Homo sapiens
Ontology highlight
ABSTRACT:
PROVIDER: PRJNA269761 | ENA |
REPOSITORIES: ENA
Similar Datasets
Project description:Oscillatory gene expression is fundamental to mammalian development, but technologies to monitor expression oscillations are limited. We have developed a statistical approach called Oscope to identify and characterize the transcriptional dynamics of oscillating genes in single-cell RNA-seq data from an unsynchronized cell population. Applications to a number of data sets, include a single-cell RNA-seq data set of human embroyonic stem cells (hESCs), demonstrate advantages of the approach and also identify a potential artifact in the Fluidigm C1 platform. Total 213 H1 single cells and 247 H1-Fucci single cells were sequenced. The 213 H1 cells were used to evaluate Oscope in identifying oscillatory genes. The H1-Fucci cells were used to confirm the cell cycle gene cluster identified by Oscope in the H1 hESCs.
2015-06-24 | E-GEOD-64016 | biostudies-arrayexpress
Project description:Oscillatory gene expression is fundamental to mammalian development, but technologies to monitor expression oscillations are limited. We have developed a statistical approach called Oscope to identify and characterize the transcriptional dynamics of oscillating genes in single-cell RNA-seq data from an unsynchronized cell population. Applications to a number of data sets, include a single-cell RNA-seq data set of human embroyonic stem cells (hESCs), demonstrate advantages of the approach and also identify a potential artifact in the Fluidigm C1 platform.
2015-06-24 | GSE64016 | GEO
Project description:Single cell RNA-seq data of human hESCs to evaluate SCnorm: robust normalization of single-cell rna-seq data
| PRJNA339754 | ENA
Project description:RNA-Sequencing is a transformative method that captures the quantitative dynamics of a transcriptome with exquisite sensitivity and single-base resolution. There are, however, few computational pipelines for RNA-Seq with statistical tests that evince sufficient robustness and power as demanded by the difficult combination of small sample sizes and high variability in sequence read counts. To this end, we developed GENE-counter, a complete software pipeline for analyzing RNA-Seq data for genome-wide expression differences between replicated treatment groups. One important component of GENE-counter is a statistical test based on the NBP parameterization of the negative binomial distribution for identifying differentially expressed genome features. We used GENE-counter to analyze RNA-Seq data derived from Arabidopsis thaliana infected with a strain of defense-eliciting bacteria. We identified 308 genes that were differentially induced. Using alternative methods, we provided support for the induced expression and biological relevance of a substantial proportion of the genes. These results suggest the NBP parameterization of the negative binomial distribution is well suited for explaining RNA-Seq data and the statistical test makes GENE-counter a powerful pipeline for studying genome-wide expression changes. GENE-counter is freely available at http://changlab.cgrb.oregonstate.edu/. Our RNA-seq data is uploaded on the NCBI short read archive (SRA) under the SRA025952.
2010-12-03 | GSE25818 | GEO
Project description:RNA-Sequencing is a transformative method that captures the quantitative dynamics of a transcriptome with exquisite sensitivity and single-base resolution. There are, however, few computational pipelines for RNA-Seq with statistical tests that evince sufficient robustness and power as demanded by the difficult combination of small sample sizes and high variability in sequence read counts. To this end, we developed GENE-counter, a complete software pipeline for analyzing RNA-Seq data for genome-wide expression differences between replicated treatment groups. One important component of GENE-counter is a statistical test based on the NBP parameterization of the negative binomial distribution for identifying differentially expressed genome features. We used GENE-counter to analyze RNA-Seq data derived from Arabidopsis thaliana infected with a strain of defense-eliciting bacteria. We identified 308 genes that were differentially induced. Using alternative methods, we provided support for the induced expression and biological relevance of a substantial proportion of the genes. These results suggest the NBP parameterization of the negative binomial distribution is well suited for explaining RNA-Seq data and the statistical test makes GENE-counter a powerful pipeline for studying genome-wide expression changes. GENE-counter is freely available at http://changlab.cgrb.oregonstate.edu/. Our RNA-seq data is uploaded on the NCBI short read archive (SRA) under the SRA025952. 6 samples total. Two treatments with three biological replicates each. MgCl2 is the mock treatment, and hrcC is the experimental treatment.
