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Virus-Derived Small RNAs and microRNAs in Health and Disease. 病毒衍生小rna和微rna在健康和疾病中的作用。
IF 6
Annual Review of Biomedical Data Science Pub Date : 2023-08-10 DOI: 10.1146/annurev-biodatasci-122220-111429
Vasileios Gouzouasis, Spyros Tastsoglou, Antonis Giannakakis, Artemis G Hatzigeorgiou
{"title":"Virus-Derived Small RNAs and microRNAs in Health and Disease.","authors":"Vasileios Gouzouasis,&nbsp;Spyros Tastsoglou,&nbsp;Antonis Giannakakis,&nbsp;Artemis G Hatzigeorgiou","doi":"10.1146/annurev-biodatasci-122220-111429","DOIUrl":"https://doi.org/10.1146/annurev-biodatasci-122220-111429","url":null,"abstract":"<p><p>MicroRNAs (miRNAs) are short noncoding RNAs that can regulate all steps of gene expression (induction, transcription, and translation). Several virus families, primarily double-stranded DNA viruses, encode small RNAs (sRNAs), including miRNAs. These virus-derived miRNAs (v-miRNAs) help the virus evade the host's innate and adaptive immune system and maintain an environment of chronic latent infection. In this review, the functions of the sRNA-mediated virus-host interactions are highlighted, delineating their implication in chronic stress, inflammation, immunopathology, and disease. We provide insights into the latest viral RNA-based research-in silico approaches for functional characterization of v-miRNAs and other RNA types. The latest research can assist toward the identification of therapeutic targets to combat viral infections.</p>","PeriodicalId":29775,"journal":{"name":"Annual Review of Biomedical Data Science","volume":null,"pages":null},"PeriodicalIF":6.0,"publicationDate":"2023-08-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9960509","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Computational Methods for Single-Cell Proteomics. 单细胞蛋白质组学的计算方法。
IF 6
Annual Review of Biomedical Data Science Pub Date : 2023-08-10 Epub Date: 2023-04-11 DOI: 10.1146/annurev-biodatasci-020422-050255
Sophia M Guldberg, Trine Line Hauge Okholm, Elizabeth E McCarthy, Matthew H Spitzer
{"title":"Computational Methods for Single-Cell Proteomics.","authors":"Sophia M Guldberg, Trine Line Hauge Okholm, Elizabeth E McCarthy, Matthew H Spitzer","doi":"10.1146/annurev-biodatasci-020422-050255","DOIUrl":"10.1146/annurev-biodatasci-020422-050255","url":null,"abstract":"<p><p>Advances in single-cell proteomics technologies have resulted in high-dimensional datasets comprising millions of cells that are capable of answering key questions about biology and disease. The advent of these technologies has prompted the development of computational tools to process and visualize the complex data. In this review, we outline the steps of single-cell and spatial proteomics analysis pipelines. In addition to describing available methods, we highlight benchmarking studies that have identified advantages and pitfalls of the currently available computational toolkits. As these technologies continue to advance, robust analysis tools should be developed in tandem to take full advantage of the potential biological insights provided by these data.</p>","PeriodicalId":29775,"journal":{"name":"Annual Review of Biomedical Data Science","volume":null,"pages":null},"PeriodicalIF":6.0,"publicationDate":"2023-08-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10621466/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10023948","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Gene Interactions in Human Disease Studies-Evidence Is Mounting. 人类疾病研究中的基因相互作用——证据越来越多。
IF 6
Annual Review of Biomedical Data Science Pub Date : 2023-08-10 DOI: 10.1146/annurev-biodatasci-102022-120818
Pankhuri Singhal, Shefali Setia Verma, Marylyn D Ritchie
{"title":"Gene Interactions in Human Disease Studies-Evidence Is Mounting.","authors":"Pankhuri Singhal,&nbsp;Shefali Setia Verma,&nbsp;Marylyn D Ritchie","doi":"10.1146/annurev-biodatasci-102022-120818","DOIUrl":"https://doi.org/10.1146/annurev-biodatasci-102022-120818","url":null,"abstract":"<p><p>Despite monumental advances in molecular technology to generate genome sequence data at scale, there is still a considerable proportion of heritability in most complex diseases that remains unexplained. Because many of the discoveries have been single-nucleotide variants with small to moderate effects on disease, the functional implication of many of the variants is still unknown and, thus, we have limited new drug targets and therapeutics. We, and many others, posit that one primary factor that has limited our ability to identify novel drug targets from genome-wide association studies may be due to gene interactions (epistasis), gene-environment interactions, network/pathway effects, or multiomic relationships. We propose that many of these complex models explain much of the underlying genetic architecture of complex disease. In this review, we discuss the evidence from multiple research avenues, ranging from pairs of alleles to multiomic integration studies and pharmacogenomics, that supports the need for further investigation of gene interactions (or epistasis) in genetic and genomic studies of human disease. Our goal is to catalog the mounting evidence for epistasis in genetic studies and the connections between genetic interactions and human health and disease that could enable precision medicine of the future.