新一代测序技术在遗传性耳聋基因研究及诊断中的应用

超过50%的耳聋由遗传基因缺陷所致,伴随着基因组学技术的发展,耳聋分子遗传学研究逐渐成为耳科学研究的前沿领域。新一代高通量测序技术的出现,提供了以数据为导向的新的遗传性疾病研究模式,革新了人们对遗传性疾病的认识过程,使得对遗传性疾病的研究策略也发生了重大转变。近年来新一代测序技术(Next generation sequencing,NGS)在耳聋研究中的应用,大大加快了耳聋基因发现的速度,并逐渐向临床应用方向转化。文章总结了遗传性耳聋的特点和研究现状,以及新一代测序技术在耳聋研究中的应用和前景,以及基于NGS技术的耳聋基因研究和临床耳聋基因诊断的发展方向。     

Next generation sequencing based approaches to epigenomics

Next generation sequencing has brought epigenomic studies to the forefront of current research. The power of massively parallel sequencing coupled to innovative molecular and computational techniques has allowed researchers to profile the epigenome at resolutions that were unimaginable only a few years ago. With early proof of concept studies published, the field is now moving into the next phase where the importance of method standardization and rigorous quality control are becoming paramount. In this review we will describe methodologies that have been developed to profile the epigenome using next generation sequencing platforms. We will discuss these in terms of library preparation, sequence platforms and analysis techniques.     

The future of parentage analysis: From microsatellites to SNPs and beyond

Parentage analysis is a cornerstone of molecular ecology that has delivered fundamental insights into behaviour, ecology and evolution. Microsatellite markers have long been the king of parentage, their hypervariable nature conferring sufficient power to correctly assign offspring to parents. However, microsatellite markers have seen a sharp decline in use with the rise of next‐generation sequencing technologies, especially in the study of population genetics and local adaptation. The time is ripe to review the current state of parentage analysis and see how it stands to be affected by the emergence of next‐generation sequencing approaches. We find that single nucleotide polymorphisms (SNPs), the typical next‐generation sequencing marker, remain underutilized in parentage analysis but are gaining momentum, with 58 SNP‐based parentage analyses published thus far. Many of these papers, particularly the earlier ones, compare the power of SNPs and microsatellites in a parentage context. In virtually every case, SNPs are at least as powerful as microsatellite markers. As few as 100–500 SNPs are sufficient to resolve parentage completely in most situations. We also provide an overview of the analytical programs that are commonly used and compatible with SNP data. As the next‐generation parentage enterprise grows, a reliance on likelihood and Bayesian approaches, as opposed to strict exclusion, will become increasingly important. We discuss some of the caveats surrounding the use of next‐generation sequencing data for parentage analysis and conclude that the future is bright for this important realm of molecular ecology.     

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。     

基于分子标记的宏基因组16S rRNA基因高变区选择策略

随着新一代DNA测序技术出现,人们能够同时对多个DNA样本的宏基因组进行并行分析,尤其是以16S rRNA基因高变区为分子标记的测序已经成为微生物多样性研究最为简洁有效的方法. 目前二代高通量测序的读长不能覆盖16S rRNA基因的全长,需要选择一个有效的高变区进行测序.十多年来,对于16S rRNA基因高变区的选择策略没有统一的标准.本文分析了常用的高变区选择策略,指出不同环境条件是影响高变区选择的重要因素之一.在此基础上,提出了高变区选择的参考准则,同时建议应对选择的高变区进行有效评估.     

Novel bioinformatic developments for exome sequencing

With the widespread adoption of next generation sequencing technologies by the genetics community and the rapid decrease in costs per base, exome sequencing has become a standard within the repertoire of genetic experiments for both research and diagnostics. Although bioinformatics now offers standard solutions for the analysis of exome sequencing data, many challenges still remain; especially the increasing scale at which exome data are now being generated has given rise to novel challenges in how to efficiently store, analyze and interpret exome data of this magnitude. In this review we discuss some of the recent developments in bioinformatics for exome sequencing and the directions that this is taking us to. With these developments, exome sequencing is paving the way for the next big challenge, the application of whole genome sequencing.     

