1
|
Wu Q, Li Y, Wang Q, Zhao X, Sun D, Liu B. Identification of DNA motif pairs on paired sequences based on composite heterogeneous graph. Front Genet 2024; 15:1424085. [PMID: 38952710 PMCID: PMC11215013 DOI: 10.3389/fgene.2024.1424085] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2024] [Accepted: 05/22/2024] [Indexed: 07/03/2024] Open
Abstract
Motivation The interaction between DNA motifs (DNA motif pairs) influences gene expression through partnership or competition in the process of gene regulation. Potential chromatin interactions between different DNA motifs have been implicated in various diseases. However, current methods for identifying DNA motif pairs rely on the recognition of single DNA motifs or probabilities, which may result in local optimal solutions and can be sensitive to the choice of initial values. A method for precisely identifying DNA motif pairs is still lacking. Results Here, we propose a novel computational method for predicting DNA Motif Pairs based on Composite Heterogeneous Graph (MPCHG). This approach leverages a composite heterogeneous graph model to identify DNA motif pairs on paired sequences. Compared with the existing methods, MPCHG has greatly improved the accuracy of motifs prediction. Furthermore, the predicted DNA motifs demonstrate heightened DNase accessibility than the background sequences. Notably, the two DNA motifs forming a pair exhibit functional consistency. Importantly, the interacting TF pairs obtained by predicted DNA motif pairs were significantly enriched with known interacting TF pairs, suggesting their potential contribution to chromatin interactions. Collectively, we believe that these identified DNA motif pairs held substantial implications for revealing gene transcriptional regulation under long-range chromatin interactions.
Collapse
Affiliation(s)
- Qiuqin Wu
- School of Mathematics, Shandong University, Jinan, China
| | - Yang Li
- Department of Biomedical Informatics, College of Medicine, The Ohio State University, Columbus, OH, United States
| | - Qi Wang
- School of Mathematics, Shandong University, Jinan, China
| | - Xiaoyu Zhao
- School of Mathematics, Shandong University, Jinan, China
| | - Duanchen Sun
- School of Mathematics, Shandong University, Jinan, China
| | - Bingqiang Liu
- School of Mathematics, Shandong University, Jinan, China
| |
Collapse
|
2
|
Liu X, Zhang H, Zeng Y, Zhu X, Zhu L, Fu J. DRANetSplicer: A Splice Site Prediction Model Based on Deep Residual Attention Networks. Genes (Basel) 2024; 15:404. [PMID: 38674339 PMCID: PMC11048956 DOI: 10.3390/genes15040404] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2024] [Revised: 03/20/2024] [Accepted: 03/23/2024] [Indexed: 04/28/2024] Open
Abstract
The precise identification of splice sites is essential for unraveling the structure and function of genes, constituting a pivotal step in the gene annotation process. In this study, we developed a novel deep learning model, DRANetSplicer, that integrates residual learning and attention mechanisms for enhanced accuracy in capturing the intricate features of splice sites. We constructed multiple datasets using the most recent versions of genomic data from three different organisms, Oryza sativa japonica, Arabidopsis thaliana and Homo sapiens. This approach allows us to train models with a richer set of high-quality data. DRANetSplicer outperformed benchmark methods on donor and acceptor splice site datasets, achieving an average accuracy of (96.57%, 95.82%) across the three organisms. Comparative analyses with benchmark methods, including SpliceFinder, Splice2Deep, Deep Splicer, EnsembleSplice, and DNABERT, revealed DRANetSplicer's superior predictive performance, resulting in at least a (4.2%, 11.6%) relative reduction in average error rate. We utilized the DRANetSplicer model trained on O. sativa japonica data to predict splice sites in A. thaliana, achieving accuracies for donor and acceptor sites of (94.89%, 94.25%). These results indicate that DRANetSplicer possesses excellent cross-organism predictive capabilities, with its performance in cross-organism predictions even surpassing that of benchmark methods in non-cross-organism predictions. Cross-organism validation showcased DRANetSplicer's excellence in predicting splice sites across similar organisms, supporting its applicability in gene annotation for understudied organisms. We employed multiple methods to visualize the decision-making process of the model. The visualization results indicate that DRANetSplicer can learn and interpret well-known biological features, further validating its overall performance. Our study systematically examined and confirmed the predictive ability of DRANetSplicer from various levels and perspectives, indicating that its practical application in gene annotation is justified.
