Pub Date : 2023-10-01DOI: 10.1016/j.gpb.2023.10.001
Yiming Bao , Yongbiao Xue
{"title":"From BIG Data Center to China National Center for Bioinformation","authors":"Yiming Bao , Yongbiao Xue","doi":"10.1016/j.gpb.2023.10.001","DOIUrl":"10.1016/j.gpb.2023.10.001","url":null,"abstract":"","PeriodicalId":12528,"journal":{"name":"Genomics, Proteomics & Bioinformatics","volume":"21 5","pages":"Pages 900-903"},"PeriodicalIF":11.5,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10928365/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41223972","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-10-01DOI: 10.1016/j.gpb.2023.10.005
Zhang Zhang , Songnian Hu , Jun Yu
Twenty years after the completion and forty years after the proposal of the Human Genome Project (HGP), genomics, together with its twin field — bioinformatics, has entered a new paradigm, where its bioscience-related, discipline-centric applications have been creating many new research frontiers. Beijing Institute of Genomics (BIG), now also known as China National Center for Bioinformation (CNCB), will play key roles in supporting and participating in these frontier research activities. On the 20th anniversary of the establishment of BIG, we provide a brief retrospective of its historic events and ascertain strategic research directions with a broader vision for future genomics, where digital genome, digital medicine, and digital health are so structured to meet the needs of human life and healthcare, as well as their related metaverses.
{"title":"Toward A New Paradigm of Genomics Research","authors":"Zhang Zhang , Songnian Hu , Jun Yu","doi":"10.1016/j.gpb.2023.10.005","DOIUrl":"10.1016/j.gpb.2023.10.005","url":null,"abstract":"<div><div>Twenty years after the completion and forty years after the proposal of the Human Genome Project (HGP), genomics, together with its twin field — bioinformatics, has entered a new paradigm, where its bioscience-related, discipline-centric applications have been creating many new research frontiers. Beijing Institute of Genomics (BIG), now also known as China National Center for Bioinformation (CNCB), will play key roles in supporting and participating in these frontier research activities. On the 20th anniversary of the establishment of BIG, we provide a brief retrospective of its historic events and ascertain strategic research directions with a broader vision for future genomics, where digital genome, digital medicine, and digital health are so structured to meet the needs of human life and healthcare, as well as their related metaverses.</div></div>","PeriodicalId":12528,"journal":{"name":"Genomics, Proteomics & Bioinformatics","volume":"21 5","pages":"Pages 904-909"},"PeriodicalIF":11.5,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136128324","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-10-01DOI: 10.1016/j.gpb.2023.06.003
Qiang Shi , Xueyan Chen , Zemin Zhang
Over the past decade, advances in single-cell omics (SCO) technologies have enabled the investigation of cellular heterogeneity at an unprecedented resolution and scale, opening a new avenue for understanding human biology and disease. In this review, we summarize the developments of sequencing-based SCO technologies and computational methods, and focus on considerable insights acquired from SCO sequencing studies to understand normal and diseased properties, with a particular emphasis on cancer research. We also discuss the technological improvements of SCO and its possible contribution to fundamental research of the human, as well as its great potential in clinical diagnoses and personalized therapies of human disease.
