cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.35092/yhjc.11368022
title: Genome-wide summary statistics used for the analysis described in 'Rare protein-altering variants in ANGPTL7 lower intraocular pressure and protect against glaucoma'

description: The dataset deposited here contains the genome-wide association summary statistics for 7 traits characterized in UK Biobank described in the following publication:<br>Y. Tanigawa, et al., Rare protein-altering variants in <i>ANGPTL7</i> lower intraocular pressure and protect against glaucoma. PLOS Genetics. 16, e1008682 (2020).<br><br><br>The 7 traits included in the datasets are:- INI5255: Goldmann-correlated IOP (right)- INI5263: Goldmann-correlated IOP (left)- INI2005255: Goldmann-correlated IOP (median)- INI5254: corneal-compensated IOP (right)- INI5262: corneal-compensated IOP (left)- INI2005254: corneal-compensated IOP (median)- HC276: Glaucoma<br>The data files are generated by PLINK 2.0 (https://www.cog-genomics.org/plink/2.0/) and provided as a gzip compressed tab-delimitated table format and its corresponding PLINK log files (text format). Please refer to the PLINK website for the column descriptors of the data files (https://www.cog-genomics.org/plink/2.0/formats#glm_linear and https://www.cog-genomics.org/plink/2.0/formats#glm_logistic).<br>Please read our paper for more information regarding this dataset.<br><br><b>Abstract</b><br>
DOI: 10.35092/yhjc.9693332
title: FCS files to support 4-84 TG6 F3 females IFNg panel (Figs 2, 4, 5) in "The ancestral environment shapes antiviral CD8+ T cell responses across generations"

description: These are FCS files that correspond to data in figures 2, 4, and 5. FCS files to support 4-84 TG6 F3 females IFNg panel (Figs 2, 4, 5)<br>Figure 2: F0 exposure to TCDD has transgenerational effects on the CD8+ T response to IAV in the lung of female offspring. <br>Figure 4: In the MLN, F0 AHR activation alters the CD8+ T cell response of female and male F1 but not F3 generation offspring. <br>Figure 5: Transgenerational effects on the CD8+ T cell response to IAV in the lung are transmitted through both parental lineages. <br>
DOI: 10.35092/yhjc.9693395
title: FCS files to support 4-91 F3 males IFNg panel (Figs 3 & 4) in "The ancestral environment shapes antiviral CD8+ T cell responses across generations"

description: The data in these FCS file corresponds to data in figures 3 and 4. FCS files to support 4-91 F3 males IFNg panel (Figs 3 & 4)<br>Figure 3: F0 exposure to TCDD has developmental, but not transgenerational effects on the CD8+ T response to IAV in the lung of male TCDD lineage offspring. <br>Figure 4: In the MLN, F0 AHR activation alters the CD8+ T cell response of female and male F1 but not F3 generation offspring. <br>
DOI: 10.35092/yhjc.9693452
title: FCS files to support 4-91 F3 males tetramer panel (Figs 3 & 4) in "The ancestral environment shapes antiviral CD8+ T cell responses across generations"

description: These FCS files support 4-91 F3 males tetramer panel (Figs 3 & 4) <br>Figure 3: F0 exposure to TCDD has developmental, but not transgenerational effects on the CD8+ T response to IAV in the lung of male TCDD lineage offspring. <br>Figure 4: In the MLN, F0 AHR activation alters the CD8+ T cell response of female and male F1 but not F3 generation offspring. <br>
DOI: 10.35092/yhjc.9693476
title: FCS files to support 4-99 F1 IFNg panel (Figs 2 & 4) in "The ancestral environment shapes antiviral CD8+ T cell responses across generations"

description: The data in these FCS files support 4-99 F1 IFNg panel (Figs 2 & 4)<br>Figure 2: F0 exposure to TCDD has transgenerational effects on the CD8+ T response to IAV in the lung of female offspring. <br>Figure 4: In the MLN, F0 AHR activation alters the CD8+ T cell response of female and male F1 but not F3 generation offspring. <br>
DOI: 10.35092/yhjc.9695921
title: FCS files to support LB 8-125 females (Figure 6) in "The ancestral environment shapes antiviral CD8+ T cell responses across generations"

