Project description:RNA-Sequencing is a transformative method that captures the quantitative dynamics of a transcriptome with exquisite sensitivity and single-base resolution. There are, however, few computational pipelines for RNA-Seq with statistical tests that evince sufficient robustness and power as demanded by the difficult combination of small sample sizes and high variability in sequence read counts. To this end, we developed GENE-counter, a complete software pipeline for analyzing RNA-Seq data for genome-wide expression differences between replicated treatment groups. One important component of GENE-counter is a statistical test based on the NBP parameterization of the negative binomial distribution for identifying differentially expressed genome features. We used GENE-counter to analyze RNA-Seq data derived from Arabidopsis thaliana infected with a strain of defense-eliciting bacteria. We identified 308 genes that were differentially induced. Using alternative methods, we provided support for the induced expression and biological relevance of a substantial proportion of the genes. These results suggest the NBP parameterization of the negative binomial distribution is well suited for explaining RNA-Seq data and the statistical test makes GENE-counter a powerful pipeline for studying genome-wide expression changes. GENE-counter is freely available at http://changlab.cgrb.oregonstate.edu/. Our RNA-seq data is uploaded on the NCBI short read archive (SRA) under the SRA025952. 6 samples total. Two treatments with three biological replicates each. MgCl2 is the mock treatment, and hrcC is the experimental treatment.
Project description:RNA-Sequencing is a transformative method that captures the quantitative dynamics of a transcriptome with exquisite sensitivity and single-base resolution. There are, however, few computational pipelines for RNA-Seq with statistical tests that evince sufficient robustness and power as demanded by the difficult combination of small sample sizes and high variability in sequence read counts. To this end, we developed GENE-counter, a complete software pipeline for analyzing RNA-Seq data for genome-wide expression differences between replicated treatment groups. One important component of GENE-counter is a statistical test based on the NBP parameterization of the negative binomial distribution for identifying differentially expressed genome features. We used GENE-counter to analyze RNA-Seq data derived from Arabidopsis thaliana infected with a strain of defense-eliciting bacteria. We identified 308 genes that were differentially induced. Using alternative methods, we provided support for the induced expression and biological relevance of a substantial proportion of the genes. These results suggest the NBP parameterization of the negative binomial distribution is well suited for explaining RNA-Seq data and the statistical test makes GENE-counter a powerful pipeline for studying genome-wide expression changes. GENE-counter is freely available at http://changlab.cgrb.oregonstate.edu/. Our RNA-seq data is uploaded on the NCBI short read archive (SRA) under the SRA025952.
Project description:This data was used as an example to illustrate a computational method for assessing statistical significance in microarray experiments Contributed by 'The Inflammation and the Host Response to Injury Collaborative Research Program.' Keywords: Two group comparison
Project description:This data was used as an example to illustrate a computational method for assessing statistical significance in microarray experiments Contributed by 'The Inflammation and the Host Response to Injury Collaborative Research Program.' Keywords: Two group comparison Genomic response one day post traumatic injury was compared between patients having early or late respiratory recovery
Project description:We present DRUID (for Determination of Rates Using Intron Dynamics), a computational pipeline, for determining mRNA stability transcriptome-wide uses metabolic labeling and approach-to-equilibrium kinetics. Our pipeline uses endogenous introns to normalize time course data and yields more reproducible half-lives than other methods, even with datasets that were otherwise unusable. DRUID can handle datasets from a variety of organisms, spanning yeast to humans.
Project description:We developed a stringent selection pipeline for lncRNA identification, combining high-throughput RNA sequencing and computational approaches. Using this pipeline, we annotated 1,353 lncRNAs in Arabidopsis thaliana. We further found that one fifth of the lncRNAs were associated with Polycomb repressive complex 2 (PRC2). Some PRC2-associated lncRNAs could repress the transcription of their neighboring genes through mediating histone H3 lysine 27 trimethylation.