Collapse
Affiliation(s)
- Xueyan Liu
- College of Information and Intelligence, Hunan Agricultural University, Changsha 410128, China; (X.L.); (X.Z.); (L.Z.); (J.F.)
| | - Hongyan Zhang
- College of Information and Intelligence, Hunan Agricultural University, Changsha 410128, China; (X.L.); (X.Z.); (L.Z.); (J.F.)
| | - Ying Zeng
- School of Computer and Communication, Hunan Institute of Engineering, Xiangtan 411104, China;
| | - Xinghui Zhu
- College of Information and Intelligence, Hunan Agricultural University, Changsha 410128, China; (X.L.); (X.Z.); (L.Z.); (J.F.)
| | - Lei Zhu
- College of Information and Intelligence, Hunan Agricultural University, Changsha 410128, China; (X.L.); (X.Z.); (L.Z.); (J.F.)
| | - Jiahui Fu
- College of Information and Intelligence, Hunan Agricultural University, Changsha 410128, China; (X.L.); (X.Z.); (L.Z.); (J.F.)
| |
Collapse
|
3
|
Abstract
The tremendous amount of biological sequence data available, combined with the recent methodological breakthrough in deep learning in domains such as computer vision or natural language processing, is leading today to the transformation of bioinformatics through the emergence of deep genomics, the application of deep learning to genomic sequences. We review here the new applications that the use of deep learning enables in the field, focusing on three aspects: the functional annotation of genomes, the sequence determinants of the genome functions and the possibility to write synthetic genomic sequences.
Collapse
|
4
|
Scalzitti N, Kress A, Orhand R, Weber T, Moulinier L, Jeannin-Girardon A, Collet P, Poch O, Thompson JD. Spliceator: multi-species splice site prediction using convolutional neural networks. BMC Bioinformatics 2021; 22:561. [PMID: 34814826 PMCID: PMC8609763 DOI: 10.1186/s12859-021-04471-3] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2021] [Accepted: 11/09/2021] [Indexed: 12/14/2022] Open
Abstract
Background Ab initio prediction of splice sites is an essential step in eukaryotic genome annotation. Recent predictors have exploited Deep Learning algorithms and reliable gene structures from model organisms. However, Deep Learning methods for non-model organisms are lacking. Results We developed Spliceator to predict splice sites in a wide range of species, including model and non-model organisms. Spliceator uses a convolutional neural network and is trained on carefully validated data from over 100 organisms. We show that Spliceator achieves consistently high accuracy (89–92%) compared to existing methods on independent benchmarks from human, fish, fly, worm, plant and protist organisms. Conclusions Spliceator is a new Deep Learning method trained on high-quality data, which can be used to predict splice sites in diverse organisms, ranging from human to protists, with consistently high accuracy. Supplementary Information The online version contains supplementary material available at 10.1186/s12859-021-04471-3.
Collapse
Affiliation(s)
- Nicolas Scalzitti
- Complex Systems and Translational Bioinformatics (CSTB), ICube Laboratory, UMR7357, University of Strasbourg, 1 rue Eugène Boeckel, 67000, Strasbourg, France
| | - Arnaud Kress
- Complex Systems and Translational Bioinformatics (CSTB), ICube Laboratory, UMR7357, University of Strasbourg, 1 rue Eugène Boeckel, 67000, Strasbourg, France.,BiGEst-ICube Platform, ICube Laboratory, UMR7357, 1 rue Eugène Boeckel, 67000, Strasbourg, France
| | - Romain Orhand
- Complex Systems and Translational Bioinformatics (CSTB), ICube Laboratory, UMR7357, University of Strasbourg, 1 rue Eugène Boeckel, 67000, Strasbourg, France
| | - Thomas Weber
- Complex Systems and Translational Bioinformatics (CSTB), ICube Laboratory, UMR7357, University of Strasbourg, 1 rue Eugène Boeckel, 67000, Strasbourg, France
| | - Luc Moulinier
- Complex Systems and Translational Bioinformatics (CSTB), ICube Laboratory, UMR7357, University of Strasbourg, 1 rue Eugène Boeckel, 67000, Strasbourg, France.,BiGEst-ICube Platform, ICube Laboratory, UMR7357, 1 rue Eugène Boeckel, 67000, Strasbourg, France
| | - Anne Jeannin-Girardon
- Complex Systems and Translational Bioinformatics (CSTB), ICube Laboratory, UMR7357, University of Strasbourg, 1 rue Eugène Boeckel, 67000, Strasbourg, France
| | - Pierre Collet
- Complex Systems and Translational Bioinformatics (CSTB), ICube Laboratory, UMR7357, University of Strasbourg, 1 rue Eugène Boeckel, 67000, Strasbourg, France
| | - Olivier Poch
- Complex Systems and Translational Bioinformatics (CSTB), ICube Laboratory, UMR7357, University of Strasbourg, 1 rue Eugène Boeckel, 67000, Strasbourg, France
| | - Julie D Thompson
- Complex Systems and Translational Bioinformatics (CSTB), ICube Laboratory, UMR7357, University of Strasbourg, 1 rue Eugène Boeckel, 67000, Strasbourg, France.
