Bioinformatics Approaches for Fetal DNA Fraction Estimation in Noninvasive Prenatal Testing
Bioinformatics在生命科学研究中的应用
Bioinformatics在生命科学研究中的应用生物信息学是生命科学与信息科学的交叉学科,通过开发计算机算法、统计学方法和数据库技术,对生命科学中的大规模生物学数据进行分析、解释和管理。
在当今迅速发展的生命科学研究中,生物信息学的应用已经变得至关重要。
本文将探讨生物信息学在生命科学研究中的多个重要领域的应用,包括基因组学、转录组学、蛋白质组学和药物设计。
生物信息学在基因组学中的应用基因组学研究涉及整个基因组的组成、结构、功能和表达。
生物信息学为基因组学研究提供了一套强大的工具和方法。
通过生物信息学技术,科学家们能够分析基因组中的大量DNA序列数据,识别基因和基因的功能元件,预测基因的调控区域,并进行进化分析。
生物信息学还可以用于分析基因组中的遗传变异,探究疾病与遗传因素的关系,推动个性化医学的发展。
生物信息学在转录组学中的应用转录组学研究探究的是细胞或组织中的全部RNA转录本,即基因在特定条件下的表达情况。
生物信息学在转录组学研究中扮演着重要的角色。
通过大规模测序技术,科学家们可以获得大量的转录组数据。
生物信息学技术可以比对这些转录组数据与已知的DNA序列数据库进行分析,帮助我们理解基因的调控机制、鉴定新的基因和预测功能未知的RNA分子。
另外,在癌症研究中,生物信息学分析转录组数据还可以帮助寻找潜在的癌症标志物和预测患者的预后。
生物信息学在蛋白质组学中的应用蛋白质组学研究旨在理解蛋白质的表达、结构和相互作用。
生物信息学在蛋白质质谱数据分析和蛋白质结构预测等方面发挥了关键作用。
蛋白质质谱数据可以通过生物信息学工具进行分析,用于鉴定和定量蛋白质样本中的不同蛋白质,并研究它们之间的相互作用。
此外,生物信息学还可以预测蛋白质的三维结构,帮助科学家理解蛋白质的功能和相互作用机制,以及设计新的药物靶点。
生物信息学在药物设计中的应用药物设计旨在开发新的药物分子以治疗疾病。
生物信息学在药物设计中的应用有助于提高药物研发的效率。
bioinformatics 医学英语
bioinformatics 医学英语Bioinformatics is an interdisciplinary field that combines biology, computer science, and information technology to analyze and interpret biological data. In the context of medicine, bioinformatics focuses on using computational tools and methods to make sense of vast amounts of genomic, transcriptomic, proteomic, and other biological data to better understand various diseases and develop personalized treatment strategies.Bioinformaticians in medicine use algorithms, statistical models, and machine learning techniques to analyze and compare DNA sequences, identify genes and protein structures, predict protein functions, and study gene expression patterns. They also develop databases and software tools for storing, managing, and analyzing biological data, allowing researchers to access and share valuable information.By leveraging bioinformatics tools and techniques, researchers and clinicians can uncover novel insights into the underlying causes of diseases, discover biomarkers for early disease detection, and develop targeted therapies. It has been particularly useful in cancer research, where bioinformatics is used to identify specific gene mutations and genetic variations that drive cancer growth, helping to develop targeted therapies and personalized treatment plans. Overall, bioinformatics in medicine plays a crucial role in advancing our understanding of diseases, improving diagnosis and treatment, and ultimately paving the way for precision medicine.。
生物信息学 英文教科书
生物信息学英文教科书1. "Bioinformatics: A Practical Guide to the Analysis of Genes and Proteins" (Third Edition) by David W. MountThis textbook provides a comprehensive introduction to bioinformatics, covering topics such as sequence analysis, genomics, transcriptomics, proteomics, and systems biology. It includes practical examples and exercises to help readers apply the concepts.2. "Introduction to Bioinformatics" (Second Edition) by Arthur M. LeskThis book offers a broad overview of bioinformatics, including sequence analysis, database searching, phylogenetic inference, and genome analysis. It also covers bioinformatics tools and techniques used in experimental biology.3. "Bioinformatics for Dummies" by John M. Walker and Todd W. J. DavisThis beginner-friendly guide introduces the fundamentals of bioinformatics in an easy-to-understand manner. It covers topics like sequence alignment, database searching, and phylogenetic trees, with a focus on practical applications.4. "Computational Biology: A Practical Introduction to Bioinformatics and its Applications" by Udit Sharma and Navdeep KaurThis textbook provides a comprehensive overview of bioinformatics, including sequence analysis, genome annotation, protein structure prediction, and biological networks. It includes real-life examples and case studies.These textbooks offer in-depth coverage of bioinformatics concepts and techniques, and they can serve as valuable references for students, researchers, and professionals in the field. The specific choice of a textbook may depend on the reader's background, level of expertise, and specific interests within bioinformatics.。
【bioinfo】生物信息学——代码遇见生物学的地方
【bioinfo】⽣物信息学——代码遇见⽣物学的地⽅注:从进⼊⽣信领域到现在,已经过去快8年了。
⽣物信息学包含了我最喜欢的三门学科:⽣物学、计算机科学和数学。
但是如果突然问起,什么是⽣物信息学,我还是⽆法给出⼀个让⾃⼰满意的答案。
于是便有了这篇博客。
起源据说在1970年,荷兰科学家Paulien Hogeweg和Ben Hesper最早在荷兰语中创造了"bioinformatica"⼀词,英语中的"bioinformatics" 在1978年⾸次被使⽤。
这两位科学家当时使⽤该词来表⽰:The study of information processes in biotic systems.该定义中有两个关键词:⽣物系统(biotic systems)和信息过程(information processes)。
但是这⾥的"信息过程"不太好理解。
此外,从该领域的著名期刊——"bioinformatics"期刊名称的变化也可以从另⼀个⾓度来考证"⽣物信息学"这个词的接受程度。
"bioinformatics"创⽴于1985年,改名前的期刊名为:Computer Applications in the Biosciences (CABIOS)同时也是国际计算⽣物学会(the International Society for Computational Biology, ISCB)的会刊,在1998年改为现在的名字。
各个不同时期的定义wiki【定义1】⾸先看⼀下维基百科对⽣物信息学的解释:Bioinformatics /ˌbaɪ.oʊˌɪnfərˈmætɪks/ (About this soundlisten) is an interdisciplinary field that develops methods and softwaretools for understanding biological data. As an interdisciplinary field of science, bioinformatics combines biology, computerscience, information engineering, mathematics and statistics to analyze and interpret biological data. Bioinformatics has beenused for in silico analyses of biological queries using mathematical and statistical techniques.Bioinformatics and computational biology involve the analysis of biological data, particularly DNA, RNA, and proteinsequences. The field of bioinformatics experienced explosive growth starting in the mid-1990s, driven largely by the HumanGenome Project and by rapid advances in DNA sequencing technology.The primary goal of bioinformatics is to increase the understanding of biological processes.这⾥的定义强调交叉学科以及对⽣物学数据的理解,认为最主要的⽣物学数据是DNA、RNA和蛋⽩质的序列数据。
bioinformatics with editor和with reviewer变换
