1
|
Gupta S, Kesarwani V, Bhati U, Jyoti, Shankar R. PTFSpot: deep co-learning on transcription factors and their binding regions attains impeccable universality in plants. Brief Bioinform 2024; 25:bbae324. [PMID: 39013383 PMCID: PMC11250369 DOI: 10.1093/bib/bbae324] [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: 03/19/2024] [Revised: 06/07/2024] [Accepted: 06/19/2024] [Indexed: 07/18/2024] Open
Abstract
Unlike animals, variability in transcription factors (TFs) and their binding regions (TFBRs) across the plants species is a major problem that most of the existing TFBR finding software fail to tackle, rendering them hardly of any use. This limitation has resulted into underdevelopment of plant regulatory research and rampant use of Arabidopsis-like model species, generating misleading results. Here, we report a revolutionary transformers-based deep-learning approach, PTFSpot, which learns from TF structures and their binding regions' co-variability to bring a universal TF-DNA interaction model to detect TFBR with complete freedom from TF and species-specific models' limitations. During a series of extensive benchmarking studies over multiple experimentally validated data, it not only outperformed the existing software by >30% lead but also delivered consistently >90% accuracy even for those species and TF families that were never encountered during the model-building process. PTFSpot makes it possible now to accurately annotate TFBRs across any plant genome even in the total lack of any TF information, completely free from the bottlenecks of species and TF-specific models.
Collapse
Affiliation(s)
- Sagar Gupta
- Studio of Computational Biology & Bioinformatics, The Himalayan Centre for High-throughput Computational Biology, (HiCHiCoB, A BIC supported by DBT, India), Biotechnology Division, CSIR-Institute of Himalayan Bioresource Technology (CSIR-IHBT), Palampur, Himachal Pradesh 176061, India
- Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, Uttar Pradesh 201002, India
| | - Veerbhan Kesarwani
- Studio of Computational Biology & Bioinformatics, The Himalayan Centre for High-throughput Computational Biology, (HiCHiCoB, A BIC supported by DBT, India), Biotechnology Division, CSIR-Institute of Himalayan Bioresource Technology (CSIR-IHBT), Palampur, Himachal Pradesh 176061, India
- Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, Uttar Pradesh 201002, India
| | - Umesh Bhati
- Studio of Computational Biology & Bioinformatics, The Himalayan Centre for High-throughput Computational Biology, (HiCHiCoB, A BIC supported by DBT, India), Biotechnology Division, CSIR-Institute of Himalayan Bioresource Technology (CSIR-IHBT), Palampur, Himachal Pradesh 176061, India
- Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, Uttar Pradesh 201002, India
| | - Jyoti
- Studio of Computational Biology & Bioinformatics, The Himalayan Centre for High-throughput Computational Biology, (HiCHiCoB, A BIC supported by DBT, India), Biotechnology Division, CSIR-Institute of Himalayan Bioresource Technology (CSIR-IHBT), Palampur, Himachal Pradesh 176061, India
- Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, Uttar Pradesh 201002, India
| | - Ravi Shankar
- Studio of Computational Biology & Bioinformatics, The Himalayan Centre for High-throughput Computational Biology, (HiCHiCoB, A BIC supported by DBT, India), Biotechnology Division, CSIR-Institute of Himalayan Bioresource Technology (CSIR-IHBT), Palampur, Himachal Pradesh 176061, India
- Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, Uttar Pradesh 201002, India
| |
Collapse
|
2
|
Wang RH, Ng YK, Zhang X, Wang J, Li SC. Coding genomes with gapped pattern graph convolutional network. Bioinformatics 2024; 40:btae188. [PMID: 38603603 PMCID: PMC11034989 DOI: 10.1093/bioinformatics/btae188] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2023] [Revised: 03/11/2024] [Accepted: 04/05/2024] [Indexed: 04/13/2024] Open
Abstract
MOTIVATION Genome sequencing technologies reveal a huge amount of genomic sequences. Neural network-based methods can be prime candidates for retrieving insights from these sequences because of their applicability to large and diverse datasets. However, the highly variable lengths of genome sequences severely impair the presentation of sequences as input to the neural network. Genetic variations further complicate tasks that involve sequence comparison or alignment. RESULTS Inspired by the theory and applications of "spaced seeds," we propose a graph representation of genome sequences called "gapped pattern graph." These graphs can be transformed through a Graph Convolutional Network to form lower-dimensional embeddings for downstream tasks. On the basis of the gapped pattern graphs, we implemented a neural network model and demonstrated its performance on diverse tasks involving microbe and mammalian genome data. Our method consistently outperformed all the other state-of-the-art methods across various metrics on all tasks, especially for the sequences with limited homology to the training data. In addition, our model was able to identify distinct gapped pattern signatures from the sequences. AVAILABILITY AND IMPLEMENTATION The framework is available at https://github.com/deepomicslab/GCNFrame.
