探寻新型HCV同源病毒的自然宿主

丙型肝炎病毒(hepatitis C virus,HCV)感染是一个世界性的公共卫生问题,该病毒感染后可引起急、慢性肝炎。由于HCV的宿主范围较窄,一直未找到合适的动物模型来研究该病毒的致病机制、免疫预防等,至今也无证据表明动物源的HCV同源病毒可能跨种传播给人类。最近,研究者在小型野生动物(如啮齿类动物、蝙蝠)和家养动物(如犬、马、牛)中相继发现了新型HCV同源病毒(HCV样病毒和GBV样病毒),这些病毒分别属于丙型肝炎病毒属(hepacivirus)或持续性G病毒属(pegivirus),研究这些病毒的基因组结构和生物学特征有助于HCV的起源及其致病机制和免疫等研究。本综述从HCV基本特征、相关病毒谈起,着重介绍新型HCV同源病毒,并探寻其自然宿主,进而讨论了人类重要病原之一HCV的起源问题。     

人类基因组结构变异检测方法

本研究介绍了基因组结构变异检测的生物信息学基本方法和前沿技术。对基于第二代测序技术的四种检测方法(读对方法,读深方法,分裂片段方法和序列拼接方法)的原理和特点进行了详细解读,分析了第二代测序技术应用在检测结构变异上的特点与发展趋势。最后介绍了三代测序、Linked-reads和光学物理图谱等新技术在基因组结构变异检测中的应用,论述了融合新技术的结构变异检测方法的特点与优势。     

新型冠状病毒基因组G-四链体结构的初步研究

21世纪以来,冠状病毒频频引起危害人类健康的重要传染病,其中包括2003年严重急性呼吸综合征冠状病毒(SARS-CoV)、2012年中东呼吸综合征冠状病毒(MERS-CoV)和新型冠状病毒(SARS-CoV-2),目前对这些病毒引发的疾病并无特效的治疗药物。G-四链体(G-quadruplex,G4)是在DNA或RNA的鸟嘌呤富集区形成的非典型二级结构,可存在于人类和病毒基因组中,G-四链体的不同位置对病毒复制和感染等过程发挥重要调控作用。本研究针对七种与人类疾病相关的冠状病毒以及与SARS-CoV-2同源性较高的三种蝙蝠相关病毒,通过全基因组序列分析潜在四链体形成序列(Potential quadruplex-forming sequences,PQS),结果发现,十种病毒中均存在一定数量的PQS基序,同时对SARS-CoV-2 G-四链体存在位置及形成潜力进行评估,并分析了不同变异株间G-四链体基序的保守性。本研究对SARS-CoV-2基因组中G-四链体进行初步预测与探讨,旨在为COVID-19治疗提供一种新的药物靶点,使其更好地应用于临床研究。     

群体基因组结构变异检测工作流

结构变异作为人类基因组上的一种大规模的变异类型,对分子与细胞进程、调节功能、基因表达调控、个体表型具有重要的影响,检测群体中基因组结构变异有助于绘制群体基因组变异图谱,刻画群体遗传进化特征,为疾病诊治、精准医疗的发展提供支撑。本研究提出一种面向高通量测序的群体基因组结构变异检测工作流,该工作流通过使用多种高性能基因组结构变异检测算法实现全面、精准的结构变异挖掘,使用多层融合与过滤获得高精度群体结构变异候选集合,利用基因型重新校正、变异修剪、类型校对,最终完整绘制群体基因组结构变异图谱。基于该工作流对由267个样本组成的人群进行群体结构变异检测,检测出了96 202个结构变异,其变异种类和频率分布与其他国际基因组计划相符,这些结果证明了本工作流具有良好的群体结构变异检测能力。同时,工作流通过并行的方式在内存可控的基础上显著降低了分析时间,为大规模人群基因组结构变异的高效检测提供了重要支撑。     

