dataesr/scientific-paragraphs-categorization huggingface dataset:
GrainGenes The GrainGenes repository (https://wheat. pw. usda. gov, Yao et al. 2022, Fig. 2. 3) is a digital platform and a community service provider that has been continuously supported by U. S. congressional funds since 1992 through the U. S. Department of Agriculture. Its stakeholders are primarily global small grain research communities who work on wheat, barley, rye and oat  (Blake et al. 2022). Unlike many other small grain repositories, GrainGenes has decades-worth of genetic data: GrainGenes contains rich, peerreviewed, curated data content  (Odell et al. 2017) , ranging from genetic to genomic, phenotypic to traits, and people to publications, with a myriad of search and visualisation tools to enhance data findability and information discovery. GrainGenes also provides services, such as the GrainGenes email list and a Twitter feed, for small grain communities through communicating community announcements, open positions, upcoming conference information and grant opportunities.
The range of genome browsers at GrainGenes for wheat-related species attest the data growth as a result of increasingly accessible sequencing platforms, advanced assembly algorithms and annotation pipelines (https://wheat. pw. usda. gov/ GG3/genome_browser). GrainGenes, in addition to IWGSC's CHINESE SPRING v1 and v2 assemblies, houses assemblies and annotations for Aegilops longissima, A. speltoides, A. sharonensis, five Aegilops tauschii accessions, wild emmer (ZAVITAN) and durum wheat SVEVO, as well as Triticum aestivum genomes from the 10+ Wheat Genome project and the hexaploid wheat pangenome. The genome browsers at GrainGenes are shared with the Triticeae Toolbox (T3) database for the benefit of small grain researchers. In its IWGSC CHINESE SPRING v1 genome browser, GrainGenes has many tracks overlapped with the IWGSC's data depository at Wheat@ URGI and Ensembl Plants.
In addition to those tracks, T3 created several tracks for variants, genome-wide association studies (GWAS), primers and quantitative trait loci (QTLs). The GrainGenes team created the guanine-quadruplex (G4) track, for this newly emergent transcription regulation element class  (Cagirici and Sen 2020). Some of GrainGenes' genome browsers overlap with the genome browsers at other repositories such as Wheat@URGI or Ensembl Plants. This duplication of displays is not in excess, but ultimately serve the interest of small grains researchers, because having the same datasets at multiple repositories allows users to harness different tools built on top of these datasets, for example, BLAST services at GrainGenes (https://wheat. pw. usda. gov/blast/) or the Ensembl Variant Effect Predictor at Ensembl Plants.
One of the added values of using genome browsers at GrainGenes is their integration with the BLAST service at GrainGenes. When users run their nucleotide/protein sequences at GrainGenes, the results are linked to hit regions in the browsers, which allow users to go to those regions with a single mouse click. GrainGenes also uniquely allows rubber banding selection of a genome region on its JBrowse-based browsers for automatic copy pasting of underlying sequence data for subsequent BLASTing. Those who are not familiar with genome browser operations and their relationship to other pages at GrainGenes can benefit from the several YouTube tutorial videos that were created by the GrainGenes team. This is especially useful for those who would like to learn how to jump from genomic to reach genetic data, and vice versa in GrainGenes.
Ensembl Plants The Ensembl Plants platform (https://plants. ensembl. org) provides a Web browser, databases, tools and programmatic access to integrated public genomic data for a breadth of plant species  (Cunningham et al. 2022, Fig. 2. 4 ). Ensembl Plants imports genomes and community gene annotations into the platform, annotates genomic repeat regions, imports variation data and identifies homologues via Ensembl's comparative genomics analysis pipeline. Users can access bioinformatics tools such as BLAST  (Altschul et al. 1990 ) for sequence similarity searching or the Ensembl Variant Effect wheatinitiative. org/10-wheat-genome-project), making 26 triticeae genomes in total. Each of the bread wheat cultivars displays the annotation from IWGSC RefSeq v1. 1 projected onto the cultivar assembly.
