dataesr/scientific-paragraphs-categorization huggingface dataset:
Detection and validation of sex-linked single nucleotide polymorphisms In order to detect SNPs linked to the sex-determining region, we run SEX-DETector++. This yielded 4113 sex-linked SNPs (with a posterior probability high than 0. 6), 90. 5% of them on chromosome 2. As the sex-determining regions is assumed to be small  [4, 6]  and the low number of offspring in our cross gives us access to few recombination events, the sex-linked region in our analysis may appear larger than the actual sex-determining region (SNP close to the sex locus tend to be linked to sex in a particular cross). To determine the boundaries of the sex-determining region, independent public data were analyzed in order to identify shared XY SNPs between different populations. WG-reseq and RNA-seq data from wild and cultivated grapevines were mapped against the V.
sylvestris genome with gsnap v2018-07-04; SNPs were called with varscan v2. 4. 3. We searched for candidate XY-SNPs that were heterozygote in all male samples and homozygote in all female samples of the validation dataset. Validated XY-SNPs spanned 111 kb on chromosome 2 (from 4,810,929 to 4,921,949 bp), which indicated the boundaries of the sex-determining region. In order to complement SEX-DETector++ analysis, we also performed an empirical one. We searched for SNPs that overlapped the sex-determining region and were homozygote in female individuals of the cross and heterozygote in male individuals, allowing two missing alleles. This empirical analysis retrieved all 1406 XY SNPs identified by SEX-DETector++ plus 459 additional ones (+ 32. 6%). This final dataset of 1865 XY SNPs was used for downstream analyses.
Determination of the synonymous divergence between X and Y alleles in the sex-linked region The dataset of XY SNPs was used to build X and Y allelic pseudosequences in coding regions, using a custom python script to substitute reference positions by X or Y SNPs respectively in the genome in respect with strand, and to extract and concatenate coding DNA sequences for each gene of the sex locus. The yn00 program of the PAML suite was used to estimate synonymous divergence (dS) with standard error estimation  [73]. The same procedure was used to calculate the dS between the Y V. sylvestris haplotype and the Yh haplotype of Cabernet Sauvignon. Structural characterization of the sex-linked region in the V. sylvestris genome Structural gene annotations in the sex-linked regions were extracted from Eugene (see annotation section).
Reads numbers were summed by gene and normalized by the total number of reads in the variant file for the library and the length of transcripts. Organ-specific expression of sex-linked genes in V. vinifera We retrieved and mapped transcriptomic data of V. sylvestris and V. vinifera in several organs and conditions against the genome of V. sylvestris (berries; developing seeds; leaves under normal, drought, and pathogenic conditions; stem; early and mature flower buds; Additional file 1: Table  S12 ). We measured the gene expression levels in the different conditions, and computed an index of organ-specificity (Tau) on normalized expression levels (log (read count per kilobase per millions of mapped reads)). The Tau specificity index ranges between 0 and 1 and typically display two modes near 0 and 1 indicating ubiquitous and organ-specific genes respectively  [79].
Whole-genome re-sequencing of a cross between C1-5 (female Vitis sylvestris) and Martigny_2 (male Vitis sylvestris) These two accessions were crossed in INRAE Colmar ampelographic collection. The accession C1-5 female Vitis sylvestris (plant accession number at INRAE Colmar 8500. Col. C1-5) originated from the geographic location Sainte-Croix-en-Plaine, Haut-Rhin (68), France, and the male accession Martigny_2 (plant accession number at INRAE Colmar 8500. Col. 1) was an accession originating from the geographic location Martigny, Swiss. The progeny was grown at INRAE Colmar greenhouses. The DNA samples of both parents, of five male descendants (44613. Col. 5026T, 44613. Col. 5028T, 44613. Col. 5029T, 44613. Col. 5033T, and 44613. Col. 5053T) and five female descendants (44613. Col. 5035T, 44613.
