Project description:A caffeine-resistant Saccharomyces cerevisiae mutant strain was obtained using an evolutionary engineering strategy based on successive batch cultivation at gradually increasing caffeine levels. The mutant strain Caf905-2 was selected at a caffeine concentration where its reference strain could not grow at all. Whole-genome transcriptomic analysis of Caf905-2 was performed with respect to its reference strain.
Project description:A propolis-resistant Saccharomyces cerevisiae mutant strain was obtained using an evolutionary engineering strategy based on successive batch cultivation under gradually increasing propolis levels. The mutant strain FD 11 was selected at a propolis concentration that the reference strain could not grow at all. Whole-genome transcriptomic analysis of FD11 was performed with respect to its reference strain to determine differences in gene expression levels between the two strains. Saccharomyces cerevisiae
Project description:Saccharomyces cerevisiae IMS0002 which, after metabolic and evolutionary engineering, ferments the pentose sugar arabinose. Glucose and arabinose-limited anaerobic chemostat cultures of IMS0002 and its non-evolved ancestor IMS0001 were subjected to transcriptome analysis to identify key genetic changes contributing to efficient arabinose utilization by strain IMS0002.
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.