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

Cao, Lei; Zhang, Quanbao; Song, Hongtao; Lin, Kui; Pang, Erli

doi:10.1016/j.isci.2022.105345

Cited by 3 publications

(4 citation statements)

References 45 publications

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“…MkcDBGAS was also applied to the Iso-Seq data of Amborella . Full-length transcripts and AS events of Amborella were downloaded from DeepASmRNA [ 30 ], involving 1973 AS transcript pairs and 3312 AS events containing 325 ES, 1236 AA, 444 AD and 1307 IR. The 8918 full-length transcripts were put into MkcDBGAS using the model of A. thaliana.…”

Section: Resultsmentioning

confidence: 99%

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

Zhang,

Cao,

Song

et al. 2023

Briefings in Bioinformatics

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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.

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Section: Resultsmentioning

confidence: 99%

“…Cao et al. designed DeepASmRNA [ 30 ], using adjacent high-scoring segment pairs in the results of BLAST to greatly increase the recall while maintaining the precision. DeepASmRNA classified the AS events using an attention-based deep learning model.…”

Section: Introductionmentioning

confidence: 99%

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

Zhang,

Cao,

Song

et al. 2023

Briefings in Bioinformatics

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“…On the other hand, Zhou et al [153] combined convolutional neural networks with transformers in a deep learning model, INTERACT, to predict the effects of genetic variations on DNA methylation levels. Cao et al [154] presented DeepASmRNA, an attentionbased convolutional neural network model, showing promising results for predicting alternative splicing events.…”

Section: Other Topicsmentioning

confidence: 99%

Transformer Architecture and Attention Mechanisms in Genome Data Analysis: A Comprehensive Review

Choi

Lee

2023

Biology

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The emergence and rapid development of deep learning, specifically transformer-based architectures and attention mechanisms, have had transformative implications across several domains, including bioinformatics and genome data analysis. The analogous nature of genome sequences to language texts has enabled the application of techniques that have exhibited success in fields ranging from natural language processing to genomic data. This review provides a comprehensive analysis of the most recent advancements in the application of transformer architectures and attention mechanisms to genome and transcriptome data. The focus of this review is on the critical evaluation of these techniques, discussing their advantages and limitations in the context of genome data analysis. With the swift pace of development in deep learning methodologies, it becomes vital to continually assess and reflect on the current standing and future direction of the research. Therefore, this review aims to serve as a timely resource for both seasoned researchers and newcomers, offering a panoramic view of the recent advancements and elucidating the state-of-the-art applications in the field. Furthermore, this review paper serves to highlight potential areas of future investigation by critically evaluating studies from 2019 to 2023, thereby acting as a stepping-stone for further research endeavors.

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“…Several models have been developed for predicting and identifying alternative splicing events combining deep learning approaches ( Table 2 ). For example, DeepASmRNA is a convolutional neural network (CNN) model capable of identifying alternative splicing events with over 90% accuracy ( Cao et al., 2022 ). The Deep Splicing Code model uses raw RNA sequences to classify exons based on their alternative splicing behavior and performs well in identifying splice sites and motifs ( Louadi et al., 2019 ).…”

Section: Deep Learning Based Alternative Splicing Studymentioning

confidence: 99%

Advances in alternative splicing identification: deep learning and pantranscriptome

Shen,

Hu,

Huang

et al. 2023

Front. Plant Sci.

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In plants, alternative splicing is a crucial mechanism for regulating gene expression at the post-transcriptional level, which leads to diverse proteins by generating multiple mature mRNA isoforms and diversify the gene regulation. Due to the complexity and variability of this process, accurate identification of splicing events is a vital step in studying alternative splicing. This article presents the application of alternative splicing algorithms with or without reference genomes in plants, as well as the integration of advanced deep learning techniques for improved detection accuracy. In addition, we also discuss alternative splicing studies in the pan-genomic background and the usefulness of integrated strategies for fully profiling alternative splicing.

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

Cited by 3 publications

References 45 publications

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

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

Transformer Architecture and Attention Mechanisms in Genome Data Analysis: A Comprehensive Review

Advances in alternative splicing identification: deep learning and pantranscriptome

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