cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.35092/yhjc.12198225.v1
title: Live-cell imaging data for DASC (disassembly asymmetry score classification) analysis of calthrin-mediated endocytosis

description: This dataset is for sharing all of the raw images analyzed in the paper, “<b><i>DASC, a sensitive classifier for measuring discrete early stages in clathrin-mediated endocytosis</i></b>”, https://doi.org/10.7554/eLife.53686. In the paper, we proposed a new method that can unbiasedly classify two populations of clathrin structures during endocytosis, clathrin coated pits and abortive coats. Based on this new classification, we measured phenotypes of siRNA knockdown of 11 endocytic accessory protein (EAP), analyzed clathrin-AP2 dual channel images and measured curvature formation during endocytosis.<br><br>Images in all four experiments are included in this data set, ‘EAP knockdown’, ‘alphaPIP2-’ as a positive control experiment, ‘CLC_AP2_dual’ as another control experiment, and ‘EpiTIRF’ for curvature measurement. Each experiment has various conditions, see the metadata file for details.<br><br>Readers can follow this README to obtain the entire data and then apply DASC, one experiment by one experiment, to reproduce the results in the associated paper. The software of DASC is available on GitHub: https://github.com/DanuserLab/cmeAnalysis. See the instruction on the GitHub page for how to use DASC to analyze the images.<br><br>Files in this data are compressed. Each ‘.tif.bz’ file is a compressed tif image sequence of fluorescently labeled cells. The names of these compressed files include descriptive information of ‘experiment’, ‘condition’, ‘date’, ‘cell name’ and ‘channel name’ (optional). These descriptors are separated by ‘#’.<br><br>The compressed files can be automatically decompressed by running the Matlab script ('Decompression.m') in a Linux system.
DOI: 10.35092/yhjc.12272015
title: Glycosylated Swiss-model molecular dynamics trajectory of SARS-CoV-2 spike glycoprotein

description: Molecular dynamics simulation trajectory. The system is the SARS-CoV-2 spike glycoprotein with glycans modeled on using GLYCAM-Web. SARS-CoV2 spike (S) protein structure – A 3D structure of the prefusion form of the S protein (RefSeq: YP_009724390.1, UniProt: P0DTC2 SPIKE_SARS2), based on a Cryo-EM structure (PDB code 6VSB), was obtained from the SWISS-MODEL server (swissmodel.expasy.org). The model has 95% coverage (residues 27 to 1146) of the S protein. The site specific glycans used to model a glycoform representative of the data obtained from the S glycoprotein expressed in HEK293 cells, are presented below: Hybrid: DManpα1-6[DManpα1-3]DManpα1-6[DGlcpNAcβ1-2DManpα1-3]DManpβ1-4DGlcpNAcβ1-4DGlcpNAcβ1-OH Glycosites: 657 FA2: DGlcpNAcβ1-2DManpα1-6[DGlcpNAcβ1-2DManpα1-3]DManpβ1-4DGlcpNAcβ1-4[LFucpα1-6]DGlcpNAcβ1-OH Glycosites: 149, 165, 331, 343, 616, 1134 A2: DGlcpNAcβ1-2DManpα1-6[DGlcpNAcβ1-2DManpα1-3]DManpβ1-4DGlcpNAcβ1-4DGlcpNAcβ1-OH Glycosites: 1098 FA2B: DGlcpNAcβ1-2DManpα1-6[DGlcpNAcβ1-4][DGlcpNAcβ1-2DManpα1-3]DManpβ1-4DGlcpNAcβ1-4[LFucpα1-6]DGlcpNAcβ1-OH Glycosites: 74, 282 M5: DManpα1-6[DManpα1-3]DManpα1-6[DManpα1-3]DManpβ1-4DGlcpNAcβ1-4DGlcpNAcβ1-OH Glycosites: 61, 122, 603, 709, 717, 801, 1074 M8: DManpα1-2DManpα1-6[DManpα1-2DManpα1-3]DManpα1-6[DManpα1-2DManpα1-3]DManpβ1-4DGlcpNAcβ1-4DGlcpNAcβ1-OH Glycosites: 234 Glycan naming system. In cases where the mass spectrometry data allows for multiple structures, such as FA2/FA1B, the glycan with the least ambiguity was selected. For example, FA2 was selected over FA1B, as the position of the single antennae is ambiguous.<br>Dataset supports the following preprint: 3D Models of glycosylated SARS-CoV-2 spike protein suggest challenges and opportunities for vaccine development. Oliver C. Grant, David Montgomery, Keigo Ito, Robert J. Woods; bioRxiv 2020.04.07.030445; doi: https://doi.org/10.1101/2020.04.07.030445
DOI: 10.35092/yhjc.12272015.v1
title: Glycosylated Swiss-model molecular dynamics trajectory of SARS-CoV-2 spike glycoprotein

