dataesr/scientific-paragraphs-categorization huggingface dataset:
NAtURE MEDiciNE Methods gnomAD variant annotation and curation. The gnomAD resource, including both sample and variant quality control (including sample ancestry assignment), is fully described in our companion paper  9. Analysis was conducted using gnomAD v. 2. 1. 1. Putative LoF variants were defined as stop-gained, frameshift or essential splice site (splice donor or splice acceptor) as annotated by the Ensembl Variant Effect Predictor 37. Variants were included if they were annotated as LoF on any of the three high-confidence GENCODE annotated protein-coding transcripts that are expressed in the lung, liver or kidney. All variants also underwent transcript expression-aware annotation which evaluates cumulative expression status of transcripts harboring a variant in the Genotype Tissue Expression (GTEx) project dataset  38.
All high-confidence variants were found in exons with high evidence of expression across all relevant tissues in GTEx. In addition, all were high-confidence pLoF on the canonical transcript, which is the only transcript to include the kinase domain. Variants were filtered out if they were flagged as low confidence by LOFTEE  9. For the remaining variants, manual curation was performed, including inspection of variant quality metrics, read distribution and the presence of nearby variants using the integrative genome viewer and splice-site prediction algorithms using Alamut. A single splice-site variant (12-40626187-T-C), found in 77 gnomAD carriers, was identified in an individual with RNA-seq data in the GTEx project. The RNA-seq reads were manually inspected to look for any effect on splicing.
Assessing the read distribution of a linked heterozygous variant in this individual showed convincingly that the variant has no discernible effect on transcript splicing (Extended Data Fig. 3 ). All available tissues were assessed with reads from lung tissue shown in Extended Data Fig. 3. The variant was also identified in eight UK Biobank carriers and in 23andMe and was similarly excluded from these cohorts. This study complied with all relevant ethical regulations and was overseen by the Broad Institute's Office of Research Subject Protection and the Partners Human Research Committee. Informed consent was obtained from all participants. Sanger validation of gnomAD variant carriers. Sanger validation was performed on genomic DNA derived from peripheral blood under the following PCR conditions: 98 °C 2 min; 30 cycles 20 s 98 °C, 20 s 54 °C, 1 min 72 °C; 3 min 72 °C using Herculase II Fusion DNA polymerase (Agilent, 600679).
The Swedish schizophrenia and bipolar studies. Cases with schizophrenia or bipolar disorder were identified from Swedish national hospitalization registers  41, 42. Controls were selected at random from population registers. All individuals had whole-exome sequencing data  43. All available ICD codes from inpatient hospitalizations and outpatient specialist treatment contacts were provided for each patient. The national FINRISK study. The FINRISK study has been carried out for 40 years since 1972 every 5 years using independent, random and representative population samples from different parts of Finland. For this work, we used sequencing and health register data from FINRISK surveys between 1992 and 2007 (ref. 44 ). Full health records including ICD10 codes were reviewed by study coordinators who provided us with yes/no answers for each of our phenotype classes.
The BioMe biobank at the Charles Bronfman Institute for Personalized Medicine at Mount Sinai. The Mount Sinai BioMe Biobank, founded in September 2007, is an ongoing, broadly consented electronic health record (EHR)-linked bio and data repository that enrolls participants nonselectively from the Mount Sinai Medical Center patient population (New York City). BioMe participants represent broad racial, ethnic and socioeconomic diversity with a distinct and population-specific disease burden, characteristic of the communities served by Mount Sinai Hospital. Currently comprising over 47,000 participants, BioMe participants are of African (24%), Hispanic/Latino (35%), European (32% of whom 40% are Ashkenazi Jewish) and other/mixed ancestry. BioMe is linked to Mount Sinai's system-wide Epic EHR, which captures a full spectrum of biomedical phenotypes, including clinical outcomes, covariate and exposure data from past, present and future healthcare encounters.
The median number of outpatient encounters is 21 per participant, reflecting predominant enrollment of participants with common chronic conditions from primary care facilities. Clinical phenotype data have been meticulously harmonized and validated. Genome-wide genotype data and whole-exome sequencing data are available for >30,000 participants. In addition, WGS data are available for >11,000 participants. The full EHRs of three BioMe LRRK2 pLoF carriers were reviewed by local clinicians and we were provided with detailed summaries. Estonian Biobank of the Estonian Genome Center, University of Tartu. The Estonian Biobank cohort is composed of volunteers from the general Estonian resident adult population  45. The current number of participants of close to 165,000 (representing 15% of the Estonian adult population) makes it ideally suited to population-based studies.