Connectivity Mapping for Candidate Therapeutics Identification Using Next Generation Sequencing RNA-Seq Data

The advent of next generation sequencing technologies (NGS) has expanded the area of genomic research, offering high coverage and increased sensitivity over older microarray platforms. Although the current cost of next generation sequencing is still exceeding that of microarray approaches, the rapid advances in NGS will likely make it the platform of choice for future research in differential gene expression. Connectivity mapping is a procedure for examining the connections among diseases, genes and drugs by differential gene expression initially based on microarray technology, with which a large collection of compound-induced reference gene expression profiles have been accumulated. In this work, we aim to test the feasibility of incorporating NGS RNA-Seq data into the current connectivity mapping framework by utilizing the microarray based reference profiles and the construction of a differentially expressed gene signature from a NGS dataset. This would allow for the establishment of connections between the NGS gene signature and those microarray reference profiles, alleviating the associated incurring cost of re-creating drug profiles with NGS technology. We examined the connectivity mapping approach on a publicly available NGS dataset with androgen stimulation of LNCaP cells in order to extract candidate compounds that could inhibit the proliferative phenotype of LNCaP cells and to elucidate their potential in a laboratory setting. In addition, we also analyzed an independent microarray dataset of similar experimental settings. We found a high level of concordance between the top compounds identified using the gene signatures from the two datasets. The nicotine derivative cotinine was returned as the top candidate among the overlapping compounds with potential to suppress this proliferative phenotype. Subsequent lab experiments validated this connectivity mapping hit, showing that cotinine inhibits cell proliferation in an androgen dependent manner. Thus the results in this study suggest a promising prospect of integrating NGS data with connectivity mapping.     

Estimating Individual Admixture Proportions from Next Generation Sequencing Data

Inference of population structure and individual ancestry is important both for population genetics and for association studies. With next generation sequencing technologies it is possible to obtain genetic data for all accessible genetic variations in the genome. Existing methods for admixture analysis rely on known genotypes. However, individual genotypes cannot be inferred from low-depth sequencing data without introducing errors. This article presents a new method for inferring an individual’s ancestry that takes the uncertainty introduced in next generation sequencing data into account. This is achieved by working directly with genotype likelihoods that contain all relevant information of the unobserved genotypes. Using simulations as well as publicly available sequencing data, we demonstrate that the presented method has great accuracy even for very low-depth data. At the same time, we demonstrate that applying existing methods to genotypes called from the same data can introduce severe biases. The presented method is implemented in the NGSadmix software available at http://www.popgen.dk/software.     

HLA Typing from 1000 Genomes Whole Genome and Whole Exome Illumina Data

Specific HLA genotypes are known to be linked to either resistance or susceptibility to certain diseases or sensitivity to certain drugs. In addition, high accuracy HLA typing is crucial for organ and bone marrow transplantation. The most widespread high resolution HLA typing method used to date is Sanger sequencing based typing (SBT), and next generation sequencing (NGS) based HLA typing is just starting to be adopted as a higher throughput, lower cost alternative. By HLA typing the HapMap subset of the public 1000 Genomes paired Illumina data, we demonstrate that HLA-A, B and C typing is possible from exome sequencing samples with higher than 90% accuracy. The older 1000 Genomes whole genome sequencing read sets are less reliable and generally unsuitable for the purpose of HLA typing. We also propose using coverage % (the extent of exons covered) as a quality check (QC) measure to increase reliability.     

C4水稻,我们的新挑战

自从上个世纪60年代末C4光合途径发现以来,人们对工程改造现有C3粮食作物使之具有C4光合能力进行了大量努力。目前,大量分子、生理和基因组水平研究的进展和证据表明,该目标将可能在10～15年之内实现。本综述结合目前国际C4研究的现状,详述了该领域目前所涉各项研究内容的理论依据。我们首先总结过去的经典杂交实验,然后论证新一代测序技术与C4光合研究模式系统狐尾草（Setaria viridis）的发展极大的促进了我们对C4光合特征遗传发育相关基因的发现与鉴定。最后,我们强调虽然C4光合工程改造的研究目前已在世界各国大规模展开,但其最终成功仍有赖于不同国家研究基金及私立慈善基金的大力和长期共同资助。     

