dataesr/scientific-paragraphs-categorization huggingface dataset:
While inclusion of the G protein signaling apparatus and neuropeptide ligands is appropriate for the broad category of G protein signaling, the GO categories do not differentiate between GPCRs with a high likelihood of function from the poorly classified 7TM proteins. In addition, many of the nlp genes listed in GO:0007186 are functionally uncharacterized, and, thus, it is not clear if they are bona fide GPCR ligands or could interact with other receptors outside of GPCR signaling  (Li and Kim 2008). Therefore, WormCat improves on GO analysis for these datasets by providing more nuanced information on the function of these genes in GPCR pathways. Neuronal genes from adult  (Kaletsky et al. 2018 ) and larval  (Spencer et al. 2011)  gene sets also showed strong enrichment in Cat2 and Cat3 classifications within Neuronal function, such as Synaptic function, neuropeptide, and neurotransmitter (nt) receptor (Figure  5, D  and E , Table  S9 , and Table  S10 ).
Drug interactions limiting lifespan induce changes in sterol metabolism C. elegans is particularly suited to studies determining gene expression changes in response to a panel of treatments in a whole animal, and to correlate these changes to physiological function. For example,  Admasu et al. (2018)  generated a complex gene expression dataset by performing parallel RNA-seq on animals treated with five lifespan-increasing drugs that affect distinct pathways (Allantoin, Rapamycin, Metformin, Psora-5, and Rifampicin). They used five pairwise combinations and three triple-drug combinations to determine if any combination lead to further lifespan extension, and to identify gene expression profiles associated with increased longevity  (Admasu et al. 2018). They found that one triple-drug combination (Rifa/Psora/Allan) activated lipogenic metabolism through the transcription factor SBP-1/ SREBP-1, and determined that the drug-induced longevity was dependent on SBP-1 function  (Admasu et al.
2009) , and visualized the data with REVIGO  (Supek et al. 2011 ) (Figure  S11  and Table  S11 ). WormCat and GO showed multiple similarities. For example, WormCat and GO identified extracellular matrix-linked categories in all three triple combinations (WormCat: EC MATERIAL; GOrilla: GO:0030198: extracellular matrix organization) (Figure  S9  and Table  S11 ). However, WormCat identified Metabolism: lipid in all three combinations, whereas GO analysis by GOrilla only identified categories linked to lipid metabolism (GO:0006629: lipid metabolic process (q = 5. 63 3 10 203 ), GO:0044255 cellular lipid metabolic process (q = 1. 49 3 10 202 ) and GO:0006631 fatty acid metabolic process (q = 2. 16 3 10 202 ) in the Rifa/Rapa/Psora dataset (Table  S11 ). WormCat also showed a much higher enrichment score for Metabolism: lipid, P = 2.
2015) ; however, they were unable to assign statistical significance to any of the groups. WormCat identified similar groups as the  LaMacchia et al. (2015)  functional classification for the "glycogen low" candidates. For example, we identified Metabolism: mitochondria, complex I, III, IV, and V and found statistical enrichment in these categories (Figure  7B  and Table  S14 ). However, signaling had no enrichment (Table  S14 ). Thus, WormCat can identify statistically relevant pathways in genome-scale RNAi screen data. To provide a direct comparison between WormCat and GO with this dataset, we determined the GO term associated with the "glycogen low" data by GOrilla  (Eden et al. 2009) , and visualized the data with REVIGO  (Supek et al. 2011) (Figure S12 and Table S14). A total of 185 separate GO terms were identified in this data set compared to the 4 Cat1 level terms identified by WormCat (Metabolism, Lysosome, Proteolysis Proteasome, and Trafficking) (Figure  7B  and Table  S14 ).
Discussion WormCat provides new insights into comparative RNA-seq data Current technology allows for the routine use of genome-scale experiments for the generation of gene expression data. The goal of these experiments is often to identify classes of genes that add insight to biological functions, as well as to highlight selected genes for individual analysis. GO analysis, while widely used, is difficult to apply to datasets with multiple combinations of treatments or genetic perturbations. Further, for C. elegans, current GO analysis is often inaccurate, and misses useful physiological and molecular information. Here, we have shown that WormCat can annotate gene categories, provide enrichment statistics, and display user-friendly graphics for gene sets identified from C. elegans gene expression studies.
