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Mouratidis I, Baltoumas FA, Chantzi N, Patsakis M, Chan CS, Montgomery A, Konnaris MA, Aplakidou E, Georgakopoulos GC, Das A, Chartoumpekis DV, Kovac J, Pavlopoulos GA, Georgakopoulos-Soares I. kmerDB: A database encompassing the set of genomic and proteomic sequence information for each species. Comput Struct Biotechnol J 2024; 23:1919-1928. [PMID: 38711760 PMCID: PMC11070822 DOI: 10.1016/j.csbj.2024.04.050] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2023] [Revised: 04/17/2024] [Accepted: 04/18/2024] [Indexed: 05/08/2024] Open
Abstract
The decrease in sequencing expenses has facilitated the creation of reference genomes and proteomes for an expanding array of organisms. Nevertheless, no established repository that details organism-specific genomic and proteomic sequences of specific lengths, referred to as kmers, exists to our knowledge. In this article, we present kmerDB, a database accessible through an interactive web interface that provides kmer-based information from genomic and proteomic sequences in a systematic way. kmerDB currently contains 202,340,859,107 base pairs and 19,304,903,356 amino acids, spanning 54,039 and 21,865 reference genomes and proteomes, respectively, as well as 6,905,362 and 149,305,183 genomic and proteomic species-specific sequences, termed quasi-primes. Additionally, we provide access to 5,186,757 nucleic and 214,904,089 peptide sequences absent from every genome and proteome, termed primes. kmerDB features a user-friendly interface offering various search options and filters for easy parsing and searching. The service is available at: www.kmerdb.com.
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Affiliation(s)
- Ioannis Mouratidis
- Institute for Personalized Medicine, Department of Biochemistry and Molecular Biology, The Pennsylvania State University College of Medicine, Hershey, PA, USA
- Huck Institutes of the Life Sciences, The Pennsylvania State University, University Park, PA, USA
| | - Fotis A. Baltoumas
- Institute for Fundamental Biomedical Research, BSRC "Alexander Fleming", Vari, 16672, Greece
| | - Nikol Chantzi
- Institute for Personalized Medicine, Department of Biochemistry and Molecular Biology, The Pennsylvania State University College of Medicine, Hershey, PA, USA
| | - Michail Patsakis
- Institute for Personalized Medicine, Department of Biochemistry and Molecular Biology, The Pennsylvania State University College of Medicine, Hershey, PA, USA
| | - Candace S.Y. Chan
- Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, CA, USA
| | - Austin Montgomery
- Institute for Personalized Medicine, Department of Biochemistry and Molecular Biology, The Pennsylvania State University College of Medicine, Hershey, PA, USA
| | - Maxwell A. Konnaris
- Institute for Personalized Medicine, Department of Biochemistry and Molecular Biology, The Pennsylvania State University College of Medicine, Hershey, PA, USA
- Huck Institutes of the Life Sciences, The Pennsylvania State University, University Park, PA, USA
- Department of Statistics, The Pennsylvania State University, University Park, PA, USA
| | - Eleni Aplakidou
- Institute for Fundamental Biomedical Research, BSRC "Alexander Fleming", Vari, 16672, Greece
- Department of Basic Sciences, School of Medicine, University of Crete, Heraklion, Greece
| | - George C. Georgakopoulos
- National Technical University of Athens, School of Electrical and Computer Engineering, Athens, Greece
| | - Anshuman Das
- Institute for Personalized Medicine, Department of Biochemistry and Molecular Biology, The Pennsylvania State University College of Medicine, Hershey, PA, USA
| | - Dionysios V. Chartoumpekis
- Service of Endocrinology, Diabetology and Metabolism, Lausanne University Hospital, Lausanne, Switzerland
| | - Jasna Kovac
- Department of Food Science, The Pennsylvania State University, University Park, PA 16802, USA
| | - Georgios A. Pavlopoulos
- Institute for Fundamental Biomedical Research, BSRC "Alexander Fleming", Vari, 16672, Greece
- Center for New Biotechnologies and Precision Medicine, School of Medicine, National and Kapodistrian University of Athens, Athens, 11527, Greece
| | - Ilias Georgakopoulos-Soares
- Institute for Personalized Medicine, Department of Biochemistry and Molecular Biology, The Pennsylvania State University College of Medicine, Hershey, PA, USA
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Kuang Z, Yan X, Yuan Y, Wang R, Zhu H, Wang Y, Li J, Ye J, Yue H, Yang X. Advances in stress-tolerance elements for microbial cell factories. Synth Syst Biotechnol 2024; 9:793-808. [PMID: 39072145 PMCID: PMC11277822 DOI: 10.1016/j.synbio.2024.06.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2024] [Revised: 06/10/2024] [Accepted: 06/27/2024] [Indexed: 07/30/2024] Open
Abstract
Microorganisms, particularly extremophiles, have evolved multiple adaptation mechanisms to address diverse stress conditions during survival in unique environments. Their responses to environmental coercion decide not only survival in severe conditions but are also an essential factor determining bioproduction performance. The design of robust cell factories should take the balance of their growing and bioproduction into account. Thus, mining and redesigning stress-tolerance elements to optimize the performance of cell factories under various extreme conditions is necessary. Here, we reviewed several stress-tolerance elements, including acid-tolerant elements, saline-alkali-resistant elements, thermotolerant elements, antioxidant elements, and so on, providing potential materials for the construction of cell factories and the development of synthetic biology. Strategies for mining and redesigning stress-tolerance elements were also discussed. Moreover, several applications of stress-tolerance elements were provided, and perspectives and discussions for potential strategies for screening stress-tolerance elements were made.
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Affiliation(s)
- Zheyi Kuang
- School of Intelligence Science and Technology, Xinjiang University, Urumqi, 830017, China
| | - Xiaofang Yan
- School of Biology and Biological Engineering, South China University of Technology, Guangzhou, 510006, China
| | - Yanfei Yuan
- School of Biology and Biological Engineering, South China University of Technology, Guangzhou, 510006, China
| | - Ruiqi Wang
- School of Intelligence Science and Technology, Xinjiang University, Urumqi, 830017, China
| | - Haifan Zhu
- School of Intelligence Science and Technology, Xinjiang University, Urumqi, 830017, China
| | - Youyang Wang
- School of Intelligence Science and Technology, Xinjiang University, Urumqi, 830017, China
| | - Jianfeng Li
- School of Biology and Biological Engineering, South China University of Technology, Guangzhou, 510006, China
| | - Jianwen Ye
- School of Biology and Biological Engineering, South China University of Technology, Guangzhou, 510006, China
| | - Haitao Yue
- School of Intelligence Science and Technology, Xinjiang University, Urumqi, 830017, China
- Laboratory of Synthetic Biology, School of Life Science and Technology, Xinjiang University, Urumqi, 830017, China
| | - Xiaofeng Yang
- School of Biology and Biological Engineering, South China University of Technology, Guangzhou, 510006, China
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Molina-Moreno M, González-Díaz I, Mikut R, Díaz-de-María F. A self-supervised embedding of cell migration features for behavior discovery over cell populations. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2024; 255:108337. [PMID: 39067139 DOI: 10.1016/j.cmpb.2024.108337] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/20/2023] [Revised: 04/26/2024] [Accepted: 07/17/2024] [Indexed: 07/30/2024]
Abstract
BACKGROUND AND OBJECTIVE Recent studies point out that the dynamics and interaction of cell populations within their environment are related to several biological processes in immunology. Hence, single-cell analysis in immunology now relies on spatial omics. Moreover, recent literature suggests that immunology scenarios are hierarchically organized, including unknown cell behaviors appearing in different proportions across some observable control and therapy groups. These dynamic behaviors play a crucial role in identifying the causes of processes such as inflammation, aging, and fighting off pathogens or cancerous cells. In this work, we use a self-supervised learning approach to discover these behaviors associated with cell dynamics in an immunology scenario. MATERIALS AND METHODS Specifically, we study the different responses of control group and therapy groups in a scenario involving inflammation due to infarct, with a focus on neutrophil migration within blood vessels. Starting from a set of hand-crafted spatio-temporal features, we use a recurrent neural network to generate embeddings that properly describe the dynamics of the migration processes. The network is trained using a novel multi-task contrastive loss that, on the one hand, models the hierarchical structure of our scenario (groups-behaviors-samples) and, on the other, ensures temporal consistency within the embedding, enforcing that subsequent temporal samples obtained from a given cell stay close in the latent space. RESULTS Our experimental results demonstrate that the resulting embeddings improve the separability of cell behaviors and log-likelihood of the therapies, when compared to the hand-crafted feature extraction and recent methods from the state of the art, even with dimensionality reduction (16 vs. 21 hand-crafted features). CONCLUSIONS Our approach enables single-cell analyses at a population level, being able to automatically discover shared behaviors among different groups. This, in turn, enables the prediction of the therapy effectiveness based on their proportions within a study group.
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Affiliation(s)
- Miguel Molina-Moreno
- Department of Signal Theory and Communications, Universidad Carlos III de Madrid, Avda. de la Universidad, 30, Leganés, 28911, Spain; Department of Immunobiology, Yale University, Amistad Street Building, 10 Amistad St, New Haven, 06520, USA.
| | - Iván González-Díaz
- Department of Signal Theory and Communications, Universidad Carlos III de Madrid, Avda. de la Universidad, 30, Leganés, 28911, Spain.
| | - Ralf Mikut
- Institute for Automation and Applied Informatics, Karlsruhe Institute of Technology, Hermann-von-Helmholtz-Platz, 1, Eggenstein-Leopoldshafen, 76344, Baden-Württemberg, Germany.
| | - Fernando Díaz-de-María
- Department of Signal Theory and Communications, Universidad Carlos III de Madrid, Avda. de la Universidad, 30, Leganés, 28911, Spain.
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Gyawali N, Hao Y, Lin G, Huang J, Bika R, Daza L, Zheng H, Cruppe G, Caragea D, Cook D, Valent B, Liu S. Using recurrent neural networks to detect supernumerary chromosomes in fungal strains causing blast diseases. NAR Genom Bioinform 2024; 6:lqae108. [PMID: 39165675 PMCID: PMC11333962 DOI: 10.1093/nargab/lqae108] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2023] [Revised: 06/27/2024] [Accepted: 08/06/2024] [Indexed: 08/22/2024] Open
Abstract
The genomes of the fungus Magnaporthe oryzae that causes blast diseases on diverse grass species, including major crops, have indispensable core-chromosomes and may contain supernumerary chromosomes, also known as mini-chromosomes. These mini-chromosomes are speculated to provide effector gene mobility, and may transfer between strains. To understand the biology of mini-chromosomes, it is valuable to be able to detect whether a M. oryzae strain possesses a mini-chromosome. Here, we applied recurrent neural network models for classifying DNA sequences as arising from core- or mini-chromosomes. The models were trained with sequences from available core- and mini-chromosome assemblies, and then used to predict the presence of mini-chromosomes in a global collection of M. oryzae isolates using short-read DNA sequences. The model predicted that mini-chromosomes were prevalent in M. oryzae isolates. Interestingly, at least one mini-chromosome was present in all recent wheat isolates, but no mini-chromosomes were found in early isolates collected before 1991, indicating a preferential selection for strains carrying mini-chromosomes in recent years. The model was also used to identify assembled contigs derived from mini-chromosomes. In summary, our study has developed a reliable method for categorizing DNA sequences and showcases an application of recurrent neural networks in predictive genomics.
