1
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Kowalski MH, Wessels HH, Linder J, Dalgarno C, Mascio I, Choudhary S, Hartman A, Hao Y, Kundaje A, Satija R. Multiplexed single-cell characterization of alternative polyadenylation regulators. Cell 2024; 187:4408-4425.e23. [PMID: 38925112 DOI: 10.1016/j.cell.2024.06.005] [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: 02/09/2023] [Revised: 03/12/2024] [Accepted: 06/05/2024] [Indexed: 06/28/2024]
Abstract
Most mammalian genes have multiple polyA sites, representing a substantial source of transcript diversity regulated by the cleavage and polyadenylation (CPA) machinery. To better understand how these proteins govern polyA site choice, we introduce CPA-Perturb-seq, a multiplexed perturbation screen dataset of 42 CPA regulators with a 3' scRNA-seq readout that enables transcriptome-wide inference of polyA site usage. We develop a framework to detect perturbation-dependent changes in polyadenylation and characterize modules of co-regulated polyA sites. We find groups of intronic polyA sites regulated by distinct components of the nuclear RNA life cycle, including elongation, splicing, termination, and surveillance. We train and validate a deep neural network (APARENT-Perturb) for tandem polyA site usage, delineating a cis-regulatory code that predicts perturbation response and reveals interactions between regulatory complexes. Our work highlights the potential for multiplexed single-cell perturbation screens to further our understanding of post-transcriptional regulation.
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Affiliation(s)
- Madeline H Kowalski
- New York Genome Center, New York, NY, USA; Center for Genomics and Systems Biology, New York University, New York, NY, USA; New York University Grossman School of Medicine, New York, NY, USA
| | - Hans-Hermann Wessels
- New York Genome Center, New York, NY, USA; Center for Genomics and Systems Biology, New York University, New York, NY, USA.
| | - Johannes Linder
- Department of Genetics, Stanford University, Stanford, CA, USA; Department of Computer Science, Stanford University, Stanford, CA, USA
| | | | - Isabella Mascio
- New York Genome Center, New York, NY, USA; Center for Genomics and Systems Biology, New York University, New York, NY, USA
| | - Saket Choudhary
- New York Genome Center, New York, NY, USA; Center for Genomics and Systems Biology, New York University, New York, NY, USA
| | | | - Yuhan Hao
- New York Genome Center, New York, NY, USA; Center for Genomics and Systems Biology, New York University, New York, NY, USA
| | - Anshul Kundaje
- Department of Genetics, Stanford University, Stanford, CA, USA; Department of Computer Science, Stanford University, Stanford, CA, USA
| | - Rahul Satija
- New York Genome Center, New York, NY, USA; Center for Genomics and Systems Biology, New York University, New York, NY, USA; New York University Grossman School of Medicine, New York, NY, USA.
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2
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Hwang H, Jeon H, Yeo N, Baek D. Big data and deep learning for RNA biology. Exp Mol Med 2024; 56:1293-1321. [PMID: 38871816 PMCID: PMC11263376 DOI: 10.1038/s12276-024-01243-w] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2024] [Revised: 02/27/2024] [Accepted: 03/05/2024] [Indexed: 06/15/2024] Open
Abstract
The exponential growth of big data in RNA biology (RB) has led to the development of deep learning (DL) models that have driven crucial discoveries. As constantly evidenced by DL studies in other fields, the successful implementation of DL in RB depends heavily on the effective utilization of large-scale datasets from public databases. In achieving this goal, data encoding methods, learning algorithms, and techniques that align well with biological domain knowledge have played pivotal roles. In this review, we provide guiding principles for applying these DL concepts to various problems in RB by demonstrating successful examples and associated methodologies. We also discuss the remaining challenges in developing DL models for RB and suggest strategies to overcome these challenges. Overall, this review aims to illuminate the compelling potential of DL for RB and ways to apply this powerful technology to investigate the intriguing biology of RNA more effectively.
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Affiliation(s)
- Hyeonseo Hwang
- School of Biological Sciences, Seoul National University, Seoul, Republic of Korea
| | - Hyeonseong Jeon
- Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul, Republic of Korea
- Genome4me Inc., Seoul, Republic of Korea
| | - Nagyeong Yeo
- School of Biological Sciences, Seoul National University, Seoul, Republic of Korea
| | - Daehyun Baek
- School of Biological Sciences, Seoul National University, Seoul, Republic of Korea.
- Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul, Republic of Korea.
- Genome4me Inc., Seoul, Republic of Korea.
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3
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Guo Y, Zhou D, Li P, Li C, Cao J. Context-Aware Poly(A) Signal Prediction Model via Deep Spatial-Temporal Neural Networks. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2024; 35:8241-8253. [PMID: 37015693 DOI: 10.1109/tnnls.2022.3226301] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/19/2023]
Abstract
Polyadenylation [Poly(A)] is an essential process during messenger RNA (mRNA) maturation in biological eukaryote systems. Identifying Poly(A) signals (PASs) from the genome level is the key to understanding the mechanism of translation regulation and mRNA metabolism. In this work, we propose a deep dual-dynamic context-aware Poly(A) signal prediction model, called multiscale convolution with self-attention networks (MCANet), to adaptively uncover the spatial-temporal contextual dependence information. Specifically, the model automatically learns and strengthens informative features from the temporalwise and the spatialwise dimension. The identity connectivity performs contextual feature maps of Poly(A) data by direct connections from previous layers to subsequent layers. Then, a fully parametric rectified linear unit (FP-RELU) with dual-dynamic coefficients is devised to make the training of the model easier and enhance the generalization ability. A cross-entropy loss (CL) function is designed to make the model focus on samples that are easy to misclassify. Experiments on different Poly(A) signals demonstrate the superior performance of the proposed MCANet, and an ablation study shows the effectiveness of the network design for the feature learning and prediction of Poly(A) signals.
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4
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Koido M, Tomizuka K, Terao C. Fundamentals for predicting transcriptional regulations from DNA sequence patterns. J Hum Genet 2024:10.1038/s10038-024-01256-3. [PMID: 38730006 DOI: 10.1038/s10038-024-01256-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2024] [Revised: 04/10/2024] [Accepted: 04/25/2024] [Indexed: 05/12/2024]
Abstract
Cell-type-specific regulatory elements, cataloged through extensive experiments and bioinformatics in large-scale consortiums, have enabled enrichment analyses of genetic associations that primarily utilize positional information of the regulatory elements. These analyses have identified cell types and pathways genetically associated with human complex traits. However, our understanding of detailed allelic effects on these elements' activities and on-off states remains incomplete, hampering the interpretation of human genetic study results. This review introduces machine learning methods to learn sequence-dependent transcriptional regulation mechanisms from DNA sequences for predicting such allelic effects (not associations). We provide a concise history of machine-learning-based approaches, the requirements, and the key computational processes, focusing on primers in machine learning. Convolution and self-attention, pivotal in modern deep-learning models, are explained through geometrical interpretations using dot products. This facilitates understanding of the concept and why these have been used for machine learning for DNA sequences. These will inspire further research in this genetics and genomics field.
