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Xiao M, Wei R, Yu J, Gao C, Yang F, Zhang L. CpG Island Definition and Methylation Mapping of the T2T-YAO Genome. GENOMICS, PROTEOMICS & BIOINFORMATICS 2024; 22:qzae009. [PMID: 39142816 DOI: 10.1093/gpbjnl/qzae009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/06/2023] [Revised: 12/05/2023] [Accepted: 12/08/2023] [Indexed: 08/16/2024]
Abstract
Precisely defining and mapping all cytosine (C) positions and their clusters, known as CpG islands (CGIs), as well as their methylation status, are pivotal for genome-wide epigenetic studies, especially when population-centric reference genomes are ready for timely application. Here, we first align the two high-quality reference genomes, T2T-YAO and T2T-CHM13, from different ethnic backgrounds in a base-by-base fashion and compute their genome-wide density-defined and position-defined CGIs. Second, by mapping some representative genome-wide methylation data from selected organs onto the two genomes, we find that there are about 4.7%-5.8% sequence divergency of variable categories depending on quality cutoffs. Genes among the divergent sequences are mostly associated with neurological functions. Moreover, CGIs associated with the divergent sequences are significantly different with respect to CpG density and observed CpG/expected CpG (O/E) ratio between the two genomes. Finally, we find that the T2T-YAO genome not only has a greater CpG coverage than that of the T2T-CHM13 genome when whole-genome bisulfite sequencing (WGBS) data from the European and American populations are mapped to each reference, but also shows more hyper-methylated CpG sites as compared to the T2T-CHM13 genome. Our study suggests that future genome-wide epigenetic studies of the Chinese populations rely on both acquisition of high-quality methylation data and subsequent precision CGI mapping based on the Chinese T2T reference.
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Affiliation(s)
- Ming Xiao
- College of Computer Science, Sichuan University, Chengdu 610065, China
| | - Rui Wei
- College of Computer Science, Sichuan University, Chengdu 610065, China
- Key Laboratory of Systems Health Science of Zhejiang Province, School of Life Science, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Hangzhou 310024, China
| | - Jun Yu
- CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing 100101, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Chujie Gao
- College of Computer Science, Sichuan University, Chengdu 610065, China
| | - Fengyi Yang
- College of Computer Science, Sichuan University, Chengdu 610065, China
| | - Le Zhang
- College of Computer Science, Sichuan University, Chengdu 610065, China
- Key Laboratory of Systems Health Science of Zhejiang Province, School of Life Science, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Hangzhou 310024, China
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2
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Zhang L, Xiong Z, Xiao M. A Review of the Application of Spatial Transcriptomics in Neuroscience. Interdiscip Sci 2024; 16:243-260. [PMID: 38374297 DOI: 10.1007/s12539-024-00603-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2023] [Revised: 12/29/2023] [Accepted: 01/02/2024] [Indexed: 02/21/2024]
Abstract
Since spatial transcriptomics can locate and distinguish the gene expression of functional genes in special regions and tissue, it is important for us to investigate the brain development, the development mechanism of brain diseases, and the relationship between brain structure and function in Neuroscience (or Brain science). While previous studies have introduced the crucial spatial transcriptomic techniques and data analysis methods, there are few studies to comprehensively overview the key methods, data resources, and technological applications of spatial transcriptomics in Neuroscience. For these reasons, we first investigate several common spatial transcriptomic data analysis approaches and data resources. Second, we introduce the applications of the spatial transcriptomic data analysis approaches in Neuroscience. Third, we summarize the integrating spatial transcriptomics with other technologies in Neuroscience. Finally, we discuss the challenges and future research directions of spatial transcriptomics in Neuroscience.
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Affiliation(s)
- Le Zhang
- College of Computer Science, Sichuan University, Chengdu, 610065, China
| | - Zhenqi Xiong
- College of Computer Science, Sichuan University, Chengdu, 610065, China
| | - Ming Xiao
- College of Computer Science, Sichuan University, Chengdu, 610065, China.