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Affiliation(s)
- Nikesh Gyawali
- Department of Computer Science, Kansas State University, Manhattan, KS 66506, USA
| | - Yangfan Hao
- Department of Plant Pathology, Kansas State University, Manhattan, KS 66506, USA
| | - Guifang Lin
- Department of Plant Pathology, Kansas State University, Manhattan, KS 66506, USA
| | - Jun Huang
- Department of Plant Pathology, Kansas State University, Manhattan, KS 66506, USA
| | - Ravi Bika
- Department of Plant Pathology, Kansas State University, Manhattan, KS 66506, USA
| | - Lidia Calderon Daza
- Department of Plant Pathology, Kansas State University, Manhattan, KS 66506, USA
| | - Huakun Zheng
- Department of Plant Pathology, Kansas State University, Manhattan, KS 66506, USA
| | - Giovana Cruppe
- Department of Plant Pathology, Kansas State University, Manhattan, KS 66506, USA
| | - Doina Caragea
- Department of Computer Science, Kansas State University, Manhattan, KS 66506, USA
| | - David Cook
- Department of Plant Pathology, Kansas State University, Manhattan, KS 66506, USA
| | - Barbara Valent
- Department of Plant Pathology, Kansas State University, Manhattan, KS 66506, USA
| | - Sanzhen Liu
- Department of Plant Pathology, Kansas State University, Manhattan, KS 66506, USA
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5
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Diao B, Luo J, Guo Y. A comprehensive survey on deep learning-based identification and predicting the interaction mechanism of long non-coding RNAs. Brief Funct Genomics 2024; 23:314-324. [PMID: 38576205 DOI: 10.1093/bfgp/elae010] [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] [Received: 12/06/2023] [Revised: 02/25/2024] [Accepted: 03/14/2024] [Indexed: 04/06/2024] Open
Abstract
Long noncoding RNAs (lncRNAs) have been discovered to be extensively involved in eukaryotic epigenetic, transcriptional, and post-transcriptional regulatory processes with the advancements in sequencing technology and genomics research. Therefore, they play crucial roles in the body's normal physiology and various disease outcomes. Presently, numerous unknown lncRNA sequencing data require exploration. Establishing deep learning-based prediction models for lncRNAs provides valuable insights for researchers, substantially reducing time and costs associated with trial and error and facilitating the disease-relevant lncRNA identification for prognosis analysis and targeted drug development as the era of artificial intelligence progresses. However, most lncRNA-related researchers lack awareness of the latest advancements in deep learning models and model selection and application in functional research on lncRNAs. Thus, we elucidate the concept of deep learning models, explore several prevalent deep learning algorithms and their data preferences, conduct a comprehensive review of recent literature studies with exemplary predictive performance over the past 5 years in conjunction with diverse prediction functions, critically analyze and discuss the merits and limitations of current deep learning models and solutions, while also proposing prospects based on cutting-edge advancements in lncRNA research.
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Affiliation(s)
- Biyu Diao
- Department of Breast Surgery, The First Affiliated Hospital of Ningbo University, No. 59, Liuting Street, Haishu District, Ningbo 315000, China
| | - Jin Luo
- Department of Breast Surgery, The First Affiliated Hospital of Ningbo University, No. 59, Liuting Street, Haishu District, Ningbo 315000, China
| | - Yu Guo
- Department of Breast Surgery, The First Affiliated Hospital of Ningbo University, No. 59, Liuting Street, Haishu District, Ningbo 315000, China
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Graham JP, Zhang Y, He L, Gonzalez-Fernandez T. CRISPR-GEM: A Novel Machine Learning Model for CRISPR Genetic Target Discovery and Evaluation. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.07.01.601587. [PMID: 39005295 PMCID: PMC11244939 DOI: 10.1101/2024.07.01.601587] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/16/2024]
Abstract
CRISPR gene editing strategies are shaping cell therapies through precise and tunable control over gene expression. However, achieving reliable therapeutic effects with improved safety and efficacy requires informed target gene selection. This depends on a thorough understanding of the involvement of target genes in gene regulatory networks (GRNs) that regulate cell phenotype and function. Machine learning models have been previously used for GRN reconstruction using RNA-seq data, but current techniques are limited to single cell types and focus mainly on transcription factors. This restriction overlooks many potential CRISPR target genes, such as those encoding extracellular matrix components, growth factors, and signaling molecules, thus limiting the applicability of these models for CRISPR strategies. To address these limitations, we have developed CRISPR-GEM, a multi-layer perceptron (MLP)-based synthetic GRN constructed to accurately predict the downstream effects of CRISPR gene editing. First, input and output nodes are identified as differentially expressed genes between defined experimental and target cell/tissue types respectively. Then, MLP training learns regulatory relationships in a black-box approach allowing accurate prediction of output gene expression using only input gene expression. Finally, CRISPR-mimetic perturbations are made to each input gene individually and the resulting model predictions are compared to those for the target group to score and assess each input gene as a CRISPR candidate. The top scoring genes provided by CRISPR-GEM therefore best modulate experimental group GRNs to motivate transcriptomic shifts towards a target group phenotype. This machine learning model is the first of its kind for predicting optimal CRISPR target genes and serves as a powerful tool for enhanced CRISPR strategies across a range of cell therapies.
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Affiliation(s)
- Josh P Graham
- Department of Bioengineering, Lehigh University, Bethlehem, PA, USA
| | - Yu Zhang
- Department of Bioengineering, Lehigh University, Bethlehem, PA, USA
- Department of Electrical and Computer Engineering, Lehigh University, Bethlehem, PA, USA
| | - Lifang He
- Department of Computer Science and Engineering, Lehigh University, Bethlehem, PA, USA
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Ahmed FS, Aly S, Liu X. EPI-Trans: an effective transformer-based deep learning model for enhancer promoter interaction prediction. BMC Bioinformatics 2024; 25:216. [PMID: 38890584 PMCID: PMC11184834 DOI: 10.1186/s12859-024-05784-9] [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: 12/19/2023] [Accepted: 04/15/2024] [Indexed: 06/20/2024] Open
Abstract
BACKGROUND Recognition of enhancer-promoter Interactions (EPIs) is crucial for human development. EPIs in the genome play a key role in regulating transcription. However, experimental approaches for classifying EPIs are too expensive in terms of effort, time, and resources. Therefore, more and more studies are being done on developing computational techniques, particularly using deep learning and other machine learning techniques, to address such problems. Unfortunately, the majority of current computational methods are based on convolutional neural networks, recurrent neural networks, or a combination of them, which don't take into consideration contextual details and the long-range interactions between the enhancer and promoter sequences. A new transformer-based model called EPI-Trans is presented in this study to overcome the aforementioned limitations. The multi-head attention mechanism in the transformer model automatically learns features that represent the long interrelationships between enhancer and promoter sequences. Furthermore, a generic model is created with transferability that can be utilized as a pre-trained model for various cell lines. Moreover, the parameters of the generic model are fine-tuned using a particular cell line dataset to improve performance. RESULTS Based on the results obtained from six benchmark cell lines, the average AUROC for the specific, generic, and best models is 94.2%, 95%, and 95.7%, while the average AUPR is 80.5%, 66.1%, and 79.6% respectively. CONCLUSIONS This study proposed a transformer-based deep learning model for EPI prediction. The comparative results on certain cell lines show that EPI-Trans outperforms other cutting-edge techniques and can provide superior performance on the challenge of recognizing EPI.
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Affiliation(s)
- Fatma S Ahmed
- Department of Computer Science and Technology, Xiamen University, Xiamen, 361005, China.
- Department of Electrical Engineering, Aswan University, Aswan, 81542, Egypt.
| | - Saleh Aly
- Department of Electrical Engineering, Aswan University, Aswan, 81542, Egypt.
- Department of Information Technology, Majmaah University, 11952, Majmaah, Saudi Arabia.
| | - Xiangrong Liu
- Department of Computer Science and Technology, Xiamen University, Xiamen, 361005, China
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Shumaly S, Darvish F, Li X, Kukharenko O, Steffen W, Guo Y, Butt HJ, Berger R. Estimating sliding drop width via side-view features using recurrent neural networks. Sci Rep 2024; 14:12033. [PMID: 38797765 PMCID: PMC11128450 DOI: 10.1038/s41598-024-62194-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2023] [Accepted: 05/14/2024] [Indexed: 05/29/2024] Open
Abstract
High speed side-view videos of sliding drops enable researchers to investigate drop dynamics and surface properties. However, understanding the physics of sliding requires knowledge of the drop width. A front-view perspective of the drop is necessary. In particular, the drop's width is a crucial parameter owing to its association with the friction force. Incorporating extra cameras or mirrors to monitor changes in the width of drops from a front-view perspective is cumbersome and limits the viewing area. This limitation impedes a comprehensive analysis of sliding drops, especially when they interact with surface defects. Our study explores the use of various regression and multivariate sequence analysis (MSA) models to estimate the drop width at a solid surface solely from side-view videos. This approach eliminates the need to incorporate additional equipment into the experimental setup. In addition, it ensures an unlimited viewing area of sliding drops. The Long Short Term Memory (LSTM) model with a 20 sliding window size has the best performance with the lowest root mean square error (RMSE) of 67 µm. Within the spectrum of drop widths in our dataset, ranging from 1.6 to 4.4 mm, this RMSE indicates that we can predict the width of sliding drops with an error of 2.4%. Furthermore, the applied LSTM model provides a drop width across the whole sliding length of 5 cm, previously unattainable.
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Affiliation(s)
- Sajjad Shumaly
- Max Planck Institute for Polymer Research (MPI-P), Ackermannweg 10, 55128, Mainz, Germany
| | - Fahimeh Darvish
- Max Planck Institute for Polymer Research (MPI-P), Ackermannweg 10, 55128, Mainz, Germany
| | - Xiaomei Li
- Max Planck Institute for Polymer Research (MPI-P), Ackermannweg 10, 55128, Mainz, Germany
| | - Oleksandra Kukharenko
- Max Planck Institute for Polymer Research (MPI-P), Ackermannweg 10, 55128, Mainz, Germany
| | - Werner Steffen
- Max Planck Institute for Polymer Research (MPI-P), Ackermannweg 10, 55128, Mainz, Germany
| | - Yanhui Guo
- Department of Computer Science, University of Illinois Springfield, Springfield, IL, USA
| | - Hans-Jürgen Butt
- Max Planck Institute for Polymer Research (MPI-P), Ackermannweg 10, 55128, Mainz, Germany
| | - Rüdiger Berger
- Max Planck Institute for Polymer Research (MPI-P), Ackermannweg 10, 55128, Mainz, Germany.
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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.
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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
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10
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Kwak IY, Kim BC, Lee J, Kang T, Garry DJ, Zhang J, Gong W. Proformer: a hybrid macaron transformer model predicts expression values from promoter sequences. BMC Bioinformatics 2024; 25:81. [PMID: 38378442 PMCID: PMC10877777 DOI: 10.1186/s12859-024-05645-5] [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: 05/24/2023] [Accepted: 01/08/2024] [Indexed: 02/22/2024] Open
Abstract
The breakthrough high-throughput measurement of the cis-regulatory activity of millions of randomly generated promoters provides an unprecedented opportunity to systematically decode the cis-regulatory logic that determines the expression values. We developed an end-to-end transformer encoder architecture named Proformer to predict the expression values from DNA sequences. Proformer used a Macaron-like Transformer encoder architecture, where two half-step feed forward (FFN) layers were placed at the beginning and the end of each encoder block, and a separable 1D convolution layer was inserted after the first FFN layer and in front of the multi-head attention layer. The sliding k-mers from one-hot encoded sequences were mapped onto a continuous embedding, combined with the learned positional embedding and strand embedding (forward strand vs. reverse complemented strand) as the sequence input. Moreover, Proformer introduced multiple expression heads with mask filling to prevent the transformer models from collapsing when training on relatively small amount of data. We empirically determined that this design had significantly better performance than the conventional design such as using the global pooling layer as the output layer for the regression task. These analyses support the notion that Proformer provides a novel method of learning and enhances our understanding of how cis-regulatory sequences determine the expression values.
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Affiliation(s)
- Il-Youp Kwak
- Department of Applied Statistics, Chung‑Ang University, Seoul, Republic of Korea
| | - Byeong-Chan Kim
- Department of Applied Statistics, Chung‑Ang University, Seoul, Republic of Korea
| | - Juhyun Lee
- Department of Applied Statistics, Chung‑Ang University, Seoul, Republic of Korea
| | - Taein Kang
- Department of Applied Statistics, Chung‑Ang University, Seoul, Republic of Korea
| | - Daniel J Garry
- Cardiovascular Division, Department of Medicine, Lillehei Heart Institute, University of Minnesota, 2231 6th St SE, Minneapolis, MN, 55455, USA.
