1
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Xu Q, Bao X, Lin Z, Tang L, He LN, Ren J, Zuo Z, Hu K. AStruct: detection of allele-specific RNA secondary structure in structuromic probing data. BMC Bioinformatics 2024; 25:91. [PMID: 38429654 DOI: 10.1186/s12859-024-05704-x] [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: 10/23/2023] [Accepted: 02/14/2024] [Indexed: 03/03/2024] Open
Abstract
BACKGROUND Uncovering functional genetic variants from an allele-specific perspective is of paramount importance in advancing our understanding of gene regulation and genetic diseases. Recently, various allele-specific events, such as allele-specific gene expression, allele-specific methylation, and allele-specific binding, have been explored on a genome-wide scale due to the development of high-throughput sequencing methods. RNA secondary structure, which plays a crucial role in multiple RNA-associated processes like RNA modification, translation and splicing, has emerged as an essential focus of relevant research. However, tools to identify genetic variants associated with allele-specific RNA secondary structures are still lacking. RESULTS Here, we develop a computational tool called 'AStruct' that enables us to detect allele-specific RNA secondary structure (ASRS) from RT-stop based structuromic probing data. AStruct shows robust performance in both simulated datasets and public icSHAPE datasets. We reveal that single nucleotide polymorphisms (SNPs) with higher AStruct scores are enriched in coding regions and tend to be functional. These SNPs are highly conservative, have the potential to disrupt sites involved in m6A modification or protein binding, and are frequently associated with disease. CONCLUSIONS AStruct is a tool dedicated to invoke allele-specific RNA secondary structure events at heterozygous SNPs in RT-stop based structuromic probing data. It utilizes allelic variants, base pairing and RT-stop information under different cell conditions to detect dynamic and functional ASRS. Compared to sequence-based tools, AStruct considers dynamic cell conditions and outperforms in detecting functional variants. AStruct is implemented in JAVA and is freely accessible at: https://github.com/canceromics/AStruct .
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Affiliation(s)
- Qingru Xu
- State Key Laboratory of Oncology in South China, Cancer Center, Collaborative Innovation Center for Cancer Medicine, School of Life Sciences, Sun Yat-sen University, Guangzhou, 510060, China
- Department of Data Science, Dana-Farber Cancer Institute, Boston, MA, 02215, USA
| | - Xiaoqiong Bao
- State Key Laboratory of Oncology in South China, Cancer Center, Collaborative Innovation Center for Cancer Medicine, School of Life Sciences, Sun Yat-sen University, Guangzhou, 510060, China
| | - Zhuobin Lin
- Guangdong Key Laboratory of Liver Disease Research, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Lin Tang
- State Key Laboratory of Oncology in South China, Cancer Center, Collaborative Innovation Center for Cancer Medicine, School of Life Sciences, Sun Yat-sen University, Guangzhou, 510060, China
| | - Li-Na He
- State Key Laboratory of Oncology in South China, Cancer Center, Collaborative Innovation Center for Cancer Medicine, School of Life Sciences, Sun Yat-sen University, Guangzhou, 510060, China
| | - Jian Ren
- State Key Laboratory of Oncology in South China, Cancer Center, Collaborative Innovation Center for Cancer Medicine, School of Life Sciences, Sun Yat-sen University, Guangzhou, 510060, China
| | - Zhixiang Zuo
- State Key Laboratory of Oncology in South China, Cancer Center, Collaborative Innovation Center for Cancer Medicine, School of Life Sciences, Sun Yat-sen University, Guangzhou, 510060, China.
| | - Kunhua Hu
- Guangdong Key Laboratory of Liver Disease Research, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China.
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2
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Sun H, Fu B, Qian X, Xu P, Qin W. Nuclear and cytoplasmic specific RNA binding proteome enrichment and its changes upon ferroptosis induction. Nat Commun 2024; 15:852. [PMID: 38286993 PMCID: PMC10825125 DOI: 10.1038/s41467-024-44987-9] [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: 06/21/2022] [Accepted: 01/11/2024] [Indexed: 01/31/2024] Open
Abstract
The key role of RNA-binding proteins (RBPs) in posttranscriptional regulation of gene expression is intimately tied to their subcellular localization. Here, we show a subcellular-specific RNA labeling method for efficient enrichment and deep profiling of nuclear and cytoplasmic RBPs. A total of 1221 nuclear RBPs and 1333 cytoplasmic RBPs were enriched and identified using nuclear/cytoplasm targeting enrichment probes, representing an increase of 54.4% and 85.7% compared with previous reports. The probes were further applied in the omics-level investigation of subcellular-specific RBP-RNA interactions upon ferroptosis induction. Interestingly, large-scale RBPs display enhanced interaction with RNAs in nucleus but reduced association with RNAs in cytoplasm during ferroptosis process. Furthermore, we discovered dozens of nucleoplasmic translocation candidate RBPs upon ferroptosis induction and validated representative ones by immunofluorescence imaging. The enrichment of Tricarboxylic acid cycle in the translocation candidate RBPs may provide insights for investigating their possible roles in ferroptosis induced metabolism dysregulation.
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Affiliation(s)
- Haofan Sun
- State Key Laboratory of Medical Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics, Beijing, 102206, China
| | - Bin Fu
- State Key Laboratory of Medical Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics, Beijing, 102206, China
| | - Xiaohong Qian
- State Key Laboratory of Medical Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics, Beijing, 102206, China
| | - Ping Xu
- State Key Laboratory of Medical Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics, Beijing, 102206, China
| | - Weijie Qin
- State Key Laboratory of Medical Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics, Beijing, 102206, China.