Collapse
Affiliation(s)
- Ruo Han Wang
- Department of Computer Science, City University of Hong Kong Shenzhen Research Institute, Shen Zhen, 518063, China
- Department of Computer Science, City University of Hong Kong, Hong Kong, 999077, China
| | - Yen Kaow Ng
- Department of Computer Science, City University of Hong Kong Shenzhen Research Institute, Shen Zhen, 518063, China
- Department of Computer Science, City University of Hong Kong, Hong Kong, 999077, China
| | - Xianglilan Zhang
- State Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology and Epidemiology, Beijing, 100071, China
| | - Jianping Wang
- Department of Computer Science, City University of Hong Kong Shenzhen Research Institute, Shen Zhen, 518063, China
- Department of Computer Science, City University of Hong Kong, Hong Kong, 999077, China
| | - Shuai Cheng Li
- Department of Computer Science, City University of Hong Kong Shenzhen Research Institute, Shen Zhen, 518063, China
- Department of Computer Science, City University of Hong Kong, Hong Kong, 999077, China
| |
Collapse
|
3
|
Mahmoud MAB. Classification of DNA Sequence Based on a Non-gradient Algorithm: Pseudoinverse Learners. Methods Mol Biol 2024; 2744:359-373. [PMID: 38683331 DOI: 10.1007/978-1-0716-3581-0_23] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/01/2024]
Abstract
This chapter proposes a prototype-based classification approach for analyzing DNA barcodes that uses a spectral representation of DNA sequences and a non-gradient neural network. Biological sequences can be viewed as data components with higher non-fixed dimensions, which correspond to the length of the sequences. Through computational procedures such as one-hot encoding, numerical encoding plays an important role in DNA sequence evaluation (OHE). However, the OHE method has some disadvantages: (1) It does not add any details that could result in an additional predictive variable, and (2) if the variable has many classes, OHE significantly expands the feature space. To address these shortcomings, this chapter proposes a computationally efficient framework for classifying DNA sequences of living organisms in the image domain. A multilayer perceptron trained by a pseudoinverse learning autoencoder (PILAE) algorithm is used in the proposed strategy. The learning control parameters and the number of hidden layers do not have to be specified during the PILAE training process. As a result, the PILAE classifier outperforms other deep neural network (DNN) strategies such as the VGG-16 and Xception models.
Collapse
Affiliation(s)
- Mohammed A B Mahmoud
- Faculty of Computer Science, October University for Modern Sciences and Arts, Cairo, Egypt.
| |
Collapse
|
4
|
Akbari Rokn Abadi S, Tabatabaei S, Koohi S. KDeep: a new memory-efficient data extraction method for accurately predicting DNA/RNA transcription factor binding sites. J Transl Med 2023; 21:727. [PMID: 37845681 PMCID: PMC10580661 DOI: 10.1186/s12967-023-04593-7] [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: 07/01/2023] [Accepted: 10/04/2023] [Indexed: 10/18/2023] Open
Abstract
This paper addresses the crucial task of identifying DNA/RNA binding sites, which has implications in drug/vaccine design, protein engineering, and cancer research. Existing methods utilize complex neural network structures, diverse input types, and machine learning techniques for feature extraction. However, the growing volume of sequences poses processing challenges. This study introduces KDeep, employing a CNN-LSTM architecture with a novel encoding method called 2Lk. 2Lk enhances prediction accuracy, reduces memory consumption by up to 84%, reduces trainable parameters, and improves interpretability by approximately 79% compared to state-of-the-art approaches. KDeep offers a promising solution for accurate and efficient binding site prediction.
Collapse
Affiliation(s)
| | | | - Somayyeh Koohi
- Department of Computer Engineering, Sharif University of Technology, Tehran, Iran.
| |
Collapse
|
5
|
Zhang Q, Xu Y, Wang S, Wu Y, Ye Y, Yuan CA, Gribova V, Filaretov VF, Huang DS. Using Fully Convolutional Network to Locate Transcription Factor Binding Sites Based on DNA Sequence and Conservation Information. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2023; 20:2690-2699. [PMID: 36374878 DOI: 10.1109/tcbb.2022.3219831] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
Abstract
Transcription factors (TFs) play a part in gene expression. TFs can form complex gene expression regulation system by combining with DNA. Thereby, identifying the binding regions has become an indispensable step for understanding the regulatory mechanism of gene expression. Due to the great achievements of applying deep learning (DL) to computer vision and language processing in recent years, many scholars are inspired to use these methods to predict TF binding sites (TFBSs), achieving extraordinary results. However, these methods mainly focus on whether DNA sequences include TFBSs. In this paper, we propose a fully convolutional network (FCN) coupled with refinement residual block (RRB) and global average pooling layer (GAPL), namely FCNARRB. Our model could classify binding sequences at nucleotide level by outputting dense label for input data. Experimental results on human ChIP-seq datasets show that the RRB and GAPL structures are very useful for improving model performance. Adding GAPL improves the performance by 9.32% and 7.61% in terms of IoU (Intersection of Union) and PRAUC (Area Under Curve of Precision and Recall), and adding RRB improves the performance by 7.40% and 4.64%, respectively. In addition, we find that conservation information can help locate TFBSs.