- Stem Cell Institute, University of Minnesota, Minneapolis, MN, 55455, USA.
- Paul and Sheila Wellstone Muscular Dystrophy Center, University of Minnesota, Minneapolis, MN, 55455, USA.
| | - Jianyi Zhang
- Department of Biomedical Engineering, The University of Alabama at Birmingham, Birmingham, AL, 35233, USA
| | - Wuming Gong
- Cardiovascular Division, Department of Medicine, Lillehei Heart Institute, University of Minnesota, 2231 6th St SE, Minneapolis, MN, 55455, USA.
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11
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Chuah CW, He W, Huang DS. GMean-a semi-supervised GRU and K-mean model for predicting the TF binding site. Sci Rep 2024; 14:2539. [PMID: 38291225 PMCID: PMC10827707 DOI: 10.1038/s41598-024-52933-4] [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: 10/29/2023] [Accepted: 01/25/2024] [Indexed: 02/01/2024] Open
Abstract
The transcription factor binding site is a deoxyribonucleic acid sequence that binds to transcription factors. Transcription factors are proteins that regulate the transcription gene. Abnormal turnover of transcription factors can lead to uncontrolled cell growth. Therefore, discovering the relationships between transcription factors and deoxyribonucleic acid sequences is an important component of bioinformatics research. Numerous deep learning and machine learning language models have been developed to accomplish these tasks. Our goal in this work is to propose a GMean model for predicting unlabelled deoxyribonucleic acid sequences. The GMean model is a hybrid model with a combination of gated recurrent unit and K-mean clustering. The GMean model is developed in three phases. The labelled and unlabelled data are processed based on k-mers and tokenization. The labelled data is used for training. The unlabelled data are used for testing and prediction. The experimental data consists of deoxyribonucleic acid experimental of GM12878, K562 and HepG2. The experimental results show that GMean is feasible and effective in predicting deoxyribonucleic acid sequences, as the highest accuracy is 91.85% in predicting K562 and HepG2. This is followed by the prediction of the sequence between GM12878 and K562 with an accuracy of 89.13%. The lowest accuracy is the prediction of the sequence between HepG2 and GM12828, which is 88.80%.
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Affiliation(s)
- Chai Wen Chuah
- Guangdong University of Science and Technology, Songsan Hu, Dongguang, 523070, Guangdong, China.
| | - Wanxian He
- Guangxi Academy of Sciences, 98 Daling Road, Nanning, 530007, Guangxi, China
| | - De-Shuang Huang
- Eastern Institute for Advanced Study, Eastern Institute of Technology, Tongxin Road No. 568, Ningbo, 315201, China
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12
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Gupta A, Kang K, Pathania R, Saxton L, Saucedo B, Malik A, Torres-Tiji Y, Diaz CJ, Dutra Molino JV, Mayfield SP. Harnessing genetic engineering to drive economic bioproduct production in algae. Front Bioeng Biotechnol 2024; 12:1350722. [PMID: 38347913 PMCID: PMC10859422 DOI: 10.3389/fbioe.2024.1350722] [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: 12/05/2023] [Accepted: 01/16/2024] [Indexed: 02/15/2024] Open
Abstract
Our reliance on agriculture for sustenance, healthcare, and resources has been essential since the dawn of civilization. However, traditional agricultural practices are no longer adequate to meet the demands of a burgeoning population amidst climate-driven agricultural challenges. Microalgae emerge as a beacon of hope, offering a sustainable and renewable source of food, animal feed, and energy. Their rapid growth rates, adaptability to non-arable land and non-potable water, and diverse bioproduct range, encompassing biofuels and nutraceuticals, position them as a cornerstone of future resource management. Furthermore, microalgae's ability to capture carbon aligns with environmental conservation goals. While microalgae offers significant benefits, obstacles in cost-effective biomass production persist, which curtails broader application. This review examines microalgae compared to other host platforms, highlighting current innovative approaches aimed at overcoming existing barriers. These approaches include a range of techniques, from gene editing, synthetic promoters, and mutagenesis to selective breeding and metabolic engineering through transcription factors.
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Affiliation(s)
- Abhishek Gupta
- Mayfield Laboratory, Department of Molecular Biology, School of Biological Sciences, University of California San Diego, San Diego, CA, United States
| | - Kalisa Kang
- Mayfield Laboratory, Department of Molecular Biology, School of Biological Sciences, University of California San Diego, San Diego, CA, United States
| | - Ruchi Pathania
- Mayfield Laboratory, Department of Molecular Biology, School of Biological Sciences, University of California San Diego, San Diego, CA, United States
| | - Lisa Saxton
- Mayfield Laboratory, Department of Molecular Biology, School of Biological Sciences, University of California San Diego, San Diego, CA, United States
| | - Barbara Saucedo
- Mayfield Laboratory, Department of Molecular Biology, School of Biological Sciences, University of California San Diego, San Diego, CA, United States
| | - Ashleyn Malik
- Mayfield Laboratory, Department of Molecular Biology, School of Biological Sciences, University of California San Diego, San Diego, CA, United States
| | - Yasin Torres-Tiji
- Mayfield Laboratory, Department of Molecular Biology, School of Biological Sciences, University of California San Diego, San Diego, CA, United States
| | - Crisandra J. Diaz
- Mayfield Laboratory, Department of Molecular Biology, School of Biological Sciences, University of California San Diego, San Diego, CA, United States
| | - João Vitor Dutra Molino
- Mayfield Laboratory, Department of Molecular Biology, School of Biological Sciences, University of California San Diego, San Diego, CA, United States
| | - Stephen P. Mayfield
- Mayfield Laboratory, Department of Molecular Biology, School of Biological Sciences, University of California San Diego, San Diego, CA, United States
- California Center for Algae Biotechnology, University of California San Diego, San Diego, CA, United States
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13
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Elshafey BG, Elfadadny A, Metwally S, Saleh AG, Ragab RF, Hamada R, Mandour AS, Hendawy AO, Alkazmi L, Ogaly HA, Batiha GES. Association between biochemical parameters and ultrasonographic measurement for the assessment of hepatic lipidosis in dairy cows. ITALIAN JOURNAL OF ANIMAL SCIENCE 2023. [DOI: 10.1080/1828051x.2023.2170284] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Affiliation(s)
- Besheer G. Elshafey
- Department of Animal Internal Medicine, Faculty of Veterinary Medicine, Damanhour University, Damanhour, Egypt
| | - Ahmed Elfadadny
- Department of Animal Internal Medicine, Faculty of Veterinary Medicine, Damanhour University, Damanhour, Egypt
| | - Samy Metwally
- Department of Infectious Diseases, Faculty of Veterinary Medicine, Damanhour University, Damanhour, Egypt
| | - Asmaa G. Saleh
- Department of Animal Internal Medicine, Faculty of Veterinary Medicine, Damanhour University, Damanhour, Egypt
| | - Rokaia F. Ragab
- Department of Biochemistry, Faculty of Veterinary Medicine, Damanhour University, Damanhour, Egypt
| | - Rania Hamada
- Department of Clinical Pathology, Faculty of Veterinary Medicine, Damanhour University, Damanhour, Egypt
| | - Ahmed S. Mandour
- Department of Animal Medicine (Internal Medicine), Faculty of Veterinary Medicine, Suez Canal University, Ismailia, Egypt
| | - Amin Omar Hendawy
- Department of Animal and Poultry Production, Faculty of Agriculture, Damanhour University, Damanhour, Egypt
| | - Luay Alkazmi
- Biology Department, Faculty of Applied Sciences, Umm Al-Qura University, Makkah, Saudi Arabia
| | - Hanan A. Ogaly
- Chemistry Department, College of Science, King Khalid University, Abha, Saudi Arabia
- Biochemistry and Molecular Biology Department, Faculty of Veterinary Medicine, Cairo University, Giza, Egypt
| | - Gaber El-Saber Batiha
- Department of Pharmacology and Therapeutics, Faculty of Veterinary Medicine, Damanhour University, Damanhour, Egypt
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14
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Proft S, Leiz J, Heinemann U, Seelow D, Schmidt-Ott KM, Rutkiewicz M. Discovery of a non-canonical GRHL1 binding site using deep convolutional and recurrent neural networks. BMC Genomics 2023; 24:736. [PMID: 38049725 PMCID: PMC10696883 DOI: 10.1186/s12864-023-09830-3] [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: 08/17/2023] [Accepted: 11/22/2023] [Indexed: 12/06/2023] Open
Abstract
BACKGROUND Transcription factors regulate gene expression by binding to transcription factor binding sites (TFBSs). Most models for predicting TFBSs are based on position weight matrices (PWMs), which require a specific motif to be present in the DNA sequence and do not consider interdependencies of nucleotides. Novel approaches such as Transcription Factor Flexible Models or recurrent neural networks consequently provide higher accuracies. However, it is unclear whether such approaches can uncover novel non-canonical, hitherto unexpected TFBSs relevant to human transcriptional regulation. RESULTS In this study, we trained a convolutional recurrent neural network with HT-SELEX data for GRHL1 binding and applied it to a set of GRHL1 binding sites obtained from ChIP-Seq experiments from human cells. We identified 46 non-canonical GRHL1 binding sites, which were not found by a conventional PWM approach. Unexpectedly, some of the newly predicted binding sequences lacked the CNNG core motif, so far considered obligatory for GRHL1 binding. Using isothermal titration calorimetry, we experimentally confirmed binding between the GRHL1-DNA binding domain and predicted GRHL1 binding sites, including a non-canonical GRHL1 binding site. Mutagenesis of individual nucleotides revealed a correlation between predicted binding strength and experimentally validated binding affinity across representative sequences. This correlation was neither observed with a PWM-based nor another deep learning approach. CONCLUSIONS Our results show that convolutional recurrent neural networks may uncover unanticipated binding sites and facilitate quantitative transcription factor binding predictions.
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Affiliation(s)
- Sebastian Proft
- Exploratory Diagnostic Sciences, Berlin Institute of Health, Charité - Universitätsmedizin Berlin, 10117, Berlin, Germany
- Institute of Medical Genetics and Human Genetics, Charité - Universitätsmedizin Berlin, Freie Universität Berlin and Humboldt-Universität zu Berlin, 13353, Berlin, Germany
| | - Janna Leiz
- Department of Nephrology and Hypertension, Hannover Medical School, 30625, Hannover, Germany
- Department of Nephrology and Intensive Care Medicine, Charité - Universitätsmedizin Berlin, Freie Universität Berlin and Humboldt-Universität zu Berlin, 12203, Berlin, Germany
- Molecular and Translational Kidney Research, Max-Delbrück-Center for Molecular Medicine in the Helmholtz Association, 13125, Berlin, Germany
| | - Udo Heinemann
- Macromolecular Structure and Interaction, Max Delbrück Center for Molecular Medicine in the Helmholtz Association, 13125, Berlin, Germany.
| | - Dominik Seelow
- Exploratory Diagnostic Sciences, Berlin Institute of Health, Charité - Universitätsmedizin Berlin, 10117, Berlin, Germany.
- Institute of Medical Genetics and Human Genetics, Charité - Universitätsmedizin Berlin, Freie Universität Berlin and Humboldt-Universität zu Berlin, 13353, Berlin, Germany.
| | - Kai M Schmidt-Ott
- Department of Nephrology and Hypertension, Hannover Medical School, 30625, Hannover, Germany.
- Department of Nephrology and Intensive Care Medicine, Charité - Universitätsmedizin Berlin, Freie Universität Berlin and Humboldt-Universität zu Berlin, 12203, Berlin, Germany.
