Compilation of Aggregate Genomic Data for General Research Use
Ontology highlight
ABSTRACT: This study contains all authorized aggregate genomic data from datasets currently in dbGaP that are approved for general research use (GRU) and have no further limitations beyond those outlined in model Data Use Certification Agreement. Access to this study will be granted for one year and any additional authorized aggregate GRU datasets that become available during this one-year period. This study does not include individual-level data.
REPOSITORIES: dbGaP
Similar Datasets
Project description:<p>This study contains all authorized aggregate genomic data from datasets currently in dbGaP that are approved for general research use (GRU) and have no further limitations beyond those outlined in model Data Use Certification Agreement. Access to this study will be granted for one year and any additional authorized aggregate GRU datasets that become available during this one-year period. This study does not include individual-level data.</p>
| phs000501.v1.p1 | EGA
Project description:<p>This study contains all authorized whole genome sequence data of the HeLa cell line from datasets currently in dbGaP. These data have been approved for health, medical, and/or biomedical research purposes. Access to these data can be granted for one year. Accessible data will include the studies listed on this page and any additional authorized datasets that become available during this one-year period.</p> <p><a href="http://acd.od.nih.gov/hlgda.htm">The HeLa Genome Data Access Working Group of the Advisory Committee to the Director (ACD)</a> will review requests from the research community for access to these datasets and assess whether the requests align with the terms of use defined in the HeLa Genome Data Use Agreement. The Working Group's findings will be reported to the ACD, and the ACD will make recommendations to the NIH Director about whether a request should be approved or disapproved. The NIH Director will decide whether access to the data will be granted.</p>
| phs000640 | dbGaP
Project description:<p>This study contains all authorized whole genome sequence data of the HeLa cell line from datasets currently in dbGaP. These data have been approved for health, medical, and/or biomedical research purposes. Access to these data can be granted for one year. Accessible data will include the studies listed on this page and any additional authorized datasets that become available during this one-year period.</p> <p><a href="http://acd.od.nih.gov/hlgda.htm">The HeLa Genome Data Access Working Group of the Advisory Committee to the Director (ACD)</a> will review requests from the research community for access to these datasets and assess whether the requests align with the terms of use defined in the HeLa Genome Data Use Agreement. The Working Group's findings will be reported to the ACD, and the ACD will make recommendations to the NIH Director about whether a request should be approved or disapproved. The NIH Director will decide whether access to the data will be granted.</p>
| phs000640.v1.p1 | EGA
Project description:We have established a certification system for antibodies to be used in chromatin immunoprecipitation assays coupled to massive parallel sequencing (ChIP-seq). This certification comprises a standardized ChIP procedure and the attribution of a numerical quality control indicator (QCi) to biological replicate experiments. The QCi computation is based on a universally applicable quality assessment that quantitates the global deviation of randomly sampled subsets of ChIP-seq dataset with the original genome-aligned sequence reads. Comparison with a QCi database for >28,000 ChIP-seq assays were used to attribute quality grades (ranging from âAAAâ to âDDDâ) to a given dataset. In the present report we used the numerical QC system to assess the factors influencing the quality of ChIP-seq assays, including the nature of the target, the sequencing depth and the commercial source of the antibody. We have used this approach specifically to certify mono and polyclonal antibodies obtained from Active Motif directed against the histone modification marks H3K4me3, H3K27ac and H3K9ac for ChIP-seq. The antibodies received the grades AAA to BBA (www.ngs-qc.org). We propose to attribute such quantitative grading of all antibodies attributed with the label âChIP-seq gradeâ. The NGS-QC team has setup a certification procedure for Antibodies dedicated to ChIP-seq applications based on (1)a highly reproducible pipeline for performing ChIP-seq assays including the use of an automated liquid handling platform (TECAN EVO75) for ChIP and sequencing library preparation steps and (2) the use of the NGS-QC Generator tool for providing a ChIP-seq quality grade and to evaluate other parameters like the Optimal sequencing depth, the degree of reproducibility among biological replicates and the comparison of the quality grades of the certified antibody with datasets retrieved in the public domain. This quality certification structure makes possible to assign a measured Quality Stamp to ChIP-seq grade Antibodies.
2016-01-07 | E-GEOD-76618 | biostudies-arrayexpress
Project description:Antibody performance in ChIP-sequencing assays: From quality scores of public datasets to quantitative certification
| PRJNA308201 | ENA
Project description:The cattle industry is the largest of the agricultural commodities in the United States and generated over $101 billion in farm cash receipts during 2016; 28% of all US farm cash receipts. Although the sequence of the bovine reference genome has been publicly available since 2009, annotation of functional genome elements is largely incomplete, resulting in limitations for exploiting the genome to phenome relationship. This project generate high-quality detailed transcript and miRNA status datasets from a comprehensive set of bovine tissues, developmental stages, and cells through a set of rationally selected assays.
2022-07-30 | E-MTAB-11699 | biostudies-arrayexpress
Project description:This experiment aimed to identify protoplast-induced genes in tomato seedling, for future use in single cell datasets.
2023-10-01 | GSE212404 | GEO
Project description:High throughput single-cell RNA sequencing (sc-RNAseq) has become a frequently used tool to assess immune cell function and heterogeneity. Recently, the combined measurement of RNA and protein expression by sequencing was developed, which is commonly known as CITE-Seq. Acquisition of protein expression data along with transcriptome data resolves some of the limitations inherent to only assessing transcript, but also nearly doubles the sequencing read depth required per single cell. Furthermore, there is still a paucity of analysis tools to visualize combined transcript-protein datasets. Here, we describe a novel targeted transcriptomics approach that combines analysis of over 400 genes with simultaneous measurement of over 40 proteins on more than 25,000 cells. This targeted approach requires only about 1/10 of the read depth compared to a whole transcriptome approach while retaining high sensitivity for low abundance transcripts. To analyze these multi-omic transcript-protein datasets, we adapted One-SENSE for intuitive visualization of the relationship of proteins and transcripts on a single-cell level.
2020-03-30 | GSE135325 | GEO
Project description:Although the data-independent acquisition (DIA) technology and the related algorithms are developing rapidly, the results of DIA data analyzing without the use of spectra library from data-dependent acquisition (DDA) data remain unsatisfying. Here we proposed an untargeted analysis method, Dear-DIA-XMBD, for directly analyzing DIA data. Dear-DIA-XMBD first integrates the deep variational autoencoder and triplet loss to learn the representations of extracted ion chromatograms of fragments, then uses the k-means clustering algorithm to aggregate fragments with similar representations into the same classes, and finally establishes the inverted index tables to determine the precursor of fragment cluster between precursors and peptides, and between fragments and peptides. We show that Dear-DIA-XMBD performs superiorly with the highly complicated DIA data of different species obtained by different instrument platforms. We trained the Dear-DIA-XMBD on an Escherichia coli SWATH dataset. We benchmarked the performance of Dear-DIA-XMBD by using the highly complicated sample datasets, which consist of L929 mouse dataset, SWATH-MS Gold Standard (SGS) human dataset (PeptideAtlas PASS00289), HYE124 dataset (PXD002952) with 64 variable windows (TripleTOF 6600).
2021-11-11 | PXD029694 | iProX