Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Oxygen downshift experiment with E.coli W3110

ABSTRACT: Dynamical response to oxygen downshift under fermentation conditions was tested by taking sample before (S1) and after (S2, S3 and S4) the oxygen downshift. The dynamical changes relevant for ongoing research on physiology were applied.

ORGANISM(S): Escherichia coli

PROVIDER: GSE12006 | GEO | 2008/07/08

SECONDARY ACCESSION(S): PRJNA113277

REPOSITORIES: GEO

ACCESS DATA

Json Xml

Similar Datasets

Transcription profiling of E.coli W3110 oxygen downshift

Project description:Dynamical response to oxygen downshift under fermentation conditions was tested by taking sample before (S1) and after (S2, S3 and S4) the oxygen downshift. The dynamical changes relevant for ongoing research on physiology were applied. Experiment Overall Design: Four microarray chips were analyzed: Experiment Overall Design: S1 was taken 15 min before the oxygen downshift Experiment Overall Design: S2, S3 and S4 were taken 15 min, 45 min and 75 min after the oxygen downshift, respectively

2008-10-21 | E-GEOD-12006 | biostudies-arrayexpress

Methylome analysis of epiRIL344 BC1-S1 to BC1-S6 aerial part

Project description:DNA methylation profiling in epiRIL344 BC1-S1 to BC1-S6 aerial part (epiRIL344_BC1-S1 - epiRIL344_BC1-S2 - epiRIL344_BC1-S3 - epiRIL344_BC1-S4 - epiRIL344_BC1-S5 - epiRIL344_BC1-S6) Keywords: Genome binding/occupancy profiling by genome tiling array

2012-09-03 | GSE37285 | GEO

RNA sequencing of Medicago seed tissues under optimal and heat stress conditions

Project description:We analysed transcriptome changes between seed devlopement (4 stages corresponding at before (S1) and after (S2) desiccation tolerance acquisition and before (S3) and after (S4) longevity acquisition) in dissected seed tissues (embryo, E; Endosperm Eo and Seed Coat SC)

2020-11-03 | GSE160725 | GEO

Methylome analysis of epiRIL344 BC1-S1 to BC1-S6 aerial part

2012-09-03 | E-GEOD-37285 | biostudies-arrayexpress

Expression profile analysis of circular RNAs during embryonic lung development of C57BL/6 mice

Project description:Objectives: The contribution of circular RNAs (circRNAs) in the regulation of embryonic lung development is not yet clear. This study aims to uncover the differentially expressed circRNAs in mouse embryonic lung tissue. Methods: High-throughput sequencing (HTS) was used to determine the cirRNA expression profiles at four time points of mouse embryonic lung tissue [embryonic (E) Day 14.5 (E14.5/S1), (E16.5/S2), (E18.5/S3), and newborn (N) Day 7.5 (N7.5/S4)]. CircRNAs that were significantly differential were subjected to bioinformatic analysis and functional prediction for their host genes. Finally, quantitative real-time reverse transcriptase polymerase chain reaction (qRT-PCR) was conducted to examine their levels. Results: Dynamically expressed circRNAs in the mouse embryonic lung tissue were identified. After that, a total of 231 circRNAs differentially expressed between the three groups (S1 vs. S2, S1 vs. S3 and S1 vs. S4) in embryonic lung tissue (fold change ≥ 2.0; p < 0.05). These circRNAs were subjected to bioinformatic analysis to predict their potential biological functions. Finally, six circRNAs (circRNA3_Ttn, circRNA56_Galnt18, circRNA38_Alg12, circRNA42_Filip1l, circRNA43_Ptprm, and circRNA57_Ncoa3) that were further validated using qRT-PCR. Conclusions: These findings illustrate the vital functions of circRNAs in mouse embryonic lung development. Combined with our results and previous researches, circRNAs may serve as potential miRNA sponges in the embryonic lung development. Our research is conducive to further explore the development of embryonic lungs, and the dynamic expressed circRNAs may be novel molecules that regulate mouse lung development.

2019-08-07 | GSE131839 | GEO

Diffuse large B-cell lymphoma (DLBCL) from primary sites and metastatic lymph nodes

Project description:Analysis of diffuse large B-cell lymphoma (DLBCL) from primary sites (named as S1, S2 and S3) and metastatic lymph nodes (named as L1, L2 and L3). Results provide insight into the targets involved in t DLBCL dissemination.

