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Hwang H, Jeon H, Yeo N, Baek D. Big data and deep learning for RNA biology. Exp Mol Med 2024:10.1038/s12276-024-01243-w. [PMID: 38871816 DOI: 10.1038/s12276-024-01243-w] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2024] [Revised: 02/27/2024] [Accepted: 03/05/2024] [Indexed: 06/15/2024] Open
Abstract
The exponential growth of big data in RNA biology (RB) has led to the development of deep learning (DL) models that have driven crucial discoveries. As constantly evidenced by DL studies in other fields, the successful implementation of DL in RB depends heavily on the effective utilization of large-scale datasets from public databases. In achieving this goal, data encoding methods, learning algorithms, and techniques that align well with biological domain knowledge have played pivotal roles. In this review, we provide guiding principles for applying these DL concepts to various problems in RB by demonstrating successful examples and associated methodologies. We also discuss the remaining challenges in developing DL models for RB and suggest strategies to overcome these challenges. Overall, this review aims to illuminate the compelling potential of DL for RB and ways to apply this powerful technology to investigate the intriguing biology of RNA more effectively.
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Affiliation(s)
- Hyeonseo Hwang
- School of Biological Sciences, Seoul National University, Seoul, Republic of Korea
| | - Hyeonseong Jeon
- Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul, Republic of Korea
- Genome4me Inc., Seoul, Republic of Korea
| | - Nagyeong Yeo
- School of Biological Sciences, Seoul National University, Seoul, Republic of Korea
| | - Daehyun Baek
- School of Biological Sciences, Seoul National University, Seoul, Republic of Korea.
- Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul, Republic of Korea.
- Genome4me Inc., Seoul, Republic of Korea.
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Shen F, Hu C, Huang X, He H, Yang D, Zhao J, Yang X. Advances in alternative splicing identification: deep learning and pantranscriptome. FRONTIERS IN PLANT SCIENCE 2023; 14:1232466. [PMID: 37790793 PMCID: PMC10544900 DOI: 10.3389/fpls.2023.1232466] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/31/2023] [Accepted: 08/28/2023] [Indexed: 10/05/2023]
Abstract
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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Affiliation(s)
- Fei Shen
- Institute of Biotechnology, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China
| | - Chenyang Hu
- Institute of Biotechnology, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China
- Shanxi Key Lab of Chinese Jujube, College of Life Science, Yan’an University, Yan’an, Shanxi, China
| | - Xin Huang
- Institute of Biotechnology, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China
| | - Hao He
- Institute of Biotechnology, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China
| | - Deng Yang
- Institute of Biotechnology, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China
| | - Jirong Zhao
- Shanxi Key Lab of Chinese Jujube, College of Life Science, Yan’an University, Yan’an, Shanxi, China
| | - Xiaozeng Yang
- Institute of Biotechnology, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China
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Tian Z, Fang H, Teng Z, Ye Y. GOGCN: Graph Convolutional Network on Gene Ontology for Functional Similarity Analysis of Genes. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2023; 20:1053-1064. [PMID: 35687647 DOI: 10.1109/tcbb.2022.3181300] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/04/2023]
Abstract
The measurement of gene functional similarity plays a critical role in numerous biological applications, such as gene clustering, the construction of gene similarity networks. However, most existing approaches still rely heavily on traditional computational strategies, which are not guaranteed to achieve satisfactory performance. In this study, we propose a novel computational approach called GOGCN to measure gene functional similarity by modeling the Gene Ontology (GO) through Graph Convolutional Network (GCN). GOGCN is a graph-based approach that performs sufficient representation learning for terms and relations in the GO graph. First, GOGCN employs the GCN-based knowledge graph embedding (KGE) model to learn vector representations (i.e., embeddings) for all entities (i.e., terms). Second, GOGCN calculates the semantic similarity between two terms based on their corresponding vector representations. Finally, GOGCN estimates gene functional similarity by making use of the pair-wise strategy. During the representation learning period, GOGCN promotes semantic interaction between terms through GCN, thereby capturing the rich structural information of the GO graph. Further experimental results on various datasets suggest that GOGCN is superior to the other state-of-the-art approaches, which shows its reliability and effectiveness.
