cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.35092/yhjc.12636125.v1
title: Phospho-proteomics tandem mass tag datasets from cells with CEP350 genetic alteration | Datasets Supporting: Tumor Suppressive Functions of CEP350 in Cutaneous Melanoma Cells

description: Signaling changes induced by haploinsufficient loss or over-expression of CEP350 by global phospho-serine/threonine profiling melanoma cells expressing oncogenic BRAF-V600E. Raw data produced by the Proteomics and Metabolomics Core Facility and data analysis performed by the Biostatistics and Bioinformatics Shared Resource at the H. Lee Moffitt Cancer Center & Research Institute.<br>Supplementary datasets and other information accompanying manuscript: Tumor Suppressive Functions of CEP350 in Cutaneous Melanoma Cells by Aziz Aiderus, Bin Fang, John M. Koomen and Michael B. Mann.<br>Abstract: We previously identified Cep350 as a novel melanoma haploinsufficient melanoma tumor suppressor gene using SB transposon-mediated mutagenesis to drive melanoma progression in Braf(V600E) mutant (SB|Braf) mice functionally demonstrated that the human CEP350 ortholog is a new melanoma tumor-suppressor gene in human cancer cell lines (Mann et al., Nature Genetics, 2015). Further dissection of the latent tumor suppressive functions of CEP350 in cutaneous melanoma cells is essential for understanding its role in melanoma imitation and progression. In this work, we investigated the role of the novel tumor suppressive functions of CEP350 in cutaneous melanoma cells using comparative informatics, molecular oncology, and proteomics approaches to demonstrate that CEP350 acts via altered cytoskeletal dynamics to contribute to BRAF-V600E driven melanoma.
DOI: 10.35092/yhjc.9995672.v5
title: Cell Health - Cell Painting Single Cell Profiles

description: Single Cell Databases of Cell Painting Profiles for the Cell Health Project. These data are used to aggregate profiles in a CRISPR knockout experiment. The data are used to predict cell health assays.DataWe collected Cell Painting measurements on a CRISPR experiment. The experiment targeted 59 genes, which included 119 unique guides (~2 per gene), across 3 cell lines. The cell lines included A549, ES2, and HCC44.About 40% of all CRISPR guides were reproducible. This is ok since we are not actually interested in the CRISPR treatment specifically, but instead, just its corresponding readout in each cell health assay.ApproachWe performed the following approach:Split data into 85% training and 15% test sets.Normalized data by plate (z-score).Selected optimal hyperparamters using 5-fold cross-validationTrained elastic net regression models to predict each of the 70 cell health assay readouts, independently.Trained using shuffled data as well.Report performance on training and test sets.We also trained logistic regression classifiers using the same approach above<br>See https://github.com/broadinstitute/cell-health for more details.<br>
DOI: 10.15156/bio/sh1229255.08fu
title: SH1229255.08FU

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/sh1229256.08fu
title: SH1229256.08FU

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/sh1229257.08fu
title: SH1229257.08FU

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/th028951
title: TH028951
title: Haemonchidae

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/th028952
title: TH028952
title: Heligmosomatidae

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: 10.25549/one-c4-50191
title: Tom F., letters (undated)

description: Tom F., note (undated)
DOI: 10.35092/yhjc.11400966.v2
title: All Long Peptides Screened by the NCI Surgery Branch for Reactivity Against TIL

description: All starting mutated Nmers as described in the manuscript titled "A machine learning model for ranking candidate HLA class I neoantigens based on known neoepitopes from multiple human tumor types"
DOI: 10.35092/yhjc.11400972.v2
title: Training Nmers randomly subsampled to reduce expression bias

description: Mutated Nmers from the balanced training set described in the manuscript titled "A machine learning model for ranking candidate HLA class I neoantigens based on known neoepitopes from multiple human tumor types". These Nmers have been randomly selected to reduce the expression bias in the data amongst the screened nmers
DOI: 10.35092/yhjc.11400975.v2
title: Training Mmps randomly subsampled to reduce expression bias

description: Mmps derived from the Nmers in the balanced training set in the manuscript titled "A machine learning model for ranking candidate HLA class I neoantigens based on known neoepitopes from multiple human tumor types". The nmers in the balanced training set were randomly selected from each decile in the full training data to reduce the expression bias in the data amongst the screened nmers<br>
DOI: 10.35092/yhjc.11400984.v2
title: Test Set of Long Peptides Screened by NCI Surgery Branch for Reactivity Against TIL

