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Erfanian N, Heydari AA, Feriz AM, Iañez P, Derakhshani A, Ghasemigol M, Farahpour M, Razavi SM, Nasseri S, Safarpour H, Sahebkar A. Deep learning applications in single-cell genomics and transcriptomics data analysis. Biomed Pharmacother 2023; 165:115077. [PMID: 37393865 DOI: 10.1016/j.biopha.2023.115077] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2023] [Revised: 06/22/2023] [Accepted: 06/23/2023] [Indexed: 07/04/2023] Open
Abstract
Traditional bulk sequencing methods are limited to measuring the average signal in a group of cells, potentially masking heterogeneity, and rare populations. The single-cell resolution, however, enhances our understanding of complex biological systems and diseases, such as cancer, the immune system, and chronic diseases. However, the single-cell technologies generate massive amounts of data that are often high-dimensional, sparse, and complex, thus making analysis with traditional computational approaches difficult and unfeasible. To tackle these challenges, many are turning to deep learning (DL) methods as potential alternatives to the conventional machine learning (ML) algorithms for single-cell studies. DL is a branch of ML capable of extracting high-level features from raw inputs in multiple stages. Compared to traditional ML, DL models have provided significant improvements across many domains and applications. In this work, we examine DL applications in genomics, transcriptomics, spatial transcriptomics, and multi-omics integration, and address whether DL techniques will prove to be advantageous or if the single-cell omics domain poses unique challenges. Through a systematic literature review, we have found that DL has not yet revolutionized the most pressing challenges of the single-cell omics field. However, using DL models for single-cell omics has shown promising results (in many cases outperforming the previous state-of-the-art models) in data preprocessing and downstream analysis. Although developments of DL algorithms for single-cell omics have generally been gradual, recent advances reveal that DL can offer valuable resources in fast-tracking and advancing research in single-cell.
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Affiliation(s)
- Nafiseh Erfanian
- Student Research Committee, Birjand University of Medical Sciences, Birjand, Iran
| | - A Ali Heydari
- Department of Applied Mathematics, University of California, Merced, CA, USA; Health Sciences Research Institute, University of California, Merced, CA, USA
| | - Adib Miraki Feriz
- Student Research Committee, Birjand University of Medical Sciences, Birjand, Iran
| | - Pablo Iañez
- Cellular Systems Genomics Group, Josep Carreras Research Institute, Barcelona, Spain
| | - Afshin Derakhshani
- Department of Biochemistry and Molecular Biology, University of Calgary, Calgary, AB, Canada
| | | | - Mohsen Farahpour
- Department of Electronics, Faculty of Electrical and Computer Engineering, University of Birjand, Birjand, Iran
| | - Seyyed Mohammad Razavi
- Department of Electronics, Faculty of Electrical and Computer Engineering, University of Birjand, Birjand, Iran
| | - Saeed Nasseri
- Cellular and Molecular Research Center, Birjand University of Medical Sciences, Birjand, Iran
| | - Hossein Safarpour
- Cellular and Molecular Research Center, Birjand University of Medical Sciences, Birjand, Iran.
| | - Amirhossein Sahebkar
- Biotechnology Research Center, Pharmaceutical Technology Institute, Mashhad University of Medical Sciences, Mashhad, Iran; Applied Biomedical Research Center, Mashhad University of Medical Sciences, Mashhad, Iran; Department of Biotechnology, School of Pharmacy, Mashhad University of Medical Sciences, Mashhad, Iran.