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3
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A Computer Simulation of SARS-CoV-2 Mutation Spectra for Empirical Data Characterization and Analysis. Biomolecules 2022; 13:biom13010063. [PMID: 36671448 PMCID: PMC9855923 DOI: 10.3390/biom13010063] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2022] [Revised: 12/21/2022] [Accepted: 12/23/2022] [Indexed: 12/31/2022] Open
Abstract
It is very important to compute the mutation spectra, and simulate the intra-host mutation processes by sequencing data, which is not only for the understanding of SARS-CoV-2 genetic mechanism, but also for epidemic prediction, vaccine, and drug design. However, the current intra-host mutation analysis algorithms are not only inaccurate, but also the simulation methods are unable to quickly and precisely predict new SARS-CoV-2 variants generated from the accumulation of mutations. Therefore, this study proposes a novel accurate strand-specific SARS-CoV-2 intra-host mutation spectra computation method, develops an efficient and fast SARS-CoV-2 intra-host mutation simulation method based on mutation spectra, and establishes an online analysis and visualization platform. Our main results include: (1) There is a significant variability in the SARS-CoV-2 intra-host mutation spectra across different lineages, with the major mutations from G- > A, G- > C, G- > U on the positive-sense strand and C- > U, C- > G, C- > A on the negative-sense strand; (2) our mutation simulation reveals the simulation sequence starts to deviate from the base content percentage of Alpha-CoV/Delta-CoV after approximately 620 mutation steps; (3) 2019-NCSS provides an easy-to-use and visualized online platform for SARS-Cov-2 online analysis and mutation simulation.
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4
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Ma F, Xiao M, Zhu L, Jiang W, Jiang J, Zhang PF, Li K, Yue M, Zhang L. An integrated platform for Brucella with knowledge graph technology: From genomic analysis to epidemiological projection. Front Genet 2022; 13:981633. [PMID: 36186430 PMCID: PMC9516312 DOI: 10.3389/fgene.2022.981633] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2022] [Accepted: 08/30/2022] [Indexed: 11/20/2022] Open
Abstract
Motivation:Brucella, the causative agent of brucellosis, is a global zoonotic pathogen that threatens both veterinary and human health. The main sources of brucellosis are farm animals. Importantly, the bacteria can be used for biological warfare purposes, requiring source tracking and routine surveillance in an integrated manner. Additionally, brucellosis is classified among group B infectious diseases in China and has been reported in 31 Chinese provinces to varying degrees in urban areas. From a national biosecurity perspective, research on brucellosis surveillance has garnered considerable attention and requires an integrated platform to provide researchers with easy access to genomic analysis and provide policymakers with an improved understanding of both reported patients and detected cases for the purpose of precision public health interventions. Results: For the first time in China, we have developed a comprehensive information platform for Brucella based on dynamic visualization of the incidence (reported patients) and prevalence (detected cases) of brucellosis in mainland China. Especially, our study establishes a knowledge graph for the literature sources of Brucella data so that it can be expanded, queried, and analyzed. When similar “epidemiological comprehensive platforms” are established in the distant future, we can use knowledge graph to share its information. Additionally, we propose a software package for genomic sequence analysis. This platform provides a specialized, dynamic, and visual point-and-click interface for studying brucellosis in mainland China and improving the exploration of Brucella in the fields of bioinformatics and disease prevention for both human and veterinary medicine.
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Affiliation(s)
- Fubo Ma
- West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, China
| | - Ming Xiao
- College of Computer Science, Sichuan University, Chengdu, China
| | - Lin Zhu
- China Animal Health and Epidemiology Center, Qingdao, Shandong, China
| | - Wen Jiang
- College of Computer Science, Sichuan University, Chengdu, China
| | - Jizhe Jiang
- College of Computer Science, Sichuan University, Chengdu, China
| | - Peng-Fei Zhang
- Department of Medical Oncology, Cancer Center, West China Hospital, Sichuan University, Chengdu, China
- Med-X Center for Informatics, Sichuan University, Chengdu, China
| | - Kang Li
- West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, China
- Shanghai Artificial Intelligence Laboratory, Shanghai, China
| | - Min Yue
- Hainan Institute of Zhejiang University, Sanya, China
- *Correspondence: Le Zhang, ; Min Yue,
| | - Le Zhang
- College of Computer Science, Sichuan University, Chengdu, China
- Key Laboratory of Systems Biology, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Hangzhou, China
- Key Laboratory of Systems Health Science of Zhejiang Province, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Hangzhou, China
- *Correspondence: Le Zhang, ; Min Yue,
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5
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Artificial intelligence in cancer target identification and drug discovery. Signal Transduct Target Ther 2022; 7:156. [PMID: 35538061 PMCID: PMC9090746 DOI: 10.1038/s41392-022-00994-0] [Citation(s) in RCA: 68] [Impact Index Per Article: 34.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2021] [Revised: 03/14/2022] [Accepted: 04/05/2022] [Indexed: 02/08/2023] Open
Abstract
Artificial intelligence is an advanced method to identify novel anticancer targets and discover novel drugs from biology networks because the networks can effectively preserve and quantify the interaction between components of cell systems underlying human diseases such as cancer. Here, we review and discuss how to employ artificial intelligence approaches to identify novel anticancer targets and discover drugs. First, we describe the scope of artificial intelligence biology analysis for novel anticancer target investigations. Second, we review and discuss the basic principles and theory of commonly used network-based and machine learning-based artificial intelligence algorithms. Finally, we showcase the applications of artificial intelligence approaches in cancer target identification and drug discovery. Taken together, the artificial intelligence models have provided us with a quantitative framework to study the relationship between network characteristics and cancer, thereby leading to the identification of potential anticancer targets and the discovery of novel drug candidates.