Furthermore, our visualization strategy allows comparison across multiple datasets, facilitating the identification of categories with shared biological functions. Our initial, script-based, smaller-scale version of WormCat highlighted changes in metabolic gene expression in C. elegans with changes in levels of the methyl donor SAM or methyltransferases modifying H3K4me3  (Ding et al. 2018). In this study, we have expanded the annotation list, One TTK, spe-6, has been previously identified in a screen for mutants with defects in sperm development  (Varkey et al. 1993). Our results suggest that many genes in this family could have important functions in spermatogenesis, and that the appearance of MSPs and TTKs in a dataset could also serve as a marker for maleness. Finally, we used WormCat to analyze a dataset consisting of RNA-seq from C.
Strengths and weaknesses of WormCat We developed WormCat to overcome some of the limitations of GO analysis when analyzing C. elegans gene expression data, and to utilize specific phenotype data available in WormBase. In addition, we specifically engineered WormCat to classify data for the identification of coexpressed or cofunctioning gene sets. Finally, we developed two graphical outputs: a scaled heat map/bubble plot and a sunburst plot. The modular nature of the bubble plot allows multiple datasets to be grouped and compared, while the sunburst plot gives a concise view of single datasets, as may be obtained with screening data. Our validation with random gene testing and analysis of C. elegans gene expression data from metabolic, tissue-specific, and drug-treated animals shows that WormCat is a robust tool that provides biologically relevant gene enrichment information.
This comparison would be difficult to make with GO, as many common GO servers do not supply the genes with each category in an easily accessible manner. Directly comparing the genes within WormCat and GO categories from our previously published dataset of gene expression after sams-1 knockdown, we found that WormCat identified a broader set of lipid metabolic genes than GO analysis from GOrilla, and that the genes identified only by GO analysis might be better classified in different categories to reflect their biological functions. Thus, WormCat provides an alternative to GO with advantages in output that improve data interpretation and access to gene annotations that allow deeper comparisons among categories. In some cases, WormCat also identifies categories that are not found by GO. However, there are several limitations to WormCat.
First, while multiple researchers with varied expertise curated our annotation list, some genes may be misannotated, or some Cat2 or Cat3 groups may fit better in other Cat1 classifications. We will update the WormCat annotation list at periodic intervals while providing access to the previous annotation lists. Second, each C. elegans gene received a single, nested, annotation, rather than a group of annotations as in GO. We chose to prioritize the visualization of enriched gene sets in this instance, using a single annotation per gene to permit graphing in scaled heat maps. Access to the program and annotation lists for the local application also allows users to customize the annotation lists according to their preferences. Annotation lists of genome-scale data are likely to contain errors.
We have defined several sources of error, and have taken corrective steps. In some cases, a gene may be simply misannotated. For example, a component of the General transcription machinery was placed in Signaling by the annotator. In others, the classification system may be incorrect. An example of this would be classifying enzymes that modify small molecules as protein modification. To estimate the misclassification error rate, we generated a list of 3000 random WormBase IDs. We mapped each ID to our annotation list and rechecked the annotations. We found 29/2294 genes (1. 3%) whose annotations were incorrect by our criteria (13 of these were Unknown genes that could be classified in other categories). This suggests 300 genes in the entire dataset may be misannotated by our criteria, many representing Unknown genes that could acquire classification.
We will periodically update the WormCat annotation lists to accommodate new gene information and correct errors. It is important to note that some gene classifications depend on criteria that are open to interpretation. For example, transcription factors regulating genes within a pathway are grouped within a linked category to allow identification of cofunctioning genes. For instance, efl-1, a master regulator of cell cycle genes, is annotated as Cell cycle: transcriptional regulator, instead of with the more broadly acting transregulatory factors in Transcription factor: E2F. To allow for different interpretations of the annotation strategy, we have set up a GitHub site (https://github. com/dphiggs01/wormcat), where the annotation list and scripts for executing WormCat can be downloaded and customized by the user to accommodate differences in annotation preference.