Participants were recruited throughout Estonia by medical personnel and participants receive a standardized health examination, donate blood and fill out a 16-module questionnaire on health-related topics such as lifestyle, diet and clinical diagnoses. A detailed phenotype summary from a health survey and linked data including ICD10 codes, clinical laboratory values and treatment and medication information is annually updated through linkage with national electronic health databases and registries. UK Biobank variant detection and curation. The 49,960 exome-sequenced individuals from the UK Biobank were restricted to a subset of 46,062 unrelated individuals of European ancestry. Relatedness was determined using KING kinship coefficient estimates from the genotype relatedness file with a cutoff of 0.
0884 to include pairs of individuals with greater than third-degree relatedness. European ancestry was determined by projecting individuals onto the 1000 Genomes Project phase 3 (ref. 46 ) principal-component analysis (PCA) coordinate space, followed by Aberrant R package  47  clustering to retain only those individuals falling within the 1000 Genomes Project EUR PC1 and PC2 limits (λ = 4. 5). We further removed individuals who self-reported as non-European ethnicity. We identified all individuals with putative LoF variants detected in the FE analysis pipeline, which used GATK 3. 0 for variant calling and filtering  33. We did not use the SPB pipeline calls due to advertised errors in the Regeneron Genetics Center pipeline at the time we were conducting these analyses. Variants were included if they were annotated as LoF on any transcript expressed in the lung, liver or kidney.
As with the gnomAD analysis, variants were filtered out if they were flagged as low confidence by LOFTEE, before manual curation of the remaining variants. This curation included inspection of variant quality metrics, read distribution and the presence of nearby variants using integrative genome viewer and splice-site prediction algorithms using Alamut. In addition, 266 individuals in the full genotyped cohort of 488,288 samples who were carriers of the G2019S risk allele were identified. One individual who was a carrier for both a LRRK2 pLoF variant and G2019S was excluded from all analyses. Carriers of G2019S were not included in the 'noncarrier' cohort in any of the analyses. LRRK2 pLoF carriers, G2019S risk allele carriers and noncarriers are well matched for both sex (Extended Data Fig.
4 ) and age (Extended Data Fig. 5 ). UK Biobank phenotype analysis. Blood serum and urine biomarkers. The first recorded value of all fields relating to 'blood biochemistry' (field codes 30600-30890) and 'urine assays' (field codes 30510-30535) was extracted for all individuals. The distribution of values for all biomarkers was plotted (Supplementary Fig. 1 ) and a two-sided Wilcoxon test was used to test for a difference between LRRK2 pLoF carriers and noncarriers.
NAtURE MEDiciNE These data were also extracted for G2019S risk allele carriers and these individuals were compared to both pLoF carriers and carriers of neither G2019S nor LRRK2 pLoF variants. There was no significant difference in any of the 34 biomarkers between pLoF and G2019S carriers after accounting for multiple testing (Supplementary Table  15 ). When comparing G2019S carriers to noncarriers we found significant associations with cystatin C and phosphate levels. Clinical measures of kidney function. ACR was calculated by dividing the urine microalbumin value (field code 30500; mg l -1 ) by the urine creatinine value (field code 30510; μmol l -1 ) multiplied by a factor of 0. 0001131222. Estimated glomerular filtration rate was calculated using the CKD Epidemiology Collaboration (CKD-EPI) creatinine equation  48.
During initial selection and sequencing, expansion of the database led to inclusion of a number of additional individuals genotyped for one of the pLoF variants, rs183902574. We performed custom calling on these individuals and found 354 deemed as high-confidence carriers (Supplementary Table  4 ). As these individuals were not Sanger sequenced, all subsequent analyses were performed both including and excluding these individuals. Participants provided informed consent and participated in the research online, under a protocol approved by the institutional review board, Ethical & Independent Review Services, an organization accredited by the Association for the Accreditation of Human Research Protection Programs. Testing the power to detect an age effect in 23andMe. As a positive control for age analysis, we tested the apolipoprotein E (APOE) Alzheimer's disease risk allele rs429358, which has a known effect on lifespan.
