dataesr/scientific-paragraphs-categorization huggingface dataset:
comment moving forward with Fair data The amount of preliminary data deposited and the enthusiasm from the community for the PoDP reaffirm the need for such a repository of paired public datasets. Feedback from early users also indicated an eagerness to include additional kinds of data in the future. Presently, the PoDP is expressly for linking MS/MS data and whole-genome or metagenome data. Potentially, the PoDP could be developed to include other types of spectral data, like full scan (MS 1 ) metabolomics mass spectrometry data and NMR, as well as proteomics data. Additionally, different kinds of genomic data could be facilitated, including 16S rRNA or other amplicon sequences, transcriptomic data, and genetic manipulation or heterologous expression data. Such additions will further fuel integrated omics analysis tools and approaches, a field that has gained much traction recently  25.
The PoDP requires researchers to deposit their data in public databases, stimulating the upload of data by early users, which is exemplified by more than 1,800 GNPS-MassIVE and MetaboLights submissions just prior to submitting these data in the PoDP. As a FAIR data platform, the PoDP not only facilitates reuse of data, but also promotes the work of researchers who submit their data to the PoDP, through increased publication visibility. Future efforts to (re)use these data by connecting to other platforms and programs for analyzing paired data, such as NPLinker  24  , will further the field of natural product prediction and discovery. Reporting Summary. Further information on research design is available in the Nature Research Reporting Summary linked to this article.
Acknowledgements The research reported in this publication was supported by an  ASDI eScience Grant  ( ASDI. 2017. 030 )from the  Netherlands eScience Center  (to J. J. J. v. d. H. and M. H. M. ), a  National Institutes of Health (NIH) Genome  to  Natural Products Network supplementary award  (no. U01GM110706  to M. H. M. ), a  Wageningen Graduate School Postdoc Talent Program fellowship  (to M. A. S. ), a  Marie Sklodowska-Curie Individual Fellowship  from the  European Union  ( MSCA-IF-EF-ST-897121  to M. A. S. ), the  National Science Foundation (NSF)  ( 1817955  to L. M. S. and  1817887  to R. J. D. ), a  Fundaçao para a Ciencia e Tecnologia (FCT) fellowship  ( SFRH/BD/136367/2018  to R. C. B. ), the  National Cancer Institute of the NIH  (award no. F32CA221327  to M. W. M. ), the  University of California, San Diego, Scripps Institution of Oceanography , and two grant from the  NIH  ( Awards GM118815  and  107550  to L.
Code availability The software is licensed under the Apache 2. 0 open source license and the source code can be found on GitHub (https://github. com/iomega/paired-data-form), which includes the dependencies of the software. Each software release is archived to Zenodo at https://doi. org/10. 5281/ zenodo. 2656630. A full description of how the platform was built can be found on https://pairedomicsdata. bioinformatics. nl/methods.
Rheumatoid arthritis (RA) is a multifactorial, complex autoimmune disease that involves various genetic, environmental, and epigenetic factors. Systems biology approaches provide the means to study complex diseases by integrating different layers of biological information. Combining multiple data types can help compensate for missing or conflicting information and limit the possibility of false positives. In this work, we aim to unravel mechanisms governing the regulation of key transcription factors in RA and derive patient-specific models to gain more insights into the disease heterogeneity and the response to treatment. We first use publicly available transcriptomic datasets (peripheral blood) relative to RA and machine learning to create an RA-specific transcription factor (TF) co-regulatory network.
One such machine learning tool is CoRegNet, which is an R/Bioconductor package that infers co-regulatory networks of transcription factors (TFs) and target genes by analysing transcriptomic data and estimating TFs activity profiles. Moreover, the software also allows for network enrichment by integrating regulation evidence for TF binding sites, protein-protein interaction data, and chromatin immunoprecipitation (ChIP) data from various databases to support cooperative TFs  [17]. In this work, we present a framework for integrating signalling and transcriptional regulation cascades with genomic mutations, combining data-driven approaches with prior knowledge in the form of an integrative RA-specific network. To do so, we use publicly available transcriptomic data of white blood cells from patients suffering from RA and the tool CoRegNet to infer a co-regulatory network.