- College of Chemistry and Materials Science, Hebei University, Baoding, 071002, China.
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3
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Singh M, Kumar S. Effect of single nucleotide polymorphisms on the structure of long noncoding RNAs and their interaction with RNA binding proteins. Biosystems 2023; 233:105021. [PMID: 37703988 DOI: 10.1016/j.biosystems.2023.105021] [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: 02/21/2023] [Revised: 07/25/2023] [Accepted: 09/06/2023] [Indexed: 09/15/2023]
Abstract
Long non-coding RNAs (lncRNA) are emerging as a new class of regulatory RNAs with remarkable potential to be utilized as therapeutic targets against many human diseases. Several genome-wide association studies (GWAS) have catalogued Single Nucleotide Polymorphisms (SNPs) present in the noncoding regions of the genome from where lncRNAs originate. In this study, we have selected 67 lncRNAs with GWAS-tagged SNPs and have also investigated their role in affecting the local secondary structures. Majority of the SNPs lead to changes in the secondary structure of lncRNAs to a different extent by altering the base pairing patterns. These structural changes in lncRNA are also manifested in form of alteration in the binding site for RNA binding proteins (RBPs) along with affecting their binding efficacies. Ultimately, these structural modifications may influence the transcriptional and post-transcriptional pathways of these RNAs, leading to the causation of diseases. Hence, it is important to understand the possible underlying mechanism of RBPs in association with GWAS-tagged SNPs in human diseases.
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Affiliation(s)
- Mandakini Singh
- Department of Life Science, National Institute of Technology, Rourkela, Odisha, 769008, India
| | - Santosh Kumar
- Department of Life Science, National Institute of Technology, Rourkela, Odisha, 769008, India.
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4
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Cao S, Zhu H, Cui J, Liu S, Li Y, Shi J, Mo J, Wang Z, Wang H, Hu J, Chen L, Li Y, Xia L, Xiao S. Allele-specific RNA N 6-methyladenosine modifications reveal functional genetic variants in human tissues. Genome Res 2023; 33:1369-1380. [PMID: 37714712 PMCID: PMC10547253 DOI: 10.1101/gr.277704.123] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2023] [Accepted: 06/13/2023] [Indexed: 09/17/2023]
Abstract
An intricate network of cis- and trans-elements acts on RNA N 6-methyladenosine (m6A), which in turn may affect gene expression and, ultimately, human health. A complete understanding of this network requires new approaches to accurately measure the subtle m6A differences arising from genetic variants, many of which have been associated with common diseases. To address this gap, we developed a method to accurately and sensitively detect transcriptome-wide allele-specific m6A (ASm6A) from MeRIP-seq data and applied it to uncover 12,056 high-confidence ASm6A modifications from 25 human tissues. We also identified 1184 putative functional variants for ASm6A regulation, a subset of which we experimentally validated. Importantly, we found that many of these ASm6A-associated genetic variants were enriched for common disease-associated and complex trait-associated risk loci, and verified that two disease risk variants can change m6A modification status. Together, this work provides a tool to detangle the dynamic network of RNA modifications at the allelic level and highlights the interplay of m6A and genetics in human health and disease.
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Affiliation(s)
- Shuo Cao
- Department of Developmental Biology, School of Basic Medical Sciences, Southern Medical University, Guangzhou 510515, China
| | - Haoran Zhu
- Department of Developmental Biology, School of Basic Medical Sciences, Southern Medical University, Guangzhou 510515, China
| | - Jinru Cui
- Department of Developmental Biology, School of Basic Medical Sciences, Southern Medical University, Guangzhou 510515, China
| | - Sun Liu
- Department of Developmental Biology, School of Basic Medical Sciences, Southern Medical University, Guangzhou 510515, China
| | - Yuhe Li
- Department of Developmental Biology, School of Basic Medical Sciences, Southern Medical University, Guangzhou 510515, China
| | - Junfang Shi
- Department of Developmental Biology, School of Basic Medical Sciences, Southern Medical University, Guangzhou 510515, China
| | - Junyuan Mo
- Department of Developmental Biology, School of Basic Medical Sciences, Southern Medical University, Guangzhou 510515, China
| | - Zihan Wang
- Department of Developmental Biology, School of Basic Medical Sciences, Southern Medical University, Guangzhou 510515, China
| | - Hailan Wang
- Department of Developmental Biology, School of Basic Medical Sciences, Southern Medical University, Guangzhou 510515, China
| | - Jiaxin Hu
- Department of Developmental Biology, School of Basic Medical Sciences, Southern Medical University, Guangzhou 510515, China
| | - Lizhi Chen
- Department of Developmental Biology, School of Basic Medical Sciences, Southern Medical University, Guangzhou 510515, China
| | - Yuan Li
- Department of Developmental Biology, School of Basic Medical Sciences, Southern Medical University, Guangzhou 510515, China
| | - Laixin Xia
- Department of Developmental Biology, School of Basic Medical Sciences, Southern Medical University, Guangzhou 510515, China;
| | - Shan Xiao
- Department of Developmental Biology, School of Basic Medical Sciences, Southern Medical University, Guangzhou 510515, China;
- Guangdong Provincial Key Laboratory of Cardiac Function and Microcirculation, Guangzhou 510515, China
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5