- Molecular and Translational Kidney Research, Max-Delbrück-Center for Molecular Medicine in the Helmholtz Association, 13125, Berlin, Germany.
| | - Maria Rutkiewicz
- Macromolecular Structure and Interaction, Max Delbrück Center for Molecular Medicine in the Helmholtz Association, 13125, Berlin, Germany
- Department of Structural Biology of Eukaryotes, Institute of Bioorganic Chemistry, Polish Academy of Sciences, Poznań, 61-704, Poland
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15
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Chung Y, Lee H. Joint triplet loss with semi-hard constraint for data augmentation and disease prediction using gene expression data. Sci Rep 2023; 13:18178. [PMID: 37875602 PMCID: PMC10598120 DOI: 10.1038/s41598-023-45467-8] [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: 04/24/2023] [Accepted: 10/19/2023] [Indexed: 10/26/2023] Open
Abstract
The accurate prediction of patients with complex diseases, such as Alzheimer's disease (AD), as well as disease stages, including early- and late-stage cancer, is challenging owing to substantial variability among patients and limited availability of clinical data. Deep metric learning has emerged as a promising approach for addressing these challenges by improving data representation. In this study, we propose a joint triplet loss model with a semi-hard constraint (JTSC) to represent data in a small number of samples. JTSC strictly selects semi-hard samples by switching anchors and positive samples during the learning process in triplet embedding and combines a triplet loss function with an angular loss function. Our results indicate that JTSC significantly improves the number of appropriately represented samples during training when applied to the gene expression data of AD and to cancer stage prediction tasks. Furthermore, we demonstrate that using an embedding vector from JTSC as an input to the classifiers for AD and cancer stage prediction significantly improves classification performance by extracting more accurate features. In conclusion, we show that feature embedding through JTSC can aid in classification when there are a small number of samples compared to a larger number of features.
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Affiliation(s)
- Yeonwoo Chung
- School of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology, Gwangju, 61005, Republic of Korea
| | - Hyunju Lee
- School of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology, Gwangju, 61005, Republic of Korea.
- Artificial Intelligence Graduate School, Gwangju Institute of Science and Technology, Gwangju, 61005, Republic of Korea.
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16
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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.
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Affiliation(s)
| | | | - Somayyeh Koohi
- Department of Computer Engineering, Sharif University of Technology, Tehran, Iran.
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17
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Milito A, Aschern M, McQuillan JL, Yang JS. Challenges and advances towards the rational design of microalgal synthetic promoters in Chlamydomonas reinhardtii. JOURNAL OF EXPERIMENTAL BOTANY 2023; 74:3833-3850. [PMID: 37025006 DOI: 10.1093/jxb/erad100] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/09/2022] [Accepted: 03/24/2023] [Indexed: 06/19/2023]
Abstract
Microalgae hold enormous potential to provide a safe and sustainable source of high-value compounds, acting as carbon-fixing biofactories that could help to mitigate rapidly progressing climate change. Bioengineering microalgal strains will be key to optimizing and modifying their metabolic outputs, and to render them competitive with established industrial biotechnology hosts, such as bacteria or yeast. To achieve this, precise and tuneable control over transgene expression will be essential, which would require the development and rational design of synthetic promoters as a key strategy. Among green microalgae, Chlamydomonas reinhardtii represents the reference species for bioengineering and synthetic biology; however, the repertoire of functional synthetic promoters for this species, and for microalgae generally, is limited in comparison to other commercial chassis, emphasizing the need to expand the current microalgal gene expression toolbox. Here, we discuss state-of-the-art promoter analyses, and highlight areas of research required to advance synthetic promoter development in C. reinhardtii. In particular, we exemplify high-throughput studies performed in other model systems that could be applicable to microalgae, and propose novel approaches to interrogating algal promoters. We lastly outline the major limitations hindering microalgal promoter development, while providing novel suggestions and perspectives for how to overcome them.
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Affiliation(s)
- Alfonsina Milito
- Centre for Research in Agricultural Genomics (CRAG), CSIC-IRTA-UAB-UB, Campus UAB, Bellaterra, Barcelona, Spain
| | - Moritz Aschern
- Centre for Research in Agricultural Genomics (CRAG), CSIC-IRTA-UAB-UB, Campus UAB, Bellaterra, Barcelona, Spain
| | - Josie L McQuillan
- Department of Chemical and Biological Engineering, University of Sheffield, Mappin Street, Sheffield, S1 3JD, UK
| | - Jae-Seong Yang
- Centre for Research in Agricultural Genomics (CRAG), CSIC-IRTA-UAB-UB, Campus UAB, Bellaterra, Barcelona, Spain
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18
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Farhadi F, Allahbakhsh M, Maghsoudi A, Armin N, Amintoosi H. DiMo: discovery of microRNA motifs using deep learning and motif embedding. Brief Bioinform 2023; 24:bbad182. [PMID: 37165972 DOI: 10.1093/bib/bbad182] [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] [Received: 10/27/2022] [Revised: 04/17/2023] [Accepted: 04/21/2023] [Indexed: 05/12/2023] Open
Abstract
MicroRNAs are small regulatory RNAs that decrease gene expression after transcription in various biological disciplines. In bioinformatics, identifying microRNAs and predicting their functionalities is critical. Finding motifs is one of the most well-known and important methods for identifying the functionalities of microRNAs. Several motif discovery techniques have been proposed, some of which rely on artificial intelligence-based techniques. However, in the case of few or no training data, their accuracy is low. In this research, we propose a new computational approach, called DiMo, for identifying motifs in microRNAs and generally macromolecules of small length. We employ word embedding techniques and deep learning models to improve the accuracy of motif discovery results. Also, we rely on transfer learning models to pre-train a model and use it in cases of a lack of (enough) training data. We compare our approach with five state-of-the-art works using three real-world datasets. DiMo outperforms the selected related works in terms of precision, recall, accuracy and f1-score.
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Affiliation(s)
- Fatemeh Farhadi
- Department of Bioinformatics, University of Zabol, Zabol, Iran
| | | | - Ali Maghsoudi
- Department of Bioinformatics, University of Zabol, Zabol, Iran
| | - Nadieh Armin
- Computer Engineering Department, Ferdowsi University of Mashhad, Mashhad, Iran
| | - Haleh Amintoosi
- Computer Engineering Department, Ferdowsi University of Mashhad, Mashhad, Iran
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19
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Dunham AS, Beltrao P, AlQuraishi M. High-throughput deep learning variant effect prediction with Sequence UNET. Genome Biol 2023; 24:110. [PMID: 37161576 PMCID: PMC10169183 DOI: 10.1186/s13059-023-02948-3] [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: 08/10/2022] [Accepted: 04/20/2023] [Indexed: 05/11/2023] Open
Abstract
Understanding coding mutations is important for many applications in biology and medicine but the vast mutation space makes comprehensive experimental characterisation impossible. Current predictors are often computationally intensive and difficult to scale, including recent deep learning models. We introduce Sequence UNET, a highly scalable deep learning architecture that classifies and predicts variant frequency from sequence alone using multi-scale representations from a fully convolutional compression/expansion architecture. It achieves comparable pathogenicity prediction to recent methods. We demonstrate scalability by analysing 8.3B variants in 904,134 proteins detected through large-scale proteomics. Sequence UNET runs on modest hardware with a simple Python package.
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Affiliation(s)
- Alistair S Dunham
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridgeshire, CB10 1SD, UK.
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridgeshire, CB10 1RQ, UK.
| | - Pedro Beltrao
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridgeshire, CB10 1SD, UK
- Department of Biology, Institute of Molecular Systems Biology, ETH Zurich, 8093, Zurich, Switzerland
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20
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Kalakoti Y, Clarancia Peter S, Gawande S, Sundar D. Modulation of DNA-protein interactions by proximal genetic elements as uncovered by interpretable deep learning. J Mol Biol 2023; 435:168121. [PMID: 37100167 DOI: 10.1016/j.jmb.2023.168121] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2023] [Revised: 04/14/2023] [Accepted: 04/19/2023] [Indexed: 04/28/2023]
Abstract
Transcription factors (TF) recognize specific motifs in the genome that are typically 6-12 bp long to regulate various aspects of the cellular machinery. Presence of binding motifs and favorable genome accessibility are key drivers for a consistent TF-DNA interaction. Although these pre-requisites may occur thousands of times in the genome, there seems to be a high degree of selectivity for the sites that are actually bound. Here, we present a deep-learning framework that identifies and characterizes the upstream and downstream genetic elements to the binding motif, for their role in enforcing the mentioned selectivity. The proposed framework is based on an interpretable recurrent neural network architecture that enables for the relative analysis of sequence context features. We apply the framework to model twenty-six transcription factors and score the TF-DNA binding at a base-pair resolution. We find significant differences in activations of DNA context features for bound and unbound sequences. In addition to standardized evaluation protocols, we offer outstanding interpretability that enables us to identify and annotate DNA sequence with possible elements that modulate TF-DNA binding. Also, differences in data processing have a huge influence on the overall model performance. Overall, the proposed framework allows for novel insights on the non-coding genetic elements and their role in facilitating a stable TF-DNA interaction.
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Affiliation(s)
- Yogesh Kalakoti
- Department of Biochemical Engineering & Biotechnology, Indian Institute of Technology (IIT) Delhi, New Delhi - 110016, India.
| | | | - Swaraj Gawande
- Department of Computer Science and Engineering, Indian Institute of Technology (IIT) Delhi - 110016, India.
| | - Durai Sundar
- Department of Biochemical Engineering & Biotechnology, Indian Institute of Technology (IIT) Delhi, New Delhi - 110016, India; Yardi School of Artificial Intelligence, Indian Institute of Technology (IIT) Delhi, New Delhi - 110016, India.
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21
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Kravchuk EV, Ashniev GA, Gladkova MG, Orlov AV, Vasileva AV, Boldyreva AV, Burenin AG, Skirda AM, Nikitin PI, Orlova NN. Experimental Validation and Prediction of Super-Enhancers: Advances and Challenges. Cells 2023; 12:cells12081191. [PMID: 37190100 DOI: 10.3390/cells12081191] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2023] [Revised: 04/07/2023] [Accepted: 04/14/2023] [Indexed: 05/17/2023] Open
Abstract
Super-enhancers (SEs) are cis-regulatory elements of the human genome that have been widely discussed since the discovery and origin of the term. Super-enhancers have been shown to be strongly associated with the expression of genes crucial for cell differentiation, cell stability maintenance, and tumorigenesis. Our goal was to systematize research studies dedicated to the investigation of structure and functions of super-enhancers as well as to define further perspectives of the field in various applications, such as drug development and clinical use. We overviewed the fundamental studies which provided experimental data on various pathologies and their associations with particular super-enhancers. The analysis of mainstream approaches for SE search and prediction allowed us to accumulate existing data and propose directions for further algorithmic improvements of SEs' reliability levels and efficiency. Thus, here we provide the description of the most robust algorithms such as ROSE, imPROSE, and DEEPSEN and suggest their further use for various research and development tasks. The most promising research direction, which is based on topic and number of published studies, are cancer-associated super-enhancers and prospective SE-targeted therapy strategies, most of which are discussed in this review.