人类基因组结构变异检测研究进展

高通量、高分辨率基因组学技术的出现推动了人类基因组中长度在1kb～3Mb的亚显微水平结构变异检测方法的发展,这些结构变异主要包括基因拷贝数变异、倒置、插入、缺失、重复及其他基因重排．而传统的细胞遗传学技术达不到如此高的分辨率．本文介绍了目前主要的基因组结构变异的检测技术,包括基于芯片的比较基因组杂交技术和代表性寡核苷酸芯片分析技术,基于PCR的多重扩增探针杂交技术和依赖于连接反应的多重探针扩增技术,配对末端图谱技术等．还比较和分析了各种方法的优劣势并提出了目前结构变异数据库存在的问题．最后讨论了这些变异对于人类表型多态性、疾病易感性、药物反应程度及群体遗传学的影响．     

植物蛋白的体外泛素化检测方法

泛素激活酶(E1)、泛素耦联酶(E2)和泛素连接酶(E3)是蛋白质泛素化修饰的关键酶。在真核基因组上有大量基因编码这些泛素化相关的酶类或蛋白。检测这些泛素化修饰酶及其底物蛋白的生化特性和特异性是分析其生物学功能的重要内容。该文提供了一种简便快速检测体外泛素化反应的方法, 不仅可通过检测对DTT敏感的硫酯键的形成来判断E2的活性、检测E3的体外泛素化活性, 而且可以检测E2-E3和E3-底物的特异性。所用蛋白主要来源于拟南芥(Arabidopsis thaliana), 包括分属于绝大多数E2亚家族的成员, 可用于不同RING类型E3的活性检测。该方法不仅可以采用多种E2进行E3活性分析, 而且可以分析不同组合的E2-RING E3、RING E3-底物的泛素化活性等, 亦可应用于真核生物蛋白质尤其是植物蛋白的体外泛素化活性分析。     

福建省慢性HBV感染者HBV基因S区、基本核心启动子区和前C区基因变异特征分析

为研究福建省慢性HBV感染者HBV基因多样性及变异规律,了解该人群HBV-DNA的病毒学特征。收集慢性HBV感染者血清标本,通过巢式PCR法扩增其HBV基因序列,比对NCBI数据库中标准基因型序列,分析HBV基因S区,基本核心启动子区(BCP)及前C区的序列变异情况,并对这些变异可能造成的病毒抗原表达,疫苗逃逸,患者病症改变等情况进行探讨。最终成功扩增82例HBV全长基因序列,其中B基因型56例,C基因型26例。基因组特定功能区序列分析发现慢性HBV感染者HBV基因在S区(23.2%)、BCP区(61.0%)和前C区(29.3%)均出现了不同程度的变异。其中主蛋白(HBsAg)主要抗原决定簇a决定簇45.8%位点出现了变异,这些变异位点中包括与肝炎重症化及免疫逃逸密切相关的位点(aa126、aa129、aa145等)。位点G1896A(19.5%),G1764A(11.0%)和A1762T(9.8%)依然是BCP/前C区的主要突变位点。而位点A1752G(25.6%)高突变率的出现在BCP区应引起关注。此外位点G1764A(χ~2=5.742,P=0.030)、A1896G(χ~2=14.392,P=0.000)以及A1762T/G1764A(χ~2=7.289,P=0.012)的突变更容易发生在HBeAg阴性的样品中;而位点A1846T(χ~2=11.882,P=0.003)、A1762T(χ~2=6.561,P=0.038)和A1896G(χ~2=6.958,P=0.030)的突变与HBV-DNA的病毒载量存在一定相关性。总之,福建省慢性HBV感染者在HBV不同基因功能区域均存在不同程度的变异,一些与HBeAg表达情况、HBV-DNA载量、疫苗免疫逃避及肝细胞癌发生具有相关联的变异位点已经出现,BCP区A1752G位点的高频率出现应值得关注,对于这些变异位点的患者应加强监测。     