Acknowledgements This work benefitted from the computing facilities of the  CC LBBE/PRABI. Whole-genome PacBio was conducted at the  IGM Genomics Center, University of California, San Diego ,  La Jolla , CA. RNA-seq and cross re-sequencing were performed by the GenomEast platform, a member of the " France Génomique "  consortium  ( ANR-10-INBS-0009 ). Sylvestris C1-2 Illumina re-sequencing was performed by the  Etude du Polymorphisme des Génomes Végétaux (EPGV, INRAE, Evry) facility. We thank  Guillaume Barnabé  for his help with transcriptome assembly. We thank  Jérôme Gouzy  for help with curation of the trinity dataset used for gene annotation and  Aline Muyle  for providing information on the code of SEX-DETector perl,  Gisèle Butterlin , and  Angélique Ardiller  for technical assistance and the  UEAV experimental platform (Unité d'Expérimentation Agronomique et Viticole)  for plant material maintenance at  INRAE Colmar , and  Jernej Jakše  and  Urban Kunej  ( Univ.
Availability of data and materials All raw reads, genome assembly, and gene annotation are available in ENA under the bioproject PRJEB37020  [82]. The source code of SEX-DETector++ is available at the url https://gitlab. in2p3. fr/sex-det-family/sex-detector-plusplus [83]  available under the GNU General Public License v3. 0 and archived at Zenodo  [84].
The emergence of large gene expression datasets has revealed the need for improved tools to identify enriched gene categories and visualize enrichment patterns. While gene ontogeny (GO) provides a valuable tool for gene set enrichment analysis, it has several limitations. First, it is difficult to graph multiple GO analyses for comparison. Second, genes from some model systems are not well represented. For example, 30% of Caenorhabditis elegans genes are missing from the analysis in commonly used databases. To allow categorization and visualization of enriched C. elegans gene sets in different types of genome-scale data, we developed WormCat, a web-based tool that uses a near-complete annotation of the C. elegans genome to identify coexpressed gene sets and scaled heat map for enrichment visualization.
We tested the performance of WormCat using a variety of published transcriptomic datasets, and show that it reproduces major categories identified by GO. Importantly, we also found previously unidentified categories that are informative for interpreting phenotypes or predicting biological function. For example, we analyzed published RNA-seq data from C. elegans treated with combinations of lifespan-extending drugs, where one combination paradoxically shortened lifespan. Using WormCat, we identified sterol metabolism as a category that was not enriched in the single or double combinations, but emerged in a triple combination along with the lifespan shortening. Thus, WormCat identified a gene set with potential. phenotypic relevance not found with previous GO analysis. In conclusion, WormCat provides a powerful tool for the analysis and visualization of gene set enrichment in different types of C.
2019). Because the multiplicity of GO term parent-child relationships can produce complex data structures, specialized ontologies such as GO-Slim use a restricted set of terms, searching biological processes as default  (Mi et al. 2019). P-values provide relevance for enriched GO terms. Visualization of gene set enrichment data are important for identifying critical elements and communication of information. PANTHER provides pie or bar charts of individual searches  (Mi et al. 2019). The GOrilla platform generates tables of P-values  (Eden et al. 2009 ) and links to another service, REVIGO, that uses semantic graphs to visualize GO terms data  (Supek et al. 2011). Thus, the GO databases provide a widely used platform for classifying, comparing, and visualizing functional genomic data. However, as outlined below, GO is of limited use for the analysis of Caenorhabditis elegans data and visualization of multiplexed datasets.
elegans gene expression profiling data, it has several limitations. First, 30% of C. elegans genes are not annotated in GO databases  (Ding et al. 2018 ), excluding these genes from the analysis. Thus, these genes are arbitrarily excluded from enrichment statistics. Second, the visualization of enrichment data from comparative RNA-seq datasets is difficult, and this is true not only for C. elegans datasets but for gene expression profile comparisons in any organism. Most users display the output data as lists with P-values  (MacNeil et al. 2013)  or as pie or bar charts  (Ding et al. 2015) , which are challenging to multiplex for comparison of multiple datasets. Finally, it can be challenging to determine which input genes are associated with a given GO classification, which is critical for interpreting the accuracy and biological importance of enriched gene sets.
