E-MTAB-8621
Ontology highlight
ABSTRACT:
PROVIDER: E-MTAB-8621 | ExpressionAtlas | 2020-01-13
REPOSITORIES: ExpressionAtlas
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Project description:RNA-seq of yeast cell cycle grown on ethanol, used for absolute gene expression quantification
| PRJEB35903 | ENA
Project description:RNA-seq of yeast cell cycle, used for absolute gene expression quantification
| PRJEB35742 | ENA
Project description:Flis2015 - Plant clock gene circuit
(P2011.1.2 PLM_71 ver 1)
This model is described in the article:
Defining the robust
behaviour of the plant clock gene circuit with absolute RNA
timeseries and open infrastructure.
Flis A, Fernández AP, Zielinski
T, Mengin V, Sulpice R, Stratford K, Hume A, Pokhilko A, Southern
MM, Seaton DD, McWatters HG, Stitt M, Halliday KJ, Millar
AJ.
Open Biol 2015 Oct; 5(10):
Abstract:
Our understanding of the complex, transcriptional feedback
loops in the circadian clock mechanism has depended upon
quantitative, timeseries data from disparate sources. We
measure clock gene RNA profiles in Arabidopsis thaliana
seedlings, grown with or without exogenous sucrose, or in
soil-grown plants and in wild-type and mutant backgrounds. The
RNA profiles were strikingly robust across the experimental
conditions, so current mathematical models are likely to be
broadly applicable in leaf tissue. In addition to providing
reference data, unexpected behaviours included co-expression of
PRR9 and ELF4, and regulation of PRR5 by GI. Absolute RNA
quantification revealed low levels of PRR9 transcripts (peak
approx. 50 copies cell(-1)) compared with other clock genes,
and threefold higher levels of LHY RNA (more than 1500 copies
cell(-1)) than of its close relative CCA1. The data are
disseminated from BioDare, an online repository for focused
timeseries data, which is expected to benefit mechanistic
modelling. One data subset successfully constrained clock gene
expression in a complex model, using publicly available
software on parallel computers, without expert tuning or
programming. We outline the empirical and mathematical
justification for data aggregation in understanding highly
interconnected, dynamic networks such as the clock, and the
observed design constraints on the resources required to make
this approach widely accessible.
cL_m_degr, param m1, modified to ensure light rate > dark rate.
This model is hosted on
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MODEL1510190000.
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2024-09-02 | BIOMD0000000597 | BioModels
Project description:Multi-omic absolute quantitative analysis of the yeast (Saccharomyces cerevisiae) mitotic cell cycle. Transcriptomics (RNA-Seq), proteomics (SILAC/ iBAQ), phosphoproteomics (SILAC/ iBAQ, enrichment with TiO2), and untargeted metabolomics (Metabolon, Inc.) were performed, all in biological triplicate. Three sub-projects from this central project were generated, in order of priority: (1) growth on glucose (n= 30 samples) (2) growth on ethanol (n= 21) and (3) pheromone effect (n= 51 samples; combined glucose and ethanol samples). For each sample, every omic type was analysed (n=4; transcriptomic, proteomic, phosphoproteomic, and metabolomic). Total omic samples generated for project = 204.
2020-03-26 | PXD016519 | Pride
Project description:RNA-seq of yeast cell cycle grown on glucose, synchronized with CDC15-2.
| PRJEB35940 | ENA
Project description:The sexual cycle of Ustilago maydis includes the succession of a saprophytic haploid yeast stage to a virulent hyphal dikaryotic stage, product of the mating of sexually compatible yeast cells. This dimorphic transition may be replicated in vitro by different means. Recently, it was shown that ethanol induced filamentous growth on solid medium, and we observed that this process also occurred in liquid media. Since the utilization of a six- or a two-carbon source involves different metabolic pathways, we considered that the morphogenetic process might be associated with this alteration. Accordingly, we analyzed the transcriptome of U. maydis grown in glucose or in ethanol using the Illumina RNA Seq. Around 18 million reads were obtained from each treatment. From the 6788 U. maydis genes, 542 were differentially expressed (158 upregulated and 384 downregulated) during incubation with ethanol compared to glucose-grown cells, revealing a noticeable alteration in the metabolism of the fungus through their adaptation to either carbon source. The present phenomenon is a further example of how an alteration in a two-dimensional mechanism (metabolism) can affect a three-dimensional process (cell morphogenesis).
2021-01-18 | GSE164981 | GEO
Project description:To determine the contribution of yeast genes to acute lethal ethanol stress, we performed fitness profiling of a pooled haploid yeast deletion library. This deletion library contains 4,500 deletions of most non-essential yeast genes. Comparing the abundance of post-stress gene deletions to pre-stress gene deletions allows for the quantification of the effects of each gene deletion on survival
2020-09-17 | GSE151784 | GEO
Project description:RNA-seq of yeast cell cycle grown on glucose, synchronized with CDC15-2.
2020-01-10 | E-MTAB-8626 | ExpressionAtlas
Project description:We use Saccharomyces cerevisiae grown on ethanol to perform absolute quantitative multi-omics analysis to map interactions of different cellular processes during the yeast cell cycle.
2021-11-01 | E-MTAB-8621 | biostudies-arrayexpress
Project description:Data analysis is a critical part of quantitative proteomics studies in interpreting biological questions. Numerous computational tools including protein quantification, imputation, and differential expression (DE) analysis were generated in the past decade. However, searching optimized tools is still an unsolved issue. Moreover, due to the rapid development of RNA-Seq technology, a vast number of DE analysis methods are created. Applying these newly developed RNA-Seq-oriented tools to proteomics data is still a question that needs to be addressed. In order to benchmark these analysis methods, a proteomics dataset constituted the proteins derived from human, yeast, and drosophila with different ratios were generated. Based on this dataset, DE analysis tools (including array-based and RNA-Seq based), imputation algorithms, and protein quantification methods were compared and benchmarked. This study provided useful information on analyzing quantitative proteomics datasets. All the methods used in this study were integrated into Perseus which are available at https://www.maxquant.org/perseus.
2022-11-16 | PXD029891 | JPOST Repository