The value of gene set enrichment is also highly dependent on the criteria used to specify the regulated genes. In the present study, we used the same criteria as the respective authors, except that we separated up and downregulated genes where necessary. For example, in the  Kaletsky et al. (2018)  tissue-specific data, the authors provided data for all genes expressed in each tissue, enriched genes (expressed at FDR. 0. 05, and log 2 fold change. 2 relative to other tissues), or unique genes (log 2 RPKM. 5) significantly differentially expressed in comparison to the expression of each of the three other tissues (FDR. 0. 05, log 2 fold change. 2 for each comparison)  (Kaletsky et al. 2018). We found the best resolution of WormCat categories between the tissues occurred with the enriched datasets, rather than with all genes or unique gene sets.
Application to other organisms By developing WormCat specifically for analyzing C. elegans gene sets, we were able to take advantage of available data on WormBase, but this limited the applicability of our annotation list with other organisms. Although researchers in mammalian fields can access pathway analysis pipelines such as Ingenuity Pathway Analysis (Qiagen;  Krämer et al. 2014 ) that identify functionally linked genes, these programs do not necessarily provide a simple graphical output for comparative analysis. WormCat analysis generating the scaled heat/bubble charts can be adapted for use with other organisms by running the program locally with altered annotation lists. Replacing gene IDs and the Cat1, Cat2, and Cat3 values with any annotation allows customization of the pipeline to any other organism.
However, GOrilla only identified categories linked to transcription factors (GO:0006355: regulation of transcription, DNA-templated, GO:0051252: regulation of RNA metabolic process, GO:2001141: regulation of RNA biosynthetic process, GO:1903506 regulation of nucleic acid-templated transcription, and GO:0019219 regulation of nucleobase-containing compound metabolic process) in the Rifa/Psora/Allan dataset. No individual class of.
5 scenario (one of the strongest IPCC-AR5 (fifth assessment report of the intergovernmental panel of climate change) representative concentration pathways (RCPs) in terms of greenhouse gasses (GHG) concentration increase for the end of the 21st century) using GCM-RCM simulation pairs from the EURO-CORDEX initiative. We classify different RCM simulations depending on their aerosol representation in the model and their driving GCM. A modeling chain approach is used: the climate model outputs (SSR, surface temperature) are used as inputs for the PV parametric model, which is then used to calculate PV productivity. The results are shown for summer, since that is the season when both aerosol loads and solar energy are maximal in Europe. The manuscript is organized as follows: section 2 describes the regional models and simulations used in the study, as well as the aerosol datasets for those with evolving scenarios.
Climate simulations 2. 1. EURO-CORDEX Within the Coordinated Regional Climate Downscaling Experiment (CORDEX), EURO-CORDEX  [16]  develops climate projections focused on the European continent at different horizontal resolutions (0. 44°, 0. 11°). In scenario mode, these simulations are driven by different GCMs  [17]  within climate model intercomparison project phase 5 (CMIP5) and provide multi-decadal simulations over the Euro-Mediterranean area, typically from 1950 to 2100 following various IPCC-AR5 scenarios (RCP8. 5, RCP4. 5, RCP2. 6). Among the whole list of simulations included in the EURO-CORDEX database, aerosols are described very differently depending on the RCM (see tables B1 and B2 in the appendix). RCMs use different aerosol datasets, different levels of complexity for their representation and different temporal evolutions.
In particular, it is worth noting that most of the EURO-CORDEX RCMs do not present evolving aerosols in the future projections. Only a few RCMs (namely RACMO22E, the ARPEGE-ALADIN family Had-GEM3 and WRF3. 5/WRF331) include an aerosol dataset that evolves depending on the year, following the chosen RCP scenarios and the driving GCM. For the purpose of this work, we use the EURO-CORDEX whole list of 0. 11°simulations, available at the time of the study, under the RCP8. 5 hypothesis and then a relevant six-member ensemble, which is a subsampling of the whole list, is selected. For this selection we choose two driving GCMs (EC-EARTH and CNRM-CM5) and four RCMs (RACMO22E, ALA-DIN53, CCLM4-8-17 and RCA4). Each GCM drives three RCM simulations, one with evolving aerosols and two with constant aerosols (see table 1 for a detailed description of the GCM-RCM pairs).
