Overview of available methods for diverse RNA-Seq data analyses  

作　　者：CHEN Geng WANG Charles SHI TieLiu 

机构地区：[1]Center for Bioinformatics and Computational Biology, Institute of Biomedical Sciences, School of Life Science, East China Normal University, Shanghai 200241, China [2]Functional Genomics Core, Beckman Research Institute, City of Hope Comprehensive Cancer Center, Duarte, CA 91010, USA [3]Shanghai Information Center for Life Sciences, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai 200031, China

出　　处：《Science China(Life Sciences)》2011年第12期1121-1128,共8页中国科学（生命科学英文版）

基　　金：supported by the National Basic Research Program of China (Grant Nos. 2010CB945401, 2007CB108800);National Natural Science Foundation of China (Grant Nos. 30870575,31071162,31000590);Science and Technology Commission of Shanghai Municipality (Grant No. 11DZ2260300)

摘　　要：RNA-Seq technology is becoming widely used in various transcriptomics studies;however,analyzing and interpreting the RNA-Seq data face serious challenges.With the development of high-throughput sequencing technologies,the sequencing cost is dropping dramatically with the sequencing output increasing sharply.However,the sequencing reads are still short in length and contain various sequencing errors.Moreover,the intricate transcriptome is always more complicated than we expect.These challenges proffer the urgent need of efficient bioinformatics algorithms to effectively handle the large amount of transcriptome sequencing data and carry out diverse related studies.This review summarizes a number of frequently-used applications of transcriptome sequencing and their related analyzing strategies,including short read mapping,exon-exon splice junction detection,gene or isoform expression quantification,differential expression analysis and transcriptome reconstruction.RNA-Seq technology is becoming widely used in various transcriptomics studies; however, analyzing and interpreting the RNA-Seq data face serious challenges. With the development of high-throughput sequencing technologies, the sequencing cost is dropping dramatically with the sequencing output increasing sharply. However, the sequencing reads are still short in length and contain various sequencing errors. Moreover, the intricate transcriptome is always more complicated than we expect. These challenges proffer the urgent need of efficient bioinformatics algorithms to effectively handle the large amount of tran- scriptome sequencing data and carry out diverse related studies. This review summarizes a number of frequently-used applica- tions of transcriptome sequencing and their related analyzing strategies, including short read mapping, exon-exon splice junc- tion detection, gene or isoform expression quantification, differential expression analysis and transcriptome reconstruction.

关 键 词：next generation sequencing TRANSCRIPTOME RNA-Seq data analysis TRANSCRIPTOMICS 

分 类 号：Q811.4[生物学—生物工程] TP18[自动化与计算机技术—控制理论与控制工程]