Data Description and Pre-Processing We used a transcriptomic dataset of white blood cells from RA patients and healthy donors (GSE117769) sequenced at CSL Limited/bio21 Institute (30 Flemington Rd, Parkville, Australia) using the Illumina HiSeq 2500 platform (Illumina, Inc. ). The dataset Figure  1. Workflow for creating an integrative and executable network for rheumatoid arthritis (RA). The steps include network inference and identification of master regulators, the combination of the co-regulatory network with curated signalling cascades, the analysis of omics data of anti-TNF treatment, and the addition of Boolean rules to create adaptable and flexible personalised models for in silico simulations.
Data Description and Pre-Processing We used a transcriptomic dataset of white blood cells from RA patients and healthy donors (GSE117769) sequenced at CSL Limited/bio21 Institute (30 Flemington Rd, Parkville, Australia) using the Illumina HiSeq 2500 platform (Illumina, Inc. ). The dataset describes 120 subjects in total (51 RA and 50 controls, and 19 patients with either ankylosing spondylitis or psoriatic arthritis). From this dataset, we extracted 96 samples (46 RA and 50 controls). For the 46 RA samples, 43 were of Caucasian ancestry and 3 were of unknown ancestry. Moreover, two samples consisted of duplicates of the exact origin (female, control, unknown ancestry), and their expression matrix average was used for the analysis. We conducted a preliminary analysis on the expression matrix data with DESeq2 version 1.
Inference of the Co-Regulatory Network (CoRegNet) We used CoRegNet  [17]  R package version 1. 26. 0 to infer the co-regulatory network with the normalised gene expression matrix of the pre-processing step. The CoRegNet package implements the H-LICORN algorithm, allowing identifying cooperative gene regulators  [25]. We enriched the inferred network with protein-protein and regulation evidence and further refined it with an unsupervised method using the unweighted mean. The co-regulatory network inferred with CoRegNet is composed of the significant edges between TF with a False Discovery Rate (FDR)  [26]  of 5%.
Differential Expression Analysis (DEA) Using Independent Datasets We conducted multiple DEA using two different datasets. One dataset contained normalised counts from RNA sequencing data of CD4+ T cells, including different responses of RA patients to anti-TNF treatment (GSE138747). The dataset comprises two cohorts of RA patients treated with adalimumab (37 patients) and etanercept (41 patients), which were analysed independently. The second dataset comprises raw counts from RNA sequencing data of whole blood cells of biologic naive RA patients from baseline and after three months of treatment with infliximab or adalimumab (GSE129705). This dataset contains two different cohorts of 40 and 36 RA patients, which were also analysed independently. Thus, using DESeq2, we conducted two DEA on comparing responders and non-responders' gene expression levels for both drugs (adalimumab and etanercept) and two DEA on the comparison of baseline and after three months of anti-TNF treatment gene expression level.
List of Variants DisGeNET  [32]  contains the most extensive publicly available collection of genes and variants associated with human diseases. From this database, we extracted 2387 variants associated with RA. Then, we filtered out variants with a variant disease association (VDA) and evidence index (EI) score lower than 0. 7. The VDA score is computed using the number of curated and non-curated publications supporting the variant disease association, while the EI score is computed using contradictory results in publications supporting the variant. A 0. 7 threshold gives us at least one curated publication supporting the variant and the disease association resulting in 1635 variants. Within these 1635 variants, we identified 731 associated genes that were subsequently used as an overlay for the global RA-specific network.
Shiny App The co-regulatory network inferred with CoRegNet, the RA map Activity Flow extracted network, and the merged global RA network, along with their overlays, were integrated into a web-based Shiny application using R  [33]. The web application uses a Cytoscape viewer based on the R package cyjShiny  [34]  and is freely available (https: //quentin-miagoux. shinyapps. io/global_ra_network (accessed on 1 July 2021)).
Inference of a Boolean Network for In Silico Simulations The subnetwork obtained in Section 2. 7 was imported in Cytoscape and exported in SBML format using the BiNoM plugin and its function "export to SBML". The file was subsequently imported in CellDesigner to adjust the layout and remove co-regulatory interactions between TFs to focus only on upstream regulators. Finally, the CellDesigner SBML file was used to infer a Boolean model in an SMBL-Qual format using CaSQ (CellDesigner as SBML-Qual) v0. 9. 11. Then, the SMBL-Qual file was imported into Cell Collective to perform real-time simulation experiments. Using the "Simulation" tab on Cell Collective, we mimicked the downregulation of the components in the datasets used (before/after treatment, responders/nonresponders, mutation carriers) under different initial conditions for each input (TNF, IL6, and TGFB1).