Collapse
|
6
|
Du Z, Huang T, Uversky VN, Li J. Predicting TF Proteins by Incorporating Evolution Information Through PSSM. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2023; 20:1319-1326. [PMID: 35981062 DOI: 10.1109/tcbb.2022.3199758] [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
Transcription factors (TFs) are DNA binding proteins involved in the regulation of gene expression. They exist in all organisms and activate or repress transcription by binding to specific DNA sequences. Traditionally, TFs have been identified by experimental methods that are time-consuming and costly. In recent years, various computational methods have been developed to identify TF to overcome these limitations. However, there is a room for further improvement in the predictive performance of these tools in terms of accuracy. We report here a novel computational tool, TFnet, that provides accurate and comprehensive TF predictions from protein sequences. The accuracy of these predictions is substantially better than the results of the existing TF predictors and methods. Especially, it outperforms comparable methods significantly when sequence similarity to other known sequences in the database drops below 40%. Ablation tests reveal that the high predictive performance stems from innovative ways used in TFnet to derive sequence Position-Specific Scoring Matrix (PSSM) and encode inputs.
Collapse
|
7
|
Zhou J, Wang X, Wei Z, Meng J, Huang D. 4acCPred: Weakly supervised prediction of N4-acetyldeoxycytosine DNA modification from sequences. MOLECULAR THERAPY - NUCLEIC ACIDS 2022; 30:337-345. [DOI: 10.1016/j.omtn.2022.10.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/09/2022] [Accepted: 10/12/2022] [Indexed: 11/06/2022]
|
8
|
Fang M, He Y, Du Z, Uversky VN. DeepCLD: An Efficient Sequence-Based Predictor of Intrinsically Disordered Proteins. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2022; 19:3154-3159. [PMID: 34727037 DOI: 10.1109/tcbb.2021.3124273] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
Intrinsic disorder is common in proteins, plays important roles in protein functionality, and is commonly associated with various human diseases. To have an accurate tool for the annotation of intrinsic disorder in proteins, this paper proposes a novel algorithm, DeepCLD, for sequence-based prediction of intrinsically disordered proteins. This algorithm uses amino acid position specific scoring matrix (PSSM) to capture the intrinsic variability characteristic of sequence patterns, ResNet to preserve feature space structure, and bidirectional CudnnLSTM as recurrent layer to further improve the efficiency. Futhermore, DeepCLD also utilized the attention mechanism to solve the problem of gradient disappearing in deep network. Comparative analyses show that DeepCLD has faster training speed and higher prediction accuracy than comparable methods.
Collapse
|
9
|
Zhang Q, Zhang Y, Wang S, Chen ZH, Gribova V, Filaretov VF, Huang DS. Predicting In-Vitro DNA-Protein Binding With a Spatially Aligned Fusion of Sequence and Shape. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2022; 19:3144-3153. [PMID: 34882561 DOI: 10.1109/tcbb.2021.3133869] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
Discovery of transcription factor binding sites (TFBSs) is of primary importance for understanding the underlying binding mechanic and gene regulation process. Growing evidence indicates that apart from the primary DNA sequences, DNA shape landscape has a significant influence on transcription factor binding preference. To effectively model the co-influence of sequence and shape features, we emphasize the importance of position information of sequence motif and shape pattern. In this paper, we propose a novel deep learning-based architecture, named hybridShape eDeepCNN, for TFBS prediction which integrates DNA sequence and shape information in a spatially aligned manner. Our model utilizes the power of the multi-layer convolutional neural network and constructs an independent subnetwork to adapt for the distinct data distribution of heterogeneous features. Besides, we explore the usage of continuous embedding vectors as the representation of DNA sequences. Based on the experiments on 20 in-vitro datasets derived from universal protein binding microarrays (uPBMs), we demonstrate the superiority of our proposed method and validate the underlying design logic.
