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Cotranslational Mechanisms of Protein Biogenesis and Complex Assembly in Eukaryotes. 真核生物蛋白质生物生成和复合体组装的共翻译机制。
IF 6 Q1 MATHEMATICAL & COMPUTATIONAL BIOLOGY Pub Date : 2022-08-10 Epub Date: 2022-04-26 DOI: 10.1146/annurev-biodatasci-121721-095858
Fabián Morales-Polanco, Jae Ho Lee, Natália M Barbosa, Judith Frydman

The formation of protein complexes is crucial to most biological functions. The cellular mechanisms governing protein complex biogenesis are not yet well understood, but some principles of cotranslational and posttranslational assembly are beginning to emerge. In bacteria, this process is favored by operons encoding subunits of protein complexes. Eukaryotic cells do not have polycistronic mRNAs, raising the question of how they orchestrate the encounter of unassembled subunits. Here we review the constraints and mechanisms governing eukaryotic co- and posttranslational protein folding and assembly, including the influence of elongation rate on nascent chain targeting, folding, and chaperone interactions. Recent evidence shows that mRNAs encoding subunits of oligomeric assemblies can undergo localized translation and form cytoplasmic condensates that might facilitate the assembly of protein complexes. Understanding the interplay between localized mRNA translation and cotranslational proteostasis will be critical to defining protein complex assembly in vivo.

蛋白质复合物的形成对大多数生物功能至关重要。蛋白质复合物生物生成的细胞机制尚不十分清楚,但一些共翻译和翻译后组装的原理已开始显现。在细菌中,编码蛋白质复合体亚基的操作子有利于这一过程。真核细胞没有多聚核苷酸 mRNA,这就提出了它们如何协调未组装亚基相遇的问题。在此,我们回顾了真核生物共翻译和翻译后蛋白质折叠和组装的制约因素和机制,包括伸长率对新生链靶向、折叠和伴侣相互作用的影响。最近的证据表明,编码寡聚体组装亚基的 mRNA 可以进行局部翻译并形成细胞质凝聚物,从而促进蛋白质复合体的组装。了解局部 mRNA 翻译和共翻译蛋白稳态之间的相互作用对于确定体内蛋白质复合体的组装至关重要。
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引用次数: 0
Phenotypic Causal Inference Using Genome-Wide Association Study Data: Mendelian Randomization and Beyond. 利用全基因组关联研究数据进行表型因果推断:孟德尔随机化及其他。
IF 7 Q1 MATHEMATICAL & COMPUTATIONAL BIOLOGY Pub Date : 2022-08-10 Epub Date: 2022-04-01 DOI: 10.1146/annurev-biodatasci-122120-024910
Venexia M Walker, Jie Zheng, Tom R Gaunt, George Davey Smith

statistics for genome-wide association studies (GWAS) are increasingly available for downstream analyses. Meanwhile, the popularity of causal inference methods has grown as we look to gather robust evidence for novel medical and public health interventions. This has led to the development of methods that use GWAS summary statistics for causal inference. Here, we describe these methods in order of their escalating complexity, from genetic associations to extensions of Mendelian randomization that consider thousands of phenotypes simultaneously. We also cover the assumptions and limitations of these approaches before considering the challenges faced by researchers performing causal inference using GWAS data. GWAS summary statistics constitute an important data source for causal inference research that offers a counterpoint to nongenetic methods when triangulating evidence. Continued efforts to address the challenges in using GWAS data for causal inference will allow the full impact of these approaches to be realized.

