Single-molecule Real-time (SMRT) Isoform Sequencing (Iso-Seq) in Plants: The Status of the Bioinformatics Tools to Unravel the Transcriptome Complexity - Gao, Yubang; Xi, Feihu; zhang, Hangxiao; Liu, Xuqing; Wang, Huiyuan; zhao, Liangzhen; Reddy, Anireddy S.N.; Gu, Lianfeng (Current Bioinformatics, 2019)
Background: The advent of the Single-Molecule Real-time (SMRT) Isoform Sequencing (Iso-Seq) has paved the way to obtain longer full-length transcripts. This method was found to be much superior in identifying full-length splice variants and other post-transcriptional events as compared to the Next Generation Sequencing (NGS)-based short read sequencing (RNA-Seq). Several different bioinformatics tools to analyze the Iso-Seq data have been developed and some of them are still being refined to address different aspects of transcriptome complexity. However, a comprehensive summary of the available tools and their utility is still lacking. Objective: Here, we summarized the existing Iso-Seq analysis tools and presented an integrated bioinformatics pipeline for Iso-Seq analysis, which overcomes the limitations of NGS and generates long contiguous Full-Length Non-Chimeric (FLNC) reads for the analysis of posttranscriptional events. Results: In this review, we summarized recent applications of Iso-Seq in plants, which include improved genome annotations, identification of novel genes and lncRNAs, identification of fulllength splice isoforms, detection of novel Alternative Splicing (AS) and Alternative Polyadenylation (APA) events. In addition, we also discussed the bioinformatics pipeline for comprehensive Iso-Seq data analysis, including how to reduce the error rate in the reads and how to identify and quantify post-transcriptional events. Furthermore, the visualization approach of Iso-Seq was discussed as well. Finally, we discussed methods to combine Iso-Seq data with RNA-Seq for transcriptome quantification. Conclusion: Overall, this review demonstrates that the Iso-Seq is pivotal for analyzing transcriptome complexity and this new method offers unprecedented opportunities to comprehensively understand transcripts diversity.
Keywords: novel genes