Collapse
|
10
|
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
|
11
|
Zhang Y, Bao W, Cao Y, Cong H, Chen B, Chen Y. A survey on protein–DNA-binding sites in computational biology. Brief Funct Genomics 2022; 21:357-375. [DOI: 10.1093/bfgp/elac009] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2022] [Revised: 04/07/2022] [Accepted: 04/22/2022] [Indexed: 01/08/2023] Open
Abstract
Abstract
Transcription factors are important cellular components of the process of gene expression control. Transcription factor binding sites are locations where transcription factors specifically recognize DNA sequences, targeting gene-specific regions and recruiting transcription factors or chromatin regulators to fine-tune spatiotemporal gene regulation. As the common proteins, transcription factors play a meaningful role in life-related activities. In the face of the increase in the protein sequence, it is urgent how to predict the structure and function of the protein effectively. At present, protein–DNA-binding site prediction methods are based on traditional machine learning algorithms and deep learning algorithms. In the early stage, we usually used the development method based on traditional machine learning algorithm to predict protein–DNA-binding sites. In recent years, methods based on deep learning to predict protein–DNA-binding sites from sequence data have achieved remarkable success. Various statistical and machine learning methods used to predict the function of DNA-binding proteins have been proposed and continuously improved. Existing deep learning methods for predicting protein–DNA-binding sites can be roughly divided into three categories: convolutional neural network (CNN), recursive neural network (RNN) and hybrid neural network based on CNN–RNN. The purpose of this review is to provide an overview of the computational and experimental methods applied in the field of protein–DNA-binding site prediction today. This paper introduces the methods of traditional machine learning and deep learning in protein–DNA-binding site prediction from the aspects of data processing characteristics of existing learning frameworks and differences between basic learning model frameworks. Our existing methods are relatively simple compared with natural language processing, computational vision, computer graphics and other fields. Therefore, the summary of existing protein–DNA-binding site prediction methods will help researchers better understand this field.
Collapse
|
12
|
Mi JX, Feng J, Huang KY. Designing efficient convolutional neural network structure: A survey. Neurocomputing 2022. [DOI: 10.1016/j.neucom.2021.08.158] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
|
13
|
Zhang S, Ma A, Zhao J, Xu D, Ma Q, Wang Y. Assessing deep learning methods in cis-regulatory motif finding based on genomic sequencing data. Brief Bioinform 2022; 23:bbab374. [PMID: 34607350 PMCID: PMC8769700 DOI: 10.1093/bib/bbab374] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2021] [Revised: 08/22/2021] [Accepted: 08/23/2021] [Indexed: 12/28/2022] Open
Abstract
Identifying cis-regulatory motifs from genomic sequencing data (e.g. ChIP-seq and CLIP-seq) is crucial in identifying transcription factor (TF) binding sites and inferring gene regulatory mechanisms for any organism. Since 2015, deep learning (DL) methods have been widely applied to identify TF binding sites and predict motif patterns, with the strengths of offering a scalable, flexible and unified computational approach for highly accurate predictions. As far as we know, 20 DL methods have been developed. However, without a clear and systematic assessment, users will struggle to choose the most appropriate tool for their specific studies. In this manuscript, we evaluated 20 DL methods for cis-regulatory motif prediction using 690 ENCODE ChIP-seq, 126 cancer ChIP-seq and 55 RNA CLIP-seq data. Four metrics were investigated, including the accuracy of motif finding, the performance of DNA/RNA sequence classification, algorithm scalability and tool usability. The assessment results demonstrated the high complementarity of the existing DL methods. It was determined that the most suitable model should primarily depend on the data size and type and the method's outputs.
Collapse
Affiliation(s)
- Shuangquan Zhang
- Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education, College of Computer Science and Technology, Jilin University, Changchun, 130012, China
| | - Anjun Ma
- Department of Biomedical Informatics, College of Medicine, The Ohio State University, Columbus, OH, 43210, USA
| | - Jing Zhao
- Department of Biomedical Informatics, College of Medicine, The Ohio State University, Columbus, OH, 43210, USA
| | - Dong Xu
- Department of Electrical Engineering and Computer Science, and Christopher S. Bond Life Science Center, University of Missouri, MO, 65211, USA
| | - Qin Ma
- Department of Biomedical Informatics, College of Medicine, The Ohio State University, Columbus, OH, 43210, USA
| | - Yan Wang
- Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education, College of Computer Science and Technology, Jilin University, Changchun, 130012, China
- School of Artificial Intelligence, Jilin University, Changchun, 130012, China
| |
Collapse
|
14
|
Mei LC, Wang YL, Wu FX, Wang F, Hao GF, Yang GF. HISNAPI: a bioinformatic tool for dynamic hot spot analysis in nucleic acid-protein interface with a case study. Brief Bioinform 2021; 22:bbaa373. [PMID: 33406224 PMCID: PMC7929440 DOI: 10.1093/bib/bbaa373] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2020] [Revised: 11/19/2020] [Accepted: 11/23/2020] [Indexed: 01/18/2023] Open
Abstract
Protein-nucleic acid interactions play essential roles in many biological processes, such as transcription, replication and translation. In protein-nucleic acid interfaces, hotspot residues contribute the majority of binding affinity toward molecular recognition. Hotspot residues are commonly regarded as potential binding sites for compound molecules in drug design projects. The dynamic property is a considerable factor that affects the binding of ligands. Computational approaches have been developed to expedite the prediction of hotspot residues on protein-nucleic acid interfaces. However, existing approaches overlook hotspot dynamics, despite their essential role in protein function. Here, we report a web server named Hotspots In silico Scanning on Nucleic Acid and Protein Interface (HISNAPI) to analyze hotspot residue dynamics by integrating molecular dynamics simulation and one-step free energy perturbation. HISNAPI is capable of not only predicting the hotspot residues in protein-nucleic acid interfaces but also providing insights into their intensity and correlation of dynamic motion. Protein dynamics have been recognized as a vital factor that has an effect on the interaction specificity and affinity of the binding partners. We applied HISNAPI to the case of SARS-CoV-2 RNA-dependent RNA polymerase, a vital target of the antiviral drug for the treatment of coronavirus disease 2019. We identified the hotspot residues and characterized their dynamic behaviors, which might provide insight into the target site for antiviral drug design. The web server is freely available via a user-friendly web interface at http://chemyang.ccnu.edu.cn/ccb/server/HISNAPI/ and http://agroda.gzu.edu.cn:9999/ccb/server/HISNAPI/.