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Biener-Ramanujan E, Rosier F, Coetzee SG, McGovern DDP, Hazelett D, Targan SR, Gonsky R. Diagnostic and therapeutic potential of RNASET2 in Crohn's disease: Disease-risk polymorphism modulates allelic-imbalance in expression and circulating protein levels and recombinant-RNASET2 attenuates pro-inflammatory cytokine secretion. Front Immunol 2022; 13:999155. [PMID: 36466822 PMCID: PMC9709281 DOI: 10.3389/fimmu.2022.999155] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2022] [Accepted: 09/30/2022] [Indexed: 08/28/2023] Open
Abstract
Ribonuclease T2 gene (RNASET2) variants are associated in genome wide association studies (GWAS) with risk for several autoimmune diseases, including Crohn's disease (CD). In T cells, a functional and biological relationship exists between TNFSF15-mediated enhancement of IFN-γ production, mucosal inflammation and RNASET2. Disease risk variants are associated with decreased mRNA expression and clinical characteristics of severe CD; however, functional classifications of variants and underlying molecular mechanisms contributing to pathogenesis remain largely unknown. In this study we demonstrate that allelic imbalance of RNASET2 disease risk variant rs2149092 is associated with transcriptional and post-transcriptional mechanisms regulating transcription factor binding, promoter-transactivation and allele-specific expression. RNASET2 mRNA expression decreases in response to multiple modes of T cell activation and recovers following elimination of activator. In CD patients with severe disease necessitating surgical intervention, preoperative circulating RNASET2 protein levels were decreased compared to non-IBD subjects and rebounded post-operatively following removal of the inflamed region, with levels associated with allelic carriage. Furthermore, overexpression or treatment with recombinant RNASET2 significantly reduced IFN-γ secretion. These findings reveal that RNASET2 cis- and trans-acting variation contributed regulatory complexity and determined expression and provide a basis for linking genetic variation with CD pathobiology. These data may ultimately identify RNASET2 as an effective therapeutic target in a subset of CD patients with severe disease.
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Affiliation(s)
- Eva Biener-Ramanujan
- Inflammatory Bowel & Immunobiology Research Institute, Cedars-Sinai, Los Angeles, CA, United States
| | - Florian Rosier
- Inflammatory Bowel & Immunobiology Research Institute, Cedars-Sinai, Los Angeles, CA, United States
| | - Simon G. Coetzee
- Department of Biomedical Sciences, Cedars−Sinai Medical Center, Los Angeles, CA, United States
| | - Dermot D. P. McGovern
- Inflammatory Bowel & Immunobiology Research Institute, Cedars-Sinai, Los Angeles, CA, United States
| | - Dennis Hazelett
- Department of Biomedical Sciences, Cedars−Sinai Medical Center, Los Angeles, CA, United States
| | - Stephan R. Targan
- Inflammatory Bowel & Immunobiology Research Institute, Cedars-Sinai, Los Angeles, CA, United States
| | - Rivkah Gonsky
- Inflammatory Bowel & Immunobiology Research Institute, Cedars-Sinai, Los Angeles, CA, United States
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6
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Liu Y, Li R, Luo J, Zhang Z. Inferring RNA-binding protein target preferences using adversarial domain adaptation. PLoS Comput Biol 2022; 18:e1009863. [PMID: 35202389 PMCID: PMC8870515 DOI: 10.1371/journal.pcbi.1009863] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2021] [Accepted: 01/25/2022] [Indexed: 11/18/2022] Open
Abstract
Precise identification of target sites of RNA-binding proteins (RBP) is important to understand their biochemical and cellular functions. A large amount of experimental data is generated by in vivo and in vitro approaches. The binding preferences determined from these platforms share similar patterns but there are discernable differences between these datasets. Computational methods trained on one dataset do not always work well on another dataset. To address this problem which resembles the classic "domain shift" in deep learning, we adopted the adversarial domain adaptation (ADDA) technique and developed a framework (RBP-ADDA) that can extract RBP binding preferences from an integration of in vivo and vitro datasets. Compared with conventional methods, ADDA has the advantage of working with two input datasets, as it trains the initial neural network for each dataset individually, projects the two datasets onto a feature space, and uses an adversarial framework to derive an optimal network that achieves an optimal discriminative predictive power. In the first step, for each RBP, we include only the in vitro data to pre-train a source network and a task predictor. Next, for the same RBP, we initiate the target network by using the source network and use adversarial domain adaptation to update the target network using both in vitro and in vivo data. These two steps help leverage the in vitro data to improve the prediction on in vivo data, which is typically challenging with a lower signal-to-noise ratio. Finally, to further take the advantage of the fused source and target data, we fine-tune the task predictor using both data. We showed that RBP-ADDA achieved better performance in modeling in vivo RBP binding data than other existing methods as judged by Pearson correlations. It also improved predictive performance on in vitro datasets. We further applied augmentation operations on RBPs with less in vivo data to expand the input data and showed that it can improve prediction performances. Lastly, we explored the predictive interpretability of RBP-ADDA, where we quantified the contribution of the input features by Integrated Gradients and identified nucleotide positions that are important for RBP recognition.
