ASTool: An Easy-to-Use Tool to Accurately Identify Alternative Splicing Events from Plant RNA-Seq Data

iFLAS: positive-unlabeled learning facilitates full-length transcriptome-based identification and functional exploration of alternatively spliced isoforms in maize

The advent of full-length transcriptome sequencing technologies has accelerated the discovery of novel splicing isoforms. However, existing alternative splicing (AS) tools are either tailored for short-read RNA-Seq data or designed for human and animal studies. The disparities in AS patterns between plants and animals still pose a challenge to the reliable identification and functional exploration of novel isoforms in plants. Here, we developed integrated full-length alternative splicing analysis (iFLAS), a plant-optimized AS toolkit that introduced a semi-supervised machine learning method known as positive-unlabeled (PU) learning to accurately identify novel isoforms. iFLAS also enables the investigation of AS functions from various perspectives, such as differential AS, poly(A) tail length, and allele-specific AS (ASAS) analyses. By applying iFLAS to three full-length transcriptome sequencing datasets, we systematically identified and functionally characterized maize (Zea mays) AS patterns. We found intron retention not only introduces premature termination codons, resulting in lower expression levels of isoforms, but may also regulate the length of 3'UTR and poly(A) tail, thereby affecting the functional differentiation of isoforms. Moreover, we observed distinct ASAS patterns in two genes within heterosis offspring, highlighting their potential value in breeding. These results underscore the broad applicability of iFLAS in plant full-length transcriptome-based AS research.

MkcDBGAS: a reference-free approach to identify comprehensive alternative splicing events in a transcriptome

Alternative splicing (AS) is an essential post-transcriptional mechanism that regulates many biological processes. However, identifying comprehensive types of AS events without guidance from a reference genome is still a challenge. Here, we proposed a novel method, MkcDBGAS, to identify all seven types of AS events using transcriptome alone, without a reference genome. MkcDBGAS, modeled by full-length transcripts of human and Arabidopsis thaliana, consists of three modules. In the first module, MkcDBGAS, for the first time, uses a colored de Bruijn graph with dynamic- and mixed- kmers to identify bubbles generated by AS with precision higher than 98.17% and detect AS types overlooked by other tools. In the second module, to further classify types of AS, MkcDBGAS added the motifs of exons to construct the feature matrix followed by the XGBoost-based classifier with the accuracy of classification greater than 93.40%, which outperformed other widely used machine learning models and the state-of-the-art methods. Highly scalable, MkcDBGAS performed well when applied to Iso-Seq data of Amborella and transcriptome of mouse. In the third module, MkcDBGAS provides the analysis of differential splicing across multiple biological conditions when RNA-sequencing data is available. MkcDBGAS is the first accurate and scalable method for detecting all seven types of AS events using the transcriptome alone, which will greatly empower the studies of AS in a wider field.

PlaASDB: a comprehensive database of plant alternative splicing events in response to stress

BACKGROUND

Alternative splicing (AS) is a co-transcriptional regulatory mechanism of plants in response to environmental stress. However, the role of AS in biotic and abiotic stress responses remains largely unknown. To speed up our understanding of plant AS patterns under different stress responses, development of informative and comprehensive plant AS databases is highly demanded.

DESCRIPTION

In this study, we first collected 3,255 RNA-seq data under biotic and abiotic stresses from two important model plants (Arabidopsis and rice). Then, we conducted AS event detection and gene expression analysis, and established a user-friendly plant AS database termed PlaASDB. By using representative samples from this highly integrated database resource, we compared AS patterns between Arabidopsis and rice under abiotic and biotic stresses, and further investigated the corresponding difference between AS and gene expression. Specifically, we found that differentially spliced genes (DSGs) and differentially expressed genes (DEG) share very limited overlapping under all kinds of stresses, suggesting that gene expression regulation and AS seemed to play independent roles in response to stresses. Compared with gene expression, Arabidopsis and rice were more inclined to have conserved AS patterns under stress conditions.

CONCLUSION

PlaASDB is a comprehensive plant-specific AS database that mainly integrates the AS and gene expression data of Arabidopsis and rice in stress response. Through large-scale comparative analyses, the global landscape of AS events in Arabidopsis and rice was observed. We believe that PlaASDB could help researchers understand the regulatory mechanisms of AS in plants under stresses more conveniently. PlaASDB is freely accessible at http://zzdlab.com/PlaASDB/ASDB/index.html .

DeepASmRNA: Reference-free prediction of alternative splicing events with a scalable and interpretable deep learning model

Alternative splicing is crucial for a wide range of biological processes. However, limited by the availability of reference genomes, genome-wide patterns of alternative splicing remain unknown in most nonmodel organisms. We present an attention-based convolutional neural network model, DeepASmRNA, for predicting alternative splicing events using only transcriptomic data. DeepASmRNA consists of two parts: identification of alternatively spliced transcripts and classification of alternative splicing events, which outperformed the state-of-the-art method, AStrap, and other deep learning models. Then, we utilize transfer learning to increase the performance in species with limited training data and use an interpretation method to decipher splicing codes. Finally, applying Amborella, DeepASmRNA can identify more AS events than AStrap while maintaining the same level of precision, suggesting that DeepASmRNA has superior sensitivity to identify alternative splicing events. In summary, DeepASmRNA is scalable and interpretable for detecting genome-wide patterns of alternative splicing in species without a reference genome.

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DeepASmRNA uses only the transcriptome to predict alternative splicing events

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DeepASmRNA identifies adjacent HSPs to greatly improve the recall

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DeepASmRNA uses attention-based convolutional neural network to classify AS events

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Transfer learning is used to increase the predictive power of a target species

Plant Biology and Biotechnology: Focus on Genomics and Bioinformatics

The study of molecular mechanisms of plant stress response is important for agrobiotechnology applications as it was discussed at series of recent bioinformatics conferences [...].