Collapse
Affiliation(s)
- Long-Can Mei
- College of Chemistry, Central China Normal University
| | | | | | | | | | - Guang-Fu Yang
- Pesticide Science from Nankai University, Tianjin, China
| |
Collapse
|
15
|
Li M, Wang Y, Li F, Zhao Y, Liu M, Zhang S, Bin Y, Smith AI, Webb GI, Li J, Song J, Xia J. A Deep Learning-Based Method for Identification of Bacteriophage-Host Interaction. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2021; 18:1801-1810. [PMID: 32813660 PMCID: PMC8703204 DOI: 10.1109/tcbb.2020.3017386] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
Multi-drug resistance (MDR) has become one of the greatest threats to human health worldwide, and novel treatment methods of infections caused by MDR bacteria are urgently needed. Phage therapy is a promising alternative to solve this problem, to which the key is correctly matching target pathogenic bacteria with the corresponding therapeutic phage. Deep learning is powerful for mining complex patterns to generate accurate predictions. In this study, we develop PredPHI (Predicting Phage-Host Interactions), a deep learning-based tool capable of predicting the host of phages from sequence data. We collect >3000 phage-host pairs along with their protein sequences from PhagesDB and GenBank databases and extract a set of features. Then we select high-quality negative samples based on the K-Means clustering method and construct a balanced training set. Finally, we employ a deep convolutional neural network to build the predictive model. The results indicate that PredPHI can achieve a predictive performance of 81 percent in terms of the area under the receiver operating characteristic curve on the test set, and the clustering-based method is significantly more robust than that based on randomly selecting negative samples. These results highlight that PredPHI is a useful and accurate tool for identifying phage-host interactions from sequence data.
Collapse
|
16
|
Buongiorno D, Cascarano GD, De Feudis I, Brunetti A, Carnimeo L, Dimauro G, Bevilacqua V. Deep learning for processing electromyographic signals: A taxonomy-based survey. Neurocomputing 2021. [DOI: 10.1016/j.neucom.2020.06.139] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
|
17
|
Huang D, Song B, Wei J, Su J, Coenen F, Meng J. Weakly supervised learning of RNA modifications from low-resolution epitranscriptome data. Bioinformatics 2021; 37:i222-i230. [PMID: 34252943 PMCID: PMC8336446 DOI: 10.1093/bioinformatics/btab278] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022] Open
Abstract
Motivation Increasing evidence suggests that post-transcriptional ribonucleic acid (RNA) modifications regulate essential biomolecular functions and are related to the pathogenesis of various diseases. Precise identification of RNA modification sites is essential for understanding the regulatory mechanisms of RNAs. To date, many computational approaches for predicting RNA modifications have been developed, most of which were based on strong supervision enabled by base-resolution epitranscriptome data. However, high-resolution data may not be available. Results We propose WeakRM, the first weakly supervised learning framework for predicting RNA modifications from low-resolution epitranscriptome datasets, such as those generated from acRIP-seq and hMeRIP-seq. Evaluations on three independent datasets (corresponding to three different RNA modification types and their respective sequencing technologies) demonstrated the effectiveness of our approach in predicting RNA modifications from low-resolution data. WeakRM outperformed state-of-the-art multi-instance learning methods for genomic sequences, such as WSCNN, which was originally designed for transcription factor binding site prediction. Additionally, our approach captured motifs that are consistent with existing knowledge, and visualization of the predicted modification-containing regions unveiled the potentials of detecting RNA modifications with improved resolution. Availability implementation The source code for the WeakRM algorithm, along with the datasets used, are freely accessible at: https://github.com/daiyun02211/WeakRM Supplementary information Supplementary data are available at Bioinformatics online.