Aerosol description in ALADIN53 The aerosol dataset used in ALADIN53 runs is based on AOD fields for five different species: black carbon, organic carbon, dust, sea salt, and sulfate. It is described in Szopa et al (2013)  [20]  and built from the global LMDz-OR-INCA climate-chemistry coupled model. For the historical period it accounts for reference aerosol emissions, including seasonal cycles for each species and trends for the anthropogenic aerosol species (sulfate, black carbon, and organic carbon). The future simulations include the same temporal variations but follow the corresponding RCP8. 5 scenario emissions. It is worth noting that this aerosol dataset has been used in two CMIP5 GCMs (IPSL-CM5 and CNRM-CM5) and in all the ARPEGE-ALADIN historical and scenario simulations performed in CORDEX whatever the domain, resolution, scenario, or running institute.
Aerosol description in RACMO22E A spatial and vertically distributed dataset derived from the CAM inventory  [21]  is used in RACMO22E simulations. It accounts for six species (see table  1 ). The historical run also follows the reference emissions  [21]  and from 2006 it evolves in time with the RCP  [22]. As well as for ALADIN53, the same aerosol implementation is used in RACMO22E and EC-EARTH. The same aerosol datasets based on historical and RCP-scenario CMIP emissions has been used in all simulations of EC-EARTH and RACMO22E.
Only a moderate dust accumulation degree is considered. The second step transforms the 'effective' irradiation into power (productivity) (kWh/kWp) output considering the electrical performance of the system, where the productivity is the energy produced (kWh) divided by the power capacity (kWp) of the modeled PV plant. The system includes characteristics of a general PV module and inverter, the arrangement of the generator and some efficiency losses. The characteristics of the general PV system are the same as the ones described in  [29, 30]. Power from the PV generator also depends on the cell's temperature. Mean temperature from climate models is also used to compute the cell's temperature using the procedure explained in  [30]. The whole process and the R package, solaR, used for the computation are described in detail in  [31].
Uncertainties due to the different AOD datasets used as driving conditions in the two RCMs and in the representation of cloud-aerosol-radiation interactions make it difficult to provide a robust answer regarding the magnitude of the change in PV production. However, despite these limitations, the direct radiative forcing of aerosols had a dominating role in SSR trends over Europe  [6, 35]. An ensemble with more simulations using evolving aerosols would also reinforce the robustness of our results. For the mid-century, an increase in PV potential is projected over Europe when the evolution of aerosols over the area is considered. The magnitude of the change depends on the country, the most impacted areas being those in Central Europe, with an important potential increase of more than 10% in summer for some countries.
Tanré et al (1984) out the aerosol datasets included in the EURO-CORDEX RCMs used in the study is presented (in table B1 and B2 of the appendix, information about the whole list of EURO-CORDEX models is presented). It can be observed that only two RCMs include time-evolving aerosols: RACMO22E and ALADIN53. Each of these RCMs has a different dataset of aerosols for future projections. The two other models do not have a realistic representation of aerosols in terms of AOD, as CCLM4-8-17 uses the climatology ofTanré et al (1984).
Data availability The data that support the findings of this study are available upon reasonable request from the authors. In addition, the data that support the findings from climate simulations from CMIP5 and EURO-COR-DEX are available on the Portal Earth System Grid Federation (ESGF node); therefore, the input variables for the PV model can be downloaded from there. The PV model used in this study is an open source tool written in R language  [31] , which can be used freely and the code can be downloaded from github athttps://github. com/oscarperpinan/solar.
1 Black carbon, POM, sulfate, sea salt, dust AeroCom, Kinne et al 2006 [41] 2D dataset of 1 x 1 spatial resolution Monthly mean values for the base for each class year 2000 GERICS REMO2009 Sea, land, desert,urban Tanré et al 1984 [18] Low-resolution 2D map, prescribed No seasonal cycle, no trend REMO2015 vertical profiles.