description: These FCS files correspond to the female data shown in Figure 6.<br>Figure 6: Lack of AHR in hematopoietic cells has differential effects on CD8+ T cell expansion in female and male offspring during IAV infection. <br>At maturity, 9-11 male or female developmentally exposed Vav1creAhrfx/fx and Ahrfx/fx offspring were infected with IAV. MLN cells were harvested and stained as described in the Methods section.
DOI: 10.35092/yhjc.9995672.v2
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.<br>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: 10.35092/yhjc.9995672.v3
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.<br>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: 10.35092/yhjc.9995672.v4
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.<br>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/sh1229238.08fu
title: SH1229238.08FU

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/sh1229239.08fu
title: SH1229239.08FU

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/th028934
title: TH028934
title: Spirocercidae

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/th028935
title: TH028935
title: Spiruridae

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-c4-49640
title: T. F., letters (1968)

description: T. F., letters (1968)
DOI: 10.35092/yhjc.10324877
title: Eye tracking data for participants with Autism Spectrum Disorders for "Distinct neural mechanisms of social orienting and mentalizing revealed by independent measures of neural and eye movement typicality"

description: Eye movement data, before any preprocessing, from participants with Autism Spectrum Disorder while watching 24 short movie (14s long) movie clips.<br>Dataset structure is participants x movie x direction (horizontal, vertical) x time (sampled at 1000Hz).<br><br>See collection for related datasets: https://doi.org/10.35092/yhjc.c.4741556<br><br>See publication for details: Ramot, M., Walsh, C., Reimann, G.E., & Martin, A. Distinct neural mechanisms of social orienting and mentalizing revealed by independent measures of neural and eye movement typicality. Commun Biol 3, 48 (2020). https://doi.org/10.1038/s42003-020-0771-1<br>
DOI: 10.35092/yhjc.11368022.v1
title: Genome-wide summary statistics used for the analysis described in 'Rare protein-altering variants in ANGPTL7 lower intraocular pressure and protect against glaucoma'

description: The dataset deposited here contains the genome-wide association summary statistics for 7 traits characterized in UK Biobank described in the following publication:<br>Y. Tanigawa, et al., Rare protein-altering variants in <i>ANGPTL7</i> lower intraocular pressure and protect against glaucoma. PLOS Genetics. 16, e1008682 (2020).<br><br><br>The 7 traits included in the datasets are:- INI5255: Goldmann-correlated IOP (right)- INI5263: Goldmann-correlated IOP (left)- INI2005255: Goldmann-correlated IOP (median)- INI5254: corneal-compensated IOP (right)- INI5262: corneal-compensated IOP (left)- INI2005254: corneal-compensated IOP (median)- HC276: Glaucoma<br>The data files are generated by PLINK 2.0 (https://www.cog-genomics.org/plink/2.0/) and provided as a gzip compressed tab-delimitated table format and its corresponding PLINK log files (text format). Please refer to the PLINK website for the column descriptors of the data files (https://www.cog-genomics.org/plink/2.0/formats#glm_linear and https://www.cog-genomics.org/plink/2.0/formats#glm_logistic).<br>Please read our paper for more information regarding this dataset.<br><br><b>Abstract</b><br>
DOI: 10.35092/yhjc.11369166
title: Gene-based test results used for the analysis described in 'Rare protein-altering variants in ANGPTL7 lower intraocular pressure and protect against glaucoma'

description: The dataset deposited here contains the results of gene-based tests for 7 traits characterized in UK Biobank described in the following publication:<br>Y. Tanigawa, et al., Rare protein-altering variants in <i>ANGPTL7</i> lower intraocular pressure and protect against glaucoma. PLOS Genetics. 16, e1008682 (2020).<br><br><br>The 7 traits included in the datasets are:- INI5255: Goldmann-correlated IOP (right)- INI5263: Goldmann-correlated IOP (left)- INI2005255: Goldmann-correlated IOP (median)- INI5254: corneal-compensated IOP (right)- INI5262: corneal-compensated IOP (left)- INI2005254: corneal-compensated IOP (median)- HC276: Glaucoma<br>Please read our paper for more information regarding this dataset.<br><b>Abstract</b><br>
DOI: 10.35092/yhjc.11369166.v1
title: Gene-based test results used for the analysis described in 'Rare protein-altering variants in ANGPTL7 lower intraocular pressure and protect against glaucoma'