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Alatawneh R, Salomon Y, Eshel R, Orenstein Y, Birnbaum RY. Deciphering transcription factors and their corresponding regulatory elements during inhibitory interneuron differentiation using deep neural networks. Front Cell Dev Biol 2023; 11:1034604. [PMID: 36891511 PMCID: PMC9986276 DOI: 10.3389/fcell.2023.1034604] [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: 09/01/2022] [Accepted: 01/23/2023] [Indexed: 02/22/2023] Open
Abstract
During neurogenesis, the generation and differentiation of neuronal progenitors into inhibitory gamma-aminobutyric acid-containing interneurons is dependent on the combinatorial activity of transcription factors (TFs) and their corresponding regulatory elements (REs). However, the roles of neuronal TFs and their target REs in inhibitory interneuron progenitors are not fully elucidated. Here, we developed a deep-learning-based framework to identify enriched TF motifs in gene REs (eMotif-RE), such as poised/repressed enhancers and putative silencers. Using epigenetic datasets (e.g., ATAC-seq and H3K27ac/me3 ChIP-seq) from cultured interneuron-like progenitors, we distinguished between active enhancer sequences (open chromatin with H3K27ac) and non-active enhancer sequences (open chromatin without H3K27ac). Using our eMotif-RE framework, we discovered enriched motifs of TFs such as ASCL1, SOX4, and SOX11 in the active enhancer set suggesting a cooperativity function for ASCL1 and SOX4/11 in active enhancers of neuronal progenitors. In addition, we found enriched ZEB1 and CTCF motifs in the non-active set. Using an in vivo enhancer assay, we showed that most of the tested putative REs from the non-active enhancer set have no enhancer activity. Two of the eight REs (25%) showed function as poised enhancers in the neuronal system. Moreover, mutated REs for ZEB1 and CTCF motifs increased their in vivo activity as enhancers indicating a repressive effect of ZEB1 and CTCF on these REs that likely function as repressed enhancers or silencers. Overall, our work integrates a novel framework based on deep learning together with a functional assay that elucidated novel functions of TFs and their corresponding REs. Our approach can be applied to better understand gene regulation not only in inhibitory interneuron differentiation but in other tissue and cell types.
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Affiliation(s)
- Rawan Alatawneh
- Department of Life Sciences, Faculty of Natural Sciences, Ben-Gurion University of the Negev, Beer-Sheva, Israel.,The Zlotowski Center for Neuroscience, Ben-Gurion University of the Negev, Beer-Sheva, Israel
| | - Yahel Salomon
- School of Electrical and Computer Engineering, Ben-Gurion University of the Negev, Beer-Sheva, Israel
| | - Reut Eshel
- Department of Life Sciences, Faculty of Natural Sciences, Ben-Gurion University of the Negev, Beer-Sheva, Israel.,The Zlotowski Center for Neuroscience, Ben-Gurion University of the Negev, Beer-Sheva, Israel
| | - Yaron Orenstein
- School of Electrical and Computer Engineering, Ben-Gurion University of the Negev, Beer-Sheva, Israel.,Department of Computer Science, Bar-Ilan University, Ramat Gan, Israel.,The Mina and Everard Goodman Faculty of Life Sciences, Bar-Ilan University, Ramat Gan, Israel
| | - Ramon Y Birnbaum
- Department of Life Sciences, Faculty of Natural Sciences, Ben-Gurion University of the Negev, Beer-Sheva, Israel.,The Zlotowski Center for Neuroscience, Ben-Gurion University of the Negev, Beer-Sheva, Israel
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3
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Cazares TA, Rizvi FW, Iyer B, Chen X, Kotliar M, Bejjani AT, Wayman JA, Donmez O, Wronowski B, Parameswaran S, Kottyan LC, Barski A, Weirauch MT, Prasath VBS, Miraldi ER. maxATAC: Genome-scale transcription-factor binding prediction from ATAC-seq with deep neural networks. PLoS Comput Biol 2023; 19:e1010863. [PMID: 36719906 PMCID: PMC9917285 DOI: 10.1371/journal.pcbi.1010863] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2022] [Revised: 02/10/2023] [Accepted: 01/10/2023] [Indexed: 02/01/2023] Open
Abstract
Transcription factors read the genome, fundamentally connecting DNA sequence to gene expression across diverse cell types. Determining how, where, and when TFs bind chromatin will advance our understanding of gene regulatory networks and cellular behavior. The 2017 ENCODE-DREAM in vivo Transcription-Factor Binding Site (TFBS) Prediction Challenge highlighted the value of chromatin accessibility data to TFBS prediction, establishing state-of-the-art methods for TFBS prediction from DNase-seq. However, the more recent Assay-for-Transposase-Accessible-Chromatin (ATAC)-seq has surpassed DNase-seq as the most widely-used chromatin accessibility profiling method. Furthermore, ATAC-seq is the only such technique available at single-cell resolution from standard commercial platforms. While ATAC-seq datasets grow exponentially, suboptimal motif scanning is unfortunately the most common method for TFBS prediction from ATAC-seq. To enable community access to state-of-the-art TFBS prediction from ATAC-seq, we (1) curated an extensive benchmark dataset (127 TFs) for ATAC-seq model training and (2) built "maxATAC", a suite of user-friendly, deep neural network models for genome-wide TFBS prediction from ATAC-seq in any cell type. With models available for 127 human TFs, maxATAC is the largest collection of high-performance TFBS prediction models for ATAC-seq. maxATAC performance extends to primary cells and single-cell ATAC-seq, enabling improved TFBS prediction in vivo. We demonstrate maxATAC's capabilities by identifying TFBS associated with allele-dependent chromatin accessibility at atopic dermatitis genetic risk loci.
