DNA测序技术的发展进程

随着DNA测序技术的发展,生物信息学、分子遗传学、精准医学以及基因组学等新兴学科也随之涌现,推动着生命科学研究的不断深入。本文就DNA测序技术发展进程中的几代测序技术的原理及优缺点进行介绍,并对以后测序技术的发展进行展望。     

2022年精准医学发展态势

精准医学的形成是科技自身发展的客观必然,也是公众对健康需求的推动,体现了医学科学发展趋势,也代表了临床实践发展的方向。经过几年的快速发展,精准医学研究理念和范式已广泛推广,精准医学体系逐渐成熟走向应用。该文在系统梳理2022年精准医学领域的发展布局与举措,前瞻领域发展新趋势、研究新进展、产业新突破的基础上,展望了领域未来发展前景。当前,精准医学的科学价值与健康维护作用进一步凸显,各国系统布局、长期规划、持续加码支持精准医学发展;大型人群队列平台建设广泛开展、疾病研究与疾病精准防诊治方案开始成熟,诊断方案与治疗药物的开发思路及审批标准也开始发生转变,精准医学发展进入新阶段。未来,随高质量大型队列的建设、生命组学技术的发展,以及相关政策体系的完善,精准医学将呈现巨大发展前景。     

在恒河猴基因组学框架下研究人类演化与调控

恒河猴作为人类近缘的模式生物,在基础与转化医学研究中具有独特优势,但其应用受到功能基因组学数据匮乏、基因结构混乱、研究平台缺乏等限制。近年来,深度测序技术的发展为突破这些技术瓶颈提供了机遇。现综述在深度测序技术支撑下,以恒河猴为背景开展的基因组学与分子演化研究,以期抛砖引玉,推动非人灵长类领域的研究进程。     

环境微生物宏基因组学研究中的生物信息学方法

高通量测序技术的发展促进了组学技术在环境微生物研究中的广泛应用,而宏基因组学是目前最为关键和成熟的组学方法。生物信息学在微生物宏基因组学研究中具有至关重要的作用。它贯穿于宏基因组学的数据收集和存储、数据处理和分析等各个阶段,既是宏基因组学推广的最大瓶颈,也是目前宏基因组学研究发展的关键所在。本文主要介绍和归纳了目前在高通量宏基因组测序中常用的生物信息学分析平台及其重要的信息分析工具。未来几年之内,测序成本的下降和测序深度的增加将进一步增大宏基因组学研究在数据存储、数据处理和数据挖掘层面的难度,因此相应生物信息学技术与方法的研究和发展也势在必行。近期内我们应该首先加强基础性分析和存储平台的建设以方便普通环境微生物研究者使用,同时针对目前生物信息分析的瓶颈步骤和关键任务重点突破,逐步发展。     

昆虫肠道的宏基因组学:微生物大数据的新疆界

微生物作为自然界中普遍存在的生命体,通常以"微生物群落"的形式共存。这些物种相互协作适应环境变化的同时,也对环境产生了长期而深刻的影响。随着人类对于微生物了解的深入,微生物群落基础研究及其在健康和环境等领域的应用研究日益重要。昆虫肠道内存在种类繁多、数量庞大的微生物,一方面,这些肠道微生物种群结构的多样性与昆虫种类、龄期、消化道形式、食物的来源、环境等都息息相关。另一方面,这些菌群也对宿主的一些生理活动有着一定的影响。随着高通量测序技术、组学技术的发展,昆虫肠道宏基因组大数据挖掘和应用已经成为研究热点,极大地推动人类微生物资源利用的能力。本文概述了昆虫肠道微生物宏基因组学的发展现状和发展趋势,特别是肠道宏基因组学大数据的挖掘工具和应用,以及现阶段昆虫肠道宏基因组学的研究进展、应用、优势和瓶颈,并对今后昆虫肠道微生物组大数据研究方向进行展望。     