Collapse
Affiliation(s)
- Daiyun Huang
- Department of Biological Sciences, Xi'an Jiaotong-Liverpool University, Suzhou, Jiangsu 215123, China.,Department of Computer Science, University of Liverpool, Liverpool L69 7ZB, UK
| | - Bowen Song
- Department of Mathematical Sciences, Xi'an Jiaotong-Liverpool University, Suzhou, Jiangsu 215123, China.,Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool L69 7ZB, UK
| | - Jingjue Wei
- Department of Mathematical Sciences, Xi'an Jiaotong-Liverpool University, Suzhou, Jiangsu 215123, China
| | - Jionglong Su
- School of AI and Advanced Computing, XJTLU Entrepreneur College (Taicang), Xi'an Jiaotong-Liverpool University, Suzhou, Jiangsu 215123, China.,AI University Research Centre, Xi'an Jiaotong-Liverpool University, Suzhou, Jiangsu 215123, China
| | - Frans Coenen
- Department of Computer Science, University of Liverpool, Liverpool L69 7ZB, UK
| | - Jia Meng
- Department of Biological Sciences, Xi'an Jiaotong-Liverpool University, Suzhou, Jiangsu 215123, China.,Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool L69 7ZB, UK.,AI University Research Centre, Xi'an Jiaotong-Liverpool University, Suzhou, Jiangsu 215123, China
| |
Collapse
|
18
|
Zhang J, Chen Q, Liu B. DeepDRBP-2L: A New Genome Annotation Predictor for Identifying DNA-Binding Proteins and RNA-Binding Proteins Using Convolutional Neural Network and Long Short-Term Memory. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2021; 18:1451-1463. [PMID: 31722485 DOI: 10.1109/tcbb.2019.2952338] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
DNA-binding proteins (DBPs) and RNA-binding proteins (RBPs) are two kinds of crucial proteins, which are associated with various cellule activities and some important diseases. Accurate identification of DBPs and RBPs facilitate both theoretical research and real world application. Existing sequence-based DBP predictors can accurately identify DBPs but incorrectly predict many RBPs as DBPs, and vice versa, resulting in low prediction precision. Moreover, some proteins (DRBPs) interacting with both DNA and RNA play important roles in gene expression and cannot be identified by existing computational methods. In this study, a two-level predictor named DeepDRBP-2L was proposed by combining Convolutional Neural Network (CNN) and the Long Short-Term Memory (LSTM). It is the first computational method that is able to identify DBPs, RBPs and DRBPs. Rigorous cross-validations and independent tests showed that DeepDRBP-2L is able to overcome the shortcoming of the existing methods and can go one further step to identify DRBPs. Application of DeepDRBP-2L to tomato genome further demonstrated its performance. The webserver of DeepDRBP-2L is freely available at http://bliulab.net/DeepDRBP-2L.
Collapse
|
19
|
DeepD2V: A Novel Deep Learning-Based Framework for Predicting Transcription Factor Binding Sites from Combined DNA Sequence. Int J Mol Sci 2021; 22:ijms22115521. [PMID: 34073774 PMCID: PMC8197256 DOI: 10.3390/ijms22115521] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2021] [Revised: 04/29/2021] [Accepted: 05/12/2021] [Indexed: 12/13/2022] Open
Abstract
Predicting in vivo protein-DNA binding sites is a challenging but pressing task in a variety of fields like drug design and development. Most promoters contain a number of transcription factor (TF) binding sites, but only a small minority has been identified by biochemical experiments that are time-consuming and laborious. To tackle this challenge, many computational methods have been proposed to predict TF binding sites from DNA sequence. Although previous methods have achieved remarkable performance in the prediction of protein-DNA interactions, there is still considerable room for improvement. In this paper, we present a hybrid deep learning framework, termed DeepD2V, for transcription factor binding sites prediction. First, we construct the input matrix with an original DNA sequence and its three kinds of variant sequences, including its inverse, complementary, and complementary inverse sequence. A sliding window of size k with a specific stride is used to obtain its k-mer representation of input sequences. Next, we use word2vec to obtain a pre-trained k-mer word distributed representation model. Finally, the probability of protein-DNA binding is predicted by using the recurrent and convolutional neural network. The experiment results on 50 public ChIP-seq benchmark datasets demonstrate the superior performance and robustness of DeepD2V. Moreover, we verify that the performance of DeepD2V using word2vec-based k-mer distributed representation is better than one-hot encoding, and the integrated framework of both convolutional neural network (CNN) and bidirectional LSTM (bi-LSTM) outperforms CNN or the bi-LSTM model when used alone. The source code of DeepD2V is available at the github repository.
