基因芯片与高通量测序技术的原理与应用的比较

随着后基因组时代的到来,基因芯片和高通量测序已成为生物化学和分子生物学研究中的两大重要技术。从检测效率、准确性以及自动化程度证实,这两大技术都较传统的遗传学方法有了新的突破。基因芯片技术是一种具有高通量、高效率以及高自动化特点的方法,发展至今无论在核心技术还是工业应用方面都得到广泛的推广。高通量DNA测序技术建立较晚,但是其发展速度快,特别是在技术方面的更新换代极快,不断地改进使得测序的高通量、高准确率在生命科学中的应用也是占据不可逾越的优势。二者在原理上存在着显著的差异,却在应用方面上常常交融。基于此背景,本文以基因芯片技术与高通量测序技术二者在原理和基因拷贝数变异、肠道微生物、农业等应用方面作简要论述和对比。     

基于高通量测序技术对染色体外环状DNA研究的最新进展

染色体外环状DNA (extrachromosomal circular DNA, eccDNA)是一种真核生物染色体外的闭合环状DNA结构，长度和染色体起源具有较高异质性。ecc DNA这一名称目前主要指大小在数百kb以内的小分子染色体外环状DNA，包括micro DNA、小多分散环状DNA (small polydispersed circular DNA, spcDNA)以及其他未分类的小分子eccDNA等。高通量测序技术(high-throughput sequencing, HTS)是一种可以同时对百万条DNA分子进行序列测定的技术，又名新一代测序技术(next generation sequencing, NGS)，具有高通量、高灵敏度、高准确度等优势。近年来高通量测序结合生物信息学分析技术不仅在揭示eccDNA染色体起源、分子结构、发生机制和潜在功能以及循环系统中的eccDNA分子特征研究等方面发挥了重要作用，而且推动了eccDNA在甲基化等表观遗传学方面的研究。生物信息学软件的发展和eccDNA分析算法的开发也对其研究提供了重要帮助。血浆以及尿液等液体活检常用体液样本中...     

基因芯片与高通量DNA测序技术前景分析

基因芯片与第二代DNA测序是两种重要的高通量基因组学研究技术,对于揭示基因组的结构与功能已经并正在发挥重要的推动作用．基因芯片技术建立了10多年,技术日渐成熟,在功能基因组、系统生物学、药物基因组的研究中已经得到了广泛的应用．2003年,454公司首先建立了高通量的第二代测序技术,其他公司相继推出了Solexa和Solid测序技术．虽然第二代测序技术建立的时间不长,但发展非常快,已经应用于基因组,包括测序和表观基因组学以及功能基因组学研究的许多方面．本文简要综述了基因芯片和第二代测序技术及其应用进展,并分析了这两种高通量基因组学技术的前景．     

高通量测序技术及其应用

高通量测序技术是DNA测序发展历程的一个里程碑,它为现代生命科学研究提供了前所未有的机遇。详细介绍了以454、Solexa和SOLiD为代表的第二代高通量测序技术,以HeliScope TIRM和Pacific Biosciences SMRT为代表的单分子测序技术,以及最近Life Science公司推出的Ion Personal Genome Machine (PGM)测序技术等高通量测序技术的最新进展。在此基础上,阐述了高通量测序技术在基因组测序、转录组测序、基因表达调控、转录因子结合位点的检测以及甲基化等研究领域的应用。最后,讨论了高通量测序技术在成本和后续数据分析等方面存在的问题及其未来的发展前景。     

DNA测序技术的发展历史与最新进展

DNA测序技术是现代分子生物学研究中最常用的技术。从1977年第一代测序技术的出现,经过30多年的发展,DNA测序技术取得重大进展,以高通量为特点的第二代测序技术逐步成熟并商业化,以单分子测序为特点的第三代测序技术也已经出现。介绍每一代测序技术的特点,并重点介绍了第二代测序技术及其应用。展望新的测序技术对于未来生物学研究及人们自身健康与人类疾病等方面研究的影响。     

高通量DNA甲基化数据的处理和分析方法

DNA甲基化作为一种表观遗传学修饰,在调控基因表达、X染色体失活、印记基因等方面都发挥着重要的作用.不同的DNA甲基化的预处理方法结合二代测序产生了大量的高通量甲基化数据,这些数据的存储、处理和分析是当前亟需解决的问题.在本文中,总结了目前存在的三种高通量DNA甲基化检测技术（限制性内切酶法,亲和纯化法,重亚硫酸盐转换法）,以及针对这些技术产生的高通量数据开发的存储、处理和分析工具.另外,还注重介绍了单碱基水平的DNA甲基化检测技术,BS-Seq的测序原理、数据处理流程以及后续的分析工具.     