description: Molecular dynamics simulation trajectory. The system is the SARS-CoV-2 spike glycoprotein with glycans modeled on using GLYCAM-Web. SARS-CoV2 spike (S) protein structure – A 3D structure of the prefusion form of the S protein (RefSeq: YP_009724390.1, UniProt: P0DTC2 SPIKE_SARS2), based on a Cryo-EM structure (PDB code 6VSB), was obtained from the SWISS-MODEL server (swissmodel.expasy.org). The model has 95% coverage (residues 27 to 1146) of the S protein. The site specific glycans used to model a glycoform representative of the data obtained from the S glycoprotein expressed in HEK293 cells, are presented below: Hybrid: DManpα1-6[DManpα1-3]DManpα1-6[DGlcpNAcβ1-2DManpα1-3]DManpβ1-4DGlcpNAcβ1-4DGlcpNAcβ1-OH Glycosites: 657 FA2: DGlcpNAcβ1-2DManpα1-6[DGlcpNAcβ1-2DManpα1-3]DManpβ1-4DGlcpNAcβ1-4[LFucpα1-6]DGlcpNAcβ1-OH Glycosites: 149, 165, 331, 343, 616, 1134 A2: DGlcpNAcβ1-2DManpα1-6[DGlcpNAcβ1-2DManpα1-3]DManpβ1-4DGlcpNAcβ1-4DGlcpNAcβ1-OH Glycosites: 1098 FA2B: DGlcpNAcβ1-2DManpα1-6[DGlcpNAcβ1-4][DGlcpNAcβ1-2DManpα1-3]DManpβ1-4DGlcpNAcβ1-4[LFucpα1-6]DGlcpNAcβ1-OH Glycosites: 74, 282 M5: DManpα1-6[DManpα1-3]DManpα1-6[DManpα1-3]DManpβ1-4DGlcpNAcβ1-4DGlcpNAcβ1-OH Glycosites: 61, 122, 603, 709, 717, 801, 1074 M8: DManpα1-2DManpα1-6[DManpα1-2DManpα1-3]DManpα1-6[DManpα1-2DManpα1-3]DManpβ1-4DGlcpNAcβ1-4DGlcpNAcβ1-OH Glycosites: 234 Glycan naming system. In cases where the mass spectrometry data allows for multiple structures, such as FA2/FA1B, the glycan with the least ambiguity was selected. For example, FA2 was selected over FA1B, as the position of the single antennae is ambiguous.<br>Dataset supports the following preprint: 3D Models of glycosylated SARS-CoV-2 spike protein suggest challenges and opportunities for vaccine development. Oliver C. Grant, David Montgomery, Keigo Ito, Robert J. Woods; bioRxiv 2020.04.07.030445; doi: https://doi.org/10.1101/2020.04.07.030445
DOI: 10.35092/yhjc.12298838
title: The snpnet polygenic risk score coefficients for 35 lab biomarkers described in 'Genetics of 35 blood and urine biomarkers in the UK Biobank'