T3 houses many Web-based tools for breeders. It has capabilities that allow users to (1) upload their raw genome-wide association (GWAS) and genotype-by-sequencing datasets onto the Website, (2) perform computations such as principal component analyses and (3) visualise histograms for phenotypic observations, screen-plots of principal component eigenvalues, Q-Q plots displaying observed and expected -log 10 p-values and Manhattan plots. In addition, T3 provides Web-based tools to generate selection indexes for multiple traits simultaneously, which is a useful method for breeding programs to select and advance germplasms. As mentioned in the previous section, T3 has a very close collaboration with GrainGenes. Both databases maintain and share the same genome browsers, which enable users to go back and forth between two databases seamlessly.
Gramene (https://www. gramene. org, Tello-Ruiz et  al. 2021). Gramene offers a rich data content and a wide range of tools for comparative functional genomics for 118 reference genomes and 124,010 gene family trees (Release #65, May 2022). These genomes encompass a wide range of species, including various accessions of wheat (similar to other databases discussed in this chapter). Gramene is also the home of the Plant Reactome portal  (Gupta et al. 2022 ), which contains pathways information and gene expression displays for 106 species. Gramene has a close partnership with Ensembl Plants and displays genomes, gene models, variations and annotations collaboratively. In addition to multiple visualisation and analysis tools, such as Ensembl genome browsers, BLAST and FTP download, it also houses the Ensembl-Compara-based GeneTrees visualiser tool for sequence-based protein family classification  (Vilella et al.
Acknowledgements The authors would like to thank the following people from Ensembl Plants:  Guy Naamati ,  Shradha Saraf  and former members  Bruno Contreras-Moreira ,  Dan Bolser ,  Arnaud Kerhornou  and  Paul Kersey ; from  INRAE-URGI :  Anne-Françoise Adam-Blondon ,  Cyril Pommier ,  Célia Michotey ,  Raphaël Flores ,  Nicolas Francillonne ,  Erik Kimmel ; the GrainGenes team members. Thanks to the  International Wheat Genome Sequencing Consortium  and its sponsors, the  Wheat Initiative  and especially the WheatIS expert working group, the  Plant Bioinformatics Facility  (https://doi. org /10. 15454/1. 5572414581735654E12), the following projects:  BreedWheat  ( ANR-10-BTBR-03 ,  France Agrimer ,  FSOV ),  Whealbi  ( EU FP7-613556 ),  AGENT  (European Union's  Horizon 2020 research and innovation programme  under Grant Agreement No.
862613 ),  Wheat Genomics for Sustainable Agriculture  ( BB/J00328X/1 ),  Designing Future Wheat  ( BB/P016855/1 ),  Elixir : the research infrastructure for life-science data and the  European Molecular Biology Laboratory. 2 Wheat Data Integration and FAIRification: IWGSC, GrainGenes … Walkowiak S et al  (2020)  Multiple wheat genomes reveal global variation  in modern breeding. Nature 588:277-283 Wilkinson MD et al ( 2016a ) The  FAIR guiding principles for scientific data management  and stewardship. Sci Data 3: 160018  Wilkinson PA et al ( 2016b ) CerealsDB 3. 0: expansion of resources and data integration. BMC Bioinform 17:256 Yao E et al (2022) GrainGenes: a data-rich repository for small grains genetics and genomics. Database (Oxford) 2022:baac034 Zhu T et al (2021) Optical maps refine the bread wheat Triticum aestivum cv.
RNA-Seq expression data are available as read counts and transcripts per kilobase million mapped reads for the IWGSC RefSeq v1. 1 annotation. A transcriptome atlas developed from 850 RNA-Seq datasets representing 32 tissues at different growth stages and stresses were mapped to the IWGSC RefSeq annotations v1. 0 and v1. 1 (Ramírez-González et al. 2018).
along with seven diploid and tetraploid species: Triticum durum cv. CAPPELLI, Triticum durum cv. STRONGFIELD, Triticum durum cv. SVEVO, Triticum monococcum, Triticum urartu, Aegilops speltoides and Aegilops sharonensis (IWGSC  2014). Download and BLAST services are available for these datasets at https://wheat-urgi. versailles. inrae. fr/Seq-Repository/Assemblies. More broadly, the IWGSC is responsible for organising workshops and seminars and making genomics tools available to the community (https:// www. wheatgenome. org/) as shown in Fig. 2. 2. For example, the Apollo portal from national Australian Research Data Commons (https:// apollo-portal. genome. edu. au/) has been set up to allow the curation of the IWGSC v2. 1 annotation.
