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Peng B, Sun G, Fan Y. iProL: identifying DNA promoters from sequence information based on Longformer pre-trained model. BMC Bioinformatics 2024; 25:224. [PMID: 38918692 PMCID: PMC11201334 DOI: 10.1186/s12859-024-05849-9] [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: 04/29/2024] [Accepted: 06/19/2024] [Indexed: 06/27/2024] Open
Abstract
Promoters are essential elements of DNA sequence, usually located in the immediate region of the gene transcription start sites, and play a critical role in the regulation of gene transcription. Its importance in molecular biology and genetics has attracted the research interest of researchers, and it has become a consensus to seek a computational method to efficiently identify promoters. Still, existing methods suffer from imbalanced recognition capabilities for positive and negative samples, and their recognition effect can still be further improved. We conducted research on E. coli promoters and proposed a more advanced prediction model, iProL, based on the Longformer pre-trained model in the field of natural language processing. iProL does not rely on prior biological knowledge but simply uses promoter DNA sequences as plain text to identify promoters. It also combines one-dimensional convolutional neural networks and bidirectional long short-term memory to extract both local and global features. Experimental results show that iProL has a more balanced and superior performance than currently published methods. Additionally, we constructed a novel independent test set following the previous specification and compared iProL with three existing methods on this independent test set.
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Affiliation(s)
- Binchao Peng
- School of Computer Science and Information Security, Guilin University of Electronic Technology, Guilin, 541004, China
| | - Guicong Sun
- School of Computer Science and Information Security, Guilin University of Electronic Technology, Guilin, 541004, China
| | - Yongxian Fan
- School of Computer Science and Information Security, Guilin University of Electronic Technology, Guilin, 541004, China.
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Zhang P, Zhang H, Wu H. iPro-WAEL: a comprehensive and robust framework for identifying promoters in multiple species. Nucleic Acids Res 2022; 50:10278-10289. [PMID: 36161334 PMCID: PMC9561371 DOI: 10.1093/nar/gkac824] [Citation(s) in RCA: 26] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2022] [Revised: 08/24/2022] [Accepted: 09/14/2022] [Indexed: 11/27/2022] Open
Abstract
Promoters are consensus DNA sequences located near the transcription start sites and they play an important role in transcription initiation. Due to their importance in biological processes, the identification of promoters is significantly important for characterizing the expression of the genes. Numerous computational methods have been proposed to predict promoters. However, it is difficult for these methods to achieve satisfactory performance in multiple species. In this study, we propose a novel weighted average ensemble learning model, termed iPro-WAEL, for identifying promoters in multiple species, including Human, Mouse, E.coli, Arabidopsis, B.amyloliquefaciens, B.subtilis and R.capsulatus. Extensive benchmarking experiments illustrate that iPro-WAEL has optimal performance and is superior to the current methods in promoter prediction. The experimental results also demonstrate a satisfactory prediction ability of iPro-WAEL on cross-cell lines, promoters annotated by other methods and distinguishing between promoters and enhancers. Moreover, we identify the most important transcription factor binding site (TFBS) motif in promoter regions to facilitate the study of identifying important motifs in the promoter regions. The source code of iPro-WAEL is freely available at https://github.com/HaoWuLab-Bioinformatics/iPro-WAEL.
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Affiliation(s)
- Pengyu Zhang
- School of Software, Shandong University, Jinan, 250101, Shandong, China.,College of Information Engineering, Northwest A&F University, Yangling, 712100, Shaanxi, China
| | - Hongming Zhang
- College of Information Engineering, Northwest A&F University, Yangling, 712100, Shaanxi, China
| | - Hao Wu
- School of Software, Shandong University, Jinan, 250101, Shandong, China
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Li F, Chen J, Ge Z, Wen Y, Yue Y, Hayashida M, Baggag A, Bensmail H, Song J. Computational prediction and interpretation of both general and specific types of promoters in Escherichia coli by exploiting a stacked ensemble-learning framework. Brief Bioinform 2020; 22:2126-2140. [PMID: 32363397 DOI: 10.1093/bib/bbaa049] [Citation(s) in RCA: 48] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2019] [Revised: 02/25/2020] [Accepted: 03/11/2020] [Indexed: 12/12/2022] Open
Abstract
Promoters are short consensus sequences of DNA, which are responsible for transcription activation or the repression of all genes. There are many types of promoters in bacteria with important roles in initiating gene transcription. Therefore, solving promoter-identification problems has important implications for improving the understanding of their functions. To this end, computational methods targeting promoter classification have been established; however, their performance remains unsatisfactory. In this study, we present a novel stacked-ensemble approach (termed SELECTOR) for identifying both promoters and their respective classification. SELECTOR combined the composition of k-spaced nucleic acid pairs, parallel correlation pseudo-dinucleotide composition, position-specific trinucleotide propensity based on single-strand, and DNA strand features and using five popular tree-based ensemble learning algorithms to build a stacked model. Both 5-fold cross-validation tests using benchmark datasets and independent tests using the newly collected independent test dataset showed that SELECTOR outperformed state-of-the-art methods in both general and specific types of promoter prediction in Escherichia coli. Furthermore, this novel framework provides essential interpretations that aid understanding of model success by leveraging the powerful Shapley Additive exPlanation algorithm, thereby highlighting the most important features relevant for predicting both general and specific types of promoters and overcoming the limitations of existing 'Black-box' approaches that are unable to reveal causal relationships from large amounts of initially encoded features.
