dataesr/scientific-paragraphs-categorization huggingface dataset:
Results of quality control pipeline applied to the cancer datasets At least one issue was identified by the QC pipeline in 41 (25%) of 163 total cancer datasets, or in 21 (42. 9%) of 49 studies (Supplementary table  4 ). These included serious meta data errors (defined as incorrect labelling of effect allele or effect allele frequency columns) in 5 (10. 2%) of 49 studies. In three datasets, alleles associated with higher cancer risk in the GWAS catalog were associated with lower risk in the test dataset for a substantial proportion of SNPs (Figures  3 4 5 ), suggesting that the effect allele column actually refers to the non-effect allele. This was clearest for GliomaScan (ID 967) where 19/21 SNPs were discordant with the GWAS catalog but was less clear for the NBS (ID 106) and BC-NHL (ID 5) datasets.
In the BC-NHL, although all SNPs were discordant to the GWAS catalog, the number available for comparison was small and Z scores were not highly significant by GWAS standards (Z ≤ 2. 5). Therefore, we could not rule out chance deviations from the GWAS catalog for this dataset. In addition, the ancestry of the BC-NHL (East Asian) was different to the ancestry of the reference dataset (European). Therefore, the observed conflict for the BC-NHL dataset could also reflect differences in LD between populations. In the NBS (ID 106), equal numbers of SNPs were highly discordant and highly concordant to the GWAS catalog. Due to the ambiguity of the effect allele we decided to drop the BC-NHL (ID 5) and NBS (ID 106) datasets. Reported effect alleles were compatible with reported cancer hits in the GWAS catalog for other cancer datasets (Supplementary figure  7 ).
We identified three datasets where reported allele frequency was inconsistent with allele frequency in reference datasets (Figures  3, 6  & 7 ). This included GliomaScan (ID 967) where allele frequency was inversely correlated with allele frequency across all SNPs in ancestry matched reference datasets, indicating that the reported effect allele frequency corresponded to the non-effect allele (Figure  3 ). In the UCSF_AGS/SFAGS (ID 133) and TNC (ID=132), there were two groups of SNPs showing positive or inverse correlations with allele frequency in ancestry matched datasets (Figures  6  & 7 ), indicating that the reported effect allele frequency actually corresponds to minor allele frequency and that the minor allele was not consistently the effect allele. For these datasets, we decided to set effect allele frequency to missing.
Allele frequency conflicts were not observed for other cancer datasets (Supplementary figures 8-11). We identified 35 datasets where the slope was >1. 2 or <0. 8 in models that regressed the expected log odds ratio on the reported effect size (Supplementary figure  12 ). Of these, 10 had slopes greater than 23. Further investigation revealed that the summary data for these 10 datasets had been generated in linear mixed models of cancer in participants from UK Biobank. Effect sizes from such models can be interpreted as the change in absolute risk per copy of the effect allele, explaining the conflict with the expected log odds ratio. We retained these datasets but transformed the reported effect size into a log odds ratio scale. Of the remaining 25 datasets, all had slopes <0. 8 and we confirmed that the reported effect sizes were log odds ratios by consulting the original study publications.
For 18 of the 25 datasets, the deviation between the expected and reported log odds ratio was largely attributable to SNP-level sample size reporting errors, low imputation quality or a small number of SNPs with unusually large log odds ratios (>1 or <-1). For example, in the ACCC dataset (ID 3) with results for 20,952 SNPs from a meta-analysis of up-to 15 independent studies, results for a subset of 1562 SNPs had come from 5 or fewer studies (and therefore were analysed in a smaller subset of participants). When restricted to the subset of SNPs that came from ≥14 studies, the slope increased from 0. 29 to 0. 83 (Supplementary figure  13 ). In the GICC/MDA dataset (a meta-analysis of two independent studies), there were two distinct groups of SNPs, one group where results had come from both studies and a second group where results had come from a single study (Supplementary figure  14 ).