We constructed a web-based gene set enrichment analysis tool we named WormCat (WormCatalog) that works independently from GO to identify potentially coexpressed or cofunctioning genes in genome-wide expression studies or functional screens. WormCat (www. wormcat. com) , uses a concise list of nested categories where each gene is first assigned to a category based on physiological function, and then to a molecular function or cellular location. WormCat provides a scaled bubble chart that allows the visualization and direct comparison of complex datasets. The tool also provides csv files containing input gene annotations, P-values from Fisher's exact tests, and Bonferroni multiple hypothesis testing corrections. We used WormCat to identify functional gene sets in published gene expression data and large-scale RNAi screens.
WormCat reproducibly identified prior GO classifications, and provided an easy way to interpret visualization that enables the facile and intuitive comparison of multiple published datasets. We also identified new groups of enriched categories with potentially important biological significance, showing that WormCat provides enrichment information not revealed by GO. Taken together, WormCat offers an alternative and complementary tool for categorizing and visualizing data for genome-wide C. elegans studies, and may provide a platform for similar annotations in other model organisms and humans.
Data availability The authors state that all data necessary for confirming the conclusions presented in the article are represented fully within the article. The code and annotation lists are available under MIT Open Source License, and can be downloaded from the GitHub repository https://github. com/dphiggs01/ wormcat along with version-control information. Alternatively, WormCat can be installed directly as an R package using the devtools library. Supplemental material has been deposited at figshare and includes 12 supplemental figures and 14 supplemental tables. Supplemental material available at figshare: https://doi. org/10. 25386/genetics. 10312070. GO searches: Genes lists were entered as test sets into GOrilla (http://cbl-gorilla. cs. technion. ac. il/)  (Eden et al. 2009)  with the WormCat annotation list used as background so that the same background set was used when comparing WormCat and GOrilla.
Many of these genes are C. elegans-or nematode-specific; however, some have homology to human genes of unknown function. WormBase also aggregates microarray and RNA-seq information, and annotates genes that respond to pharmacological treatments  (Lee et al. 2018). We also used this information to differentiate genes within Unknown: regulated by multiple stresses that respond to at least two commonly used stressors. This classification does not imply that these genes have a function in the stress response. It does allow identification of genes with otherwise unknown functions that are common responders to stress. This classification may be useful to distinguish RNA-seq datasets that respond similarly to pharmacological stressors or can serve as a source to identify specific genes of interest for additional study.
We also included pseudogenes and noncoding RNAs in our annotation list. These genes commonly appear in RNA-seq data; including them in the annotation list allows them to be labeled within the user's input dataset. In this way, we were able to leverage multiple data sources to categorize C. elegans genes into potentially functional biological groups. WormCat. com allows web-based searches of input genes and generates scaled bubble charts and gene lists WormCat. com maps annotations to input genes then determine category enrichment for Cat1, Cat2, and Cat3 (Figure  1B ). Determination of category enrichment in a gene set of interest compared to the entire genome can rely on several commonly used statistics such as the Fisher's exact test and the Mann-Whitney test  (Mi et al. 2019). We used Fisher's exact test to determine if categories were over-represented because it is accurate down to small sample sizes, which may occur in high-resolution classifications  (McDonald 2014).
csv). Genes that matched at least one Cat1, Cat2, and Cat3 classification are returned with Fisher's exact test  Cat2. csv, or Cat3. csv). Next, Cat1, Cat2, and Cat3 matches with an FDR correction of ,0. 01 are returned as CSV files named Cat1. apv, Cat2. apv, and Cat3. apv (appropriate P-value). Finally, the Cat. apv files are used to generate two types of graphical output. First, it constructs scaled heat map bubble charts (Cat1. , Cat2. , Cat3. sgv) where color signifies P-value, and size specifies the number of genes in the category (Figure  1D ). The scaling for these graphs is fixed so that multiple datasets can be graphed together. Second, a sunburst graph is built with concentric rings of Cat1, Cat2, and Cat3 values (Figure  1E ). In these graphs, rings sections correspond to categories, with section sizes proportional to numbers of genes in the category.
