Homo sapiens
Ontology highlight
ABSTRACT:
OTHER RELATED OMICS DATASETS IN: GSE47774
PROVIDER: PRJNA243413 | ENA |
REPOSITORIES: ENA
Similar Datasets
Project description:A comprehensive assessment of RNA-seq accuracy, reproducibility and information content by the Sequence Quality Control consortium
| PRJNA208369 | ENA
Project description:nCounter miRNA Expression Assay data for all profiled miRQC samples prepared by the miRQC Consortium. In this study, we systematically compared 12 commercially available miRNA expression platforms by measuring an identical set of standardized positive and negative control samples, including human universal reference RNA, human brain RNA and titrations thereof, human serum samples, and synthetic spikes from miRNA family members with varying homology. We developed novel and robust quality metrics to objectively assess platform performance of very different technologies such as small RNA sequencing, RT-qPCR and (microarray) hybridization. We assessed reproducibility, sensitivity, accuracy, specificity, and concordance of differential expression. miRQC Consortium multi-platform comparision. *This represents the NanoString component only
2013-12-30 | E-GEOD-49600 | biostudies-arrayexpress
Project description:nCounter miRNA Expression Assay data for all profiled miRQC samples prepared by the miRQC Consortium. In this study, we systematically compared 12 commercially available miRNA expression platforms by measuring an identical set of standardized positive and negative control samples, including human universal reference RNA, human brain RNA and titrations thereof, human serum samples, and synthetic spikes from miRNA family members with varying homology. We developed novel and robust quality metrics to objectively assess platform performance of very different technologies such as small RNA sequencing, RT-qPCR and (microarray) hybridization. We assessed reproducibility, sensitivity, accuracy, specificity, and concordance of differential expression.
2013-12-30 | GSE49600 | GEO
Project description:Comparison to RNA-Seq showed different strengths and weaknesses for different regimes of expression strength. At sufficient read-depth, both platforms seemed comparable for gene-level expression profiling. Comparison to RNA-Seq showed comparable accuracy and precision with microarrays for ERCC spike-ins at medium to high expression levels. At low expression levels, the array showed signal attenuation but better precision, while RNA-Seq maintained accuracy albeit with highly inflated variance. In summary, both platforms can be meaningfully applied in a similar range of expression strength. Assessment of (precision, accuracy, reproducibility, mutual information, titration order consistency, and known mixing ratio recovery) by measurement of known-ratio mixtures of two RNA reference samples. The array probes 776 complex genes representative of the AceView gene model annotation, as well as 92 ERCC spike-in controls.
2015-04-03 | E-GEOD-48016 | biostudies-arrayexpress
Project description:The International Council on Harmonization (ICH) S7B and E14 regulatory guidelines are sensitive but not specific for predicting which drugs are pro‐arrhythmic. In response, the Comprehensive In Vitro Proarrhythmia Assay (CiPA) was proposed that integrates multi‐ion channel pharmacology data in vitro into a human cardiomyocyte model in silico for proarrhythmia risk assessment. Previously, we reported the model optimization and proarrhythmia metric selection based on CiPA training drugs. In this study, we report the application of the prespecified model and metric to independent CiPA validation drugs. Over two validation datasets, the CiPA model performance meets all pre‐specified measures for ranking and classifying validation drugs, and outperforms alternatives, despite some in vitro data differences between the two datasets due to different experimental conditions and quality control procedures. This suggests that the current CiPA model/metric may be fit for regulatory use, and standardization of experimental protocols and quality control criteria could increase the model prediction accuracy even further.
2021-11-26 | MODEL2111260002 | BioModels
Project description:We present a multi-platform assessment and a global resource for epigenetics research from the FDA's Epigenomics Quality Control (EpiQC) Group. The study design leverages seven human cell lines that are designated as reference materials and publicly available from the National Institute of Standards and Technology (NIST) and Genome in a Bottle (GIAB) consortium. These samples were subject to a variety of genome-wide methylation interrogation approaches across six independent laboratories, with a primary focus on 5-methylcytosine modifications.