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6
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ASTM: Developing the web service for anthrax related spatiotemporal characteristics and meteorology study. QUANTITATIVE BIOLOGY 2022. [DOI: 10.15302/j-qb-022-0288] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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7
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Liu S, You Y, Tong Z, Zhang L. Developing an Embedding, Koopman and Autoencoder Technologies-Based Multi-Omics Time Series Predictive Model (EKATP) for Systems Biology research. Front Genet 2021; 12:761629. [PMID: 34764986 PMCID: PMC8576451 DOI: 10.3389/fgene.2021.761629] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2021] [Accepted: 09/27/2021] [Indexed: 11/13/2022] Open
Abstract
It is very important for systems biologists to predict the state of the multi-omics time series for disease occurrence and health detection. However, it is difficult to make the prediction due to the high-dimensional, nonlinear and noisy characteristics of the multi-omics time series data. For this reason, this study innovatively proposes an Embedding, Koopman and Autoencoder technologies-based multi-omics time series predictive model (EKATP) to predict the future state of a high-dimensional nonlinear multi-omics time series. We evaluate this EKATP by using a genomics time series with chaotic behavior, a proteomics time series with oscillating behavior and a metabolomics time series with flow behavior. The computational experiments demonstrate that our proposed EKATP can substantially improve the accuracy, robustness and generalizability to predict the future state of a time series for multi-omics data.
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Affiliation(s)
- Suran Liu
- College of Computer Science, Sichuan University, Chengdu, China
| | - Yujie You
- College of Computer Science, Sichuan University, Chengdu, China
| | - Zhaoqi Tong
- College of Software Engineering, Sichuan University, Chengdu, China
| | - Le Zhang
- College of Computer Science, Sichuan University, Chengdu, China
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MCDB: A comprehensive curated mitotic catastrophe database for retrieval, protein sequence alignment, and target prediction. Acta Pharm Sin B 2021; 11:3092-3104. [PMID: 34729303 PMCID: PMC8546929 DOI: 10.1016/j.apsb.2021.05.032] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2021] [Revised: 03/12/2021] [Accepted: 05/06/2021] [Indexed: 02/05/2023] Open
Abstract
Mitotic catastrophe (MC) is a form of programmed cell death induced by mitotic process disorders, which is very important in tumor prevention, development, and drug resistance. Because rapidly increased data for MC is vigorously promoting the tumor-related biomedical and clinical study, it is urgent for us to develop a professional and comprehensive database to curate MC-related data. Mitotic Catastrophe Database (MCDB) consists of 1214 genes/proteins and 5014 compounds collected and organized from more than 8000 research articles. Also, MCDB defines the confidence level, classification criteria, and uniform naming rules for MC-related data, which greatly improves data reliability and retrieval convenience. Moreover, MCDB develops protein sequence alignment and target prediction functions. The former can be used to predict new potential MC-related genes and proteins, and the latter can facilitate the identification of potential target proteins of unknown MC-related compounds. In short, MCDB is such a proprietary, standard, and comprehensive database for MC-relate data that will facilitate the exploration of MC from chemists to biologists in the fields of medicinal chemistry, molecular biology, bioinformatics, oncology and so on. The MCDB is distributed on http://www.combio-lezhang.online/MCDB/index_html/.