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Affiliation(s)
- Ying Liu
- College of Computer Science and Electronic Engineering, Hunan University, Changsha, Hunan, China
- Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, Ontario, Canada
| | - Ruihui Li
- Department of Computer Science and Engineering, The Chinese University of Hong Kong, Hong Kong, China
| | - Jiawei Luo
- College of Computer Science and Electronic Engineering, Hunan University, Changsha, Hunan, China
| | - Zhaolei Zhang
- Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, Ontario, Canada
- Department of Computer Science, University of Toronto, Toronto, Ontario, Canada
- Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada
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7
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Abstract
Diploidy has profound implications for population genetics and susceptibility to genetic diseases. Although two copies are present for most genes in the human genome, they are not necessarily both active or active at the same level in a given individual. Genomic imprinting, resulting in exclusive or biased expression in favor of the allele of paternal or maternal origin, is now believed to affect hundreds of human genes. A far greater number of genes display unequal expression of gene copies due to cis-acting genetic variants that perturb gene expression. The availability of data generated by RNA sequencing applied to large numbers of individuals and tissue types has generated unprecedented opportunities to assess the contribution of genetic variation to allelic imbalance in gene expression. Here we review the insights gained through the analysis of these data about the extent of the genetic contribution to allelic expression imbalance, the tools and statistical models for gene expression imbalance, and what the results obtained reveal about the contribution of genetic variants that alter gene expression to complex human diseases and phenotypes.
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Affiliation(s)
- Siobhan Cleary
- School of Mathematics, Statistics and Applied Mathematics, National University of Ireland, Galway H91 H3CY, Ireland;
| | - Cathal Seoighe
- School of Mathematics, Statistics and Applied Mathematics, National University of Ireland, Galway H91 H3CY, Ireland;
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8
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Roos D, de Boer M. Mutations in cis that affect mRNA synthesis, processing and translation. Biochim Biophys Acta Mol Basis Dis 2021; 1867:166166. [PMID: 33971252 DOI: 10.1016/j.bbadis.2021.166166] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2020] [Revised: 05/03/2021] [Accepted: 05/04/2021] [Indexed: 12/17/2022]
Abstract
Genetic mutations that cause hereditary diseases usually affect the composition of the transcribed mRNA and its encoded protein, leading to instability of the mRNA and/or the protein. Sometimes, however, such mutations affect the synthesis, the processing or the translation of the mRNA, with similar disastrous effects. We here present an overview of mRNA synthesis, its posttranscriptional modification and its translation into protein. We then indicate which elements in these processes are known to be affected by pathogenic mutations, but we restrict our review to mutations in cis, in the DNA of the gene that encodes the affected protein. These mutations can be in enhancer or promoter regions of the gene, which act as binding sites for transcription factors involved in pre-mRNA synthesis. We also describe mutations in polyadenylation sequences and in splice site regions, exonic and intronic, involved in intron removal. Finally, we include mutations in the Kozak sequence in mRNA, which is involved in protein synthesis. We provide examples of genetic diseases caused by mutations in these DNA regions and refer to databases to help identify these regions. The over-all knowledge of mRNA synthesis, processing and translation is essential for improvement of the diagnosis of patients with genetic diseases.
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Affiliation(s)
- Dirk Roos
- Sanquin Blood Supply Organization, Dept. of Blood Cell Research, Landsteiner Laboratory, Amsterdam University Medical Centre, location AMC, University of Amsterdam, Amsterdam, the Netherlands.
| | - Martin de Boer
- Sanquin Blood Supply Organization, Dept. of Blood Cell Research, Landsteiner Laboratory, Amsterdam University Medical Centre, location AMC, University of Amsterdam, Amsterdam, the Netherlands
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9
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Sun L, Xu K, Huang W, Yang YT, Li P, Tang L, Xiong T, Zhang QC. Predicting dynamic cellular protein-RNA interactions by deep learning using in vivo RNA structures. Cell Res 2021; 31:495-516. [PMID: 33623109 PMCID: PMC7900654 DOI: 10.1038/s41422-021-00476-y] [Citation(s) in RCA: 45] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2020] [Accepted: 01/19/2021] [Indexed: 01/31/2023] Open
Abstract
Interactions with RNA-binding proteins (RBPs) are integral to RNA function and cellular regulation, and dynamically reflect specific cellular conditions. However, presently available tools for predicting RBP-RNA interactions employ RNA sequence and/or predicted RNA structures, and therefore do not capture their condition-dependent nature. Here, after profiling transcriptome-wide in vivo RNA secondary structures in seven cell types, we developed PrismNet, a deep learning tool that integrates experimental in vivo RNA structure data and RBP binding data for matched cells to accurately predict dynamic RBP binding in various cellular conditions. PrismNet results for 168 RBPs support its utility for both understanding CLIP-seq results and largely extending such interaction data to accurately analyze additional cell types. Further, PrismNet employs an "attention" strategy to computationally identify exact RBP-binding nucleotides, and we discovered enrichment among dynamic RBP-binding sites for structure-changing variants (riboSNitches), which can link genetic diseases with dysregulated RBP bindings. Our rich profiling data and deep learning-based prediction tool provide access to a previously inaccessible layer of cell-type-specific RBP-RNA interactions, with clear utility for understanding and treating human diseases.