When restricted to the dataset contributed to by both studies, the slope increased from 0. 67 to 0. 81. In the GECCO (IDs 59, 63, 64 and 65) and the HNMSC (IDs 70 & 71) datasets, the deviation of the slopes from one was at least partly driven by a very small number of outlier SNPs that had been derived from a subset of studies (for the HNMSC), or subset of genotyping batches (for GECCO) within each dataset's respective meta-analysis (Supplementary figures 15 & 16). When restricted to the SNPs that had come from the maximum number of studies or genotyping batches, the slopes increased towards one. The slopes improved because the reported sample size was only applicable to those SNPs that were analysed in all studies or genotype batches. We decided to drop SNPs if the number of contributing studies or batches to that SNP was less than the median number of studies or batches.
Across 16 datasets where this information was available, there were 9351 SNPs that met this criterion, including 868 in the fatty acid SNP set. In the TNC (ID 132), NB-UGC (ID 104) and ECAC (ID 25) datasets, the deviation of the slopes from 1 was partly attributable to low imputation quality for some SNPs (Supplementary figure  17 ). We also found that the % deviation of the expected from the reported log odds ratio was strongly and inversely related to metrics of imputation quality (Supplementary figure  18 ) but not P values for deviation from Hardy Weinberg Equilibrium or P values for heterogeneity between studies (Supplementary figures 19 & 20). Across datasets where this information was available, there were 46,534 SNPs with imputation quality scores < 0. 8, including 1119 in the fatty acid SNP set.
Effect size discrepancies in the OCAC and PRACTICAL datasets were mainly attributable to a very small number of SNPs with unusually large log odds ratios (>1 or < -1) (Supplementary figure  12 ). The number of SNPs across all datasets with log odds ratios greater than 1 or less than -1 was 368, including 5 SNPs in the fatty acid SNP set. Additional effect size discrepancies were identified in two datasets where the correlation was less than 0. 99 between Z p scores (Z scores from P values) and Z b scores (Z scores inferred from effect sizes and standard errors) (Figures  6  and 8 ). In one dataset, this was due to three SNPs with very large effect sizes (Z>99) but with P values very close to 1 (>0. 9). The second dataset showed a very irregular non-linear relationship between the two sets of Z scores (Figure  8 ).
Final collection of cancer summary datasets Overall, we identified 69,052 genetic associations (17. 2% of the total) across 163 datasets with probable effect allele meta-data errors, low metrics of imputation quality (defined as info or imputation r 2 score <0. 8), large sample size errors, unusually large log odds ratios or effect sizes that didn't closely correspond to their reported P values. Of these, 5,269 corresponded to the fatty acid SNP set (2. 4%). An analytical issue, meta data error or summary data error was identified in 22 of 55 studies (6 fatty acid GWAS and 49 cancer GWAS). A serious error, which we define as having the potential to introduce substantial bias into MR analyses, was identified in 6 of 55 studies (10. 9%). These included the effect allele meta data errors identified in the cancer studies and the large number of false positive genetic associations with fatty acids identified in CHARGE.
After excluding unreliable associations and restricting the summary data to the fatty acid SNP set, there were 219,842 genetic associations with cancer in 160 datasets. The genetic associations represent analyses of 87 unique cancer types conducted in 609,863 cancer cases and 1,311,961 controls across 51 studies (Table  1  and Supplementary table 5  17, [99] [100] [101] [102] [103]. The median n mber of cases per cancer type was 3,101 (min =155, max= 139,445).
Discussion Our pipeline flagged analytical issues or meta and summary data errors in 23 studies (2 fatty acid GWAS and 21 cancer GWAS), including errors in 7 studies with the potential to introduce substantial bias into downstream MR analyses. Issues identified included a large number of false positive genetic associations for fatty acids, incorrect specification of the effect allele and effect allele frequency columns, inconsistent effect size scales amongst cancer studies, reported effect sizes and standard errors that did not correspond to reported P values, large numbers of low quality imputed SNPs, SNPs with incorrect sample sizes and SNPs with unusually large effect sizes.