影像基因组学分析方法研究进展

在生物医学大数据背景下,精准医学的研究重点之一是基因型数据和表型数据的融合及关联分析,通过数据融合及关联分析,认识疾病表型特征与基因多态性及基因活动之间的关系。影像基因组学作为一个新兴研究领域,它将疾病影像数据和基因组数据整合,并挖掘两者之间的联系,从而发现能够反映基因多态或表达的影像特征,在此基础上建立基于影像特征的非侵入式疾病诊断方法,是目前生物医学最有前景的研究领域之一。综述了影像基因组学领域的研究方法,包括基因组数据分析、影像数据分析以及基因组数据-影像数据融合分析方法。在此基础上,介绍了影像基因组学目前在临床上的典型应用,包括疾病的辅助诊断、预后预测和疗效评估。最后,对影像基因组学的未来发展进行了展望。     

医学真菌基因组研究进展（下）

近年来,一系列重要医学致病真菌全基因组数据陆续被公布,使人类对这些致病菌的认识提高到全新水平。本文在回溯医学真菌基因组学和基因组测序技术发展历程、综述其发展现状及应用的基础上,再分别介绍重要医学真菌全基因组测序的进展。     

医学真菌基因组学研究进展(上)

近年来,一系列重要医学致病真菌全基因组数据陆续被公布,使人类对这些致病菌的认识提高到全新水平.本文在回溯医学真菌基因组学和基因组测序技术发展历程、综述其发展现状及应用的基础上,再分别介绍重要医学真菌全基因组测序的进展.     

2019年启航献礼

精准医疗是应用现代分子生物学、分子病理学、分子遗传学、分子影像技术、生物信息技术以及目前火热的大数据技术、智能化技术等,结合患者生活环境和临床数据,实现精准的疾病分类和诊断,制定具有个性化的疾病预防和诊疗方案,包括对风险的精确预测、疾病精确诊断、疾病精确分类、药物精确应用、疗效精确评估、疗后精确预测等。精准医疗是医学自身发展的客观必然,是人民群众对健康新需求的使然。精准医疗的核心价值是造福于患者,造福于人类,尤其是在当今中国,人民生活水平普遍得到改善和提高,人民对健康的追求达到了一个新的高度。     

病毒宏基因组学技术及其在医学领域的应用

病毒宏基因组学(Viral metagenomics,VM)无需知晓核酸序列,直接以环境中的病毒群落为研究对象,对研究环境中病毒的多样性、快速鉴定出已知和未知病毒,实时监测特定病毒的动态变化等具有重要价值。下一代测序(Next-generation sequencing,NGS)平台具备快速、自动化和高通量等综合优势,因此相对于一代Sanger测序技术,其测序能力大大提高。同时,近年来测序技术的不断改进、成本的不断降低、数据分析流程的普及以及对数据深入挖掘能力的显著性改进表明,在可预见的未来,病毒宏基因组测序将会成为常规检测项目,为未知病毒检测提供新的技术手段和思路。本文将从病毒宏基因组学的兴起,研究过程和在医学领域中的应用等方面进行综述。     

Lessons learnt on the analysis of large sequence data in animal genomics

The ’omics revolution has made a large amount of sequence data available to researchers and the industry. This has had a profound impact in the field of bioinformatics, stimulating unprecedented advancements in this discipline. Mostly, this is usually looked at from the perspective of human ’omics, in particular human genomics. Plant and animal genomics, however, have also been deeply influenced by next‐generation sequencing technologies, with several genomics applications now popular among researchers and the breeding industry. Genomics tends to generate huge amounts of data, and genomic sequence data account for an increasing proportion of big data in biological sciences, due largely to decreasing sequencing and genotyping costs and to large‐scale sequencing and resequencing projects. The analysis of big data poses a challenge to scientists, as data gathering currently takes place at a faster pace than does data processing and analysis, and the associated computational burden is increasingly taxing, making even simple manipulation, visualization and transferring of data a cumbersome operation. The time consumed by the processing and analysing of huge data sets may be at the expense of data quality assessment and critical interpretation. Additionally, when analysing lots of data, something is likely to go awry—the software may crash or stop—and it can be very frustrating to track the error. We herein review the most relevant issues related to tackling these challenges and problems, from the perspective of animal genomics, and provide researchers that lack extensive computing experience with guidelines that will help when processing large genomic data sets.     