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Affiliation(s)
- Tareian A. Cazares
- Immunology Graduate Program, University of Cincinnati College of Medicine, Cincinnati, Ohio, United States of America
| | - Faiz W. Rizvi
- Systems Biology and Physiology Graduate Program, University of Cincinnati College of Medicine, Cincinnati, Ohio, United States of America
| | - Balaji Iyer
- Division of Biomedical Informatics, Cincinnati Children’s Hospital Medical Center, Cincinnati, Ohio, United States of America
- Department of Electrical Engineering and Computer Science, University of Cincinnati, Cincinnati, Ohio, United States of America
| | - Xiaoting Chen
- The Center for Autoimmune Genetics and Etiology (CAGE), Cincinnati Children’s Hospital Medical Center, Cincinnati, Ohio, United States of America
| | - Michael Kotliar
- Division of Allergy and Immunology, Cincinnati Children’s Hospital Medical Center, Cincinnati, Ohio, United States of America
| | - Anthony T. Bejjani
- Molecular and Developmental Biology Graduate Program, University of Cincinnati College of Medicine, Cincinnati, Ohio, United States of America
| | - Joseph A. Wayman
- Division of Immunobiology, Cincinnati Children’s Hospital Medical Center, Cincinnati, Ohio, United States of America
| | - Omer Donmez
- The Center for Autoimmune Genetics and Etiology (CAGE), Cincinnati Children’s Hospital Medical Center, Cincinnati, Ohio, United States of America
| | - Benjamin Wronowski
- Division of Allergy and Immunology, Cincinnati Children’s Hospital Medical Center, Cincinnati, Ohio, United States of America
| | - Sreeja Parameswaran
- The Center for Autoimmune Genetics and Etiology (CAGE), Cincinnati Children’s Hospital Medical Center, Cincinnati, Ohio, United States of America
| | - Leah C. Kottyan
- The Center for Autoimmune Genetics and Etiology (CAGE), Cincinnati Children’s Hospital Medical Center, Cincinnati, Ohio, United States of America
- Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, Ohio, United States of America
- Division of Human Genetics, Cincinnati Children’s Hospital Medical Center, Cincinnati, Ohio, United States of America
| | - Artem Barski
- Division of Allergy and Immunology, Cincinnati Children’s Hospital Medical Center, Cincinnati, Ohio, United States of America
- Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, Ohio, United States of America
- Division of Human Genetics, Cincinnati Children’s Hospital Medical Center, Cincinnati, Ohio, United States of America
| | - Matthew T. Weirauch
- Division of Biomedical Informatics, Cincinnati Children’s Hospital Medical Center, Cincinnati, Ohio, United States of America
- The Center for Autoimmune Genetics and Etiology (CAGE), Cincinnati Children’s Hospital Medical Center, Cincinnati, Ohio, United States of America
- Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, Ohio, United States of America
- Division of Human Genetics, Cincinnati Children’s Hospital Medical Center, Cincinnati, Ohio, United States of America
- Division of Developmental Biology, Cincinnati Children’s Hospital Medical Center, Cincinnati, Ohio, United States of America
| | - V. B. Surya Prasath
- Division of Biomedical Informatics, Cincinnati Children’s Hospital Medical Center, Cincinnati, Ohio, United States of America
- Department of Electrical Engineering and Computer Science, University of Cincinnati, Cincinnati, Ohio, United States of America
- Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, Ohio, United States of America
| | - Emily R. Miraldi
- Division of Biomedical Informatics, Cincinnati Children’s Hospital Medical Center, Cincinnati, Ohio, United States of America
- Department of Electrical Engineering and Computer Science, University of Cincinnati, Cincinnati, Ohio, United States of America
- Division of Immunobiology, Cincinnati Children’s Hospital Medical Center, Cincinnati, Ohio, United States of America
- Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, Ohio, United States of America
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4
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Vadapalli S, Abdelhalim H, Zeeshan S, Ahmed Z. Artificial intelligence and machine learning approaches using gene expression and variant data for personalized medicine. Brief Bioinform 2022; 23:6590150. [PMID: 35595537 DOI: 10.1093/bib/bbac191] [Citation(s) in RCA: 24] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2022] [Revised: 04/02/2022] [Accepted: 04/26/2022] [Indexed: 12/16/2022] Open
