dataesr/scientific-paragraphs-categorization huggingface dataset:
Comparison networks The NeuroSynth term "navigation" was used to localize cortical regions involved in spatial navigation (64 studies; search conducted on  Nov. 3, 2016). An FDR threshold of 0. 01 was used to control the false-positive rate. Moreover, the default mode network and the frontoparietal multiple demand networks were defined using the EvLabN60 dataset (see Data Sets), with the probabilistic activation maps of fMRI contrast Easy versus Hard, and Hard versus Easy, respectively, based on the spatial working memory task. Again, a probability threshold of 50% was applied to identify voxels showing consistent activation (t > 2. 3) across subjects. Next, the AICHA atlas was used to identify regions for each functional network. Our purpose was to define a similar number of top regions for each comparison network, to support similarly powered analyses of all networks in the downstream analyses.
Functional connectivity in the BIL&GIN data set The preprocessing of the BIL&GIN data set was done by the Bordeaux group (MJ). Preprocessing procedures included head motion correction and registration onto the anatomical T1 image, the latter being stereotaxic registered on the MNI152 standard space. In addition, time series for white matter and cerebrospinal fluid, the six head motion parameters, and the temporal linear trend were removed from the stereotaxic normalized rs-fMRI data using regression analysis, and time series data were temporally filtered using a least squares linear-phase finite impulse response (FIR) filter design bandpass (0. 01-0. 1 Hz). For each participant and each region, a time series was then calculated by averaging the rs-fMRI time series of all voxels located within that region.
Functional connectivity in the GEB data set The preprocessing of the GEB data set was done by the Beijing group ( JL). Preprocessing procedures included head motion correction, spatial smoothing, intensity normalization, and removal of linear trend, using the FEAT preprocessing workflow implemented with Nipype  (Gorgolewski et al. , 2011). A temporal band-pass filter (0. 01-0. 1 Hz) was applied to reduce low-frequency drifts and high-frequency noise. To eliminate physiological noise, such as fluctuations caused by motion or cardiac and respiratory cycles, nuisance signals were regressed out. Nuisance regressors were averaged cerebrospinal fluid signal, averaged white matter signal, global signal averaged across the whole brain, six head realignment parameters obtained by rigid-body head motion correction, and the derivatives of each of these signals.
Construction of the Gene Expression Networks Transcriptomic networks were constructed based on gene expression profiles in the human brain from the AHBA (above). Specifically, we first extracted the normalized expression score for each gene, from each sampling site, and each donor. For genes for which the expression had been measured using multiple microarray probes in the AHBA data, average values were calculated per gene at each sampling site and in each donor. Given that AICHA atlas regions are defined in the standard MNI space, the location of each sampling site was then translated into the standard space using alleninf, 2018. Gene expression data from within a given region were then averaged per gene and across donors, to obtain a single expression measure of each gene per region. We restricted our analyses to cerebral cortical regions with at least two sampling sites summed across all donors, and data quality was then ensured by including only genes showing relatively high interindividual consistency of expression levels, that is, we restricted our analyses to the top 5% of all genes based on differential stability across donors as assessed over the entire cerebral cortex (Figure  S2 ).
This was a set of 867 genes that had differential stability greater than 0. 357, as calculated previously  (Hawrylycz et al. , 2015). The concept of differential stability was quantified as the averaged Pearson correlation between pairs of brains over a set of cerebral cortical regions  (Hawrylycz et al. , 2015). The percentage threshold applied (5%) seems to be strict, but the expression consistency of most genes is relatively low in cortical regions in the Allen data  (Hawrylycz et al. , 2015) , so that a 5% threshold still includes genes with stability across donors of less than 0. 4 (Figure  S2 ). Excluding genes with very low interindividual consistency was necessary to link average gene expression data from the Allen brain data set to functional connectivity in other data sets. In addition, we repeated our analysis using a lower stability threshold (i.