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Affiliation(s)
- Ekaterina V Kravchuk
- Prokhorov General Physics Institute of the Russian Academy of Sciences, 38 Vavilov St., 119991 Moscow, Russia
- Faculty of Biology, Lomonosov Moscow State University, Leninskiye Gory, MSU, 1-12, 119991 Moscow, Russia
| | - German A Ashniev
- Prokhorov General Physics Institute of the Russian Academy of Sciences, 38 Vavilov St., 119991 Moscow, Russia
- Faculty of Biology, Lomonosov Moscow State University, Leninskiye Gory, MSU, 1-12, 119991 Moscow, Russia
- Faculty of Bioengineering and Bioinformatics, Lomonosov Moscow State University, GSP-1, Leninskiye Gory, MSU, 1-73, 119234 Moscow, Russia
| | - Marina G Gladkova
- Faculty of Bioengineering and Bioinformatics, Lomonosov Moscow State University, GSP-1, Leninskiye Gory, MSU, 1-73, 119234 Moscow, Russia
| | - Alexey V Orlov
- Prokhorov General Physics Institute of the Russian Academy of Sciences, 38 Vavilov St., 119991 Moscow, Russia
| | - Anastasiia V Vasileva
- Prokhorov General Physics Institute of the Russian Academy of Sciences, 38 Vavilov St., 119991 Moscow, Russia
| | - Anna V Boldyreva
- Prokhorov General Physics Institute of the Russian Academy of Sciences, 38 Vavilov St., 119991 Moscow, Russia
| | - Alexandr G Burenin
- Prokhorov General Physics Institute of the Russian Academy of Sciences, 38 Vavilov St., 119991 Moscow, Russia
| | - Artemiy M Skirda
- Prokhorov General Physics Institute of the Russian Academy of Sciences, 38 Vavilov St., 119991 Moscow, Russia
| | - Petr I Nikitin
- Prokhorov General Physics Institute of the Russian Academy of Sciences, 38 Vavilov St., 119991 Moscow, Russia
| | - Natalia N Orlova
- Prokhorov General Physics Institute of the Russian Academy of Sciences, 38 Vavilov St., 119991 Moscow, Russia
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22
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Pan X, Coban Akdemir ZH, Gao R, Jiang X, Sheynkman GM, Wu E, Huang JH, Sahni N, Yi SS. AD-Syn-Net: systematic identification of Alzheimer's disease-associated mutation and co-mutation vulnerabilities via deep learning. Brief Bioinform 2023; 24:bbad030. [PMID: 36752347 PMCID: PMC10025433 DOI: 10.1093/bib/bbad030] [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: 07/09/2022] [Revised: 12/19/2022] [Accepted: 01/13/2023] [Indexed: 02/09/2023] Open
Abstract
Alzheimer's disease (AD) is one of the most challenging neurodegenerative diseases because of its complicated and progressive mechanisms, and multiple risk factors. Increasing research evidence demonstrates that genetics may be a key factor responsible for the occurrence of the disease. Although previous reports identified quite a few AD-associated genes, they were mostly limited owing to patient sample size and selection bias. There is a lack of comprehensive research aimed to identify AD-associated risk mutations systematically. To address this challenge, we hereby construct a large-scale AD mutation and co-mutation framework ('AD-Syn-Net'), and propose deep learning models named Deep-SMCI and Deep-CMCI configured with fully connected layers that are capable of predicting cognitive impairment of subjects effectively based on genetic mutation and co-mutation profiles. Next, we apply the customized frameworks to data sets to evaluate the importance scores of the mutations and identified mutation effectors and co-mutation combination vulnerabilities contributing to cognitive impairment. Furthermore, we evaluate the influence of mutation pairs on the network architecture to dissect the genetic organization of AD and identify novel co-mutations that could be responsible for dementia, laying a solid foundation for proposing future targeted therapy for AD precision medicine. Our deep learning model codes are available open access here: https://github.com/Pan-Bio/AD-mutation-effectors.
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Affiliation(s)
- Xingxin Pan
- Livestrong Cancer Institutes, and Department of Oncology, Dell Medical School, The University of Texas at Austin, Austin, TX 78712, USA
| | - Zeynep H Coban Akdemir
- Human Genetics Center, Department of Epidemiology, Human Genetics, and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA
| | - Ruixuan Gao
- Departments of Chemistry and Biological Sciences, University of Illinois Chicago, Chicago, IL 60607, USA
| | - Xiaoqian Jiang
- School of Biomedical Informatics, University of Texas Health Science Center, Houston, TX 77030, USA
| | - Gloria M Sheynkman
- Department of Molecular Physiology and Biological Physics, University of Virginia, Charlottesville, VA 22903, USA
- Department of Biochemistry and Molecular Genetics, School of Medicine, University of Virginia, Charlottesville, VA 22903, USA
- Center for Public Health Genomics, and UVA Comprehensive Cancer Center, University of Virginia, Charlottesville, VA 22903, USA
| | - Erxi Wu
- Livestrong Cancer Institutes, and Department of Oncology, Dell Medical School, The University of Texas at Austin, Austin, TX 78712, USA
- Neuroscience Institute and Department of Neurosurgery, Baylor Scott & White Health, Temple, TX 76502, USA
- Department of Surgery, Texas A & M University Health Science Center, College of Medicine, Temple, TX 76508, USA
- Department of Pharmaceutical Sciences, Texas A & M University Health Science Center, College of Pharmacy, College Station, TX 77843, USA
| | - Jason H Huang
- Neuroscience Institute and Department of Neurosurgery, Baylor Scott & White Health, Temple, TX 76502, USA
- Department of Surgery, Texas A & M University Health Science Center, College of Medicine, Temple, TX 76508, USA
| | - Nidhi Sahni
- Department of Epigenetics and Molecular Carcinogenesis, The University of Texas MD Anderson Cancer Center, Houston, TX 77054, USA
- Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
- Quantitative and Computational Biosciences Program, Baylor College of Medicine, Houston, TX 77030, USA
| | - S Stephen Yi
- Livestrong Cancer Institutes, and Department of Oncology, Dell Medical School, The University of Texas at Austin, Austin, TX 78712, USA
- Oden Institute for Computational Engineering and Sciences (ICES), The University of Texas at Austin, Austin, TX 78712, USA
- Interdisciplinary Life Sciences Graduate Programs (ILSGP), College of Natural Sciences, The University of Texas at Austin, Austin, TX 78712, USA
- Department of Biomedical Engineering, Cockrell School of Engineering, The University of Texas at Austin, Austin, TX 78712, USA
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Luo H, Shan W, Chen C, Ding P, Luo L. Improving language model of human genome for DNA-protein binding prediction based on task-specific pre-training. Interdiscip Sci 2023; 15:32-43. [PMID: 36136096 DOI: 10.1007/s12539-022-00537-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2022] [Revised: 08/30/2022] [Accepted: 09/07/2022] [Indexed: 11/27/2022]
Abstract
The DNA-protein binding plays a pivotal role in regulating gene expression and evolution, and computational identification of DNA-protein has drawn more and more attention in bioinformatics. Recently, variants of BERT are also used to capture the semantic information of DNA sequences for predicting DNA-protein bindings. In this study, we leverage a task-specific pre-training strategy on BERT using large-scale multi-source DNA-protein binding data and present TFBert. TFBert treats DNA sequences as natural sentences and k-mer nucleotides as words. It can effectively extract upstream and downstream nucleotide context information by pre-training the 690 unlabeled ChIP-seq datasets. Experiments show that the pre-trained model can achieve promising performance on every single dataset in the 690 ChIP-seq datasets after simple fine tuning, especially on small datasets. The average AUC is 94.7%, outperforming existing popular methods. In conclusion, this study provides a variant of BERT based on pre-training and achieved state-of-the-art results in predicting DNA-protein bindings. We believe that TFBert can provide insights into other biological sequence classification problems.
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Affiliation(s)
- Hanyu Luo
- School of Computer Science, University of South China, Hengyang, Hunan, 421001, People's Republic of China
| | - Wenyu Shan
- School of Computer Science, University of South China, Hengyang, Hunan, 421001, People's Republic of China
| | - Cheng Chen
- School of Computer Science, University of South China, Hengyang, Hunan, 421001, People's Republic of China
| | - Pingjian Ding
- School of Computer Science, University of South China, Hengyang, Hunan, 421001, People's Republic of China
| | - Lingyun Luo
- School of Computer Science, University of South China, Hengyang, Hunan, 421001, People's Republic of China. .,Hunan Medical Big Data International Science and Technology Innovation Cooperation Base, Hengyang, Hunan, 421001, People's Republic of China.
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24
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He Y, Zhang Q, Wang S, Chen Z, Cui Z, Guo ZH, Huang DS. Predicting the Sequence Specificities of DNA-Binding Proteins by DNA Fine-Tuned Language Model With Decaying Learning Rates. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2023; 20:616-624. [PMID: 35389869 DOI: 10.1109/tcbb.2022.3165592] [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
DNA-binding proteins (DBPs) play vital roles in the regulation of biological systems. Although there are already many deep learning methods for predicting the sequence specificities of DBPs, they face two challenges as follows. Classic deep learning methods for DBPs prediction usually fail to capture the dependencies between genomic sequences since their commonly used one-hot codes are mutually orthogonal. Besides, these methods usually perform poorly when samples are inadequate. To address these two challenges, we developed a novel language model for mining DBPs using human genomic data and ChIP-seq datasets with decaying learning rates, named DNA Fine-tuned Language Model (DFLM). It can capture the dependencies between genome sequences based on the context of human genomic data and then fine-tune the features of DBPs tasks using different ChIP-seq datasets. First, we compared DFLM with the existing widely used methods on 69 datasets and we achieved excellent performance. Moreover, we conducted comparative experiments on complex DBPs and small datasets. The results show that DFLM still achieved a significant improvement. Finally, through visualization analysis of one-hot encoding and DFLM, we found that one-hot encoding completely cut off the dependencies of DNA sequences themselves, while DFLM using language models can well represent the dependency of DNA sequences. Source code are available at: https://github.com/Deep-Bioinfo/DFLM.
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25
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Wang Y. Intelligent auxiliary system for music performance under edge computing and long short-term recurrent neural networks. PLoS One 2023; 18:e0285496. [PMID: 37155635 PMCID: PMC10166492 DOI: 10.1371/journal.pone.0285496] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2022] [Accepted: 04/24/2023] [Indexed: 05/10/2023] Open
Abstract
Music performance action generation can be applied in multiple real-world scenarios as a research hotspot in computer vision and cross-sequence analysis. However, the current generation methods of music performance actions have consistently ignored the connection between music and performance actions, resulting in a strong sense of separation between visual and auditory content. This paper first analyzes the attention mechanism, Recurrent Neural Network (RNN), and long and short-term RNN. The long and short-term RNN is suitable for sequence data with a strong temporal correlation. Based on this, the current learning method is improved. A new model that combines attention mechanisms and long and short-term RNN is proposed, which can generate performance actions based on music beat sequences. In addition, image description generative models with attention mechanisms are adopted technically. Combined with the RNN abstract structure that does not consider recursion, the abstract network structure of RNN-Long Short-Term Memory (LSTM) is optimized. Through music beat recognition and dance movement extraction technology, data resources are allocated and adjusted in the edge server architecture. The metric for experimental results and evaluation is the model loss function value. The superiority of the proposed model is mainly reflected in the high accuracy and low consumption rate of dance movement recognition. The experimental results show that the result of the loss function of the model is at least 0.00026, and the video effect is the best when the number of layers of the LSTM module in the model is 3, the node value is 256, and the Lookback value is 15. The new model can generate harmonious and prosperous performance action sequences based on ensuring the stability of performance action generation compared with the other three models of cross-domain sequence analysis. The new model has an excellent performance in combining music and performance actions. This paper has practical reference value for promoting the application of edge computing technology in intelligent auxiliary systems for music performance.
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Affiliation(s)
- Yi Wang
- KU School of Music, Lawrence, Kansas, United States of America
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26
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He X, Liu X, Zuo F, Shi H, Jing J. Artificial intelligence-based multi-omics analysis fuels cancer precision medicine. Semin Cancer Biol 2023; 88:187-200. [PMID: 36596352 DOI: 10.1016/j.semcancer.2022.12.009] [Citation(s) in RCA: 35] [Impact Index Per Article: 35.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2022] [Revised: 12/16/2022] [Accepted: 12/29/2022] [Indexed: 01/02/2023]
Abstract
With biotechnological advancements, innovative omics technologies are constantly emerging that have enabled researchers to access multi-layer information from the genome, epigenome, transcriptome, proteome, metabolome, and more. A wealth of omics technologies, including bulk and single-cell omics approaches, have empowered to characterize different molecular layers at unprecedented scale and resolution, providing a holistic view of tumor behavior. Multi-omics analysis allows systematic interrogation of various molecular information at each biological layer while posing tricky challenges regarding how to extract valuable insights from the exponentially increasing amount of multi-omics data. Therefore, efficient algorithms are needed to reduce the dimensionality of the data while simultaneously dissecting the mysteries behind the complex biological processes of cancer. Artificial intelligence has demonstrated the ability to analyze complementary multi-modal data streams within the oncology realm. The coincident development of multi-omics technologies and artificial intelligence algorithms has fuelled the development of cancer precision medicine. Here, we present state-of-the-art omics technologies and outline a roadmap of multi-omics integration analysis using an artificial intelligence strategy. The advances made using artificial intelligence-based multi-omics approaches are described, especially concerning early cancer screening, diagnosis, response assessment, and prognosis prediction. Finally, we discuss the challenges faced in multi-omics analysis, along with tentative future trends in this field. With the increasing application of artificial intelligence in multi-omics analysis, we anticipate a shifting paradigm in precision medicine becoming driven by artificial intelligence-based multi-omics technologies.