基于支持向量机的室颤信号检测算法

目的:实现室颤信号与非室颤信号的分类,进而实现室颤信号的检测。方法:本文引入了一种基于支持向量机(Support Vec-tor Machine,SVM)和改进的越限区间算法(TCI)的新算法,其中支持向量机在处理分类和模式识别等问题中具有很大的优势。该算法采用4s的滑动窗技术,并利用改进后的越限区间算法(Threshold Crossing Interval,TCI)方法提取心电信号的特征。新算法的实现如下:在每一滑动窗内采用改进的后的绝对值阈值,计算中间2s内的平均越限间隔值。并以此TCI值作为特征参数,输入一个预先设计好的二分类支持向量机中,从而实现分类。结果:成功实现了室颤信号的检测,通过计算该方法的灵敏度、精确度、预测性和准确度且与其他方法相比较,可知此新算法总体可靠性优于其他方法。结论:该算法能够实现室颤信号的实时监测,且简单易行,易于实现,较适合实时的心电监测以及除颤仪器。     

基因组拷贝数变异研究进展

基因组结构变异分为两个层次:显微水平(microscopic)和亚显微水平(submicroscopic)。显微水平的基因组结构变异主要是指显微镜下可见的染色体畸变,包括整倍体或非整倍体、缺失、插入、倒位、易位、脆性位点等结构变异。亚显微水平的基因组结构变异是指DNA片段长度在1Kb-3Mb的基因组结构变异,包括缺失、插入、重复、重排、倒位、DNA拷贝数目变化(copy numbervariation,CNV),这些统称为CNV或者CNP(copy number polymorphisms,CNP)。对CNV的研究能够帮助研究者建立遗传检测假说,进而发现疾病易感基因,同时加深对表型变异的理解,为今后研究人类生物功能、进化、疾病奠定基础。本文主要从CNV的研究历史、分子机制、研究方法、研究意义等四个方面进行综述.。     

SARS和MERS中和抗体假病毒检测方法的建立

严重急性呼吸综合征冠状病毒(SARS-CoV)和中东呼吸综合征冠状病毒(MERS-CoV)均属冠状病毒科(Coronaviridae),能够引起人类严重疾病,特别是SARS-CoV可以导致严重急性呼吸综合征,而且容易造成暴发式流行,引起社会恐慌。本研究利用含有SARS-CoV和MERS-CoV棘突蛋白(Spike protein,S)基因的表达质粒pcD-NA3.1-SS、pcDNA3.1-MS,成功构建了高滴度假病毒,在此基础上通过对病毒接种量进行优化,建立了稳定可靠的假病毒中和抗体检测方法,并通过检测SARS恢复期病人血清以及免疫后小鼠血清对方法的特异性、稳定性以及重复性进行分析,充分证明了该方法是有效的血清中和抗体的检测手段。本研究成功构建了不依赖于BSL-3级生物安全条件的SARS和MERS假病毒,不仅可应用于血清中和抗体检测,还为后续单克隆抗体及抗病毒药物筛选和评价、疫苗研发及病毒感染机制研究等奠定了基础。     

A new disease-specific machine learning approach for the prediction of cancer-causing missense variants

High-throughput genotyping and sequencing techniques are rapidly and inexpensively providing large amounts of human genetic variation data. Single Nucleotide Polymorphisms (SNPs) are an important source of human genome variability and have been implicated in several human diseases, including cancer. Amino acid mutations resulting from non-synonymous SNPs in coding regions may generate protein functional changes that affect cell proliferation. In this study, we developed a machine learning approach to predict cancer-causing missense variants. We present a Support Vector Machine (SVM) classifier trained on a set of 3163 cancer-causing variants and an equal number of neutral polymorphisms. The method achieve 93% overall accuracy, a correlation coefficient of 0.86, and area under ROC curve of 0.98. When compared with other previously developed algorithms such as SIFT and CHASM our method results in higher prediction accuracy and correlation coefficient in identifying cancer-causing variants.     