Furthermore, we performed sensitivity and dose-response analyses using five different initial conditions described in Table  3. The same SBML-Qual file was also used for analysis with the software GINsim  [35]  after a post-processing modification step for node name recognition. We used the nightly build version 3. 0. 0b-SNAPSHOT, and the functions Reduce model for the reduced version, Compute stable states to obtain the stable states of the model and Run simulation with configurations of perturbations for the in silico knock out (KO) experiments.
Inference of the Co-Regulatory Network We selected a transcriptomic dataset from the GEO database (GSE117769) to infer the co-regulatory network, including 120 samples (51 RA and 50 control, and 19 patients with either ankylosing spondylitis or psoriatic arthritis). After a series of pre-processing checks, including the sample origins, duplicates, and quality of the data using a PCA on the matrix expression with normalisation and variance stabilising transformation (shown in Figure  S1 ), we kept for further analysis a total of 90 samples (48 Controls and 42 RA patients). Then, of the remaining samples, we obtained normalised counts using DESeq2, on which we finally applied CoRegNet to infer the co-regulatory network, which is presented in Figure  2. This network includes a total of 19 TFs, 14 co-regulatory interactions, and a total of 373 regulated target genes.
Two Use Cases: Identification of Key TFs Using DEG from RA Patients Undergoing Anti-TNF Treatment Two datasets of RNAseq expression data coming from RA patients undergoing anti-TNF treatment were analysed to obtain DEG. The first dataset focuses on responders and non-responders to RA treatment, including 37 and 41 RA patients treated with adalimumab and etanercept, respectively. The second one involves untreated and treated ↓ ↓ JUN ↓ FOS ↓ ↓ CoRegNet TNFAIP3 ↓ BCL6 ↓ MXD1 ↓ MNDA ↓ DAZAP2 ↓ FOSB ↓ NFKBIA ↓ ↓ JUN ↓.
For the dataset of before and after anti-TNF treatment of RA patients, MAPK14 and MAPK1 activity levels were set to zero, and simulations turning the inputs sequentially active revealed that when setting either TNF, TGFB1, or IL6 on, all TFs are expressed (Figure  6a-c ). Furthermore, when mimicking the downregulation of MAP2K1, ILK, and DAXX for the dataset of responders/non-responders to anti-TNF treatment, we observed that when setting TNF and TGFB1 on, all TFs are expressed (Figure  6d, e ). However, when IL6 is set on, only the TF NFKBIA is expressed (Figure  6f ). Finally, when mimicking the downregulation of DAXX and NFKB1 for the mutation carrier from DisGeNET, we observe that when setting either TNF, TGFB1, or IL6 on, all TFs are expressed (Figure  6g-i ). TNF treatment, while the inputs (IL6, TNF, and TGFB1) along with a few intermediate nodes are characterised as mutation carriers.
Dose-Response and Sensitivity Analysis For the dose-response, we studied five different initial conditions shown in Table  3  that mimic different scenarios' effects in combination with TNF activity status. The first condition corresponds to TNF blockade and simultaneous impairment of IL6 and TGFB1 signalling; the second corresponds to having the IL6 cascade active, the third to having the TGFB1 active, the fourth to having both IL6 and TGFB1 active, and lastly, the fifth condition mimics what happens to the system when only the TNF input is active. active revealed that when setting either TNF, TGFB1, or IL6 on, all TFs are expressed (Figure  6a-c ). Furthermore, when mimicking the downregulation of MAP2K1, ILK, and DAXX for the dataset of responders/non-responders to anti-TNF treatment, we observed that when setting TNF and TGFB1 on, all TFs are expressed (Figure  6d ,e).
Wild-Type Stable-State Analysis and KO Simulations We performed stable-state analysis for the model using the software GINsim (Figure  8 ). The analysis for the wild type (no perturbations) revealed five steady states (fixed points) and no complex attractor. The configurations of these five stable states as far as the molecules of interest are concerned (grey, blue, and pink nodes of Figure  8 ) are shown in Table  4. Next, we wanted to see how the downregulation of the TFs observed after the anti-TNF treatment could be counterbalanced by the other pathways, given that in non-responders, the expression of these TFs was kept intact, despite the administered treatment. Therefore, we performed an environment sensitivity analysis to identify which of the two model inputs (IL6 and TGFB1) has the most significant impact on the up-regulation of the TFs included in the model when TNF activity is blocked.
