SUS1
Ontology highlight
ABSTRACT:
ORGANISM(S): Saccharomyces cerevisiae
PROVIDER: GSE920 | GEO | 2004/01/02
SECONDARY ACCESSION(S): PRJNA87939
REPOSITORIES: GEO
Similar Datasets
Project description:Transcriptome analysis of sus1 mutant in reference to its parental wild type reference. Iterative global median method was used for between sample normalization. z-test was performed to evaluate differential gene expression.
2010-06-09 | E-GEOD-920 | biostudies-arrayexpress
Project description:Mice over-expressing human PITX2A in cornea by the keratocan upstream regulatory sequences. Normalization procedure: Data normalized to spikes. Values below 0.01 were set to 0.01. All of the genes in each sample were divided by the median of a user specified list of positive control genes (chip-to-chip normalization). Keywords: repeat sample
2005-04-06 | GSE1283 | GEO
Project description:We have analyzed the global effect of the conserved transcription-mRNA export factor Sus1 on transcription and its association with chromatin. We used genomic run-on experiments to show that Sus has a broad impact on the stability of most RNA polymerase II-transcribed genes. Genome association of Sus1 by the chromatin immunoprecipitation technique showed that Sus1 was widely distributed throughout coding regions, tending to accumulate towards the 3’ ends of highly transcribed transcription factor IID (TFIID) and Spt-Ada-Gcn5 acetyltransferase (SAGA) dependent genes. This accumulation depends on growth conditions, the transcriptional rate and whether the genes are TFIID- or SAGA-regulated. Validation of Sus1 occupancy data also revealed that Sus1 appears at tRNAs. Concomitantly, deletion of SUS1 leads to tRNA overexpression. In addition, we discovered that distinctive SAGA subunits Spt8 and Spt7 play a key role in Sus1 enrichment at the 3’ ends of SAGA-regulated genes upon temperature shift and at tRNAs. Thus, our study identifies the molecular mechanisms by which a SAGA factor is recruited genome-wide, and provides evidence of a more general role for this conserved coactivator in eukaryotic transcription.
2018-12-31 | GSE65883 | GEO
Project description:Eukaryotic transcription is regulated through two complexes, the general transcription factor IID (TFIID) and the coactivator Spt-Ada-Gcn5 acetyltransferase (SAGA). Recent findings confirm that both TFIID and SAGA contribute to the synthesis of nearly all transcripts and are recruited genome-wide in yeast. However, how this broad recruitment confers selectivity under specific conditions remains an open question. Here we find that the SAGA DUBm subunit Sus1 associates upstream regulatory regions of many yeast genes and that heat shock drastically changes Sus1 binding pattern. While Sus1 binding to TFIID-dominated genes is not affected by temperature, its recruitment to SAGA-dominated genes and RP genes is significantly disturbed under heat shock, being Sus1 relocated to environmental stress responsive genes in these conditions. Moreover, in contrast to recent results showing that SAGA deubiquitinating enzyme Ubp8 is dispensable for RNA synthesis, genomic run-on experiments demonstrate that Sus1 contributes to synthesis and stability for a wide range of transcripts. Our study provides support for a model in which SAGA acts as a global transcriptional regulator in yeast but acquires differential binding affinities under thermal stress conditions.
2016-05-15 | GSE65902 | GEO
Project description:We have analyzed the global effect of the conserved transcription-mRNA export factor Sus1 on transcription and its association with chromatin. We used genomic run-on experiments to show that Sus has a broad impact on the stability of most RNA polymerase II-transcribed genes. Genome association of Sus1 by the chromatin immunoprecipitation technique showed that Sus1 was widely distributed throughout coding regions, tending to accumulate towards the 3â ends of highly transcribed transcription factor IID (TFIID) and Spt-Ada-Gcn5 acetyltransferase (SAGA) dependent genes. This accumulation depends on growth conditions, the transcriptional rate and whether the genes are TFIID- or SAGA-regulated. Validation of Sus1 occupancy data also revealed that Sus1 appears at tRNAs. Concomitantly, deletion of SUS1 leads to tRNA overexpression. In addition, we discovered that distinctive SAGA subunits Spt8 and Spt7 play a key role in Sus1 enrichment at the 3â ends of SAGA-regulated genes upon temperature shift and at tRNAs. Thus, our study identifies the molecular mechanisms by which a SAGA factor is recruited genome-wide, and provides evidence of a more general role for this conserved coactivator in eukaryotic transcription. Genome wide analysis of Sus1 in wild type and Spt7 and Spt8 deletion mutants using ChIP-exo and genomic run-on experiments
2016-05-15 | E-GEOD-65902 | biostudies-arrayexpress
Project description:We analyzed the effect of SAC3, SUS1 and SRC1 on the transcription rates, mRNA stabilities and mRNA levels by doing GRO experiments in a deletion mutants and the partial C-truncated version of Sac3 comparing with a wild type. Some data for mRNA amounts (sus1 ans src1 mutants) are not included because were already in GEO database: GSE920 and GSE6370 accession numbers.
2015-12-31 | GSE63769 | GEO
Project description:Isobaric labeling has the promise of combining high sample multiplexing with precise quantification. However, normalization issues and the missing value problem of complete n-plexes hamper quantification across more than one n-plex. Here we introduce two novel algorithms implemented in MaxQuant that substantially improve the data analysis with multiple n-plexes. First, isobaric matching between runs (IMBR) makes use of the three-dimensional MS1 features to transfer identifications from identified to unidentified MS/MS spectra between LC-MS runs in order to utilize reporter ion intensities in unidentified spectra for quantification. On typical datasets, we observe a significant gain in quantifiable n-plexesMS/MS spectra that can be used for quantification. Second, we introduce a novel PSM-level normalization, applicable to data with and without common reference channel. It is a weighted median-based method, in which the weights reflect the number of ions that were used for fragmentation. On a typical dataset, we observe complete removal of batch effects and dominance of the biological sample grouping after normalization. This dataset is one of the datasets used for the study. It is TMT 10-plex with a reference channel.
2020-06-22 | PXD019881 | Pride
Project description:RNA-sequencing of PEO1 cells grown in suspension (SUS1 and SUS3) was performed to evaluate transcriptional reprogramming during anokis escape. CRISPR/Cas9 screen of PEO1 cells grown in adherent and suspension culture settings for 5 days or 10 days. GeCKO v2 library transduced in PEO1 cell line and cells were selected with puromycin. PEO1 line was authenticated prior to sequencing. PEO1 parental were confirmed to be BRCA2-mutated (5139C>G).
2018-12-04 | GSE123290 | GEO
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
Project description:We analyzed the effect of SAC3, SUS1 and SRC1 on the transcription rates, mRNA stabilities and mRNA levels by doing GRO experiments in a deletion mutants and the partial C-truncated version of Sac3 comparing with a wild type. Some data for mRNA amounts (sus1 ans src1 mutants) are not included because were already in GEO database: GSE920 and GSE6370 accession numbers. This study focus on the transcriptional activity of RNA pol II in yeast genes. S. cerevisiae cells grown in YPD to exponential phase were subjected to Genomic Run On. Data were normalized by the ArrayStat software. Home-made macroarrays containing entire ORFs were used.
2015-12-31 | E-GEOD-63769 | biostudies-arrayexpress