</p>","PeriodicalId":29775,"journal":{"name":"Annual Review of Biomedical Data Science","volume":null,"pages":null},"PeriodicalIF":6.0,"publicationDate":"2023-08-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9960535","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 3
Importance of Diversity in Precision Medicine: Generalizability of Genetic Associations Across Ancestry Groups Toward Better Identification of Disease Susceptibility Variants. 多样性在精准医学中的重要性:跨祖先群体遗传关联的普遍性，有助于更好地识别疾病易感性变异。
IF 6
Annual Review of Biomedical Data Science Pub Date : 2023-08-10 Epub Date: 2023-05-17 DOI: 10.1146/annurev-biodatasci-122220-113250
Lauren A Cruz, Jessica N Cooke Bailey, Dana C Crawford
{"title":"Importance of Diversity in Precision Medicine: Generalizability of Genetic Associations Across Ancestry Groups Toward Better Identification of Disease Susceptibility Variants.","authors":"Lauren A Cruz, Jessica N Cooke Bailey, Dana C Crawford","doi":"10.1146/annurev-biodatasci-122220-113250","DOIUrl":"10.1146/annurev-biodatasci-122220-113250","url":null,"abstract":"<p><p>Genome-wide association studies (GWAS) revolutionized our understanding of common genetic variation and its impact on common human disease and traits. Developed and adopted in the mid-2000s, GWAS led to searchable genotype-phenotype catalogs and genome-wide datasets available for further data mining and analysis for the eventual development of translational applications. The GWAS revolution was swift and specific, including almost exclusively populations of European descent, to the neglect of the majority of the world's genetic diversity. In this narrative review, we recount the GWAS landscape of the early years that established a genotype-phenotype catalog that is now universally understood to be inadequate for a complete understanding of complex human genetics. We then describe approaches taken to augment the genotype-phenotype catalog, including the study populations, collaborative consortia, and study design approaches aimed to generalize and then ultimately discover genome-wide associations in non-European descent populations. The collaborations and data resources established in the efforts to diversify genomic findings undoubtedly provide the foundations of the next chapters of genetic association studies with the advent of budget-friendly whole-genome sequencing.</p>","PeriodicalId":29775,"journal":{"name":"Annual Review of Biomedical Data Science","volume":null,"pages":null},"PeriodicalIF":6.0,"publicationDate":"2023-08-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10720270/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9960536","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Human Microbiomes and Disease for the Biomedical Data Scientist. 生物医学数据科学家的人类微生物组和疾病。
IF 6
Annual Review of Biomedical Data Science Pub Date : 2023-08-10 DOI: 10.1146/annurev-biodatasci-020722-043017
Jonathan L Golob
{"title":"Human Microbiomes and Disease for the Biomedical Data Scientist.","authors":"Jonathan L Golob","doi":"10.1146/annurev-biodatasci-020722-043017","DOIUrl":"https://doi.org/10.1146/annurev-biodatasci-020722-043017","url":null,"abstract":"<p><p>The human microbiome is complex, variable from person to person, essential for health, and related to both the risk for disease and the efficacy of our treatments. There are robust techniques to describe microbiota with high-throughput sequencing, and there are hundreds of thousands of already-sequenced specimens in public archives. The promise remains to use the microbiome both as a prognostic factor and as a target for precision medicine. However, when used as an input in biomedical data science modeling, the microbiome presents unique challenges. Here, we review the most common techniques used to describe microbial communities, explore these unique challenges, and discuss the more successful approaches for biomedical data scientists seeking to use the microbiome as an input in their studies.</p>","PeriodicalId":29775,"journal":{"name":"Annual Review of Biomedical Data Science","volume":null,"pages":null},"PeriodicalIF":6.0,"publicationDate":"2023-08-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9960518","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Single-Cell RNA Sequencing for Studying Human Cancers. 单细胞RNA测序用于研究人类癌症。