S. Department of Energy 284 Wheat@URGI INRAE-URGI 19,844,409 (including IWGSC data repository and GnpIS) GrainGenes USDA-ARS 23,309 (including Wheat Gene Catalogue at Komugi) Ensembl Plants (including EVA) EMBL-EBI 3,071,899 The Triticeae Toolbox Triticeae CAP 223,013 (including UniProt) Gramene CSH, OSU 229,851 AgroLD SouthGreen 137,060 CIMMYT publications and datasets CIMMYT 1,788 CR-EST, GBIS and MetaCrop IPK 250,877 CrowsNest PGSB 13,324 KnetMiner Rothamsted Research 108,474 PlantPhenoDB IPG PAS 6 Wheat pangenome UWA 167,167.
Movies were recorded in a 1024 * 1024 px 2 window (340 * 340 µm 2 ) at 10 frames per second. The local temperature was regulated through illumination with the 395-nm and 555-nm light sources of the lightbox (SI Appendix for more details). All the particles in the field of view were located and tracked using the TrackPy library in Python  (56). Stuck particles and particles that got stuck during the experiment were omitted from the analysis. The self-propulsion velocity of every individual particle was determined by measuring their displacements over 0. 5 s windows (5 frames). The chirality and rotational diffusion were determined by investigating the distribution of angular displacements at different lag times in 30-s windows (SI Appendix, section 7 for more details), corresponding to the time windows over which temperature was changed.
Data, Materials, and Software Availability. All study data are included in the article and/or SI Appendix. All the data and codes have been deposited in the following publicly accessible database https://doi. org/10. 3929/ethz-b-000598257. ACKNOWLEDGMENTS. L. A. acknowledges the financial support from the EuropeanSoftMatterInfrastructure(EUSMI)proposalnumberS180600105. This project has received funding from the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation program grant agreement No. 101001514. We thank Alexander Kuehne and Dirk Rommels for their help with particle synthesis and discussion.
The difference with an autoencoder is that t-SNE does not try to minimize a reconstruction error but instead tries to preserve the neighborhood distances. autoencoder and recursively feedforward the generated output as the input until the output gets to a fixed point. They have experimented with a dataset of handwritten numerical digits (the MNIST dataset for handwritten digits recognition) and generated new types of visual patterns that they name "digits that are not". We have applied their approach on our autoencoder trained on Celtic melodies, as illustrated in Figure  23 , with Figure  24  showing an example of progressive refinement of a melody. Note that each melody generated corresponds to some attractor of the network and their number is finite  26.
Results To address this limitation, we developed Var|Decrypt, a web-based tool designed to greatly facilitate WES data browsing and analysis. Var|Decrypt offers a wide range of gene and variant filtering possibilities, clustering and enrichment tools, providing an efficient way to derive patient-specific functional information and to prioritize gene variants for functional analyses. We applied Var|Decrypt on WES datasets of 10 acute erythroid leukemia patients, a rare and aggressive form of leukemia, and recovered known disease oncogenes in addition to novel putative drivers. We additionally validated the performance of Var|Decrypt using an independent dataset of ~ 90 multiple myeloma WES, recapitulating the identified deregulated genes and pathways, showing the general applicability and versatility of Var|Decrypt for WES analysis.
Conclusion Despite years of use of WES in human health for diagnosis and discovery of disease drivers, WES data analysis still remains a complex task requiring advanced bioinformatic skills. In that context, there is a need for userfriendly all-in-one dedicated tools for data analysis, to allow biologists and clinicians to extract relevant biological information from patient datasets. Here, we provide Var|Decrypt (trial version accessible here: https:// varde crypt. com/ app/ varde crypt), a simple and intuitive Rshiny application created to fill this gap.
It is, therefore, extensively used in multiple clinical centers and studies over the world (Fig. 1 ). As a growing number of patient genomes became sequenced, increasing amounts of detected variants were published and added into public databases. The number of known disease genes has, therefore, dramatically increased over the past decade, which reinforces diagnostic test performance  [4]. Clinics in the USA  [5] , France  [6]  and the Netherlands  [7]  for instance report WES as a promising tool for systematic use in patients. However, despite its extensive use and significant advantages, extracting relevant information from WES data still represents a challenge (e. g. , identifying recurrently mutated genes from cohorts of patients, or the biological pathways which are significantly affected).