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Affiliation(s)
- Masaru Koido
- Laboratory of Complex Trait Genomics, Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of Tokyo, Tokyo, Japan.
- Laboratory for Statistical and Translational Genetics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan.
| | - Kohei Tomizuka
- Laboratory for Statistical and Translational Genetics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
| | - Chikashi Terao
- Laboratory for Statistical and Translational Genetics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan.
- Clinical Research Center, Shizuoka General Hospital, Shizuoka, Japan.
- The Department of Applied Genetics, The School of Pharmaceutical Sciences, University of Shizuoka, Shizuoka, Japan.
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5
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Liu X, Chen H, Li Z, Yang X, Jin W, Wang Y, Zheng J, Li L, Xuan C, Yuan J, Yang Y. InPACT: a computational method for accurate characterization of intronic polyadenylation from RNA sequencing data. Nat Commun 2024; 15:2583. [PMID: 38519498 PMCID: PMC10960005 DOI: 10.1038/s41467-024-46875-8] [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: 06/12/2023] [Accepted: 03/12/2024] [Indexed: 03/25/2024] Open
Abstract
Alternative polyadenylation can occur in introns, termed intronic polyadenylation (IPA), has been implicated in diverse biological processes and diseases, as it can produce noncoding transcripts or transcripts with truncated coding regions. However, a reliable method is required to accurately characterize IPA. Here, we propose a computational method called InPACT, which allows for the precise characterization of IPA from conventional RNA-seq data. InPACT successfully identifies numerous previously unannotated IPA transcripts in human cells, many of which are translated, as evidenced by ribosome profiling data. We have demonstrated that InPACT outperforms other methods in terms of IPA identification and quantification. Moreover, InPACT applied to monocyte activation reveals temporally coordinated IPA events. Further application on single-cell RNA-seq data of human fetal bone marrow reveals the expression of several IPA isoforms in a context-specific manner. Therefore, InPACT represents a powerful tool for the accurate characterization of IPA from RNA-seq data.
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Affiliation(s)
- Xiaochuan Liu
- The Province and Ministry Co-sponsored Collaborative Innovation Center for Medical Epigenetics, Tianjin Key Laboratory of Inflammatory Biology, The Second Hospital of Tianjin Medical University, Department of Bioinformatics, School of Basic Medical Sciences, Tianjin Medical University, Tianjin, 300070, China
| | - Hao Chen
- Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences, Tianjin Medical University, Tianjin, 300070, China
| | - Zekun Li
- The Province and Ministry Co-sponsored Collaborative Innovation Center for Medical Epigenetics, Tianjin Key Laboratory of Inflammatory Biology, The Second Hospital of Tianjin Medical University, Department of Bioinformatics, School of Basic Medical Sciences, Tianjin Medical University, Tianjin, 300070, China
| | - Xiaoxiao Yang
- The Province and Ministry Co-sponsored Collaborative Innovation Center for Medical Epigenetics, Tianjin Key Laboratory of Inflammatory Biology, The Second Hospital of Tianjin Medical University, Department of Bioinformatics, School of Basic Medical Sciences, Tianjin Medical University, Tianjin, 300070, China
- Department of Pharmacology, School of Basic Medical Sciences, Tianjin Medical University, Tianjin, 300070, China
| | - Wen Jin
- The Province and Ministry Co-sponsored Collaborative Innovation Center for Medical Epigenetics, Tianjin Key Laboratory of Inflammatory Biology, The Second Hospital of Tianjin Medical University, Department of Bioinformatics, School of Basic Medical Sciences, Tianjin Medical University, Tianjin, 300070, China
- Department of Pharmacology, School of Basic Medical Sciences, Tianjin Medical University, Tianjin, 300070, China
| | - Yuting Wang
- The Province and Ministry Co-sponsored Collaborative Innovation Center for Medical Epigenetics, Tianjin Key Laboratory of Inflammatory Biology, The Second Hospital of Tianjin Medical University, Department of Bioinformatics, School of Basic Medical Sciences, Tianjin Medical University, Tianjin, 300070, China
- Department of Pharmacology, School of Basic Medical Sciences, Tianjin Medical University, Tianjin, 300070, China
| | - Jian Zheng
- Department of Immunology, School of Basic Medical Sciences, Tianjin Medical University, Tianjin, 300070, China
| | - Long Li
- Department of Immunology, School of Basic Medical Sciences, Tianjin Medical University, Tianjin, 300070, China
| | - Chenghao Xuan
- Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences, Tianjin Medical University, Tianjin, 300070, China.
| | - Jiapei Yuan
- State Key Laboratory of Experimental Hematology, National Clinical Research Center for Blood Diseases, Haihe Laboratory of Cell Ecosystem, Institute of Hematology and Blood Diseases Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Tianjin, 300020, China.
- Tianjin Institutes of Health Science, Tianjin, 301600, China.
| | - Yang Yang
- The Province and Ministry Co-sponsored Collaborative Innovation Center for Medical Epigenetics, Tianjin Key Laboratory of Inflammatory Biology, The Second Hospital of Tianjin Medical University, Department of Bioinformatics, School of Basic Medical Sciences, Tianjin Medical University, Tianjin, 300070, China.
- Department of Pharmacology, School of Basic Medical Sciences, Tianjin Medical University, Tianjin, 300070, China.
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6
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McKeever PM, Sababi AM, Sharma R, Khuu N, Xu Z, Shen SY, Xiao S, McGoldrick P, Orouji E, Ketela T, Sato C, Moreno D, Visanji N, Kovacs GG, Keith J, Zinman L, Rogaeva E, Goodarzi H, Bader GD, Robertson J. Single-nucleus multiomic atlas of frontal cortex in amyotrophic lateral sclerosis with a deep learning-based decoding of alternative polyadenylation mechanisms. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.12.22.573083. [PMID: 38187588 PMCID: PMC10769403 DOI: 10.1101/2023.12.22.573083] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/09/2024]
Abstract
The understanding of how different cell types contribute to amyotrophic lateral sclerosis (ALS) pathogenesis is limited. Here we generated a single-nucleus transcriptomic and epigenomic atlas of the frontal cortex of ALS cases with C9orf72 (C9) hexanucleotide repeat expansions and sporadic ALS (sALS). Our findings reveal shared pathways in C9-ALS and sALS, characterized by synaptic dysfunction in excitatory neurons and a disease-associated state in microglia. The disease subtypes diverge with loss of astrocyte homeostasis in C9-ALS, and a more substantial disturbance of inhibitory neurons in sALS. Leveraging high depth 3'-end sequencing, we found a widespread switch towards distal polyadenylation (PA) site usage across ALS subtypes relative to controls. To explore this differential alternative PA (APA), we developed APA-Net, a deep neural network model that uses transcript sequence and expression levels of RNA-binding proteins (RBPs) to predict cell-type specific APA usage and RBP interactions likely to regulate APA across disease subtypes.