On the website, each ring section is clickable to generate a subgraph-based division within a section. For example, clicking a single Cat1 section would generate a subgraph with all the Cat2 and Cat3 subdivisions located within. This graphical output is likely to be most useful for visualization of a single RNA-seq dataset, or genetic screening data. Thus, WormCat provides multiple outputs to allow inspection of individual input genes, generation of gene tables, and Pvalues, and graphical visualization of enrichments.
elegans is a robust model system for studying development and differentiation. The study of hermaphrodite germline development has been of particular interest, as it first produces sperm, after which it switches to oocyte production  (Hubbard and Greenstein 2005). This concurs with distinct gene expression programs for both processes  (Greenstein 2005; L'hernault 2006). Recently, the Kimble laboratory performed RNA-seq on dissected germlines from genetically female [fog-2(q71)] and genetically male [fem-3(q96)] animals  (Ortiz et al. 2014 ) (Figure  3A ). Genes expressed in both germlines were called gender-neutral (GN), in contrast to genes that are specific to female (Oo, oogenic) or male (Sp, spermatogenic) germlines  (Ortiz et al. 2014). We used WormCat to determine enrichment categories in each dataset.
We found that GN genes are strongly enriched for growth, DNA, transcription, and mRNA functions (Figure  3B  and Table  S7 ), which is expected because the germline is undergoing extensive mitotic and meiotic divisions. We further found that Chromosome dynamics and Meiotic functions were enriched in the GN dataset (Figure  3C  and Table  S7 ), as were mRNA functions of Processing and Binding (Figure  3D  and Table  S7 ). Oo genes were enriched for mRNA binding proteins, especially the zinc finger (ZF) class (Figure  3D  and Table  S7 ). These include such as maternally deposited oma-1, pie-1, pos-1, and mex-1, mex-5, and mex-6 mRNAs, which are known to function in oocytes  (Lee and Schedl 2006 ) (Table  S7 ). ZF proteins with unknown nucleic acid binding specificity were also enriched in the Oo dataset (Figure  3D  and Table  S7 ), suggesting that many of these may also be produced in the maternal germline.
In an independent dataset comparing RNA from germline-less [glp-4(bn2)], oocyte [fem-3(gof)] and sperm-producing [fem-1(lof)] animals by microarray analysis  (Reinke et al. 2000) , we also observed enrichment in categories for mRNA functions, transcription, development, and cell cycle control (Figure  S3 , A-D and Table  S8 ). As expected, Sp genes are enriched for Major Sperm Proteins (MSPs), which are necessary for sperm crawling (Figure  3B  and Table  S7 ). Interestingly, a class of potential cytoskeletal regulators, tau-tubulin kinases (TTKs), were also enriched in Sp genes (64 of 71, P-value of 8. 8 3 10 234 ) (Figure  3E  and Table  S7 ). One TTK, spe-6, was previously isolated in a screen for spermatogenesis defects, and is thought to be involved in phosphorylation of MSPs to allow the sperm to crawl  (Varkey et al.
1993). Underscoring the potential importance of the TTKs in the male germline, WormCat also produced an enrichment in tau tubulin kinases in the  Reinke et al. (2000)  spermatogenic gene sets (Figure  S3E  and Table  S8 ). Thus, WormCat has identified a class of kinases that may be important for sperm-specific functions (Figure  3F ). To directly compare gene set enrichment from WormCat and GO, we analyzed each of these germline-enriched datasets with GOrilla and used REVIGO  (Supek et al. 2011)  for visualization (Figure  S4, A-C , Figure  S5, A  and B , Table  S7 , and Table  S8 ). For the GN genes, the top 5 of the 544 significantly enriched categories were nucleic acid metabolic process (GO:0090304), nucleobase-containing compound metabolic process (GO:0006139), heterocycle metabolic process (GO:0046483), cellular aromatic compound metabolic process (GO:0006725), and organic cyclic compound metabolic process (GO:1901360) (Figure  S4A  and Table  S7 , see tabs 7, 8).
These GO categories are highly overlapping and linked to multiple general processes involving nucleic acids. One gene GO:0006139, gut-2, an LSM RNA binding protein, was present in 23 different GO categories (Table  S7 ). A comparison of these GO categories found that each contains genes placed in distinct WormCat categories. For example, gut-2 was placed in mRNA Functions in WormCat, ama-1, the RNA Pol II large subunit, placed in Transcription: General Machinery, brc-1, the BRCA1 ortholog, placed in DNA and nsun-5, a mitochondrial RNA methyltransferase placed in Metabolism: mitochondria. These WormCat categories are the top five identified in the GN dataset (Figure  3B  and Table  S7 ). Thus, while WormCat and GO are both identify nucleic acid-related processed as among the most highly enriched in the GN dataset, the WormCat data are more concise and easily aligned with the molecular processes.