description: Mutated Nmers From the Test set described in the manuscript titled "A machine learning model for ranking candidate HLA class I neoantigens based on known neoepitopes from multiple human tumor types"
DOI: 10.35092/yhjc.11400987.v2
title: Test Set of Mutated Minimal Peptides screened by NCI Surgery Branch for Reactivity Against TIL

description: Mutated Minimal Peptides From the Test set described in the manuscript titled "A machine learning model for ranking candidate HLA class I neoantigens based on known neoepitopes from multiple human tumor types"
DOI: 10.35092/yhjc.12298838.v1
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.12355382.v1
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.12401165.v2
title: Supplemental Table 7: RNAseq data of chicken embryonic gonads exposed to ethynylestradiol or vehicle: Expression of repetitive sequences

description: Supplemental Table 7 supporting: Keiko Shioda et al. “Transcriptomic and epigenetic preservation of genetic sex identity in estrogen-feminized male chicken embryonic gonads” <br>Supplemental Table 7: Normalized repetitive sequence expression.<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 shows TMM-normalized RNA-seq data (counts per million) of these chicken embryonic gonads for expression of repetitive sequences annotated by RepeatMasker in the chicken reference genome sequence galGal6.
DOI: 10.35092/yhjc.12401177.v2
title: Supplemental Table 9: List of genes showing differential promoter methylation in chicken embryonic gonads with various degrees of feminization after in ovo exposure to ethynylestradiol

description: Supplemental Table 9 supporting: Keiko Shioda et al. “Transcriptomic and epigenetic preservation of genetic sex identity in estrogen-feminized male chicken embryonic gonads” <br>Supplemental Table 9: Genes with differentially methylated regions on transcriptional start sites.<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 shows list of genes showing differemtial promoter methylation in chicken embryonic gonads showing various degrees of feminization after exposure to ethynylestradiol determined by RNA-seq. Induction or suppression comparing various degrees of feminization scores (FS= 0,1,2,3) are shown in the tag labels.<br>This table shows 6 groups of TSS linked to differentially methylated genes (TDMGs) – namely, FS0 &gt; FS3 (demethylated in FS=3 normal ovaries but not in FS=0 normal ZZ-testes; 341 TDMGs), FS0 &lt; FS3 (3243 TDMGs), FS0 &gt; FS1/2 (927 TDMGs), FS0 &lt; FS1/2 (5352 TDMGs), FS3 &gt; FS1/2 (6535 TDMGs), and FS3 &lt; FS1/2 (1696 TDMGs). <br>
DOI: 10.35092/yhjc.12552218.v1
title: Supplemental Table 1 for "Leucine supplementation augments skeletal muscle mitochondrial respiration during recovery following 7 days of physical inactivity in older adults"

description: Mitochondrial respiration measures determined using OroBors high resolution respirometry Protocol 1. Since all OroBoros measurements originate from the same muscle biopsy sample, these are presented as technical replicates (with the exception of State 4<sub>o</sub>). All outcomes have units of (pmol O<sub>2</sub>/sec/mg tissue).<br><br>Supplemental Table supporting: <br>"Leucine supplementation augments skeletal muscle mitochondrial respiration during recovery following 7 days of physical inactivity in older adults"<br><br>Emily J. Arentson-Lantz, Jasmine Mikovic, Nisha Bhattarai, Christopher S. Fry, Severine Lamone, and Doug Paddon-Jones<br><br>In older adults, leucine supplementation attenuates the loss of skeletal muscle mass and function during bed rest. We sought to determine if leucine could also preserve and/or restore mitochondrial function and muscle oxidative capacity during periods of disuse and rehabilitation. Healthy older adults (69.1 ±1.1 years) consumed a structured diet with supplemental leucine (LEU: 0.06 g/ kg body weight/ meal; n=8) or alanine (CON: 0.06 g/ kg body weight/meal; n=8) during 7 days of bed rest and 5 days of inpatient rehabilitation. A 75 g oral glucose tolerance test was performed at baseline (PreBR), after bed rest (PostBR) and rehabilitation (PostRehab) and used to calculate an indicator of insulin sensitivity, metabolic clearance rate. (MCR). Tissue samples from the <i>m. vastus lateralis</i> were collected PreBR, PostBR, and PostRehab to assess mitochondrial respiratory capacity and protein markers of the oxidative phosphorylation and a marker of the antioxidant defense systems. During bed rest, leucine tended to preserve insulin sensitivity (Change in MCR, CON vs. LEU: -3.5 ± 0.82 vs LEU: -0.98 ± 0.88, p=0.054), but had no effect on mitochondrial respiratory capacity (Change in State 3<sub>+succinate</sub> CON vs. LEU -3.3 ± 6.1 vs. 7.3 ± 4.1 pmol O<sub>2</sub>/sec/mg tissue, p=0.19) Following rehabilitation, leucine increased ATP-linked respiration (CON vs. LEU: -8.9 ± 6.2 vs. 18.8 ± 3.7 pmol O<sub>2</sub>/sec/mg tissue, p=0.014). While the expression of mitochondrial respiratory and antioxidant proteins was not impacted, leucine supplementation preserved specific pathways of mitochondrial respiration, insulin sensitivity and a marker of oxidative stress during bed rest and rehabilitation.
DOI: 10.35092/yhjc.13370915
title: Dataset supporting: Normalizing and denoising protein expression data from droplet-based single cell profiling