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7
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Stroup EK, Ji Z. Deep learning of human polyadenylation sites at nucleotide resolution reveals molecular determinants of site usage and relevance in disease. Nat Commun 2023; 14:7378. [PMID: 37968271 PMCID: PMC10651852 DOI: 10.1038/s41467-023-43266-3] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2023] [Accepted: 11/05/2023] [Indexed: 11/17/2023] Open
Abstract
The genomic distribution of cleavage and polyadenylation (polyA) sites should be co-evolutionally optimized with the local gene structure. Otherwise, spurious polyadenylation can cause premature transcription termination and generate aberrant proteins. To obtain mechanistic insights into polyA site optimization across the human genome, we develop deep/machine learning models to identify genome-wide putative polyA sites at unprecedented nucleotide-level resolution and calculate their strength and usage in the genomic context. Our models quantitatively measure position-specific motif importance and their crosstalk in polyA site formation and cleavage heterogeneity. The intronic site expression is governed by the surrounding splicing landscape. The usage of alternative polyA sites in terminal exons is modulated by their relative locations and distance to downstream genes. Finally, we apply our models to reveal thousands of disease- and trait-associated genetic variants altering polyadenylation activity. Altogether, our models represent a valuable resource to dissect molecular mechanisms mediating genome-wide polyA site expression and characterize their functional roles in human diseases.
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Affiliation(s)
- Emily Kunce Stroup
- Department of Pharmacology, Feinberg School of Medicine, Northwestern University, Chicago, IL, 60611, USA
| | - Zhe Ji
- Department of Pharmacology, Feinberg School of Medicine, Northwestern University, Chicago, IL, 60611, USA.
- Department of Biomedical Engineering, McCormick School of Engineering, Northwestern University, Evanston, IL, 60628, USA.
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8
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Carrion SA, Michal JJ, Jiang Z. Alternative Transcripts Diversify Genome Function for Phenome Relevance to Health and Diseases. Genes (Basel) 2023; 14:2051. [PMID: 38002994 PMCID: PMC10671453 DOI: 10.3390/genes14112051] [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: 10/13/2023] [Revised: 11/06/2023] [Accepted: 11/07/2023] [Indexed: 11/26/2023] Open
Abstract
Manipulation using alternative exon splicing (AES), alternative transcription start (ATS), and alternative polyadenylation (APA) sites are key to transcript diversity underlying health and disease. All three are pervasive in organisms, present in at least 50% of human protein-coding genes. In fact, ATS and APA site use has the highest impact on protein identity, with their ability to alter which first and last exons are utilized as well as impacting stability and translation efficiency. These RNA variants have been shown to be highly specific, both in tissue type and stage, with demonstrated importance to cell proliferation, differentiation and the transition from fetal to adult cells. While alternative exon splicing has a limited effect on protein identity, its ubiquity highlights the importance of these minor alterations, which can alter other features such as localization. The three processes are also highly interwoven, with overlapping, complementary, and competing factors, RNA polymerase II and its CTD (C-terminal domain) chief among them. Their role in development means dysregulation leads to a wide variety of disorders and cancers, with some forms of disease disproportionately affected by specific mechanisms (AES, ATS, or APA). Challenges associated with the genome-wide profiling of RNA variants and their potential solutions are also discussed in this review.
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Affiliation(s)
| | | | - Zhihua Jiang
- Department of Animal Sciences and Center for Reproductive Biology, Washington State University, Pullman, WA 99164-7620, USA; (S.A.C.); (J.J.M.)
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9
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Zhang L, Zhou Q, Zhang J, Cao K, Fan C, Chen S, Jiang H, Wu F. Liver transcriptomic and proteomic analyses provide new insight into the pathogenesis of liver fibrosis in mice. Genomics 2023; 115:110738. [PMID: 37918454 DOI: 10.1016/j.ygeno.2023.110738] [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: 04/17/2023] [Revised: 09/25/2023] [Accepted: 10/30/2023] [Indexed: 11/04/2023]
Abstract
BACKGROUND Liver fibrosis (LF) is a kind of progressive liver injury reaction. The goal of this study was to achieve a more detailed understanding of the molecular changes in response to CCl4-induced LF through the identification of a differentially expressed liver transcriptomic and proteomic. RESULTS A total of 1224 differentially expressed genes (DEGs) and 302 differentially expressed proteins (DEPs) were significantly identified at the transcriptomic and proteomic level, respectively, and 69 genes (hereafter "cor-DEGs-DEPs" genes) were detected at both levels. Pathway enrichment analysis showed that these cor-DEGs-DEPs genes were significantly enriched in 133 pathways. Importantly, among the cor-DEGs-DEPs genes, Gstm1, Gstm3, Ephx1 and Gstp1 were shown to be associated with metabolic pathways, and confirmed by RT-qPCR and parallel reaction monitoring (PRM) verification. CONCLUSIONS Through the combined analysis of transcriptomic and proteomic data, this study provides valuable insights into the potential mechanism of the pathogenesis of LF, and lays a theoretical foundation for the further development of targeted therapy for LF.
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Affiliation(s)
- Lili Zhang
- Experimental Center of Clinical Research, The First Affiliated Hospital of Anhui University of Chinese Medicine, Hefei, China; School of Pharmacy, Anhui University of Chinese Medicine, Hefei, China.
| | - Qiumei Zhou
- Experimental Center of Clinical Research, The First Affiliated Hospital of Anhui University of Chinese Medicine, Hefei, China.
| | - Jiafu Zhang
- Department of Pharmacy, The First Affiliated Hospital of Anhui University of Chinese Medicine, Hefei, China.
| | - Kefeng Cao
- Departments of Laboratory Medicine, Traditional Chinese Medical Hospital of Taihe County, Fuyang, China.
| | - Chang Fan
- Experimental Center of Clinical Research, The First Affiliated Hospital of Anhui University of Chinese Medicine, Hefei, China; School of Pharmacy, Anhui University of Chinese Medicine, Hefei, China.
| | - Sen Chen
- Experimental Center of Clinical Research, The First Affiliated Hospital of Anhui University of Chinese Medicine, Hefei, China; School of Pharmacy, Anhui University of Chinese Medicine, Hefei, China.
| | - Hui Jiang
- Experimental Center of Clinical Research, The First Affiliated Hospital of Anhui University of Chinese Medicine, Hefei, China; School of Pharmacy, Anhui University of Chinese Medicine, Hefei, China.
| | - Furong Wu
- Department of Pharmacy, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, China.
