tailieunhanh - Báo cáo y học: "Gene ontology analysis for RNA-seq: accounting for selection 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: Gene ontology analysis for RNA-seq: accounting for selection bias. | Young et al. Genome Biology 2010 11 R14 http 2010 11 2 R14 w Genome Biology METHOD Open Access Gene ontology analysis for RNA-seq accounting for selection bias Matthew D Young Matthew J Wakefield Gordon K Smyth and Alicia Oshlack Abstract We present GOseq an application for performing Gene Ontology GO analysis on RNA-seq data. GO analysis is widely used to reduce complexity and highlight biological processes in genome-wide expression studies but standard methods give biased results on RNA-seq data due to over-detection of differential expression for long and highly expressed transcripts. Application of GOseq to a prostate cancer data set shows that GOseq dramatically changes the results highlighting categories more consistent with the known biology. Background Next generation sequencing of RNA RNA-seq gives unprecedented detail about the transcriptional landscape of an organism. Not only is it possible to accurately measure expression levels of transcripts in a sample 1 but this new technology promises to deliver a range of additional benefits such as the investigation of alternative splicing 2 allele specific expression 3 and RNA editing 4 . However in order to accurately make use of the data it is vital that analysis techniques are developed to take into account the technical features of RNA-seq output. As many of the specific technical properties of RNA-seq data are not present in previous technologies such as microarrays naive application of the same analysis methodologies developed for these older technologies may lead to bias in the results. In RNA-seq experiments the expression level of a transcript is estimated from the number of reads that map to that transcript. In many applications the expected read count for a transcript is proportional to the gene s expression level multiplied by its transcript length. Therefore even when two transcripts are expressed at the same level differences in length will yield differing numbers of total reads.