{"title":"Decoding Human Biology and Disease Using Single-cell Omics Technologies","authors":"Qiang Shi , Xueyan Chen , Zemin Zhang","doi":"10.1016/j.gpb.2023.06.003","DOIUrl":"10.1016/j.gpb.2023.06.003","url":null,"abstract":"<div><div>Over the past decade, advances in <strong>single-cell omics</strong> (SCO) technologies have enabled the investigation of <strong>cellular heterogeneity</strong> at an unprecedented resolution and scale, opening a new avenue for understanding human biology and <strong>disease</strong>. In this review, we summarize the developments of sequencing-based SCO technologies and <strong>computational methods</strong>, and focus on considerable insights acquired from SCO sequencing studies to understand normal and diseased properties, with a particular emphasis on <strong>cancer research</strong>. We also discuss the technological improvements of SCO and its possible contribution to fundamental research of the human, as well as its great potential in clinical diagnoses and personalized therapies of human disease.</div></div>","PeriodicalId":12528,"journal":{"name":"Genomics, Proteomics & Bioinformatics","volume":"21 5","pages":"Pages 926-949"},"PeriodicalIF":11.5,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10928380/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41147316","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-10-01DOI: 10.1016/j.gpb.2022.11.014
Bin Huang , Lupeng Kong , Chao Wang , Fusong Ju , Qi Zhang , Jianwei Zhu , Tiansu Gong , Haicang Zhang , Chungong Yu , Wei-Mou Zheng , Dongbo Bu
Protein structure prediction is an interdisciplinary research topic that has attracted researchers from multiple fields, including biochemistry, medicine, physics, mathematics, and computer science. These researchers adopt various research paradigms to attack the same structure prediction problem: biochemists and physicists attempt to reveal the principles governing protein folding; mathematicians, especially statisticians, usually start from assuming a probability distribution of protein structures given a target sequence and then find the most likely structure, while computer scientists formulate protein structure prediction as an optimization problem — finding the structural conformation with the lowest energy or minimizing the difference between predicted structure and native structure. These research paradigms fall into the two statistical modeling cultures proposed by Leo Breiman, namely, data modeling and algorithmic modeling. Recently, we have also witnessed the great success of deep learning in protein structure prediction. In this review, we present a survey of the efforts for protein structure prediction. We compare the research paradigms adopted by researchers from different fields, with an emphasis on the shift of research paradigms in the era of deep learning. In short, the algorithmic modeling techniques, especially deep neural networks, have considerably improved the accuracy of protein structure prediction; however, theories interpreting the neural networks and knowledge on protein folding are still highly desired.
蛋白质结构预测是一个跨学科研究课题，吸引了来自生物化学、医学、物理学、数学和计算机科学等多个领域的研究人员。这些研究人员采用不同的研究范式来解决相同的结构预测问题：生物化学家和物理学家试图揭示蛋白质折叠的原理；数学家，尤其是统计学家，通常从假设目标序列中蛋白质结构的概率分布出发，然后找出最可能的结构；而计算机科学家则将蛋白质结构预测表述为一个优化问题--寻找能量最低的结构构象，或将预测结构与原生结构之间的差异最小化。这些研究范式属于 L. Breiman 提出的两种统计建模文化，即数据建模和算法建模。最近，我们也见证了深度学习在蛋白质结构预测方面的巨大成功。在这篇综述中，我们对蛋白质结构预测方面的工作进行了调查。我们比较了不同领域研究人员所采用的研究范式，重点关注深度学习时代研究范式的转变。总之，算法建模技术，尤其是深度神经网络，大大提高了蛋白质结构预测的准确性；然而，解释神经网络的理论和蛋白质折叠方面的知识仍是亟待解决的问题。
{"title":"Protein Structure Prediction: Challenges, Advances, and the Shift of Research Paradigms","authors":"Bin Huang , Lupeng Kong , Chao Wang , Fusong Ju , Qi Zhang , Jianwei Zhu , Tiansu Gong , Haicang Zhang , Chungong Yu , Wei-Mou Zheng , Dongbo Bu","doi":"10.1016/j.gpb.2022.11.014","DOIUrl":"10.1016/j.gpb.2022.11.014","url":null,"abstract":"<div><div><strong>Protein structure prediction</strong> is an interdisciplinary research topic that has attracted researchers from multiple fields, including biochemistry, medicine, physics, mathematics, and computer science. These researchers adopt various research paradigms to attack the same structure prediction problem: biochemists and physicists attempt to reveal the principles governing <strong>protein folding</strong>; mathematicians, especially statisticians, usually start from assuming a probability distribution of protein structures given a target sequence and then find the most likely structure, while computer scientists formulate protein structure prediction as an optimization problem — finding the structural conformation with the lowest energy or minimizing the difference between predicted structure and native structure. These research paradigms fall into the two statistical modeling cultures proposed by Leo Breiman, namely, data modeling and algorithmic modeling. Recently, we have also witnessed the great success of <strong>deep learning</strong> in protein structure prediction. In this review, we present a survey of the efforts for protein structure prediction. We compare the research paradigms adopted by researchers from different fields, with an emphasis on the shift of research paradigms in the era of deep learning. In short, the algorithmic modeling techniques, especially deep neural networks, have considerably improved the accuracy of protein structure prediction; however, theories interpreting the neural networks and knowledge on protein folding are still highly desired.</div></div>","PeriodicalId":12528,"journal":{"name":"Genomics, Proteomics & Bioinformatics","volume":"21 5","pages":"Pages 913-925"},"PeriodicalIF":11.5,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10928435/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9593946","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-10-01DOI: 10.1016/j.gpb.2023.10.004