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Key Words
- Data mining
- Database
- GO, Gene Ontology
- IUPAC, International Union of Pure and Applied Chemistry
- InChI Key, International Chemical Identifier hash
- InChI, International Chemical Identifier
- MC, Mitotic Catastrophe
- MCDB, Mitotic Catastrophe Database
- Mitotic catastrophe
- PDB, Protein Data Bank
- PMID, PubMed identifier
- Protein sequence analysis
- PubChem, Public Chemistry
- PubMed, Public Medicine
- SMILES, Simplified Molecular Input Line Entry Specification
- Target prediction
- UniProt, Universal Protein Resource
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Xiao M, Liu G, Xie J, Dai Z, Wei Z, Ren Z, Yu J, Zhang L. 2019nCoVAS: Developing the Web Service for Epidemic Transmission Prediction, Genome Analysis, and Psychological Stress Assessment for 2019-nCoV. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2021; 18:1250-1261. [PMID: 33406042 PMCID: PMC8769043 DOI: 10.1109/tcbb.2021.3049617] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/30/2020] [Revised: 10/02/2020] [Accepted: 01/03/2021] [Indexed: 06/12/2023]
Abstract
Since the COVID-19 epidemic is still expanding around the world and poses a serious threat to human life and health, it is necessary for us to carry out epidemic transmission prediction, whole genome sequence analysis, and public psychological stress assessment for 2019-nCoV. However, transmission prediction models are insufficiently accurate and genome sequence characteristics are not clear, and it is difficult to dynamically assess the public psychological stress state under the 2019-nCoV epidemic. Therefore, this study develops a 2019nCoVAS web service (http://www.combio-lezhang.online/2019ncov/home.html) that not only offers online epidemic transmission prediction and lineage-associated underrepresented permutation (LAUP) analysis services to investigate the spreading trends and genome sequence characteristics, but also provides psychological stress assessments based on such an emotional dictionary that we built for 2019-nCoV. Finally, we discuss the shortcomings and further study of the 2019nCoVAS web service.
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Affiliation(s)
- Ming Xiao
- College of Computer ScienceSichuan UniversityChengdu610065PR China
| | - Guangdi Liu
- College of Computer and Information ScienceSouthwest UniversityChong-Qing400715PR China
| | - Jianghang Xie
- College of Computer ScienceSichuan UniversityChengdu610065PR China
| | - Zichun Dai
- College of Computer ScienceSichuan UniversityChengdu610065PR China
| | - Zihao Wei
- College of Computer ScienceSichuan UniversityChengdu610065PR China
| | - Ziyao Ren
- College of Computer ScienceSichuan UniversityChengdu610065PR China
| | - Jun Yu
- CAS Key Laboratory of Genome Sciences and InformationBeijing Institute of Genomics, Chinese Academy of SciencesBeijing100101PR China
| | - Le Zhang
- College of Computer ScienceSichuan UniversityChengdu610065PR China
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10
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Badai J, Bu Q, Zhang L. Review of Artificial Intelligence Applications and Algorithms for Brain Organoid Research. Interdiscip Sci 2020; 12:383-394. [PMID: 32833194 DOI: 10.1007/s12539-020-00386-4] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2020] [Revised: 08/04/2020] [Accepted: 08/12/2020] [Indexed: 02/07/2023]
Abstract
The human brain organoid is a miniature three-dimensional tissue culture that can simulate the structure and function of the brain in an in vitro culture environment. Although we consider that human brain organoids could be used to understand brain development and diseases, experimental models of human brain organoids are so highly variable that we apply artificial intelligence (AI) techniques to investigate the development mechanism of the human brain. Therefore, this study briefly reviewed commonly used AI applications for human brain organoid-magnetic resonance imaging, electroencephalography, and gene editing techniques, as well as related AI algorithms. Finally, we discussed the limitations, challenges, and future study direction of AI-based technology for human brain organoids.
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Affiliation(s)
- Jiayidaer Badai
- College of Computer Science, Sichuan University, Chengdu, 610065, China
| | - Qian Bu
- Department of Food Engineering, College of Biomass Science and Engineering, Sichuan University, Chengdu, 610065, China
| | - Le Zhang
- College of Computer Science, Sichuan University, Chengdu, 610065, China. .,Medical Big Data Center of Sichuan University, Chengdu, 610065, China. .,PERA Corporation Ltd., Beijing, 100025, China.