Identification of "consensus genes" correlated with the sentence-processing network We identified a set of "consensus genes" (N = 41), that is, those that had positive CGI scores in all six analyses of the sentence-processing network: the three definition strategies SmSA, SSA, and OcSA, by the two independent rs-fMRI data sets BIL&GIN and GEB. These were therefore the genes that made the most consistent individual contributions to the significant overall correlations between expression similarity and functional connectivity, regardless of the definition of sentence network regions. For each of the three comparison networks (spatial navigation, frontoparietal multiple demand, and default mode networks) we also identified the same number of genes (i. e. , 41) showing the highest GCIs, based on the averaged score of each gene in the two analyses relevant to each of those networks, that is, with the two rs-fMRI data sets.
, 2014). We also queried another singlecell gene expression database  (Zeisel et al. , 2015)  to investigate the cell-type specificity of genes. Several recently published single-cell, or single-nucleus, gene expression data sets from human neocortex also became available while this study was in progress  (Fan et al. , 2018; Lake et al. , 2018; Li et al. , 2018; Zhong et al. , 2018). We also queried these data sets.
Enrichment Analysis Using GWAS Summary Statistics for ASD, Schizophrenia, and Intelligence We tested the hypothesis that the consensus set of sentence-processing network genes was enriched for genetic association signals with ASD, schizophrenia, or intelligence, in the publicly available results from very large-scale genome-wide association studies, based on casecontrol or general population cohorts. (There were no large-scale GWAS results yet available for reading/language measures in the general population, or disorders such as dyslexia or language impairment which involve linguistic deficits. ) Specifically, we ran gene set analyses using MAGMA software ( 2020). MAGMA was run with default settings. Because each gene contains multiple individual polymorphisms within it, gene-based association scores were derived using the SNP-wise mean model, which considers the sum of -log(p-values) as derived from GWAS analysis, for SNPs located within the transcribed region of a given gene (using NCBI 37.
, 2017)  and showed little overlap with the sentence-processing networks defined in the preceding text (Figure  2 ). We obtained 19, 17, and 12 areas for the SNN, MDN, and DMN, respectively. More information about the spatial distribution of each definition and their overlaps can be seen in Table  S1. Connectivity within a given functionally defined set of regions was estimated based on inter-regional synchronization of rs-fMRI time courses. The connectivity patterns based on two independent rs-fMRI data sets (BIL&GIN and GEB; see Methods) were highly correlated for all functional networks (rho > 0. 80; Figure  2 ), indicating high reproducibility of the grouplevel functional connectivity pattern with respect to AICHA atlas regions. Corresponding gene expression networks for each functionally defined set of regions were calculated based on postmortem cortical gene expression data, and using pairwise similarities of regional gene expression profiles (see Materials and Methods).
Gene Contribution Index With a leave-one-out procedure, we obtained gene contribution index (GCI) scores for all individual genes, and for each functional network definition, which indicated the extent to which each gene affected the overall connectivity-transcriptome correlation for a given network (see Materials and Methods). To investigate the similarity of gene contribution patterns across different networks, we calculated the correlation between the GCI scores of each pair of networks. As expected, for the three different definitions of the sentence-processing network, which involved largely overlapping sets of cortical regions, the GCI scores showed substantial correlations, which were also reproducible across the two independent rs-fMRI data sets (Figure  3A ; BIL&GIN: mean r = 0. 40, from 0.
For example, MET and CTXN3 showed 3. 57-and 1. 53-fold expression in neurons compared to the maximum expression in other cell types, respectively. Data on 7 of the 41 genes were available in another single-cell gene expression database  (Zeisel et al. , 2015) , and 2 of these showed enrichment in interneurons: ANK1 and SHD. Among the six genes driving the gene ontology enrichment results related to neural projection development, the following four showed their highest expressions in neurons: ROBO1, TRPC6, SPON2, and GPRIN1. In addition, among the five genes driving the gene ontology enrichment related to the actin cytoskeleton, data for three were available: MET showed highest expression in neurons, and ARHGAP25 and ZYX in microglia. We also queried several recently available single-cell or single-nucleus gene expression data sets on the human neocortex  (Fan et al.