Cuiping Li , Lina Ma , Dong Zou , Rongqin Zhang , Xue Bai , Lun Li , Gangao Wu , Tianhao Huang , Wei Zhao , Enhui Jin , Yiming Bao , Shuhui Song
The Resource for Coronavirus 2019 (RCoV19) is an open-access information resource dedicated to providing valuable data on the genomes, mutations, and variants of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). In this updated implementation of RCoV19, we have made significant improvements and advancements over the previous version. Firstly, we have implemented a highly refined genome data curation model. This model now features an automated integration pipeline and optimized curation rules, enabling efficient daily updates of data in RCoV19. Secondly, we have developed a global and regional lineage evolution monitoring platform, alongside an outbreak risk pre-warning system. These additions provide a comprehensive understanding of SARS-CoV-2 evolution and transmission patterns, enabling better preparedness and response strategies. Thirdly, we have developed a powerful interactive mutation spectrum comparison module. This module allows users to compare and analyze mutation patterns, assisting in the detection of potential new lineages. Furthermore, we have incorporated a comprehensive knowledgebase on mutation effects. This knowledgebase serves as a valuable resource for retrieving information on the functional implications of specific mutations. In summary, RCoV19 serves as a vital scientific resource, providing access to valuable data, relevant information, and technical support in the global fight against COVID-19. The complete contents of RCoV19 are available to the public at https://ngdc.cncb.ac.cn/ncov/.
{"title":"RCoV19: A One-stop Hub for SARS-CoV-2 Genome Data Integration, Variant Monitoring, and Risk Pre-warning","authors":"Cuiping Li , Lina Ma , Dong Zou , Rongqin Zhang , Xue Bai , Lun Li , Gangao Wu , Tianhao Huang , Wei Zhao , Enhui Jin , Yiming Bao , Shuhui Song","doi":"10.1016/j.gpb.2023.10.004","DOIUrl":"10.1016/j.gpb.2023.10.004","url":null,"abstract":"<div><div>The Resource for Coronavirus 2019 (RCoV19) is an open-access information resource dedicated to providing valuable data on the genomes, <strong>mutations</strong>, and <strong>variants</strong> of the severe acute respiratory syndrome coronavirus 2 (<strong>SARS-CoV-2</strong>). In this updated implementation of RCoV19, we have made significant improvements and advancements over the previous version. Firstly, we have implemented a highly refined genome data curation model. This model now features an automated integration pipeline and optimized curation rules, enabling efficient daily updates of data in RCoV19. Secondly, we have developed a global and regional lineage evolution monitoring platform, alongside an outbreak risk <strong>pre-warning</strong> system. These additions provide a comprehensive understanding of SARS-CoV-2 evolution and transmission patterns, enabling better preparedness and response strategies. Thirdly, we have developed a powerful interactive mutation spectrum comparison module. This module allows users to compare and analyze mutation patterns, assisting in the detection of potential new lineages. Furthermore, we have incorporated a comprehensive knowledgebase on mutation effects. This knowledgebase serves as a valuable resource for retrieving information on the functional implications of specific mutations. In summary, RCoV19 serves as a vital scientific resource, providing access to valuable data, relevant information, and technical support in the global fight against COVID-19. The complete contents of RCoV19 are available to the public at <span><span>https://ngdc.cncb.ac.cn/ncov/</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":12528,"journal":{"name":"Genomics, Proteomics & Bioinformatics","volume":"21 5","pages":"Pages 1066-1079"},"PeriodicalIF":11.5,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10928372/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"66784787","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-10-01DOI: 10.1016/j.gpb.2022.12.004
Lina Ma , Dong Zou , Lin Liu , Huma Shireen , Amir A. Abbasi , Alex Bateman , Jingfa Xiao , Wenming Zhao , Yiming Bao , Zhang Zhang
Biological databases serve as a global fundamental infrastructure for the worldwide scientific community, which dramatically aid the transformation of big data into knowledge discovery and drive significant innovations in a wide range of research fields. Given the rapid data production, biological databases continue to increase in size and importance. To build a catalog of worldwide biological databases, we curate a total of 5825 biological databases from 8931 publications, which are geographically distributed in 72 countries/regions and developed by 1975 institutions (as of September 20, 2022). We further devise a z-index, a novel index to characterize the scientific impact of a database, and rank all these biological databases as well as their hosting institutions and countries in terms of citation and z-index. Consequently, we present a series of statistics and trends of worldwide biological databases, yielding a global perspective to better understand their status and impact for life and health sciences. An up-to-date catalog of worldwide biological databases, as well as their curated meta-information and derived statistics, is publicly available at Database Commons (https://ngdc.cncb.ac.cn/databasecommons/).
