title: Detecting differentially expressed genes by smoothing effect of gene length on variance estimation.
abstract: Next-generation sequencing technologies are widely used in genome research, and RNA sequencing (RNA-Seq) is becoming the main application for gene expression profiling. A large number of computational methods have been developed for analyzing differentially expressed (DE) genes in RNA-Seq data. However, most existing algorithms prefer to call long genes as DE. Short DE genes are rarely detected. In this work, we set out to gain insight into the influence of gene length on RNA-Seq data analysis and to figure out the effect of gene length on variance estimation of RNA-Seq read counts, which is important for statistic test to identify DE genes. We proposed a balanced method of hunting for short DE genes with significance by smoothing a gene length factor. Computational experiments indicate that our method performs well. Software available: http://www.iipl.fudan.edu.cn/lenseq/.
authors.affiliation: 1 Shanghai Key Laboratory of Intelligent Information Processing, School of Computer Science, Fudan University, Shanghai, P. R. China.
authors.forename: Jinyang
authors.initials: J
authors.lastname: Tang
authors.affiliation: 1 Shanghai Key Laboratory of Intelligent Information Processing, School of Computer Science, Fudan University, Shanghai, P. R. China.
authors.forename: Fei
authors.initials: F
authors.lastname: Wang
mesh_terms: D000465:Algorithms; D000818:Animals; D001483:Base Sequence; D001921:Brain / Q000502:physiology; D003198:Computer Simulation; D020869:Gene Expression Profiling / Q000379:methods*; D006801:Humans; D051379:Mice; D020411:Oligonucleotide Array Sequence Analysis; D017423:Sequence Analysis, RNA / Q000379:methods*; D012984:Software
keywords: Differential expression; RNA-Seq; gene length