WES data analysis requires advanced bioinformatics tools and skills, which prevents non-specialists such as wet lab scientists or clinicians from being able to navigate within the datasets and perform custom analyses. A great challenge for scientists and clinicians with limited or no bioinformatics skills is, therefore, to be able to manipulate WES data in order to extract biological meaningful information, and relevant genes or pathways to guide functional testing and therapeutic approaches. WES data analysis consists in two main features: first, the detection of variants or mutations, and second, the post-variant calling analysis for prioritization of genes or variants within patient cohorts. The detection of germinal or somatic variants from matched tumor-control samples is still a complex task  [8] , despite the availability of dedicated tools.
Bertier et al. [3]  reported a total of 23 different challenges related mainly to the production, analysis and sharing of WES data  [3]. They mentioned that the interpretation of variants of unknown significance was one of the most reported challenges across all articles. Often, the interpretation of these data requires specially trained staff  [3]. In order to understand the biological meaning of these variants and/or differences between tumoral and control samples, researchers tend to focus on variant annotation and filtering to eliminate those with low relevance (variant-centered approach). Several tools have been developed in this aspect such as SNPsift  [9] , GEMINI  [10]  and VCF tools  [11] , Ingenuity ® Variant Analysis ™ Software  [12] , Golden Helix SNP and Variation Suite  [13] , BiERapp  [14] , EVA  [15] , Exomiser  [16] , Variant Ranker  [17] , BrowseVCF  [18] , TGex  [19] , and VCF-Miner  [20].
Another strategy for prioritizing variants in WES data is attempting to connect the discovered variants with known diseases, biological processes, pathways, etc. using a variety of open-source databases (gene-centered approach). Some available tools such as GeMSTONE  [21]  and BiERapp  [14]  perform some enrichment analyses, but either are available online only, or do not support variant and enrichment results visualization. Other web-based tools such as Enrichr  [22] , GOrilla  [23] , wKGGSeq  [24] , geneontology  [25]  and g: profiler  [26]  which are often used to analyze transcriptome datasets, can perform some enrichment analyses using gene lists. However, these tools require users who wish to analyze WES data to extract and prepare the input data according to the tool recommendations.
Exome-seq analysis pipeline To provide an all-in-one solution, we first implemented an Exome-seq variant analysis pipeline (available as supplementary material, see Additional file 1 and Additional file 8: Fig. S2  for details). This pipeline can be used with raw sequencing data (e. g. , FASTQ files) to generate variant calling files (vcf ) and input files for downstream processing by Var|Decrypt. For users wishing to use their own vcf files, or vcf files from publicly available repositories as input, we built-in a pre-processing tool called Pre-Var|Decrypt (see https:// gitlab. com/ moham madsa lma/ varde crypt) allowing to process vcf files to generate Var|Decrypt input files (see below). This step only needs to be performed once for each batch of samples (i. e. , patient cohorts), the resulting files can then be stored or directly used in Var|Decrypt for downstream analyses.
WES data processing using Var|Decrypt In order to facilitate WES data analysis and functional interpretation, we developed Var|Decrypt, an easy-touse and user-friendly RShiny tool, which can be deployed via Docker on several operating systems (Linux, macOS), downloaded and installed from open-source to run via Rstudio. In addition, we provide a link to an online trial version of Var|Decrypt (https:// varde crypt. com/ app/ varde crypt) with access to a test dataset, allowing users to quickly be able to assess the tool and evaluate its capacities. Var|Decrypt includes several R packages to perform different post-VCF downstream analyses, which usually require users with scripting skills to perform tasks such as installing packages, preparing the input data and calling the appropriate function.