description: The dataset deposited here contains the results of gene-based tests for 7 traits characterized in UK Biobank described in the following publication:<br>Y. Tanigawa, et al., Rare protein-altering variants in <i>ANGPTL7</i> lower intraocular pressure and protect against glaucoma. PLOS Genetics. 16, e1008682 (2020).<br><br><br>The 7 traits included in the datasets are:- INI5255: Goldmann-correlated IOP (right)- INI5263: Goldmann-correlated IOP (left)- INI2005255: Goldmann-correlated IOP (median)- INI5254: corneal-compensated IOP (right)- INI5262: corneal-compensated IOP (left)- INI2005254: corneal-compensated IOP (median)- HC276: Glaucoma<br>Please read our paper for more information regarding this dataset.<br><b>Abstract</b><br>
DOI: 10.35092/yhjc.11370204
title: Pooled analysis of radiation hybrids to identify genes for cell growth and paclitaxel action

description: SUMMARYData from bulk segregant analysis of radiation hybrids to identify genes for cell growth and paclitaxel action.<br>LaTeX DOCUMENTSThe LaTeX documents "master.tex", "main.tex" and "supp.tex" are located in the "Words" directory and provide the key to navigate the scripts and data. These documents can be found by search. The pdf output file of the latex documents is "master.pdf".<br>SCRIPTSThe scripts necessary to analyze the data are in the "Data_Figs" directory and can also be found by search. The vast majority of the data files needed to run the scripts are in the directory "RH_pools_workspace_1". A few miscellaneous data files are in the "Data_Figs" directory.<br>FINDING RELEVANT SCRIPTSFor example, the second paragraph in the "Results" section ("master.pdf") commences with the phrase "We created six independent RH pools...", and provides some basic statistics on the RH pools. Searching for this phrase in "main.tex" reveals the names of two R scripts "clone_sem_1.R" and "graph_Human_retent_2.R" above the paragraph. These scripts provide the corresponding results. <br>Inspection of the script "clone_sem_1.R" shows that it uses the data file "clone.txt", while the script "graph_Human_retent_2.R" uses the data files "RH_pool_human_total_align.txt", "RH_pool_hamster_total_align.txt", "RH_human_gseq.txt", "RH_hamster_gseq.txt", "gencode_gtf_ensembl_ucsc_v31.txt", "clone.txt" and "cell_label_info.txt". All these data files can be found in the directory "RH_pools_workspace_1".<br>FINDING RELEVANT PARTS OF SCRIPTSStatistics in the LaTeX documents have been quoted to unrealistic levels of precision, but are rounded in the pdf output file, "master.pdf". However, the redundant digits can be useful. For example, in the last paragraph on pg S12 of the "Supporting Information" (section "Overlap of interaction loci with growth and paclitaxel loci"), we are informed that "There were 15 genes that overlapped between the interaction loci and the 859 unique growth loci (odds ratio = 22.6, P = 8.3 × 10−15, Fisher’s Exact Test)..."<br>Reference to "supp.tex" reveals that the pertinent script is "g_d_comb_fish_2.R" and that the exact P value is "8.281682e-15". Searching "g_d_comb_fish_2.R" for "8.281682e-15" takes the reader to the relevant part of the script.<br>
DOI: 10.35092/yhjc.11370204.v1
title: Pooled analysis of radiation hybrids to identify genes for cell growth and paclitaxel action

