木本植物全基因组测序研究进展

近年来, 植物全基因组测序的结果正如雨后春笋般涌现, 木本植物全基因组测序也在紧锣密鼓地展开。但由于木本植物通常基因组较大, 基因组结构较为复杂, 在测序、测序后的组装、注释、功能分析等均存在较大的困难。在基因组测序分析的经费预算方面也存在着较大的压力。因此, 有必要对这方面的研究进展及其存在问题进行分析比较, 以提高林木全基因组研究方面的效率。文章在比较分析已经发展起来的3代基因测序技术(Sanger测序法、合成测序法和单分子测序法)的基础上, 选择4种已经公布的木本植物(杨树、葡萄、番木瓜、苹果), 从全基因组测序的研究背景、测序结果及应用的研究进展和存在问题等方面进行了述评, 对未来要开展的木本植物全基因组测序前的准备工作(材料选择、遗传图谱和连锁图谱的构建、测序技术的选择), 全基因组测序结果的生物信息学分析和应用进行了讨论。     

微生物全基因组测序研究进展

本文综述了近年来大规模微生物基因组核酸序列测定的最新研究进展,介绍微生物全基因组测序的基本方法、序列的收集组装,序列缺口的填补,以及序列资料的计算机分析整理。大规模基因组测序完成后,未来面临的更大挑战是在ＤＮＡ序列基础上认识微生物的完整生物学功能。为此本文也介绍了有关基因功能分析的新技术,并对微生物基因组功能分析的未来发展作了展望。     

牛全基因组高通量测序技术研究进展

现代科技迅速发展的今天,无疑是分子生物学的世界。基因组测序是对生物的遗传结构进行分析的一种技术。作为一项尤为重要的生物技术。在近几年来得到了迅猛的发展以及应用,并取得了跨越性的进展,在很多领域取得了革命性的成就。无论是在人类疾病的防治,还是在畜牧遗传育种发面都发挥着重要的作用。本综述主要介绍了第一代测序技术、第二代测序技术以及第三代测序技术的原理,并对三者的优缺点进行了比较说明,还分别阐述了全基因组高通量技术在肉牛的起源、遗传育种与优良性状的选育和奶牛的疾病防治、生产性能的提高等方面的研究进展,对当下的高通量测序技术存在的问题进行了讨论,并对其未来进行了展望。     

马属动物全基因组高通量测序研究进展

马属(Equus)动物的祖先大约在5500万年前出现,经过持续分化形成现今的马、驴和斑马,统称马属动物。马作为家畜中的重要一员,是推动人类文明发展的载体,在人类的饮食、战争、农耕、运输和娱乐等领域做出了巨大贡献。然而,人类为了满足需求,或多或少影响着马的进化方向,从而在长时间自然和人工选择过程中形成了多种独具特色性状的不同马种。驴和骡在全球的存栏量也较多,在人类的生产和生活中起到的作用同样不可忽视,不但为人类提供了生产力而且还提供了食物和营养保健品。可见,马属动物对人类的重要性。近年来,高通量测序技术和生物信息学分析方法被广泛运用于家畜的遗传学研究。人们利用高通量测序手段探索马属动物在进化过程中的种群变化历史,解析形成独特性状的分子机制,为其育种工作提供有效的数据支持。本文综述了马属动物全基因组高通量测序的研究进展,以及利用该技术在马属动物的进化历史和功能基因挖掘研究领域所取得的成就,以期今后对马属动物的深入研究、产业开发和利用等方面提供参考信息。     

复杂基因组测序技术研究进展

复杂基因组指的是无法使用常规测序和组装手段直接解析的一类基因组,通常指包含高比例重复序列、高杂合度、极端GC含量、存在难消除异源DNA污染的基因组。为了解决复杂基因组的测序和组装问题,需要分别从基因组测序实验方法、测序技术平台、组装算法与策略3个方面进行深入研究。本文详细介绍了复杂基因组测序组装相关的现有技术与方法,并结合复杂基因组经典实例介绍了复杂基因组测序的技术解决途径和发展历程,可为制订合适的复杂基因组测序策略提供参考。     

一个被全基因组测序挽救的家庭

15岁的Noah Beery终于可以跟双胞胎妹妹Alexis Beery爬山了。对于正常健廉的家庭来说,这不是什么大不了的事情;而被病磨折磨了十几年的Beery家庭,却因为这弥足珍贵的家庭外出而激动万分。母亲RettaBeery一直坚强地从未放弃子女治疗,此时终于热泪盈眶：“这是我们从来不敢想象的画面。”     

耐热黑曲霉3.316菌株全基因组测序及分析

黑曲霉3.316是一株耐热型丝状真菌,最高生长温度达47℃,在工业发酵中有着巨大的应用潜力.为了更加充分地在工业发酵中利用其耐热特性,需要对菌株信息进行全面了解.通过PacBio Sequel测序平台的CLR测序方式对黑曲霉3.316菌株进行全基因组测序.结果表明,基因组最后得到15个contigs,总长度为34 95...     