Leveraging Identity-by-Descent for Accurate Genotype Inference in Family Sequencing Data

Sequencing family DNA samples provides an attractive alternative to population based designs to identify rare variants associated with human disease due to the enrichment of causal variants in pedigrees. Previous studies showed that genotype calling accuracy can be improved by modeling family relatedness compared to standard calling algorithms. Current family-based variant calling methods use sequencing data on single variants and ignore the identity-by-descent (IBD) sharing along the genome. In this study we describe a new computational framework to accurately estimate the IBD sharing from the sequencing data, and to utilize the inferred IBD among family members to jointly call genotypes in pedigrees. Through simulations and application to real data, we showed that IBD can be reliably estimated across the genome, even at very low coverage (e.g. 2X), and genotype accuracy can be dramatically improved. Moreover, the improvement is more pronounced for variants with low frequencies, especially at low to intermediate coverage (e.g. 10X to 20X), making our approach effective in studying rare variants in cost-effective whole genome sequencing in pedigrees. We hope that our tool is useful to the research community for identifying rare variants for human disease through family-based sequencing.     

选择性剪接的理论预测

可变剪接作为真核生物转录后加工机制普遍存在于不同组织、不同发育时期或不同病理状态下的基因表达调控过程中。对同一基因不同剪接变体的研究可使我们更深入的了解发育、进化、疾病发生机理等基本的生物学问题。讨论了一些基于序列比对而建立的可变剪接的理论预测方法,尚存不足之处,有待于进一步完善。     

Detecting disease-causing mutations in the human genome by haplotype matching

Comparisons between haplotypes from affected patients and the human reference genome are frequently used to identify candidates for disease-causing mutations, even though these alignments are expected to reveal a high level of background neutral polymorphism. This limits the scope of genetic studies to relatively small genomic intervals, because current methods for distinguishing potential causal mutations from neutral variation are inefficient. Here we describe a new strategy for detecting mutations that is based on comparing affected haplotypes with closely matched control sequences from healthy individuals, rather than with the human reference genome. We use theory, simulation, and a real data set to show that this approach is expected to reduce the number of sequence variants that must be subjected to follow-up analysis by at least a factor of 20 when closely matched control sequences are selected from a reference panel with as few as 100 control genomes. We also define a reference data resource that would allow efficient application of this strategy to large critical intervals across the genome.     

Rare Variant Association Testing Under Low-Coverage Sequencing

Deep sequencing technologies enable the study of the effects of rare variants in disease risk. While methods have been developed to increase statistical power for detection of such effects, detecting subtle associations requires studies with hundreds or thousands of individuals, which is prohibitively costly. Recently, low-coverage sequencing has been shown to effectively reduce the cost of genome-wide association studies, using current sequencing technologies. However, current methods for disease association testing on rare variants cannot be applied directly to low-coverage sequencing data, as they require individual genotype data, which may not be called correctly due to low-coverage and inherent sequencing errors. In this article, we propose two novel methods for detecting association of rare variants with disease risk, using low coverage, error-prone sequencing. We show by simulation that our methods outperform previous methods under both low- and high-coverage sequencing and under different disease architectures. We use real data and simulation studies to demonstrate that to maximize the power to detect associations for a fixed budget, it is desirable to include more samples while lowering coverage and to perform an analysis using our suggested methods.     

The power comparison of the haplotype-based collapsing tests and the variant-based collapsing tests for detecting rare variants in pedigrees

Background

Both common and rare genetic variants have been shown to contribute to the etiology of complex diseases. Recent genome-wide association studies (GWAS) have successfully investigated how common variants contribute to the genetic factors associated with common human diseases. However, understanding the impact of rare variants, which are abundant in the human population (one in every 17 bases), remains challenging. A number of statistical tests have been developed to analyze collapsed rare variants identified by association tests. Here, we propose a haplotype-based approach. This work inspired by an existing statistical framework of the pedigree disequilibrium test (PDT), which uses genetic data to assess the effects of variants in general pedigrees. We aim to compare the performance between the haplotype-based approach and the rare variant-based approach for detecting rare causal variants in pedigrees.