A detailed tutorial on how to use Var|Decrypt in a simple and intuitive way is available at (https:// gitlab. com/ moham madsa lma/ varde crypt/-/ wikis/ Var% 7CDec rypt) and as a video tutorial on the front page of Var|Decrypt online version. Var|Decrypt imports the output results from the Exome-seq pipeline or vcf files processed through Pre-Var|Decrypt (Additional file 1) and provides many built-in enrichment analyses options, helping researchers to develop or confirm hypotheses, to easily explore the differences between normal and tumor samples, and to prioritize variants, genes and pathways for functional analyses. Var|Decrypt is a fast-operating tool which provides multiple outputs within short time frames (i. e. , seconds to minutes for loading and processing a full dataset, Additional file 6: Table  S5 ).
Overall presentation of Var|Decrypt The Var|Decrypt interface is composed of several tabs allowing users to get a general overview of the Exomeseq data and to browse the mutated gene lists or focus on single genes, single variant types (e. g. , stop gain and frameshift deletions). The 'Somatic variants explorer' tab provides a gene list and summary table containing all detected mutated genes (we define the somatic variants as being the ones specifically acquired in the tumor sample as compared to the control cells; variants or mutations present in the control cells are considered as germline variants as they are not somatically acquired) (Additional file 8: Fig. S2 ). For each gene in the table, the total number of variants detected is indicated, together with the different types of variants identified, and the percentage of patients bearing a mutation in a particular gene.
Whereas the germline variants from a patient are usually used to filter-out nonspecific variants in cancer samples, Var|Decrypt also allows working on the germline variants ('Germline variant explorer') which is useful for the study of Mendelian genetic disorders or family case studies (not shown here). The 'General statistics' tab provides information on the frequency of variant types within the cohort using a color code for the different types (e. g. , frameshifts, non-sense, missense, etc. ), the class of SNV (e. g. , C > T, T > G, etc. ) which may be useful to check if a particular bias is present in the samples or in the disease under study (Fig. 2 ). This tab also provides information on the total number of variants per sample, a feature that helps to quickly spot any outlier within the datasets.
Identifying the recurrently mutated gene fraction within a patient cohort Var|Decrypt offers the opportunity to quickly and easily browse WES data in order to identify recurrently mutated genes. By navigating in the somatic menus, users can in one click access the gene mutations frequencies (i. e. , gene mutation percentage within the cohort), an important feature allowing to point at key genes likely involved in the disease phenotype. One key step in the discovery of cancer drivers is to be able to pinpoint the recurrently mutated genes within patient cohorts, as recurrently mutated genes likely represent true oncogenic drivers or genes important to sustain the cells' transformed state. However, despite all the filtering steps applied in various Exome-Seq analysis pipelines, a very large number of variants usually remains, especially in cancer samples.
As a result, by setting a threshold of a maximum of 4 variants per gene in a maximum of 20% of the patients, we could get rid of the apparently highly variable and likely false-positive mutated genes in the final list (Fig. 3 ). Another commonly used strategy to enrich for putative causative variants is to filter the mutated gene lists against cancer gene databases such as COSMIC, OncoKB or NCG  [29] [30] [31]. We also implemented a filtering option allowing to focus on the mutated genes that are tagged as cancer-associated from such databases. The resulting outputs, therefore, are highly enriched in putative oncogenic drivers, allowing to explore the mutational landscape of human cancers. As confirmation, applying such filtering strategy on our AEL WES data produced a mutated gene list enriched for previously reported AELassociated gene mutations  [30, 31]  such as the epigenetic modifiers TET2, NCOR1, NCOR2, BCOR, BCORL1, the CBP(CREBBP)/p300(EP300) co-activators, the polycomb repressive complex proteins EZH2, ASXL1, ASXL2, and the cohesin complex component RAD21 (Additional file 2: Table  S1 ).
For Fig. 3  Custom filtering of putative false-positive mutations. The top 20 mutated genes are show before (A) and after (B) applying the custom filters. This shows that without this filtering step, a number of genes score positive in 100% of the patients, including gene families containing variable number of tandem repeats (e. g. , the MUC gene family). After applying a threshold (maximum of 4 variants per gene in a single patient, in a maximum of 20% of the patients) and selecting the option to retain genes present in the COSMIC database, the resulting mutated gene list is highly enriched in known oncogenic drivers and previously reported AEL-mutated genes each category, users can choose between three different graphical outputs including bar graphs, association or enrichment factor along with color-coded p-value representations (Fig.