*(a) IsoDOT work flow. The dash line indicates that known isoform annotation (i.e., transcriptome annotation) is optional, (b) A gene with 3 exons and 3 possible isoforms. (c) A matrix of input data. Each row corresponding to an exon set. The column “Count” is the number of RNA-seq fragments at each exon set, and the columns “isoform k” for k = 1,2,3 give the effective lengths of each exon set within each isoform, and specifically, η _{A,k} is the effective length of exon set A for the k-th isoform. NB(μ,φ) indicates a negative binomial distribution with mean μ, and dispersion parameter φ.*

**Availability** – An R package of IsoDOT is available at http://www.bios.unc.edu/∼weisun/software/isoform.htm.

Sun W, Liu Y, Crowley JJ, Chen TH, Zhou H, Chu H, Huang S, Kuan PF, Li Y, Miller DR, Shaw GD, Wu Y, Zhabotynsky V, McMillan L, Zou F, Sullivan PF, de Villena FP. (2015) **IsoDOT Detects Differential RNA-isoform Expression/Usage with respect to a Categorical or Continuous Covariate with High Sensitivity and Specificity**. *J Am Stat Assoc* 110(511):975-986. [article]

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Now, a team led by researchers at the University of Pennsylvania Perelman School of Medicine have developed MetaDiff, a random-effects meta-regression model that naturally fits for the above purposes. Through extensive simulations and analysis of an RNA-Seq dataset on human heart failure, they show that the random-effects meta-regression approach is computationally fast, reliable, and can improve the power of differential expression analysis while controlling for false positives due to the effect of covariates or confounding variables. In contrast, several existing methods either fail to control false discovery rate or have reduced power in the presence of covariates or confounding variables.

*Analogy between meta-regression and isoform differential expression analysis in RNA-Seq*

These results indicate that random-effects meta-regression offers a flexible framework for differential expression analysis of isoforms, particularly when gene expression is influenced by other variables.

**Availability** – The source code, compiled JAR package and documentation of MetaDiff are freely available at https://github.com/jiach/MetaDiff

Jia C, Guan W, Yang A, Xiao R, Tang WH, Moravec CS, Margulies KB, Cappola TP, Li M, Li C. (2015) **MetaDiff: differential isoform expression analysis using random-effects meta-regression**. *BMC Bioinformatics* 16(1):208. [article]