) , pqsfinder  (25, 26) , and G4Hunter (27,28) tools were downloaded from the website indicated in the respective publication to be used for RG4 prediction in user-specified sequences.
Database content QUADRatlas has been implemented as a relational Post-greSQL database, coupled to a reactive, app-like web interface. Its current version contains 60616 human genes corresponding to 231957 transcripts. It includes 217 424 RG4s, 42 563 of which (19. 6%) are experimentally derived and 174 861 (80. 4%) represent the coding transcriptomewide predictions obtained with three tools and their consensus. We compiled a catalog of 1117 RNA-binding proteins which bind to RG4s (RG4BPs), both in their folded or unfolded conformation. Out of these 1117 RG4BPs, 1089 were identified through 8 previously published RNA pull-down datasets  (5, (30) (31) (32) (33) (34) (35) (36) , while 28 were obtained through the manual curation of 95 publications on the subject (see Meth-ods). Whenever known, we associated RG4BPs to the RG4s they bind, obtaining 34 737 such interactions.
RG4BPs are annotated for the RG4-dependent RNA biology functions and biological processes they are known to exert, totaling 1175 entries. Finally, RG4BPs and all genes were also connected to the diseases they are involved with (66571 annotations). Recognizing that the interaction of RG4s with RBPs is key to their function, we then complemented known RG4BP-RG4 interactions with the extensive dataset of EN-CODE eCLIP assays  (17) , including 818 231 binding sites for 148 RBPs in two cell lines (  HepG2  and K562 ). An overview of the database content and features is presented in Figure  1.
Analysis functions Along with its data exploration features, QUADRatlas offers four analysis functions to support the generation of hypotheses about RG4s and their interactions with RG4BPs. The RBP-RG4 overlap analysis requires the user to specify an RNA-binding protein of interest and select the source of RG4s to be considered (e. g. rG4-seq). This function then computes the positional overlap of the binding sites of the selected RBP with the RG4s, returning a downloadable table of overlaps and a custom genome browser. This analysis can thus predict whether that RBP can systematically bind to RG4s and identify those specific elements it might interact with. The RG4 enrichment analysis starts from a list of genes/transcripts of interest to the user (e. g. differentially expressed in an RNA-seq experiment) and a source of RG4s to be considered (e.
Data download QUADRatlas offers several ways to obtain part or all of the data it contains. First, the whole database in SQL format can be downloaded from the website's Download page. On the same page, our users can also obtain an annotated table of RG4-binding proteins, including phenotypes and links to other databases. When using a search or an analysis function, individual results tables are always downloadable as CSV files. Finally, the batch download function allows downloading all data included in the database for a user-specified list of genes. Each dataset (genes annotation, RG4s, RG4BPs association and binding sites) will be output  in a tab-separated file consisting of several tables, straightforward to inspect but also machine-readable.
Web interface and usage QUADRatlas is available at https://rg4db. cibio. unitn. it. Its interface was implemented by using the Angular framework and the Angular Material UI component library to provide a reactive, app-like user experience. The web interface and the database communicate via a REST API served by a Django backend. Figure  3  presents screenshots of the various components of the search and results page for the RG4BP search function. Each function is accessible through a top menu on the main page and displays instructions coupled with a parameters box allowing the user to configure the analysis (Figure  3A ). Results are presented as a set of collapsible panels. Several columns link to external databases, providing additional details on specific features of the data (e. g. posttranslational modification of an RG4BP).
Results can also be explored via a custom genome browser (implemented via JBrowse2 (  42 )), including the genome sequence, gene/transcript annotations, and all the RG4-specific data contained in QUADRatlas. JBrowse2 provides the typical genome browser experience and is therefore easy for any user to navigate. Users can customize display options and select which tracks to display through the menu in the top left corner of the browser. A vector image of the current display can also be downloaded in the SVG format for use in publications and presentations (Figure  3F ). Finally, a detailed user manual, including directions for parameter selection and screenshots of all interface components coupled with descriptions of their content, is available on the website Help page.
CONCLUSIONS QUADRatlas (https://rg4db. cibio. unitn. it) is a onestop resource for the analysis of RNA G-quadruplexes (RG4s) and their interactions with RG4-binding proteins (RG4BPs). The database includes a comprehensive catalog of experimentally-derived and computationally predicted RG4s enriched with biological function and disease associations. A broad set of RBP binding sites coupled with RBP-RG4 overlap, RG4 enrichment, and prediction functions provide a yet unavailable level of integration of RG4 knowledge with post-transcriptional regulation datasets. Ultimately, QUADRatlas will allow generating mechanistic hypotheses about the RG4-mediated control of gene expression and its role in disease. This capability will enable exploiting RG4s to develop new, highly-specific therapeutic strategies.
Future expansions of the database will focus first on adding data for other organisms, especially widely used models (e. g. the mouse). QUADRatlas already supports such development and includes basic annotation for M. musculus. However, a demanding curation work will be necessary to gather and process all the required data, both experimentally derived and predicted. Another line of work will focus on including non-single-base resolution RG4 datasets such as those produced by G4RP-seq (8) and BG4seq  (43). This objective will require defining a strategy to locate individual RG4 elements in the larger regions obtained with such techniques. Also, we plan to include single nucleotide polymorphisms and indels affecting RG4s, which would provide an additional tool to explore the potential impact of such elements in disease.