Abstract
Precision medicine uses genetic, environmental and lifestyle factors to more accurately diagnose and treat disease in specific groups of patients, and it is considered one of the most promising medical efforts of our time. The use of genetics is arguably the most data-rich and complex components of precision medicine. The grand challenge today is the successful assimilation of genetics into precision medicine that translates across different ancestries, diverse diseases and other distinct populations, which will require clever use of artificial intelligence (AI) and machine learning (ML) methods. Our goal here was to review and compare scientific objectives, methodologies, datasets, data sources, ethics and gaps of AI/ML approaches used in genomics and precision medicine. We selected high-quality literature published within the last 5 years that were indexed and available through PubMed Central. Our scope was narrowed to articles that reported application of AI/ML algorithms for statistical and predictive analyses using whole genome and/or whole exome sequencing for gene variants, and RNA-seq and microarrays for gene expression. We did not limit our search to specific diseases or data sources. Based on the scope of our review and comparative analysis criteria, we identified 32 different AI/ML approaches applied in variable genomics studies and report widely adapted AI/ML algorithms for predictive diagnostics across several diseases.
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Affiliation(s)
- Sreya Vadapalli
- Rutgers Institute for Health, Health Care Policy and Aging Research, Rutgers University, 112 Paterson St, New Brunswick, NJ, USA
| | - Habiba Abdelhalim
- Rutgers Institute for Health, Health Care Policy and Aging Research, Rutgers University, 112 Paterson St, New Brunswick, NJ, USA
| | - Saman Zeeshan
- Rutgers Cancer Institute of New Jersey, Rutgers University, 195 Little Albany St, New Brunswick, NJ, USA
| | - Zeeshan Ahmed
- Rutgers Institute for Health, Health Care Policy and Aging Research, Rutgers University, 112 Paterson St, New Brunswick, NJ, USA.,Department of Medicine, Robert Wood Johnson Medical School, Rutgers Biomedical and Health Sciences, 125 Paterson St, New Brunswick, NJ, USA
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Nothof SA, Magdinier F, Van-Gils J. Chromatin Structure and Dynamics: Focus on Neuronal Differentiation and Pathological Implication. Genes (Basel) 2022; 13:genes13040639. [PMID: 35456445 PMCID: PMC9029427 DOI: 10.3390/genes13040639] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2022] [Revised: 03/28/2022] [Accepted: 03/31/2022] [Indexed: 02/07/2023] Open
Abstract
Chromatin structure is an essential regulator of gene expression. Its state of compaction contributes to the regulation of genetic programs, in particular during differentiation. Epigenetic processes, which include post-translational modifications of histones, DNA methylation and implication of non-coding RNA, are powerful regulators of gene expression. Neurogenesis and neuronal differentiation are spatio-temporally regulated events that allow the formation of the central nervous system components. Here, we review the chromatin structure and post-translational histone modifications associated with neuronal differentiation. Studying the impact of histone modifications on neuronal differentiation improves our understanding of the pathophysiological mechanisms of chromatinopathies and opens up new therapeutic avenues. In addition, we will discuss techniques for the analysis of histone modifications on a genome-wide scale and the pathologies associated with the dysregulation of the epigenetic machinery.
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Affiliation(s)
- Sophie A. Nothof
- Marseille Medical Genetics, Aix Marseille University, Inserm, CEDEX 05, 13385 Marseille, France; (S.A.N.); (F.M.)
| | - Frédérique Magdinier
- Marseille Medical Genetics, Aix Marseille University, Inserm, CEDEX 05, 13385 Marseille, France; (S.A.N.); (F.M.)
| | - Julien Van-Gils
- Marseille Medical Genetics, Aix Marseille University, Inserm, CEDEX 05, 13385 Marseille, France; (S.A.N.); (F.M.)
- Reference Center AD SOOR, AnDDI-RARE, Inserm U 1211, Medical Genetics Department, Bordeaux University, Center Hospitalier Universitaire de Bordeaux, 33076 Bordeaux, France
- Correspondence:
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