Center for Bioinformatics and Computational Biology, Institute of Biomedical Sciences, School of Life Science, East China Normal University, Shanghai 200241, China.
Sci China Life Sci. 2011 Dec;54(12):1121-8. doi: 10.1007/s11427-011-4255-x. Epub 2012 Jan 7.
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 技术在各种转录组学研究中得到了广泛应用；然而，RNA-Seq 数据的分析和解释面临着严峻的挑战。随着高通量测序技术的发展，测序成本急剧下降，测序通量大幅增加。然而，测序读段仍然较短，并且包含各种测序错误。此外，复杂的转录组通常比我们预期的更为复杂。这些挑战迫切需要高效的生物信息学算法来有效地处理大量的转录组测序数据，并开展各种相关研究。本综述总结了转录组测序的一些常用应用及其相关分析策略，包括短读段映射、外显子-外显子剪接连接检测、基因或异构体表达定量、差异表达分析和转录组重构。