IF 6
Annual Review of Biomedical Data Science Pub Date : 2023-08-10 DOI: 10.1146/annurev-biodatasci-020722-091857
Dvir Aran
{"title":"Single-Cell RNA Sequencing for Studying Human Cancers.","authors":"Dvir Aran","doi":"10.1146/annurev-biodatasci-020722-091857","DOIUrl":"https://doi.org/10.1146/annurev-biodatasci-020722-091857","url":null,"abstract":"<p><p>Since the first publication a decade ago describing the use of single-cell RNA sequencing (scRNA-seq) in the context of cancer, over 200 datasets and thousands of scRNA-seq studies have been published in cancer biology. scRNA-seq technologies have been applied across dozens of cancer types and a diverse array of study designs to improve our understanding of tumor biology, the tumor microenvironment, and therapeutic responses, and scRNA-seq is on the verge of being used to improve decision-making in the clinic. Computational methodologies and analytical pipelines are key in facilitating scRNA-seq research. Numerous computational methods utilizing the most advanced tools in data science have been developed to extract meaningful insights. Here, we review the advancements in cancer biology gained by scRNA-seq and discuss the computational challenges of the technology that are specific to cancer research.</p>","PeriodicalId":29775,"journal":{"name":"Annual Review of Biomedical Data Science","volume":null,"pages":null},"PeriodicalIF":6.0,"publicationDate":"2023-08-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9967040","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 1
An Overview of Deep Generative Models in Functional and Evolutionary Genomics. 功能和进化基因组学中的深度生成模型概述。
IF 6
Annual Review of Biomedical Data Science Pub Date : 2023-08-10 Epub Date: 2023-05-03 DOI: 10.1146/annurev-biodatasci-020722-115651
Burak Yelmen, Flora Jay
{"title":"An Overview of Deep Generative Models in Functional and Evolutionary Genomics.","authors":"Burak Yelmen, Flora Jay","doi":"10.1146/annurev-biodatasci-020722-115651","DOIUrl":"10.1146/annurev-biodatasci-020722-115651","url":null,"abstract":"<p><p>Following the widespread use of deep learning for genomics, deep generative modeling is also becoming a viable methodology for the broad field. Deep generative models (DGMs) can learn the complex structure of genomic data and allow researchers to generate novel genomic instances that retain the real characteristics of the original dataset. Aside from data generation, DGMs can also be used for dimensionality reduction by mapping the data space to a latent space, as well as for prediction tasks via exploitation of this learned mapping or supervised/semi-supervised DGM designs. In this review, we briefly introduce generative modeling and two currently prevailing architectures, we present conceptual applications along with notable examples in functional and evolutionary genomics, and we provide our perspective on potential challenges and future directions.</p>","PeriodicalId":29775,"journal":{"name":"Annual Review of Biomedical Data Science","volume":null,"pages":null},"PeriodicalIF":6.0,"publicationDate":"2023-08-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9967062","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Challenges and Opportunities for Data Science in Women's Health. 妇女健康数据科学的挑战与机遇。
IF 7
Annual Review of Biomedical Data Science Pub Date : 2023-08-10 Epub Date: 2023-04-11 DOI: 10.1146/annurev-biodatasci-020722-105958
Todd L Edwards, Catherine A Greene, Jacqueline A Piekos, Jacklyn N Hellwege, Gabrielle Hampton, Elizabeth A Jasper, Digna R Velez Edwards
{"title":"Challenges and Opportunities for Data Science in Women's Health.","authors":"Todd L Edwards, Catherine A Greene, Jacqueline A Piekos, Jacklyn N Hellwege, Gabrielle Hampton, Elizabeth A Jasper, Digna R Velez Edwards","doi":"10.1146/annurev-biodatasci-020722-105958","DOIUrl":"10.1146/annurev-biodatasci-020722-105958","url":null,"abstract":"<p><p>The intersection of women's health and data science is a field of research that has historically trailed other fields, but more recently it has gained momentum. This growth is being driven not only by new investigators who are moving into this area but also by the significant opportunities that have emerged in new methodologies, resources, and technologies in data science. Here, we describe some of the resources and methods being used by women's health researchers today to meet challenges in biomedical data science. We also describe the opportunities and limitations of applying these approaches to advance women's health outcomes and the future of the field, with emphasis on repurposing existing methodologies for women's health.</p>","PeriodicalId":29775,"journal":{"name":"Annual Review of Biomedical Data Science","volume":null,"pages":null},"PeriodicalIF":7.0,"publicationDate":"2023-08-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10877578/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9967041","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Human Genomics of COVID-19 Pneumonia: Contributions of Rare and Common Variants. COVID-19 肺炎的人类基因组学：罕见和常见变异的贡献