越来越多的全基因组关联研究(GWAS)统计数据可用于下游分析。同时,随着我们希望为新型医疗和公共卫生干预措施收集可靠证据,因果推断方法也越来越受欢迎。因此,我们开发了使用 GWAS 摘要统计进行因果推断的方法。在此,我们将按照复杂程度的递增顺序介绍这些方法,从遗传关联到孟德尔随机化的扩展(同时考虑数千种表型)。在考虑研究人员利用 GWAS 数据进行因果推断所面临的挑战之前,我们还将介绍这些方法的假设和局限性。GWAS 统计摘要是因果推断研究的一个重要数据源,在三角测量证据时可与非遗传方法相抗衡。继续努力解决使用 GWAS 数据进行因果推断时所面临的挑战,将使这些方法的影响得以充分发挥。
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引用次数: 0
Best Practices on Big Data Analytics to Address Sex-Specific Biases in Our Understanding of the Etiology, Diagnosis, and Prognosis of Diseases. 大数据分析的最佳实践,以解决我们在了解疾病的病因、诊断和预后时存在的性别偏见。
IF 7 Q1 MATHEMATICAL & COMPUTATIONAL BIOLOGY Pub Date : 2022-08-10 Epub Date: 2022-05-13 DOI: 10.1146/annurev-biodatasci-122120-025806
Su Golder, Karen O'Connor, Yunwen Wang, Robin Stevens, Graciela Gonzalez-Hernandez

A bias in health research to favor understanding diseases as they present in men can have a grave impact on the health of women. This paper reports on a conceptual review of the literature on machine learning or natural language processing (NLP) techniques to interrogate big data for identifying sex-specific health disparities. We searched Ovid MEDLINE, Embase, and PsycINFO in October 2021 using synonyms and indexing terms for (a) "women," "men," or "sex"; (b) "big data," "artificial intelligence," or "NLP"; and (c) "disparities" or "differences." From 902 records, 22 studies met the inclusion criteria and were analyzed. Results demonstrate that the inclusion by sex is inconsistent and often unreported, although the inclusion of men in these studies is disproportionately less than women. Even though artificial intelligence and NLP techniques are widely applied in healthresearch, few studies use them to take advantage of unstructured text to investigate sex-related differences or disparities. Researchers are increasingly aware of sex-based data bias, but the process toward correction is slow. We reflect on best practices on using big data analytics to address sex-specific biases in understanding the etiology, diagnosis, and prognosis of diseases.

健康研究中偏向于理解男性所患疾病的偏见会对女性的健康产生严重影响。本文对有关机器学习或自然语言处理(NLP)技术的文献进行了概念性综述,这些技术用于查询大数据以识别特定性别的健康差异。我们在 2021 年 10 月使用以下同义词和索引词对 Ovid MEDLINE、Embase 和 PsycINFO 进行了检索:(a) "女性"、"男性 "或 "性别";(b) "大数据"、"人工智能 "或 "NLP";(c) "差异 "或 "差别"。在 902 条记录中,有 22 项研究符合纳入标准并进行了分析。结果表明,按性别纳入研究的情况并不一致,而且往往未作报告,尽管男性在这些研究中的比例比女性少得多。尽管人工智能和 NLP 技术已广泛应用于健康研究,但很少有研究利用它们来研究非结构化文本中与性别相关的差异或差距。研究人员越来越意识到基于性别的数据偏差,但纠正过程却很缓慢。我们反思了在了解疾病的病因、诊断和预后方面使用大数据分析来解决性别偏见的最佳实践。
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引用次数: 0
Discovering Biological Conflict Systems Through Genome Analysis: Evolutionary Principles and Biochemical Novelty. 通过基因组分析发现生物冲突系统:进化原理和生化新颖性。
IF 6 Q1 MATHEMATICAL & COMPUTATIONAL BIOLOGY Pub Date : 2022-05-24 DOI: 10.1146/annurev-biodatasci-122220-101119
L. Aravind, L. Iyer, A. M. Burroughs
Biological replicators, from genes within a genome to whole organisms, are locked in conflicts. Comparative genomics has revealed a staggering diversity of molecular armaments and mechanisms regulating their deployment, collectively termed biological conflict systems. These encompass toxins used in inter- and intraspecific interactions, self/nonself discrimination, antiviral immune mechanisms, and counter-host effectors deployed by viruses and intragenomic selfish elements. These systems possess shared syntactical features in their organizational logic and a set of effectors targeting genetic information flow through the Central Dogma, certain membranes, and key molecules like NAD+. These principles can be exploited to discover new conflict systems through sensitive computational analyses. This has led to significant advances in our understanding of the biology of these systems and furnished new biotechnological reagents for genome editing, sequencing, and beyond. We discuss these advances using specific examples of toxins, restriction-modification, apoptosis, CRISPR/second messenger-regulated systems, and other enigmatic nucleic acid-targeting systems. Expected final online publication date for the Annual Review of Biomedical Data Science, Volume 5 is August 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.
