RESUMEN
RNA-Seq is a widely used technology that allows an efficient genome-wide quantification of gene expressions for, for example, differential expression (DE) analysis. After a brief review of the main issues, methods and tools related to the DE analysis of RNA-Seq data, this article focuses on the impact of both the replicate number and library size in such analyses. While the main drawback of previous relevant studies is the lack of generality, we conducted both an analysis of a two-condition experiment (with eight biological replicates per condition) to compare the results with previous benchmark studies, and a meta-analysis of 17 experiments with up to 18 biological conditions, eight biological replicates and 100 million (M) reads per sample. As a global trend, we concluded that the replicate number has a larger impact than the library size on the power of the DE analysis, except for low-expressed genes, for which both parameters seem to have the same impact. Our study also provides new insights for practitioners aiming to enhance their experimental designs. For instance, by analyzing both the sensitivity and specificity of the DE analysis, we showed that the optimal threshold to control the false discovery rate (FDR) is approximately 2-r, where r is the replicate number. Furthermore, we showed that the false positive rate (FPR) is rather well controlled by all three studied R packages: DESeq, DESeq2, and edgeR. We also analyzed the impact of both the replicate number and library size on gene ontology (GO) enrichment analysis. Interestingly, we concluded that increases in the replicate number and library size tend to enhance the sensitivity and specificity, respectively, of the GO analysis. Finally, we recommend to RNA-Seq practitioners the production of a pilot data set to strictly analyze the power of their experimental design, or the use of a public data set, which should be similar to the data set they will obtain. For individuals working on tomato research, on the basis of the meta-analysis, we recommend at least four biological replicates per condition and 20 M reads per sample to be almost sure of obtaining about 1000 DE genes if they exist.
RESUMEN
Cell-wall damage caused by mutations of cell-wall-related genes triggers a compensatory mechanism which eventually results in hyperaccumulation of chitin reaching 20% of the cell-wall dry mass. We show that activation of chitin synthesis is accompanied by a rise, from 1.3-fold to 3.5-fold according to the gene mutation, in the expression of most of the genes encoding enzymes of the chitin metabolic pathways. Evidence that GFA1, which encodes glutamine-fructose-6-Phosphate amidotransferase (Gfa1p), the first committed enzyme of this pathway, plays a major role in this process was as follows. Activation of chitin synthesis in the cell-wall mutants correlated with activation of GFA1 and with a proportional increase in Gfa1p activity. Overexpression of GFA1 caused an approximately threefold increase in chitin in the transformed cells, whereas chitin content was barely affected by the joint overexpression of CHS3 and CHS7. Introduction of a gfa1-97 allele mutation in the cell-wall-defective gas1Delta mutant or cultivation of this mutant in a hyperosmotic medium resulted in reduction in chitin synthesis that was proportional to the decrease in Gfa1p activity. Finally, the stimulation of chitin production was also accompanied by an increase in pools of fructose 6-Phosphate, a substrate of Gfa1p. In quantitative terms, we estimated the flux-coefficient control of Gfa1p to be in the range of 0.90, and found that regulation of the chitin metabolic pathway was mainly hierarchical, i.e. dominated by regulation of the amount of newly synthesized GFA1 protein. In the search for the mechanism by which GFA1 is activated in response to cell-wall perturbations, we could only show that neither MCM1 nor RLM1, which encode two transcriptional factors of the MADS box family that are required for expression of cell-cycle and cell-wall-related genes, was involved in this process.