2017-06-30 | GSE81968 | GEO

Differentially expressed genes between SPOPmut and control in mouse prostate organoids

Project description:To provide further insight to the signaling pathways deregulated by SPOP mutation and determine the relevance of these models to human prostate cancer, we performed RNA-seq on SPOP mutant organoids and controls. RNA-seq reads mapped to human and mouse SPOP confirmed appropriate expression of the F133V transgenic transcript without overexpression compared to endogenous mouse Spop. Quantification of gene expression was performed via RSEQtools using GENCODE as reference gene–annotation set. Both SPOPmut and SPOPwt were done in the same run. S0 was done in first run; S1 and S2 were done in second run. S3, S4 and S5 were done in third run. S5mut and S5wt were excluded from differentially expressed genes analysis, due to the different mouse line.

2017-03-13 | GSE94839 | GEO

Hippo signaling coordinates cellular quiescence with terminal maturation in iNKT cell development and fate decisions [MoGene-2_0]

Project description:Hippo kinases Mst1 and Mst2 act as molecular rheostats for the terminal maturation and effector differentiation programs of iNKT cells. iNKT developmental stages 1, 2, and 3 (S1, S2, S3) were sorted from the thymus of WT (n = 3) and Cd4CreMst1fl/fl (n = 3) mice

2020-04-07 | GSE130043 | GEO

Identification of tRNA sequences in Lactococcus lactis

Project description:we report the identification and sequences of the tRNAome of industrially relevant microorganism Lactococcus lactis Three Next Generation sequencing runs annotated as S1, S2 and S3 were performed. Cells were harvested at exponential phase and tRNA was isolated. S1 and S2 were spiked with Phe-tRNAGAA from yeast and Lys-tRNAUUU from E. coli prior to cell lysis. S3 was spiked with Phe-tRNAGAA from yeast and Lys-tRNAUUU from E. coli before the library preparation to estimate the possible loss of tRNA in the extraction process.

2014-07-29 | E-GEOD-55392 | biostudies-arrayexpress

Expression profiles of 30-gene hepatocellular carcinoma molecular classification signature in surgically resected Japanese hepatocellular carcinoma patients

Project description:Background & Aims: Hepatocellular carcinoma (HCC) is the second most lethal cancer due to lack of effective therapies. Although promising, HCC molecular classification, which enriches potential responders to specific therapies, has not yet been assessed in clinical trials of anti-HCC drugs. We aimed to overcome these challenges by developing clinicopathological surrogate indices of HCC molecular classification. Methods: HCC classification defined in our previous transcriptome meta-analysis (S1, S2, and S3 subclasses) was implemented in an FDA-approved diagnostic platform (Elements assay, NanoString). Ninety-six HCC tumors (training set) were assayed to develop molecular subclass-predictive indices based on clinicopathological features, which were independently validated in 99 HCC tumors (validation set). Molecular deregulations associated with the histopathological features were determined by pathway analysis. Sample sizes for HCC clinical trials enriched with specific molecular subclasses were determined. Results: HCC subclass-predictive indices were: steatohepatitic (SH)-HCC variant and immune cell infiltrate for S1 subclass, macrotrabecular/compact pattern, lack of pseudoglandular pattern, and high serum alpha-fetoprotein (>400 ng/mL) for S2 subclass, and microtrabecular pattern, lack of SH-HCC and clear cell variants, and lower histological grade for S3 subclass. Macrotrabecular/compact pattern, a predictor of S2 subclass, was associated with activation of therapeutically targetable oncogene YAP and stemness markers EPCAM/KRT19. BMP4 was associated with pseudoglandular pattern. Subclass-predictive indices-based patient enrichment reduced clinical trial sample sizes from 121, 184, and 53 to 30, 43, and 22 for S1, S2, and S3 subclass-targeting therapies, respectively. Conclusions: HCC molecular subclasses can be enriched by clinicopathological indices tightly associated with deregulation of therapeutically targetable molecular pathways.

2015-06-17 | GSE59548 | GEO

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

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