2021-10-26 | GSE186383 | GEO
Project description:We present a multi-platform assessment and a global resource for epigenetics research from the FDA's Epigenomics Quality Control (EpiQC) Group. The study design leverages seven human cell lines that are designated as reference materials and publicly available from the National Institute of Standards and Technology (NIST) and Genome in a Bottle (GIAB) consortium. These samples were subject to a variety of genome-wide methylation interrogation approaches across six independent laboratories, with a primary focus on 5-methylcytosine modifications.
2023-04-21 | GSE230132 | GEO
Project description:Proteome studies using mass spectrometry (MS)-based quantification is a main approach for the discovery of new biomarkers. However, a number of analytical conditions in front and during MS data acquisition can affect the accuracy of the obtained outcome. Therefore, comprehensive quality assessment of the acquired data plays a central role in quantitative proteomics, though due to immense complexity of MS data it is often neglected. Here, we practically address the quality assessment of quantitative MS data describing key steps for the evaluation including the levels: raw data, identification and quantification. With this, four independent datasets from cerebrospinal fluid, an important biofluid for neurodegenerative diseases biomarker studies, were assessed demonstrating that already sample processing-based differences are reflected on all three levels but with a varying impact on the quality of the quantitative data.
2023-04-12 | PXD037650 | Pride
Project description:Enhanced cross-linking immunoprecipitation (eCLIP) featuring a size-matched input control has been recently applied to profile the binding sites of more than one hundred RNA binding proteins (RBPs). However computational pipelines and quality control metrics needed to process CLIP data at scale have yet to be well defined. Here, we describe our ENCODE eCLIP processing pipeline (https://github.com/YeoLab/eclip), enabling users to go from raw reads to processed peaks that are enriched above paired input, reproducible across biological replicates, and can be directly compared against the public ENCODE eCLIP resource. In particular, we discuss processing steps designed to address common artifacts, including properly quantifying unique RNA fragments bound by both unique genomic- and repetitive element-mapped reads. Using manual quality annotation of 350 ENCODE eCLIP experiments, we develop metrics for quality assessment of eCLIP experiments prior to and after sequencing, including library yield, number of unique fragments in the library, total binding relative information, and biological reproducibility. In particular, we quantify the commonly believed linkage between depth of sequencing and peak discovery, and derive methods for estimating required sequencing depth based on pre-sequencing metrics. Finally we provide recommendations for the common question of integrating RBP binding information with RNA-seq to generate splicing maps representing the positional effect of binding on alternative splicing. These pipelines and QC metrics enable large-scale processing and analysis of eCLIP data, and will help to standardize rigorous analysis of RBP binding data.
2018-06-08 | GSE107767 | GEO
Project description:Enhanced cross-linking immunoprecipitation (eCLIP) featuring a size-matched input control has been recently applied to profile the binding sites of more than one hundred RNA binding proteins (RBPs). However computational pipelines and quality control metrics needed to process CLIP data at scale have yet to be well defined. Here, we describe our ENCODE eCLIP processing pipeline (https://github.com/YeoLab/eclip), enabling users to go from raw reads to processed peaks that are enriched above paired input, reproducible across biological replicates, and can be directly compared against the public ENCODE eCLIP resource. In particular, we discuss processing steps designed to address common artifacts, including properly quantifying unique RNA fragments bound by both unique genomic- and repetitive element-mapped reads. Using manual quality annotation of 350 ENCODE eCLIP experiments, we develop metrics for quality assessment of eCLIP experiments prior to and after sequencing, including library yield, number of unique fragments in the library, total binding relative information, and biological reproducibility. In particular, we quantify the commonly believed linkage between depth of sequencing and peak discovery, and derive methods for estimating required sequencing depth based on pre-sequencing metrics. Finally we provide recommendations for the common question of integrating RBP binding information with RNA-seq to generate splicing maps representing the positional effect of binding on alternative splicing. These pipelines and QC metrics enable large-scale processing and analysis of eCLIP data, and will help to standardize rigorous analysis of RBP binding data.
2018-06-08 | GSE107766 | GEO