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: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
Project description:A large number of computational methods have been recently developed for analyzing differential gene expression (DE) in RNA-seq data. We report on a comprehensive evaluation of the commonly used DE methods using the SEQC benchmark data set and data from ENCODE project. We evaluated a number of key features including: normalization, accuracy of DE detection and DE analysis when one condition has no detectable expression. We found significant differences among the methods. Furthermore, computational methods designed for DE detection from expression array data perform comparably to methods customized for RNA-seq. Most importantly, our results demonstrate that increasing the number of replicate samples significantly improves detection power over increased sequencing depth. The Sequencing Quality Control Consortium generated two datasets from two reference RNA samples in order to evaluate transcriptome profiling by next-generation sequencing technology. Each sample contains one of the reference RNA source and a set of synthetic RNAs from the External RNA Control Consortium (ERCC) at known concentrations. Group A contains 5 replicates of the Strategene Universal Human Reference RNA (UHRR), which is composed of total RNA from 10 human cell lines, with 2% by volume of ERCC mix 1. Group B includes 5 replicate samples of the Ambion Human Brain Reference RNA (HBRR) with 2% by volume of ERCC mix 2. The ERCC spike-in control is a mixture of 92 synthetic polyadenylated oligonucleotides of 250-2000 nucleotides long that are meant to resemble human transcripts.
2013-08-20 | E-GEOD-49712 | biostudies-arrayexpress
Project description:Additional toxicity testing and regulation are needed as the number of compounds chronically released into aquatic coastal environments increases. Decision makers require comprehensive and efficient screening tools to detect adverse biological effects and to identify their cause. Transcriptomic analysis can complement traditional ecotoxicology data by providing mechanistic insight, identifying sub-lethal organismal responses and identifying contaminant classes underlying observed toxicity. Before transcriptomic information is used in monitoring and risk assessment, it is necessary to determine its reproducibility and detect key steps that impact the reliable identification of differentially expressed genes. A custom 15K-probe microarray was used to conduct transcriptomics analyses across six laboratories. Estuarine amphipods were exposed to cyfluthrin-spiked or control sediments for 10 days. Two sample types were generated: the first consisted of total RNA extracts (Ex) from exposed and control samples (extracted by one laboratory) and the second type consisted of exposed and control whole body amphipods (WB) from which RNA was extracted by each laboratory. A subset of genes were consistently identified as differentially expressed across all laboratories and sample types. Genes with the highest magnitude of differential expression (> 2 fold) were more likely to be consistently identified as differentially expressed across laboratories. Differentially expressed data had a higher degree of concordance across all laboratories (W = 0.7) in samples with similar RNA quality (Ex) when compared to WB samples (W = 0.5). Our results revealed that several factors can affect data comparability including RNA sample preparation, labeling, instrumentation and technical expertise.
2015-01-26 | GSE64206 | GEO
Project description:Additional toxicity testing and regulation are needed as the number of compounds chronically released into aquatic coastal environments increases. Decision makers require comprehensive and efficient screening tools to detect adverse biological effects and to identify their cause. Transcriptomic analysis can complement traditional ecotoxicology data by providing mechanistic insight, identifying sub-lethal organismal responses and identifying contaminant classes underlying observed toxicity. Before transcriptomic information is used in monitoring and risk assessment, it is necessary to determine its reproducibility and detect key steps that impact the reliable identification of differentially expressed genes. A custom 15K-probe microarray was used to conduct transcriptomics analyses across six laboratories. Estuarine amphipods were exposed to cyfluthrin-spiked or control sediments for 10 days. Two sample types were generated: the first consisted of total RNA extracts (Ex) from exposed and control samples (extracted by one laboratory) and the second type consisted of exposed and control whole body amphipods (WB) from which RNA was extracted by each laboratory. A subset of genes were consistently identified as differentially expressed across all laboratories and sample types. Genes with the highest magnitude of differential expression (> 2 fold) were more likely to be consistently identified as differentially expressed across laboratories. Differentially expressed data had a higher degree of concordance across all laboratories (W = 0.7) in samples with similar RNA quality (Ex) when compared to WB samples (W = 0.5). Our results revealed that several factors can affect data comparability including RNA sample preparation, labeling, instrumentation and technical expertise.
2015-01-26 | GSE64205 | GEO