2010-12-03 | E-GEOD-25818 | biostudies-arrayexpress
Project description:Human pluripotent stem cells (hPSCs) offer a unique cellular model to study lineage specifications of the primary germ layers during human development. We profiled single-cell RNA-seq (scRNA-seq) on four lineage-specific progenitor cells derived from hESCs. Our scRNA-seq analyses revealed each type of progenitors display various extend of heterogeneity. Specifically, definitive endoderm cells (DECs) not only show a greater degree of heterogeneity, but are also enriched in metabolic signatures. Followed by detailed temporal scRNA-seq profiling along DEC differentiation, we reconstructed a differentiation trajectory using a novel statistical pipeline named Wave-Crest. Wave-Crest further identifies candidate regulators during the transitioning phase from Brachyury (T)+ mesendoderm towards CXCR4+ DEC state. To functionally test identified novel regulators; we generated a live cell monitoring system, a T-2A-EGFP knock-in reporter cell line via CRISPR/CAS9. We demonstrated that, among the top candidate genes, KLF8 plays a pivotal role modulating mesendoderm to DEC differentiation. In this submission, 1810 raw fastq files are provided; 212 are re-analysis from GSE64016. Four expected count matrices are provided - 1) 1018 single cells from snapshot progenitors; 2) 758 single cells from time couse profiling; 3) 19 bulk RNA-seq sample from snapshot progenitors; 4) 15 bulk RNA-seq sample from time course profiling. Total 1018 single cells from snapshot progenitors and 758 single cells from time couse profiling. Matchd population bulk RNA-seq samples for both the progenitors snapshot (19 samples) and time course profiling (15 samples) also included in this submission. These data set are used to detect the transitioning phase from mesendoderm to definitive endoderm.
2016-07-21 | E-GEOD-75748 | biostudies-arrayexpress
Project description:Human pluripotent stem cells (hPSCs) offer a unique cellular model to study lineage specifications of the primary germ layers during human development. We profiled single-cell RNA-seq (scRNA-seq) on four lineage-specific progenitor cells derived from hESCs. Our scRNA-seq analyses revealed each type of progenitors display various extend of heterogeneity. Specifically, definitive endoderm cells (DECs) not only show a greater degree of heterogeneity, but are also enriched in metabolic signatures. Followed by detailed temporal scRNA-seq profiling along DEC differentiation, we reconstructed a differentiation trajectory using a novel statistical pipeline named Wave-Crest. Wave-Crest further identifies candidate regulators during the transitioning phase from Brachyury (T)+ mesendoderm towards CXCR4+ DEC state. To functionally test identified novel regulators; we generated a live cell monitoring system, a T-2A-EGFP knock-in reporter cell line via CRISPR/CAS9. We demonstrated that, among the top candidate genes, KLF8 plays a pivotal role modulating mesendoderm to DEC differentiation. In this submission, 1810 raw fastq files are provided; 212 are re-analysis from GSE64016. Four expected count matrices are provided - 1) 1018 single cells from snapshot progenitors; 2) 758 single cells from time couse profiling; 3) 19 bulk RNA-seq sample from snapshot progenitors; 4) 15 bulk RNA-seq sample from time course profiling.
2016-07-21 | GSE75748 | GEO
Project description:SHARE-seq single-cell RNA-seq data profiling hESCs overexpressing transcription factors
2023-01-05 | GSE217460 | GEO
Project description:Transcription factors (TFs) can relay signals through temporal alterations in their levels. The cell stress responsive TF p53 exhibits different dynamics depending on the type of stress, which influence the magnitude and dynamics of expression of its target genes and subsequent cellular outcomes. The mechanisms decoding p53 dynamics into levels and dynamics of RNA and protein of its targets remain unclear. We systematically quantified p53 target mRNA and protein levels over time under two p53 dynamical regimes – oscillatory and sustained – using RNA-seq and quantitative mass spectrometry. We categorized the mRNA dynamical patterns arising from oscillatory or sustained p53 expression, as well as the corresponding protein dynamics. We found that in both cases oscillatory dynamics allowed for the greatest variety of dynamical patterns of the downstream species. Target proteins from a wide range of functional categories were induced under both p53 dynamic conditions, with induction levels being overall higher under sustained dynamics. Mathematical modeling combined with analysis of empirical data revealed three mechanisms of decoding p53 dynamics: adjustment of mRNA and protein degradation rates, adjustment of activation thresholds for expression of mRNA and corresponding protein levels, and usage of coherent and incoherent feed-forward loop motifs in generating exclusive responses to p53 oscillatory or sustained dynamics, respectively. Our results highlight the diversity of mechanisms that decode complex TF dynamics into dynamical patterns of mRNA and proteins.
2022-02-11 | PXD027030 | Pride
Project description:SHARE-seq single-cell ATAC- and RNA-seq data profiling hESCs overexpressing transcription factors
2023-01-05 | GSE217215 | GEO