Effect allele meta data errors Of the issues identified, incorrect specification of the effect allele column is the most serious, as it will lead to inferences of causal effect in the wrong direction  105, 106  (when the null hypothesis is false), and was flagged in 3 (6. 1%) of the 49 cancer studies. A related, albeit less serious, error is incorrect specification of the effect allele frequency column, which can cause harmonisation problems for palindromic SNPs. Failure to harmonise palindromic SNPs between exposure and outcome studies may lead to increased heterogeneity in MR findings, which could in turn bias results towards the null (assuming the null hypothesis is false and that the palindromic SNPs are valid instruments). A conventional approach for avoiding these meta data errors is to compare allele frequency between the GWAS of interest and an external reference dataset  13  or to confirm the effect allele through correspondence with study authors (especially when these are ambiguously labelled) or through consultation of readme files.
Summary data errors Potential summary data errors were flagged by mismatches between expected and reported effect sizes. We found that a substantial proportion of the mismatches were attributable to imputed SNPs, SNPs with incorrect sample sizes and SNPs with unusually large effect sizes. The sample size errors were due to the strategy of using the maximum reported sample size to represent sample size across all SNPs. However, not all samples in a given GWAS necessarily contribute to the analysis of every SNP, which is particularly common in large meta-analysis consortia with many independent studies. Incomplete sample overlap amongst SNPs within a GWAS could introduce bias into methods that assume a constant sample size, such as summary-data methods that rely on an external LD reference panel to model the correlation structure amongst SNPs in a genetic instrument.
General recommendations When obtaining summary GWAS data via correspondence with study authors, we recommend that researchers should request access to full GWAS summary data, as this allows a far more comprehensive assessment of summary data reliability than is possible with only subsets of data. When full access is not possible, researchers should request summary data for SNPs that are established GWAS hits for their outcome of interest (i. e. not just the SNPs being used to instrument the exposure), which can then be used to confirm the identity of the effect allele through comparisons with the GWAS catalog. In addition, researchers could request summary data corresponding to the SNPs in our 1000 genomes reference set, which contains 2297 SNPs with the same minor allele across all 1000 genomes super populations, and which can be used to identify allele frequency issues.
An advantage of using our 1000 genomes reference set is that effect allele frequency conflicts can be identified without knowledge of the ancestral background of the test dataset. Alternatively, a similar quality control check can be achieved by comparing allele frequencies between the exposure and outcome studies of interest (assuming they are closely matched on ancestry). Where possible, researchers should also confirm the identity of effect allele meta data through correspondence with the data providers. We also recommend that researchers confirm with data providers the nature of all post GWAS filtering procedures that have been applied to the summary data. For example, in our own collaboration we ask each cancer study to confirm that their summary data has been through the same QC procedures described in their GWAS publications.
Failure to perform this check could lead to the inclusion of large numbers of low quality and unreliable genetic associations. It is also advisable to confirm effect size scales, to support the correct interpretation of results. These considerations supplement previously developed guidelines for conducting MR studies  4, 109, 110. Our approach of comparing expected to reported effect sizes, and of comparing summary association statistics to external reference datasets, offers an additional safeguard against the aforementioned errors and analytical issues. A limitation of this approach is that not all flagged datasets will necessarily be problematic because other factors, such as covariate adjustment in the original GWAS or deviations from Hardy Weinberg equilibrium for reasons other than measurement error, could also cause deviations between expected and reported effect sizes.
Two-sample population assumption One of the key assumptions made in two-sample MR is that the studies used to define the exposure and the outcome come from the same population. The comparison of allele frequencies between test datasets and reference populations can in principle be used to evaluate this assumption. For example, in our own analyses, allele frequencies in the European origin cancer studies and 1000 genomes European super population were consistently strongly correlated (the same applied to the East Asian origin studies and the 1000 genomes East Asian super population), indicating that the reported study ancestries were broadly accurate. However, our QC procedure was not designed to specifically test for ancestral origins and was restricted to SNPs with a narrow allele frequency range.