bioinformatics with editor和with reviewer变换Bioinformatics is a rapidly growing field that combines biology and computer science to analyze and interpret biological data. Within this field, there are two primary roles that play a crucial role in the development and dissemination of research findings - the editor and the reviewer. Both of these roles are essential in ensuring the quality and validity of scientific research.Editors in the field of bioinformatics are responsible for overseeing the publication process of research articles. They play a crucial role in selecting manuscripts for publication and ensuring that they meet the standards of the journal. Editors are typically experts in the field and have a deep understanding of the latest advancements and techniques in bioinformatics. They evaluate the significance and novelty of the research, as well as the overall quality of the manuscript.The first step in the editorial process is the submission of a research manuscript by the authors. Editors then review the initial submission to determine if it meets the journal's scope and criteria. If the manuscript is deemed suitable for further evaluation, it is sentout for peer review. The editor identifies potential reviewers who possess the necessary expertise to assess the scientific validity and rigor of the research presented in the manuscript.The role of the reviewer is to critically evaluate the manuscript and provide feedback to the editor. Reviewers are typically researchers or experts in the field who have a deep understanding of the topic being investigated. They assess the methodology, data analysis, and interpretation of the results to ensure that they are accurate and supported by the presented evidence.Reviewers also assess the clarity of the manuscript and provide constructive feedback on how it can be improved. They may suggest additional experiments, analysis, or clarifications to strengthen the research. The feedback from the reviewer is anonymous and confidential, allowing them to provide unbiased assessments of the manuscript.Once the review process is complete, the editor carefully considers the feedback provided by the reviewers. They evaluate the strengths and weaknesses of the manuscript and determine whether it should be accepted, revised, or rejected. If revisions arerequired, the editor communicates the reviewer's feedback to the authors and provides them with an opportunity to address the concerns raised.Authors then revise their manuscript based on the reviewer's feedback and resubmit it for re-evaluation. The editor evaluates the revised manuscript to ensure that the author's responses adequately address the concerns raised by the reviewers. If the editor is satisfied with the revisions, the manuscript may proceed to the final stages of production, including copyediting, proofreading, and formatting.The role of the editor continues throughout the publication process, ensuring that the manuscript is formatted correctly and that it adheres to the journal's guidelines. Editors are also responsible for coordinating with the authors and reviewers to address any remaining concerns or questions.In conclusion, both the editor and the reviewer play crucial roles in the publication process of bioinformatics research. The editor oversees the entire process, from initial submission to finalpublication, ensuring that the research meets the journal's standards. Reviewers, on the other hand, assess the scientific validity and rigor of the research, providing feedback to improve the manuscript. Together, the editor and reviewer ensure the quality and integrity of bioinformatics research findings.。
bioinformatics 医学英语
bioinformatics 医学英语【原创实用版】目录1.生物信息学简介2.生物信息学与医学的关系3.生物信息学在医学领域的应用4.生物信息学对医学英语的要求5.医学英语的发展与挑战正文生物信息学是一门跨学科的科学,它结合了生物学、计算机科学和信息科学,致力于处理和分析生物学和生命科学领域的大量数据。
随着生物技术和生物医学的发展,生物信息学在医学领域的应用越来越广泛,成为现代医学研究的重要组成部分。
生物信息学与医学有着紧密的联系。
医学研究中的许多实验和观察都需要对生物大分子(如蛋白质和核酸)进行分析,而生物信息学提供了处理这些数据的方法和工具。
例如,通过生物信息学技术,研究人员可以对基因序列进行比对和分析,揭示疾病的遗传基础;可以对蛋白质结构进行预测和模拟,了解其功能和作用机制。
生物信息学在医学领域的应用广泛,包括基因组学、蛋白质组学、代谢组学等多个方面。
这些应用为医学研究带来了新的机遇,也带来了挑战。
其中,一个重要的挑战就是对医学英语的需求。
医学英语是医学领域的专业语言,它具有严谨、准确、规范等特点,对于生物信息学研究至关重要。
医学英语的发展与挑战也是生物信息学研究需要关注的问题。
随着医学英语的广泛应用,其在词汇、语法、表达方式等方面也在不断发展和变化。
这给生物信息学研究带来了一定的困难,因为需要对医学英语进行准确的处理和分析。
同时,医学英语的发展也为生物信息学研究提供了新的契机,使得研究人员可以更好地理解和应用医学知识。
总之,生物信息学在医学领域的应用日益广泛,对医学英语的需求也越来越大。
医学英语的发展与挑战也为生物信息学研究带来了新的机遇和困难。
制药行业中的生物信息学与药物靶点预测分析
制药行业中的生物信息学与药物靶点预测分析生物信息学在制药行业的应用旨在利用生物信息学技术进行药物靶点预测分析,以加快药物研发过程并提高药物研究的成功率。
本文将介绍生物信息学在制药行业中的应用,并阐述药物靶点预测分析的重要性和方法。
生物信息学是一门融合了生物学、计算机科学和统计学等多个领域的学科,致力于收集、分析和解释生物学数据。
在制药行业中,生物信息学扮演着重要的角色,旨在加速药物研发的过程,提高药物研究的效率。
药物靶点预测分析是一种通过生物信息学方法,确定候选药物可能的靶点,并评估其与靶点的相互作用,以确定潜在的药物治疗效果的方法。
这种分析方法可以在药物研发的早期阶段就筛选出具有潜在治疗作用的候选药物,减少不必要的实验和研究,节省时间和资源。
药物靶点是药物分子在生物体中发挥作用的特定靶点,通常是特定的蛋白质,如酶、受体等。
在药物研发过程中,选择恰当的药物靶点至关重要。
生物信息学技术可通过分析蛋白质的序列、结构以及与其他分子的相互作用等信息,预测潜在的药物靶点,从而指导药物研发的方向。
生物信息学在药物靶点预测分析中的应用包括以下几个方面。
首先,生物信息学技术可以分析已知的药物靶点数据库,并利用计算方法评估某个分子与已知药物靶点的相似性。
这种相似性分析可以为研究人员提供候选药物靶点,并为药物研发的方向提供指导。
其次,生物信息学技术可以预测某个分子与特定蛋白质之间的相互作用。
通过分析蛋白质的结构,利用计算方法模拟分子与蛋白质之间的结合情况,可以评估分子与靶点的亲和力,从而确定是否具有潜在的治疗作用。
此外,生物信息学技术还可以根据药物分子的属性,通过机器学习算法对药物的靶点进行预测。
通过对大量已知药物的数据进行学习,可以建立预测模型,从而对新的药物分子进行靶点预测。
除了这些方法之外,生物信息学技术还可以利用系统生物学的方法,通过对药物与生物体的整体相互作用网络进行分析,预测药物可能的靶点。
这种方法能够从整体上揭示药物的作用机制,为研究人员提供宝贵的信息。
bioinformatics在基因组学中的应用
bioinformatics在基因组学中的应用随着生物技术的不断发展,基因组学逐渐成为生命科学研究的重要领域。
基因组学研究一般以DNA序列为主要研究对象,而DNA序列的复杂性和巨大性使得基因组学的研究很容易受到计算机算法和数据管理等方面的限制。
因此,在基因组学研究中,bioinformatics这种结合计算机算法和数据管理的交叉学科就显得尤为重要。
作为一门交叉学科,bioinformatics的本质是挖掘出生物学领域的信息,并将其转化为计算机算法和数学模型,从而更好地理解生命现象。
因此,在基因组学研究中,bioinformatics发挥着极为重要的作用。
下面我们就从基因组数据的分析、基因预测、基因表达分析以及生物信息学工具应用等方面来探讨bioinformatics在基因组学中的应用。
1. 基因组数据的分析随着DNA测序技术的不断发展,人们获得了越来越多的基因组数据,但数据量的庞大往往会对研究者的实验室资源和运算能力造成极大的限制。
因此,在基因组数据的分析中,bioinformatics所提供的算法和工具将数据分析的繁琐工作几乎全部自动化,为基因组数据的高效利用提供了保障。
