Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

S.cerevisiae - Whole organism, Ms/ms abundance data, SILAC normalized data (De_godoy,nature,2008)

ABSTRACT: abundance based on SILAC normalized data, MS/MS abundance data, Interaction consistency score: 15.7, Coverage: 60

ORGANISM(S): 4932

SUBMITTER: Christian von Mering

PROVIDER: 469118354 | PAXDB | 2008

REPOSITORIES: PAXDB

ACCESS DATA

Json Xml

Dataset's files

Source:

			Action	DRS
	bioprojects-abundance-files-v4.0.zip	Other

Items per page:

1 - 1 of 1

Publications

Comprehensive mass-spectrometry-based proteome quantification of haploid versus diploid yeast.

de Godoy Lyris M F LM Olsen Jesper V JV Cox Jürgen J Nielsen Michael L ML Hubner Nina C NC Fröhlich Florian F Walther Tobias C TC Mann Matthias M

Nature 20080928 7217

Mass spectrometry is a powerful technology for the analysis of large numbers of endogenous proteins. However, the analytical challenges associated with comprehensive identification and relative quantification of cellular proteomes have so far appeared to be insurmountable. Here, using advances in computational proteomics, instrument performance and sample preparation strategies, we compare protein levels of essentially all endogenous proteins in haploid yeast cells to their diploid counterparts. ...[more]

PMID: 18820680

Similar Datasets

PTSD Normalized data

Project description:PTSD - Posttraumatic stress disorder. 33 samples taken from PMBCs of survivors of psychological trauma, in two time points: in ER, few hours after the truma, and four months later. Some of the patients devepled chronic PTSD (17 samples) and others recovered and set to be the Control group (16 samples). This is the normalized active genes: 4512 probes from U95A chip. The raw data is available in series GSE845. Samples are labeled with 3 tags: P/C for PTSD or Control, ER/M4 - for time point of sample, D/ND for Decrement or Non-decrement symptoms over time. (e.g. sample 23C-M4-D-Norm was taken 4 months after trauma from patient 23 which belongs to the control group and showed decrease in symptoms) . Samples include the expression value, the GeneBank accession number and Affymetrix indication of valid calls. Keywords = PTSD Keywords = Normalized Keywords = PMBC Keywords: other

2004-11-20 | GSE860 | GEO

Normalized expression (normals) - samples

Project description:Normalized expression data; normals

| EGAD00010000212 | EGA

Carolina Breast Cancer Study tumor expression [RUVSeq-normalized]

Project description:Carolina Breast Cancer Study tumor expression [RUVSeq-normalized]

| PRJNA624197 | ENA

Normalized expression (discovery set) - samples

Project description:Normalized expression data; discovery set

| EGAD00010000210 | EGA

Normalized expression (validation set) - samples

Project description:Normalized expression data; validation set

| EGAD00010000211 | EGA

LDLR variant classification through activity-normalized prime editing screening

Project description:LDLR variant classification through activity-normalized prime editing screening

| PRJNA1457355 | ENA

Carolina Breast Cancer Study tumor expression [NanoString nSolver-normalized]

Project description:Carolina Breast Cancer Study tumor expression [NanoString nSolver-normalized]

| PRJNA624196 | ENA

Carolina Breast Cancer Study tumor expression [RUVSeq-normalized]

Project description:Transcription profiling by NanoString nCounter of primary breast tumors from 1219 patients from the Carolina Breast Cancer Study (CBCS) using the NanoString nCounter platform and normalized with a RUVSeq-based iterative framework. The NanoString RNA counting assay for formalin-fixed paraffin embedded samples is unique in its sensitivity, technical reproducibility, and robustness for analysis of clinical and archival samples. While commercial normalization methods are provided by NanoString, they are not optimal for all settings, particularly when samples exhibit strong technical or biological variation or where housekeeping genes have variable performance across the cohort. Here, we develop and evaluate a more comprehensive normalization procedure for NanoString data with steps for quality control, selection of housekeeping targets, normalization, and iterative data visualization and biological validation. The approach was evaluated using a large cohort from the Carolina Breast Cancer Study. The iterative process developed here eliminates technical variation more reliably than the NanoString commercial package, without diminishing biological variation, especially in long-term longitudinal multi-phase or multi-site cohorts. We also find that probe sets validated for nCounter, such as the PAM50 gene signature, are impervious to batch issues. This work emphasizes that preprocessing of gene expression data is an important component of study design. The normalized data here is processed through the RUVSeq-based iterative framework

2020-07-16 | GSE148425 | GEO

Carolina Breast Cancer Study tumor expression [NanoString nSolver-normalized]

Project description:Transcription profiling by NanoString nCounter of primary breast tumors from 1219 patients from the Carolina Breast Cancer Study (CBCS) using the NanoString nCounter platform and normalized with NanoString nSolver software. The NanoString RNA counting assay for formalin-fixed paraffin embedded samples is unique in its sensitivity, technical reproducibility, and robustness for analysis of clinical and archival samples. While commercial normalization methods are provided by NanoString, they are not optimal for all settings, particularly when samples exhibit strong technical or biological variation or where housekeeping genes have variable performance across the cohort. Here, we develop and evaluate a more comprehensive normalization procedure for NanoString data with steps for quality control, selection of housekeeping targets, normalization, and iterative data visualization and biological validation. The approach was evaluated using a large cohort from the Carolina Breast Cancer Study. The iterative process developed here eliminates technical variation more reliably than the NanoString commercial package, without diminishing biological variation, especially in long-term longitudinal multi-phase or multi-site cohorts. We also find that probe sets validated for nCounter, such as the PAM50 gene signature, are impervious to batch issues. This work emphasizes that preprocessing of gene expression data is an important component of study design. The normalized data here is processed through the RUVSeq-based iterative framework

2020-07-16 | GSE148418 | GEO

Homo sapiens

Project description:PTSD Normalized data

| PRJNA87875 | ENA