{"title":"Database Commons: A Catalog of Worldwide Biological Databases","authors":"Lina Ma , Dong Zou , Lin Liu , Huma Shireen , Amir A. Abbasi , Alex Bateman , Jingfa Xiao , Wenming Zhao , Yiming Bao , Zhang Zhang","doi":"10.1016/j.gpb.2022.12.004","DOIUrl":"10.1016/j.gpb.2022.12.004","url":null,"abstract":"<div><div><strong>Biological databases</strong> serve as a global fundamental infrastructure for the worldwide scientific community, which dramatically aid the transformation of big data into knowledge discovery and drive significant innovations in a wide range of research fields. Given the rapid data production, biological databases continue to increase in size and importance. To build a <strong>catalog</strong> of worldwide biological databases, we curate a total of 5825 biological databases from 8931 publications, which are geographically distributed in 72 countries/regions and developed by 1975 institutions (as of September 20, 2022). We further devise a <em><strong>z</strong></em><strong>-index</strong>, a novel index to characterize the scientific impact of a database, and rank all these biological databases as well as their hosting institutions and countries in terms of <strong>citation</strong> and <em>z</em>-index. Consequently, we present a series of statistics and trends of worldwide biological databases, yielding a global perspective to better understand their status and impact for life and health sciences. An up-to-date catalog of worldwide biological databases, as well as their curated meta-information and derived statistics, is publicly available at Database Commons (<span><span>https://ngdc.cncb.ac.cn/databasecommons/</span><svg><path></path></svg></span>).</div></div>","PeriodicalId":12528,"journal":{"name":"Genomics, Proteomics & Bioinformatics","volume":"21 5","pages":"Pages 1054-1058"},"PeriodicalIF":11.5,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10928426/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10787370","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-10-01DOI: 10.1016/j.gpb.2023.06.002
Tingfu Du , Chunchun Gao , Shuaiyao Lu , Qianlan Liu , Yun Yang , Wenhai Yu , Wenjie Li , Yong Qiao Sun , Cong Tang , Junbin Wang , Jiahong Gao , Yong Zhang , Fangyu Luo , Ying Yang , Yun-Gui Yang , Xiaozhong Peng
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) caused the persistent coronavirus disease 2019 (COVID-19) pandemic, which has resulted in millions of deaths worldwide and brought an enormous public health and global economic burden. The recurring global wave of infections has been exacerbated by growing variants of SARS-CoV-2. In this study, the virological characteristics of the original SARS-CoV-2 strain and its variants of concern (VOCs; including Alpha, Beta, and Delta) in vitro, as well as differential transcriptomic landscapes in multiple organs (lung, right ventricle, blood, cerebral cortex, and cerebellum) from the infected rhesus macaques, were elucidated. The original strain of SARS-CoV-2 caused a stronger innate immune response in host cells, and its VOCs markedly increased the levels of subgenomic RNAs, such as N, Orf9b, Orf6, and Orf7ab, which are known as the innate immune antagonists and the inhibitors of antiviral factors. Intriguingly, the original SARS-CoV-2 strain and Alpha variant induced larger alteration of RNA abundance in tissues of rhesus monkeys than Beta and Delta variants did. Moreover, a hyperinflammatory state and active immune response were shown in the right ventricles of rhesus monkeys by the up-regulation of inflammation- and immune-related RNAs. Furthermore, peripheral blood may mediate signaling transmission among tissues to coordinate the molecular changes in the infected individuals. Collectively, these data provide insights into the pathogenesis of COVID-19 at the early stage of infection by the original SARS-CoV-2 strain and its VOCs.