全基因组测序在病原菌分型与溯源中的应用研究进展

细菌分子分型已成为监测细菌感染性疾病的暴发流行与明确病原菌传播途径的重要工具.随着全基因组测序技术的日益兴起,公共数据库中已产生大量的细菌基因组数据,迫切需要研究人员充分认识和理解该技术,并掌握多种生物信息学工具挖掘并解读测序数据.本文系统概述了全基因组测序技术与生物信息学工具在病原菌分型与溯源中的应用,并对全基因组测...     

微生物全基因组鸟枪法测序

全基因组测序主要有二种策略,一种是分级鸟枪法测序,另一种是全基因组鸟枪法测序。微生物是一种十分重要的遗传资源,运用全基因组鸟枪法可以方便、快捷地完成其基因组的测序任务。本文对微生物全基因组鸟枪法测序中文库构建、插入片段的长短比例、反应投入量、拼接以及补洞等问题作了较细致的描述,有些步骤作了举例说明。 Abstract:Two strategies introduced for whole genome sequencing,one is clone by clone method,the other is whole genome shotgun sequencing,for microbes which are very important to us,whole genome shotgun sequencing method is very convenient.In this article we discussed the library construction、long-to-short-ratio of insert,、total number of reads should be sequenced、assembly and gap filling technologies of the whole microbial genome shotgun sequencing method while some examples presented.     

水稻基因组测序的研究进展

水稻是最重要的粮食作物之一,世界上大约有一半的人口以水稻为主要粮食。作为基因组研究的模式植物,水稻基因组的测序工作已在世界范围内展开。此项研究工作不仅能破译水稻全基因组序列,还将有助于了解其他禾本科植物的基因组信息。本对水稻基因组测序工作进展作一综述。     

全基因组测序与生物信息学分析在细菌耐药性研究中的应用

随着耐药细菌的大量出现及广泛传播,细菌耐药性成为全球备受关注的问题。耐药细菌的特征如耐药基因、毒力因子、质粒分型等以及不同菌株间亲缘关系对于细菌耐药性流行病学及分子生物学的研究有着十分重要的意义。但是传统的技术手段如聚合酶链式反应(Polymerase chain reaction,PCR)和脉冲场凝胶电泳(Pulsed field gel electrophoresis,PFGE)等得到的结果不够全面且精确度低,对于现有的研究存在很大的局限性。全基因组测序技术(Whole genome sequencing,WGS)和生物信息学分析(Bioinformatics analysis)由于能够快速详尽地得到耐药细菌的特征,也能更加精细地判断不同菌株间的进化关系,逐渐成为更加有效的技术手段,为耐药性研究提供了有效的帮助。因此,文中系统地介绍全基因组测序分析流程中的各个步骤,主要包括文库构建、平台测序以及后期数据分析三大方面的不同方法和其相应的特点,期望相关研究人员对此能够有更全面的了解,并得到一定的帮助。     

Deciding among green plants for whole genome studies

Recent comparative DNA-sequencing studies of chloroplast, mitochondrial and ribosomal genes have produced an evolutionary tree relating the diversity of green-plant lineages. By coupling this phylogenetic framework to the explosion of information on genome content, plant-genomic efforts can and should be extended beyond angiosperm crop and model systems. Including plant species representative of other crucial evolutionary nodes would produce the comparative information necessary to understand fully the organization, function and evolution of plant genomes. The simultaneous development of genomic tools for green algae, bryophytes, ‘seed-free’ vascular plants and gymnosperms should provide insights into the bases of the complex morphological, physiological, reproductive and biochemical innovations that have characterized the successful transition of green plants to land.     