Collapse
|
20
|
Du X, Hu J, Li S. Using Chou's 5-Step Rule to Predict DNA-Protein Binding with Multi-scale Complementary Feature. J Proteome Res 2021; 20:1639-1656. [PMID: 33522829 DOI: 10.1021/acs.jproteome.0c00864] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
It is well known that DNA-protein binding (DPB) prediction is not only beneficial to understand the regulation mechanism of gene expression but also a challenging task in the field of computational biology. Traditional methods for DPB prediction that depend on manually extracted features may lead to classification errors. Recently, deep learning such as convolutional neural network (CNN) has been successfully applied to classification tasks and improved DPB prediction performance significantly. Yet, these methods are based on the original DNA sequence modeling, ignoring the hidden complex dependency and complementarity between multiple sequence features. In consideration of this problem, we propose a method to fuse different sequence features and analyze them systematically through multi-scale CNN. First, sliding windows of specified lengths are set on distinct DNA sequences to generate multiple sequence features with unequal lengths. Second, multiple feature sequences are fused and encoded for feature representation. Third, multi-scale CNN with different binding motif lengths is used to automatically learn and mine the influence of internal attributes and hidden complex relations between the fusion sequence features and make full use of the complementary advantages of extracted CNN features to predict DPB. When our model is applied to 690 ChIP-seq datasets, it achieves an average AUC of 0.9112, which is significantly better than the latest methods. The results show that our method is effective for DPB prediction and is freely available at http://121.5.71.120/mscDPB/.
Collapse
Affiliation(s)
- Xiuquan Du
- Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education, Anhui University, Hefei 230601, Anhui, China.,School of Computer Science and Technology, Anhui University, Hefei 230601, Anhui, China
| | - Jiajia Hu
- School of Computer Science and Technology, Anhui University, Hefei 230601, Anhui, China
| | - Shuo Li
- Department of Medical Imaging, Western University, London, ON N6A 3K7, Canada
| |
Collapse
|
21
|
Jin D, Qin Z, Yang M, Chen P. A Novel Neural Model With Lateral Interaction for Learning Tasks. Neural Comput 2020; 33:528-551. [PMID: 33253032 DOI: 10.1162/neco_a_01345] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022]
Abstract
We propose a novel neural model with lateral interaction for learning tasks. The model consists of two functional fields: an elementary field to extract features and a high-level field to store and recognize patterns. Each field is composed of some neurons with lateral interaction, and the neurons in different fields are connected by the rules of synaptic plasticity. The model is established on the current research of cognition and neuroscience, making it more transparent and biologically explainable. Our proposed model is applied to data classification and clustering. The corresponding algorithms share similar processes without requiring any parameter tuning and optimization processes. Numerical experiments validate that the proposed model is feasible in different learning tasks and superior to some state-of-the-art methods, especially in small sample learning, one-shot learning, and clustering.
Collapse
Affiliation(s)
- Dequan Jin
- School of Mathematics and Information Science, Guangxi University, 530004, P.R.C.
| | - Ziyan Qin
- School of Mathematics and Information Science, Guangxi University, 530004, P.R.C.
| | - Murong Yang
- School of Mathematics and Information Science, Guangxi University, 530004, P.R.C.
| | - Penghe Chen
- School of Mathematics and Information Science, Guangxi University, 530004, P.R.C.
| |
Collapse
|
22
|
|
23
|
He Y, Shen Z, Zhang Q, Wang S, Huang DS. A survey on deep learning in DNA/RNA motif mining. Brief Bioinform 2020; 22:5916939. [PMID: 33005921 PMCID: PMC8293829 DOI: 10.1093/bib/bbaa229] [Citation(s) in RCA: 34] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2020] [Revised: 08/19/2020] [Accepted: 08/24/2020] [Indexed: 01/18/2023] Open
Abstract
DNA/RNA motif mining is the foundation of gene function research. The DNA/RNA motif mining plays an extremely important role in identifying the DNA- or RNA-protein binding site, which helps to understand the mechanism of gene regulation and management. For the past few decades, researchers have been working on designing new efficient and accurate algorithms for mining motif. These algorithms can be roughly divided into two categories: the enumeration approach and the probabilistic method. In recent years, machine learning methods had made great progress, especially the algorithm represented by deep learning had achieved good performance. Existing deep learning methods in motif mining can be roughly divided into three types of models: convolutional neural network (CNN) based models, recurrent neural network (RNN) based models, and hybrid CNN–RNN based models. We introduce the application of deep learning in the field of motif mining in terms of data preprocessing, features of existing deep learning architectures and comparing the differences between the basic deep learning models. Through the analysis and comparison of existing deep learning methods, we found that the more complex models tend to perform better than simple ones when data are sufficient, and the current methods are relatively simple compared with other fields such as computer vision, language processing (NLP), computer games, etc. Therefore, it is necessary to conduct a summary in motif mining by deep learning, which can help researchers understand this field.
