dataesr/scientific-paragraphs-categorization huggingface dataset:
Some of these tools include: (i) the MEME suite, allowing various motif enrichment and discovery analysis  (20) , (ii) TFBSshape allowing investigation of DNA shape features for TFBSs to provide insight on the mechanism of protein-DNA interaction  (21, 22) , (iii) CiiiDER  (23)  for TFBS prediction and analysis such as enrichment assessment in DNA sequences, (iv) RSAT, allowing motif discovery, TFBS motif analyses  (24)  and (v) i-cisTarget, which allows the prediction of cis-regulatory modules and regulatory features  (25, 26). In this paper, we present the 9th release of the JASPAR database, which provides a substantial update and expansion of TF binding profiles in the six taxonomic groups. The update includes not only binding profiles (as PFMs) but also revisited metadata. Additionally, we added word clouds to display enriched terms associated with TFs in the scientific literature.
Expansion and update of the JASPAR database TF binding profiles. In the 9th release of JASPAR, we discarded unused collections introduced in early releases of the database  (27) (28) (29)  that either did not correspond to TFspecific binding profiles or were data-type specific; we maintained the CORE and Unvalidated collections. We computed and compiled TF binding profiles obtained from CAP-SELEX (  14 ), NCAP-SELEX  (30) , SELEX-seq  (31) , PBMs  (32) , ChIP-seq  (33) (34) (35)  and DAP-seq experiments from ReMap 2022  (36)  and GEO  (37) , and ChIP-exo  (38)  data (Supplementary Data 1 -Text for detailed list of datasets and method details). After manual curation of these profiles to confirm orthogonal supports in the literature, we augmented the CORE collection with 341 new binding profiles for TFs in four taxa (Table  1 ; Figure  1 ): 148 profiles in plants (a 24% expansion for this taxon), 101 profiles in vertebrates (a 13% expansion), 85 profiles in urochordates (only one motif was present since the second release of JASPAR in 2006 (  27 )), and seven profiles in insects (a 5% expansion).
Out of these added profiles, 52 were upgraded from the Unvalidated to the CORE collection (27 and 25 for plants and vertebrates, respectively). Moreover, out of the newly introduced PFMs, 31 are associated with TF dimers. The literature that provides orthogonal evidence for the newly introduced TF binding profiles is provided in the metadata. Additionally, we updated 160 TF binding profiles across the six taxa with new PFMs (Table  1 ). High-quality PFMs lacking orthogonal support were included in the Unvalidated collection (298 new profiles; Supplementary Data 1--Supplementary Figure  S1 , Supplementary Data 2--Supplementary Table  S1 ). Specifically, 115 TF binding profiles are associated with zinc-finger TFs and 95 associated with TFs binding DNA as dimers. We provide the Unvalidated collection of TF binding profiles to the community to use with due caution since they are not yet supported with orthogonal evidence.
We extend our invitation to the user community to be involved in the motif curation process by providing either new unvalidated profiles to consider or support to existing profiles in the collection. We exhaustively revised the metadata to update information about the TF names, the structural class and family of the TF DBDs (following TFClass (  2 )), and links to external databases such as UniProt  (36) , ReMap  (36) , UniBind  (15, 16)  and DNA Readout Viewer (39), whenever possible. Finally, we removed 31 profiles from the CORE collection (22 plant, 6 vertebrate and 4 fungi profiles) as they corresponded to synonyms of already present TF profiles, had low information content, or were derived from consensus strings (Table  1 ). In addition, we removed 85 profiles from the Unvalidated collection (44 vertebrate, 40 plant and 1 fungi profiles) because: (i) the corresponding profile or a new profile for the same TF was added to the CORE collection; (ii) the profile was of insufficient quality or (iii) the profile was misannotated (Supplementary Data 2--Supplementary Table  S1 ; detailed list of all removed profiles at https://jaspar.
Word clouds of terms associated with TFs in the scientific literature Biological information about TFs, or genes in general, is scattered across many different resources, with PubMed possibly being the most extensive one. In an attempt to provide rich annotations for the TFs in JASPAR, we mined the corpus of article abstracts available in the PubMed database  (42). We compiled sets of abstracts associated with each TF and weighted each word present by its relative importance when compared to all abstracts associated with other TFs in the same taxon (Supplementary Data 1--Text for method details). For each TF, the 200 highest weighted words were used to create a word cloud summarizing the annotations associated with that TF. As an example, Figure  2  illustrates the word cloud of terms associated with the PAX6 TF in the scientific literature.