description: The dataset contains the coefficients of the polygenic risk scores for 35 biomarker traits described in the following preprint:N. Sinnott-Armstrong*, Y. Tanigawa*, et al, Genetics of 38 blood and urine biomarkers in the UK Biobank. bioRxiv, 660506 (2019). doi:10.1101/660506<br><br>Note that we are preparing a revised version of the manuscript and this dataset contains 35 (instead of 38) biomarker phenotypes.<br><br>We provide the list of 35 biomarkers in "list_of_35_biomarkers.tsv". We used the "Phenotype_name" column in this table for the file names.<br><br>For each phenotype, we provide a compressed tab-deliminated table, named "snpnet.BETAs.[Phenotype_name].tsv.gz", which contains the coefficients (weights) of the polygenic risk score and have the following columns:- CHROM: the chromosome- POS: the position- ID: the variant identifier- REF: the reference allele- ALT: the alternate allele- BETA: the coefficients (weights) of the PRS<br><br>Note that we used GRCh37/hg19 genome reference in the analysis and the BETA is always reported for the alternate allele.<br><br>We used the BASIL algorithm implemented in R snpnet package, which is described in another preprint:<br>J. Qian, et al, A Fast and Flexible Algorithm for Solving the Lasso in Large-scale and Ultrahigh-dimensional Problems. bioRxiv, 630079 (2019). doi:10.1101/630079
DOI: 10.35092/yhjc.12344351
title: The fine-mapped associations of 35 lab biomarkers described in 'Genetics of 35 blood and urine biomarkers in the UK Biobank'

description: The dataset contains the output from FINEMAP, a software to identify causal variants from genome-wide association summary statistics, for 35 biomarker traits described in the following preprint:<br>N. Sinnott-Armstrong*, Y. Tanigawa*, et al, Genetics of 38 blood and urine biomarkers in the UK Biobank. bioRxiv, 660506 (2019). doi:10.1101/660506<br><br>Note that we are preparing a revised version of the manuscript and this dataset contains 35 (instead of 38) biomarker phenotypes.<br><br>For each trait, we provide a tar archive file which contains the full output from FINEMAP for the regions with at least one genome-wide significant associations (p &lt; 5e-9) from the multi-ethnic GWAS meta-analysis within UK Biobank. The content of the tar archive is organized by directories, named as <code>chr[CHROM]/[TRAIT]/range[RANGE]</code>, and contains the following files: <code>GLOBAL_[TRAIT]_chr[CHROM]_range[RANGE].bdose.zst</code><code>GLOBAL_[TRAIT]_chr[CHROM]_range[RANGE].config.zst</code><code>GLOBAL_[TRAIT]_chr[CHROM]_range[RANGE].cred.zst</code><code>GLOBAL_[TRAIT]_chr[CHROM]_range[RANGE].ld.zst</code><code>GLOBAL_[TRAIT]_chr[CHROM]_range[RANGE].master.zst</code><code>GLOBAL_[TRAIT]_chr[CHROM]_range[RANGE].snp.zst</code><code>GLOBAL_[TRAIT]_chr[CHROM]_range[RANGE].z.zst</code> where, [TRAIT]<code></code><code></code>: trait name[CHROM]<code></code><code></code>: chromosome[RANGE]<code></code><code></code>: the range of the region in the format of <code>[start position]-[end position]</code>. We provide the list of traits and regions included in this data release in <code>FINEMAP.index.tsv</code>. This is a flat table with 3 columns: trait: the biomarker traitchr: chromosomerange: the range Note that we used GRCh37/hg19 genome reference in the analysis and the BETA is always reported for the alternate allele. Please check the FINEMAP paper and software documentation for the detailed explanation of the file formats. Benner, C. et al. FINEMAP: efficient variable selection using summary data from genome-wide association studies. Bioinformatics 32, 1493–1501 (2016).Christian Benner. FINEMAP. http://christianbenner.com/. Also, all the files in the tar archive is compressed with Zstandard (as indicated by the <code>.zst</code> extension). You can check the contents with <code>zstdcat</code> command and uncompress the files with <code>zstd -d [file.zst]</code>. The Zstandard software can be built from source, or simply available from conda (https://anaconda.org/conda-forge/zstd), pip (https://pypi.org/project/zstd/) or brew (https://formulae.brew.sh/formula/zstd). Please check Zstandard website (http://facebook.github.io/zstd/) for more information.
DOI: 10.35092/yhjc.12355382
title: The meta-analyzed GWAS summary statistics for 35 lab biomarkers described in 'Genetics of 35 blood and urine biomarkers in the UK Biobank'