线粒体基因组的高通量测序策略

综述了高通量测序技术在线粒体全基因组测序中的策略,利用该技术对线粒体全基因组进行序列测定的方法可以归纳为两种,一种是先对目标mt DNA进行富集,包括mt DNA的提取纯化,目标区域PCR扩增法以及特异性探针杂交富集法(可分为基于微阵列和基于PCR探针的杂交富集法),然后对富集出的线粒体DNA进行高通量测序;另一种是先从待测样本的基因组高通量数据中挖掘出线粒体基因组序列信息,之后利用诱饵序列或者近缘物种的线粒体全基因组参考序列,使用软件MITObim对其进行组装。此外,还给出了线粒体高通量测序的优化流程图和介绍了混合样品的线粒体高通量测序策略。     

DNA宏条形码技术在食草动物食性研究中的应用

随着测序技术的快速发展,整合DNA条形码和高通量测序的DNA宏条形码技术已经成为当前研究热点之一,在食草动物的食性鉴定中有很大潜力。放牧动物食性研究是动物营养学和草地生态学领域的重要研究内容。而与传统食性研究方法相比,宏条形码技术可通过对植物DNA条形码的高通量测序,获得样本中的物种组成进而分析动物食性。介绍了传统食性分析手段的局限,重点综述了DNA宏条形码技术的产生、操作原理以及在食草类动物食性鉴定领域中的应用,同时还简述了可能存在的挑战,并对该技术今后的发展方向进行了展望。     

基于高通量测序技术检测全基因组DNA甲基化水平的方法

DNA甲基化的研究最近几年一直是表观遗传学研究的重点。有关DNA甲基化水平检测的方法大体上有十几种,随着第二代测序技术的发展,实现了在全基因组水平上对甲基化状态进行检测。目前,基于第二代高通量测序进行全基因组DNA甲基化水平检测的方法主要有BSP-seq(亚硫酸氢盐修饰结合直接测序法)、Me DIP-seq(甲基化DNA免疫共沉淀测序法)、MBD-seq(甲基化DNA富集结合高通量测序法)。就这3种方法在原理、流程、优缺点、优化使用及后期需要用到的部分生物信息学资源等方面的研究进展作一综述,旨在为研究者在采用高通量测序方法研究DNA甲基化模式时提供一些思路。     

高通量测序技术在线虫多样性研究中的应用

土壤线虫多样性是土壤生态学研究的热点之一, 然而对土壤线虫群落组成及多样性的研究通常受到分类学和方法学的限制。当前, 分子生物学技术的快速发展丰富了我们对土壤线虫多样性的认识, 但也存在一定的局限性。本文综述了常用分子生物学技术如变性梯度凝胶电泳(denaturing gradient gel electrophoresis, DGGE)、末端限制性片段长度多态性分析(terminal restriction fragment length polymorphism, T-RFLP)、实时荧光定量PCR (quantitative real-time PCR, qPCR)和高通量测序(high-throughput sequencing, HTS)技术近年来在线虫多样性研究中的应用, 重点从土壤线虫DNA提取方法、引物和数据库的选择、高通量测序技术和形态学鉴定结果的比较等方面阐述了高通量测序技术在线虫多样性研究中的优势与不足, 并提出选择合适的线虫DNA提取方法结合特定引物和数据库进行注释分析, 仍是今后使用高通量测序技术开展线虫多样性研究的重点。当研究目标是土壤线虫多样性时, 优先推荐富集线虫悬液提取DNA的方法, 因此, 研究人员应根据具体目标选择最优组合开展实验研究。     

新一代测序技术的研究进展

大规模DNA测序技术是揭秘人类和其它生物遗传密码的重要技术,在分子生物学和基础医学领域有广泛应用。第二代测序技术的出现使DNA测序的通量大幅提高,测序的成本大幅下降,原来只有在大型测序中心才能完成的测序任务现在已经可以在更多的实验室展开。但是,早期的第二代测序技术仍然存在诸如文库构建过程复杂、测序成本依然较高等缺点。为了克服上述缺点,近三年发展了几种新的第二代和第三代测序技术,这些技术不仅继承了早期第二代测序技术通量高的优点,而且在文库构建等方面取得了重要突破,进一步简化了测序操作,降低了测序成本,缩短了测序时间。本文就几种最新的大规模测序技术的原理、特点与发展趋势进行简要介绍。     

Intragenomic variation in nuclear ribosomal markers and its implication in species delimitation,identification and barcoding in fungi

Intragenomic variation is the molecular variation within the genome among repetitive DNA. As a multigene family, nuclear ribosomal DNA (rDNA) has been widely used in fungal taxonomy for their ease in amplification and suitable variability to attain various levels of taxonomic resolution. At the intraspecific level, rDNA is believed to be under concerted evolution and the internal transcribed spacers (ITS) region is actually accepted as a universal barcoding marker for fungi. However, documentation of intragenomic variation of rDNA indicated that it can be problematic in species delimitation and identification. Fungal taxonomic studies have not generally taken into account the intragenomic variation of rDNA in a systematic manner. In this review, our objective is to address the definition, the origin and the mechanisms for maintenance of intragenomic variation, as well as its implication in the domain of fungal molecular taxonomy, particularly for species delimitation, identification and DNA barcoding. With advanced sequencing technologies (second and third generations), we also addressed how these technologies can be used to study the intragenomic variation of rDNA and also how the intragenomic variation will impact on DNA barcoding via high-throughput sequencing.     

