Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Time series study of ABA-treated Arabidopsis thaliana rosettes

ABSTRACT: This SuperSeries is composed of the SubSeries listed below.

ORGANISM(S): Arabidopsis thaliana

PROVIDER: GSE267649 | GEO | 2024/06/24

REPOSITORIES: GEO

ACCESS DATA

Json Xml

Dataset's files

Source:

			Action	DRS
		Other

Items per page:

1 - 1 of 1

Similar Datasets

Time series study of ABA-treated Arabidopsis thaliana rosettes

Project description:Time series study of ABA-treated Arabidopsis thaliana rosettes

| PRJNA1112323 | ENA

Time series study of ABA-treated Arabidopsis thaliana rosettes [Illumina TruSeq mRNA Sample Prep Kit]

Project description:The hormone abscisic acid (ABA) regulates many plant stress responses and plant development by activating a complex gene regulatory network. This time series experiment was designed to unravel the architecture and dynamics of the ABA gene regulatory network.

2024-06-24 | GSE267647 | GEO

Time series study of ABA-treated Arabidopsis thaliana rosettes [Illumina TruSeq mRNA Stranded Sample Prep Kit]

2024-06-24 | GSE267648 | GEO

Genetic architecture of variation in Arabidopsis thaliana rosettes.

Project description:Rosette morphology across Arabidopsis accessions exhibits considerable variation. Here we report a high-throughput phenotyping approach based on automatic image analysis to quantify rosette shape and dissect the underlying genetic architecture. Shape measurements of the rosettes in a core set of Recombinant Inbred Lines from an advanced mapping population (Multiparent Advanced Generation Inter-Cross or MAGIC) derived from inter-crossing 19 natural accessions. Image acquisition and analysis was scaled to extract geometric descriptors from time stamped images of growing rosettes. Shape analyses revealed heritable morphological variation at early juvenile stages and QTL mapping resulted in over 116 chromosomal regions associated with trait variation within the population. Many QTL linked to variation in shape were located near genes related to hormonal signalling and signal transduction pathways while others are involved in shade avoidance and transition to flowering. Our results suggest rosette shape arises from modular integration of sub-organ morphologies and can be considered a functional trait subjected to selective pressures of subsequent morphological traits. On an applied aspect, QTLs found will be candidates for further research on plant architecture.

| S-EPMC8849614 | biostudies-literature

Time-series analysis of the transcriptome of the re-establishment of desiccation tolerance by ABA in germinated Arabidopsis thaliana seeds.

Project description:Expression analyses of time series have become a very popular method for studying the dynamics of a wide range of biological processes. Here, we present expression analysis of a time series with the help of microarrays used to study the re-establishment of desiccation tolerance (DT) in germinated Arabidopsis thaliana seeds. Mature seeds of A. thaliana are desiccation tolerant (survive the loss of most of their water content), but they become desiccation sensitive while progressing to germination. Yet, there is a small developmental window during which DT can be re-established by treatment with the plant hormone abscisic acid (ABA). We studied germinated A. thaliana seeds at the stage of radicle protrusion during ABA incubation for 0 h, 2 h, 12 h, 24 h and 72 h. We describe in detail the methodology applied for generating and analyzing this expression data of time series. The microarray raw data (http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE62876) may be valuable for further studies on this experimental system, such as the construction of a gene co-expression network [1].

| S-EPMC4583984 | biostudies-literature

Arabidopsis thaliana growth time series

Project description:Transcriptional profiling of A. thaliana seedlings (early development) at 7 time points We investigate biomass heterosis by scoring partial correlations of transcriptional profiles.

2011-07-07 | GSE30398 | GEO

Time-series of the re-establishment of desiccation tolerance by ABA in germinated Arabidopsis thaliana seeds

Project description:Mature seeds of Arabidopsis thaliana are desiccation tolerant, but they lose DT while progressing to germination. Yet, there is a small developmental window during which DT can be rescued by treatment with abscisic acid (ABA). We used a time-series of microarrays to gain temporal resolution and identify relevant genes in the re-establishment of desiccation tolerance with ABA.

2015-04-16 | GSE62876 | GEO

Transcriptome profiling of inflorescence stem tissues from Arabidopsis thaliana series

Project description:This SuperSeries is composed of the SubSeries listed below.

2020-02-10 | GSE142034 | GEO

Sample preparation for phosphoproteomic analysis of circadian time series in Arabidopsis thaliana.

Project description:Systems biological approaches to study the Arabidopsis thaliana circadian clock have mainly focused on transcriptomics while little is known about the proteome, and even less about posttranslational modifications. Evidence has emerged that posttranslational protein modifications, in particular phosphorylation, play an important role for the clock and its output. Phosphoproteomics is the method of choice for a large-scale approach to gain more knowledge about rhythmic protein phosphorylation. Recent plant phosphoproteomics publications have identified several thousand phosphopeptides. However, the methods used in these studies are very labor-intensive and therefore not suitable to apply to a well-replicated circadian time series. To address this issue, we present and compare different strategies for sample preparation for phosphoproteomics that are compatible with large numbers of samples. Methods are compared regarding number of identifications, variability of quantitation, and functional categorization. We focus on the type of detergent used for protein extraction as well as methods for its removal. We also test a simple two-fraction separation of the protein extract.

| S-EPMC4427183 | biostudies-literature

Transcription factor target prediction using multiple short expression time series from Arabidopsis thaliana.

Project description:BackgroundThe central role of transcription factors (TFs) in higher eukaryotes has led to much interest in deciphering transcriptional regulatory interactions. Even in the best case, experimental identification of TF target genes is error prone, and has been shown to be improved by considering additional forms of evidence such as expression data. Previous expression based methods have not explicitly tried to associate TFs with their targets and therefore largely ignored the treatment specific and time dependent nature of transcription regulation.ResultsIn this study we introduce CERMT, Covariance based Extraction of Regulatory targets using Multiple Time series. Using simulated and real data we show that using multiple expression time series, selecting treatments in which the TF responds, allowing time shifts between TFs and their targets and using covariance to identify highly responding genes appear to be a good strategy. We applied our method to published TF - target gene relationships determined using expression profiling on TF mutants and show that in most cases we obtain significant target gene enrichment and in half of the cases this is sufficient to deliver a usable list of high-confidence target genes.ConclusionCERMT could be immediately useful in refining possible target genes of candidate TFs using publicly available data, particularly for organisms lacking comprehensive TF binding data. In the future, we believe its incorporation with other forms of evidence may improve integrative genome-wide predictions of transcriptional networks.

| S-EPMC2198923 | biostudies-literature

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data