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Affiliation(s)
- Fuyi Li
- Northwest A&F University, China.,Department of Biochemistry and Molecular Biology and the Infection and Immunity Program, Biomedicine Discovery Institute, Monash University, Australia
| | - Jinxiang Chen
- Biomedicine Discovery Institute and the Department of Biochemistry and Molecular Biology, Monash University from the College of Information Engineering, Northwest A&F University, China
| | - Zongyuan Ge
- Monash University and also serves as a Deep Learning Specialist at NVIDIA AI Technology Centre. Before joining Monash, he was a research scientist at IBM Research Australia doing research in medical AI during 2016-2018. His research interests are AI, computer vision, medical image, robotics and deep learning
| | - Ya Wen
- computer technology from Ningxia University, China
| | - Yanwei Yue
- medical science from Southern Medical University, China
| | - Morihiro Hayashida
- informatics from Kyoto University, Japan, in 2005. He is an Assistant Professor in the Department of Electrical Engineering and Computer Science, National Institute of Technology, Matsue College, Japan
| | - Abdelkader Baggag
- computer science from the University of Minnesota. He is a Senior Scientist at the Qatar Computing Research Institute (QCRI) and has a joint appointment as an Associate Professor at Hamad Bin Khalifa University (HBKU) in the Division of Information and Computing Technology. His research interests include data mining, linear algebra and machine learning
| | - Halima Bensmail
- University of Pierre & Marie Currie (Paris 6) in France. She is currently a Principal Scientist at QCRI-HBKU and a joint Associate Professor at the College of Computer and Science Engineering, HBKU
| | - Jiangning Song
- Monash Biomedicine Discovery Institute, Monash University, Australia. He is also affiliated with the Monash Centre for Data Science, Faculty of Information Technology, Monash University. His research interests include bioinformatics, computational biology, machine learning, data mining, and pattern recognition
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Zhang M, Li F, Marquez-Lago TT, Leier A, Fan C, Kwoh CK, Chou KC, Song J, Jia C. MULTiPly: a novel multi-layer predictor for discovering general and specific types of promoters. Bioinformatics 2019; 35:2957-2965. [PMID: 30649179 PMCID: PMC6736106 DOI: 10.1093/bioinformatics/btz016] [Citation(s) in RCA: 75] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2018] [Revised: 12/09/2018] [Accepted: 01/05/2019] [Indexed: 12/22/2022] Open
Abstract
MOTIVATION Promoters are short DNA consensus sequences that are localized proximal to the transcription start sites of genes, allowing transcription initiation of particular genes. However, the precise prediction of promoters remains a challenging task because individual promoters often differ from the consensus at one or more positions. RESULTS In this study, we present a new multi-layer computational approach, called MULTiPly, for recognizing promoters and their specific types. MULTiPly took into account the sequences themselves, including both local information such as k-tuple nucleotide composition, dinucleotide-based auto covariance and global information of the entire samples based on bi-profile Bayes and k-nearest neighbour feature encodings. Specifically, the F-score feature selection method was applied to identify the best unique type of feature prediction results, in combination with other types of features that were subsequently added to further improve the prediction performance of MULTiPly. Benchmarking experiments on the benchmark dataset and comparisons with five state-of-the-art tools show that MULTiPly can achieve a better prediction performance on 5-fold cross-validation and jackknife tests. Moreover, the superiority of MULTiPly was also validated on a newly constructed independent test dataset. MULTiPly is expected to be used as a useful tool that will facilitate the discovery of both general and specific types of promoters in the post-genomic era. AVAILABILITY AND IMPLEMENTATION The MULTiPly webserver and curated datasets are freely available at http://flagshipnt.erc.monash.edu/MULTiPly/. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Meng Zhang
- School of Science, Dalian Maritime University, Dalian, China
| | - Fuyi Li
- Biomedicine Discovery Institute and Department of Biochemistry and Molecular Biology
- Monash Centre for Data Science, Faculty of Information Technology, Monash University, Melbourne, VIC, Australia
| | - Tatiana T Marquez-Lago
- Department of Genetics, Developmental and Integrative Biology, School of Medicine, University of Alabama at Birmingham, Birmingham, AL, USA