Next, we computed a familial binding profile for each cluster, summarizing the profiles within the clusters following (47) (Supplementary Data 1--Text for method details; Supplementary Data 1--Supplementary Figure  S2 ). The familial binding profiles, also referred to as archetypes in    (47) , can be explored and downloaded at https://jaspar. genereg. net/matrix-clusters and https://jaspar. genereg. net/ downloads/, respectively. One of the primary uses of PFMs is to predict binding sites. To facilitate this, we created ready-made prediction tracks for genome visualization and interpretation. Specifically, we scanned the genomes of eight organisms (Arabidopsis thaliana, Caenorhabditis elegans, Ciona intestinalis, Danio rerio, Drosophila melanogaster, Homo sapiens, Mus musculus and Saccharomyces cerevisiae) with the JAS-PAR CORE PFMs associated with the same taxon to predict TFBSs and update the JASPAR TFBS genomic tracks.
Moreover, we created a collection of familial TF-BSs by merging overlapping TFBSs that were predicted from PFMs associated with the same familial binding profile (Supplementary Data 1--Text for method details). The TFBS predictions associated with all PFMs are available at http://expdata. cmmt. ubc. ca/JASPAR/downloads/ UCSC tracks/2022/. The familial binding TFBSs are available at https://jaspar. genereg. net/downloads/. Finally, we provide JASPAR TFBS predictions as genomic tracks, which can be visualized in genome browsers. Notably, the UCSC Genome Browser (48) now presents predicted human (for the hg19 and hg38 genome assemblies) and mouse (for the mm10 and mm39 genome assemblies) JASPAR TFBS data as a native track for the human genome with information such as TF names, TFBS prediction scores and PFM logo for each of the 12+ billion predictions (Supplementary Data 1 -Supplementary Figure  S3 ).
The enrichment tool is available as a command-line tool (https://jaspar. genereg. net/enrichment/, https://bitbucket. org/CBGR/jaspar enrichment/). As a use case, we studied the differential enrichment of predicted TFBSs at DNase-seq peaks observed in A549 cells before and after 2 h treatment with 100 nM dexamethasone. DNase-seq is an assay capturing open chromatin regions  (54). Dexamethasone is a known agonist of the glucocorticoid receptor (NR3C1), a nuclear receptor that binds the DNA upon ligand-based activation. Figure  3  provides a visual representation of the differential TFBS enrichment analysis results when considering DNase-seq peaks in treated versus untreated cells. As expected, NR3C1 (a member of the Steroid hormone receptors (NR3) family) was the top enriched TF (-log 10 (P) = 58.
pyJASPAR--serverless pythonic interface to JASPAR data All data is accessible through the JASPAR website (https://jaspar. genereg. net/), its associated RESTful API (https://jaspar. genereg. net/api/) (56) , and the JASPAR2022 R/Bioconductor data package (for reviewers: see https: //github. com/da-bar/JASPAR2022 for the temporary version before acceptance by Bioconductor). The JASPAR database can also be accessed using Biopython (57) but it requires a local MySQL server to query the underlying database, which limits its access and use. To make access to JASPAR data easier, we introduce a new Python package, pyJASPAR  (58) , which allows users to query and access all JASPAR data without setting up the underlying MySQL database. pyJASPAR is implemented in Python 3 using the Biopython motifs module and SQLite3 to provide a serverless Pythonic interface to the JASPAR database.
The package allows users to query and access TF binding profiles across various releases of JASPAR. The releases currently available are: JASPAR2014, JASPAR2016, JASPAR2018, JAS-PAR2020 and JASPAR2022. The pyJASPAR package will be updated when future JASPAR releases become available. TF binding profiles can be retrieved using JASPAR ma- pyJASPAR is open source and the code is available at https://github. com/asntech/pyjaspar/ under the GPL-3. 0 License. The module can easily be installed with Conda from the bioconda channel (https://anaconda. org/ bioconda/pyjaspar)  (59)  or from the Python Package Index with the pip command. Detailed documentation with usage examples is available at https://pyjaspar. rtfd. io/.