This effect is highly significant in this dataset (P = 1. 2 × 10 -211 ). Given that the carrier count for rs429358 is much higher than for LRRK2 pLoF, we assessed the power of the 23andMe dataset to detect an age effect associated with LRRK2 pLoF variants that is of the same effect size as the known effect of the APOE allele rs429358 by sampling carriers of this variant. We randomly selected N carriers of rs429358 from the 23andMe dataset, performed a Kolmogorov-Smirnov test on the age distribution of those carriers versus 4,000,000 noncarriers and considered the resulting P value. We repeated this process 100 times and then computed the proportion of these simulations with P < 0. 05. This tells us our power to reject the null hypothesis that APOE does not have an effect on age at α = 0. 05, if we had N carriers in the dataset.
We repeated this for different values of N between 1,000 and 20,000 (Supplementary Table  6 ). Association testing in the 23andMe dataset. Phenotype selection. The 23andMe dataset includes self-reported phenotype data for thousands of phenotypes across a diverse range of categories. These phenotypes have different sample sizes and prevalence, so the power to detect associations varies widely. We began with a curated set of 748 disease phenotypes. We then applied a liberal filter based on our power to detect an association with carrier status. More specifically, assuming a minor allele frequency of 2 × 10 -5 , we restricted to phenotypes where we had power 0. 1 to detect an association effect with odds ratio (OR) > 1. 3 (for binary traits) or β > 0. 2 (for quantitative traits) at α = 0. 0001 significance.
Analysis of LRRK2 protein levels. Cell culture. All cell lines tested negative for Mycoplasma contamination on a monthly basis with the MycoAlert Detection kit (Lonza, LT07-118) and MycoAlert Assay Control Set (Lonza, LT07-518). Cells were grown at 37 °C with 5% CO 2. Human embryonic stem cell culture. All pluripotent stem cells were approved by Harvard ESCRO protocol E00052 and E00067. Human ESCs (hESCs) were obtained from WiCell Research Institute (WA01, H1) under a material transfer agreement. Cell lines were authenticated by visual inspection of cell morphology with bright-field microscopy, staining with anti-Oct4 antibody to determine maintenance of pluripotency (Santa Cruz, sc-5279, data not shown), sent to WiCell Research Institute after 6 months of passaging or after isogenic cell line generation for karyotyping and in some cases PCA of RNA-seq data to confirm clustering with other pluripotent stem cell lines.
No variants with a <4-bp mismatch against the full 20-bp gRNA sequence or perfect match at the seed region were detected. Because a mismatch at the seed region decreases the likelihood of off-target variants and also because the single variant we detected is a known variant (rs1161563412) observed in the population without apparent phenotypic association, we concluded that no major off-target effect exists at the level of violating the main steps of our research. All the analysis for the detection of potential off-targets were conducted using pybedtools  53  and CRISPRdirect 54 software. Western blot analysis. Cell pellets were snap-frozen in liquid nitrogen and stored at -80 °C. Cells were Dounce-homogenized in ice-cold radioimmunoprecipitation assay buffer (89901; Thermo Fisher Scientific) containing protease inhibitors (Halt Protease Inhibitor, Thermo Fisher Scientific).
Sample size This study was opportunistic, and involved secondary use of all available data. No sample size was predetermined. We have included the theoretical odds ratios that our sample size is powered to detect for our pheWAS study with the 23andMe data, and a power analysis for our age distribution analysis to demonstrate the size of effect that our sample size can discover. Data exclusions Identified loss-of-function variant carriers were extensively curated and variants excluded if they were flagged as low-confidence by the Loss- of-function Transcript Effect Estimator (LOFTEE), or failed a manual inspection of variant quality metrics, read distribution and the presence of nearby variants that could rescue the loss-of-function effect. An additional variant was removed as it was shown to not fully disrupt splicing in the GTEx dataset.
