Background: Even though real-time PCR has been broadly applied in biomedical sciences, data processing procedures for the analysis of quantitative real-time PCR are still lacking; specifically in the realm of appropriate statistical treatment. Confidence interval and statistical significance considerations are not explicit in many of the current data analysis approaches. Based on the standard curve method and other useful data analysis methods, we present and compare four statistical approaches and models for the analysis of real-time PCR data. Results: In the first approach, a multiple regression analysis model was developed to derive ΔΔCt from estimation of interaction of gene and treatment effects. In the second approach, an ANCOVA (analysis of covariance) model was proposed, and the ΔΔCt can be derived from analysis of effects of variables. The other two models involve calculation ΔCt followed by a two group t- test and non-parametric analogous Wilcoxon test. SAS programs were developed for all four models and data output for analysis of a sample set are presented. In addition, a data quality control model was developed and implemented using SAS. Conclusion: Practical statistical solutions with SAS programs were developed for real-time PCR data and a sample dataset was analyzed with the SAS programs. The analysis using the various models and programs yielded similar results. Data quality control and analysis procedures presented here provide statistical elements for the estimation of the relative expression of genes using real-time PCR.

Plant transformation has enabled fundamental insights into plant biology and revolutionized commercial agriculture. Unfortunately, for most crops, tra

• The major obstacle for bioenergy production from switchgrass biomass is the low saccharification efficiency caused by cell wall recalcitrance. Saccharification efficiency is negatively correlated with both lignin content and cell wall ester‐linked p‐coumarate: ferulate (p‐CA : FA) ratio. In this study, we cloned and functionally characterized an R2R3‐MYB transcription factor from switchgrass and evaluated its potential for developing lignocellulosic feedstocks. • The switchgrass PvMYB4 cDNAs were cloned and expressed in Escherichia coli, yeast, tobacco and switchgrass for functional characterization. Analyses included determination of phylogenetic relations, in situ hybridization, electrophoretic mobility shift assays to determine binding sites in target promoters, and protoplast transactivation assays to demonstrate domains active on target promoters. • PvMYB4 binds to the AC‐I, AC‐II and AC‐III elements of monolignol pathway genes and down‐regulates these genes in vivo. Ectopic overexpression of PvMYB4 in transgenic switchgrass resulted in reduced lignin content and ester‐linked p‐CA : FA ratio, reduced plant stature, increased tillering and an approx. threefold increase in sugar release efficiency from cell wall residues. • We describe an alternative strategy for reducing recalcitrance in switchgrass by manipulating the expression of a key transcription factor instead of a lignin biosynthetic gene. PvMYB4‐OX transgenic switchgrass lines can be used as potential germplasm for improvement of lignocellulosic feedstocks and provide a platform for further understanding gene regulatory networks underlying switchgrass cell wall recalcitrance.