CONCLUSIONS AND PERSPECTIVES For the 9th release of the JASPAR database, we substantially expanded the JASPAR CORE collection by 19% (370 added motifs). The newly introduced TF binding profiles were obtained after manual curation of PFMs predicted de novo from >3500 ChIP-seq/-exo datasets (from ReMap 2022  (36)  and GEO (  60 )) or retrieved from publically available repositories. While we continued our commitment to provide non-redundant, high-quality TF binding profiles for TFs across six taxa, this release comes with an important increase in the number of profiles for urochordata, with 86 PFMs available when JASPAR has contained a single one since 2006  (27). We now also provide TFBS predictions in Ciona intestinalis using the 86 JASPAR binding profiles. This increase exemplifies how the investigation of transcriptional regulation is expanding across more model organisms.
The lack of experimental support for these profiles indicates an opportunity for the research field to explore these understudied TFs  (62). Notably, 61% of the profiles in the vertebrates Unvalidated collection is associated with C2H2 zinc finger factors. A potential contributing challenge to obtaining orthogonal evidence may be the fact that many zincfingers, which represent the largest class of TFs, have been reported to regulate a limited number or even a single gene (e. g. Zfp568 (63), ZNF558 (64), ZNF410 (65) and ZFP64  (66) ). This JASPAR update comes with a new tool to compute TFBS enrichment given user-provided input and background sequences, mimicking a similar tool available with the UniBind database  (16). The tool relies on the genomewide TFBSs predicted using PFMs from the JASPAR CORE collection.
An example is PubMed ID 21873635 that describes methods development of the Gene Ontology database (822 TFs), PubMed ID 12477932 that describes the Mammalian Gene Collection (MGC) Program (805 TFs), and PubMed ID 15618518 that analyzes the expression of 1445 TFs in the mouse brain (722 TFs). These manuscripts include general information about TFs. Therefore, we see opportunities to further improve the literature annotation engine, by decreasing the influence of outlier manuscripts and incorporating emerging natural language processing methods. PFMs are still the most widely used models to represent TF binding preferences to DNA, despite their wellestablished caveats such as fixed-length and the failure to account for nucleotide interdependencies. A novel generation of computational models based on machine learning approaches such as deep learning are arising  (67, 68).
ACKNOWLEDGMENTS We thank the user community for useful input and the scientific community for performing experimental assays of TF-DNA interactions and for publicly releasing the data. We thank  Shaun Mahony  and  Franklin Pugh  for providing early access to ChIP-exo data from (38) and  Emma Farley  for pointers to urochordata data sets. We thank  Vipin Kumar  for contribution in the early curation sessions. We thank  Walter Santana  for his technical assistance to generate the motif radial trees, the  UCSC Genome Browser  project team for their assistance with the genome tracks,  Harold Gutch  and the  NCMM IT team  for their IT support, and  Ingrid Kjelsvik  for administrative support.
FUNDING Norwegian Research Council  [187615];  Helse Sør-Øst ;  University of Oslo  through the  Centre for Molecular Medicine Norway (NCMM)  (to Mathelier group);  Norwegian Research Council  [ 288404  to R. R. P. , J. A. C. M. , Mathelier group];  Norwegian Cancer Society  [ 197884  to R. B. L. , Mathelier group];  GRAL program  [ ANR-10-LABX-49-01 ] with the frame of the  CBH-EUR-GS  [ ANR-17-EURE-0003  to Parcy group]; PhD fellowship from  CNRS Prime80  (to L. T. );  NHGRI  [ 5U41HG002371-20  to D. S. ]; BOF grant from  Ghent University  [ BOF24Y2019001901  to N. M. P. ]; PhD Fellowship from the  Provence-Alpes-C ôte d'Azur Regional Council (Région SUD) ;  Institut National de la Santé et de la Recherche Médicale (INSERM)  (to F. H. );  Novo Nordisk Foundation  [ NNF20OC0059951 ,  NNF19OC0058262 ];  Danish Cancer Foundation  [ R204-A12359 ];  Danish Independent Research Fund  [ 6110-00207B ,  7014-00120B ];  Carlsberg Foundation ;  ERC the European Union 's  Horizon 2020 research and innovation programme  (MSCA ITN pHioniC) (to Sandelin group and collaborators);  Canadian Institutes of Health Research  [ PJT-162120 ];  Natural Sciences and Engineering Research Council of Canada (NSERC)   Discovery Grant  [ RGPIN-2017-06824 ];  BC Children's Hospital Foundation and Research Institute  (to Wasserman group).