比如,生物信息学工具可以通过减少数据量来提高数据处理的效率,针对大量基因组测序数据,研究者可以利用比对算法、序列重构算法等生物信息学方法,对测序数据中的错配、缺失等错误进行矫正与校正,并对测序的结果进行分析和统计。
另外,利用生物信息学工具可以将基因组数据分析分为两部分,首先进行基于组学的分析,然后进行基于个体的分析,该方法为基因组学的快速进展提供了强有力的支撑。
2. 基因预测基因预测是基因组学研究的关键环节之一,基于基因预测的结果,研究者可以对基因结构与组织信息进行预测和分析。
利用生物信息学工具,可以通过DNA序列的匹配、剪切等算法,将基因组测序数据转化为基因序列信息,实现基因预测。
在利用生物信息学工具进行基因预测时,首先需要建立数学统计模型,基于该模型进行基因预测。
bioinformatics 医学英语
bioinformatics 医学英语摘要:一、生物信息学简介1.生物信息学的定义2.生物信息学的发展历程3.生物信息学在医学领域的重要性二、医学英语概述1.医学英语的定义2.医学英语的发展历程3.医学英语的特点三、生物信息学与医学英语的联系1.生物信息学中的医学英语应用2.医学英语对生物信息学发展的影响3.生物信息学与医学英语的共同发展趋势四、结论1.生物信息学与医学英语的相互促进作用2.我国生物信息学与医学英语的发展现状及挑战3.对我国生物信息学与医学英语发展的建议正文:生物信息学(bioinformatics)是一门研究生物大分子(如蛋白质、核酸等)信息的学科,它通过计算机技术、数学和统计学方法来解决生物学问题。
随着生物科学技术的飞速发展,生物信息学在医学领域的应用越来越广泛,对提高医疗水平、促进医学研究以及开发新药等方面具有重要意义。
医学英语(Medical English)是指应用于医学领域的英语,它涉及到基础医学、临床医学、药学、生物学等多个方面。
医学英语的发展历程与医学科学的进步紧密相连,随着全球化的推进,医学英语在国际交流、医学教育和科研中的作用日益凸显。
医学英语具有专业性强、词汇丰富、表达严谨等特点,这对生物信息学的发展产生了积极影响。
生物信息学与医学英语之间存在密切的联系。
首先,生物信息学中的医学英语应用十分广泛,无论是论文撰写、学术交流还是软件开发,都需要运用医学英语。
医学英语为生物信息学提供了丰富的专业词汇和表达,使得研究者能够更加准确地表达研究成果,提高学术交流的效率。
其次,医学英语对生物信息学的发展产生了重要影响。
随着生物信息学研究的深入,越来越多的医学问题需要通过生物信息学方法来解决。
医学英语为生物信息学研究者提供了一个共同的语言平台,使得国际间的合作变得更加便捷,促进了生物信息学的发展。
最后,生物信息学与医学英语的发展趋势呈现出相互促进的特点。
生物信息学的发展为医学英语提供了更多的实际应用场景,反过来,医学英语的完善又为生物信息学的研究提供了更好的条件。
生物信息学方法 英语
生物信息学方法英语Bioinformatics is a fascinating field that utilizes computational techniques to analyze and interpretbiological data. It's a blend of biology, computer science, statistics, and mathematics, and it's revolutionizing our understanding of the natural world.When it comes to analyzing genetic sequences, bioinformatics methods are invaluable. They can help us identify genes, predict protein function, and even understand the evolutionary relationships between species. The algorithms and software used in this field are incredibly powerful, and they're constantly evolving to meet the demands of modern research.One cool aspect of bioinformatics is how it's being used to fight diseases. Researchers are now able to analyze the genomes of patients and identify genetic mutations that may be linked to specific diseases. This information can then be used to develop targeted treatments andpersonalized medicine. It's truly amazing how far we've come in understanding the genetic basis of health and disease.Another fascinating aspect of bioinformatics is its application to metagenomics, which studies the genomes of entire microbial communities. By analyzing the genetic material extracted from environmental samples, researchers can gain insights into the diversity and function of microbial ecosystems. This information is crucial for understanding the role of microorganisms in ecosystems, as well as their potential impact on human health and the environment.But bioinformatics isn't just about analyzing data.It's also about visualization and communication. Researchers use various tools and software to create beautiful and informative visual representations of their data. These visualizations can help convey complex ideas and patterns in an accessible way, making the results.。
bioinformatics analysis is a technique
bioinformatics analysis is atechniqueBioinformatics Analysis: A Technique Shaping Modern Biomedical ResearchBioinformatics analysis is an intricate technique that revolutionizes the field of biomedical research. It involves the application of computational methods to biological data, enabling scientists to extract meaningful information from vast amounts of genetic, proteomic, and other biological datasets. This technique has become crucial in the post-genomic era, where the amount of biological data generated is exploding at an unprecedented rate.The core of bioinformatics analysis lies in the integration of multiple disciplines, including computer science, statistics, mathematics, and biology. This interdisciplinary approach allows researchers to tackle complex biological problems using advanced computational tools and algorithms. For instance, bioinformatics techniques are used to annotate and interpret genome sequences, predict protein function and interactions, analyze gene expression patterns, and identify biomarkers for various diseases.One of the most significant applications of bioinformatics analysis is in personalized medicine. By analyzing individual genetic variations, bioinformatics can help predict a person's risk for certain diseases and their response to different medications. This information can then be used to develop personalized treatment plans tailored to the unique genetic profile of each patient.Moreover, bioinformatics analysis plays a crucial role in drug discovery and development. By analyzing the interactions between drugs and their targets at the molecular level, bioinformatics can help identify potential drug candidates and predict their efficacy and safety profiles. This information can significantly shorten the drug discovery process and reduce the costs associated with clinical trials.In addition to its applications in personalized medicine and drug discovery, bioinformatics analysis also has numerous other uses. It can be used to study the evolution of species, the mechanisms of gene regulation, and the interactions between different biological systems. Bioinformatics analysis is also essential in the field of epidemiology, where it helps track the spread of diseases and identify potential outbreaks.In conclusion, bioinformatics analysis is a technique that has revolutionized biomedical research. Its interdisciplinary nature and the use of advanced computational methods have enabled researchers to extract meaningful information from vast amounts of biological data. This information has led to breakthroughs in personalized medicine, drug discovery, and other areas of biomedical research, promising better health outcomes and improved quality of life for millions of people.。
下一代免疫疗法即将问世,体内细胞重编程技术有望引领潮流