Within the spermatogenic datasets from Ortiz et al. (2014) and  Reinke et al. (2000) , WormCat identified a class of kinases, tau tubulin kinases (TTKs), that have the potential to function in sperm motility. General categories of phosphorus metabolic process (GO:0006793), phosphate-containing compound metabolic process (GO:0006796), and peptidylthreonine phosphorylation (GO:0018107) were among the top five most enriched categories by GO from the Spermatogenic dataset; however, the TTKs as a group were not selectively identified from these very broad signaling categories in either spermatogenic data set (Table  S7  and Table  S8 ). Thus, WormCat provided advantages over GO in the germline data sets by providing less redundant, and more easily interpreted, data, and, most importantly, by identifying novel categories with potential links to biological function.
Identification of postembryonic tissue-specific gene expression categories Improved technologies for cell-type-specific marker expression, nematode disruption, and deep sequencing of small RNA quantities have allowed construction of gene expression datasets from larval  (Spencer et al. 2011 ) and adult  (Kaletsky et al. 2018 ) somatic tissues. To generate data from larval cell types, the Miller laboratory used cell-type-specific tagged green fluorescent proteins to label a wide variety of larval tissues, and examined mRNA expression in tiling microarrays  (Spencer et al. 2011). RNA from each cell type would include tissue-specific, broadly expressed, and ubiquitously expressed genes. To define cell-type specific transcripts,  Spencer et al. (2011)  designated selectively enriched genes as expressed more than twofold vs.
(2011)  (Figure  4D  and Table  S9 ). In our annotation strategy, we chose to restrict genes in categories such as Neuronal function to those that are specific to that tissue, and that have a described physiological function. Genes that functioned in neurons, as well as other tissues, were placed in more general molecular function-based categories. With this approach, we hoped to reduce falsepositive identification of neuronal categories outside the nervous system, yet permit the identification of related, yet functionally less-specific groups. For example, while the WormCat analysis of the neuronal tissues in the  Spencer et al. (2011)  and  Kaletsky et al. (2018)  datasets showed strong enrichment of neuronal-specific categories, it also included categories of genes likely to function in both neurons and other tissues, or that contained genes that had not yet been classified in vivo.
These categories include Metabolism: insulin (Figure  4 , D and H and Table  S10 ), Transmembrane (TM) transport, Signaling (Figure  4 , B and F and Table  S10 ), and Transmembrane protein (Figure  4B  and Table  S10 ). This is in line with the analysis by both  Kaletsky et al. (2018)  and  Ritter et al. (2013). In order to distinguish the utility of WormCat from GO for the tissue-specific  Spencer et al. (2011)  and  Kaletsky et al. (2018)  datasets, we used GOrilla  (Eden et al. 2009 ) to generate GO analysis, and visualized the data with REVIGO  (Supek et al. 2011 ) (Figure  S6 , Figure  S7 , Figure  S8 , Table  S9 , and Table  S10 ). There were many similarities between the categories. For example, categories linked to the Cytoskeleton are highly enriched in the muscle datasets from  Kaletsky et al.
Thus, our annotation strategy allows separation of GPCRs with a high likelihood of neuronal function, yet still permits enrichment of the larger class of 7TM proteins in neuronal tissues. In order to directly compare WormCat and GO on the larval neuronal data sets, we examined category enrichment of  Spencer et al. (2011)  pan-neuronal and motor neuron genes in GO by GOrilla  (Eden et al. 2009 ), using REVIGO  (Supek et al. 2011)  for visualization (Figure  S6 , Figure  S8 , and Table  S9 ). The most enriched category in the pan-neuronal or motor neuron datasets was G protein-coupled receptor signaling (GO:0007186). Next, we used WormCat to determine how we had annotated genes within GO:0007186, and found that this GO category included genes we had classified in Signaling: Heteromeric G protein (G-alpha subunits and receptors), Neuronal Function: Synaptic function (neuropeptides and neurotransmitter receptors), and TM protein: 7TM receptor (Figure  5C  and Table  S9 ).