Time was also addressed in  (Apvrille et al. , 2004)  with a transformation from UML to RT-LOTOS. The model-based environment TimeSquare (DeAntoni and Mallet, 2012), supporting the Clock Constraint Specification Language (CCSL), provides facilities for (formal) performance analysis, in particular for the analysis of execution traces. In  (Ouchani et al. , 2013) , Ouchani et al. presented a formal verification framework for checking SysML activity diagrams. The latter are mapped into the input language of the probabilistic model checker PRISM. A calculus dedicated to activity diagrams is proposed and the mapping to PRISM is formalized. The approach is applied to an online shopping system and to real time streaming protocols. (Ando et al. , 2013)  proposed to formalize SysML state machines with CSP#.
TTool TTool is a free and open source framework targeting the design of embedded systems with UML/SysML. It supports different methodological stages, including system analysis (requirements, fault and attack trees, use cases, sequence diagrams,. ), system hardware / software partitioning with the DIPLODOCUS profile  (Apvrille et al. , 2006) , and embedded software design with the AVATAR SysML environment  (Apvrille et al. , 2020). SysML-Sec  (Apvrille and Li, 2019)  is also supported by TTool. SysML-Sec gathers together all methodological stages and adds security features (operators, formal proof) to diagrams. The model-checker presented in this paper concerns AVATAR models. Yet, we intend to extend it to the DIPLODOCUS environment.
To foster national and cross-border IT tools and procedures to ensure safe and general data protection regulationdcompliant exchange of patients' data for ERN-related services. Situation. As of now, cross-border health care is not systematically organized within EURACAN. EURACAN centres are contacted directly by patients or medical teams, who identify their contact directly or with the help of EURA-CAN sites. Approval of cross-border health care is variable across member states. Recommendation. It is essential, at the national level, in connection with the ERN, to define the situations for referral, to simplify the tools for exchange, facilitate, accelerate, and allow/reimburse direct access to centres using a simple, transparent, and flexible system. This is one of the tasks of the ad hoc national committee for the mid-term.
We also thank  Magali Bayssiere  ( Lyon Ingenierie Projets, France ) for her support to the EURACAN coordination team and  Sophie Darnis  ( Centre Léon Bérard ) for medical editorial assistance with this manuscript. FUNDING This work was supported by the  European Commission [EURACAN , grant number  EC 739521 ]; JYB:  NetSARCþ (INCA  and  DGOS , no grant number); LYRICAN [grant number  INCA-DGOS-INSERM 12563 ], Association DAM's,  Eurosarc  [grant number  FP7-278742 ],  Fondation ARC  (no grant number),  Infosarcome , (no grant number),  LabEx DEvweCAN  [grant number  ANR-10-LABX0061 ],  PIA Institut Convergence François Rabelais PLAsCAN  [grant number PLASCAN,  17-CONV-0002 ],  La Ligue de L'Ain   contre le Cancer  (no grant number),  La Ligue nationale contre le Cancer  (no grant number), and  RHU4  DEPGYN [grant number  ANR-18-RHUS-0009 ]; JH:  MH CZeDRO  [grant number MMCI, 00209805];  RI CZECRIN  [grant number  LM2018128 ].
Moreover, we have tested the technique using the Dry Blood Spot (DBS) method and found that it allows for the easy collection of blood for analysis. To develop the method, we performed the optimization of the separation and detection of lipid species on pure standards, reference human plasma (SRM1950), whole blood, and DBS. These analyses allowed an in-house lipid data bank to be built. Using the MS-Dial software, we developed an automatic process for the relative quantification of around 500 lipids species belonging to the 6 main classes of lipids (including phospholipids, sphingolipids, free fatty acids, sterols, and fatty acyl-carnitines). Then, we compared the method using the published data for SRM 1950 and a mouse blood sample, along with another sample of the same blood collected using the DBS method.
Taking into account all these technical limitations, for untargeted lipidomic profiling, in our facility, we chose to work with a supercritical fluid chromatography system using CO 2 under pressure as an eluent. This technique is especially suitable for lipids and allows for a shorter separation than liquid chromatography  [11, 12]. Recently, different studies  [8, 13]  presented the potential of Supercritical Fluid Chromatography (SFC) for profiling lipids in a very short time, especially for clinical data. Once the technique is chosen, the main challenge for an unbiased approach will be the data processing of more than 550 species of lipids. Usually, the first step is the establishment of a database that combines the precise m/z of each lipid species with the retention time under the selected chromatographic conditions, and then the suitable software has to be optimized to effectively integrate a large quantity of peaks.