Results

Extensive simulations in the sequencing setting were carried out to evaluate and compare the haplotype-based approach with the rare variant methods that drew on a more conventional collapsing strategy. As assessed through a variety of scenarios, the haplotype-based pedigree tests had enhanced statistical power compared with the rare variants based pedigree tests when the disease of interest was mainly caused by rare haplotypes (with multiple rare alleles), and vice versa when disease was caused by rare variants acting independently. For most of other situations when disease was caused both by haplotypes with multiple rare alleles and by rare variants with similar effects, these two approaches provided similar power in testing for association.

Conclusions

The haplotype-based approach was designed to assess the role of rare and potentially causal haplotypes. The proposed rare variants-based pedigree tests were designed to assess the role of rare and potentially causal variants. This study clearly documented the situations under which either method performs better than the other. All tests have been implemented in a software, which was submitted to the Comprehensive R Archive Network (CRAN) for general use as a computer program named rvHPDT.     

Detecting structural variations in the human genome using next generation sequencing

Structural variations are widespread in the human genome and can serve as genetic markers in clinical and evolutionary studies. With the advances in the next-generation sequencing technology, recent methods allow for identification of structural variations with unprecedented resolution and accuracy. They also provide opportunities to discover variants that could not be detected on conventional microarray-based platforms, such as dosage-invariant chromosomal translocations and inversions. In this review, we will describe some of the sequencing-based algorithms for detection of structural variations and discuss the key issues in future development.     

Limitations of the human reference genome for personalized genomics

Data from the 1000 genomes project (1KGP) and Complete Genomics (CG) have dramatically increased the numbers of known genetic variants and challenge several assumptions about the reference genome and its uses in both clinical and research settings. Specifically, 34% of published array-based GWAS studies for a variety of diseases utilize probes that overlap unanticipated single nucleotide polymorphisms (SNPs), indels, or structural variants. Linkage disequilibrium (LD) block length depends on the numbers of markers used, and the mean LD block size decreases from 16 kb to 7 kb,when HapMap-based calculations are compared to blocks computed from1KGP data. Additionally, when 1KGP and CG variants are compared, 19% of the single nucleotide variants (SNVs) reported from common genomes are unique to one dataset; likely a result of differences in data collection methodology, alignment of reads to the reference genome, and variant-calling algorithms. Together these observations indicate that current research resources and informatics methods do not adequately account for the high level of variation that already exists in the human population and significant efforts are needed to create resources that can accurately assess personal genomics for health, disease, and predict treatment outcomes.     

Single-cell paired-end genome sequencing reveals structural variation per cell cycle

The nature and pace of genome mutation is largely unknown. Because standard methods sequence DNA from populations of cells, the genetic composition of individual cells is lost, de novo mutations in cells are concealed within the bulk signal and per cell cycle mutation rates and mechanisms remain elusive. Although single-cell genome analyses could resolve these problems, such analyses are error-prone because of whole-genome amplification (WGA) artefacts and are limited in the types of DNA mutation that can be discerned. We developed methods for paired-end sequence analysis of single-cell WGA products that enable (i) detecting multiple classes of DNA mutation, (ii) distinguishing DNA copy number changes from allelic WGA-amplification artefacts by the discovery of matching aberrantly mapping read pairs among the surfeit of paired-end WGA and mapping artefacts and (iii) delineating the break points and architecture of structural variants. By applying the methods, we capture DNA copy number changes acquired over one cell cycle in breast cancer cells and in blastomeres derived from a human zygote after in vitro fertilization. Furthermore, we were able to discover and fine-map a heritable inter-chromosomal rearrangement t(1;16)(p36;p12) by sequencing a single blastomere. The methods will expedite applications in basic genome research and provide a stepping stone to novel approaches for clinical genetic diagnosis.