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Affiliation(s)
- Lei Sun
- MOE Key Laboratory of Bioinformatics, Beijing Advanced Innovation Center for Structural Biology and Frontier Research Center for Biological Structure, Center for Synthetic and Systems Biology, School of Life Sciences, Tsinghua University, Beijing 100084, China
- Tsinghua-Peking Center for Life Sciences, Beijing 100084, China
| | - Kui Xu
- MOE Key Laboratory of Bioinformatics, Beijing Advanced Innovation Center for Structural Biology and Frontier Research Center for Biological Structure, Center for Synthetic and Systems Biology, School of Life Sciences, Tsinghua University, Beijing 100084, China
- Tsinghua-Peking Center for Life Sciences, Beijing 100084, China
| | - Wenze Huang
- MOE Key Laboratory of Bioinformatics, Beijing Advanced Innovation Center for Structural Biology and Frontier Research Center for Biological Structure, Center for Synthetic and Systems Biology, School of Life Sciences, Tsinghua University, Beijing 100084, China
- Tsinghua-Peking Center for Life Sciences, Beijing 100084, China
| | - Yucheng T Yang
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520, USA
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT 06520, USA
| | - Pan Li
- MOE Key Laboratory of Bioinformatics, Beijing Advanced Innovation Center for Structural Biology and Frontier Research Center for Biological Structure, Center for Synthetic and Systems Biology, School of Life Sciences, Tsinghua University, Beijing 100084, China
- Tsinghua-Peking Center for Life Sciences, Beijing 100084, China
| | - Lei Tang
- MOE Key Laboratory of Bioinformatics, Beijing Advanced Innovation Center for Structural Biology and Frontier Research Center for Biological Structure, Center for Synthetic and Systems Biology, School of Life Sciences, Tsinghua University, Beijing 100084, China
- Tsinghua-Peking Center for Life Sciences, Beijing 100084, China
| | - Tuanlin Xiong
- MOE Key Laboratory of Bioinformatics, Beijing Advanced Innovation Center for Structural Biology and Frontier Research Center for Biological Structure, Center for Synthetic and Systems Biology, School of Life Sciences, Tsinghua University, Beijing 100084, China
- Tsinghua-Peking Center for Life Sciences, Beijing 100084, China
| | - Qiangfeng Cliff Zhang
- MOE Key Laboratory of Bioinformatics, Beijing Advanced Innovation Center for Structural Biology and Frontier Research Center for Biological Structure, Center for Synthetic and Systems Biology, School of Life Sciences, Tsinghua University, Beijing 100084, China.
- Tsinghua-Peking Center for Life Sciences, Beijing 100084, China.
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10
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Wang J. Integrative analyses of transcriptome data reveal the mechanisms of post-transcriptional regulation. Brief Funct Genomics 2021; 20:207-212. [PMID: 33615339 DOI: 10.1093/bfgp/elab004] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2020] [Revised: 01/18/2021] [Accepted: 01/20/2021] [Indexed: 12/13/2022] Open
Abstract
Post-transcriptional processing of RNAs plays important roles in a variety of physiological and pathological processes. These processes can be precisely controlled by a series of RNA binding proteins and cotranscriptionally regulated by transcription factors as well as histone modifications. With the rapid development of high-throughput sequencing techniques, multiomics data have been broadly used to study the mechanisms underlying the important biological processes. However, how to use these high-throughput sequencing data to elucidate the fundamental regulatory roles of post-transcriptional processes is still of great challenge. This review summarizes the regulatory mechanisms of post-transcriptional processes and the general principles and approaches to dissect these mechanisms by integrating multiomics data as well as public resources.
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Affiliation(s)
- Jinkai Wang
- Department of Medical Informatics, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou 510080, China.,Center for Stem Cell Biology and Tissue Engineering, Key Laboratory for Stem Cells and Tissue Engineering, Ministry of Education, Sun Yat-sen University, Guangzhou, China.,RNA Biomedical Institute, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou 510120, China
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11
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Li X, Zhang F, Ma J, Ruan X, Liu X, Zheng J, Liu Y, Cao S, Shen S, Shao L, Cai H, Li Z, Xue Y. NCBP3/SNHG6 inhibits GBX2 transcription in a histone modification manner to facilitate the malignant biological behaviour of glioma cells. RNA Biol 2020; 18:47-63. [PMID: 32618493 DOI: 10.1080/15476286.2020.1790140] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023] Open
Abstract
RNA-binding proteins (RBPs) are significantly dysregulated in glioma. In this study, we demonstrated the upregulation of Nuclear cap-binding subunit 3 (NCBP3) in glioma tissues and cells. Further, knockdown of NCBP3 inhibited the malignant progression of glioma. NCBP3 directly bound to small nucleolar RNA host gene 6 (SNHG6) and stabilized SNHG6 expression. In contrast, the gastrulation brain homeobox 2 (GBX2) transcription factor was downregulated in glioma tissues and cells. SNHG6 inhibited GBX2 transcription by mediating the H3K27me3 modification induced by polycomb repressive complex 2 (PRC2). Moreover, GBX2 decreased the promoter activities and downregulated the expression of the flotillin protein family 1 (FLOT1) oncogene. In conclusion, NCBP3/SNHG6 inhibits GBX2 transcription in a PRC2-dependent manner to facilitate the malignant progression of gliomas.