Conclusion We have developed a QC pipeline that can be used to flag meta and summary data errors and a range of analytical issues in GWAS results, which in turn can be used to enhance the integrity of downstream two-sample MR analyses. We applied the pipeline to the FAMRC, identifying issues or errors in 42. 9% of the studies. After resolving analytical issues and excluding problematic studies or unreliable genetic associations, we created a dataset of 219,842 genetic associations with 87 cancer types, derived from 48 separate studies, consortia or biobanks. The methods developed here are available to other researchers via the CheckSumStats R package (https://github. com/MRCIEU/CheckSumStats ). University Hospitals Bristol and Weston NHS Foundation Trust and the University of Bristol. The views expressed are those of the author(s) and not necessarily those of the NIHR or the Department of Health and Social Care.
This research was carried out in the MRC Integrative Epidemiology Unit (MC_UU_00011/1, MC_UU_00011/4). PCH was supported by Cancer Research UK (C52724/A20138 & C18281/A29019). Table  1. Cancer studies in the Fatty Acids in Cancer Mendelian Randomisation Collaboration Figure  2. Quality control report for genetic summary data from a genome-wide association of arachidonic acid in CHARGE Figure  3. Quality control report for genetic summary data from a genome-wide association of glioma in the GliomaScan dataset (ID 967) In the allele frequency subplots, each red data point corresponds to SNPs with high allele frequency conflicts, due to having an allele frequency that is greater than 0. 58 (when it is expected to be less than 0. 5) or to deviation from the reference allele frequency by more than 10 points In the allele frequency subplots, each red data point corresponds to SNPs with high allele frequency conflicts, due to having an allele frequency that is greater than 0.
58 (when it is expected to be less than 0. 5) or to deviation from the reference allele frequency by more than 10 points Figure  6. Quality control report for genetic summary data from a genome-wide association of glioma in the UCSF_AGS/SFAGS dataset (ID 133) In the allele frequency subplots, each red data point corresponds to SNPs with high allele frequency conflicts, due to having an allele frequency that is greater than 0. 58 (when it is expected to be less than 0. 5) or to deviation from the reference allele frequency by more than 10 points Figure  7. Quality control report for genetic summary data from a genome-wide association study of nasopharyngeal carcinoma in the TNC dataset (ID=132) In the allele frequency subplots, each red data point corresponds to SNPs with high allele frequency conflicts, due to having an allele frequency that is greater than 0.
58 (when it is expected to be less than 0. 5) or to deviation from the reference allele frequency by more than 10 points Figure  8. Quality control report for genetic summary data from a genome-wide association study of lung cancer in the ILCCO dataset (ID=74) In the allele frequency subplots, each red data point corresponds to SNPs with high allele frequency conflicts, due to having an allele frequency that is greater than 0. 58 (when it is expected to be less than 0. 5) or to deviation from the reference allele frequency by more than 10 points.
AU : Pleaseconfirmthatallheadinglevelsarerepresentedcorrectly: Cryo-electron tomography (cryo-ET) and subtomogram averaging (STA) are increasingly used for macromolecular structure determination in situ. Here, we introduce a set of computational tools and resources designed to enable flexible approaches to STA through increased automation and simplified metadata handling. We create a bidirectional interface between the Dynamo software package and the Warp-Relion-M pipeline, providing a framework for ab initio and geometrical approaches to multiparticle refinement in M. We illustrate the power of working within this framework by applying it to EMPIAR-10164, a publicly available dataset containing immature HIV-1 virus-like particles (VLPs), and a challenging in situ dataset containing chemosensory arrays in bacterial minicells.