下一代免疫疗法即将问世,体内细胞重编程技术有望引领潮流2022-08-05 10:07·医学营养治疗原创药明康德药明康德 2022-08-05 07:30 发表于美国编者按:在传统的小分子药物和抗体药物之外,细胞和基因疗法、RNAi和其它寡核苷酸疗法、CRISPR基因编辑疗法等更多新型的分子类型在近年逐渐走向前台,成为了生物医药产业的关注焦点,有望改写患者们的未来治疗格局。
生物医药迈入崭新时代之际,药明康德内容部也已启动“迅猛新分子”系列,邀请新分子疗法汹涌浪潮的弄潮儿进行访谈——这些访谈大咖执掌的公司都专注于开创全新类型的疗法,并在近期完成了大额早期融资,可谓是产业中冉冉升起的未来之星。
在药明康德内容部的系列访谈里,他们也将向产业介绍如何使用新型分子类型突破现有疗法的局限,带来崭新的突破!嘉宾简介:本期访谈的嘉宾Daniel Getts博士是Myeloid Therapeutics公司的联合创始人和首席执行官。
Myeloid Therapeutics公司是一家临床阶段的mRNA免疫疗法公司,主要利用髓系细胞来设计新的疗法,通过引发机体中广泛的免疫反应来治疗癌症和自身免疫性疾病。
Daniel Getts博士有着多年医药行业经验,曾先后担任Tolera Therapeutics、Cour Pharmaceuticals、TCR2 Therapeutics等多家医药公司的研发负责人,在新药靶点开发、开展临床前研究与转化医学项目等方面积累了丰富的经验,并在同行评议的学术期刊中发表了超过45篇研究,其中多篇论文发表在Nature Biotechnology, ScienceTranslational Medicine等知名学术期刊。
在药明康德内容部的系列访谈里,他将与我们分享他对于细胞免疫疗法研发的洞见。
药明康德内容部:Daniel,感谢您抽空接受我们的访谈!针对癌症和自身免疫性疾病疗法的研发,在您看来医药行业所面临的挑战有哪些,以及您的公司相应的对策是什么?Daniel Getts博士:Myeloid公司主要针对疑难、并且具有高度未竟医疗需求的病症,比如外周T细胞淋巴瘤(PTCL)、肝癌和胶质母细胞瘤。
生物信息学资源与方法
Helicobacter pylori
Buchnerasp. APS
Escherichia coli大肠杆菌
human
Arabidopsis 拟南芥
Thermotoga maritima
Thermoplasma acidophilum
mouse
Caenorhabitis elegans
rat
Borrelia burgorferi
以具有特殊功能的蛋白为基础构建的有免疫球蛋白数
据库Kabat,蛋白激酶数据库PKinase等 以三维结构原子坐标为基础构建的数据库为结构分子 生物学研究提供了有效的工具,如蛋白质二级结构构 象参数数据库DSSP,已知空间结构的蛋白质家族数据
库FSSP,已知空间结构的蛋白质及其同源蛋白数据库
蛋白质序列数据库 )
PIR ( Protein Info. Resource SWISS-PROT (http://www.expasy.ch)
结构数据库
蛋白质结构数据库
PDB (Protein Data Bank),
2014.6
What is Bioinformatics
Coined by Hwa Lim in the late 1980s, popularized in the 1990s through its association with the HGP Definitions from NIH
Bioinformatics: Research, development, or application of computational tools and approaches for expanding the use of biological, medical, behavioral or health data, including those to acquire, store, organize, archive, analyze, or visualize such data Computational Biology: The development and application of data-analytical and theoretical methods, mathematical modeling and computational simulation techniques to the study of biological, behavioral, and social systems 中文定义:生物信息学是综合运用数学、计算机科学和生物学工具,以 获取、加工、储存、分配、分析和解释生物信息,理解生物数据中 的生物学含义
bioinformatics名词解释
bioinformatics名词解释嘿,你知道 bioinformatics 吗?这可不是个一般的玩意儿啊!bioinformatics 就像是生物世界和信息世界的奇妙融合桥梁(就好比牛郎织女之间的鹊桥一样)!想象一下,在生物的海洋里,有无数的数据和信息在涌动,基因序列啦、蛋白质结构啦等等。
而 bioinformatics 呢,就是那个能把这些复杂的数据整理得井井有条的高手(就像一个超级厉害的图书管理员能把海量书籍管理得妥妥当当)!它用各种厉害的工具和方法,去分析、理解这些生物信息。
比如说,通过 bioinformatics,科学家们可以快速地比较不同物种的基因序列,发现它们之间的相似之处和差异(这就好像在一个大宝藏中寻找那些特别的宝贝)。
这能帮助我们更好地理解生物的进化、疾病的发生机制等等。
“哎呀,那 bioinformatics 岂不是超级重要?”你可能会这么问。
那当然啦!它就像是生物研究的秘密武器(跟孙悟空的金箍棒一样厉害)。
没有它,我们对生物的了解可就要大打折扣了。
再想想看,医生们也能借助 bioinformatics 来诊断疾病呢!通过分析患者的基因信息,找到疾病的根源(就如同侦探在蛛丝马迹中寻找破案的关键线索)。
这多神奇啊!总之,bioinformatics 是个充满魅力和潜力的领域,它让我们对生命的奥秘有了更深入的探索和理解。
它就像是一把打开生物奥秘大门的钥匙(哇塞,太重要啦)!所以啊,一定要好好了解它,说不定你也会被它深深吸引呢!我的观点就是:bioinformatics 是现代生物学不可或缺的一部分,它有着巨大的价值和意义,值得我们深入学习和研究。
基于生物信息学的药物靶标预测方法及应用
基于生物信息学的药物靶标预测方法及应用生物信息学(bioinformatics)是一门跨学科的研究领域,结合生物学和计算机科学,通过分析和解释生物学数据来揭示生命现象的本质。
药物靶标预测是生物信息学在药物研发领域中的一个重要应用,它能够帮助研究人员更好地理解药物与生物系统之间的相互作用,从而提高药物研发的效率和成功率。
药物靶标是指药物与生物体内特定蛋白质相互作用,并通过调控这些蛋白质的功能来治疗疾病。
传统的药物研发过程通常耗时耗力,而且成功率较低。
生物信息学的药物靶标预测方法通过结合大量的生物数据和计算方法,能够快速地筛选出候选的药物靶标,从而加速新药的研发。
目前,有多种基于生物信息学的药物靶标预测方法被广泛应用。
其中主要包括基于序列相似性、结构相似性、机器学习和网络分析的方法。
基于序列相似性的药物靶标预测方法是最早被开发和使用的方法之一。
该方法通过比对已知药物靶标与未知蛋白质序列的相似性,来推断未知蛋白质的可能药物靶标。
这种方法的优势在于简单快速,但缺点是对于没有较高相似度的蛋白质对之间的关系无法准确预测。
基于结构相似性的药物靶标预测方法则是基于蛋白质结构的相似性进行预测。
蛋白质结构的相似性通常可以反映它们在功能上的相似性。
通过比对已知药物靶标与未知蛋白质结构的相似性,可以推测未知蛋白质的可能药物靶标。
这种方法的优势在于能够准确地预测结构相似性较高的蛋白质对之间的药物靶标关系,但对于结构相似性较低的蛋白质对,预测准确性较差。
机器学习方法是近年来被广泛应用于药物靶标预测的一种方法。
它基于机器学习算法,通过训练已知药物靶标与其他蛋白质特征之间的关系,来预测未知蛋白质的药物靶标。
这种方法的优势在于可以处理大量和多样化的生物数据,通过不断的学习和优化,提高预测的准确性。
机器学习方法还可以结合其他预测方法,如序列和结构相似性,进一步提高预测的准确性。
网络分析方法是一种基于生物网络的药物靶标预测方法。
生物网络是指生物体内的分子相互作用网络或代谢网络等。
全基因组低深度测序技术检测胎儿单纯性法洛四联症染色体变异
《中国产前诊断杂志(电子版)》 2019年第11卷第4期·论著· 全基因组低深度测序技术检测胎儿单纯性法洛四联症染色体变异黄慧1# 杨颖2# 王卫云1 陈芳2 杨帆1 成晨1 陈欣林1 郭健2 (1.湖北省妇幼保健院超声诊断科,湖北武汉 430070;2.深圳华大生命科学研究院,广东深圳 518120)【摘要】 目的 采用全基因组低深度测序技术检测单纯性法洛四联症胎儿中染色体异常,包含染色体非整倍体和拷贝数变异(CNVs),探讨胎儿期单纯性法洛四联症的遗传学特征。
方法 收集2014年1月至2017年12月在湖北省妇幼保健院经产前超声会诊及引产后病理检查确诊为单纯性法洛四联症的胎儿24例,利用低深度全基因组测序的方法检测染色体变异,采用生物信息学方法对测序结果进行分析,最终根据美国医学遗传学会的解读流程对检出的CNVs进行解读。
结果 在24例确诊为单纯性法洛四联症胎儿中,无染色体非整倍体的检出,5例检出有拷贝数变异,其中4例发现有致病性CNVs(16.7%,4/24)。
检出有致病性CNVs的4例胎儿中,3例为DiGeorge综合征,1例为疑似8p倒位重复伴末端缺失(8pinvertedduplication/deletion)综合征。
结论 胎儿期单纯性法洛四联症与CNVs关系密切,全基因组低覆盖度测序技术可用于发现与单纯性法洛四联症相关的CNVs。
【关键词】 全基因组低深度测序技术;单纯性法洛四联症;胎儿【中图分类号】 R714.53 【文献标识码】 A犇犗犐:10.13470/j.cnki.cjpd.2019.04.005#共同第一作者基金项目:湖北省卫生计生科研基金(WJ2018H0164、WJ2018H0132、WJ2017Z019) 通信作者:郭建,E mail:guojian@genomics.cn;陈欣林,E mail:928339431@qq.com【犃犫狊狋狉犪犮狋】 犗犫犼犲犮狋犻狏犲 Todetectthechromosomeanomalies,includingwholechromosomeaneuploidyandcopynumbervariations(CNVs)fromfetuseswithisolatedtetralogyofFallot(TOF)bythelow cov eragewhole genomesequencing,andexplorethegeneticcharacteristicsoffetalisolatedTOF.犕犲狋犺狅犱 Atotalof24fetuseswithisolatedTOFwererecruitedforthisstudyfromJanuaryin2014toDecemberin2017inMaternalandChildHealthHospitalofHubeiProvince.Allofthesefetuseshadechocardiographyatourtertiaryreferralcenter,andthediagnosisofisolatedTOFwasconfirmedbypathologicalexamina tionafterinductionoflabor.Thechromosomeanomalieswasdetectedbylow coveragewhole genomese quencingandanalyzedbybioinformaticstools.TheirpathogenicitywasinterpretedbasedontheguidelinesoftheAmericancollegeofMedicalGeneticsforsequencevariants.犚犲狊狌犾狋狊 Ofthe24fetusesconfirmedwithisolatedTOF,nochromosomeaneuploidywasdetected,andCNVswereidentifiedin5fetuses.Path ogenicCNVsweredeterminedin4fetuses,accountingfor16.7%ofisolatedTOFinourcaseseries.ThesepathogenicCNVswerefoundedtobeassociatedwithknownsyndromes,includingDiGeorgesyn drome(3cases),and8pinvertedduplication/deletionsyndrome(1case,neededtobeconfirmed).犆狅狀犮犾狌 狊犻狅狀狊 CNVsarelikelytobeoneoftheimportantcausesofisolatedTOFinfetuses.Andthelow coveragewhole genomesequencingtechnologyisafeasiblediagnostictechniqueforfetuseswithIsolatedTOF.【犓犲狔狑狅狉犱狊】 Low coveragewhole genomesequencing;IsolatedtetralogyofFallot;Fetus 52·论著·《中国产前诊断杂志(电子版)》 2019年第11卷第4期 法洛四联症(tetralogyofFallot,TOF)是一种常见的严重性心脏病,临床症状为肺动脉狭窄、主动脉骑跨、室间隔缺损和右心室肥大[1]。