were used for immunoblotting: LRRK2 (1:500, 75-253, UC Davis/NIH NeuroMab Facility, lot# 455. 7JD. 04, clone N241A/34), α-actinin (1:5000, A7811, Sigma, Batch# 024M4758, monoclonal EA-53), GAPDH (1:10,000, sc-25778, Santa Cruz, FL-335), anti-rabbit IgG HRP (1:5000, 7074, Cell Signaling), and anti-mouse IgG HRP (1:5000, 7076, Cell Signaling). ValidationThese antibodies were selected based on their use in other publications and validation through IF and WB by the companies providing the antibodies. Company provided information is as follows: LRRK2: Immunogen: Fusion protein amino acids 970-2527 (C-terminus) of human LRRK2 (also known as Leucine-rich repeat serine/threonine-protein kinase 2, Dardarin and PARK8, accession number Q5S007) Mouse: 89% identity (1393/1557 amino acids identical) Rat: 89% identity (1392/1557 amino acids identical) <30% identity with LRRK1 Epitope mapped to within amino acids 1836-1845 (EGDLLVNPDQ) by PEPperPRINT through work funded by The Michael J.
Acknowledgements We thank the research participants and employees of 23andMe, Inc. and the research participants in gnomAD and the  UK Biobank , for making this work possible. N. Whiffin is supported by a  Rosetrees  and  Stoneygate Imperial College Research Fellowship. E. V. Minikel is supported by  National Institutes of Health   F31 AI22592. K. J. K. was supported by  NIGMS   F32 GM115208. This work was supported by the  Michael J. Fox Foundation for Parkinson's Research  grant  12868 ,  NIDDK U54DK105566 ,  NIGMS R01GM104371 ,  Wellcome Trust  ( 107469/Z/15/Z );  Medical Research Council (UK) ;  NIHR Royal Brompton Biomedical Research Unit; NIHR Imperial Biomedical Research Centre. L. F. was supported by the  Swiss National Science Foundation  ( Advanced Postdoc. Mobility 177853 ). T. E.
Data analysis Data analysis Custom code was used to analyse data and create the figures in this study. This code has been shared in a GitHub repository. The Genome Analysis ToolKit (GATK) was used to analyses sequencing data generated from CRISPR edited cell lines, according to best practice guidelines. Pybedtools and CRISPRdirect software were used to detect potential off-target edits. The Integrative genome viewer (IGV) and Alamut software were used to curate variants. For manuscripts utilizing custom algorithms or software that are central to the research but not yet described in published literature, software must be made available to editors/reviewers. We strongly encourage code deposition in a community repository (e. g. GitHub). See the Nature Research guidelines for submitting code & software for further information.
Data Policy information about availability of data All manuscripts must include a data availability statement. This statement should provide the following information, where applicable: -Accession codes, unique identifiers, or web links for publicly available datasets -A list of figures that have associated raw data -A description of any restrictions on data availability The data presented in this manuscript and the code used to make the figures are available in https://github. com/macarthur-lab/gnomad_lrrk2.
Materials were used for immunoblotting: LRRK2 (1:500, 75-253, UC Davis/NIH NeuroMab Facility, lot# 455. 7JD. 04, clone N241A/34), α-actinin (1:5000, A7811, Sigma, Batch# 024M4758, monoclonal EA-53), GAPDH (1:10,000, sc-25778, Santa Cruz, FL-335), anti-rabbit IgG HRP (1:5000, 7074, Cell Signaling), and anti-mouse IgG HRP (1:5000, 7076, Cell Signaling). ValidationThese antibodies were selected based on their use in other publications and validation through IF and WB by the companies providing the antibodies. Company provided information is as follows: LRRK2: Immunogen: Fusion protein amino acids 970-2527 (C-terminus) of human LRRK2 (also known as Leucine-rich repeat serine/threonine-protein kinase 2, Dardarin and PARK8, accession number Q5S007) Mouse: 89% identity (1393/1557 amino acids identical) Rat: 89% identity (1392/1557 amino acids identical) <30% identity with LRRK1 Epitope mapped to within amino acids 1836-1845 (EGDLLVNPDQ) by PEPperPRINT through work funded by The Michael J.
For example, in a manually-curated dataset of 1007 symmetries in SCOP domains  [5] , only about 30 domains came from transmembrane proteins. To understand how well-suited the available methods are for detecting membrane protein symmetries and to facilitate future investigations of the effect of the lipid bilayer on a protein's predisposition for symmetry, we compiled a benchmark set of Membrane protein Structures And Their Symmetries (MemSTATS). We chose to describe membrane proteins in terms of complexes and their membranespanning chains rather than domains. While many reputable databases and benchmarks use domains as the fundamental unit  [9, 10] , their definition for membrane proteins is often ambiguous  [11]. Instead, we organize our data around protein chains, each being a single gene product  [12] , which naturally allows us to investigate both internal and quaternary symmetries in every entry of the dataset.