GWAS results A GWAS meta-analysis was conducted of 57 BD cohorts collected in Europe, North America and Australia (Table  S1 ), totaling 41,917 BD cases and 371,549 controls of European descent (Effective N = 101,962, see online methods). For 52 cohorts, individual-level genotype and phenotype data were shared with the PGC and cases met international consensus criteria (DSM-IV, ICD-9 or ICD-10) for lifetime BD, established using structured diagnostic interviews, clinician-administered checklists or medical record review. BD GWAS summary statistics were received for five external cohorts (iPSYCH  30  , deCODE genetics  31  , Estonian Biobank  32  , Trøndelag Health Study (HUNT)  33  and UK Biobank  34  ), in which most cases were ascertained using ICD codes. The GWAS meta-analysis identified 64 independent loci associated with BD at genomewide significance (P < 5E-08) (Figure  1 , Table  1 , Table  S2 ).
Enrichment analyses Genome-wide analyses using MAGMA 37 indicated significant enrichment of BD associations in 161 genes (Table  S4 ) and 4 gene sets, related to synaptic signaling (Table  S5 ). The BD association signal was enriched amongst genes expressed in different brain tissues (Table  S6 ), especially genes with high specificity of gene expression in neurons (both excitatory and inhibitory) versus other cell types, within cortical and subcortical brain regions in mice (Supplementary Figure  2 )  38. In human brain samples, signal enrichment was also observed in hippocampal pyramidal neurons and interneurons of the prefrontal cortex and hippocampus, compared with other cell types (Supplementary Figure  2 ). In a gene-set analysis of the targets of individual drugs (from the Drug-Gene Interaction Database DGIdb v.
2  39  and the Psychoactive Drug Screening Database Ki DB  40  ), the targets of the calcium channel blockers mibefradil and nisoldipine were significantly enriched (Table  S7 ). Grouping drugs according to their Anatomical Therapeutic Chemical (ATC) classes  41  , there was significant enrichment in the targets of four broad drug classes (Table  S8 ): psycholeptics (drugs with a calming effect on behavior) (especially hypnotics and sedatives, antipsychotics and anxiolytics), calcium channel blockers, antiepileptics and (general) anesthetics. (Table  S8 ).
In summary, these new data advance our understanding of the biological etiology of BD and prioritize a set of candidate genes for functional follow-up experiments. Several lines of evidence converge on the involvement of calcium channel signaling, providing a promising avenue for future therapeutic development. (PGC3: U01 MH109528; PGC2: U01 MH094421; PGC1: U01 MH085520). Statistical analyses were carried out on the NL Genetic Cluster Computer (http://www. geneticcluster. org) hosted by SURFsara and the Mount Sinai high performance computing cluster (http://hpc. mssm. edu), which is supported by the Office of Research Infrastructure of the National Institutes of Health under award numbers S10OD018522 and S10OD026880. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.
Genotyping, quality control and imputation For 52 cohorts internal to the PGC, genotyping was performed following local protocols and genotypes were called using standard genotype calling softwares from commercial sources (Affymetrix and Illumina). Subsequently, standardized quality control, imputation and statistical analyses were performed centrally using RICOPILI (Rapid Imputation for COnsortias PIpeLIne) (version 2018_Nov_23. 001)  94 , separately for each cohort. Briefly, the quality control parameters for retaining SNPs and subjects were: SNP missingness < 0. 05 (before sample removal), subject missingness < 0. 02, autosomal heterozygosity deviation (Fhet < 0. 2), SNP missingness < 0. 02 (after sample removal), difference in SNP missingness between cases and controls < 0. 02, SNP Hardy-Weinberg equilibrium (P > 10E-10 in psychiatric cases and P > 10E-06 in controls).
Relatedness was calculated across cohorts using identity by descent and one of each pair of related individuals (pi_hat > 0. 2) was excluded. Principal components (PCs) were generated using genotyped SNPs in each cohort separately using EIGENSTRAT v6. 1. 4  95. Based on visual inspection of plots of PCs for each dataset (which were all of European descent according to self-report/clinical data), we excluded samples to obtain more clearly homogeneous datasets. Genotype imputation was performed using the pre-phasing/ imputation stepwise approach implemented in Eagle v2. 3. 5  96  and Minimac3  97  to the Haplotype Reference Consortium (HRC) reference panel v1. 0  98. Data on the X chromosome were available for 50 cohorts internal to the PGC and one external cohort (HUNT), and the X chromosome was imputed to the HRC reference panel in males and females separately within each cohort.