description: The dataset contains meta-analyzed GWAS summary statistics for 35 biomarker traits described in the following preprint:<br>N. Sinnott-Armstrong*, Y. Tanigawa*, et al, Genetics of 38 blood and urine biomarkers in the UK Biobank. bioRxiv, 660506 (2019). doi:10.1101/660506<br><br>Note that we are preparing a revised version of the manuscript and this dataset contains 35 (instead of 38) biomarker phenotypes.<br><br>We provide the list of 35 biomarkers in "list_of_35_biomarkers.tsv". We used the "Phenotype_name" column in this table for the file names. For each phenotype, we provide two compressed tab-delimited files, named "[Phenotype_name].array.gz" and "[Phenotype_name].imp.gz", which contain the summary statistics for genetic variants on the genotyping array and the imputed dataset, respectively.<br><br>We used METAL for the meta-analysis for 4 populations (White British, non-British White, African, and South Asian) within UK Biobank. The files have the following columns: CHROM: the chromosomePOS: the positionMarkerName: the variant identifierREF: the reference alleleALT: the alternate alleleEffect: the effect size (BETA) estimateStdErr: the standard error of effect size estimateP-value: the p-value of the associationDirection: the direction of effect sizeHetISq, HetChiSq, HetDf, HetPVal: heterogeneity statistics from METAL Note that we used GRCh37/hg19 genome reference in the analysis and the BETA is always reported for the alternate allele.<br><br>Please also check the METAL documentation (https://genome.sph.umich.edu/wiki/METAL_Documentation).<br><br>The summary statistic files are compressed with <code>bgzip</code> and indexed with <code>tabix</code> (the <code>.tbi</code> files). One should be able to read those files with the standard <code>gzip</code>/<code>zcat</code>.
DOI: 10.35092/yhjc.12355424
title: The multi-PRS weights computed with the 35 lab biomarkers described in 'Genetics of 35 blood and urine biomarkers in the UK Biobank'