biopharmaceuticals 的意思
biopharmaceuticals 的意思
生物制药是指利用生物技术生产的药物,通常通过生物制造过程获得。
这些生物制药通常包括蛋白质药物、抗体、疫苗和基因治疗药物等。
与传统药物相比,生物制药具有更高的复杂性和专一性,因此在治疗许多疾病方面具有更好的疗效。
生物制药的发展源远流长,早在20世纪90年代,第一批生物制药就已经问世,其中包括丙型肝炎抗病毒药物和白血病抗癌药物。
随着科学技术的不断发展,生物制药领域也在不断拓展,如今已经涉及到肿瘤治疗、免疫治疗、神经退行性疾病治疗等多个领域。
生物制药的制造过程通常包括以下几个步骤:基因克隆、表达、纯化和验证。
首先,通过基因克隆技术将需要表达的基因插入到适当的表达宿主中,然后通过生物反应器等设备进行表达,提取并纯化目标蛋白质,最终通过验证确保产品的质量和纯度。
生物制药的研发和生产过程相对较为复杂,需要投入大量的研发资金和人力物力,因此其研发周期较长,但具有较高的市场价值。
另外,生物制药相对于化学合成药物而言,更容易与生物体相容,能够更好地发挥药效,减少对患者的副作用。
随着生物技术的不断进步,生物制药领域也在不断创新,涌现出越来越多的新药。
有些生物制药甚至可以通过基因工程技术实现个体化治疗,为患者提供更加精准的治疗方案。
此外,生物制药还可以通过提高药物的靶向性和特异性,降低药物的毒副作用,从而提高药物的安全性和有效性。
总的来说,生物制药作为一种新型的药物疗法,具有巨大的发展潜力。
随着生物技术的不断进步和商业化的推动,生物制药必将在未来取得更大的突破,为人类健康事业做出更大的贡献。
非侵入性产前诊断——母体血中胎儿DNA检测
非侵入性产前诊断——母体血中胎儿DNA检测于君【摘要】目前产前诊断主要通过绒毛取样或羊膜腔穿刺获得胎儿遗传物质,可能会对妊娠妇女和胎儿造成影响.近年来,通过检测母血中胎儿DNA进行非侵人性产前诊断飞速发展,胎儿性别和RhD血型鉴定已广泛应用于临床,单基因遗传病和非整倍体的准确诊断也进行了实验论证.相信在未来几年,检测母血中胎儿DNA进行非侵人性产前诊断将在临床上有更广泛的应用.【期刊名称】《国际生殖健康/计划生育杂志》【年(卷),期】2012(031)004【总页数】4页(P308-311)【关键词】产前诊断;染色体;DNA;非整倍体;性别预选;血型鉴定和交叉配血;遗传性疾病,先天性;实验室技术和方法;细胞遗传学分析【作者】于君【作者单位】250002 济南,济南军区空军后勤部卫生处【正文语种】中文近年来,随着围生儿死亡率和出生缺陷率的逐渐降低,产前诊断逐渐成为产科的重要组成部分。
目前,临床上胎儿先天性缺陷或遗传性疾病的诊断主要通过侵入性手段进行,如绒毛取样、羊膜腔或脐静脉穿刺,但这些侵入性产前诊断技术都有造成妊娠妇女流产的风险[1]。
因此,近年来非侵入性产前诊断技术的研究备受关注。
大量研究表明,胎儿细胞和胎儿游离DNA存在于母体外周血中,从而使利用母体外周血获取胎儿遗传物质进行非侵入性产前诊断成为可能。
然而,胎儿细胞在母体外周血中含量极少,其有核红细胞占母体外周血中有核细胞的1/105~1/109,而且胎儿细胞在产后几年内长期存在于母体血液中[2],因此,如何富集如此微量的胎儿细胞是非侵入性产前诊断技术面临的瓶颈。
目前的富集技术比较复杂,且成本较高,仍不能广泛应用于临床[3]。
1997年,Lo等[4]首先在男性胎儿的妊娠母体血浆和血清中发现了胎儿Y染色体特异DNA序列,10 μL血浆或血清中胎儿DNA的检出率高达70%~80%。
然后该研究组通过实时定量聚合酶链反应(PCR)技术检测到胎儿DNA浓度占母体血循环总DNA的3.4%~6.2%,随妊娠时间的增加而增加,并提示从妊娠7周开始就可以在母体外周血中检测到胎儿DNA[5]。
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International Journal ofMolecular SciencesReviewBioinformatics Approaches for Fetal DNA Fraction Estimation in Noninvasive Prenatal TestingXianlu Laura Peng1,2and Peiyong Jiang1,2,*1Li Ka Shing Institute of Health Sciences,The Chinese University of Hong Kong,Hong Kong,China;laurapeng@.hk2Department of Chemical Pathology,The Chinese University of Hong Kong,Prince of Wales Hospital, Hong Kong,China*Correspondence:jiangpeiyong@.hk;Tel.:+852-3763-6056Academic Editor:William Chi-shing ChoReceived:18January2017;Accepted:11February2017;Published:20February2017Abstract:The discovery of cell-free fetal DNA molecules in plasma of pregnant women has created a paradigm shift in noninvasive prenatal testing(NIPT).Circulating cell-free DNA in maternal plasma has been increasingly recognized as an important proxy to detect fetal abnormalities in a noninvasive manner.A variety of approaches for NIPT using next-generation sequencing have been developed,which have been rapidly transforming clinical practices nowadays.In such approaches, the fetal DNA fraction is a pivotal parameter governing the overall performance and guaranteeing the proper clinical interpretation of testing results.In this review,we describe the current bioinformatics approaches developed for estimating the fetal DNA fraction and discuss their pros and cons. Keywords:noninvasive prenatal testing;circulating cell-free DNA;fetal DNA fraction1.IntroductionThe discovery of circulating cell-free fetal DNA in maternal plasma[1]has created a paradigm shift in noninvasive prenatal testing(NIPT),which has rapidly made its way into clinical practices worldwide,for example,cell-free DNA-based chromosomal aneuploidy detection[2–8]and diagnosis of monogenic diseases[9–15].The circulating cell-free DNA(cfDNA)in a pregnant woman is a mixture of predominant maternal DNA derived from the hematopoietic system of the mother[16,17]and fetal DNA released through the apoptosis of cytotrophoblast cells during fetal development[18,19]. The proportion of fetal DNA molecules among the total cfDNA molecules in maternal circulation is expressed as fetal DNA fraction,which is a paramount factor for determining the overall performance of NIPT[15,20–22]and interpreting clinical assessments[7,23–25].In noninvasive fetal aneuploidy detection,the fetal DNA fraction in maternal plasma is linearly correlated with the extent of chromosomal abnormalities present in plasma of pregnant women[3,6,7]. The fetal DNA concentration below4%in a maternal plasma sample would suggest a potential issue present in the quality control(QC)step,because the limited amount of fetal DNA molecules to be detected and analyzed may give rise to a false negative result[20,26–28].Therefore,it is important to estimate the fetal DNA fraction accurately,making sure that it has passed the QC threshold to guarantee a sufficient amount of fetal DNA present in a testing sample and make it possible to arrive at a proper interpretation of the sequencing result.In addition,the fetal DNA fraction has been incorporated into bioinformatics diagnostic algorithms by a number of laboratories[7,23,24].Monogenic diseases comprise a larger proportion of genetic diseases than chromosomal aneuploidies[15].However,the cfDNA-based NIPT for single-gene diseases is much more challenging, because the cfDNA in maternal plasma is generally of minor population,hampering the reliable deduction of the maternal inherence of fetus at single-nucleotide resolution.Technologically,the Int.J.Mol.Sci.2017,18,453;doi:10.3390//journal/ijmsdevelopment of relative haplotype dosage analysis(RHDO),which utilizes information regarding parental haplotypesflanking the variants of interest,has been demonstrated to greatly improve the accuracy of single-gene disorder detection[9,10,13].More recently,researchers have illustrated that the use of linked-read sequencing technology allows for directly ascertaining parental haplotypes surrounding the genes of