, 2011) , and one based on a single task contrast (OcSA;  Fedorenko et al. , 2010). Reassuringly, our three approaches to defining sentence-level processing regions yielded similar sets of regions, especially with respect to areas in the inferior frontal and middle temporal gyri. Likely because of the overlap between the three network definitions, their transcriptomic correlates were also similar, as well as the contributions of individual genes. This overall concordance supports the validity of the different definitions, allowing us to define a consensus set of genes that emerged consistently across all definitions and both of the rs-fMRI data sets, and is thus not dependent solely on any individual approach for defining the functional network. Although multiple cognitive processes are likely to be involved in sentence processing, at least two of the key elements, syntax and semantics, are tightly integrated in the brain, whereas combinatorial processing is a key part of both of them  (Fedorenko, Mineroff, Siegelman, & Blank, 2018).
Further studies may investigate how using more or less inclusive definitions of these functional networks affect relationships with gene expression. The present study was based on gene expression data from a small number of donors in the Allen Brain data base. Postmortem brain tissues suitable for transcriptomic analysis are difficult to collect from individuals who were healthy immediately before death, which is necessary because RNA degrades within hours after death. Thus, the availability of high-quality gene expression data from the human brain is necessarily limited. Moreover, most of the Allen brain data, and all the data used for the present study, are based on the older and relatively noisy transcriptomic technology of microarrays, rather than the more accurate, latest method of RNA sequencing.
, 2010; Steinmetz & Seitz, 1991)  as well as the cytoarchitectonic features of higher-level cognitive areas (e. g. , BA 44 and 45)  (Fischl et al. , 2008). To address these issues, we focused on only a subset of genes with the highest differential stability across donors, in order to ensure a reliable and representative, average map of gene expression, which was necessary for this study. Interdonor differential stability is particularly low for cortical regions in the Allen data set, for the majority of individual genes (Figure  S2 ), so that only a minority of genes have a useful degree of consistency for group-based analysis. Having made a selection based on high stability across donors, the reproducibility of our genetic findings with respect to two independent rs-fMRI data sets supported their validity.
Unlike these previous studies, the present study provided new evidence for the gene expression correlates of a high-level language network, and additionally identified a set of individual genes likely to be important for cortical language functions. A limitation of our study is the use of transcriptomic data from blocks of cerebral cortical tissues, which comprised many cell types. Future databases based on single-cell transcriptomics would likely provide further insights. Although efforts to produce such data are underway for a limited number of human cerebral cortical regions, no database currently exists with broad mapping over the cerebral cortex. It will be a major undertaking in the future for the human brain science community to achieve a widespread cerebral cortical gene expression map at single cell resolution.
More importantly, we found high correlations (up to 0. 92) of the functional connectivity patterns extracted from two independent rs-fMRI data sets (i. e. , BIL&GIN and GEB), and we focused on consensus genes arising from the analysis of these two data sets. Thus, any motion-induced artifacts would have minimal impact on our analyses. In summation, we provide a first description of the overall transcriptomic correlates of brain networks underlying high-level linguistic processing, as well as identify a set of individual genes likely to be most important. These findings help elucidate the molecular basis of language networks as distinct from functional networks important for other aspects of cognition. A link of this genetic infrastructure to ASD is also suggested by our data. Finally, we propose functional connectivity and gene expression analysis as a complementary approach to existing genetic epidemiological and genetic association approaches, for understanding complex cognitive traits.
| Structural analysis A homology model of human wild-type GRK1 based on the bovine structure (PDB: 4PNI) was produced using SwissModel  (Waterhouse et al. , 2018). The locations of the RH and PK domains, ATP binding site, polypeptide substrate binding site and activation loop were identified using InterPro  (Mitchell et al. , 2019). The structure was visualized, and regions of interest labeled using PyMOL  (Schrodinger, 2015). Residue depth and connectivity within the structure was determined using Site-Directed Mutator (SDM;  Pandurangan et al. , 2017). To further explain variant impact, changes in residue physical characteristics; molecular weight, isoelectric point, and hydrophobicity  (Laskowski et al. , 2020)  were reviewed.
| Scoring missense variants A number of bioinformatics tools were reviewed to identify which approach best classified the variants. Scoring methods included: SDM  (Pandurangan et al. , 2017) , VarSite  (Laskowski et al. , 2020) , SIFT v6. 2. 1  (Sim et al. , 2012) , Polyphen-2 v2. 2. 2r394  (Adzhubei et al. , 2010 ), CADD v1. 3 (Kircher et al. , 2014) , Rhapsody  (Ponzoni et al. , 2019) , and ConSurf  (Ashkenazy et al. , 2016). All statistical analyses were performed in GraphPad Prism 8. 0. 2 (for macOS, GraphPad Software, www. graphpad. com) using Welch's t-test.