生物复制因子,从基因组内的基因到整个生物体,都处于冲突之中。比较基因组学揭示了分子武器的惊人多样性和调节其部署的机制,统称为生物冲突系统。这些包括在种间和种内相互作用中使用的毒素,自我/非自我歧视,抗病毒免疫机制,病毒和基因组内自私元件部署的反宿主效应物。这些系统在组织逻辑上具有共同的语法特征,并具有一组针对遗传信息流的效应器,这些遗传信息流通过中央教条、某些膜和关键分子如NAD+。这些原理可以通过敏感的计算分析来发现新的冲突系统。这使得我们对这些系统的生物学理解取得了重大进展,并为基因组编辑、测序等提供了新的生物技术试剂。我们用毒素、限制性修饰、细胞凋亡、CRISPR/第二信使调节系统和其他神秘的核酸靶向系统的具体例子来讨论这些进展。预计《生物医学数据科学年度评论》第5卷的最终在线出版日期为2022年8月。修订后的估计数请参阅http://www.annualreviews.org/page/journal/pubdates。
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引用次数: 8
Developing and Implementing Predictive Models in a Learning Healthcare System: Traditional and Artificial Intelligence Approaches in the Veterans Health Administration. 在学习医疗保健系统中开发和实施预测模型:退伍军人健康管理中的传统和人工智能方法。
IF 6 Q1 MATHEMATICAL & COMPUTATIONAL BIOLOGY Pub Date : 2022-05-24 DOI: 10.1146/annurev-biodatasci-122220-110053
D. Atkins, C. A. Makridis, G. Alterovitz, R. Ramoni, C. Clancy
Predicting clinical risk is an important part of healthcare and can inform decisions about treatments, preventive interventions, and provision of extra services. The field of predictive models has been revolutionized over the past two decades by electronic health record data; the ability to link such data with other demographic, socioeconomic, and geographic information; the availability of high-capacity computing; and new machine learning and artificial intelligence methods for extracting insights from complex datasets. These advances have produced a new generation of computerized predictive models, but debate continues about their development, reporting, validation, evaluation, and implementation. In this review we reflect on more than 10 years of experience at the Veterans Health Administration, the largest integrated healthcare system in the United States, in developing, testing, and implementing such models at scale. We report lessons from the implementation of national risk prediction models and suggest an agenda for research. Expected final online publication date for the Annual Review of Biomedical Data Science, Volume 5 is August 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.
预测临床风险是医疗保健的重要组成部分,可以为有关治疗、预防性干预和提供额外服务的决策提供信息。在过去的二十年里,电子健康记录数据使预测模型领域发生了革命性的变化;将此类数据与其他人口、社会经济和地理信息联系起来的能力;高容量计算的可用性;以及从复杂数据集中提取见解的新机器学习和人工智能方法。这些进步已经产生了新一代的计算机化预测模型,但是关于它们的开发、报告、验证、评估和实现的争论仍在继续。在这篇综述中,我们回顾了退伍军人健康管理局(美国最大的综合医疗保健系统)10多年来在大规模开发、测试和实施这些模型方面的经验。我们报告了实施国家风险预测模型的经验教训,并提出了研究议程。预计《生物医学数据科学年度评论》第5卷的最终在线出版日期为2022年8月。修订后的估计数请参阅http://www.annualreviews.org/page/journal/pubdates。
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引用次数: 2
Extracellular Vesicle-Based Multianalyte Liquid Biopsy as a Diagnostic for Cancer. 基于细胞外囊泡的多分析物液体活检诊断癌症。
IF 6 Q1 MATHEMATICAL & COMPUTATIONAL BIOLOGY Pub Date : 2022-05-13 DOI: 10.1146/annurev-biodatasci-122120-113218
Andrew A. Lin, V. Nimgaonkar, D. Issadore, E. Carpenter
Liquid biopsy is the analysis of materials shed by tumors into circulation, such as circulating tumor cells, nucleic acids, and extracellular vesicles (EVs), for the diagnosis and management of cancer. These assays have rapidly evolved with recent FDA approvals of single biomarkers in patients with advanced metastatic disease. However, they have lacked sensitivity or specificity as a diagnostic in early-stage cancer, primarily due to low concentrations in circulating plasma. EVs, membrane-enclosed nanoscale vesicles shed by tumor and other cells into circulation, are a promising liquid biopsy analyte owing to their protein and nucleic acid cargoes carried from their mother cells, their surface proteins specific to their cells of origin, and their higher concentrations over other noninvasive biomarkers across disease stages. Recently, the combination of EVs with non-EV biomarkers has driven improvements in sensitivity and accuracy; this has been fueled by the use of machine learning (ML) to algorithmically identify and combine multiple biomarkers into a composite biomarker for clinical prediction. This review presents an analysis of EV isolation methods, surveys approaches for and issues with using ML in multianalyte EV datasets, and describes best practices for bringing multianalyte liquid biopsy to clinical implementation. Expected final online publication date for the Annual Review of Biomedical Data Science, Volume 5 is August 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.
