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Báo cáo y học: "Improving RNA-Seq expression estimates by correcting for fragment bias"

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: Improving RNA-Seq expression estimates by correcting for fragment bias. | Roberts et al. Genome Biology 2011 12 R22 http genomebiology.eom 2011 12 3 R22 Genome Biology METHOD Open Access Improving RNA-Seq expression estimates by correcting for fragment bias 1 23 2 23 1.4 Adam Roberts Cole Trapnell 1 Julie Donaghey John L Rinn 1 and Lior Pachter1 Abstract The biochemistry of RNA-Seq library preparation results in cDNA fragments that are not uniformly distributed within the transcripts they represent. This non-uniformity must be accounted for when estimating expression levels and we show how to perform the needed corrections using a likelihood based approach. We find improvements in expression estimates as measured by correlation with independently performed qRT-PCR and show that correction of bias leads to improved replicability of results across libraries and sequencing technologies. Background RNA-Seq technology offers the possibility of accurately measuring transcript abundances in a sample of RNA by sequencing of double stranded cDNA 1 . Unfortunately current technological limitations of sequencers require that the cDNA molecules represent only partial fragments of the RNA being probed. The cDNA fragments are obtained by a series of steps often including reverse transcription primed by random hexamers RH or by oligo dT . Most protocols also include a fragmentation step typically RNA hydrolysis or nebulization or alternatively cDNA fragmentation by DNase I treatment or sonication. Many sequencing technologies also require constrained cDNA lengths so a final gel cutting step for size selection may be included. Figure 1 shows how some of these procedures are combined in a typical experiment. The randomness inherent in many of the preparation steps for RNA-Seq leads to fragments whose starting points relative to the transcripts from which they were sequenced appear to be chosen approximately uniformly at random. This observation has been the basis of assumptions underlying a number of RNA-Seq analysis approaches that in computer science .