description: The dataset contains the multi-PRS weights computed with the 35 biomarker traits described in the following preprint:<br>N. Sinnott-Armstrong*, Y. Tanigawa*, et al, Genetics of 38 blood and urine biomarkers in the UK Biobank. bioRxiv, 660506 (2019). doi:10.1101/660506<br><br>Note that we are preparing a revised version of the manuscript and this dataset contains 35 (instead of 38) biomarker phenotypes.<br><br>The list of disease endpoints included in this dataset is: angina, alcoholic cirrhosis, gallstones, hypertension, cholecystitis, kidney failure, heart failure, myocardial infarction, gout, and type 2 diabetes (T2D).<br><br>We provide weights of the 23 polygenic risk scores characterized by multi-PRS. The list of models are summarized in list_of_multi-PRS-models.tsv. This index file the following columns: Filename: the filename of polygenic risk score weights in this dataset.Trait: the disease outcome.Covariate_adjustment: a binary variable indicating whether the multi-PRS model is trained with covariate (age, sex, and PC1-10) adjustment.Family_history_adjustment: a binary variable indicating whether the multi-PRS model is trained with family history.Note: Additional information when relevant. For the PRS models listed with "Covariate_adjustment == TRUE", we fit multi-PRS regression model adjusted by age, sex, and 10 principal components where as the ones with "Covariate_adjustment == FALSE" we did not use those covariates.<br><br>For T2D, we have two sets of models: (1) models trained for Eastwood et al. T2D cases in UK Biobank and (2) models trained for Eastwood et al. T2D cases in UK Biobank vs. filtered controls with HbA1c &lt; 39.<br><br>For myocardial infarction, we provide a model with family history adjustment, <code>weights_familyhistory.HC326.tsv.gz</code>. This model is trained with covariates (age, sex, and 10 principal components) and family history of heart disease as covariates.<br><br>Please read our manuscript for more details. For each PRS model listed in <code>list_of_multi-PRS-models.tsv</code>, we provide a compressed tab-delimited files, which contain the multi-PRS weights. The files have the following columns: CHROM: the chromosomePOS: the positionID: the variant identifierREF: the reference alleleALT: the alternate alleleA1: the risk alleleweights.: the coefficients (weights) of the PRS Note that we used GRCh37/hg19 genome reference in the analysis and the BETA is always reported for the alternate allele.<br><br>The multi-PRS weights files are compressed with <code>gzip</code>. One should be able to read those files with the standard <code>gzip</code>/<code>zcat</code>.
DOI: 10.15156/bio/sh1229247.08fu
title: SH1229247.08FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh1229248.08fu
title: SH1229248.08FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/th028943
title: TH028943
title: Tylodoridae

description: Here we present the novel 'taxon hypothesis' (TH) concept to accommodate fungal 'species hypothesis' (SH) into existing classification frameworks for taxonomic communication. As supplied with Digital Object Identifiers (DOIs), THs are flexible in time and enable direct cross-communication of higher level taxa along with evolving classification schemes. Every TH includes given number of SHs and consequently fixed number of fungal rDNA ITS sequences. DNA sequences generated by High-throughput or Sanger sequencing studies and identified against UNITE SHs will be automatically assigned to THs as well.
DOI: 10.15156/bio/th028944
title: TH028944
title: Heligmosomidae

description: Here we present the novel 'taxon hypothesis' (TH) concept to accommodate fungal 'species hypothesis' (SH) into existing classification frameworks for taxonomic communication. As supplied with Digital Object Identifiers (DOIs), THs are flexible in time and enable direct cross-communication of higher level taxa along with evolving classification schemes. Every TH includes given number of SHs and consequently fixed number of fungal rDNA ITS sequences. DNA sequences generated by High-throughput or Sanger sequencing studies and identified against UNITE SHs will be automatically assigned to THs as well.
DOI: 10.35092/yhjc.11872434
title: Supplemental Tables A to F for "Cord blood DNA methylation reflects cord blood C-reactive protein levels..."

description: Results using EPIC microarray to investigate maternal CRP in the pregnancy and delivery with cord blood DNA methylation. Results from model 1 adjusting for maternal age, smoking, income, pre-pregnancy BMI, and plate. Model 2 additionally adjusts for cell type distribution using the cord blood reference as published by Bakulski et al. 2016. Model 3 adjusts for model 1 covariates and cell type distribution using the cord blood reference published by Gervin, Salas et al. 2019. Results are from first trimester (week 8), second trimester (week 20), third trimester (week 36), cord blood, cumulative (AUC) or persistent (extreme tertiles) CRP.<br>Supports the publication:<br>Yeung, E.H., Guan, W., Zeng, X. et al. Cord blood DNA methylation reflects cord blood C-reactive protein levels but not maternal levels: a longitudinal study and meta-analysis. Clin Epigenet 12, 60 (2020). https://doi.org/10.1186/s13148-020-00852-2<br>
DOI: 10.35092/yhjc.12367658
title: Early Life Stress Mouse Brain MRI Dataset