The goal of our work is to develop a new analytical strategy for a high-throughput and comprehensive lipidomic analysis of biological samples applicable for large clinical studies using SFC on a sub-2 µm particle-bridged diethylamine polar column for the separation of a wide range of hydrophobic and hydrophilic lipid classes in one analysis, including the identification and quantitation of individual lipid species using electrospray ionization (ESI)-MS Xevo G2-XS time of flight (QTOF). First of all, our lipidomic method was validated through the analysis of the reference material human plasma, NIST (National Institute of Standards and Technology; SRM 1950), and by comparing it with already published data  [14]. Dried blood spots (DBSs) are whole blood collected on filter paper from a simple finger prick, which provides a minimally invasive method for collecting blood samples in nonclinical settings.
In-House Database Development The total characterization of the different internal standards belonging to each lipid class were performed using MS/MS experiments. We validated each structure in terms of the presence of specific daughter ions (Table  1 ). For instance, phosphatidylcholine and sphingomyelin were characterized using the PC ion at m/z 184. 0726  [21]  and ceramides and hexosyl ceramide using the sphingoid base backbone at m/z 264. 2682  [22]. Moreover, we validate the characterization and retention time of each lipid class through the analysis of different lipid subspecies (Table  1 ). The huge advantage of working with a DEA column is the rapid and efficient chromatographic separation by lipid class due to the SFC properties. Thus, thanks to the high mass accuracy of the QTOF mass spectrometer and the fully chromatographic separation of the lipid class, the identification of other subspecies of each family becomes easy and unambiguous.
Based on the molecular adducts, the Lipid Maps database, and human plasma lipidic extract characterization, we developed an in-house database containing a unique mass to charge ratio and a retention time for lipid subspecies. This was achieved through a manual search for each compound in a plasma lipidomic extract, with a tolerance of 10 ppm, and the verification of the isotopic profile and adducts. The metabolomic guidelines  [23]  were used to determine the level of identification of different lipids. Thus, using commercially pure internal standards (and free cholesterol), MS and MSMS experiments were performed, allowing us to annotate them at level 1. Metabolites that are characterized by their m/z ratio and their retention time according to their lipid class and that do not have insaturation in the fatty acyl chain are annotated at level 2.
Finally, metabolites characterized by their m/z ratio and their retention time according to their lipid class and that have insaturation in the fatty acyl chain are annotated at level 3. For example, in our chromatographic method, isomeric species, such as PC 18:0/16:1 and PC 18:1/16:0, are not separated (data not shown). Thus, in our database, we annotate these lipids as a single species, including the total number of carbon and double bonds, such as PC 34:1, and assign them to level 3 for identification. Finally, our in-house database contains 503 lipids belonging to the 17 main class of lipids, which are as follows: 28 cholesteryl esters and free cholesterols, 60 TGs, 63 DGs, 34 Free Fatty Acids, 36 ceramides, 84 PCs, 7 mono hexosyl ceramides (galactosyl and glucosyl ceramides are not separated in our chromatographic conditions), 27 SMs, 25 LPCs, 11 fatty acyl-carnitines, 72 PEs, 3 LacCers, 8 LPEs, 21 PGs, 16 PIs, and 7 PSs (Supplementary Materials Table  S3 ).
MS-DIAL Processing Workflow Data processing was performed using the MS-DIAL software  [24]. Raw Waters data were first converted to analysis base files (abf) in order to create a MS-DIAL project. As the Waters QTOF data have a supplementary function, corresponding to the lock spray analysis, a package must be downloaded on the Waters web site and installed in the abf converter software. To process our data in MS-DIAL, two projects are necessary: one in positive ionization mode for the identification of CEs, TGs, Cers, MHCers, LacCers, SMs, PCs, PEs, LPCs, LPEs, and CARs and one in negative ionization mode for the identification of FAs, PGs, PIs, and PSs. Numerous parameters can be changed in MS-DIAL for data collection, peak picking, and identification, as well as alignment. Based on the mass accuracy of the QTOF mass spectrometer and the repeatability of the retention time between the analyses, all parameters were carefully optimized.
All MS-DIAL parameters are described in Supplementary Materials Table  S2. Nevertheless, we observed numerous errors in the integration of the peaks belonging to the CE and TG classes. The key parameter in our data processing was the mass slice width, displayed in the peak collection window. The errors made in integration could be due to the important abundance of CEs and TGs in plasma, but it may also be due to the peak tailing observed in the chromatogram (Figure  1 ). Thus, in order to correct the problem, two methods of analysis parameter settings have been developed for the positive ionization mode. We used our in-house database for the identification of lipids, after a careful optimization of the MS tolerance to precisely determine each retention time.