A literature search revealed a further thirteen published Oguchi disease causing GRK1 variants from 12 studies  (Azam et al. , 2009; Cideciyan et al. , 1998; Godara et al. , 2012; Hayashi et al. , 2007; Jespersgaard et al. , 2019; Li et al. , 2017; Mucciolo et al. , 2018; Oishi et al. , 2007; Skorczyk-Werner et al. , 2015; Teke et al. , 2016; Yamamoto et al. , 1997; Q. Zhang et al. , 2005). The nomenclature for these variants was updated based on the current HGVS nomenclature guidelines and the GRCh37 version of the Human Genome, and together with the new variants reported here, these have been included in a Leiden Open Variation Database (LOVD) of GRK1 variants (http://dna2. leeds. ac. uk/GRK1_LOVD/genes/GRK1).
We therefore calculated the same set of pathogenic prediction scores to assess the likely pathogenicity of these variants of unknown significance. The p. Met185Val variant, although rare in gnomAD, produced scores that were comparable with the NDAVs for each tool, suggesting it does not contribute to disease. However, both p. Ala353Ser and p. Ala387Val, produced scores that were compatible with DAVs, suggesting that the two individuals in gnomAD that are homozygous for these variants may have Oguchi disease. gnomAD contains exome and genome sequence data from over 130,000 unrelated individuals, with only those individuals affected by a severe pediatric disorder being removed. It is therefore possible that individuals with CSNB or Oguchi disease are present in the gnomAD database which could explain the presence of two homozygous, likely DAVs.
In summary, we present eight novel variants in GRK1 as a cause of Oguchi disease and perform an in-depth analysis of all GRK1 variants to understand their contribution to disease. We describe three mechanisms likely to account for all GRK1-related disease, each of which ultimately result in a failure to phosphorylate light-activated rhodopsin. We assessed the ability of different in silico pathogenicity scores to differentiate DAVs and NDAVs and found that Rhapsody out-performed all tools tested for this data set. Finally, we have created an LOVD database into which all the known DAVs and several common NDAVs described herein have been entered, to guide future variant interpretation in GRK1-associated disease.
Ethical approval for this study was obtained from the Yorkshire and The Humber -Leeds East Research Ethics Committee (REC F I G U R E 1 Distribution of disease associated variants within GRK1. All variants shown are annotated according to human genome GRCh37/hg19, using GRK1 gene and protein accession numbers NM_002929 and NP_002920, respectively. Loss of function variants are shown above the gene/protein and missense variants are given below. (a) Genomic organization of GRK1 showing the location of all novel and published Oguchi disease causing variants. (b) Domain structure of GRK1 showing the location of all GRK1 variants. The start and end amino-acid positions of each domain are based on Lodowski et al. (2006). NT, N-terminus; RH, regulator of G-protein signaling homology reference: 17/YH/0032).
DAVs, disease associated variants; NDAVs, nondisease associated variants F I G U R E 3 -Location of disease associated and nondisease associated variants in a homology model of GRK1. Homology model of human GRK1 based upon bovine GRK1 structure (PDB: 4PNI). Wildtype residues of disease associated variants are labeled in red (Leu157, Gly199, Leu308, Arg332, Glu362, Ala377, Val380, Pro391, and Arg438) and nondisease associated variants in blue (Leu54, Thr97, Ile241, Glu464, and Ser536). The protein kinase domain is colored in purple, RH domain in orange, activation loop in green, ATP binding residues are represented as yellow sticks and polypeptide substrate binding residues as cyan sticks. (a) A visualization of variant location in the overall structure. Variants are highlighted as beads. The smaller representations to the right, with the ATP and polypeptide binding surface displayed demonstrate how intramolecular residue changes may deform the binding site surface.