| |
Collapse
|
5
|
Deng Z, Zhang J, Li J, Zhang X. Application of Deep Learning in Plant-Microbiota Association Analysis. Front Genet 2021; 12:697090. [PMID: 34691142 PMCID: PMC8531731 DOI: 10.3389/fgene.2021.697090] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2021] [Accepted: 08/31/2021] [Indexed: 01/04/2023] Open
Abstract
Unraveling the association between microbiome and plant phenotype can illustrate the effect of microbiome on host and then guide the agriculture management. Adequate identification of species and appropriate choice of models are two challenges in microbiome data analysis. Computational models of microbiome data could help in association analysis between the microbiome and plant host. The deep learning methods have been widely used to learn the microbiome data due to their powerful strength of handling the complex, sparse, noisy, and high-dimensional data. Here, we review the analytic strategies in the microbiome data analysis and describe the applications of deep learning models for plant–microbiome correlation studies. We also introduce the application cases of different models in plant–microbiome correlation analysis and discuss how to adapt the models on the critical steps in data processing. From the aspect of data processing manner, model structure, and operating principle, most deep learning models are suitable for the plant microbiome data analysis. The ability of feature representation and pattern recognition is the advantage of deep learning methods in modeling and interpretation for association analysis. Based on published computational experiments, the convolutional neural network and graph neural networks could be recommended for plant microbiome analysis.
Collapse
Affiliation(s)
- Zhiyu Deng
- Key Laboratory of Plant Germplasm Enhancement and Specialty Agriculture, Wuhan Botanical Garden, Chinese Academy of Sciences, Wuhan, China.,Center of Economic Botany, Core Botanical Gardens, Chinese Academy of Sciences, Wuhan, China.,University of Chinese Academy of Sciences, Beijing, China
| | - Jinming Zhang
- Department of Infectious Diseases, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China
| | - Junya Li
- Key Laboratory of Plant Germplasm Enhancement and Specialty Agriculture, Wuhan Botanical Garden, Chinese Academy of Sciences, Wuhan, China.,Center of Economic Botany, Core Botanical Gardens, Chinese Academy of Sciences, Wuhan, China.,University of Chinese Academy of Sciences, Beijing, China
| | - Xiujun Zhang
- Key Laboratory of Plant Germplasm Enhancement and Specialty Agriculture, Wuhan Botanical Garden, Chinese Academy of Sciences, Wuhan, China.,Center of Economic Botany, Core Botanical Gardens, Chinese Academy of Sciences, Wuhan, China
| |
Collapse
|
6
|
Umarov R, Li Y, Arakawa T, Takizawa S, Gao X, Arner E. ReFeaFi: Genome-wide prediction of regulatory elements driving transcription initiation. PLoS Comput Biol 2021; 17:e1009376. [PMID: 34491989 PMCID: PMC8448322 DOI: 10.1371/journal.pcbi.1009376] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2021] [Revised: 09/17/2021] [Accepted: 08/23/2021] [Indexed: 11/19/2022] Open
Abstract
Regulatory elements control gene expression through transcription initiation (promoters) and by enhancing transcription at distant regions (enhancers). Accurate identification of regulatory elements is fundamental for annotating genomes and understanding gene expression patterns. While there are many attempts to develop computational promoter and enhancer identification methods, reliable tools to analyze long genomic sequences are still lacking. Prediction methods often perform poorly on the genome-wide scale because the number of negatives is much higher than that in the training sets. To address this issue, we propose a dynamic negative set updating scheme with a two-model approach, using one model for scanning the genome and the other one for testing candidate positions. The developed method achieves good genome-level performance and maintains robust performance when applied to other vertebrate species, without re-training. Moreover, the unannotated predicted regulatory regions made on the human genome are enriched for disease-associated variants, suggesting them to be potentially true regulatory elements rather than false positives. We validated high scoring "false positive" predictions using reporter assay and all tested candidates were successfully validated, demonstrating the ability of our method to discover novel human regulatory regions.