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10
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Kowalski MH, Wessels HH, Linder J, Choudhary S, Hartman A, Hao Y, Mascio I, Dalgarno C, Kundaje A, Satija R. CPA-Perturb-seq: Multiplexed single-cell characterization of alternative polyadenylation regulators. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.02.09.527751. [PMID: 36798324 PMCID: PMC9934614 DOI: 10.1101/2023.02.09.527751] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 06/18/2023]
Abstract
Most mammalian genes have multiple polyA sites, representing a substantial source of transcript diversity that is governed by the cleavage and polyadenylation (CPA) regulatory machinery. To better understand how these proteins govern polyA site choice we introduce CPA-Perturb-seq, a multiplexed perturbation screen dataset of 42 known CPA regulators with a 3' scRNA-seq readout that enables transcriptome-wide inference of polyA site usage. We develop a statistical framework to specifically identify perturbation-dependent changes in intronic and tandem polyadenylation, and discover modules of co-regulated polyA sites exhibiting distinct functional properties. By training a multi-task deep neural network (APARENT-Perturb) on our dataset, we delineate a cis-regulatory code that predicts responsiveness to perturbation and reveals interactions between distinct regulatory complexes. Finally, we leverage our framework to re-analyze published scRNA-seq datasets, identifying new regulators that affect the relative abundance of alternatively polyadenylated transcripts, and characterizing extensive cellular heterogeneity in 3' UTR length amongst antibody-producing cells. Our work highlights the potential for multiplexed single-cell perturbation screens to further our understanding of post-transcriptional regulation in vitro and in vivo.
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Affiliation(s)
- Madeline H. Kowalski
- New York Genome Center, New York, NY, USA
- Center for Genomics and Systems Biology, New York University, New York, NY, USA
- New York University Grossman School of Medicine, New York, NY, USA
| | - Hans-Hermann Wessels
- New York Genome Center, New York, NY, USA
- Center for Genomics and Systems Biology, New York University, New York, NY, USA
| | - Johannes Linder
- Department of Genetics, Stanford University, Stanford USA
- Department of Computer Science, Stanford University, Stanford USA
| | - Saket Choudhary
- New York Genome Center, New York, NY, USA
- Center for Genomics and Systems Biology, New York University, New York, NY, USA
| | | | - Yuhan Hao
- New York Genome Center, New York, NY, USA
- Center for Genomics and Systems Biology, New York University, New York, NY, USA
| | - Isabella Mascio
- New York Genome Center, New York, NY, USA
- Center for Genomics and Systems Biology, New York University, New York, NY, USA
| | | | - Anshul Kundaje
- Department of Genetics, Stanford University, Stanford USA
- Department of Computer Science, Stanford University, Stanford USA
| | - Rahul Satija
- New York Genome Center, New York, NY, USA
- Center for Genomics and Systems Biology, New York University, New York, NY, USA
- New York University Grossman School of Medicine, New York, NY, USA
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11
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Linder J, Koplik SE, Kundaje A, Seelig G. Deciphering the impact of genetic variation on human polyadenylation using APARENT2. Genome Biol 2022; 23:232. [PMID: 36335397 PMCID: PMC9636789 DOI: 10.1186/s13059-022-02799-4] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2022] [Accepted: 10/19/2022] [Indexed: 11/08/2022] Open
Abstract
BACKGROUND 3'-end processing by cleavage and polyadenylation is an important and finely tuned regulatory process during mRNA maturation. Numerous genetic variants are known to cause or contribute to human disorders by disrupting the cis-regulatory code of polyadenylation signals. Yet, due to the complexity of this code, variant interpretation remains challenging. RESULTS We introduce a residual neural network model, APARENT2, that can infer 3'-cleavage and polyadenylation from DNA sequence more accurately than any previous model. This model generalizes to the case of alternative polyadenylation (APA) for a variable number of polyadenylation signals. We demonstrate APARENT2's performance on several variant datasets, including functional reporter data and human 3' aQTLs from GTEx. We apply neural network interpretation methods to gain insights into disrupted or protective higher-order features of polyadenylation. We fine-tune APARENT2 on human tissue-resolved transcriptomic data to elucidate tissue-specific variant effects. By combining APARENT2 with models of mRNA stability, we extend aQTL effect size predictions to the entire 3' untranslated region. Finally, we perform in silico saturation mutagenesis of all human polyadenylation signals and compare the predicted effects of [Formula: see text] million variants against gnomAD. While loss-of-function variants were generally selected against, we also find specific clinical conditions linked to gain-of-function mutations. For example, we detect an association between gain-of-function mutations in the 3'-end and autism spectrum disorder. To experimentally validate APARENT2's predictions, we assayed clinically relevant variants in multiple cell lines, including microglia-derived cells. CONCLUSIONS A sequence-to-function model based on deep residual learning enables accurate functional interpretation of genetic variants in polyadenylation signals and, when coupled with large human variation databases, elucidates the link between functional 3'-end mutations and human health.
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Affiliation(s)
| | | | - Anshul Kundaje
- Department of Genetics, Stanford University, Stanford, USA
- Department of Computer Science, Stanford University, Stanford, USA
| | - Georg Seelig
- Paul G. Allen School of Computer Science and Engineering, University of Washington, Seattle, USA
- Department of Electrical and Computer Engineering, University of Washington, Seattle, USA
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12
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Guo Y, Shen H, Li W, Li C, Jin C. Deep Effective k-mer representation learning for polyadenylation signal prediction via co-occurrence embedding. Knowl Based Syst 2022. [DOI: 10.1016/j.knosys.2022.109887] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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13
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Ye W, Lian Q, Ye C, Wu X. A Survey on Methods for Predicting Polyadenylation Sites from DNA Sequences, Bulk RNA-seq, and Single-cell RNA-seq. GENOMICS, PROTEOMICS & BIOINFORMATICS 2022:S1672-0229(22)00121-8. [PMID: 36167284 PMCID: PMC10372920 DOI: 10.1016/j.gpb.2022.09.005] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/19/2022] [Revised: 08/17/2022] [Accepted: 09/19/2022] [Indexed: 05/08/2023]
Abstract
Alternative polyadenylation (APA) plays important roles in modulating mRNA stability, translation, and subcellular localization, and contributes extensively to shaping eukaryotic transcriptome complexity and proteome diversity. Identification of poly(A) sites (pAs) on a genome-wide scale is a critical step toward understanding the underlying mechanism of APA-mediated gene regulation. A number of established computational tools have been proposed to predict pAs from diverse genomic data. Here we provided an exhaustive overview of computational approaches for predicting pAs from DNA sequences, bulk RNA sequencing (RNA-seq) data, and single-cell RNA sequencing (scRNA-seq) data. Particularly, we examined several representative tools using bulk RNA-seq and scRNA-seq data from peripheral blood mononuclear cells and put forward operable suggestions on how to assess the reliability of pAs predicted by different tools. We also proposed practical guidelines on choosing appropriate methods applicable to diverse scenarios. Moreover, we discussed in depth the challenges in improving the performance of pA prediction and benchmarking different methods. Additionally, we highlighted outstanding challenges and opportunities using new machine learning and integrative multi-omics techniques, and provided our perspective on how computational methodologies might evolve in the future for non-3' untranslated region, tissue-specific, cross-species, and single-cell pA prediction.