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Affiliation(s)
- Xiwen Li
- Department of Neurobiology, School of Life Sciences, China Medical University , Shenyang, China.,Key Laboratory of Cell Biology, Ministry of Public Health of China, China Medical University , Shenyang, China.,Key Laboratory of Medical Cell Biology, Ministry of Education of China, China Medical University , Shenyang, China
| | - Fangfang Zhang
- Department of Neurobiology, School of Life Sciences, China Medical University , Shenyang, China.,Key Laboratory of Cell Biology, Ministry of Public Health of China, China Medical University , Shenyang, China.,Key Laboratory of Medical Cell Biology, Ministry of Education of China, China Medical University , Shenyang, China
| | - Jun Ma
- Department of Neurobiology, School of Life Sciences, China Medical University , Shenyang, China.,Key Laboratory of Cell Biology, Ministry of Public Health of China, China Medical University , Shenyang, China.,Key Laboratory of Medical Cell Biology, Ministry of Education of China, China Medical University , Shenyang, China
| | - Xuelei Ruan
- Department of Neurobiology, School of Life Sciences, China Medical University , Shenyang, China.,Key Laboratory of Cell Biology, Ministry of Public Health of China, China Medical University , Shenyang, China.,Key Laboratory of Medical Cell Biology, Ministry of Education of China, China Medical University , Shenyang, China
| | - Xiaobai Liu
- Department of Neurosurgery, Shengjing Hospital of China Medical University , Shenyang, China.,Liaoning Clinical Medical Research Center in Nervous System Disease , Shenyang, China.,Key Laboratory of Neuro-oncology in Liaoning Province , Shenyang, China
| | - Jian Zheng
- Department of Neurosurgery, Shengjing Hospital of China Medical University , Shenyang, China.,Liaoning Clinical Medical Research Center in Nervous System Disease , Shenyang, China.,Key Laboratory of Neuro-oncology in Liaoning Province , Shenyang, China
| | - Yunhui Liu
- Department of Neurosurgery, Shengjing Hospital of China Medical University , Shenyang, China.,Liaoning Clinical Medical Research Center in Nervous System Disease , Shenyang, China.,Key Laboratory of Neuro-oncology in Liaoning Province , Shenyang, China
| | - Shuo Cao
- Department of Neurobiology, School of Life Sciences, China Medical University , Shenyang, China.,Key Laboratory of Cell Biology, Ministry of Public Health of China, China Medical University , Shenyang, China.,Key Laboratory of Medical Cell Biology, Ministry of Education of China, China Medical University , Shenyang, China
| | - Shuyuan Shen
- Department of Neurobiology, School of Life Sciences, China Medical University , Shenyang, China.,Key Laboratory of Cell Biology, Ministry of Public Health of China, China Medical University , Shenyang, China.,Key Laboratory of Medical Cell Biology, Ministry of Education of China, China Medical University , Shenyang, China
| | - Lianqi Shao
- Department of Neurobiology, School of Life Sciences, China Medical University , Shenyang, China.,Key Laboratory of Cell Biology, Ministry of Public Health of China, China Medical University , Shenyang, China.,Key Laboratory of Medical Cell Biology, Ministry of Education of China, China Medical University , Shenyang, China
| | - Heng Cai
- Department of Neurosurgery, Shengjing Hospital of China Medical University , Shenyang, China.,Liaoning Clinical Medical Research Center in Nervous System Disease , Shenyang, China.,Key Laboratory of Neuro-oncology in Liaoning Province , Shenyang, China
| | - Zhen Li
- Department of Neurosurgery, Shengjing Hospital of China Medical University , Shenyang, China.,Liaoning Clinical Medical Research Center in Nervous System Disease , Shenyang, China.,Key Laboratory of Neuro-oncology in Liaoning Province , Shenyang, China
| | - Yixue Xue
- Department of Neurobiology, School of Life Sciences, China Medical University , Shenyang, China.,Key Laboratory of Cell Biology, Ministry of Public Health of China, China Medical University , Shenyang, China.,Key Laboratory of Medical Cell Biology, Ministry of Education of China, China Medical University , Shenyang, China
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12
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Van Nostrand EL, Pratt GA, Yee BA, Wheeler EC, Blue SM, Mueller J, Park SS, Garcia KE, Gelboin-Burkhart C, Nguyen TB, Rabano I, Stanton R, Sundararaman B, Wang R, Fu XD, Graveley BR, Yeo GW. Principles of RNA processing from analysis of enhanced CLIP maps for 150 RNA binding proteins. Genome Biol 2020; 21:90. [PMID: 32252787 PMCID: PMC7137325 DOI: 10.1186/s13059-020-01982-9] [Citation(s) in RCA: 102] [Impact Index Per Article: 25.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2019] [Accepted: 03/03/2020] [Indexed: 02/08/2023] Open
Abstract
BACKGROUND A critical step in uncovering rules of RNA processing is to study the in vivo regulatory networks of RNA binding proteins (RBPs). Crosslinking and immunoprecipitation (CLIP) methods enable mapping RBP targets transcriptome-wide, but methodological differences present challenges to large-scale analysis across datasets. The development of enhanced CLIP (eCLIP) enabled the mapping of targets for 150 RBPs in K562 and HepG2, creating a unique resource of RBP interactomes profiled with a standardized methodology in the same cell types. RESULTS Our analysis of 223 eCLIP datasets reveals a range of binding modalities, including highly resolved positioning around splicing signals and mRNA untranslated regions that associate with distinct RBP functions. Quantification of enrichment for repetitive and abundant multicopy elements reveals 70% of RBPs have enrichment for non-mRNA element classes, enables identification of novel ribosomal RNA processing factors and sites, and suggests that association with retrotransposable elements reflects multiple RBP mechanisms of action. Analysis of spliceosomal RBPs indicates that eCLIP resolves AQR association after intronic lariat formation, enabling identification of branch points with single-nucleotide resolution, and provides genome-wide validation for a branch point-based scanning model for 3' splice site recognition. Finally, we show that eCLIP peak co-occurrences across RBPs enable the discovery of novel co-interacting RBPs. CONCLUSIONS This work reveals novel insights into RNA biology by integrated analysis of eCLIP profiling of 150 RBPs with distinct functions. Further, our quantification of both mRNA and other element association will enable further research to identify novel roles of RBPs in regulating RNA processing.