Wild-Type Stable-State Analysis and KO Simulations We performed stable-state analysis for the model using the software GINsim (Figure  8 ). The analysis for the wild type (no perturbations) revealed five steady states (fixed points) and no complex attractor. The configurations of these five stable states as far as the molecules of interest are concerned (grey, blue, and pink nodes of Figure  8 ) are shown in Table  4. For the second set of conditions, we observe that when TNF is blocked and DAXX, ILK, and MAP2K1 are downregulated, the IL6 signal alone is not enough to activate the TFs JUN, FOS, and JUND and the kinases MAPK14 and MAPK1 (Table  6 , ss2). However, when both IL6 and TGFB1 signals are on, all TFs are activated, and the activity level of kinase MAPK14 is restored (Table  6, ss3 ).
Discussion In the present work, we combine gene co-regulation with mechanistic signalling cascades to provide information about upstream regulation. Furthermore, we use the integrative RA network to analyse transcriptomic data regarding anti-TNF treatment and map information about known disease-associated mutation carriers. Lastly, we use the tool CaSQ to add Boolean dynamics to a subnetwork of interest to mimic the effects of the anti-TNF treatment and estimate the impact of IL6 and TGFB1 and the downregulated genes on the activation profile of the identified TFs. The nineteen TFs identified as master regulators have been implicated in RA and autoimmunity, as the literature evidence supports. Six out of the nineteen TFs were also present in the RA map, which is a state-of-the-art mechanistic network for the disease built using manual curation.
These six TFs, namely JUN, JUND, FOS, NFKBIA, ETS1, and TNFAIP3, were used as a functional overlap between the co-regulation and the signalling events, enabling us to obtain a network comprising upstream cascades, active TFs, and target genes. We used the integrative network as a template to analyse two independent datasets regarding anti-TNF treatment. First, we observed the downregulation of some of the TFs previously identified as master regulators. Second, to study the impact of the treatment in parallel with the activity of other signalling cascades, we extracted subgraphs from the integrative network. Finally, we selected the cascades of TNF, IL6, and TGFB1, up to the first affected TF to reduce complexity and focus on the upstream regulators. We selected IL6, as it is one of the targets of the biologic treatment in RA  [36, [41] [42] [43]  and TGF-beta because it is an immunomodulatory cytokine highly expressed in RA patients, with a role that is yet to be determined  [44] [45] [46].
Towards this direction, dual-targeted therapies have been proposed, either with the development of dual-target agents, blocking IL6 and TNF simultaneously, for example  [47] , or by administering two biologics at the same time. However, the administration of combined biologics has been linked to increased adverse effects, and it is currently under study to evaluate better dosage schemes  [48, 49]. Simulations with combined KOs mimicking the effect of anti-TNF treatment in combination with the downregulation of genes observed in the analysed datasets confirmed the dependency of the TFs activation state on the presence of inputs and further highlighted specific conditions. For example, when TNF is blocked, and DAXX, ILK and MAP2K1 are downregulated, the IL6 signal alone is not enough to activate the TFs JUN, FOS, JUND, and the kinases MAPK14 and MAPK1 (Table  6 , ss2).
In this way, patient-specific models could be created based on integrated, personalised data, such as clinical information, comorbidities, and genetic factors (mutations in specific genes). In addition, single-cell datasets could also provide insights into the disease heterogeneity at the cellular level. Executable, integrated networks can accelerate the building of personalised models, as mapping dysregulated genes could reveal potentially impacted pathways shedding light on therapy response. Dynamic analysis and in silico simulations can also inform about the outcome of combined perturbations, predicting the emergent behaviour of the system. Integrating multi-omics data is a key step in understanding pathogenetic mechanisms of multifactorial diseases, where one level of information does not suffice to explain the complex phenotypic traits.