液体活检是分析肿瘤进入循环的物质,如循环肿瘤细胞、核酸和细胞外囊泡(EVs),用于癌症的诊断和管理。随着美国食品药品监督管理局最近批准晚期转移性疾病患者使用单一生物标志物,这些检测方法迅速发展。然而,它们作为早期癌症的诊断缺乏敏感性或特异性,主要是由于循环血浆中的低浓度。EVs是一种由肿瘤和其他细胞脱落到循环中的膜包裹的纳米级囊泡,是一种很有前途的液体活检分析物,因为它们的蛋白质和核酸货物是从其母细胞携带的,它们的表面蛋白是其来源细胞特异性的,并且在整个疾病阶段的浓度高于其他非侵入性生物标志物。最近,电动汽车与非电动汽车生物标志物的结合推动了灵敏度和准确性的提高;这是由使用机器学习(ML)以算法识别多种生物标志物并将其组合成用于临床预测的复合生物标志物所推动的。这篇综述分析了EV分离方法,调查了在多分析细胞EV数据集中使用ML的方法和问题,并描述了将多分析细胞液体活检应用于临床的最佳实践。《生物医学数据科学年度评论》第5卷预计最终在线出版日期为2022年8月。请参阅http://www.annualreviews.org/page/journal/pubdates用于修订估算。
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引用次数: 5
Exchange of Human Data Across International Boundaries. 跨越国际边界的人类数据交换。
IF 6 Q1 MATHEMATICAL & COMPUTATIONAL BIOLOGY Pub Date : 2022-05-10 DOI: 10.1146/annurev-biodatasci-122220-110811
H. Bentzen
There is a need to share personal data across jurisdictional boundaries. However, the laws regulating such transfers are not harmonized, and sometimes even conflict, causing challenges and occasional data stalls. This review describes the legal landscape for transfer of human data across international boundaries. The European Union's data protection legislation is used as the starting point for illustrating the legislation of countries across the world, how these diverge, and one's options for exchanging human data internationally in a legally compliant manner. Expected final online publication date for the Annual Review of Biomedical Data Science, Volume 5 is August 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.
有必要跨司法管辖区共享个人数据。然而,规范此类传输的法律并不统一,有时甚至会发生冲突,造成挑战和偶尔的数据停滞。这篇综述描述了跨越国际边界转移人类数据的法律环境。欧盟的数据保护立法被用作说明世界各国立法的起点,这些立法是如何分歧的,以及以合法的方式在国际上交换人类数据的选择。《生物医学数据科学年度评论》第5卷预计最终在线出版日期为2022年8月。请参阅http://www.annualreviews.org/page/journal/pubdates用于修订估算。
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引用次数: 1
Computational Approaches for Understanding Sequence Variation Effects on the 3D Genome Architecture. 理解序列变异对三维基因组结构影响的计算方法。
IF 6 Q1 MATHEMATICAL & COMPUTATIONAL BIOLOGY Pub Date : 2022-05-10 DOI: 10.1146/annurev-biodatasci-102521-012018
P. Avdeyev, Jian Zhou
Decoding how genomic sequence and its variations affect 3D genome architecture is indispensable for understanding the genetic architecture of various traits and diseases. The 3D genome organization can be significantly altered by genome variations and in turn impact the function of the genomic sequence. Techniques for measuring the 3D genome architecture across spatial scales have opened up new possibilities for understanding how the 3D genome depends upon the genomic sequence and how it can be altered by sequence variations. Computational methods have become instrumental in analyzing and modeling the sequence effects on 3D genome architecture, and recent development in deep learning sequence models have opened up new opportunities for studying the interplay between sequence variations and the 3D genome. In this review, we focus on computational approaches for both the detection and modeling of sequence variation effects on the 3D genome, and we discuss the opportunities presented by these approaches. Expected final online publication date for the Annual Review of Biomedical Data Science, Volume 5 is August 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.