I G U R E 4 -Rhapsody top scoring substitutions mapped onto the homology model of GRK1. Homology model of human GRK1 based upon bovine GRK1 structure (PDB: 4PNI). Wildtype residues of top-scoring ((≥0. 896) Rhapsody variants are highlighted as beads, and labeled, colored and numbered based on residue number. The colors and labels within the bar chart of top-scoring Rhapsody variants mimic this format. Locations with more than four variants in the top 93 scores include: G68, L253, L308, Y315, L318, Y383, L430, and G440.
ACKNOWLEDGMENTS The authors would like to thank all the families for agreeing to participate in this study, and  Roman A. Laskowski  and  Arun Prasad Pandurangan  for assistance with VarSite and SDM respectively, and  Ivet Bahar ,  Luca Ponzoni  and the rest of the  Bahar group  for their help with Rhapsody. This study was supported by the  European Retinal Disease Consortium (ERDC) , the  European Reference Network for Rare Eye Disease (ERN-EYE)  and the  Japan Eye Genetics Consortium  (https://www. jegc. org/). This study was funded by  RP Fighting Blindness and Fight for Sight  as part of the  UK Inherited Retinal Disease Consortium (RP Genome  Project  GR586 ) and the  UK National Health Service (NHS) Foundation Trust , the  National Institute for Health Research Biomedical Research Centre  ( NIHR-BRC ) at  Moorfields Eye Hospital  and the  NIHR-BRC at Great Ormond Street Institute of Child Health ,  Moorfields Eye Charity  and the  Great Britain Sasakawa Foundation.
JAP is supported by a  UKRI   Future Leaders Fellowship  ( MR/ T02044X/1 ). MSCG is on the  Wellcome Trust 4 -year PhD studentship in The Astbury Centre funded by The  University of Leeds. GA is supported by a  Fight For Sight (UK) Early Career Investigator Award. SAH acknowledges the  EPSRC  for their support ( EP/S0306971/1 ). OM is funded by  Wellcome  Grant  206619_Z_17_Z. This study was made possible through access to the data and findings generated by the 100,000  Genomes Project managed by Genomics England Limited  (a wholly owned company of the  Department of Health and Social Care ). The 100,000  Genomes  Project is funded by the  National Institute for Health Research and NHS England. The  Wellcome Trust ,  Cancer Research UK  and the  Medical Research Council  have also funded research infrastructure.
DATA AVAILABILITY STATEMENT All disease associated GRK1 variants described to date have been collated into a Leiden Open Variation Database (http:// dna2. leeds. ac. uk/GRK1_LOVD/genes/GRK1). The variants reported in this paper have been deposited into the ClinVar database (https://www. ncbi. nlm. nih. gov/clinvar/) at the National Center for Biotechnology Information under accession numbers SCV001245346 -SCV001245364. Supplemental Data includes three figures and three tables. The 3D homology model of Human GRK1 based upon the bovine GRK1 crystal structure (PDB: 4PNI) is available on request from the corresponding author. The full data that supports the findings of this study are available in the supplementary material of this article.
F I G U R E 4 -Rhapsody top scoring substitutions mapped onto the homology model of GRK1. Homology model of human GRK1 based upon bovine GRK1 structure (PDB: 4PNI). Wildtype residues of top-scoring ((≥0. 896) Rhapsody variants are highlighted as beads, and labeled, colored and numbered based on residue number. The colors and labels within the bar chart of top-scoring Rhapsody variants mimic this format. Locations with more than four variants in the top 93 scores include: G68, L253, L308, Y315, L318, Y383, L430, and G440.
Results Metagenomic search for viral rhodopsins genes and sequence analysis. To obtain a comprehensive set of rhodopsins in the vast metagenomic sequence database produced by the Tara Ocean project, we compared 36 rhodopsin sequences representative of the previously identified major groups to the sequences of all open reading frames from Tara Ocean contigs. This search retrieved 2584 Type 1 rhodopsins, of which 385 belonged to VR1 and 172 belonged to VR2 as confirmed by phylogenetic analysis that also supported the monophyly of viral rhodopsins family (Fig. 1a  and Supplementary Fig. 1 ). Consistent with the monophyly of viral rhodopsins and the separation of the VR1 and VR2 groups, the examination of sequence alignments detected several amino acid motifs that partially differed between the two groups.