Figure1. Data and analysis features offered by QUADRatlas. The core of the database consists of experimentally derived and predicted RG4s coupled with an extensive catalog of RG4-binding proteins. These proteins can bind to folded or unfolded RG4s and were obtained by collecting and integrating eight screens for RG4 interactors (see Methods). Functional annotations and eCLIP-derived RBP binding sites are also included to empower hypothesis generation. On top of this rich dataset, QUADRatlas offers several visualizations (e. g. a custom genome browser) and analysis capabilities (e. g. RBP-RG4 overlap analysis), coupled with highly flexible options for downloading the data it contains.
The amount and variety of data is increasing drastically for several years. These data are often represented as networks, which are then explored with approaches arising from network theory. Recent years have witnessed the extension of network exploration methods to leverage more complex and richer network frameworks. Random walks, for instance, have been extended to explore multilayer networks. However, current random walk approaches are limited in the combination and heterogeneity of network layers they can handle. New analytical and numerical random walk methods are needed to cope with the increasing diversity and complexity of multilayer networks. We propose here MultiXrank, a Python package that enables Random Walk with Restart (RWR) on any kind of multilayer network with an optimized implementation.
This package is supported by a universal mathematical formulation of the RWR. We evaluated MultiXrank with leave-one-out cross-validation and link prediction, and introduced protocols to measure the impact of the addition or removal of multilayer network data on prediction performances. We further measured the sensitivity of MultiXrank to input parameters by in-depth exploration of the parameter space. Finally, we illustrate the versatility of MultiXrank with different use-cases of unsupervised node prioritization and supervised classification in the context of human genetic diseases.
D ata amount and variety have soared as never seen be- fore. These data offer a unique opportunity to better understand complex systems. Among the different modes of representation of data, networks appear as particularly successful. Network representations are indeed particularly interesting to refine raw data and extract relevant features, patterns, and classes. They are exploited for years in the study of complex systems, and a wide and powerful range of tools from graph theory are available for their exploration. However, the exploration and integration of large multidimensional datasets remains a major challenge in many scientific fields. For instance, a comprehensive understanding of biological systems would require the integrated analysis of dozens of different datasets produced at different molecular, cellular or tissular scales.
In RWR, the degrees of freedom are summarized in the Transition rate matrix, and corresponds to the available transition between each node of the graph. The challenges of these different RWR extension lay in the normalization of the Transition rate matrices. To the best of our knowledge, this normalization is currently only solved for multilayer networks composed of two heterogeneous multiplex networks and the case of N multiplex networks remains unsolved. We propose here MultiXrank, a new framework composed of a method and a Python package to execute RWR process on universal multilayer networks. We first introduce the mathematical bases of this universal RWR, which corresponds to a generalization of the approach from  (10). We evaluated MultiXrank with leave one out cross-validation and link prediction protocols.
These evaluations highlight in particular the critical influence of the bipartite networks. We then present an in-depth exploration of the parameter space to measure the stability of the RWR output scores under variations of the input parameters. We finally illustrate the versatility of MultiXrank with three different use-cases in the context of human genetic diseases: two unsupervised strategies for node prioritization and a supervised strategy to train a classifier. The MultiXrank Python package is freely available at https://github. com/anthbapt/multixrank with an optimized implementation allowing its application to large multilayer networks.
The probability condition of normalization gives the constraint: L k j=1 τij = 1, ∀ i. Numerical implementation: MultiXrank. Our RWR on universal multilayer networks method is implemented as a Python package called MultiXrank with an optimized implementation. Default parameters allow exploring homogeneously the multilayer network (supplementary section 1. B). The running time of the package depends on the number of edges of the multilayer network (see complexity analyses in supplementary section 2. A). The package is available on GitHub github/MultiXrank, and can be installed with standard pip installation command: pypi/MultiXrank.
S14 ) to robust toprankings with well-clustered projections in the PCA space for the biological multilayer network composed of 3 types of nodes (genes, diseases and drugs, supplementary Fig. S15 ). Overall, our parameter space study reveals different sensitivities to input parameters depending on the multilayer network explored. The protocol is available within the MultiXrank package and can be used to characterize in-depth the sensitivity to input parameters of any multilayer network.
Mul-tiXrank can produce such a similarity measure respecting the global topology of the network. Using MultiXrank output scores with subsequent embedding would allow taking advantage of the embedding space on the robustness to increase the predictive power of the gene-disease association prediction task  (46). We illustrate the usefulness of MultiXrank with various use-case applications related to biology and human diseases, providing guidelines for users. The MultiXrank package can be applied to any kind of multilayer network such as social, economic, or ecological multilayer networks. MultiXrank is optimized and can handle multilayer networks containing up to millions edges. To consider billion-scale network problems, several strategies can be considered, such as the Block Elimination Approach for RWR (BEAR) that can be exact or approximate  (47)  or the Best of Preprocessing and Iterative approaches (BEPI) that is an approximate approach  (48).