interest,making RHDO analysis a universal NIPT method for single-gene diseases[29].This work has made an important step forward towards the real clinical utility regarding cfDNA-based single-gene disease testing.Such RHDO analysis took advantage of the fetal DNA fraction as a key parameter to determine the statistical thresholds,indicating if a particular maternal haplotype presumably inherited by the fetus exhibits a statistically significant over-presentation in maternal plasma of a pregnant woman[9,23].In this review,we discuss a number of existing approaches for the determination of fetal DNA fraction,as well as their advantages and disadvantages(Table1).The simplified principles for these approaches are diagrammatically depicted in Figure1.Figure1.Schematic illustration of current approaches for the determination of fetal DNA fraction in maternal circulating cell-free DNA(cfDNA).(a)Y chromosomal(chr)sequence-based fetal DNA fraction estimate[3,22];(b)Single-nucleotide polymorphism(SNP)-based approach.A direct way to estimate the fetal DNA fraction is to use the SNP loci,where both mother and father are homozygous but with different alleles.The resulting fetal genotype is obligately heterozygous.In maternal plasma,the fetal DNA fraction can be directly deduced by calculating the proportion of fetal specific alleles[9,30].Based on this concept,two extended versions of SNP-based methods for fetal DNA fraction estimate have been developed,namely FetalQuant and FetalQuant SD,which can be used without the need of both paternal and maternal genotype information[31,32];(c)cfDNA count-based approach.Read densities across the genome-wide50KB windows arefitted into a neural network model to predict the fetal DNA fraction[33];(d)Differential methylation-based approaches[17,26,34,35];(e)cfDNA size-based approach.The proportion of short cfDNA molecules is correlated with fetal DNA fraction[36];(f)Nucleosome track-based approach.Cell-free DNA distribution at the nucleosomal core and linkerregions is correlated with fetal DNA fraction[37].Table1.The summary of current approaches for estimating fetal DNA fraction. Approaches Advantages LimitationsY Chromosome[3,22]Simple and accurate NOT applicable for pregnancies with female fetusesMaternal plasma DNA sequencingdata with parental genotypes[9,30]Direct and accuratePaternal DNA may notbe availableTargeted sequencing of maternal plasma DNA(FetalQuant)[31]Sequencing maternal plasma DNAonly;accurateHigh sequencing depth is requiredShallow-depth sequencing ofmaternal plasma DNA coupled with maternal genotypes(FetalQuant SD)[32]Shallow-depth sequencing ofmaternal plasma DNA;accurateMaternal genotype requirementwill add additional costs;therecalibration curve is required tobe rebuilt for different sequencingand genotyping platformsShallow-depth maternal plasma DNA sequencing data(SeqFF)[33]Only shallow-depth sequencing ofmaternal plasma DNA;single-endsequencing;easy to be integratedinto the routine noninvasiveprenatal testing(NIPT)Large-scale samples are needed totrain the neutral network;need toimprove the accuracy when thefetal DNA fraction is below5%Differantial methylation [17,26,34,35]AccurateEither bisulfite conversion ordigestion withmethylation-sensitive restrictionenzymes may affect the accuracy;genome-wide bisulfite sequencingis too expensive and prohibitivefor the routine NIPTcfDNA fragment size[36]Only shallow-depth sequencing ofmaternal plasma DNA;easy to beintegrated into the routine NIPTModerate accuracy;paired-endsequencing would increasethe costsNucleosome track[37]Only shallow-depth sequencing ofmaternal plasma DNALower accuracy;high-depthsequencing data is requiredduring the training step2.Current Approaches Developed to Estimate Fetal DNA Fraction2.1.Y Chromosome-Based ApproachIn the early works,genetic markers located on Y chromosome which are paternally inherited, such as gene SRY,DYS14and ZFY,were used to indicate the fraction of fetal DNA molecules based on PCR assays[23,38,39].For instance,the ratio of the concentration of the sequences from Y chromosome to that of an autosome was used for the determination of fetal DNA fraction.In the context of NIPT using massively parallel sequencing,the proportion of all sequence reads from Y chromosome can be translated to the fetal DNA fraction[3,22].Although these methods are simple and accurate,they are only applicable to pregnancies carrying male fetuses.2.2.Maternal Plasma DNA Sequencing Data with Parental Genotype-Based ApproachWith the use of parental genotypes,fetal-specific alleles in maternal plasma can be readily identified from the sequence reads.Briefly,the fetal genotypes are obligately heterozygous at single-nucleotide polymorphism(SNP)loci,where both father and mother are homozygous but with different genotypes(e.g.,A/A for paternal genotype and C/C for maternal genotype).Then the fetal DNA fraction can be quantified by calculating the ratio of fetal-specific alleles(A)to the total alleles in plasma DNA[7,9,30,40].Even though this method is a direct and accurate way to assess the fetal DNA fraction and generally considered as a gold standard[9],the feasibility of this approachis sometimes hindered by the requirement of parental genotypes,because(1)only maternal blood samples would be collected and maternal plasma DNA are subject to sequence for NIPT in most clinical settings;and(2)it is not uncommon that the genotype of the biological father may not be available in practice[41].2.3.High-Depth Sequencing Data of Maternal Plasma DNA-Based ApproachTo obviate the requirement of parental genotype information,an approach called FetalQuant was developed to measure the fetal DNA fraction through the analysis of maternal plasma DNA sequencing data at high depth using targeted massively parallel sequencing[31].In this method, a binomial mixture model was employed tofit the observed allelic counts with the use of the underlying four types of maternal-fetal genotype combinations(AA AA,AA AB,AB AA,AB AB,where the main text and subscript represent the maternal and fetal genotypes,respectively).In this model,the fetal fraction was determined through the maximum likelihood estimation.The predicted result of this method is very close to the one deduced by the parental genotypes-based approach(the correlation coefficient is not available).However,the limitation of this approach would be that the sequencing depth is required to be as high as~120×by targeted sequencing to robustly determine the fetal alleles[31]. 