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Affiliation(s)
- Xiujing He
- Laboratory of Integrative Medicine, Clinical Research Center for Breast, State Key Laboratory of Biotherapy, West China Hospital, Sichuan University and Collaborative Innovation Center, Chengdu, Sichuan, PR China
| | - Xiaowei Liu
- Laboratory of Integrative Medicine, Clinical Research Center for Breast, State Key Laboratory of Biotherapy, West China Hospital, Sichuan University and Collaborative Innovation Center, Chengdu, Sichuan, PR China
| | - Fengli Zuo
- Laboratory of Integrative Medicine, Clinical Research Center for Breast, State Key Laboratory of Biotherapy, West China Hospital, Sichuan University and Collaborative Innovation Center, Chengdu, Sichuan, PR China
| | - Hubing Shi
- Laboratory of Integrative Medicine, Clinical Research Center for Breast, State Key Laboratory of Biotherapy, West China Hospital, Sichuan University and Collaborative Innovation Center, Chengdu, Sichuan, PR China
| | - Jing Jing
- Laboratory of Integrative Medicine, Clinical Research Center for Breast, State Key Laboratory of Biotherapy, West China Hospital, Sichuan University and Collaborative Innovation Center, Chengdu, Sichuan, PR China.
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27
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Roy R, Marakkar S, Vayalil MP, Shahanaz A, Anil AP, Kunnathpeedikayil S, Rawal I, Shetty K, Shameer Z, Sathees S, Prasannakumar AP, Mathew OK, Subramanian L, Shameer K, Yadav KK. Drug-food Interactions in the Era of Molecular Big Data, Machine Intelligence, and Personalized Health. RECENT ADVANCES IN FOOD, NUTRITION & AGRICULTURE 2022; 13:27-50. [PMID: 36173075 PMCID: PMC10258917 DOI: 10.2174/2212798412666220620104809] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/23/2021] [Revised: 03/04/2022] [Accepted: 03/30/2022] [Indexed: 12/29/2022]
Abstract
The drug-food interaction brings forth changes in the clinical effects of drugs. While favourable interactions bring positive clinical outcomes, unfavourable interactions may lead to toxicity. This article reviews the impact of food intake on drug-food interactions, the clinical effects of drugs, and the effect of drug-food in correlation with diet and precision medicine. Emerging areas in drug-food interactions are the food-genome interface (nutrigenomics) and nutrigenetics. Understanding the molecular basis of food ingredients, including genomic sequencing and pharmacological implications of food molecules, helps to reduce the impact of drug-food interactions. Various strategies are being leveraged to alleviate drug-food interactions; measures including patient engagement, digital health, approaches involving machine intelligence, and big data are a few of them. Furthermore, delineating the molecular communications across dietmicrobiome- drug-food-drug interactions in a pharmacomicrobiome framework may also play a vital role in personalized nutrition. Determining nutrient-gene interactions aids in making nutrition deeply personalized and helps mitigate unwanted drug-food interactions, chronic diseases, and adverse events from their onset. Translational bioinformatics approaches could play an essential role in the next generation of drug-food interaction research. In this landscape review, we discuss important tools, databases, and approaches along with key challenges and opportunities in drug-food interaction and its immediate impact on precision medicine.
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Affiliation(s)
- Romy Roy
- Molecular Robotics, Cochin, Kerala, India
| | | | | | - Alisha Shahanaz
- Molecular Robotics, Cochin, Kerala, India
- Sanaria Inc, Rockville, MD, USA
| | - Athira Panicker Anil
- Molecular Robotics, Cochin, Kerala, India
- Mar Athanasious College for Advanced Studies, Tiruvalla, India
| | - Shameer Kunnathpeedikayil
- Molecular Robotics, Cochin, Kerala, India
- Thiruvalla, Kerala; People Care Health LLP Thrissur, Kerala, India
| | | | | | | | - Saraswathi Sathees
- Molecular Robotics, Cochin, Kerala, India
- University of Washington Seattle, Washington WA, USA
| | | | | | - Lakshminarayanan Subramanian
- Department of Computer Science, Courant Institute of Mathematical Sciences, New York University, New York, NY, USA
| | - Khader Shameer
- Northwell Health, New York, NY, USA and Faculty of Medicine, Imperial College London, London, UK
| | - Kamlesh K. Yadav
- School of Engineering Medicine, and
- Department of Translational Medical Sciences, Center for Genomic and Precision Medicine, Texas A&M University, Houston, TX 77030, USA
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Khatamifard SK, Chowdhury Z, Pande N, Razaviyayn M, Kim C, Karpuzcu UR. GeNVoM: Read Mapping Near Non-Volatile Memory. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2022; 19:3482-3496. [PMID: 34613917 DOI: 10.1109/tcbb.2021.3118018] [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
DNA sequencing is the physical/biochemical process of identifying the location of the four bases (Adenine, Guanine, Cytosine, Thymine) in a DNA strand. As semiconductor technology revolutionized computing, modern DNA sequencing technology (termed Next Generation Sequencing, NGS) revolutionized genomic research. As a result, modern NGS platforms can sequence hundreds of millions of short DNA fragments in parallel. The sequenced DNA fragments, representing the output of NGS platforms, are termed reads. Besides genomic variations, NGS imperfections induce noise in reads. Mapping each read to (the most similar portion of) a reference genome of the same species, i.e., read mapping, is a common critical first step in a diverse set of emerging bioinformatics applications. Mapping represents a search-heavy memory-intensive similarity matching problem, therefore, can greatly benefit from near-memory processing. Intuition suggests using fast associative search enabled by Ternary Content Addressable Memory (TCAM) by construction. However, the excessive energy consumption and lack of support for similarity matching (under NGS and genomic variation induced noise) renders direct application of TCAM infeasible, irrespective of volatility, where only non-volatile TCAM can accommodate the large memory footprint in an area-efficient way. This paper introduces GeNVoM, a scalable, energy-efficient and high-throughput solution. Instead of optimizing an algorithm developed for general-purpose computers or GPUs, GeNVoM rethinks the algorithm and non-volatile TCAM-based accelerator design together from the ground up. Thereby GeNVoM can improve the throughput by up to 3.67×; the energy consumption, by up to 1.36×, when compared to an ASIC baseline, which represents one of the highest-throughput implementations known.
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29
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Lu X, Li J, Zhu Z, Yuan Y, Chen G, He K. Predicting miRNA-Disease Associations via Combining Probability Matrix Feature Decomposition With Neighbor Learning. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2022; 19:3160-3170. [PMID: 34260356 DOI: 10.1109/tcbb.2021.3097037] [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
Predicting the associations of miRNAs and diseases may uncover the causation of various diseases. Many methods are emerging to tackle the sparse and unbalanced disease related miRNA prediction. Here, we propose a Probabilistic matrix decomposition combined with neighbor learning to identify MiRNA-Disease Associations utilizing heterogeneous data(PMDA). First, we build similarity networks for diseases and miRNAs, respectively, by integrating semantic information and functional interactions. Second, we construct a neighbor learning model in which the neighbor information of individual miRNA or disease is utilized to enhance the association relationship to tackle the spare problem. Third, we predict the potential association between miRNAs and diseases via probability matrix decomposition. The experimental results show that PMDA is superior to other five methods in sparse and unbalanced data. The case study shows that the new miRNA-disease interactions predicted by the PMDA are effective and the performance of the PMDA is superior to other methods.
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30
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Bernardino M, Beiko R. Genome-scale prediction of bacterial promoters. Biosystems 2022; 221:104771. [PMID: 36099980 DOI: 10.1016/j.biosystems.2022.104771] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2022] [Revised: 08/18/2022] [Accepted: 08/27/2022] [Indexed: 11/02/2022]
Abstract
A key step in the transcription of RNA is the binding of the RNA polymerase protein complex to a short promoter sequence that is typically upstream of the gene to be expressed. Automated identification of promoters would serve as a valuable complement to experimental validation in determining which genes are likely to be expressed and when; however, promoter sequences are short and highly variable, which makes them very difficult to accurately classify. The many tools developed to identify promoters in DNA have generally been tested on small and balanced subsets of genomic sequence, and the results may not reflect their expected performance on genomes with millions of DNA base pairs where promoters are likely to comprise less than ∼1% of the sequence. Here we introduce Expositor, a neural-network-based method that uses different types of DNA encodings and tunable sensitivity and specificity parameters. Expositor showed higher sensitivity and precision on the E. coli K-12 MG1655 chromosome than other tested approaches. Expositor predictions were more consistent in the homologous subset of sequence from a strain of Salmonella than they were with another strain of E. coli. We also examined the accuracy of Expositor in distinguishing different classes of promoters and found that misclassification between classes was consistent with the biological similarity between promoters.
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Affiliation(s)
- Miria Bernardino
- Faculty of Computer Science, Dalhousie University, Halifax, Canada.
| | - Robert Beiko
- Faculty of Computer Science, Dalhousie University, Halifax, Canada.
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31
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Wu QW, Cao RF, Xia JF, Ni JC, Zheng CH, Su YS. Extra Trees Method for Predicting LncRNA-Disease Association Based On Multi-Layer Graph Embedding Aggregation. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2022; 19:3171-3178. [PMID: 34529571 DOI: 10.1109/tcbb.2021.3113122] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
Lots of experimental studies have revealed the significant associations between lncRNAs and diseases. Identifying accurate associations will provide a new perspective for disease therapy. Calculation-based methods have been developed to solve these problems, but these methods have some limitations. In this paper, we proposed an accurate method, named MLGCNET, to discover potential lncRNA-disease associations. Firstly, we reconstructed similarity networks for both lncRNAs and diseases using top k similar information, and constructed a lncRNA-disease heterogeneous network (LDN). Then, we applied Multi-Layer Graph Convolutional Network on LDN to obtain latent feature representations of nodes. Finally, the Extra Trees was used to calculate the probability of association between disease and lncRNA. The results of extensive 5-fold cross-validation experiments show that MLGCNET has superior prediction performance compared to the state-of-the-art methods. Case studies confirm the performance of our model on specific diseases. All the experiment results prove the effectiveness and practicality of MLGCNET in predicting potential lncRNA-disease associations.
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32
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Chandler M, Jain S, Halman J, Hong E, Dobrovolskaia MA, Zakharov AV, Afonin KA. Artificial Immune Cell, AI-cell, a New Tool to Predict Interferon Production by Peripheral Blood Monocytes in Response to Nucleic Acid Nanoparticles. SMALL (WEINHEIM AN DER BERGSTRASSE, GERMANY) 2022; 18:e2204941. [PMID: 36216772 PMCID: PMC9671856 DOI: 10.1002/smll.202204941] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/11/2022] [Revised: 09/15/2022] [Indexed: 06/16/2023]
Abstract
Nucleic acid nanoparticles, or NANPs, rationally designed to communicate with the human immune system, can offer innovative therapeutic strategies to overcome the limitations of traditional nucleic acid therapies. Each set of NANPs is unique in their architectural parameters and physicochemical properties, which together with the type of delivery vehicles determine the kind and the magnitude of their immune response. Currently, there are no predictive tools that would reliably guide the design of NANPs to the desired immunological outcome, a step crucial for the success of personalized therapies. Through a systematic approach investigating physicochemical and immunological profiles of a comprehensive panel of various NANPs, the research team developes and experimentally validates a computational model based on the transformer architecture able to predict the immune activities of NANPs. It is anticipated that the freely accessible computational tool that is called an "artificial immune cell," or AI-cell, will aid in addressing the current critical public health challenges related to safety criteria of nucleic acid therapies in a timely manner and promote the development of novel biomedical tools.
