Nanopore sequencing from Oxford Nanopore Technologies (ONT) and Pacific BioSciences (PacBio) single-molecule real-time (SMRT) long-read isoform sequencing (Iso-Seq) are revolutionizing the way transcriptomes are analyzed. These methods offer many advantages over most widely used high-throughput short-read RNA sequencing (RNA-Seq) approaches and allow a comprehensive analysis of transcriptomes in identifying full-length splice isoforms and several other post-transcriptional events. In addition, direct RNA-Seq provides valuable information about RNA modifications, which are lost during the PCR amplification step in other methods. Here, we present a comprehensive summary of important applications of these technologies in plants, including identification of complex alternative splicing (AS), full-length splice variants, fusion transcripts, and alternative polyadenylation (APA) events. Furthermore, we discuss the impact of the newly developed nanopore direct RNA-Seq in advancing epitranscriptome research in plants. Additionally, we summarize computational tools for identifying and quantifying full-length isoforms and other co/post-transcriptional events and discussed some of the limitations with these methods. Sequencing of transcriptomes using these new single-molecule long-read methods will unravel many aspects of transcriptome complexity in unprecedented ways as compared to previous short-read sequencing approaches. Analysis of plant transcriptomes with these new powerful methods that require minimum sample processing is likely to become the norm and is expected to uncover novel co/post-transcriptional gene regulatory mechanisms that control biological outcomes during plant development and in response to various stresses.

BackgroundMoso bamboo (Phyllostachys edulis) is a well-known bamboo species of high economic value in the textile industry due to its rapid growth. Phytohormones, which are master regulators of growth and development, serve as important endogenous signals. However, the mechanisms through which phytohormones regulate growth in moso bamboo remain unknown to date.ResultsHere, we reported that exogenous gibberellins (GA) applications resulted in a significantly increased internode length and lignin condensation. Transcriptome sequencing revealed that photosynthesis-related genes were enriched in the GA-repressed gene class, which was consistent with the decrease in leaf chlorophyll concentrations and the lower rate of photosynthesis following GA treatment. Exogenous GA applications on seedlings are relatively easy to perform, thus we used 4-week-old whole seedlings of bamboo for GA- treatment followed by high throughput sequencing. In this study, we identified 932 cis-nature antisense transcripts (cis-NATs), and 22,196 alternative splicing (AS) events in total. Among them, 42 cis-nature antisense transcripts (cis-NATs) and 442 AS events were differentially expressed upon exposure to exogenous GA3, suggesting that post-transcriptional regulation might be also involved in the GA3 response. Targets of differential expression of cis-NATs included genes involved in hormone receptor, photosynthesis and cell wall biogenesis. For example, LAC4 and its corresponding cis-NATs were GA3-induced, and may be involved in the accumulation of lignin, thus affecting cell wall composition.ConclusionsThis study provides novel insights illustrating how GA alters post-transcriptional regulation and will shed light on the underlying mechanism of growth modulated by GA in moso bamboo.Electronic supplementary materialThe online version of this article (10.1186/s12870-018-1336-z) contains supplementary material, which is available to authorized users.

Circular RNAs, including circular exonic RNAs (circRNA), circular intronic RNAs (ciRNA) and exon-intron circRNAs (EIciRNAs), are a new type of noncoding RNAs. Growing shoots of moso bamboo (Phyllostachys edulis) represent an excellent model of fast growth and their circular RNAs have not been studied yet. To understand the potential regulation of circular RNAs, we systematically characterized circular RNAs from eight different developmental stages of rapidly growing shoots. Here, we identified 895 circular RNAs including a subset of mutually inclusive circRNA. These circular RNAs were generated from 759 corresponding parental coding genes involved in cellulose, hemicellulose and lignin biosynthetic process. Gene co-expression analysis revealed that hub genes, such as DEFECTIVE IN RNA-DIRECTED DNA METHYLATION 1 (DRD1), MAINTENANCE OF METHYLATION (MOM), dicerlike 3 (DCL3) and ARGONAUTE 1 (AGO1), were significantly enriched giving rise to circular RNAs. The expression level of these circular RNAs presented correlation with its linear counterpart according to transcriptome sequencing. Further protoplast transformation experiments indicated that overexpressing circ-bHLH93 generating from transcription factor decreased its linear transcript. Finally, the expression profiles suggested that circular RNAs may have interplay with miRNAs to regulate their cognate linear mRNAs, which was further supported by overexpressing miRNA156 decreasing the transcript of circ-TRF-1 and linear transcripts of TRF-1. Taken together, the overall profile of circular RNAs provided new insight into an unexplored category of long noncoding RNA regulation in moso bamboo.

Background: The BLAST (Basic Local Alignment Search Tool) algorithm has been widely used for sequence similarity searching. Analogously, the public phenotype images must be efficiently retrieved using biological images as queries and identify the phenotype with high similarity. Due to the accumulation of genotype-phenotype-mapping data, a system of searching for similar phenotypes is not available due to the bottleneck of image processing. Objective: In this study, we focus on the identification of similar query phenotypic images by searching the biological phenotype database, including information about loss-of-function and gain-of-function. Methods: We propose a deep convolutional autoencoder architecture to segment the biological phenotypic images and develop a phenotype retrieval system to enable a better understanding of genotype–phenotype correlation. Results: This study shows how deep convolutional autoencoder architecture can be trained on images from biological phenotypes to achieve state-of-the-art performance in a phenotypic images retrieval system. Conclusion: Taken together, the phenotype analysis system can provide further information on the correlation between genotype and phenotype. Additionally, it is obvious that the neural network model of image segmentation and the phenotype retrieval system is equally suitable for any species, which has enough phenotype images to train the neural network.

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.