二代测序应用于检测GT1-7细胞中KISS1和GnRH启动子的DNA甲基化状态的研究

目的:利用二代测序技术检测GT1-7细胞中KISS1和GnRH基因启动子范围内的甲基化状态,并用金标准的亚硫酸氢盐修饰后的克隆测序作为对照,比较二代测序与金标准克隆测序在研究DNA甲基化检测中的差别。方法:提取GT1-7细胞基因组DNA并进行亚硫酸氢盐处理。进行巢式PCR,将PCR产物进行二代测序。同时采用金标准的亚硫酸氢盐修饰后克隆测序的方法作为对照,对相同批次的PCR产物进行克隆测序。结果:PCR产物二代测序结果表明KISS1和GnRH两个基因的27个CpG甲基化位点信息完整,结果准确。挑取10个克隆进行一代测序结果表明序列无丢失,KISS1和GnRH两个基因的27个CpG甲基化位点信息完整。结论:利用高通量的二代测序技术能够有效的对DNA甲基化的PCR产物进行检测,二代测序和克隆测序都是研究DNA甲基化的有效方法,但前者与克隆测序相比每一个读取序列(reads)都相当于一个单克隆,且二代测序每个区段得到成百上千个reads,因此二代测序结果更加精确。     

Sequencing technologies and genome sequencing

The high-throughput - next generation sequencing (HT-NGS) technologies are currently the hottest topic in the field of human and animals genomics researches, which can produce over 100 times more data compared to the most sophisticated capillary sequencers based on the Sanger method. With the ongoing developments of high throughput sequencing machines and advancement of modern bioinformatics tools at unprecedented pace, the target goal of sequencing individual genomes of living organism at a cost of $1,000 each is seemed to be realistically feasible in the near future. In the relatively short time frame since 2005, the HT-NGS technologies are revolutionizing the human and animal genome researches by analysis of chromatin immunoprecipitation coupled to DNA microarray (ChIP-chip) or sequencing (ChIP-seq), RNA sequencing (RNA-seq), whole genome genotyping, genome wide structural variation, de novo assembling and re-assembling of genome, mutation detection and carrier screening, detection of inherited disorders and complex human diseases, DNA library preparation, paired ends and genomic captures, sequencing of mitochondrial genome and personal genomics. In this review, we addressed the important features of HT-NGS like, first generation DNA sequencers, birth of HT-NGS, second generation HT-NGS platforms, third generation HT-NGS platforms: including single molecule Heliscope™, SMRT™ and RNAP sequencers, Nanopore, Archon Genomics X PRIZE foundation, comparison of second and third HT-NGS platforms, applications, advances and future perspectives of sequencing technologies on human and animal genome research.     

Mutation Discovery in Regions of Segmental Cancer Genome Amplifications with CoNAn-SNV: A Mixture Model for Next Generation Sequencing of Tumors

Next generation sequencing has now enabled a cost-effective enumeration of the full mutational complement of a tumor genome-in particular single nucleotide variants (SNVs). Most current computational and statistical models for analyzing next generation sequencing data, however, do not account for cancer-specific biological properties, including somatic segmental copy number alterations (CNAs)-which require special treatment of the data. Here we present CoNAn-SNV (Copy Number Annotated SNV): a novel algorithm for the inference of single nucleotide variants (SNVs) that overlap copy number alterations. The method is based on modelling the notion that genomic regions of segmental duplication and amplification induce an extended genotype space where a subset of genotypes will exhibit heavily skewed allelic distributions in SNVs (and therefore render them undetectable by methods that assume diploidy). We introduce the concept of modelling allelic counts from sequencing data using a panel of Binomial mixture models where the number of mixtures for a given locus in the genome is informed by a discrete copy number state given as input. We applied CoNAn-SNV to a previously published whole genome shotgun data set obtained from a lobular breast cancer and show that it is able to discover 21 experimentally revalidated somatic non-synonymous mutations in a lobular breast cancer genome that were not detected using copy number insensitive SNV detection algorithms. Importantly, ROC analysis shows that the increased sensitivity of CoNAn-SNV does not result in disproportionate loss of specificity. This was also supported by analysis of a recently published lymphoma genome with a relatively quiescent karyotype, where CoNAn-SNV showed similar results to other callers except in regions of copy number gain where increased sensitivity was conferred. Our results indicate that in genomically unstable tumors, copy number annotation for SNV detection will be critical to fully characterize the mutational landscape of cancer genomes.     

Next generation sequencing and bioinformatic bottlenecks: the current state of metagenomic data analysis

The recent technological advances in next generation sequencing have brought the field closer to the goal of reconstructing all genomes within a community by presenting high throughput sequencing at much lower costs. While these next-generation sequencing technologies have allowed a massive increase in available raw sequence data, there are a number of new informatics challenges and difficulties that must be addressed to improve the current state, and fulfill the promise of, metagenomics.     

