A balanced method detecting differentially expressed genes for RNA-sequencing data*

As RNA-seq becomes the main application for gene expression profiling, many computational methods have been developed for analysis of differential expression (DE) genes in RNA-seq data. However, most existing algorithms prefer to call long genes as DE. In this work, we set out to gain insights into the inuence of gene length on RNA-seq data analysis, and figure out the effect on variance estimation of RNA-seq data, which is important for identification of DE genes. We proposed a balanced method hunting for short DE genes with a gene length factor. Computational experiments indicate the good performance of our method.