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Affiliation(s)
- Morgan Chandler
- Department of Chemistry, University of North Carolina at Charlotte, Charlotte, NC 28223, USA
| | - Sankalp Jain
- National Center for Advancing Translational Sciences, National Institutes of Health, Rockville, MD 20850, USA
| | - Justin Halman
- Department of Chemistry, University of North Carolina at Charlotte, Charlotte, NC 28223, USA
| | - Enping Hong
- Nanotechnology Characterization Lab, Cancer Research Technology Program, Frederick National Laboratory for Cancer Research, Frederick, MD 21702, USA
| | - Marina A. Dobrovolskaia
- Nanotechnology Characterization Lab, Cancer Research Technology Program, Frederick National Laboratory for Cancer Research, Frederick, MD 21702, USA
| | - Alexey V. Zakharov
- National Center for Advancing Translational Sciences, National Institutes of Health, Rockville, MD 20850, USA
| | - Kirill A. Afonin
- Department of Chemistry, University of North Carolina at Charlotte, Charlotte, NC 28223, USA
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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.
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34
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Towards a better understanding of TF-DNA binding prediction from genomic features. Comput Biol Med 2022; 149:105993. [DOI: 10.1016/j.compbiomed.2022.105993] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2022] [Revised: 07/12/2022] [Accepted: 08/14/2022] [Indexed: 11/17/2022]
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35
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Maruyama O, Li Y, Narita H, Toh H, Au Yeung WK, Sasaki H. CMIC: predicting DNA methylation inheritance of CpG islands with embedding vectors of variable-length k-mers. BMC Bioinformatics 2022; 23:371. [PMID: 36096737 PMCID: PMC9469632 DOI: 10.1186/s12859-022-04916-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2021] [Accepted: 09/05/2022] [Indexed: 11/10/2022] Open
Abstract
Background Epigenetic modifications established in mammalian gametes are largely reprogrammed during early development, however, are partly inherited by the embryo to support its development. In this study, we examine CpG island (CGI) sequences to predict whether a mouse blastocyst CGI inherits oocyte-derived DNA methylation from the maternal genome. Recurrent neural networks (RNNs), including that based on gated recurrent units (GRUs), have recently been employed for variable-length inputs in classification and regression analyses. One advantage of this strategy is the ability of RNNs to automatically learn latent features embedded in inputs by learning their model parameters. However, the available CGI dataset applied for the prediction of oocyte-derived DNA methylation inheritance are not large enough to train the neural networks. Results We propose a GRU-based model called CMIC (CGI Methylation Inheritance Classifier) to augment CGI sequence by converting it into variable-length k-mers, where the length k is randomly selected from the range \documentclass[12pt]{minimal}
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\begin{document}$$k_{\max }$$\end{document}kmax, N times, which were then used as neural network input. N was set to 1000 in the default setting. In addition, we proposed a new embedding vector generator for k-mers called splitDNA2vec. The randomness of this procedure was higher than the previous work, dna2vec. Conclusions We found that CMIC can predict the inheritance of oocyte-derived DNA methylation at CGIs in the maternal genome of blastocysts with a high F-measure (0.93). We also show that the F-measure can be improved by increasing the parameter N, that is, the number of sequences of variable-length k-mers derived from a single CGI sequence. This implies the effectiveness of augmenting input data by converting a DNA sequence to N sequences of variable-length k-mers. This approach can be applied to different DNA sequence classification and regression analyses, particularly those involving a small amount of data. Supplementary Information The online version contains supplementary material available at 10.1186/s12859-022-04916-3.
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Affiliation(s)
| | - Yinuo Li
- Graduate School of Design, Kyushu University, Fukuoka, Japan
| | | | - Hidehiro Toh
- Division of Epigenomics and Development, Medical Institute of Bioregulation, Kyushu University, Fukuoka, Japan
| | - Wan Kin Au Yeung
- Division of Epigenomics and Development, Medical Institute of Bioregulation, Kyushu University, Fukuoka, Japan.
| | - Hiroyuki Sasaki
- Division of Epigenomics and Development, Medical Institute of Bioregulation, Kyushu University, Fukuoka, Japan.
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Naik M, Rueda L, Vasighizaker A. Identification of Enriched Regions in ChIP-Seq Data via a Linear-Time Multi-Level Thresholding Algorithm. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2022; 19:2842-2850. [PMID: 34398762 DOI: 10.1109/tcbb.2021.3104734] [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
Chromatin immunoprecipitation (ChIP-Seq) has emerged as a superior alternative to microarray technology as it provides higher resolution, less noise, greater coverage and wider dynamic range. While ChIP-Seq enables probing of DNA-protein interaction over the entire genome, it requires the use of sophisticated tools to recognize hidden patterns and extract meaningful data. Over the years, various attempts have resulted in several algorithms making use of different heuristics to accurately determine individual peaks corresponding to unique DNA-protein. However, finding all the significant peaks with high accuracy in a reasonable time is still a challenge. In this work, we propose the use of Multi-level thresholding algorithm, which we call LinMLTBS, used to identify the enriched regions on ChIP-Seq data. Although various suboptimal heuristics have been proposed for multi-level thresholding, we emphasize on the use of an algorithm capable of obtaining an optimal solution, while maintaining linear-time complexity. Testing various algorithm on various ENCODE project datasets shows that our approach attains higher accuracy relative to previously proposed peak finders while retaining a reasonable processing speed.
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Liu J, Zhou D. Minimum Functional Length Analysis of K-Mer Based on BPNN. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2022; 19:2920-2925. [PMID: 34310316 DOI: 10.1109/tcbb.2021.3098512] [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
BP neural network (BPNN), as a multilayer feed-forward network, can realize the deep cognition to target data and high accuracy to output results. However, there were still no related research of k-mer based on BPNN yet. In present study, BPNN was used to train and test binary classification data of each classification mode respectively. All k-mer were divided into two categories according to the X + Y content or completely random mode. Results showed that 1) For classification mode of X + Y content, the accuracy of k-mers classification was 100 percent, no matter k ≤ 6 or k ≥ 7; 2) For completely random classification mode, the accuracy of classification is 100 percent for k-mers of k ≤ 6; But for k-mers of k ≥ 7, the accuracy is less than 100 percent, and with the increase of k value, the accuracy of classification gradually decreases (gradually approaches 50 percent). The k-mers of k ≥ 7 should be the basic functional fragment of nucleic acid, and perform basic nucleic acid function in the DNA sequence. The k-mers of k ≤ 6 should be the basic component fragment of nucleic acid, and no longer perform basic nucleic acid function.
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38
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Yang M, Ma J. Machine Learning Methods for Exploring Sequence Determinants of 3D Genome Organization. J Mol Biol 2022; 434:167666. [PMID: 35659533 DOI: 10.1016/j.jmb.2022.167666] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2021] [Revised: 05/23/2022] [Accepted: 05/27/2022] [Indexed: 01/25/2023]
Abstract
In higher eukaryotic cells, chromosomes are folded inside the nucleus. Recent advances in whole-genome mapping technologies have revealed the multiscale features of 3D genome organization that are intertwined with fundamental genome functions. However, DNA sequence determinants that modulate the formation of 3D genome organization remain poorly characterized. In the past few years, predicting 3D genome organization based on DNA sequence features has become an active area of research. Here, we review the recent progress in computational approaches to unraveling important sequence elements for 3D genome organization. In particular, we discuss the rapid development of machine learning-based methods that facilitate the connections between DNA sequence features and 3D genome architectures at different scales. While much progress has been made in developing predictive models for revealing important sequence features for 3D genome organization, new research is urgently needed to incorporate multi-omic data and enhance model interpretability, further advancing our understanding of gene regulation mechanisms through the lens of 3D genome organization.
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Affiliation(s)
- Muyu Yang
- Computational Biology Department, School of Computer Science, Carnegie Mellon University, United States. https://twitter.com/muyu_wendy_yang
| | - Jian Ma
- Computational Biology Department, School of Computer Science, Carnegie Mellon University, United States.
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39
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Yin YH, Shen LC, Jiang Y, Gao S, Song J, Yu DJ. Improving the prediction of DNA-protein binding by integrating multi-scale dense convolutional network with fault-tolerant coding. Anal Biochem 2022; 656:114878. [DOI: 10.1016/j.ab.2022.114878] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2022] [Revised: 08/18/2022] [Accepted: 08/23/2022] [Indexed: 11/01/2022]
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40
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CapsProm: a capsule network for promoter prediction. Comput Biol Med 2022; 147:105627. [DOI: 10.1016/j.compbiomed.2022.105627] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2021] [Revised: 04/05/2022] [Accepted: 04/11/2022] [Indexed: 11/21/2022]
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Alharbi WS, Rashid M. A review of deep learning applications in human genomics using next-generation sequencing data. Hum Genomics 2022; 16:26. [PMID: 35879805 PMCID: PMC9317091 DOI: 10.1186/s40246-022-00396-x] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2021] [Accepted: 07/12/2022] [Indexed: 12/02/2022] Open
Abstract
Genomics is advancing towards data-driven science. Through the advent of high-throughput data generating technologies in human genomics, we are overwhelmed with the heap of genomic data. To extract knowledge and pattern out of this genomic data, artificial intelligence especially deep learning methods has been instrumental. In the current review, we address development and application of deep learning methods/models in different subarea of human genomics. We assessed over- and under-charted area of genomics by deep learning techniques. Deep learning algorithms underlying the genomic tools have been discussed briefly in later part of this review. Finally, we discussed briefly about the late application of deep learning tools in genomic. Conclusively, this review is timely for biotechnology or genomic scientists in order to guide them why, when and how to use deep learning methods to analyse human genomic data.
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Affiliation(s)
- Wardah S Alharbi
- Department of AI and Bioinformatics, King Abdullah International Medical Research Center (KAIMRC), King Saud Bin Abdulaziz University for Health Sciences (KSAU-HS), King Abdulaziz Medical City, Ministry of National Guard Health Affairs, P.O. Box 22490, Riyadh, 11426, Saudi Arabia
| | - Mamoon Rashid
- Department of AI and Bioinformatics, King Abdullah International Medical Research Center (KAIMRC), King Saud Bin Abdulaziz University for Health Sciences (KSAU-HS), King Abdulaziz Medical City, Ministry of National Guard Health Affairs, P.O. Box 22490, Riyadh, 11426, Saudi Arabia.
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42
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Pan-cancer identification of the relationship of metabolism-related differentially expressed transcription regulation with non-differentially expressed target genes via a gated recurrent unit network. Comput Biol Med 2022; 148:105883. [PMID: 35878490 DOI: 10.1016/j.compbiomed.2022.105883] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2022] [Revised: 07/10/2022] [Accepted: 07/16/2022] [Indexed: 11/20/2022]
Abstract
The transcriptome describes the expression of all genes in a sample. Most studies have investigated the differential patterns or discrimination powers of transcript expression levels. In this study, we hypothesized that the quantitative correlations between the expression levels of transcription factors (TFs) and their regulated target genes (mRNAs) serve as a novel view of healthy status, and a disease sample exhibits a differential landscape (mqTrans) of transcription regulations compared with healthy status. We formulated quantitative transcription regulation relationships of metabolism-related genes as a multi-input multi-output regression model via a gated recurrent unit (GRU) network. The GRU model was trained using healthy blood transcriptomes and the expression levels of mRNAs were predicted by those of the TFs. The mqTrans feature of a gene was defined as the difference between its predicted and actual expression levels. A pan-cancer investigation of the differentially expressed mqTrans features was conducted between the early- and late-stage cancers in 26 cancer types of The Cancer Genome Atlas database. This study focused on the differentially expressed mqTrans features, that did not show differential expression in the actual expression levels. These genes could not be detected by conventional differential analysis. Such dark biomarkers are worthy of further wet-lab investigation. The experimental data also showed that the proposed mqTrans investigation improved the classification between early- and late-stage samples for some cancer types. Thus, the mqTrans features serve as a complementary view to transcriptomes, an OMIC type with mature high-throughput production technologies, and abundant public resources.