Two Use Cases: Identification of Key TFs Using DEG from RA Patients Undergoing Anti-TNF Treatment Two datasets of RNAseq expression data coming from RA patients undergoing anti-TNF treatment were analysed to obtain DEG. The first dataset focuses on responders and non-responders to RA treatment, including 37 and 41 RA patients treated with adalimumab and etanercept, respectively. The second one involves untreated and treated (infliximab or adalimumab) RA patients, including two cohorts of 40 and 36 RA patients. DEGs from these analyses were mapped to the global network for RA (presented in Figures S2 and S3, respectively). DEGs from responders/non-responders RA patients data mapping shows a total of 15 matching nodes, including 4 etanercept DEGs and 11 adalimumab DEGs. In addition, four matching nodes are CoRegNet and RA map TFs (NFKBIA, JUN, FOS, and TNFAIP3) and 1 CoRegNet TF only (FOSB), in the global network for RA DEGs from untreated and treated RA patients mapping show a total of 101 matching nodes, including 2 CoRegNet and RA map TFs (NFKBIA and FOS) and 4 CoRegNet TF only (BCL6 Transcription Repres-sor (BCL6), MAX Dimerisation Protein 1 (MXD1), Myeloid Cell Nuclear Differentiation Antigen (MNDA), and DAZ-Associated Protein 2 (DAZAP2)).
Data Availability Statement: Datasets (GSE117769, GSE129705, GSE138747, and DisGeNET variants) used for the analysis are publicly available. All data and code used to generate results, including networks inference, differential expression analysis, network visualisation, and Boolean model simulation, are available on a GitLab repository at https://gitlab. com/genhotel/inference-of-aglobal-integrative-network-for-rheumatoid-arthritis (accessed on 1 July 2021). The Shiny app is freely available at https://quentin-miagoux. shinyapps. io/global_ra_network (accessed on 1 July 2021).
3. 4. 4 Two Use Cases: Identification of Key TFs Using DEG from RA Patients Undergoing Anti-TNF Treatment Two datasets of RNAseq expression data coming from RA patients undergoing anti-TNF treatment were analysed to obtain DEG. The first dataset focuses on responders and non-responders to RA treatment, including 37 and 41 RA patients treated with adalimumab and etanercept, respectively. The second one involves untreated and treated (infliximab or adalimumab) RA patients, including two cohorts of 40 and 36 RA patients. DEGs from these analyses were mapped to the global network for RA (presented in Figures S2 and S3, respectively). DEGs from responders/non-responders RA patients data mapping shows a total of 15 matching nodes, including 4 etanercept DEGs and 11 adalimumab DEGs. In addition, four matching nodes are CoRegNet and RA map TFs (NFKBIA, JUN, FOS, and TNFAIP3) and 1 CoRegNet TF only (FOSB), in the global network for RA DEGs from untreated and treated RA patients mapping show a total of 101 matching nodes, including 2 CoRegNet and RA map TFs (NFKBIA and FOS) and 4 CoRegNet TF only (BCL6 Transcription Repres-sor (BCL6), MAX Dimerisation Protein 1 (MXD1), Myeloid Cell Nuclear Differentiation Antigen (MNDA), and DAZ-Associated Protein 2 (DAZAP2)).
SPoCA The SPoCA-CH-suite  (Verbeeck et al. 2014 ) is a set of segmentation procedures that allows decomposition of an EUV image into regions of similar intensity, typically active regions, CHs, and quiet Sun. It relies on an iterative clustering algorithm called fuzzy C-means, which minimizes the variance in each cluster. Typically, the CH class corresponds to the class whose center has the lowest pixel intensity value. The SDO Event Detection System runs the SPoCA-suite to extract CH information from AIA images in the 193 Å passband, and uploads the entries every four hours to the Heliophysics Events Knowledgebase  (Hurlburt et al. 2012). The code for the SPoCA-suite is available at https://github. com/bmampaey/SPoCA.
CATCH The recently developed CATCH algorithm  (Heinemann et al. 2019 ) is a threshold-based CH extraction method, which uses the intensity gradient along the CH boundary to modulate the extraction threshold. By minimizing the change in the extracted area between similar thresholds, a stable boundary can be found. CATCH also provides uncertainty estimations for all parameters. Due to its concept and set-up, CATCH can be applied to any intensity-based EUV filtergram to extract low intensity regions on the solar surface. The tool is publicly available at https://github. com/sgheinemann/CATCH, including a link to the VizieR catalogue -a sample of more than 700 CHs, ready for statistical analysis.