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Qiu S, Yu G, Lu X, Domeniconi C, Guo M. Isoform function prediction by Gene Ontology embedding. Bioinformatics 2022; 38:4581-4588. [PMID: 35997558 DOI: 10.1093/bioinformatics/btac576] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2022] [Revised: 07/13/2022] [Accepted: 08/22/2022] [Indexed: 12/24/2022] Open
Abstract
MOTIVATION High-resolution annotation of gene functions is a central task in functional genomics. Multiple proteoforms translated from alternatively spliced isoforms from a single gene are actual function performers and greatly increase the functional diversity. The specific functions of different isoforms can decipher the molecular basis of various complex diseases at a finer granularity. Multi-instance learning (MIL)-based solutions have been developed to distribute gene(bag)-level Gene Ontology (GO) annotations to isoforms(instances), but they simply presume that a particular annotation of the gene is responsible by only one isoform, neglect the hierarchical structures and semantics of massive GO terms (labels), or can only handle dozens of terms. RESULTS We propose an efficacy approach IsofunGO to differentiate massive functions of isoforms by GO embedding. Particularly, IsofunGO first introduces an attributed hierarchical network to model massive GO terms, and a GO network embedding strategy to learn compact representations of GO terms and project GO annotations of genes into compressed ones, this strategy not only explores and preserves hierarchy between GO terms but also greatly reduces the prediction load. Next, it develops an attention-based MIL network to fuse genomics and transcriptomics data of isoforms and predict isoform functions by referring to compressed annotations. Extensive experiments on benchmark datasets demonstrate the efficacy of IsofunGO. Both the GO embedding and attention mechanism can boost the performance and interpretability. AVAILABILITYAND IMPLEMENTATION The code of IsofunGO is available at http://www.sdu-idea.cn/codes.php?name=IsofunGO. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Sichao Qiu
- School of Software, Shandong University, Jinan, Shandong 250101, China.,Joint SDU-NTU Centre for Artificial Intelligence Research, Shandong University, Jinan, Shandong 250101, China
| | - Guoxian Yu
- School of Software, Shandong University, Jinan, Shandong 250101, China.,Joint SDU-NTU Centre for Artificial Intelligence Research, Shandong University, Jinan, Shandong 250101, China
| | - Xudong Lu
- School of Software, Shandong University, Jinan, Shandong 250101, China.,Joint SDU-NTU Centre for Artificial Intelligence Research, Shandong University, Jinan, Shandong 250101, China
| | | | - Maozu Guo
- College of Electrical and Information Engineering, Beijing University of Civil Engineering and Architecture, Beijing 100044, China
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Tan H, Qiu S, Wang J, Yu G, Guo W, Guo M. Weighted deep factorizing heterogeneous molecular network for genome-phenome association prediction. Methods 2022; 205:18-28. [PMID: 35690250 DOI: 10.1016/j.ymeth.2022.05.008] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2022] [Revised: 05/14/2022] [Accepted: 05/26/2022] [Indexed: 11/18/2022] Open
Abstract
Genome-phenome association (GPA) prediction can promote the understanding of biological mechanisms about complex pathology of phenotypes (i.e., traits and diseases). Traditional heterogeneous network-based GPA approaches overwhelmingly need to project heterogeneous data toward homogeneous network for data fusion and prediction, such projections result in the loss of heterogeneous network structure information. Matrix factorization based data fusion can avoid such projection by integrating multi-type data in a coherent way, but they typically perform linear factorization and cannot mine the nonlinear relationships between molecules, which compromise the accuracy of GPA analysis. Furthermore, most of them can not selectively synergy network topology and node attribution information in a principle way. In this paper, we propose a weighted deep matrix factorization based solution (WDGPA) to predict GPAs by selectively and differentially fusing heterogeneous molecular network and diverse attributes of nodes. WDGPA firstly assigns weights to inter/intra-relational data matrices and attribute data matrices, and performs deep matrix factorization on these matrices of heterogeneous network in a cooperative manner to obtain the nonlinear representations of different nodes. In addition, it performs low-rank representation learning on the attribute data with the shared nonlinear representations. In this way, both the network topology and node attributes are jointly mined to explore the representations of molecules and complex interplays between molecules and phenotypes. WDGPA then uses the representational vectors of gene and phenotype nodes to predict GPAs. Experimental results on maize and human datasets confirm that WDGPA outperforms competitive methods by a large margin under different evaluation protocols.
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Affiliation(s)
- Haojiang Tan
- School of Software, Shandong University, Jinan, China; Joint SDU-NTU Centre For AI Research (C-FAIR), Shandong University, Jinan, China.
| | - Sichao Qiu
- School of Software, Shandong University, Jinan, China; Joint SDU-NTU Centre For AI Research (C-FAIR), Shandong University, Jinan, China.
| | - Jun Wang
- Joint SDU-NTU Centre For AI Research (C-FAIR), Shandong University, Jinan, China.
| | - Guoxian Yu
- Joint SDU-NTU Centre For AI Research (C-FAIR), Shandong University, Jinan, China.
| | - Wei Guo
- Joint SDU-NTU Centre For AI Research (C-FAIR), Shandong University, Jinan, China.
| | - Maozu Guo
- College of Electrical and Information Engineering, Beijing University of Civil Engineering and Architecture, Beijing, China.
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