Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Saccharomyces cerevisiae CEN.PK113-7D

ABSTRACT: Expression data of Saccharomyces cerevisiae CEN.PK.113-7D grew in Batch and Chemostat condition using for comparison of RNA-seq and Microarray data

PROVIDER: PRJNA162311 | ENA |

REPOSITORIES: ENA

ACCESS DATA

Json Xml

Similar Datasets

Relative quantification from chemostats cultures of Saccharomyces cerevisiae CEN.PK113-7D, Kluyveromyces marxianus CBS 6556 and Yarrowia lipolytica W29

Project description:Relative quantification of protein abundances of three yeast strains (Saccharomyces cerevisiae CEN.PK113-7D, Kluyveromyces marxianus CBS6556 and Yarrowia lipolytica W29) cultivate in chemostats under different conditions. The conditions for Saccharomyces cerevisiae CEN.PK113-7D are: - Standard condition – 30°C, pH 5.5 - High temperature - 36°C, pH 5.5 - Low pH - 30°C, pH 3.5 - Osmotic stress – 30°C, pH 5.5, 1M KCl The conditions for Kluyveromyces marxianus CBS6556 are: - Standard condition – 30°C, pH 5.5 - High temperature - 40°C, pH 5.5 - Low pH - 30°C, pH 3.5 - Osmotic stress – 30°C, pH 5.5, 0.6 M KCl The conditions for Yarrowia lipolytica W29 are: - Standard condition - 28°C, pH 5.5 - High temperature - 32°C, pH 5.5 - Low pH - 28°C, pH 3.5 This study is part of the OMICS data generation of CHASSY project (European Union’s Horizon 2020 grant agreement No 720824).

2019-11-11 | PXD011426 | Pride

Approaching absolute quantification from chemostats cultures of Saccharomyces cerevisiae CEN.PK113-7D, Kluyveromyces marxianus CBS 6556 and Yarrowia lipolytica W29

Project description:Protein extracts of three yeast strains (Saccharomyces cerevisiae CEN.PK113-7D, Kluyveromyces marxianus CBS6556 and Yarrowia lipolytica W29) cultivated in chemostats under different conditions. Representative samples containing aliquots of all conditions for each yeast strain were spiked with UPS2 standard (Sigma) to estimate absolute values in fmol. The conditions for Saccharomyces cerevisiae CEN.PK113-7D are: - Standard condition : 30°C, pH 5.5 - High temperature: 36°C, pH 5.5 - Low pH: 30°C, pH 3.5 - Osmotic stress : 30°C, pH 5.5, 1M KCl The conditions for Kluyveromyces marxianus CBS6556 are: - Standard condition : 30°C, pH 5.5 - High temperature: 40°C, pH 5.5 - Low pH: 30°C, pH 3.5 - Osmotic stress: 30°C, pH 5.5, 0.6 M KCl The conditions for Yarrowia lipolytica W29 are: - Standard condition: 28°C, pH 5.5 - High temperature: 32°C, pH 5.5 - Low pH: 28°C, pH 3.5 This study is part of the OMICS data generation WP of CHASSY project (European Union’s Horizon 2020 grant agreement No 720824).

2022-02-15 | PXD012836 | Pride

Expression data of Saccharomyces cerevisiae CEN.PK.113-7D grew in Batch and Chemostat condition using for comparison of RNA-seq and Microarray data

Project description:High throughput sequencing is a powerful tool to investigate complex cellular phenotypes in functional genomics studies. Sequencing of transcriptional molecules, RNA-seq, has recently become an attractive method of choice in the studies of transcriptomes, promising several advantages compared to traditional expression analysis based on microarrays. In this study, we sought to assess the contribution of the different analytical steps involved in analysis of RNA-seq data and to cross-compare the results with those obtained through a microarray platform. We used the well-characterized Saccharomyces cervevisiae strain CEN.PK 113-7D grown under two different physiological conditions (batch and chemostat) as a case study. In our work, we addressed the influence of genetic variability on the estimation of gene expression level using three different aligners for read-mapping (Gsnap, Stampy and Tophat), the capabilities of five different statistical methods to detect differential gene expression (baySeq, Cuffdiff, DESeq, edgeR and noiSeq) and we explored the consistency between the two main approaches for RNA-seq: reference mapping and de novo assembly. High reproducibility in data generated through RNA-seq among different biological replicates (correlation ≥ 0.99) and high consistency with the results identified with RNA-seq and microarray data analysis (correlation ≥ 0.91) were observed. The results from differential gene expression identification as well as the results of integrated analysis based on the different methods are in good agreement. Overall, our study provides a useful and comprehensive comparison of the workflow for transcriptome analysis using RNA-seq technique.