{"title":"Differential Transcriptomic Landscapes of SARS-CoV-2 Variants in Multiple Organs from Infected Rhesus Macaques","authors":"Tingfu Du , Chunchun Gao , Shuaiyao Lu , Qianlan Liu , Yun Yang , Wenhai Yu , Wenjie Li , Yong Qiao Sun , Cong Tang , Junbin Wang , Jiahong Gao , Yong Zhang , Fangyu Luo , Ying Yang , Yun-Gui Yang , Xiaozhong Peng","doi":"10.1016/j.gpb.2023.06.002","DOIUrl":"10.1016/j.gpb.2023.06.002","url":null,"abstract":"<div><div>Severe acute respiratory syndrome coronavirus 2 (<strong>SARS-CoV-2</strong>) caused the persistent coronavirus disease 2019 (COVID-19) pandemic, which has resulted in millions of deaths worldwide and brought an enormous public health and global economic burden. The recurring global wave of infections has been exacerbated by growing variants of SARS-CoV-2. In this study, the virological characteristics of the original SARS-CoV-2 strain and its <strong>variants of concern</strong> (VOCs; including Alpha, Beta, and Delta) <em>in vitro</em>, as well as differential transcriptomic landscapes in multiple organs (lung, right ventricle, blood, cerebral cortex, and cerebellum) from the infected <strong>rhesus macaques</strong>, were elucidated. The original strain of SARS-CoV-2 caused a stronger innate immune response in host cells, and its VOCs markedly increased the levels of <strong>subgenomic RNA</strong><strong>s</strong>, such as <em>N</em>, <em>Orf9b</em>, <em>Orf6</em>, and <em>Orf7ab</em>, which are known as the innate immune antagonists and the inhibitors of antiviral factors. Intriguingly, the original SARS-CoV-2 strain and Alpha variant induced larger alteration of RNA abundance in tissues of rhesus monkeys than Beta and Delta variants did. Moreover, a hyperinflammatory state and active immune response were shown in the right ventricles of rhesus monkeys by the up-regulation of inflammation- and immune-related RNAs. Furthermore, peripheral blood may mediate signaling transmission among tissues to coordinate the molecular changes in the infected individuals. Collectively, these data provide insights into the pathogenesis of COVID-19 at the early stage of infection by the original SARS-CoV-2 strain and its VOCs.</div></div>","PeriodicalId":12528,"journal":{"name":"Genomics, Proteomics & Bioinformatics","volume":"21 5","pages":"Pages 1014-1029"},"PeriodicalIF":11.5,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10928377/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10154985","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-10-01DOI: 10.1016/j.gpb.2023.03.006
Jennifer A. Karlow , Erica C. Pehrsson , Xiaoyun Xing , Mark Watson , Siddhartha Devarakonda , Ramaswamy Govindan , Ting Wang
Epigenetic alterations are widespread in cancer and can complement genetic alterations to influence cancer progression and treatment outcome. To determine the potential contribution of DNAmethylation alterations to tumor phenotype in non-small cell lung cancer (NSCLC) in both smoker and never-smoker patients, we performed genome-wide profiling of DNA methylation in 17 primary NSCLC tumors and 10 matched normal lung samples using the complementary assays, methylated DNA immunoprecipitation sequencing (MeDIP-seq) and methylation sensitive restriction enzyme sequencing (MRE-seq). We reported recurrent methylation changes in the promoters of several genes, many previously implicated in cancer, including FAM83A and SEPT9 (hypomethylation), as well as PCDH7, NKX2-1, and SOX17 (hypermethylation). Although many methylation changes between tumors and their paired normal samples were shared across patients, several were specific to a particular smoking status. For example, never-smokers displayed a greater proportion of hypomethylated differentially methylated regions (hypoDMRs) and a greater number of recurrently hypomethylated promoters, including those of ASPSCR1, TOP2A, DPP9, and USP39, all previously linked to cancer. Changes outside of promoters were also widespread and often recurrent, particularly methylation loss over repetitive elements, highly enriched for ERV1 subfamilies. Recurrent hypoDMRs were enriched for several transcription factor binding motifs, often for genes involved in signaling and cell proliferation. For example, 71% of recurrent promoter hypoDMRs contained a motif for NKX2-1. Finally, the majority of DMRs were located within an active chromatin state in tissues profiled using the Roadmap Epigenomics data, suggesting that methylation changes may contribute to altered regulatory programs through the adaptation of cell type-specific expression programs.
