Staskawicz (1989): “Bacterial blight of soybean: regulation of a pathogen gene determining host cultivar specificity,” Science, 245, 1374. Cambridge, UK: Cambridge University Press. Gail (1996): “A reminder of the fallibility of the wald statistic,” American Statistician, 50, 226–227.
Chang (2011): “The NBP negative binomial model for assessing differential gene expression from RNA-Seq,” Stat. (2011): “GENE-Counter: A computational pipeline for the analysis of RNA-Seq data for gene expression differences,” PLoS ONE, 6, e25279. Reid (1987): “Parameter orthogonality and approximate conditional inference,” J. (2003): “The complete genome sequence of the Arabidopsis and tomato pathogen Pseudomonas syringae pv. (1991): “Modified signed log likelihood ratio,” Biometrika, 78, 557–563. Search in Google Scholarīarndorff-Nielsen, O.
#GENE CONSTRUCTION KIT ERROR 199 FULL#
(1986): “Infereni on full or partial parameters based on the standardized signed log likelihood ratio,” Biometrika, 73, 307–322. Huber (2010): “Differential expression analysis for sequence count data,” Genome Biol., 11, R106. We thank the reviewers for their insightful and constructive comments. We would also like to thank Mark Dasenko, Chris Sullivan, and Matthew Peterson of the CGRB core facility for their assistance with RNA-Seq preparation and data processing. IOS-1021463), and the Agricultural Research Foundation. 2011-67019-30192 from the USDA National Institute of Food and Agriculture, National Science Foundation (Grant no. 2008-35600-04691 and Agriculture and Food Research Initiative Competitive Grants Program Grant no. Work in the Chang lab is supported by the National Research Initiative Competitive Grants Program Grant no. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. Furthermore, the HOA test may be preferable even when the exact test is available because it does not require ad hoc library size adjustments.Īcknowledgements: Research reported in this publication was partially supported by the National Institute of General Medical Sciences of the National Institutes of Health under Award Number R01GM104977 (to YD, SCE, and JHC). This work helps clarify the accuracy of the unadjusted likelihood ratio test and the degree of improvement available with the HOA adjustment. We demonstrate that 1) the HOA-adjusted likelihood ratio test is practically indistinguishable from the exact test in situations where the exact test is available, 2) the type I error of the HOA test matches the nominal specification in regression settings we examined via simulation, and 3) the power of the likelihood ratio test does not appear to be affected by the HOA adjustment. We address the adequacy of available large-sample tests for the small sample sizes typically available from RNA-Seq studies and consider a higher-order asymptotic (HOA) adjustment to likelihood ratio tests. Negative binomial exact tests are available for two-group comparisons but do not extend to negative binomial regression analysis, which is important for examining gene expression as a function of explanatory variables and for adjusted group comparisons accounting for other factors. The negative binomial (NB) probability distribution has been shown to be a useful model for frequencies of mapped RNA-Seq reads and consequently provides a basis for statistical analysis of gene expression. This next generation sequencing-based method provides unprecedented depth and resolution. When choosing equipment, consider the ease of decontamination, protocol flexibility, downstream applications, throughput, budget and laboratory space available.RNA sequencing (RNA-Seq) is the current method of choice for characterizing transcriptomes and quantifying gene expression changes. In selecting products, it is helpful to work with a vendor that will provide optimized protocols, documentation, and troubleshooting assistance, since the systems can be complex with many sources of error. Real-time PCR systems have the capability for gene expression analysis, gene detection, mutation detection, methylation analysis, miRNA research, and relative quantification of target genes. Thermal cyclers denature and anneal DNA strands during amplification and reagents such as enzymes, nucleotides and buffers to build the novel DNA.Īutomated workstations are available for busy, high-throughput laboratories to simplify workflow. PCR amplifies DNA by copying the nucleic acid strands exponentially. The polymerase chain reaction (PCR) technique is ubiquitous in laboratories and is used in applications such as DNA sequencing, cloning, library generations, mutagenesis, expression profiling, and more.