2.4.Shallow-Depth Maternal Plasma DNA Sequencing Data with Maternal Genotype-Based ApproachAs an extended version of FetalQuant,FetalQuant SD[32]was recently developed based on shallow-depth sequencing data coupled with only maternal genotype information.The rationale of this approach is to take advantage of the fact that any alternative allele(non-maternal alleles)present at an SNP locus where the mother is homozygous would theoretically suggest a fetal-specific DNA allele. Briefly,the homozygous sites in a pregnant woman were identified by genotyping her blood cells using microarray technologies.Then,plasma DNA molecules with alleles different from the maternal homozygous sites(i.e.,non-maternal alleles)were identified,which were specifically derived from the father in theory.Thus,the fractions of such non-maternal alleles were hypothesized to correlate with fetal DNA fractions under the assumption that the error rates stemmed from sequencing and genotyping platforms are relatively constant across different cases.Therefore,a linear regression model wasfirst trained between the fraction of non-maternal alleles and actual fetal DNA fraction estimated by parental genotypes-based approach,and then the fetal DNA fractions were predicted with the use of the trained model in an independent validation dataset,exhibiting a very high accuracy(r=0.9950, p<0.0001,Pearson correlation)even using1million sequencing reads.However,the parameters in this model might be varied according to sequencing and genotyping platforms,because various platforms are characterized with different error properties,which may contribute to the measured non-maternal alleles.On the other hand,the extent of heterozygosity might be different in different ethnic groups,which could confound the accuracy of fetal DNA fraction prediction.The advantage of this model is that once thefinal well-trained model is achieved,it could be readily applied to any datasets,as long as they are generated from the same platform and population.2.5.Shallow-Depth Maternal Plasma DNA Sequencing Data-Based ApproachRecently,a new approach,named SeqFF,has been developed,attempting to make it possible to directly estimate fetal DNA fraction from the routine data of NIPT without any additional effort. In this approach,using single-end random sequencing of the maternal plasma,read count within each50KB autosomal region was analyzed tofit a high-dimensional regression model[33].The normalized read counts in50KB bins originating from chromosomes except chromosomes13,18,21, X,and Y were used as predictor variables,and the model coefficients were determined by making use of elastic net(Enet)and reduced-rank regression model[33].SeqFF showed a good correlation with Y chromosome-based method in two independent cohorts(r=0.932and0.938,respectively,Pearson correlation)[33].However,such high-dimensional model would require large-scale samples during training,and the performance appeared to be greatly deteriorated when the fetal DNA fraction isbelow5%,possibly because the number of cases with fetal DNA fraction<5%was not sufficient to train the Enet model.2.6.Fetal Methylation Marker-Based ApproachDNA methylation is a process by which a methyl group is added to cytosine nucleotides[42,43]. In mammalian somatic cells,the DNA methylation of cytosine in CpG dinucleotides is frequently methylated(~70%of the CpGs)[44].Different organs have been suggested to show variable methylation profiles,which would allow us to identify the tissue of origin analyzing the regions with differential methylation states[17,45].Indeed,researchers used the placenta-specific methylation markers to estimate the fetal DNA concentration[26,34].For example,a methylation-sensitive restriction enzyme has been used to digest hypomethylated maternal-derived RASSF1A promoter sequences,while it left the methylated counterparts of the fetal-derived sequences unaffected, thus allowing the discrimination of the methylated fetal DNA molecules from the unmethylated maternal background for the calculation of fetal DNA fraction[34].Similarly,based onfive differentially methylated regions comparing placental tissue and maternal buffy coat mined by using methyl-cytosine immunoprecipitation and CpG island microarrays,Nygren et al.developed a fetal quantitative assay(FQA)permitting the calculation of fetal DNA fraction in a plasma sample[26]. In FQA,by measuring the copy number of total DNA(maternal and fetal)and fetal methylated DNA after methylation-sensitive restriction enzyme digestion,the assay achieved good agreement with Y chromosome-based quantification(r=0.85,p<0.001,Pearson correlation).However,the analytical process used for quantifying these epigenetic markers involves digestion with methylation-sensitive restriction enzymes,and thus its stability needs to be further verified in large-scale datasets generated from different research centers.Furthermore,massively parallel bisulfite sequencing provides an alternative way to estimate the fetal DNA fraction according to the ratio of fetal-derived DNA molecules within differentially