Collapse
Affiliation(s)
- Ramzan Umarov
- Graduate School of Integrated Sciences for Life, Hiroshima University, Higashi-Hiroshima, Japan
- * E-mail: (RU); (XG); (EA)
| | - Yu Li
- Department of Computer Science and Engineering (CSE), The Chinese University of Hong Kong (CUHK), Hong Kong, People’s Republic of China
| | - Takahiro Arakawa
- Laboratory for Applied Regulatory Genomics Network Analysis, RIKEN Center for Integrative Medical Sciences, Yokohama, Kanagawa, Japan
| | - Satoshi Takizawa
- Laboratory for Applied Regulatory Genomics Network Analysis, RIKEN Center for Integrative Medical Sciences, Yokohama, Kanagawa, Japan
| | - Xin Gao
- King Abdullah University of Science and Technology, Computational Bioscience Research Center, Computer, Electrical and Mathematical Sciences and Engineering Division, Thuwal, Saudi Arabia
- * E-mail: (RU); (XG); (EA)
| | - Erik Arner
- Graduate School of Integrated Sciences for Life, Hiroshima University, Higashi-Hiroshima, Japan
- Laboratory for Applied Regulatory Genomics Network Analysis, RIKEN Center for Integrative Medical Sciences, Yokohama, Kanagawa, Japan
- * E-mail: (RU); (XG); (EA)
| |
Collapse
|
7
|
Routhier E, Bin Kamruddin A, Mozziconacci J. keras_dna: a wrapper for fast implementation of deep learning models in genomics. Bioinformatics 2021; 37:1593-1594. [PMID: 33135730 DOI: 10.1093/bioinformatics/btaa929] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2020] [Revised: 10/14/2020] [Accepted: 10/19/2020] [Indexed: 11/12/2022] Open
Abstract
SUMMARY Prediction of genomic annotations from DNA sequences using deep learning is today becoming a flourishing field with many applications. Nevertheless, there are still difficulties in handling data in order to conveniently build and train models dedicated for specific end-user's tasks. keras_dna is designed for an easy implementation of Keras models (TensorFlow high level API) for genomics. It can handle standard bioinformatic files formats as inputs such as bigwig, gff, bed, wig, bedGraph or fasta and returns standardized inputs for model training. keras_dna is designed to implement existing models but also to facilitate the development of news models that can have single or multiple targets or inputs. AVAILABILITY AND IMPLEMENTATION Freely available with a MIT License using pip install keras_dna or cloning the github repo at https://github.com/etirouthier/keras_dna.git. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
Collapse
Affiliation(s)
- Etienne Routhier
- Sorbonne Universite, CNRS, Laboratoire de Physique Théorique de la Matière Condensée (LPTMC), Paris F-75252, France
| | - Ayman Bin Kamruddin
- Sorbonne Universite, CNRS, Laboratoire de Physique Théorique de la Matière Condensée (LPTMC), Paris F-75252, France
- Muséum National d'Histoire Naturelle, Structure et Instabilité des Génomes, UMR7196, Paris 75231, France
| | - Julien Mozziconacci
- Sorbonne Universite, CNRS, Laboratoire de Physique Théorique de la Matière Condensée (LPTMC), Paris F-75252, France
- Muséum National d'Histoire Naturelle, Structure et Instabilité des Génomes, UMR7196, Paris 75231, France
| |
Collapse
|
8
|
Helmy M, Smith D, Selvarajoo K. Systems biology approaches integrated with artificial intelligence for optimized metabolic engineering. Metab Eng Commun 2020; 11:e00149. [PMID: 33072513 PMCID: PMC7546651 DOI: 10.1016/j.mec.2020.e00149] [Citation(s) in RCA: 32] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2020] [Revised: 10/01/2020] [Accepted: 10/07/2020] [Indexed: 12/05/2022] Open
Abstract
Metabolic engineering aims to maximize the production of bio-economically important substances (compounds, enzymes, or other proteins) through the optimization of the genetics, cellular processes and growth conditions of microorganisms. This requires detailed understanding of underlying metabolic pathways involved in the production of the targeted substances, and how the cellular processes or growth conditions are regulated by the engineering. To achieve this goal, a large system of experimental techniques, compound libraries, computational methods and data resources, including multi-omics data, are used. The recent advent of multi-omics systems biology approaches significantly impacted the field by opening new avenues to perform dynamic and large-scale analyses that deepen our knowledge on the manipulations. However, with the enormous transcriptomics, proteomics and metabolomics available, it is a daunting task to integrate the data for a more holistic understanding. Novel data mining and analytics approaches, including Artificial Intelligence (AI), can provide breakthroughs where traditional low-throughput experiment-alone methods cannot easily achieve. Here, we review the latest attempts of combining systems biology and AI in metabolic engineering research, and highlight how this alliance can help overcome the current challenges facing industrial biotechnology, especially for food-related substances and compounds using microorganisms.
Collapse
Affiliation(s)
- Mohamed Helmy
- Singapore Institute of Food and Biotechnology Innovation (SIFBI), Agency for Science, Technology and Research (A∗STAR), Singapore, Singapore
| | - Derek Smith
- Singapore Institute of Food and Biotechnology Innovation (SIFBI), Agency for Science, Technology and Research (A∗STAR), Singapore, Singapore
| | - Kumar Selvarajoo
- Singapore Institute of Food and Biotechnology Innovation (SIFBI), Agency for Science, Technology and Research (A∗STAR), Singapore, Singapore
- Synthetic Biology for Clinical and Technological Innovation (SynCTI), National University of Singapore (NUS), Singapore, Singapore
| |
Collapse
|