Introduction Cryo-electron tomography (cryo-ET) is an imaging technique rapidly gaining popularity for the direct visualisation of unique biological objects in 3D. Repeating structural motifs present in cryo-ET data can be reconstructed at higher resolution by subtomogram averaging (STA), allowing for the possibility of studying macromolecular structure in situ  [1]. STA has developed alongside single-particle analysis (SPA) in cryo-electron microscopy (cryo-EM), a technique that has benefitted significantly in the last 10 years from advances in both electron detection hardware and image processing software  [2, 3]. State-of-the-art STA workflows often co-opt tools and adapt ideas from SPA for tomography and STA  [4, 5]. An unfortunate consequence of this side-by-side development is a somewhat fragmented software ecosystem with no standardisation of file formats or metadata conventions  [6].
Despite the advent of complete or near-complete integrated solutions for cryo-ET and STA  [7] [8] [9] , optimal methodology for a given dataset often requires the creative combination of different approaches that may not all be present within a single integrated pipeline. For those new to cryo-ET, the burden of interfacing many different software packages in a complex workflow often represents a barrier to the testing of alternative approaches. Image processing for STA from cryo-ET data is a complex, multistep process that can broadly be divided into 3 blocks (  Fig 1). The first, "preprocessing," generates 3D reconstructions (tomograms) from experimental 2D micrographs. The second, "particle picking," creates putative positions and orientations for objects of interest within each volume as well as initial reference(s) for subsequent refinement.
Exhaustive, reference-based template matching approaches are widely accepted as an imperfect solution for particle picking due to the computational overhead and the need for subsequent dataset cleaning  [11, 12]. Care must also be taken to avoid model bias. Particle picking methods based on deep learning have been proposed, which may help to address some of these limitations  [13, 14]. Another class of particle picking methods, termed "geometrical particle picking," derivesAU : PleaseconfirmthattheeditstothesentenceAnotherclassofparticlepickingmethods; ter putative particle positions and orientations from a 3D model of a supporting geometry, usually generated from minimal manual annotations  [15]. These methods can be used to impose prior knowledge about how particle poses relate to the supporting geometry during refinement.
Despite these algorithmic advances, there remain relatively few examples of reconstructions from tilt series data in this resolution regime in the Electron Microscopy Data Bank (EMDB), indicating that this work remains nontrivial (S1 Fig). We posit that a possible reason for this discrepancy is the complexity of integrating the various tools available for each step of the workflow in the fragmented cryo-ET software ecosystem. Warp-RELION-M is a user-friendly solution for cryo-ET preprocessing, STA, and multi-particle refinement, yielding the highest-resolution reconstructions from cryo-ET data seen thus far  [9]. The current version of Warp (v1. 0. 9) is designed to process data collected using SerialEM and does not integrate automated tilt series alignment procedures. The pipeline assumes that an initial model is available for template matching and does not interface directly with tools for geometrical particle picking.
Dynamo is a flexible, extensible software environment for STA, providing a variety of powerful tools for the management, annotation, and analysis of cryo-ET data  [15, 19]. The combination of the tools in these software packages would yield a computational framework capable of more automated, ab initio, and geometrical approaches to multi-particle refinement in M. In this manuscript, we describe our efforts to facilitate working within this framework. In the Materials and methods section, a set of tools is presented, which automate fiducial-based tilt series alignment, simplify metadata handling, and create a bidirectional interface between Dynamo and Warp-RELION-M. In the Results and discussion section, we demonstrate the benefits of our metadata handling tools and illustrate the power of working within this computational framework on 2 datasets.
Integrating on-the-fly tilt series alignment into the Warp preprocessing pipeline Tomogram reconstruction within the Warp preprocessing pipeline currently requires the use of an external software package, IMOD  [20] , for tilt series alignment. In the absence of a fully integrated solution in the currently available version of Warp (1. 0. 9), we developed autoalign_dynamo, a package for automating fiducial-based tilt series alignment using Dynamo and Dynamo tilt series alignment workflows were released recently, and, as such, are not yet described in the literature, although details and the source code are available in the public domain  [21]. To facilitate the readers understanding, we provide a brief overview of the alignment algorithm, noting that it was not implemented by us. A binary, synthetic template of a fiducial marker is generated based on user input and used to detect candidate fiducial positions within a tilt series by cross-correlation.