description: SUMMARYData from bulk segregant analysis of radiation hybrids to identify genes for cell growth and paclitaxel action.<br>LaTeX DOCUMENTSThe LaTeX documents "master.tex", "main.tex" and "supp.tex" are located in the "Words" directory and provide the key to navigate the scripts and data. These documents can be found by search. The pdf output file of the latex documents is "master.pdf".<br>SCRIPTSThe scripts necessary to analyze the data are in the "Data_Figs" directory and can also be found by search. The vast majority of the data files needed to run the scripts are in the directory "RH_pools_workspace_1". A few miscellaneous data files are in the "Data_Figs" directory.<br>FINDING RELEVANT SCRIPTSFor example, the second paragraph in the "Results" section ("master.pdf") commences with the phrase "We created six independent RH pools...", and provides some basic statistics on the RH pools. Searching for this phrase in "main.tex" reveals the names of two R scripts "clone_sem_1.R" and "graph_Human_retent_2.R" above the paragraph. These scripts provide the corresponding results. <br>Inspection of the script "clone_sem_1.R" shows that it uses the data file "clone.txt", while the script "graph_Human_retent_2.R" uses the data files "RH_pool_human_total_align.txt", "RH_pool_hamster_total_align.txt", "RH_human_gseq.txt", "RH_hamster_gseq.txt", "gencode_gtf_ensembl_ucsc_v31.txt", "clone.txt" and "cell_label_info.txt". All these data files can be found in the directory "RH_pools_workspace_1".<br>FINDING RELEVANT PARTS OF SCRIPTSStatistics in the LaTeX documents have been quoted to unrealistic levels of precision, but are rounded in the pdf output file, "master.pdf". However, the redundant digits can be useful. For example, in the last paragraph on pg S12 of the "Supporting Information" (section "Overlap of interaction loci with growth and paclitaxel loci"), we are informed that "There were 15 genes that overlapped between the interaction loci and the 859 unique growth loci (odds ratio = 22.6, P = 8.3 × 10−15, Fisher’s Exact Test)..."<br>Reference to "supp.tex" reveals that the pertinent script is "g_d_comb_fish_2.R" and that the exact P value is "8.281682e-15". Searching "g_d_comb_fish_2.R" for "8.281682e-15" takes the reader to the relevant part of the script.<br>
DOI: 10.35092/yhjc.11374446
title: Sequencing result of a mixture of synthesized DNA barcodes

description: Raw data for the Nature Protocol manuscript entitled "clonal tracking using embedded viral barcoding and high-throughput sequencing".<br>Embedded viral barcoding in combination with high throughput sequencing is a powerful technology to track single cell clones. It can provide clonal level insights into cellular proliferation, development, differentiation, migration, and treatment efficacy. <br>Here, we present a detailed protocol of a viral barcoding procedure including the creation of barcode libraries, the viral delivery of barcodes, the recovery of barcodes, and the computational analysis of barcode sequencing data. The entire procedure can becompleted within a few weeks. <br>This barcoding method requires cells to be susceptible to viral transduction. It provides high sensitivity and throughput, and allows precise quantification of cellular progeny. It is cost efficient and does not require any advanced skills. It can also be easily adapted to many types of applications, including both in vitro and in vivo experiments.<br><br>
DOI: 10.35092/yhjc.11374446.v1
title: Sequencing result of a mixture of synthesized DNA barcodes

description: Raw data for the Nature Protocol manuscript entitled "clonal tracking using embedded viral barcoding and high-throughput sequencing".<br>Embedded viral barcoding in combination with high throughput sequencing is a powerful technology to track single cell clones. It can provide clonal level insights into cellular proliferation, development, differentiation, migration, and treatment efficacy. <br>Here, we present a detailed protocol of a viral barcoding procedure including the creation of barcode libraries, the viral delivery of barcodes, the recovery of barcodes, and the computational analysis of barcode sequencing data. The entire procedure can becompleted within a few weeks. <br>This barcoding method requires cells to be susceptible to viral transduction. It provides high sensitivity and throughput, and allows precise quantification of cellular progeny. It is cost efficient and does not require any advanced skills. It can also be easily adapted to many types of applications, including both in vitro and in vivo experiments.<br><br>
DOI: 10.35092/yhjc.11400966
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.11400966.v1
title: All Long Peptides Screened by the NCI Surgery Branch for Reactivity Against TIL

description: All starting mutated Nmers used to split into training and validation sets as described in the manuscript titled "Development of a model for ranking candidate HLA class I neoantigens based upon datasets of known neoepitopes"
DOI: 10.35092/yhjc.11400969
title: All Mutated Minimal Peptides Screened by the NCI Surgery Branch for Reactivity Against TIL

description: Mutated Minimal Peptides 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"