Overview of available methods for diverse RNA-Seq data analyses

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 increa...

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Bibliographic Details
Published inScience China. Life sciences Vol. 54; no. 12; pp. 1121 - 1128
Main Authors Chen, Geng, Wang, Charles, Shi, TieLiu
Format Journal Article
LanguageEnglish
Published Beijing Science China Press 01.12.2011
Springer Nature B.V
Subjects
Biomedical and Life Sciences
Exons
Gene Expression Profiling
Life Sciences
Reviews
RNA Splicing
RNA序列
Sequence Analysis, RNA - methods
Transcriptome
外显子
定量分析
差异表达分析
数据分析
测序技术
生物信息学
转录组
RNA-Seq data analysis
transcriptomics
next generation sequencing
transcriptome
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Summary: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.
Bibliography:next generation sequencing, transcriptome, RNA-Seq data analysis, transcriptomics
11-5841/Q
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.
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ISSN:1674-7305
1869-1889
DOI:10.1007/s11427-011-4255-x