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Affiliation(s)
- Wenbin Ye
- Pasteurien College, Suzhou Medical College of Soochow University, Soochow University, Suzhou 215000, China
| | - Qiwei Lian
- Pasteurien College, Suzhou Medical College of Soochow University, Soochow University, Suzhou 215000, China; Department of Automation, Xiamen University, Xiamen 361005, China
| | - Congting Ye
- Key Laboratory of the Coastal and Wetland Ecosystems, Ministry of Education, College of the Environment and Ecology, Xiamen University, Xiamen 361005, China
| | - Xiaohui Wu
- Pasteurien College, Suzhou Medical College of Soochow University, Soochow University, Suzhou 215000, China.
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14
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Liu Q, Fang H, Wang X, Wang M, Li S, Coin LJM, Li F, Song J. DeepGenGrep: a general deep learning-based predictor for multiple genomic signals and regions. Bioinformatics 2022; 38:4053-4061. [PMID: 35799358 DOI: 10.1093/bioinformatics/btac454] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2022] [Revised: 04/11/2022] [Accepted: 07/06/2022] [Indexed: 12/24/2022] Open
Abstract
MOTIVATION Accurate annotation of different genomic signals and regions (GSRs) from DNA sequences is fundamentally important for understanding gene structure, regulation and function. Numerous efforts have been made to develop machine learning-based predictors for in silico identification of GSRs. However, it remains a great challenge to identify GSRs as the performance of most existing approaches is unsatisfactory. As such, it is highly desirable to develop more accurate computational methods for GSRs prediction. RESULTS In this study, we propose a general deep learning framework termed DeepGenGrep, a general predictor for the systematic identification of multiple different GSRs from genomic DNA sequences. DeepGenGrep leverages the power of hybrid neural networks comprising a three-layer convolutional neural network and a two-layer long short-term memory to effectively learn useful feature representations from sequences. Benchmarking experiments demonstrate that DeepGenGrep outperforms several state-of-the-art approaches on identifying polyadenylation signals, translation initiation sites and splice sites across four eukaryotic species including Homo sapiens, Mus musculus, Bos taurus and Drosophila melanogaster. Overall, DeepGenGrep represents a useful tool for the high-throughput and cost-effective identification of potential GSRs in eukaryotic genomes. AVAILABILITY AND IMPLEMENTATION The webserver and source code are freely available at http://bigdata.biocie.cn/deepgengrep/home and Github (https://github.com/wx-cie/DeepGenGrep/). SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Quanzhong Liu
- Department of Software Engineering, College of Information Engineering, Northwest A&F University, Yangling 712100, China
| | - Honglin Fang
- Department of Software Engineering, College of Information Engineering, Northwest A&F University, Yangling 712100, China
| | - Xiao Wang
- Department of Software Engineering, College of Information Engineering, Northwest A&F University, Yangling 712100, China
| | - Miao Wang
- Department of Software Engineering, College of Information Engineering, Northwest A&F University, Yangling 712100, China
| | - Shuqin Li
- Department of Software Engineering, College of Information Engineering, Northwest A&F University, Yangling 712100, China
| | - Lachlan J M Coin
- Department of Microbiology and Immunology, The Peter Doherty Institute for Infection and Immunity, The University of Melbourne, Melbourne, VIC 3000, Australia
| | - Fuyi Li
- Department of Software Engineering, College of Information Engineering, Northwest A&F University, Yangling 712100, China.,Department of Microbiology and Immunology, The Peter Doherty Institute for Infection and Immunity, The University of Melbourne, Melbourne, VIC 3000, Australia
| | - Jiangning Song
- Biomedicine Discovery Institute and Department of Biochemistry and Molecular Biology, Monash University, Melbourne, VIC 3800, Australia.,Monash Data Futures Institute, Monash University, Melbourne, VIC 3800, Australia
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15
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Fang L, Guo L, Zhang M, Li X, Deng Z. Analysis of Polyadenylation Signal Usage with Full-Length Transcriptome in Spodoptera frugiperda (Lepidoptera: Noctuidae). INSECTS 2022; 13:803. [PMID: 36135504 PMCID: PMC9505298 DOI: 10.3390/insects13090803] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/01/2022] [Revised: 08/29/2022] [Accepted: 08/30/2022] [Indexed: 06/16/2023]
Abstract
During the messenger RNA (mRNA) maturation process, RNA polyadenylation is a key step, and is coupled to the termination of transcription. Various cis-acting elements near the cleavage site and their binding factors would affect the process of polyadenylation, and AAUAAA, a highly conserved hexamer, was the most important polyadenylation signal (PAS). PAS usage is one of the critical modification determinants targeted at mRNA post-transcription. The full-length transcriptome has recently generated a massive amount of sequencing data, revealing poly(A) variation and alternative polyadenylation (APA) in Spodoptera frugiperda. We identified 50,616 polyadenylation signals in Spodoptera frugiperda via analysis of full-length transcriptome combined with expression Sequence Tags Technology (EST). The polyadenylation signal usage in Spodoptera frugiperda is conserved, and it is similar to that of flies and other animals. AAUAAA and AUUAAA are the most highly conserved polyadenylation signals of all polyadenylation signals we identified. Additionally, we found the U/GU-rich downstream sequence element (DSE) in the cleavage site. These results demonstrate that APA in Spodoptera frugiperda plays a significant role in root growth and development. This is the first polyadenylation signal usage analysis in agricultural pests, which can deepen our understanding of Spodoptera frugiperda and provide a theoretical basis for pest control.