methylated regions[35].Using such bisulfite sequencing,the placenta has been demonstrated to exhibit a different methylation profile compared with other tissues[17,35].Therefore,a general approach, referred to as plasma DNA tissue mapping,for disentangling tissue contributors to cell-free DNA has been developed by leveraging the principle that different tissues within the body show different DNA methylation ing whole-genome bisulfite sequencing,the methylation profile of cell-free DNA across over5800DNA methylation markers was used to correlate the tissue-related methylation profiles,for the inference of the proportional contributions from different tissues in plasma[17].Using this new approach,placenta contribution was verified by genotype-based approaches.However,this genome-wide bisulfite sequencing-based tissue mapping algorithm in the present version would be too expensive for routine NIPT.2.7.Cell-Free DNA Size-Based ApproachFetal-derived and maternal-derived DNA molecules in a plasma sample have been observed to exhibit different fragmentation patterns,namely,fetal DNA being generally shorter than maternal DNA[9,46].Therefore,a higher fetal DNA fraction should be theoretically associated with an increased percentage of short DNA ing paired-end sequencing,Yu et al.developed a new method to estimate fetal DNA concentration based on the ratio between the count of fragments ranging from 100to150bp and from163to169bp[36].These size cutoffs gave their optimal performance among multiple size combinations.In the training dataset consisting of36samples,a linear regression model was established between the size ratio and fetal DNA concentration determined by the proportion of chromosome Y sequences(r=0.827,p<0.0001).Then using the derived model,the size ratio was translated to the fetal DNA fraction for each sample in the validation dataset.Intriguingly,the authors also proposed to calculate the size ratio using capillary electrophoresis of sequencing libraries directly, which is readily available before sequencing without additional costs.2.8.Cell-Free DNA Nucleosome Track-Based ApproachRecently,the investigation of nucleosomal origin of plasma DNA has been increasingly recognized as an appealing direction,which has been discussed in a number of studies[9,36,37,47].One important clue directing to such origin has been unravelled in two studies with the use of the high-resolution size profiling of maternal plasma DNA[9,36].It has been reported that the size distribution of the total maternal plasma DNA is characterized by a166bp major peak with a series of small peaks occurring at10bp periodicities,suggesting that a predominant population of plasma DNA molecules have a size of166bp.In contrast,fetal DNA molecules were found to have a dominant population with 143bp in size.It has been speculated that the166bp molecules would represent cfDNA containing the nucleosome core plus the linker[9].However,the143bp molecules would suggest molecules subject to the trimming of linker DNA[9].On the basis of this hypothetical model,Straver et al.pooled maternal plasma DNA from298cases to generate a hypothetical“nucleosome track”[37].Interestingly, the frequency of reads starting within73bp upstream and downstream regions of the inferential center of nucleosome was found to be positively correlated with the fetal DNA fraction,however,giving a relatively lower correlation coefficient than other methods(r=0.636,p=1.61×10−18,Pearson correlation).Thus,further development of a“nucleosome track”-based approach is needed for the clinical requirement.3.ConclusionsThe past decade has witnessed a tremendous advance in the technologies and bioinformatics algorithms for the analysis of circulating cfDNA.With the availability of massively parallel sequencing, noninvasive prenatal testing has become increasingly popular and presented itself as an exemplar in translational medicine research.In NIPT,a rapid,simple,accurate and cost-effective way to estimate fetal DNA fraction is highly desired,typically for the endeavors to make NIPT for single-gene diseases clinically practical.In particular,the accuracy of the estimation of low fetal DNA fraction is essential for determining the QC states and interpreting the clinical outcomes.On the other hand,the fetal DNA fraction could be related to pregnancy outcome;for example,the low fetal DNA fraction may be associated with small or dysfunctional placentas[48],suggesting its potential diagnostic value.Therefore,a large-scale validation for the accuracy of low-fetal DNA fraction estimation would still be needed for some aforementioned approaches,for example,size-,count-and nucleosome profile-based methodologies.We may expect that further in-depth analyses for such properties regarding size and nucleosome profiles would shed new insights into the mechanisms of cell-free DNA generation.As reported in the latest ultra-deep plasma DNA study[49],it was revealed that a number of preferred DNA ends in maternal plasma carry information directing to their tissue of origin(fetal-or maternal-derived DNA).The ratio of the number of fetal-preferred ends to maternal-preferred ends is positively correlated with the fetal DNA fraction in maternal plasma[49].This novel direction of cfDNA exploration regarding fragment ends has opened up new possibilities to study the complexity associated with non-randomness of plasma DNA ends,providing a new way to investigate the highly orchestrated cfDNA fragmentation patterns.More studies are needed to elucidate the relationship between the various factors as well as their interactions,for example,methylation[17],nucleosome footprints[47],and the underlying mechanisms governing the end-cutting patterns of plasma DNA. 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