Tuyển tập các báo cáo nghiên cứu về y học được đăng trên tạp chí y học Wertheim cung cấp cho các bạn kiến thức về ngành y đề tài: A scaling normalization method for differential expression analysis of RNA-seq data. | Robinson and Oshlack Genome Biology 2010 11 R25 http 2010 11 3 R25 Genome Biology METHOD Open Access A scaling normalization method for differential expression analysis of RNA-seq data Mark D Robinson1 2 Alicia Oshlack 1 Abstract The fine detail provided by sequencing-based transcriptome surveys suggests that RNA-seq is likely to become the platform of choice for interrogating steady state RNA. In order to discover biologically important changes in expression we show that normalization continues to be an essential step in the analysis. We outline a simple and effective method for performing normalization and show dramatically improved results for inferring differential expression in simulated and publicly available data sets. Background The transcriptional architecture is a complex and dynamic aspect of a cell s function. Next generation sequencing of steady state RNA RNA-seq gives unprecedented detail about the RNA landscape within a cell. Not only can expression levels of genes be interrogated without specific prior knowledge but comparisons of expression levels between genes within a sample can be made. It has also been demonstrated that splicing variants 1 2 and single nucleotide polymorphisms 3 can be detected through sequencing the transcriptome opening up the opportunity to interrogate allele-specific expression and RNA editing. An important aspect of dealing with the vast amounts of data generated from short read sequencing is the processing methods used to extract and interpret the information. Experience with microarray data has repeatedly shown that normalization is a critical component of the processing pipeline allowing accurate estimation and detection of differential expression DE 4 . The aim of normalization is to remove systematic technical effects that occur in the data to ensure that technical bias has minimal impact on the results. However the procedure for generating RNA-seq data is fundamentally different from that for .