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Affiliation(s)
- Eric L Van Nostrand
- Department of Cellular and Molecular Medicine, University of California San Diego, La Jolla, CA, USA
- Institute for Genomic Medicine, University of California San Diego, La Jolla, CA, USA
| | - Gabriel A Pratt
- Department of Cellular and Molecular Medicine, University of California San Diego, La Jolla, CA, USA
- Institute for Genomic Medicine, University of California San Diego, La Jolla, CA, USA
| | - Brian A Yee
- Department of Cellular and Molecular Medicine, University of California San Diego, La Jolla, CA, USA
- Institute for Genomic Medicine, University of California San Diego, La Jolla, CA, USA
| | - Emily C Wheeler
- Department of Cellular and Molecular Medicine, University of California San Diego, La Jolla, CA, USA
- Institute for Genomic Medicine, University of California San Diego, La Jolla, CA, USA
| | - Steven M Blue
- Department of Cellular and Molecular Medicine, University of California San Diego, La Jolla, CA, USA
- Institute for Genomic Medicine, University of California San Diego, La Jolla, CA, USA
| | - Jasmine Mueller
- Department of Cellular and Molecular Medicine, University of California San Diego, La Jolla, CA, USA
- Institute for Genomic Medicine, University of California San Diego, La Jolla, CA, USA
| | - Samuel S Park
- Department of Cellular and Molecular Medicine, University of California San Diego, La Jolla, CA, USA
- Institute for Genomic Medicine, University of California San Diego, La Jolla, CA, USA
| | - Keri E Garcia
- Department of Cellular and Molecular Medicine, University of California San Diego, La Jolla, CA, USA
- Institute for Genomic Medicine, University of California San Diego, La Jolla, CA, USA
| | - Chelsea Gelboin-Burkhart
- Department of Cellular and Molecular Medicine, University of California San Diego, La Jolla, CA, USA
- Institute for Genomic Medicine, University of California San Diego, La Jolla, CA, USA
| | - Thai B Nguyen
- Department of Cellular and Molecular Medicine, University of California San Diego, La Jolla, CA, USA
- Institute for Genomic Medicine, University of California San Diego, La Jolla, CA, USA
| | - Ines Rabano
- Department of Cellular and Molecular Medicine, University of California San Diego, La Jolla, CA, USA
- Institute for Genomic Medicine, University of California San Diego, La Jolla, CA, USA
| | - Rebecca Stanton
- Department of Cellular and Molecular Medicine, University of California San Diego, La Jolla, CA, USA
- Institute for Genomic Medicine, University of California San Diego, La Jolla, CA, USA
| | - Balaji Sundararaman
- Department of Cellular and Molecular Medicine, University of California San Diego, La Jolla, CA, USA
- Institute for Genomic Medicine, University of California San Diego, La Jolla, CA, USA
| | - Ruth Wang
- Department of Cellular and Molecular Medicine, University of California San Diego, La Jolla, CA, USA
- Institute for Genomic Medicine, University of California San Diego, La Jolla, CA, USA
| | - Xiang-Dong Fu
- Department of Cellular and Molecular Medicine, University of California San Diego, La Jolla, CA, USA
- Institute for Genomic Medicine, University of California San Diego, La Jolla, CA, USA
| | - Brenton R Graveley
- Department of Genetics and Genome Sciences, Institute for Systems Genomics, UConn Health, Farmington, CT, USA.
| | - Gene W Yeo
- Department of Cellular and Molecular Medicine, University of California San Diego, La Jolla, CA, USA.
- Institute for Genomic Medicine, University of California San Diego, La Jolla, CA, USA.
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13
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Teng H, Wei W, Li Q, Xue M, Shi X, Li X, Mao F, Sun Z. Prevalence and architecture of posttranscriptionally impaired synonymous mutations in 8,320 genomes across 22 cancer types. Nucleic Acids Res 2020; 48:1192-1205. [PMID: 31950163 PMCID: PMC7026592 DOI: 10.1093/nar/gkaa019] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2019] [Accepted: 01/07/2020] [Indexed: 02/06/2023] Open
Abstract
Somatic synonymous mutations are one of the most frequent genetic variants occurring in the coding region of cancer genomes, while their contributions to cancer development remain largely unknown. To assess whether synonymous mutations involved in post-transcriptional regulation contribute to the genetic etiology of cancers, we collected whole exome data from 8,320 patients across 22 cancer types. By employing our developed algorithm, PIVar, we identified a total of 22,948 posttranscriptionally impaired synonymous SNVs (pisSNVs) spanning 2,042 genes. In addition, 35 RNA binding proteins impacted by these identified pisSNVs were significantly enriched. Remarkably, we discovered markedly elevated ratio of somatic pisSNVs across all 22 cancer types, and a high pisSNV ratio was associated with worse patient survival in five cancer types. Intriguing, several well-established cancer genes, including PTEN, RB1 and PIK3CA, appeared to contribute to tumorigenesis at both protein function and posttranscriptional regulation levels, whereas some pisSNV-hosted genes, including UBR4, EP400 and INTS1, exerted their function during carcinogenesis mainly via posttranscriptional mechanisms. Moreover, we predicted three drugs associated with two pisSNVs, and numerous compounds associated with expression signature of pisSNV-hosted genes. Our study reveals the prevalence and clinical relevance of pisSNVs in cancers, and emphasizes the importance of considering posttranscriptional impaired synonymous mutations in cancer biology.