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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.
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44
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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]
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45
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Cho KT, Sen TZ, Andorf CM. Predicting Tissue-Specific mRNA and Protein Abundance in Maize: A Machine Learning Approach. Front Artif Intell 2022; 5:830170. [PMID: 35719692 PMCID: PMC9204276 DOI: 10.3389/frai.2022.830170] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2021] [Accepted: 04/26/2022] [Indexed: 11/13/2022] Open
Abstract
Machine learning and modeling approaches have been used to classify protein sequences for a broad set of tasks including predicting protein function, structure, expression, and localization. Some recent studies have successfully predicted whether a given gene is expressed as mRNA or even translated to proteins potentially, but given that not all genes are expressed in every condition and tissue, the challenge remains to predict condition-specific expression. To address this gap, we developed a machine learning approach to predict tissue-specific gene expression across 23 different tissues in maize, solely based on DNA promoter and protein sequences. For class labels, we defined high and low expression levels for mRNA and protein abundance and optimized classifiers by systematically exploring various methods and combinations of k-mer sequences in a two-phase approach. In the first phase, we developed Markov model classifiers for each tissue and built a feature vector based on the predictions. In the second phase, the feature vector was used as an input to a Bayesian network for final classification. Our results show that these methods can achieve high classification accuracy of up to 95% for predicting gene expression for individual tissues. By relying on sequence alone, our method works in settings where costly experimental data are unavailable and reveals useful insights into the functional, evolutionary, and regulatory characteristics of genes.
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Affiliation(s)
- Kyoung Tak Cho
- Department of Computer Science, Iowa State University, Ames, IA, United States
| | - Taner Z. Sen
- USDA-ARS, Crop Improvement and Genetics Research Unit, Albany, CA, United States
| | - Carson M. Andorf
- USDA-ARS, Corn Insects and Crop Genetics Research Unit, Ames, IA, United States
- *Correspondence: Carson M. Andorf
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46
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Zou C, Zhang Q, Wei X. Synchronization of Hyper-Lorenz System Based on DNA Strand Displacement. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2022; 19:1897-1908. [PMID: 33385311 DOI: 10.1109/tcbb.2020.3048753] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
Lorenz system is depicted by chemical reaction equations of an ideal formal chemical reaction network, and a series of reversible reactions are added into chemical reaction network in order to construct a cluster of hyper-Lorenz system. DNA as a universal substrate for chemical dynamics can approximate arbitrary dynamical characteristics of ideal formal chemical reaction network through auxiliary DNA strands and displacement reactions. Based on Lyapunov's stableness theory, a novel synchronization strategy is proposed. A 6-dimensional hyper-Lorenz system is taken as examples for simulation and shows that DNA strands displacement reactions can implement the synchronization of ideal formal chemical reaction networks. Numerical simulations indicate that synchronization based on DNA strand displacement is robust to the detection of DNA strand concentration, control of reaction rate, and noise.
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47
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Du ZH, Wu YH, Huang YA, Chen J, Pan GQ, Hu L, You ZH, Li JQ. GraphTGI: an attention-based graph embedding model for predicting TF-target gene interactions. Brief Bioinform 2022; 23:6576453. [PMID: 35511108 DOI: 10.1093/bib/bbac148] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2021] [Revised: 03/25/2022] [Accepted: 03/31/2022] [Indexed: 12/26/2022] Open
Abstract
MOTIVATION Interaction between transcription factor (TF) and its target genes establishes the knowledge foundation for biological researches in transcriptional regulation, the number of which is, however, still limited by biological techniques. Existing computational methods relevant to the prediction of TF-target interactions are mostly proposed for predicting binding sites, rather than directly predicting the interactions. To this end, we propose here a graph attention-based autoencoder model to predict TF-target gene interactions using the information of the known TF-target gene interaction network combined with two sequential and chemical gene characters, considering that the unobserved interactions between transcription factors and target genes can be predicted by learning the pattern of the known ones. To the best of our knowledge, the proposed model is the first attempt to solve this problem by learning patterns from the known TF-target gene interaction network. RESULTS In this paper, we formulate the prediction task of TF-target gene interactions as a link prediction problem on a complex knowledge graph and propose a deep learning model called GraphTGI, which is composed of a graph attention-based encoder and a bilinear decoder. We evaluated the prediction performance of the proposed method on a real dataset, and the experimental results show that the proposed model yields outstanding performance with an average AUC value of 0.8864 +/- 0.0057 in the 5-fold cross-validation. It is anticipated that the GraphTGI model can effectively and efficiently predict TF-target gene interactions on a large scale. AVAILABILITY Python code and the datasets used in our studies are made available at https://github.com/YanghanWu/GraphTGI.
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Affiliation(s)
- Zhi-Hua Du
- College of Computer Science and Software Engineering, ShenZhen University, 3688 Nanhai Avenue, Shenzhen, China
| | - Yang-Han Wu
- College of Computer Science and Software Engineering, ShenZhen University, 3688 Nanhai Avenue, Shenzhen, China
| | - Yu-An Huang
- College of Computer Science and Software Engineering, ShenZhen University, 3688 Nanhai Avenue, Shenzhen, China
| | - Jie Chen
- College of Computer Science and Software Engineering, ShenZhen University, 3688 Nanhai Avenue, Shenzhen, China
| | - Gui-Qing Pan
- College of Computer Science and Software Engineering, ShenZhen University, 3688 Nanhai Avenue, Shenzhen, China
| | - Lun Hu
- College of Computer Science and Software Engineering, ShenZhen University, 3688 Nanhai Avenue, Shenzhen, China
| | - Zhu-Hong You
- School of Computer Science, Northwestern Polytechnical University, Xi'an, China
| | - Jian-Qiang Li
- College of Computer Science and Software Engineering, ShenZhen University, 3688 Nanhai Avenue, Shenzhen, China
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48
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Nair S, Shrikumar A, Schreiber J, Kundaje A. fastISM: performant in silico saturation mutagenesis for convolutional neural networks. Bioinformatics 2022; 38:2397-2403. [PMID: 35238376 PMCID: PMC9048647 DOI: 10.1093/bioinformatics/btac135] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2021] [Revised: 02/09/2022] [Accepted: 03/01/2022] [Indexed: 11/14/2022] Open
Abstract
MOTIVATION Deep-learning models, such as convolutional neural networks, are able to accurately map biological sequences to associated functional readouts and properties by learning predictive de novo representations. In silico saturation mutagenesis (ISM) is a popular feature attribution technique for inferring contributions of all characters in an input sequence to the model's predicted output. The main drawback of ISM is its runtime, as it involves multiple forward propagations of all possible mutations of each character in the input sequence through the trained model to predict the effects on the output. RESULTS We present fastISM, an algorithm that speeds up ISM by a factor of over 10× for commonly used convolutional neural network architectures. fastISM is based on the observations that the majority of computation in ISM is spent in convolutional layers, and a single mutation only disrupts a limited region of intermediate layers, rendering most computation redundant. fastISM reduces the gap between backpropagation-based feature attribution methods and ISM. It far surpasses the runtime of backpropagation-based methods on multi-output architectures, making it feasible to run ISM on a large number of sequences. AVAILABILITY AND IMPLEMENTATION An easy-to-use Keras/TensorFlow 2 implementation of fastISM is available at https://github.com/kundajelab/fastISM. fastISM can be installed using pip install fastism. A hands-on tutorial can be found at https://colab.research.google.com/github/kundajelab/fastISM/blob/master/notebooks/colab/DeepSEA.ipynb. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Surag Nair
- Department of Computer Science, Stanford University, Stanford, CA 94305, USA
| | - Avanti Shrikumar
- Department of Computer Science, Stanford University, Stanford, CA 94305, USA
| | - Jacob Schreiber
- Department of Genetics, Stanford University, Stanford, CA 94305, USA
| | - Anshul Kundaje
- Department of Computer Science, Stanford University, Stanford, CA 94305, USA
- Department of Genetics, Stanford University, Stanford, CA 94305, USA
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Huang YA, Pan GQ, Wang J, Li JQ, Chen J, Wu YH. Heterogeneous graph embedding model for predicting interactions between TF and target gene. Bioinformatics 2022; 38:2554-2560. [PMID: 35266510 DOI: 10.1093/bioinformatics/btac148] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2021] [Revised: 02/13/2022] [Accepted: 03/09/2022] [Indexed: 11/15/2022] Open
Abstract
MOTIVATION Identifying the target genes of transcription factors (TFs) is of great significance for biomedical researches. However, using biological experiments to identify TF-target gene interactions is still time consuming, expensive and limited to small scale. Existing computational methods for predicting underlying genes for TF to target is mainly proposed for their binding sites rather than the direct interaction. To bridge this gap, we in this work proposed a deep learning prediction model, named HGETGI, to identify the new TF-target gene interaction. Specifically, the proposed HGETGI model learns the patterns of the known interaction between TF and target gene complemented with their involvement in different human disease mechanisms. It performs prediction based on random walk for meta-path sampling and node embedding in a skip-gram manner. RESULTS We evaluated the prediction performance of the proposed method on a real dataset and the experimental results show that it can achieve the average area under the curve of 0.8519 ± 0.0731 in 5-fold cross validation. Besides, we conducted case studies on the prediction of two important kinds of TF, NFKB1 and TP53. As a result, 33 and 32 in the top-40 ranking lists of NFKB1 and TP53 were successfully confirmed by looking up another public database(hTftarget). It is envisioned that the proposed HGETGI method is feasible and effective for predicting TF-target gene interactions on a large scale. AVAILABILITY AND IMPLEMENTATION The source code and dataset are available at https://github.com/PGTSING/HGETGI. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Yu-An Huang
- College of Computer Science and Software Engineering, Shenzhen University, 3688 Nanhai Avenue, Shenzhen, China
| | - Gui-Qing Pan
- College of Computer Science and Software Engineering, Shenzhen University, 3688 Nanhai Avenue, Shenzhen, China
| | - Jia Wang
- College of Computer Science and Software Engineering, Shenzhen University, 3688 Nanhai Avenue, Shenzhen, China
| | - Jian-Qiang Li
- College of Computer Science and Software Engineering, Shenzhen University, 3688 Nanhai Avenue, Shenzhen, China
| | - Jie Chen
- College of Computer Science and Software Engineering, Shenzhen University, 3688 Nanhai Avenue, Shenzhen, China
| | - Yang-Han Wu
- College of Computer Science and Software Engineering, Shenzhen University, 3688 Nanhai Avenue, Shenzhen, China
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Quan L, Sun X, Wu J, Mei J, Huang L, He R, Nie L, Chen Y, Lyu Q. Learning Useful Representations of DNA Sequences From ChIP-Seq Datasets for Exploring Transcription Factor Binding Specificities. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2022; 19:998-1008. [PMID: 32976105 DOI: 10.1109/tcbb.2020.3026787] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
Deep learning has been successfully applied to surprisingly different domains. Researchers and practitioners are employing trained deep learning models to enrich our knowledge. Transcription factors (TFs)are essential for regulating gene expression in all organisms by binding to specific DNA sequences. Here, we designed a deep learning model named SemanticCS (Semantic ChIP-seq)to predict TF binding specificities. We trained our learning model on an ensemble of ChIP-seq datasets (Multi-TF-cell)to learn useful intermediate features across multiple TFs and cells. To interpret these feature vectors, visualization analysis was used. Our results indicate that these learned representations can be used to train shallow machines for other tasks. Using diverse experimental data and evaluation metrics, we show that SemanticCS outperforms other popular methods. In addition, from experimental data, SemanticCS can help to identify the substitutions that cause regulatory abnormalities and to evaluate the effect of substitutions on the binding affinity for the RXR transcription factor. The online server for SemanticCS is freely available at http://qianglab.scst.suda.edu.cn/semanticCS/.
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