Collapse
Affiliation(s)
- Ying He
- computer science and technology at Tongji University, China
| | - Zhen Shen
- computer science and technology at Tongji University, China
| | - Qinhu Zhang
- computer science and technology at Tongji University, China
| | - Siguo Wang
- computer science and technology at Tongji University, China
| | - De-Shuang Huang
- Institute of Machines Learning and Systems Biology, Tongji University
| |
Collapse
|
24
|
Abstract
Deep neural networks have been revolutionizing the field of machine learning for the past several years. They have been applied with great success in many domains of the biomedical data sciences and are outperforming extant methods by a large margin. The ability of deep neural networks to pick up local image features and model the interactions between them makes them highly applicable to regulatory genomics. Instead of an image, the networks analyze DNA and RNA sequences and additional epigenomic data. In this review, we survey the successes of deep learning in the field of regulatory genomics. We first describe the fundamental building blocks of deep neural networks, popular architectures used in regulatory genomics, and their training process on molecular sequence data. We then review several key methods in different gene regulation domains. We start with the pioneering method DeepBind and its successors, which were developed to predict protein–DNA binding. We then review methods developed to predict and model epigenetic information, such as histone marks and nucleosome occupancy. Following epigenomics, we review methods to predict protein–RNA binding with its unique challenge of incorporating RNA structure information. Finally, we provide our overall view of the strengths and weaknesses of deep neural networks and prospects for future developments.
Collapse
Affiliation(s)
- Mira Barshai
- School of Electrical and Computer Engineering, Ben-Gurion University of the Negev, Beer-Sheva 8410501, Israel
| | - Eitamar Tripto
- Department of Biomedical Engineering, Ben-Gurion University of the Negev, Beer-Sheva 8410501, Israel
| | - Yaron Orenstein
- School of Electrical and Computer Engineering, Ben-Gurion University of the Negev, Beer-Sheva 8410501, Israel
| |
Collapse
|
25
|
Li M, Lian S, Wang F, Zhou Y, Chen B, Guan L, Wu Y. Prediction Model of Organic Molecular Absorption Energies based on Deep Learning trained by Chaos-enhanced Accelerated Evolutionary algorithm. Sci Rep 2019; 9:17261. [PMID: 31754116 PMCID: PMC6872818 DOI: 10.1038/s41598-019-53206-1] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2019] [Accepted: 10/29/2019] [Indexed: 01/24/2023] Open
Abstract
As an important physical property of molecules, absorption energy can characterize the electronic property and structural information of molecules. Moreover, the accurate calculation of molecular absorption energies is highly valuable. Present linear and nonlinear methods hold low calculation accuracies due to great errors, especially irregular complicated molecular systems for structures. Thus, developing a prediction model for molecular absorption energies with enhanced accuracy, efficiency, and stability is highly beneficial. By combining deep learning and intelligence algorithms, we propose a prediction model based on the chaos-enhanced accelerated particle swarm optimization algorithm and deep artificial neural network (CAPSO BP DNN) that possesses a seven-layer 8-4-4-4-4-4-1 structure. Eight parameters related to molecular absorption energies are selected as inputs, such as a theoretical calculating value Ec of absorption energy (B3LYP/STO-3G), molecular electron number Ne, oscillator strength Os, number of double bonds Ndb, total number of atoms Na, number of hydrogen atoms Nh, number of carbon atoms Nc, and number of nitrogen atoms NN; and one parameter representing the molecular absorption energy is regarded as the output. A prediction experiment on organic molecular absorption energies indicates that CAPSO BP DNN exhibits a favourable predictive effect, accuracy, and correlation. The tested absolute average relative error, predicted root-mean-square error, and square correlation coefficient are 0.033, 0.0153, and 0.9957, respectively. Relative to other prediction models, the CAPSO BP DNN model exhibits a good comprehensive prediction performance and can provide references for other materials, chemistry and physics fields, such as nonlinear prediction of chemical and physical properties, QSAR/QAPR and chemical information modelling, etc.
Collapse
Affiliation(s)
- Mengshan Li
- College of Physics and Electronic Information, Gannan Normal University, Ganzhou, Jiangxi, 341000, China.
| | - Suyun Lian
- College of Physics and Electronic Information, Gannan Normal University, Ganzhou, Jiangxi, 341000, China
| | - Fan Wang
- College of Physics and Electronic Information, Gannan Normal University, Ganzhou, Jiangxi, 341000, China
| | - Yanying Zhou
- College of Physics and Electronic Information, Gannan Normal University, Ganzhou, Jiangxi, 341000, China
| | - Bingsheng Chen
- College of Physics and Electronic Information, Gannan Normal University, Ganzhou, Jiangxi, 341000, China
| | - Lixin Guan
- College of Physics and Electronic Information, Gannan Normal University, Ganzhou, Jiangxi, 341000, China
| | - Yan Wu
- College of Physics and Electronic Information, Gannan Normal University, Ganzhou, Jiangxi, 341000, China
| |
Collapse
|