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Affiliation(s)
- Liying Fang
- School of Agricultural Sciences, Zhengzhou University, Zhengzhou 450001, China
| | - Lina Guo
- School of Life Sciences, Zhengzhou University, Zhengzhou 450001, China
| | - Min Zhang
- School of Agricultural Sciences, Zhengzhou University, Zhengzhou 450001, China
- School of Life Sciences, Zhengzhou University, Zhengzhou 450001, China
| | - Xianchun Li
- Department of Entomology, BIO5 Institute, University of Arizona, Tucson, AZ 85721, USA
| | - Zhongyuan Deng
- School of Agricultural Sciences, Zhengzhou University, Zhengzhou 450001, China
- School of Life Sciences, Zhengzhou University, Zhengzhou 450001, China
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16
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Abstract
The tremendous amount of biological sequence data available, combined with the recent methodological breakthrough in deep learning in domains such as computer vision or natural language processing, is leading today to the transformation of bioinformatics through the emergence of deep genomics, the application of deep learning to genomic sequences. We review here the new applications that the use of deep learning enables in the field, focusing on three aspects: the functional annotation of genomes, the sequence determinants of the genome functions and the possibility to write synthetic genomic sequences.
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17
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Li Z, Li Y, Zhang B, Li Y, Long Y, Zhou J, Zou X, Zhang M, Hu Y, Chen W, Gao X. DeeReCT-APA: Prediction of Alternative Polyadenylation Site Usage Through Deep Learning. GENOMICS, PROTEOMICS & BIOINFORMATICS 2022; 20:483-495. [PMID: 33662629 PMCID: PMC9801043 DOI: 10.1016/j.gpb.2020.05.004] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/13/2020] [Revised: 03/28/2020] [Accepted: 06/12/2020] [Indexed: 01/26/2023]
Abstract
Alternative polyadenylation (APA) is a crucial step in post-transcriptional regulation. Previous bioinformatic studies have mainly focused on the recognition of polyadenylation sites (PASs) in a given genomic sequence, which is a binary classification problem. Recently, computational methods for predicting the usage level of alternative PASs in the same gene have been proposed. However, all of them cast the problem as a non-quantitative pairwise comparison task and do not take the competition among multiple PASs into account. To address this, here we propose a deep learning architecture, Deep Regulatory Code and Tools for Alternative Polyadenylation (DeeReCT-APA), to quantitatively predict the usage of all alternative PASs of a given gene. To accommodate different genes with potentially different numbers of PASs, DeeReCT-APA treats the problem as a regression task with a variable-length target. Based on a convolutional neural network-long short-term memory (CNN-LSTM) architecture, DeeReCT-APA extracts sequence features with CNN layers, uses bidirectional LSTM to explicitly model the interactions among competing PASs, and outputs percentage scores representing the usage levels of all PASs of a gene. In addition to the fact that only our method can quantitatively predict the usage of all the PASs within a gene, we show that our method consistently outperforms other existing methods on three different tasks for which they are trained: pairwise comparison task, highest usage prediction task, and ranking task. Finally, we demonstrate that our method can be used to predict the effect of genetic variations on APA patterns and sheds light on future mechanistic understanding in APA regulation. Our code and data are available at https://github.com/lzx325/DeeReCT-APA-repo.
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Affiliation(s)
- Zhongxiao Li
- King Abdullah University of Science and Technology (KAUST), Computational Bioscience Research Center (CBRC), Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division, Thuwal 23955-6900, Saudi Arabia
| | - Yisheng Li
- Department of Biology, Southern University of Science and Technology (SUSTech), Shenzhen 518055, China
| | - Bin Zhang
- Cancer Science Institute of Singapore, Singapore 117599, Singapore
| | - Yu Li
- King Abdullah University of Science and Technology (KAUST), Computational Bioscience Research Center (CBRC), Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division, Thuwal 23955-6900, Saudi Arabia
| | - Yongkang Long
- King Abdullah University of Science and Technology (KAUST), Computational Bioscience Research Center (CBRC), Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division, Thuwal 23955-6900, Saudi Arabia,Department of Biology, Southern University of Science and Technology (SUSTech), Shenzhen 518055, China
| | - Juexiao Zhou
- Department of Biology, Southern University of Science and Technology (SUSTech), Shenzhen 518055, China
| | - Xudong Zou
- Department of Biology, Southern University of Science and Technology (SUSTech), Shenzhen 518055, China
| | - Min Zhang
- Department of Biology, Southern University of Science and Technology (SUSTech), Shenzhen 518055, China
| | - Yuhui Hu
- Department of Biology, Southern University of Science and Technology (SUSTech), Shenzhen 518055, China,Corresponding authors.
| | - Wei Chen
- Department of Biology, Southern University of Science and Technology (SUSTech), Shenzhen 518055, China,Corresponding authors.
| | - Xin Gao
- King Abdullah University of Science and Technology (KAUST), Computational Bioscience Research Center (CBRC), Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division, Thuwal 23955-6900, Saudi Arabia,Corresponding authors.
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18
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Leveraging omic features with F3UTER enables identification of unannotated 3'UTRs for synaptic genes. Nat Commun 2022; 13:2270. [PMID: 35477703 PMCID: PMC9046390 DOI: 10.1038/s41467-022-30017-z] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2021] [Accepted: 03/18/2022] [Indexed: 11/08/2022] Open
Abstract
There is growing evidence for the importance of 3' untranslated region (3'UTR) dependent regulatory processes. However, our current human 3'UTR catalogue is incomplete. Here, we develop a machine learning-based framework, leveraging both genomic and tissue-specific transcriptomic features to predict previously unannotated 3'UTRs. We identify unannotated 3'UTRs associated with 1,563 genes across 39 human tissues, with the greatest abundance found in the brain. These unannotated 3'UTRs are significantly enriched for RNA binding protein (RBP) motifs and exhibit high human lineage-specificity. We find that brain-specific unannotated 3'UTRs are enriched for the binding motifs of important neuronal RBPs such as TARDBP and RBFOX1, and their associated genes are involved in synaptic function. Our data is shared through an online resource F3UTER ( https://astx.shinyapps.io/F3UTER/ ). Overall, our data improves 3'UTR annotation and provides additional insights into the mRNA-RBP interactome in the human brain, with implications for our understanding of neurological and neurodevelopmental diseases.
