摘要
RNA-sequencing(RNA-seq),based on next-generation sequencing technologies,has rapidly become a standard and popular technology for transcriptome analysis.However,serious challenges still exist in analyzing and interpreting the RNA-seq data.With the development of high-throughput sequencing technology,the sequencing depth of RNA-seq data increases explosively.The intricate biological process of transcriptome is more complicated and diversified beyond our imagination.Moreover,most of the remaining organisms still have no available reference genome or have only incomplete genome annotations.Therefore,a large number of bioinformatics methods for various transcriptomics studies are proposed to effectively settle these challenges.This review comprehensively summarizes the various studies in RNA-seq data analysis and their corresponding analysis methods,including genome annotation,quality control and pre-processing of reads,read alignment,transcriptome assembly,gene and isoform expression quantification,differential expression analysis,data visualization and other analyses.
RNA-sequencing （RNA-seq）, based on next-generation sequencing technologies, has rapidly become a standard and popular technology for transcriptome analysis. However, serious challenges still exist in analyzing and interpreting the RNA-seq data. With the development of high-throughput sequencing technology, the sequencing depth of RNA-seq data increases explosively. The intricate biological process of transcriptome is more complicated and diversified beyond our imagination. Moreover, most of the remaining organisms still have no available reference genome or have only incomplete genome annotations. Therefore, a large number of bioinformatics methods for various transcriptomics studies are proposed to effectively settle these challenges. This review comprehensively summarizes the various studies in RNA-seq data analysis and their corresponding analysis methods, inclu- ding genome annotation, quality control and pre-processing of reads, read alignment,transcriptome assembly, gene and isoform expression quantification, differential expression analysis, data visualization and other analyses.