2012-09-14 | GSE37599 | GEO

Saccharomyces cerevisiae CEN.PK113-7D

Project description:Saccharomyces cerevisiae CEN.PK113-7D genome sequencing

| PRJNA52955 | ENA

Saccharomyces cerevisiae CEN.PK113-7D

Project description:Saccharomyces cerevisiae CEN.PK113-7D Genome sequencing

| PRJNA191134 | ENA

Experimental systems biology: Lessons from an integrated, multi-laboratory study in yeast

Project description:We undertook an inter-laboratory effort to generate high-quality quantitative data for a very large number of cellular components in yeast using transcriptome and metabolome analysis. We ensured the high-quality of the experimental data by evaluating a wide range of sampling and measurement techniques. The data were generated for two different yeast strains, each growing under two different growth conditions and based on integrated analysis of the high-throughput data we hypothesize that differences in growth rates and yields on glucose between the two strains are due to differences in protein metabolism. Two yeast strains, YSBN2 and CEN.PK 113-7D, were grown in aerobical batch as well as glucose-limited chemostat (dilution rate 0.1h-1) in biological triplicates

2010-12-01 | E-GEOD-24234 | biostudies-arrayexpress

Saccharomyces cerevisiae CEN.PK113-7D

Project description:Saccharomyces cerevisiae CEN.PK113-7D Raw sequence reads

| PRJNA354846 | ENA

Expression data of Saccharomyces cerevisiae CEN.PK.113-7D grew in Batch and Chemostat condition using for comparison of RNA-seq and Microarray data

Project description:High throughput sequencing is a powerful tool to investigate complex cellular phenotypes in functional genomics studies. Sequencing of transcriptional molecules, RNA-seq, has recently become an attractive method of choice in the studies of transcriptomes, promising several advantages compared to traditional expression analysis based on microarrays. In this study, we sought to assess the contribution of the different analytical steps involved in analysis of RNA-seq data and to cross-compare the results with those obtained through a microarray platform. We used the well-characterized Saccharomyces cervevisiae strain CEN.PK 113-7D grown under two different physiological conditions (batch and chemostat) as a case study. In our work, we addressed the influence of genetic variability on the estimation of gene expression level using three different aligners for read-mapping (Gsnap, Stampy and Tophat), the capabilities of five different statistical methods to detect differential gene expression (baySeq, Cuffdiff, DESeq, edgeR and noiSeq) and we explored the consistency between the two main approaches for RNA-seq: reference mapping and de novo assembly. High reproducibility in data generated through RNA-seq among different biological replicates (correlation M-bM-^IM-% 0.99) and high consistency with the results identified with RNA-seq and microarray data analysis (correlation M-bM-^IM-% 0.91) were observed. The results from differential gene expression identification as well as the results of integrated analysis based on the different methods are in good agreement. Overall, our study provides a useful and comprehensive comparison of the workflow for transcriptome analysis using RNA-seq technique. Microarray ananlysis were perfomed from the same RNA extraction then compare the result with RNA-seq analysis

2012-09-14 | E-GEOD-37599 | biostudies-arrayexpress

RNA sequencing experiment of S. Cerevisiae strain CEN.PK113-7D in four different chemostat conditions

Project description:The RNA sequencing experiment was performed in four different chemostat conditions as control for a CAGE sequencing experiment performed in the same conditions using the industrial relevant S. Cerevisiae strain CEN.PK113-7D

2019-01-03 | E-MTAB-6722 | biostudies-arrayexpress

CAGE experiment of S. Cerevisiae strain CEN.PK113-7D in four different chemostat conditions

Project description:The CAGE experiment was performed in four different chemostat conditions to assess if and how much the Transcription start site landscape changes in the industrial relevant S. Cerevisiae strain CEN.PK113-7D in different conditions. CAGE was also used to obtain accurate TSS annotations for each expressed gene.

2019-01-03 | E-MTAB-6650 | biostudies-arrayexpress

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