Network-Based Interpretation of Genomic Variation Data

Advances in sequencing technologies are allowing genome-wide association studies at an ever-growing scale. The interpretation of these studies requires dealing with statistical and combinatorial challenges, owing to the multi-factorial nature of human diseases and the huge space of genomic markers that are being monitored. Recently, it was proposed that using protein–protein interaction network information could help in tackling these challenges by restricting attention to markers or combinations of markers that map to close proteins in the network. In this review, we survey techniques for integrating genomic variation data with network information to improve our understanding of complex diseases and reveal meaningful associations.     

Illumina-Solexa测序数据质量评估系统的构建

目的：针对下一代测序数据量大、序列长度短的特点,研究数据分析和质量评估方法。方法：选择已发布的Illumina-Solexa平台测序数据为研究对象,通过MAQ软件将测序数据与人类全基因组序列进行比对,并以外显子区域为例,在位点水平对测序数据质量进行评估。结果：结合已有软件系统和本文自创线性算法,建立了一套包括比对、拼接在内的测序数据质量评估系统。比对分析后,发现原始测序序列共覆盖了127,113,378个位点,涉及24条染色体上的64868个外显子。其中,每个位点都被测到的外显子为0.50%,位点平均测序深度大于等于1的外显子为3.98%。结论：成功构建了基于Illumina-Solexa测序平台的数据分析和质量评估方法,其可适用于其它第二代测序平台。研究者可在质量评估的基础上完善测序试验设计,并进行SNP和突变筛选及后续功能性研究。     

DNAdigest and Repositive: Connecting the World of Genomic Data

There is no unified place where genomics researchers can search through all available raw genomic data in a way similar to OMIM for genes or Uniprot for proteins. With the recent increase in the amount of genomic data that is being produced and the ever-growing promises of precision medicine, this is becoming more and more of a problem. DNAdigest is a charity working to promote efficient sharing of human genomic data to improve the outcome of genomic research and diagnostics for the benefit of patients. Repositive, a social enterprise spin-out of DNAdigest, is building an online platform that indexes genomic data stored in repositories and thus enables researchers to search for and access a range of human genomic data sources through a single, easy-to-use interface, free of charge.     

The emerging roles of NGS-based liquid biopsy in non-small cell lung cancer

The treatment paradigm of non-small cell lung cancer (NSCLC) has evolved into oncogene-directed precision medicine. Identifying actionable genomic alterations is the initial step towards precision medicine. An important scientific progress in molecular profiling of NSCLC over the past decade is the shift from the traditional piecemeal fashion to massively parallel sequencing with the use of next-generation sequencing (NGS). Another technical advance is the development of liquid biopsy with great potential in providing a dynamic and comprehensive genomic profiling of NSCLC in a minimally invasive manner. The integration of NGS with liquid biopsy has been demonstrated to play emerging roles in genomic profiling of NSCLC by increasing evidences. This review summarized the potential applications of NGS-based liquid biopsy in the diagnosis and treatment of NSCLC including identifying actionable genomic alterations, tracking spatiotemporal tumor evolution, dynamically monitoring response and resistance to targeted therapies, and diagnostic value in early-stage NSCLC, and discussed emerging challenges to overcome in order to facilitate clinical translation in future.     

An Integrative Approach to Precision Cancer Medicine Using Patient-Derived Xenografts

Cancer is a heterogeneous disease caused by diverse genomic alterations in oncogenes and tumor suppressor genes. Despite recent advances in high-throughput sequencing technologies and development of targeted therapies, novel cancer drug development is limited due to the high attrition rate from clinical studies. Patient-derived xenografts (PDX), which are established by the transfer of patient tumors into immunodeficient mice, serve as a platform for co-clinical trials by enabling the integration of clinical data, genomic profiles, and drug responsiveness data to determine precisely targeted therapies. PDX models retain many of the key characteristics of patients’ tumors including histology, genomic signature, cellular heterogeneity, and drug responsiveness. These models can also be applied to the development of biomarkers for drug responsiveness and personalized drug selection. This review summarizes our current knowledge of this field, including methodologic aspects, applications in drug development, challenges and limitations, and utilization for precision cancer medicine.     