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19
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Context-aware dynamic neural computational models for accurate Poly(A) signal prediction. Neural Netw 2022; 152:287-299. [DOI: 10.1016/j.neunet.2022.04.025] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2021] [Revised: 03/03/2022] [Accepted: 04/22/2022] [Indexed: 11/21/2022]
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20
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Interpreting neural networks for biological sequences by learning stochastic masks. NAT MACH INTELL 2022; 4:41-54. [DOI: 10.1038/s42256-021-00428-6] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
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21
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Linder J, Seelig G. Fast activation maximization for molecular sequence design. BMC Bioinformatics 2021; 22:510. [PMID: 34670493 PMCID: PMC8527647 DOI: 10.1186/s12859-021-04437-5] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2021] [Accepted: 10/11/2021] [Indexed: 12/26/2022] Open
Abstract
BACKGROUND Optimization of DNA and protein sequences based on Machine Learning models is becoming a powerful tool for molecular design. Activation maximization offers a simple design strategy for differentiable models: one-hot coded sequences are first approximated by a continuous representation, which is then iteratively optimized with respect to the predictor oracle by gradient ascent. While elegant, the current version of the method suffers from vanishing gradients and may cause predictor pathologies leading to poor convergence. RESULTS Here, we introduce Fast SeqProp, an improved activation maximization method that combines straight-through approximation with normalization across the parameters of the input sequence distribution. Fast SeqProp overcomes bottlenecks in earlier methods arising from input parameters becoming skewed during optimization. Compared to prior methods, Fast SeqProp results in up to 100-fold faster convergence while also finding improved fitness optima for many applications. We demonstrate Fast SeqProp's capabilities by designing DNA and protein sequences for six deep learning predictors, including a protein structure predictor. CONCLUSIONS Fast SeqProp offers a reliable and efficient method for general-purpose sequence optimization through a differentiable fitness predictor. As demonstrated on a variety of deep learning models, the method is widely applicable, and can incorporate various regularization techniques to maintain confidence in the sequence designs. As a design tool, Fast SeqProp may aid in the development of novel molecules, drug therapies and vaccines.
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Affiliation(s)
- Johannes Linder
- Paul G. Allen School of Computer Science and Engineering, University of Washington, Seattle, USA
| | - Georg Seelig
- Paul G. Allen School of Computer Science and Engineering, University of Washington, Seattle, USA
- Department of Electrical and Computer Engineering, University of Washington, Seattle, USA
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22
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Tsugawa H, Rai A, Saito K, Nakabayashi R. Metabolomics and complementary techniques to investigate the plant phytochemical cosmos. Nat Prod Rep 2021; 38:1729-1759. [PMID: 34668509 DOI: 10.1039/d1np00014d] [Citation(s) in RCA: 27] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
Covering: up to 2021Plants and their associated microbial communities are known to produce millions of metabolites, a majority of which are still not characterized and are speculated to possess novel bioactive properties. In addition to their role in plant physiology, these metabolites are also relevant as existing and next-generation medicine candidates. Elucidation of the plant metabolite diversity is thus valuable for the successful exploitation of natural resources for humankind. Herein, we present a comprehensive review on recent metabolomics approaches to illuminate molecular networks in plants, including chemical isolation and enzymatic production as well as the modern metabolomics approaches such as stable isotope labeling, ultrahigh-resolution mass spectrometry, metabolome imaging (spatial metabolomics), single-cell analysis, cheminformatics, and computational mass spectrometry. Mass spectrometry-based strategies to characterize plant metabolomes through metabolite identification and annotation are described in detail. We also highlight the use of phytochemical genomics to mine genes associated with specialized metabolites' biosynthesis. Understanding the metabolic diversity through biotechnological advances is fundamental to elucidate the functions of the plant-derived specialized metabolome.
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Affiliation(s)
- Hiroshi Tsugawa
- RIKEN Center for Sustainable Resource Science, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa 230-0045, Japan. .,RIKEN Center for Integrative Medical Sciences, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa 230-0045, Japan.,Department of Biotechnology and Life Science, Tokyo University of Agriculture and Technology, 2-24-16 Nakamachi, Koganei, Tokyo 184-8588, Japan.,Graduate School of Medical Life Science, Yokohama City University, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama 230-0045, Japan
| | - Amit Rai
- RIKEN Center for Sustainable Resource Science, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa 230-0045, Japan. .,Plant Molecular Science Center, Chiba University, 1-8-1 Inohana, Chuo-ku, Chiba 260-8675, Japan
| | - Kazuki Saito
- RIKEN Center for Sustainable Resource Science, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa 230-0045, Japan. .,Plant Molecular Science Center, Chiba University, 1-8-1 Inohana, Chuo-ku, Chiba 260-8675, Japan
| | - Ryo Nakabayashi
- RIKEN Center for Sustainable Resource Science, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa 230-0045, Japan.
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23
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Li GW, Nan F, Yuan GH, Liu CX, Liu X, Chen LL, Tian B, Yang L. SCAPTURE: a deep learning-embedded pipeline that captures polyadenylation information from 3' tag-based RNA-seq of single cells. Genome Biol 2021; 22:221. [PMID: 34376223 PMCID: PMC8353616 DOI: 10.1186/s13059-021-02437-5] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2021] [Accepted: 07/16/2021] [Indexed: 01/16/2023] Open
Abstract
Single-cell RNA-seq (scRNA-seq) profiles gene expression with high resolution. Here, we develop a stepwise computational method-called SCAPTURE to identify, evaluate, and quantify cleavage and polyadenylation sites (PASs) from 3' tag-based scRNA-seq. SCAPTURE detects PASs de novo in single cells with high sensitivity and accuracy, enabling detection of previously unannotated PASs. Quantified alternative PAS transcripts refine cell identity analysis beyond gene expression, enriching information extracted from scRNA-seq data. Using SCAPTURE, we show changes of PAS usage in PBMCs from infected versus healthy individuals at single-cell resolution.
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Affiliation(s)
- Guo-Wei Li
- CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, 200031, China
| | - Fang Nan
- CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, 200031, China
| | - Guo-Hua Yuan
- CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, 200031, China
| | - Chu-Xiao Liu
- State Key Laboratory of Molecular Biology, Shanghai Key Laboratory of Molecular Andrology, CAS Center for Excellence in Molecular Cell Science, Shanghai Institute of Biochemistry and Cell Biology, University of Chinese Academy of Sciences, Chinese Academy of Sciences, 320 Yueyang Road, Shanghai, 200031, China
| | - Xindong Liu
- Institute of Pathology and Southwest Cancer Center, Southwest Hospital, Third Military Medical University (Army Medical University), Chongqing, 400038, China
| | - Ling-Ling Chen
- State Key Laboratory of Molecular Biology, Shanghai Key Laboratory of Molecular Andrology, CAS Center for Excellence in Molecular Cell Science, Shanghai Institute of Biochemistry and Cell Biology, University of Chinese Academy of Sciences, Chinese Academy of Sciences, 320 Yueyang Road, Shanghai, 200031, China
- School of Life Science and Technology, ShanghaiTech University, Shanghai, 201210, China
- School of Life Science, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Hangzhou, 310024, China
| | - Bin Tian
- Program in Gene Expression and Regulation, and Center for Systems and Computational Biology, The Wistar Institute, Philadelphia, PA, 19104, USA
| | - Li Yang
- CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, 200031, China.
- School of Life Science and Technology, ShanghaiTech University, Shanghai, 201210, China.