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11
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Lei W, Zeng H, Feng H, Ru X, Li Q, Xiao M, Zheng H, Chen Y, Zhang L. Development of an Early Prediction Model for Subarachnoid Hemorrhage With Genetic and Signaling Pathway Analysis. Front Genet 2020; 11:391. [PMID: 32373167 PMCID: PMC7186496 DOI: 10.3389/fgene.2020.00391] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2019] [Accepted: 03/30/2020] [Indexed: 01/15/2023] Open
Abstract
Subarachnoid hemorrhage (SAH) is devastating disease with high mortality, high disability rate, and poor clinical prognosis. It has drawn great attentions in both basic and clinical medicine. Therefore, it is necessary to explore the therapeutic drugs and effective targets for early prediction of SAH. Firstly, we demonstrate that LCN2 can effectively intervene or treat SAH from the perspective of cell signaling pathway. Next, three potential genes that we explored have been validated by manually reviewed experimental evidences. Finally, we turn out that the SAH early ensemble learning predictive model performs better than the classical LR, SVM, and Naïve-Bayes models.
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Affiliation(s)
- Wanjing Lei
- College of Computer Science, Sichuan University, Chengdu, China
| | - Han Zeng
- College of Computer and Information Science, Southwest University, Chongqing, China
| | - Hua Feng
- Department of Neurosurgery, Southwest Hospital, Third Military Medical University, Chongqing, China
- State Key Laboratory of Trauma, Burn and Combined Injury, Third Military Medical University, Chongqing, China
| | - Xufang Ru
- Department of Neurosurgery, Southwest Hospital, Third Military Medical University, Chongqing, China
- State Key Laboratory of Trauma, Burn and Combined Injury, Third Military Medical University, Chongqing, China
| | - Qiang Li
- Department of Neurosurgery, Southwest Hospital, Third Military Medical University, Chongqing, China
- State Key Laboratory of Trauma, Burn and Combined Injury, Third Military Medical University, Chongqing, China
| | - Ming Xiao
- College of Computer Science, Sichuan University, Chengdu, China
| | - Huiru Zheng
- School of Computing, Ulster University, Coleraine, United Kingdom
| | - Yujie Chen
- Department of Neurosurgery, Southwest Hospital, Third Military Medical University, Chongqing, China
- State Key Laboratory of Trauma, Burn and Combined Injury, Third Military Medical University, Chongqing, China
| | - Le Zhang
- College of Computer Science, Sichuan University, Chengdu, China
- College of Computer and Information Science, Southwest University, Chongqing, China
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12
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Zhang L, Dai Z, Yu J, Xiao M. CpG-island-based annotation and analysis of human housekeeping genes. Brief Bioinform 2020; 22:515-525. [PMID: 31982909 DOI: 10.1093/bib/bbz134] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2019] [Revised: 08/27/2019] [Accepted: 10/03/2019] [Indexed: 11/14/2022] Open
Abstract
By reviewing previous CpG-related studies, we consider that the transcription regulation of about half of the human genes, mostly housekeeping (HK) genes, involves CpG islands (CGIs), their methylation states, CpG spacing and other chromosomal parameters. However, the precise CGI definition and positioning of CGIs within gene structures, as well as specific CGI-associated regulatory mechanisms, all remain to be explained at individual gene and gene-family levels, together with consideration of species and lineage specificity. Although previous studies have already classified CGIs into high-CpG (HCGI), intermediate-CpG (ICGI) and low-CpG (LCGI) densities based on CpG density variation, the correlation between CGI density and gene expression regulation, such as co-regulation of CGIs and TATA box on HK genes, remains to be elucidated. First, this study introduces such a problem-solving protocol for human-genome annotation, which is based on a combination of GTEx, JBLA and Gene Ontology (GO) analysis. Next, we discuss why CGI-associated genes are most likely regulated by HCGI and tend to be HK genes; the HCGI/TATA± and LCGI/TATA± combinations show different GO enrichment, whereas the ICGI/TATA± combination is less characteristic based on GO enrichment analysis. Finally, we demonstrate that Hadoop MapReduce-based MR-JBLA algorithm is more efficient than the original JBLA in k-mer counting and CGI-associated gene analysis.
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Affiliation(s)
- Le Zhang
- College of Computer Science, Sichuan University, Chengdu, 610065, PR China
| | - Zichun Dai
- Medical Big Data Center of Sichuan University, Sichuan University, Chengdu, 610065, PR China
| | - Jun Yu
- CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing 100101, PR China
| | - Ming Xiao
- University of Chinese Academy of Sciences, Beijing 100049, PR China
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