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Affiliation(s)
- Huajing Teng
- Beijing Institutes of Life Science, Chinese Academy of Sciences, Beijing 100101, China.,Key laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Radiation Oncology, Peking University Cancer Hospital & Institute, Beijing 100142, China
| | - Wenqing Wei
- Beijing Institutes of Life Science, Chinese Academy of Sciences, Beijing 100101, China.,University of Chinese Academy of Sciences, Beijing 100049, China
| | - Qinglan Li
- Beijing Institutes of Life Science, Chinese Academy of Sciences, Beijing 100101, China.,University of Chinese Academy of Sciences, Beijing 100049, China
| | - Meiying Xue
- Beijing Institutes of Life Science, Chinese Academy of Sciences, Beijing 100101, China.,University of Chinese Academy of Sciences, Beijing 100049, China
| | - Xiaohui Shi
- Sino-Danish college, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Xianfeng Li
- Beijing Institutes of Life Science, Chinese Academy of Sciences, Beijing 100101, China.,Key laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Radiation Oncology, Peking University Cancer Hospital & Institute, Beijing 100142, China
| | - Fengbiao Mao
- Beijing Institutes of Life Science, Chinese Academy of Sciences, Beijing 100101, China
| | - Zhongsheng Sun
- Beijing Institutes of Life Science, Chinese Academy of Sciences, Beijing 100101, China
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14
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Ghanbari M, Ohler U. Deep neural networks for interpreting RNA-binding protein target preferences. Genome Res 2020; 30:214-226. [PMID: 31992613 PMCID: PMC7050519 DOI: 10.1101/gr.247494.118] [Citation(s) in RCA: 48] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2018] [Accepted: 01/07/2020] [Indexed: 11/29/2022]
Abstract
Deep learning has become a powerful paradigm to analyze the binding sites of regulatory factors including RNA-binding proteins (RBPs), owing to its strength to learn complex features from possibly multiple sources of raw data. However, the interpretability of these models, which is crucial to improve our understanding of RBP binding preferences and functions, has not yet been investigated in significant detail. We have designed a multitask and multimodal deep neural network for characterizing in vivo RBP targets. The model incorporates not only the sequence but also the region type of the binding sites as input, which helps the model to boost the prediction performance. To interpret the model, we quantified the contribution of the input features to the predictive score of each RBP. Learning across multiple RBPs at once, we are able to avoid experimental biases and to identify the RNA sequence motifs and transcript context patterns that are the most important for the predictions of each individual RBP. Our findings are consistent with known motifs and binding behaviors and can provide new insights about the regulatory functions of RBPs.
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Affiliation(s)
- Mahsa Ghanbari
- The Berlin Institute for Medical Systems Biology, Max Delbrück Center for Molecular Medicine, 10115 Berlin, Germany
| | - Uwe Ohler
- The Berlin Institute for Medical Systems Biology, Max Delbrück Center for Molecular Medicine, 10115 Berlin, Germany.,Department of Biology, Humboldt Universität zu Berlin, 10117 Berlin, Germany.,Department of Computer Science, Humboldt Universität zu Berlin, 10117 Berlin, Germany
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15
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Li Y, Zhang Y, Li X, Yi S, Xu J. Gain-of-Function Mutations: An Emerging Advantage for Cancer Biology. Trends Biochem Sci 2019; 44:659-674. [PMID: 31047772 DOI: 10.1016/j.tibs.2019.03.009] [Citation(s) in RCA: 34] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2018] [Revised: 03/21/2019] [Accepted: 03/26/2019] [Indexed: 02/08/2023]
Abstract
Advances in next-generation sequencing have identified thousands of genomic variants that perturb the normal functions of proteins, further contributing to diverse phenotypic consequences in cancer. Elucidating the functional pathways altered by loss-of-function (LOF) or gain-of-function (GOF) mutations will be crucial for prioritizing cancer-causing variants and their resultant therapeutic liabilities. In this review, we highlight the fundamental function of GOF mutations and discuss the potential mechanistic effects in the context of signaling networks. We also summarize advances in experimental and computational resources, which will dramatically help with studies on the functional and phenotypic consequences of mutations. Together, systematic investigations of the function of GOF mutations will provide an important missing piece for cancer biology and precision therapy.
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Affiliation(s)
- Yongsheng Li
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China; Department of Oncology, Dell Medical School, The University of Texas at Austin, Austin, TX 78712, USA
| | - Yunpeng Zhang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China
| | - Xia Li
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China; College of Bioinformatics, Hainan Medical University, Haikou 570100, China.
| | - Song Yi
- Department of Oncology, Dell Medical School, 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.
| | - Juan Xu
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China.
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