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24
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Guo Y, Zhou D, Li W, Cao J, Nie R, Xiong L, Ruan X. Identifying polyadenylation signals with biological embedding via self-attentive gated convolutional highway networks. Appl Soft Comput 2021. [DOI: 10.1016/j.asoc.2021.107133] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
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25
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Aptardi predicts polyadenylation sites in sample-specific transcriptomes using high-throughput RNA sequencing and DNA sequence. Nat Commun 2021; 12:1652. [PMID: 33712618 PMCID: PMC7955126 DOI: 10.1038/s41467-021-21894-x] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2020] [Accepted: 02/18/2021] [Indexed: 02/01/2023] Open
Abstract
Annotation of polyadenylation sites from short-read RNA sequencing alone is a challenging computational task. Other algorithms rooted in DNA sequence predict potential polyadenylation sites; however, in vivo expression of a particular site varies based on a myriad of conditions. Here, we introduce aptardi (alternative polyadenylation transcriptome analysis from RNA-Seq data and DNA sequence information), which leverages both DNA sequence and RNA sequencing in a machine learning paradigm to predict expressed polyadenylation sites. Specifically, as input aptardi takes DNA nucleotide sequence, genome-aligned RNA-Seq data, and an initial transcriptome. The program evaluates these initial transcripts to identify expressed polyadenylation sites in the biological sample and refines transcript 3'-ends accordingly. The average precision of the aptardi model is twice that of a standard transcriptome assembler. In particular, the recall of the aptardi model (the proportion of true polyadenylation sites detected by the algorithm) is improved by over three-fold. Also, the model-trained using the Human Brain Reference RNA commercial standard-performs well when applied to RNA-sequencing samples from different tissues and different mammalian species. Finally, aptardi's input is simple to compile and its output is easily amenable to downstream analyses such as quantitation and differential expression.
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26
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Zhang Y, Liu L, Qiu Q, Zhou Q, Ding J, Lu Y, Liu P. Alternative polyadenylation: methods, mechanism, function, and role in cancer. J Exp Clin Cancer Res 2021; 40:51. [PMID: 33526057 PMCID: PMC7852185 DOI: 10.1186/s13046-021-01852-7] [Citation(s) in RCA: 74] [Impact Index Per Article: 24.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2020] [Accepted: 01/20/2021] [Indexed: 12/12/2022] Open
Abstract
Occurring in over 60% of human genes, alternative polyadenylation (APA) results in numerous transcripts with differing 3'ends, thus greatly expanding the diversity of mRNAs and of proteins derived from a single gene. As a key molecular mechanism, APA is involved in various gene regulation steps including mRNA maturation, mRNA stability, cellular RNA decay, and protein diversification. APA is frequently dysregulated in cancers leading to changes in oncogenes and tumor suppressor gene expressions. Recent studies have revealed various APA regulatory mechanisms that promote the development and progression of a number of human diseases, including cancer. Here, we provide an overview of four types of APA and their impacts on gene regulation. We focus particularly on the interaction of APA with microRNAs, RNA binding proteins and other related factors, the core pre-mRNA 3'end processing complex, and 3'UTR length change. We also describe next-generation sequencing methods and computational tools for use in poly(A) signal detection and APA repositories and databases. Finally, we summarize the current understanding of APA in cancer and provide our vision for future APA related research.
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Affiliation(s)
- Yi Zhang
- Department of Respiratory Medicine, Sir Run Run Shaw Hospital and Institute of Translational Medicine, Zhejiang University School of Medicine, Hangzhou, 310016, Zhejiang, China
| | - Lian Liu
- Department of Respiratory Medicine, Sir Run Run Shaw Hospital and Institute of Translational Medicine, Zhejiang University School of Medicine, Hangzhou, 310016, Zhejiang, China
| | - Qiongzi Qiu
- Center for Uterine Cancer Diagnosis & Therapy Research of Zhejiang Province, Women's Reproductive Health Key Laboratory of Zhejiang Province, Department of Gynecologic Oncology, Women's Hospital and Institute of Translational Medicine, Zhejiang University School of Medicine, Hangzhou, 310006, Zhejiang, China
| | - Qing Zhou
- Center for Uterine Cancer Diagnosis & Therapy Research of Zhejiang Province, Women's Reproductive Health Key Laboratory of Zhejiang Province, Department of Gynecologic Oncology, Women's Hospital and Institute of Translational Medicine, Zhejiang University School of Medicine, Hangzhou, 310006, Zhejiang, China
| | - Jinwang Ding
- Department of Head and Neck Surgery, Cancer Hospital of the University of Chinese Academy of Sciences, Zhejiang Cancer Hospital, Key Laboratory of Head & Neck Cancer Translational Research of Zhejiang Province, Hangzhou, 310022, Zhejiang, China.
| | - Yan Lu
- Center for Uterine Cancer Diagnosis & Therapy Research of Zhejiang Province, Women's Reproductive Health Key Laboratory of Zhejiang Province, Department of Gynecologic Oncology, Women's Hospital and Institute of Translational Medicine, Zhejiang University School of Medicine, Hangzhou, 310006, Zhejiang, China.
- Cancer Center, Zhejiang University, Hangzhou, 310029, Zhejiang, China.
| | - Pengyuan Liu
- Department of Respiratory Medicine, Sir Run Run Shaw Hospital and Institute of Translational Medicine, Zhejiang University School of Medicine, Hangzhou, 310016, Zhejiang, China.
- Department of Physiology, Center of Systems Molecular Medicine, Medical College of Wisconsin, Milwaukee, WI, 53226, USA.
- Cancer Center, Zhejiang University, Hangzhou, 310029, Zhejiang, China.
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27
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Yu H, Dai Z. SANPolyA: a deep learning method for identifying Poly(A) signals. Bioinformatics 2020; 36:2393-2400. [PMID: 31904817 DOI: 10.1093/bioinformatics/btz970] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2019] [Revised: 12/05/2019] [Accepted: 01/01/2020] [Indexed: 12/21/2022] Open
Abstract
MOTIVATION Polyadenylation plays a regulatory role in transcription. The recognition of polyadenylation signal (PAS) motif sequence is an important step in polyadenylation. In the past few years, some statistical machine learning-based and deep learning-based methods have been proposed for PAS identification. Although these methods predict PAS with success, there is room for their improvement on PAS identification. RESULTS In this study, we proposed a deep neural network-based computational method, called SANPolyA, for identifying PAS in human and mouse genomes. SANPolyA requires no manually crafted sequence features. We compared our method SANPolyA with several previous PAS identification methods on several PAS benchmark datasets. Our results showed that SANPolyA outperforms the state-of-art methods. SANPolyA also showed good performance on leave-one-motif-out evaluation. AVAILABILITY AND IMPLEMENTATION https://github.com/yuht4/SANPolyA. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
| | - Zhiming Dai
- School of Data and Computer Science.,Guangdong Province Key Laboratory of Big Data Analysis and Processing, Sun Yat-Sen University, Guangzhou 510006, China
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28
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