description: Data for reproducing analysis in the manuscript:<br>Normalizing and denoising protein expression data from droplet-based single cell profiling<br>link to manuscript: https://www.biorxiv.org/content/10.1101/2020.02.24.963603v1 Data deposited here are for the purposes of reproducing the analysis results and figures reported in the manuscript above. These data are all publicly available downloaded and converted to R datasets prior to Dec 4, 2020. For a full description of all the data included in this repository and instructions for reproducing all analysis results and figures, please see the repository: https://github.com/niaid/dsb_manuscript. For usage of the dsb R package for normalizing CITE-seq data please see the repository: https://github.com/niaid/dsb If you use the dsb R package in your work please cite:<br>Mulè MP, Martins AJ, Tsang JS. Normalizing and denoising protein expression data from droplet-based single cell profiling. bioRxiv. 2020;2020.02.24.963603. General contact: John Tsang (john.tsang AT nih.gov) Questions about software/code: Matt Mulè (mulemp AT nih.gov)
DOI: 10.35092/yhjc.13370915.v1
title: Dataset supporting: Normalizing and denoising protein expression data from droplet-based single cell profiling

description: Data for reproducing analysis in the manuscript:<br>Normalizing and denoising protein expression data from droplet-based single cell profiling<br>link to manuscript: https://www.biorxiv.org/content/10.1101/2020.02.24.963603v1 Data deposited here are for the purposes of reproducing the analysis results and figures reported in the manuscript above. These data are all publicly available downloaded and converted to R datasets prior to Dec 4, 2020. For a full description of all the data included in this repository and instructions for reproducing all analysis results and figures, please see the repository: https://github.com/niaid/dsb_manuscript. For usage of the dsb R package for normalizing CITE-seq data please see the repository: https://github.com/niaid/dsb If you use the dsb R package in your work please cite:<br>Mulè MP, Martins AJ, Tsang JS. Normalizing and denoising protein expression data from droplet-based single cell profiling. bioRxiv. 2020;2020.02.24.963603. General contact: John Tsang (john.tsang AT nih.gov) Questions about software/code: Matt Mulè (mulemp AT nih.gov)
DOI: 10.35092/yhjc.14183108
title: BUAN Demo - DIPY Processed Parkinson's Progression Markers Initiative (PPMI) Data Derivatives

description: DIPY workshop 2021 Bundle Analytics (BUAN) demo data consisting of Parkinson's Progression Markers Initiative (PPMI) data derivatives. It includes Model Arcuate Fasciculus Left (AF_L), extracted AF_L tracts in common space (MNI space) and tracts in the subject's original space. It also includes anatomical measure Fractional Anisotropy (FA) for 5 patient subjects and 5 control subjects.<br>Data was processed using DIPY.<br>This data set was generated by Chandio, B.Q., <i>et al.</i> Bundle analytics, a computational framework for investigating the shapes and profiles of brain pathways across populations. <i>Sci Rep</i> <b>10, </b>17149 (2020) paper.<br>Read paper here: https://rdcu.be/b8rUr
DOI: 10.35092/yhjc.14183108.v1
title: BUAN Demo - DIPY Processed Parkinson's Progression Markers Initiative (PPMI) Data Derivatives

description: DIPY workshop 2021 Bundle Analytics (BUAN) demo data consisting of Parkinson's Progression Markers Initiative (PPMI) data derivatives. It includes Arcuate Fasciculus Left (AF_L) tracts in common space (MNI space) and tracts in the subject's original space. It also includes anatomical measure Fractional Anisotropy (FA) for 5 patient subjects and 5 control subjects.<br>Data was processed using DIPY.<br>This data set was generated by Chandio, B.Q., <i>et al.</i> Bundle analytics, a computational framework for investigating the shapes and profiles of brain pathways across populations. <i>Sci Rep</i> <b>10, </b>17149 (2020) paper.<br>Read paper here: https://rdcu.be/b8rUr
DOI: 10.15156/bio/sh1229258.08fu
title: SH1229258.08FU

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.