Finally, even if one's initial intuition in data analysis could be that "more data is better", the addition of interaction network layers also brings additional degree of freedom (4). To evaluate the pertinence of the addition of multiplex networks or the addition of layers in a multilayer system, MultiXrank includes a systematic evaluation protocol based on Leave-One-Out-Cross-Validation and Link Prediction. Overall, our results show that adding networks data does not always increase the predictive power of the RWR, as already suggested by previous studies  (9). Our evaluation protocol can be used, for the first time to our knowledge, to evaluate in-depth the signal-to-noise of multilayer system combinations.
60 min, respectively. Drawn blood was centrifuged and plasma frozen (-80 °C) until proteomic analysis. After separation of high abundant proteins, protein expression was analyzed by 2-DIGE and MALDI-TOF. To visualize the connected signaling cascade, a bio-informatical network analysis was performed. The present study was approved by the ethical committee of the University of Cologne, Germany. The study registry number is NCT03823677. In comparing HH30 to GC30, a total of seven protein spots had a doubled expression, and 22 spots had decreased gene expression. In a comparison of HH60 to GC60, a total of 27 protein spots were significantly higher expressed. HH60, as compared to GC30, revealed that a total of 37 spots had doubled expression. Vice versa, 12 spots were detected, which were higher expressed in GC30 vs.
Discussion To analyze the effect of hypobaric hypoxia on humans, serum samples donated by 19-20 independent donors were pooled per experimental group. Serum taken after exposition to hypobaric hypoxia for 30 min and 60 min (15,000 ft) and from two control groups (no hypoxia) were analyzed. Prior data showed altered protein metabolism after 30 min, so in this study we evaluated 30 min with more extensive hypobaric hypoxia as well as a doubled time period (60 min).
Data of Wang et al. [30]  from 2018 revealed that acute phase proteins and inflammatory cytokines (IL-1β, IL-6 and TNF-α) show significant changes if exposed to altitude conditions. A total of 104 male soldiers rapidly ascending from Beijing (20-60 m) to Germu, Qinghai (3200 m), were divided into an AMS (Acute-Mountain-Sickness) group and a non-AMS group according to the Lake Louis Score system. Forty-nine blood samples were collected before and on the third day after ascending to high altitude. Serum haptoglobin (Hp), transferrin (Tf) and complement C3 were detected by immune scattered nephelometry, whereas serum interleukin-1beta (IL-1β), IL-6 and tumor necrosis factor-α (TNF-α) were detected by chemical luminescence immunity analyzer. The combination of inflammatory cytokines or acute phase proteins improves the specificity for the diagnosis of AMS.
Image Analysis For image analysis, scan files of the 2D-DIGE gels were loaded into Delta 2D image analysis software (Decodon, Version: 4. 8). For the image analysis, the workflow given by the software was followed. First, the images were grouped into four groups according to sample load. Each group contained only one gel: Control I in the first group (orange); hypobaric hypoxia 30 min in the second group (red); Control II in the third group (turquoise) and hypobaric hypoxia 60 min in the fourth group (blue). The images of the internal standard were grouped separately (yellow). Next, the images were warped to each other. This action is performed prior to spot detection and is used to eliminate distortions between the gels. The warp strategy "In-Gel Standard Warping" was used. Specifically, the Cy2 gel of every sample was warped with the Cy3 and Cy5 images of the same gel.
Bio-Informatic Analysis of Proteins As a first step, statistically significantly regulated proteins from blood were identified and analyzed by network analyses (GeneMania). Afterward, those statistically significant proteins were grouped (GC30, GC60, HH30 and HH60) using hierarchical cluster analysis (Perseus). As a third step, proteins of similarly early upregulated clusters underwent further network analysis to evaluate possible corresponding proteins or functions in the blood. This approach to deal with pooled proteomic data is described in detail below.
Network Analysis of Proteins (GeneMania) Proteins from a serum analysis which were significantly altered were used for further bio-informatical analysis to identify underlying networks, signaling cascades and pathways affected. Biological functions of statistically significantly regulated proteins were identified using functional network analysis. GeneMania (http://www. genemania. org, accessed on 26 June 2021) is a tool that helps predict interactions and functions of genes in terms of network and, when available, of pathways. It provides the possibility to customize the network and allows for the choosing of data sources or highlighting of specific functions with a more comfortable graphic experience. It is developed and continually updated by the University of Toronto and is funded by the Ontario Ministry of Research and Innovation  [13].
GeneMania knowledge is based on data from large databases, which comprise Gene Expression Omnibus, BioGRID, EMBL-EBI, Pfam, Ensembl, Mouse Genome Informatics, the National Center for Biotechnology Information, InParanoid and Pathway Commons  [14]. It was developed for making predictions about a gene or protein function based on a query of a list of proteins that share a function of interest. The software allows for taking advantage of the persistent improvement and proliferation of high-throughput genomics and proteomics datasets by making up-to-date predictions of their interaction with other genes or proteins  [13, 14]. As these software programs use different algorithms, we decided to perform the bioinformatical analyses with all of them in order to retrieve the highest number of predicted interactions, maintaining an acceptable level of confidence (0.