An efficient pipeline for ancient DNA mapping and recovery of endogenous ancient DNA from whole‐genome sequencing data

Ancient DNA research has developed rapidly over the past few decades due to improvements in PCR and next‐generation sequencing (NGS) technologies, but challenges still exist. One major challenge in relation to ancient DNA research is to recover genuine endogenous ancient DNA sequences from raw sequencing data. This is often difficult due to degradation of ancient DNA and high levels of contamination, especially homologous contamination that has extremely similar genetic background with that of the real ancient DNA. In this study, we collected whole‐genome sequencing (WGS) data from 6 ancient samples to compare different mapping algorithms. To further explore more effective methods to separate endogenous DNA from homologous contaminations, we attempted to recover reads based on ancient DNA specific characteristics of deamination, depurination, and DNA fragmentation with different parameters. We propose a quick and improved pipeline for separating endogenous ancient DNA while simultaneously decreasing homologous contaminations to very low proportions. Our goal in this research was to develop useful recommendations for ancient DNA mapping and for separation of endogenous DNA to facilitate future studies of ancient DNA.     

Evaluating multiplexed next‐generation sequencing as a method in palynology for mixed pollen samples

The identification of pollen plays an important role in ecology, palaeo‐climatology, honey quality control and other areas. Currently, expert knowledge and reference collections are essential to identify pollen origin through light microscopy. Pollen identification through molecular sequencing and DNA barcoding has been proposed as an alternative approach, but the assessment of mixed pollen samples originating from multiple plant species is still a tedious and error‐prone task. Next‐generation sequencing has been proposed to avoid this hindrance. In this study we assessed mixed pollen probes through next‐generation sequencing of amplicons from the highly variable, species‐specific internal transcribed spacer 2 region of nuclear ribosomal DNA. Further, we developed a bioinformatic workflow to analyse these high‐throughput data with a newly created reference database. To evaluate the feasibility, we compared results from classical identification based on light microscopy from the same samples with our sequencing results. We assessed in total 16 mixed pollen samples, 14 originated from honeybee colonies and two from solitary bee nests. The sequencing technique resulted in higher taxon richness (deeper assignments and more identified taxa) compared to light microscopy. Abundance estimations from sequencing data were significantly correlated with counted abundances through light microscopy. Simulation analyses of taxon specificity and sensitivity indicate that 96% of taxa present in the database are correctly identifiable at the genus level and 70% at the species level. Next‐generation sequencing thus presents a useful and efficient workflow to identify pollen at the genus and species level without requiring specialised palynological expert knowledge.     

MSClust: A Multi-Seeds based Clustering algorithm for microbiome profiling using 16S rRNA sequence

Recent developments of next generation sequencing technologies have led to rapid accumulation of 16S rRNA sequences for microbiome profiling. One key step in data processing is to cluster short sequences into operational taxonomic units (OTUs). Although many methods have been proposed for OTU inferences, a major challenge is the balance between inference accuracy and computational efficiency, where inference accuracy is often sacrificed to accommodate the need to analyze large numbers of sequences. Inspired by the hierarchical clustering method and a modified greedy network clustering algorithm, we propose a novel multi-seeds based heuristic clustering method, named MSClust, for OTU inference. MSClust first adaptively selects multi-seeds instead of one seed for each candidate cluster, and the reads are then processed using a greedy clustering strategy. Through many numerical examples, we demonstrate that MSClust enjoys less memory usage, and better biological accuracy compared to existing heuristic clustering methods while preserving efficiency and scalability.     

Design of efficient simplified genomic DNA and bisulfite sequencing in large plant populations

The next generation sequencing enables generation of high resolution and high throughput data for structure sequence of any genome at a fast declining cost. This opens opportunity for population based genetic and genomic analyses. In many applications, whole genome sequencing or re-sequencing is unnecessary or prohibited by budget limits. The Reduced Representation Genome Sequencing (RRGS), which sequences only a small proportion of the genome of interest, has been proposed to deal with the situations. Several forms of RRGS are proposed and implemented in the literature. When applied to plant or crop species, the current RRGS protocols shared a key drawback that a significantly high proportion (up to 60%) of sequence reads to be generated may be of non-genomic origin but attributed to chloroplast DNA or rRNA genes, leaving an exceptional low efficiency of the sequencing experiment. We recommended and discussed here the design of optimized simplified genomic DNA and bisulfite sequencing strategies, which may greatly improves efficiency of the sequencing experiments by bringing down the presentation of the undesirable sequencing reads to less than 10% in the whole sequence reads. The optimized RAD-seq and RRBS-seq methods are potentially useful for sequence variant screening and genotyping in large plant/crop populations.