文件名称：Data analysis
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更新时间：2018-03-01 13:42:32
RNA-seq
Gene expression patterns hold valuable information regarding the specific functions of particular cell and tissue types. Initially, the analysis of transcripts was limited to testing changes in expression of only a few genes at a time using low-throughput methods such as in situ hybridization and RT-PCR. In the past decade, with the ability to study genetic information at the genome-wide scale, microarrays have become the primary high-throughput method for gene expression analysis. Based on relative changes in the amount of hybridization of cDNA, relative expression values are computed for populations of genes. Microarray analysis has certain limitations, including the inability to identify novel transcripts, a limited dynamic range for detection, and difficulty in replicability and inter-experimental comparison. RNA sequencing (RNA-Seq) overcomes many of these problems. Making use of high-throughput next-generation sequencing methods, sequencing the entire transcriptome permits both transcript discovery and robust digital quantitative analysis of gene expression levels. This document reviews basic biological principles of RNA-Seq, basic computational methods for analysis and use of RNA-Seq data, and potential medical and clinical applications of RNA-Seq.