While compiling the MemSTATS set, we aimed to describe the diversity of membrane protein symmetries, while limiting the number of trivial cases such as a repeat composed of a single transmembrane helix. The resultant set comprises 87 -helical and 23 -barrel membrane proteins, each with distinct architecture either at the quaternary or internal level. For each symmetry, the following features are specified: symmetry order, axis orientation with respect to the membrane, approximate amino-acid range of the repeats, whether the symmetry is cyclic or open (as in helical or linear symmetries), whether the symmetry has been explicitly mentioned in the literature, and whether the repeats are interdigitating (https://doi. org/10. 5281/zenodo. 3234036). Unlike previous symmetry benchmarks, which included only the symmetry order, the more extensive descriptions of symmetry in MemSTATS make it possible to assess not only whether an algorithm detects a symmetry, but also how precisely the symmetry is characterized.
Using MemSTATS, we have assessed the performance of two well-established computational algorithms for detecting internal symmetry in protein structures, namely SymD  [4]  and CE-Symm  [5, 13] , as well as two methods designed exclusively for quaternary symmetry detection, namely AnAnaS  [14, 15]  and the BioJava module used for symmetry annotations of complexes in the protein data bank, PDB  [16]  (Fig. 1 , 2, 3). We refer to the latter simply as BioJava. The two quaternary-symmetry detection methods, BioJava and AnAnaS, differ significantly in their approach for detecting the point group and symmetry axes for a complex. However, both methods strive to optimize the speed of repeat detection by taking advantage of the fact that in quaternary symmetry there is often a correspondence between repeat and chain boundaries.
g. , PDB identifier PDB: 1K4C; Fig. 1E ). Note that AnAnaS and BioJava can both correctly characterize such cases. While MemSTATS allows for the four algorithms to be benchmarked against each other, it should be noted that the set is relatively small due to the current availability of structures and the emphasis on avoiding redundancy in the types of challenges presented. Therefore, overall metrics for scoring should be carefully selected to take into account the size and imbalance in the dataset. With this in mind, we summarized the key results for each method using confusion matrices (Fig. 1A, 2A ) and also compared the probability that each algorithm produces informed predictions relative to chance (Fig. 3 ). These results indicate that while most methods detected more symmetries than they miss, a substantial fraction of symmetries were not detected, especially for cases of internal symmetry.
In addition, it is clear that identifying the boundaries of internal repeats is a non-trivial problem (Fig. 3 ). To better understand the specific situations in which each method performs well or poorly, we used MemSTATS to examine individual examples in the dataset. AnAnaS, for example, due to its analytic framework, is limited to global cyclic, dihedral, or cubic symmetries, and therefore did not detect either open symmetries, such as helical or linear symmetries (e. g. , PDB: 4HEA; Fig. 1B ), or local symmetries, in which only a subset of chains in a complex are related (e. g. , PDB: 3WMM, PDB: 2R6G, PDB: 4Y28). All other methods can, in principle, detect such local and open symmetries. BioJava, on the other hand, failed for cases where the symmetry-related chains scored below the assigned threshold of 40% sequence identity (e.
g. , PDB: 1PRC, PDB: 1ZOY, PDB: 2R6G, PDB: 4Y28, PDB: 4HEA; Fig. 1C ). Such cases were typically correctly identified by the internal symmetry algorithms, as long as the repeats constituted a large fraction of the complex (e. g. , PDB: 2R6G but not PDB: 4HEA). The ability to detect open symmetry makes internal symmetry algorithms vulnerable to obtaining a wellscoring self-alignment by just a slight translation of the structure. This so-called "slip" symmetry matches most secondary structure elements from the transformed structure to themselves and, hence, proteins with an abundance of straight parallel alpha-helices or beta-strands, for example, were especially prone to elicit this behavior (Fig. 2C ). While the various thresholds assigned in SymD and CE-Symm attempt to mitigate the detection of false positives due to slip symmetry, the problem persisted for SymD in a number of cases (e.