解码基因组序列及其变异如何影响三维基因组结构对于理解各种性状和疾病的遗传结构是必不可少的。三维基因组组织可以通过基因组变异显著改变,进而影响基因组序列的功能。跨空间尺度测量三维基因组结构的技术为理解三维基因组如何依赖于基因组序列以及序列变化如何改变基因组结构开辟了新的可能性。计算方法已经成为分析和建模序列对三维基因组结构影响的工具,而深度学习序列模型的最新发展为研究序列变异与三维基因组之间的相互作用开辟了新的机会。在这篇综述中,我们重点介绍了序列变异对三维基因组影响的检测和建模的计算方法,并讨论了这些方法所带来的机会。预计《生物医学数据科学年度评论》第5卷的最终在线出版日期为2022年8月。修订后的估计数请参阅http://www.annualreviews.org/page/journal/pubdates。
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引用次数: 1
Bioinformatics of Corals: Investigating Heterogeneous Omics Data from Coral Holobionts for Insight into Reef Health and Resilience. 珊瑚礁生物信息学:研究珊瑚礁中的异构奥密克戎数据,深入了解珊瑚礁的健康和复原力。
IF 6 Q1 MATHEMATICAL & COMPUTATIONAL BIOLOGY Pub Date : 2022-05-10 DOI: 10.1146/annurev-biodatasci-122120-030732
L. Cowen, H. Putnam
Coral reefs are home to over two million species and provide habitat for roughly 25% of all marine animals, but they are being severely threatened by pollution and climate change. A large amount of genomic, transcriptomic, and other omics data is becoming increasingly available from different species of reef-building corals, the unicellular dinoflagellates, and the coral microbiome (bacteria, archaea, viruses, fungi, etc.). Such new data present an opportunity for bioinformatics researchers and computational biologists to contribute to a timely, compelling, and urgent investigation of critical factors that influence reef health and resilience. Expected final online publication date for the Annual Review of Biomedical Data Science, Volume 5 is August 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.
珊瑚礁是200多万种物种的家园,为大约25%的海洋动物提供栖息地,但它们正受到污染和气候变化的严重威胁。从不同种类的造礁珊瑚、单细胞鞭毛藻和珊瑚微生物组(细菌、古细菌、病毒、真菌等)中获得的大量基因组学、转录组学和其他组学数据越来越多。这些新数据为生物信息学研究人员和计算生物学家提供了一个机会,可以对影响珊瑚礁健康和恢复力的关键因素进行及时、有说服力和紧急的调查。预计《生物医学数据科学年度评论》第5卷的最终在线出版日期为2022年8月。修订后的估计数请参阅http://www.annualreviews.org/page/journal/pubdates。
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引用次数: 3
Integration of Protein Structure and Population-Scale DNA Sequence Data for Disease Gene Discovery and Variant Interpretation. 整合蛋白质结构和群体规模的DNA序列数据,用于疾病基因发现和变异解释。
IF 6 Q1 MATHEMATICAL & COMPUTATIONAL BIOLOGY Pub Date : 2022-05-04 DOI: 10.1146/annurev-biodatasci-122220-112147
Bian Li, Bowen Jin, J. Capra, W. Bush
The experimental and computational techniques for capturing information about protein structures and genetic variation within the human genome have advanced dramatically in the past 20 years, generating extensive new data resources. In this review, we discuss these advances, along with new approaches for determining the impact a genetic variant has on protein function. We focus on the potential of new methods that integrate human genetic variation into protein structures to discover relationships to disease, including the discovery of mutational hotspots in cancer-related proteins, the localization of protein-altering variants within protein regions for common complex diseases, and the assessment of variants of unknown significance for Mendelian traits. We expect that approaches that integrate these data sources will play increasingly important roles in disease gene discovery and variant interpretation. Expected final online publication date for the Annual Review of Biomedical Data Science, Volume 5 is August 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.
在过去的20年里,用于获取人类基因组中蛋白质结构和遗传变异信息的实验和计算技术取得了巨大进步,产生了大量新的数据资源。在这篇综述中,我们讨论了这些进展,以及确定遗传变异对蛋白质功能影响的新方法。我们专注于将人类遗传变异整合到蛋白质结构中以发现疾病关系的新方法的潜力,包括发现癌症相关蛋白质的突变热点,在常见复杂疾病的蛋白质区域内定位蛋白质改变变异,以及评估孟德尔性状的未知意义变异。我们期望整合这些数据源的方法将在疾病基因发现和变异解释中发挥越来越重要的作用。预计《生物医学数据科学年度评论》第5卷的最终在线出版日期为2022年8月。修订后的估计数请参阅http://www.annualreviews.org/page/journal/pubdates。
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引用次数: 0
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Annual Review of Biomedical Data Science
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