肿瘤精准医学知识数据库的设计与构建

目的:整合现有前沿的大量而分散的精准医学知识以形成系统完整的知识数据库,为个体组学数据的临床应用提供依据,旨在最终实现基于组学特征的精准用药推荐。方法:采用MySQL数据库管理系统构建数据库,从FDA伴随诊断、NCCN指南、My Cancer Genome、GDSC四大权威医学资源中手动收集精准用药知识,并将原始数据标准化、结构化后以统一的格式存储。结果:成功设计并构建了肿瘤精准医学知识库,目前共收录1 940条精准用药指导,涵盖了基因突变等14种不同类型的组学特征。结论:精准医学知识数据库收录了肿瘤分子组学特征和治疗策略的关联信息,可为临床上个体化治疗方案的制订提供参考依据。数据库的建立为精准医疗临床决策支持系统的开发奠定了基础。     

RAD‐sequencing for estimating genomic relatedness matrix‐based heritability in the wild: A case study in roe deer

Estimating the evolutionary potential of quantitative traits and reliably predicting responses to selection in wild populations are important challenges in evolutionary biology. The genomic revolution has opened up opportunities for measuring relatedness among individuals with precision, enabling pedigree‐free estimation of trait heritabilities in wild populations. However, until now, most quantitative genetic studies based on a genomic relatedness matrix (GRM) have focused on long‐term monitored populations for which traditional pedigrees were also available, and have often had access to knowledge of genome sequence and variability. Here, we investigated the potential of RAD‐sequencing for estimating heritability in a free‐ranging roe deer (Capreolous capreolus) population for which no prior genomic resources were available. We propose a step‐by‐step analytical framework to optimize the quality and quantity of the genomic data and explore the impact of the single nucleotide polymorphism (SNP) calling and filtering processes on the GRM structure and GRM‐based heritability estimates. As expected, our results show that sequence coverage strongly affects the number of recovered loci, the genotyping error rate and the amount of missing data. Ultimately, this had little effect on heritability estimates and their standard errors, provided that the GRM was built from a minimum number of loci (above 7,000). Genomic relatedness matrix‐based heritability estimates thus appear robust to a moderate level of genotyping errors in the SNP data set. We also showed that quality filters, such as the removal of low‐frequency variants, affect the relatedness structure of the GRM, generating lower h² estimates. Our work illustrates the huge potential of RAD‐sequencing for estimating GRM‐based heritability in virtually any natural population.     

微生物组大数据管理与分析

高通量技术的迅猛发展促使微生物生态学研究获得了重大突破,掀起了元基因组学(Metagenomics)研究的热潮。元基因组学通常被定义为对未培养的环境样本中微生物群体的DNA序列分析。随着微生物组学数据的日益剧增,微生物大数据的高效管理与分析越来越受到研究者的关注。如何从海量的微生物组数据中挖掘出具有科研价值的数据信息并应用于实际问题成为当前的研究热点。目前已有很多计算生物学程序工具及数据库用于元基因组数据的分析与管理。本文主要综述了随着高通量测序技术的进步,国际上主要的微生物组计划及微生物组数据平台,如人类微生物组项目(human microbiome project,HMP)、地球微生物组项目(earth microbiome project,EMP)、欧盟的肠道微生物组计划(metagenomics of human intestinal tract,MetaHIT)、MG-RAST、i Microbe、整合微生物组(integration microbial genomes,IMG)以及EBI Metagenomics等;介绍了微生物数据分析的主要流程与工具;提出了建设多源异构的微生物生态数据管理与分析系统的必要性。