description: A collection of ex vivo diffusion MRI data of mouse brains acquired in a study of early life stress (PI: Dr. Arie Kaffman). There are a total of 24 mouse brains (F/M=12/12, Stressed/Control=12/12, please see the List_of_Animals.xlsx for details). Data were saved in the Analyze format and can be opened using MRICro or other software tools that support the Analyze format. For each mouse, the average diffusion weighted and fractional anisotropy (FA) images were provided.<br>Dataset supports manuscript: Jordon D. White, Tanzil M. Arefin, Alexa Pugliese, et al. Early life stress causes sex-specific changes in adult fronto-limbic connectivity that differentially drive learning. Authorea. May 26, 2020.DOI: 10.22541/au.159050220.00045500<br><br>Abstract: It is currently unclear whether early life stress (ELS) affects males and females differently. However, a growing body of work has shown that sex moderates responses to stress and injury, with important insights into sex-specific mechanisms provided by work in rodents. Unfortunately, most of the ELS studies in rodents were conducted only in males, a bias that is particularly notable in translational work that has used human imaging. Here we examine the effects of unpredictable postnatal stress (UPS), a mouse model of complex ELS, using high resolution diffusion magnetic resonance imaging. We show that UPS induces several neuroanatomical alterations that were seen in both sexes and resemble those reported in humans. In contrast, exposure to UPS induced fronto-limbic hyper-connectivity in males, but either no change or hypoconnectivity in females. Moderated-mediation analysis found that these sex-specific changes are likely to alter contextual freezing behavior in males but not in females.
DOI: 10.35092/yhjc.12386102
title: MRI dataset supporting "Comparison of T1-Post and FLAIR-Post MRI for identification of traumatic meningeal enhancement in traumatic brain injury patients"

description: This dataset supports the publication: <br>Davis TS, Nathan JE, Tinoco Martinez AS, De Vis JB, Turtzo LC, Latour LL (2020) ­­­­­Comparison of T1-Post and FLAIR-Post MRI for identification of traumatic meningeal enhancement in traumatic brain injury patients. PLoS ONE 15(7): e0234881. https://doi.org/10.1371/journal.pone.0234881<b><br></b><br>This zip archive contains the dataset of deidentified brain MRI (T1 and FLAIR pre- and post-contrast sequences in dicom format) that were analyzed by blinded readers for the T1 FLAIR manuscript. The folder numbers/subject numbers on the images correspond to the subject numbers listed in the manuscript's Table 2. That table shows how each blinded reader scored the images.
DOI: 10.35092/yhjc.12386102.v1
title: MRI dataset supporting "Comparison of T1-Post and FLAIR-Post MRI for identification of traumatic meningeal enhancement in traumatic brain injury patients"

description: <b>This dataset is in support of an accepted manuscript titled "Comparison of T1-Post and FLAIR-Post MRI for identification of traumatic meningeal enhancement in traumatic brain injury patients". </b>The DOI for the publication will be updated when available.<br>This zip archive contains the dataset of deidentified brain MRI (T1 and FLAIR pre- and post-contrast sequences in dicom format) that were analyzed by blinded readers for the T1 FLAIR manuscript. The folder numbers/subject numbers on the images correspond to the subject numbers listed in the manuscript's Table 2. That table shows how each blinded reader scored the images.
DOI: 10.35092/yhjc.12401030
title: Supplemental Table 1: Sample summary -- Chicken embryonic gonads exposed to ethynylestradiol or vehicle: Genetic Sex, Feminization Sore, CYP19A1 expression and rank, and RNAseq mapped read number

description: Supplemental Table 1 supporting: Keiko Shioda et al. “Transcriptomic and epigenetic preservation of genetic sex identity in estrogen-feminized male chicken embryonic gonads” Supplemental Table 1: Sample summary<br><br>Chicken embryos (29 total) were exposed to 20 microgram/egg ethynylestradiol or vehicle oil emulsion from incubation day 6, and their gonads were collected on day 19. This table summarizes sample ID, genetic sex, feminization sore, CYP19A1 expression and rank, and RNAseq mapped read number<br>