Saccharomyces cerevisiae
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ABSTRACT:
PROVIDER: PRJNA116733 | ENA |
REPOSITORIES: ENA
Similar Datasets
Project description:Comparison of ds-cDNA, Indirect and Direct Random Labeling Methods for gene expression analysis on the NimbleGen platform. Expression profiles from artemisinic acid-producing S. cerevisiae strain EPY330 and non-producing strain EPY338 are compared for each labeling method tested. The labeling methods and their comparison are described in detail in Ouellet et al., BMC Biotech 2009. Strains were described in detail previously in Ro et al. BMC Biotechnol 2008, 8(1):83 [PMID: 18983675]
2009-11-25 | GSE15816 | GEO
Project description:Comparison of ds-cDNA, Indirect and Direct Random Labeling Methods for gene expression analysis on the NimbleGen platform. Expression profiles from artemisinic acid-producing S. cerevisiae strain EPY330 and non-producing strain EPY338 are compared for each labeling method tested. The labeling methods and their comparison are described in detail in Ouellet et al., BMC Biotech 2009. Strains were described in detail previously in Ro et al. BMC Biotechnol 2008, 8(1):83 [PMID: 18983675] RNA pools from strains EPY330 (sample A) and EPY338 (sample B) were reverse-transcribed and labeled in triplicate with the ds-cDNA, the Indirect and the new Direct Random method and hybridized in parallel on NimbleGen 4-plex arrays.
2010-05-19 | E-GEOD-15816 | biostudies-arrayexpress
Project description:Comprehensive comparative analysis of strand-specific RNA sequencing methods
| PRJNA127357 | ENA
Project description:Saccharomyces pastorianus strains comparison
| PRJNA113471 | ENA
Project description:This experiment aims to map nucleosome positions and comparison of the same in WT NORMAL GROWTH vs WT-NUTRIENT STARVATION/isw1∆2∆ MUTANT/rsc4-∆4 MUTANT in Saccharomyces cerevisiae using a custom designed tiling array on Agilent plat form. The corresponding platform is submitted to GEO under Geo-ID GPL15842. 60mer probes with variable tiling density were designed for all the genes transcribed by RNA polymerase III. Each gene is tiled along with its 1kb downstream and upstream region with the exceptions of RPR1, SCR1, RDN5(1-6) and SNR52. Mononucleosomal DNA and size matched naked DNA was competitively hybridized to the array. Data was extracted and normalized log ratios were calculated using Agilent sofware. Normalized log2 ratio data was used in MLM to detection nucleosome positions.
2013-06-21 | GSE39802 | GEO
Project description:Performances of flax gene expression analyses were compared in two categories of Nimblegen microarrays (short 25-mers oligonucleotides and long 60-mers oligonucleotides) Results obtained in this study are described in Intra-platform comparison of flax (Linum usitatissimum L.) high-density Nimblegen DNA microarrays submitted to Journal of Computational Biology
2012-05-15 | GSE37980 | GEO
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