Predicting biological networks from genomic data

Continuing improvements in DNA sequencing technologies are providing us with vast amounts of genomic data from an ever-widening range of organisms. The resulting challenge for bioinformatics is to interpret this deluge of data and place it back into its biological context. Biological networks provide a conceptual framework with which we can describe part of this context, namely the different interactions that occur between the molecular components of a cell. Here, we review the computational methods available to predict biological networks from genomic sequence data and discuss how they relate to high-throughput experimental methods.     

Neutrality tests for sequences with missing data

Missing data are common in DNA sequences obtained through high-throughput sequencing. Furthermore, samples of low quality or problems in the experimental protocol often cause a loss of data even with traditional sequencing technologies. Here we propose modified estimators of variability and neutrality tests that can be naturally applied to sequences with missing data, without the need to remove bases or individuals from the analysis. Modified statistics include the Watterson estimator θ(W), Tajima's D, Fay and Wu's H, and HKA. We develop a general framework to take missing data into account in frequency spectrum-based neutrality tests and we derive the exact expression for the variance of these statistics under the neutral model. The neutrality tests proposed here can also be used as summary statistics to describe the information contained in other classes of data like DNA microarrays.     

Systems analysis of adaptive immunity by utilization of high-throughput technologies

A new generation of high-throughput technologies for quantitative and clonal analysis of adaptive immune responses have been developed. Functional analysis of lymphocyte populations has been accomplished via microfluidic assay systems. Additionally, lymphocyte receptor repertoires have been characterized on proteomic and genomic levels with multiplexed protein microarrays and high-throughput DNA sequencing. These tools are providing an unprecedented level of information depth on the distribution of adaptive immune cell (B and T cell) functionalities and repertoires, which develop upon activation following vaccination, pathogenic infection, or in disease states. These various high-throughput technologies have unlocked the potential to transform immunology into an information-rich science that will enable rapid expansion of the field of experimental systems immunology.     

Exome localization of complex disease association signals

Background

Recent progress in high-throughput technologies has greatly contributed to the development of DNA methylation profiling. Although there are several reports that describe methylome detection of whole genome bisulfite sequencing, the high cost and heavy demand on bioinformatics analysis prevents its extensive application. Thus, current strategies for the study of mammalian DNA methylomes is still based primarily on genome-wide methylated DNA enrichment combined with DNA microarray detection or sequencing. Methylated DNA enrichment is a key step in a microarray based genome-wide methylation profiling study, and even for future high-throughput sequencing based methylome analysis.

Results

In order to evaluate the sensitivity and accuracy of methylated DNA enrichment, we investigated and optimized a number of important parameters to improve the performance of several enrichment assays, including differential methylation hybridization (DMH), microarray-based methylation assessment of single samples (MMASS), and methylated DNA immunoprecipitation (MeDIP). With advantages and disadvantages unique to each approach, we found that assays based on methylation-sensitive enzyme digestion and those based on immunoprecipitation detected different methylated DNA fragments, indicating that they are complementary in their relative ability to detect methylation differences.

Conclusions

Our study provides the first comprehensive evaluation for widely used methodologies for methylated DNA enrichment, and could be helpful for developing a cost effective approach for DNA methylation profiling.     

Recent Progress Using High-throughput Sequencing Technologies in Plant Molecular Breeding

High-throughput sequencing is a revolutionary technological innovation in DNA sequencing.This technology has an ultra-low cost per base of sequencing and an overwhelmingly high data output.High-throughput sequencing has brought novel research methods and solutions to the research fields of genomics and post-genomics.Furthermore,this technology is leading to a new molecular breeding revolution that has landmark significance for scientific research and enables us to launch multi-level,multi-faceted,and multi-extent studies in the fields of crop genetics,genomics,and crop breeding.In this paper,we review progress in the application of high-throughput sequencing technologies to plant molecular breeding studies.     

Prediction of personalized drugs based on genetic variations provided by DNA sequencing technologies

Recent reports of death and illness caused by adverse drug reactions have boosted rational drug design research. It has been shown through sequencing of the entire human genome that human genetic variations play a key role in adverse reactions to drugs as well as in differences in the effectiveness of drug treatments. The advent of high-throughput DNA sequencing technologies with bioinformatics of system biology have allowed the easy identification of genetic variations and all other pharmacogenetic variants in a single assay, thus permitting truly personalized drug treatment. This would be particularly valuable for many patients with chronic diseases who must take many medications concurrently. In this review, we have focused on pharmacogenomics for the prediction of variable drug responses between individuals with relevant genetic variations through new DNA sequencing technologies and provided directions for personalized drug therapy in the future.