IF 7
Annual Review of Biomedical Data Science Pub Date : 2023-08-10 Epub Date: 2023-05-17 DOI: 10.1146/annurev-biodatasci-020222-021705
Aurélie Cobat, Qian Zhang, Laurent Abel, Jean-Laurent Casanova, Jacques Fellay
{"title":"Human Genomics of COVID-19 Pneumonia: Contributions of Rare and Common Variants.","authors":"Aurélie Cobat, Qian Zhang, Laurent Abel, Jean-Laurent Casanova, Jacques Fellay","doi":"10.1146/annurev-biodatasci-020222-021705","DOIUrl":"10.1146/annurev-biodatasci-020222-021705","url":null,"abstract":"<p><p>SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2) infection is silent or benign in most infected individuals, but causes hypoxemic COVID-19 pneumonia in about 10% of cases. We review studies of the human genetics of life-threatening COVID-19 pneumonia, focusing on both rare and common variants. Large-scale genome-wide association studies have identified more than 20 common loci robustly associated with COVID-19 pneumonia with modest effect sizes, some implicating genes expressed in the lungs or leukocytes. The most robust association, on chromosome 3, concerns a haplotype inherited from Neanderthals. Sequencing studies focusing on rare variants with a strong effect have been particularly successful, identifying inborn errors of type I interferon (IFN) immunity in 1-5% of unvaccinated patients with critical pneumonia, and their autoimmune phenocopy, autoantibodies against type I IFN, in another 15-20% of cases. Our growing understanding of the impact of human genetic variation on immunity to SARS-CoV-2 is enabling health systems to improve protection for individuals and populations.</p>","PeriodicalId":29775,"journal":{"name":"Annual Review of Biomedical Data Science","volume":null,"pages":null},"PeriodicalIF":7.0,"publicationDate":"2023-08-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10879986/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9960534","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Combining Molecular and Radiomic Features for Risk Assessment in Breast Cancer. 结合分子和放射学特征评估乳腺癌的风险。
IF 6
Annual Review of Biomedical Data Science Pub Date : 2023-08-10 DOI: 10.1146/annurev-biodatasci-020722-092748
Alex A Nguyen, Anne Marie McCarthy, Despina Kontos
{"title":"Combining Molecular and Radiomic Features for Risk Assessment in Breast Cancer.","authors":"Alex A Nguyen,&nbsp;Anne Marie McCarthy,&nbsp;Despina Kontos","doi":"10.1146/annurev-biodatasci-020722-092748","DOIUrl":"https://doi.org/10.1146/annurev-biodatasci-020722-092748","url":null,"abstract":"<p><p>Breast cancer risk is highly variable within the population and current research is leading the shift toward personalized medicine. By accurately assessing an individual woman's risk, we can reduce the risk of over/undertreatment by preventing unnecessary procedures or by elevating screening procedures. Breast density measured from conventional mammography has been established as one of the most dominant risk factors for breast cancer; however, it is currently limited by its ability to characterize more complex breast parenchymal patterns that have been shown to provide additional information to strengthen cancer risk models. Molecular factors ranging from high penetrance, or high likelihood that a mutation will show signs and symptoms of the disease, to combinations of gene mutations with low penetrance have shown promise for augmenting risk assessment. Although imaging biomarkers and molecular biomarkers have both individually demonstrated improved performance in risk assessment, few studies have evaluated them together. This review aims to highlight the current state of the art in breast cancer risk assessment using imaging and genetic biomarkers.</p>","PeriodicalId":29775,"journal":{"name":"Annual Review of Biomedical Data Science","volume":null,"pages":null},"PeriodicalIF":6.0,"publicationDate":"2023-08-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9967073","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
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