{"title":"Non-small Cell Lung Cancer Epigenomes Exhibit Altered DNA Methylation in Smokers and Never-smokers","authors":"Jennifer A. Karlow , Erica C. Pehrsson , Xiaoyun Xing , Mark Watson , Siddhartha Devarakonda , Ramaswamy Govindan , Ting Wang","doi":"10.1016/j.gpb.2023.03.006","DOIUrl":"10.1016/j.gpb.2023.03.006","url":null,"abstract":"<div><div>Epigenetic alterations are widespread in cancer and can complement genetic alterations to influence cancer progression and treatment outcome. To determine the potential contribution of <strong>DNA</strong> <strong>methylation</strong> alterations to tumor phenotype in <strong>non-small cell lung cancer</strong> (NSCLC) in both smoker and never-smoker patients, we performed genome-wide profiling of DNA methylation in 17 primary NSCLC tumors and 10 matched normal lung samples using the complementary assays, methylated DNA immunoprecipitation sequencing (MeDIP-seq) and methylation sensitive restriction enzyme sequencing (MRE-seq). We reported recurrent methylation changes in the promoters of several genes, many previously implicated in cancer, including <em>FAM83A</em> and <em>SEPT9</em> (hypomethylation), as well as <em>PCDH7</em>, <em>NKX2-1</em>, and <em>SOX17</em> (hypermethylation). Although many methylation changes between tumors and their paired normal samples were shared across patients, several were specific to a particular smoking status. For example, never-smokers displayed a greater proportion of hypomethylated differentially methylated regions (hypoDMRs) and a greater number of recurrently hypomethylated promoters, including those of <em>ASPSCR1</em>, <em>TOP2A</em>, <em>DPP9</em>, and <em>USP39</em>, all previously linked to cancer. Changes outside of promoters were also widespread and often recurrent, particularly methylation loss over repetitive elements, highly enriched for ERV1 subfamilies. Recurrent hypoDMRs were enriched for several transcription factor binding motifs, often for genes involved in signaling and cell proliferation. For example, 71% of recurrent promoter hypoDMRs contained a motif for NKX2-1. Finally, the majority of DMRs were located within an active chromatin state in tissues profiled using the Roadmap Epigenomics data, suggesting that methylation changes may contribute to altered regulatory programs through the adaptation of cell type-specific expression programs.</div></div>","PeriodicalId":12528,"journal":{"name":"Genomics, Proteomics & Bioinformatics","volume":"21 5","pages":"Pages 991-1013"},"PeriodicalIF":11.5,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10928376/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41147317","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-10-01DOI: 10.1016/j.gpb.2023.10.003
Jun Yu
{"title":"Toward Inclusiveness and Thoroughness: A Paradigm Shift from More-ever-omics to Holovivology","authors":"Jun Yu","doi":"10.1016/j.gpb.2023.10.003","DOIUrl":"10.1016/j.gpb.2023.10.003","url":null,"abstract":"","PeriodicalId":12528,"journal":{"name":"Genomics, Proteomics & Bioinformatics","volume":"21 5","pages":"Pages 895-896"},"PeriodicalIF":11.5,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10928371/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"54232890","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-10-01DOI: 10.1016/j.gpb.2023.10.006
Runsheng Chen
{"title":"A Historic Retrospective on the Early Bioinformatics Research in China","authors":"Runsheng Chen","doi":"10.1016/j.gpb.2023.10.006","DOIUrl":"10.1016/j.gpb.2023.10.006","url":null,"abstract":"","PeriodicalId":12528,"journal":{"name":"Genomics, Proteomics & Bioinformatics","volume":"21 5","pages":"Pages 897-899"},"PeriodicalIF":11.5,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10928369/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"71490437","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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