- Department of Cell, Developmental and Integrative Biology, School of Medicine, University of Alabama at Birmingham, Birmingham, AL, USA
| | - André Leier
- Department of Genetics, Developmental and Integrative Biology, School of Medicine, University of Alabama at Birmingham, Birmingham, AL, USA
- Department of Cell, Developmental and Integrative Biology, School of Medicine, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Cunshuo Fan
- College of Information Engineering, Northwest A&F University, Yangling, China
| | - Chee Keong Kwoh
- School of Computer Science and Engineering, Nanyang Technological University, Singapore, Singapore
| | | | - Jiangning Song
- Biomedicine Discovery Institute and Department of Biochemistry and Molecular Biology
- Monash Centre for Data Science, Faculty of Information Technology, Monash University, Melbourne, VIC, Australia
- ARC Centre of Excellence in Advanced Molecular Imaging, Monash University, Melbourne, VIC, Australia
| | - Cangzhi Jia
- School of Science, Dalian Maritime University, Dalian, China
- College of Information Engineering, Northwest A&F University, Yangling, China
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de Avila e Silva S, Forte F, T S Sartor I, Andrighetti T, J L Gerhardt G, Longaray Delamare AP, Echeverrigaray S. DNA duplex stability as discriminative characteristic for Escherichia coli σ(54)- and σ(28)- dependent promoter sequences. Biologicals 2013; 42:22-8. [PMID: 24172230 DOI: 10.1016/j.biologicals.2013.10.001] [Citation(s) in RCA: 25] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2013] [Accepted: 10/01/2013] [Indexed: 11/17/2022] Open
Abstract
The advent of modern high-throughput sequencing has made it possible to generate vast quantities of genomic sequence data. However, the processing of this volume of information, including prediction of gene-coding and regulatory sequences remains an important bottleneck in bioinformatics research. In this work, we integrated DNA duplex stability into the repertoire of a Neural Network (NN) capable of predicting promoter regions with augmented accuracy, specificity and sensitivity. We took our method beyond a simplistic analysis based on a single sigma subunit of RNA polymerase, incorporating the six main sigma-subunits of Escherichia coli. This methodology employed successfully re-discovered known promoter sequences recognized by E. coli RNA polymerase subunits σ(24), σ(28), σ(32), σ(38), σ(54) and σ(70), with highlighted accuracies for σ(28)- and σ(54)- dependent promoter sequences (values obtained were 80% and 78.8%, respectively). Furthermore, the discrimination of promoters according to the σ factor made it possible to extract functional commonalities for the genes expressed by each type of promoter. The DNA duplex stability rises as a distinctive feature which improves the recognition and classification of σ(28)- and σ(54)- dependent promoter sequences. The findings presented in this report underscore the usefulness of including DNA biophysical parameters into NN learning algorithms to increase accuracy, specificity and sensitivity in promoter beyond what is accomplished based on sequence alone.
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Affiliation(s)
- Scheila de Avila e Silva
- Universidade de Caxias do Sul, Instituto de Biotecnologia, Rua Francisco Getúlio Vargas, 1130, CEP 95070-560 Caxias do Sul, RS, Brazil.
| | - Franciele Forte
- Universidade de Caxias do Sul, Instituto de Biotecnologia, Rua Francisco Getúlio Vargas, 1130, CEP 95070-560 Caxias do Sul, RS, Brazil.
| | - Ivaine T S Sartor
- Universidade de Caxias do Sul, Instituto de Biotecnologia, Rua Francisco Getúlio Vargas, 1130, CEP 95070-560 Caxias do Sul, RS, Brazil.
| | - Tahila Andrighetti
- Universidade de Caxias do Sul, Instituto de Biotecnologia, Rua Francisco Getúlio Vargas, 1130, CEP 95070-560 Caxias do Sul, RS, Brazil.
| | - Günther J L Gerhardt
- Universidade de Caxias do Sul, Instituto de Biotecnologia, Rua Francisco Getúlio Vargas, 1130, CEP 95070-560 Caxias do Sul, RS, Brazil.
| | - Ana Paula Longaray Delamare
- Universidade de Caxias do Sul, Instituto de Biotecnologia, Rua Francisco Getúlio Vargas, 1130, CEP 95070-560 Caxias do Sul, RS, Brazil.
| | - Sergio Echeverrigaray
- Universidade de Caxias do Sul, Instituto de Biotecnologia, Rua Francisco Getúlio Vargas, 1130, CEP 95070-560 Caxias do Sul, RS, Brazil.
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Survey of the year 2008: applications of isothermal titration calorimetry. J Mol Recognit 2010; 23:395-413. [DOI: 10.1002/jmr.1025] [Citation(s) in RCA: 53] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/17/2023]
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