单细胞基因组测序技术新进展及其在生物医学中的应用

随着单细胞基因组测序技术的建立与发展,对细胞基因组特征的分析进入了单细胞水平。单细胞的基因组分辨率不但使研究人员能够在单细胞尺度上分析肿瘤细胞的异质性,也使得传统上难以检测的稀有细胞的基因组研究成为可能。这些稀有细胞往往具有重要的生物学意义或临床价值,如癌症患者血液中循环肿瘤细胞(circulatingtumorcell,CTC)的基因组检测或三代试管婴儿植入前胚胎细胞的遗传缺陷诊断与筛查(preimplantation genetic diagnosis/screening, PGD/PGS)。本文总结了近年来发展的各种单细胞基因组扩增技术及其优缺点,并介绍了单细胞基因组测序技术在肿瘤生物学和临床检测中的应用,以期为单细胞基因组测序技术在临床检测中应用开发提供参考。     

Whole genome sequencing and its applications in medical genetics

Fundamental improvement was made for genome sequencing since the next-generation sequencing (NGS) came out in the 2000s. The newer technologies make use of the power of massively-parallel short-read DNA sequencing, genome alignment and assembly methods to digitally and rapidly search the genomes on a revolutionary scale, which enable large-scale whole genome sequencing (WGS) accessible and practical for researchers. Nowadays, whole genome sequencing is more and more prevalent in detecting the genetics of diseases, studying causative relations with cancers, making genome-level comparative analysis, reconstruction of human population history, and giving clinical implications and instructions. In this review, we first give a typical pipeline of whole genome sequencing, including the lab template preparation, sequencing, genome assembling and quality control, variants calling and annotations. We compare the difference between whole genome and whole exome sequencing (WES), and explore a wide range of applications of whole genome sequencing for both mendelian diseases and complex diseases in medical genetics. We highlight the impact of whole genome sequencing in cancer studies, regulatory variant analysis, predictive medicine and precision medicine, as well as discuss the challenges of the whole genome sequencing.      

ploidyfrost: Reference-free estimation of ploidy level from whole genome sequencing data based on de Bruijn graphs

Polyploidy is ubiquitous and its consequences are complex and variable. A change of ploidy level generally influences genetic diversity and results in morphological, physiological and ecological differences between cells or organisms with different ploidy levels. To avoid cumbersome experiments and take advantage of the less biased information provided by the vast amounts of genome sequencing data, computational tools for ploidy estimation are urgently needed. Until now, although a few such tools have been developed, many aspects of this estimation, such as the requirement of a reference genome, the lack of informative results and objective inferences, and the influence of false positives from errors and repeats, need further improvement. We have developed ploidyfrost , a de Bruijn graph-based method, to estimate ploidy levels from whole genome sequencing data sets without a reference genome. ploidyfrost provides a visual representation of allele frequency distribution generated using the ggplot2 package as well as quantitative results using the Gaussian mixture model. In addition, it takes advantage of colouring information encoded in coloured de Bruijn graphs to analyse multiple samples simultaneously and to flexibly filter putative false positives. We evaluated the performance of ploidyfrost by analysing highly heterozygous or repetitive samples of Cyclocarya paliurus and a complex allooctoploid sample of Fragaria × ananassa. Moreover, we demonstrated that the accuracy of analysis results can be improved by constraining a threshold such as Cramér's V coefficient on variant features, which may significantly reduce the side effects of sequencing errors and annoying repeats on the graphical structure constructed.     

Leveraging whole genome sequencing data for demographic inference with approximate Bayesian computation

Accounting for historical demographic features, such as the strength and timing of gene flow and divergence times between closely related lineages, is vital for many inferences in evolutionary biology. Approximate Bayesian computation (ABC) is one method commonly used to estimate demographic parameters. However, the DNA sequences used as input for this method, often microsatellites or RADseq loci, usually represent a small fraction of the genome. Whole genome sequencing (WGS) data, on the other hand, have been used less often with ABC, and questions remain about the potential benefit of, and how to best implement, this type of data; we used pseudo‐observed data sets to explore such questions. Specifically, we addressed the potential improvements in parameter estimation accuracy that could be associated with WGS data in multiple contexts; namely, we quantified the effects of (a) more data, (b) haplotype‐based summary statistics, and (c) locus length. Compared with a hypothetical RADseq data set with 2.5 Mbp of data, using a 1 Gbp data set consisting of 100 Kbp sequences led to substantial gains in the accuracy of parameter estimates, which was mostly due to haplotype statistics and increased data. We also quantified the effects of including (a) locus‐specific recombination rates, and (b) background selection information in ABC analyses. Importantly, assuming uniform recombination or ignoring background selection had a negative effect on accuracy in many cases. Software and results from this method validation study should be useful for future demographic history analyses.     

Variant detection sensitivity and biases in whole genome and exome sequencing

Background

Less than two percent of the human genome is protein coding, yet that small fraction harbours the majority of known disease causing mutations. Despite rapidly falling whole genome sequencing (WGS) costs, much research and increasingly the clinical use of sequence data is likely to remain focused on the protein coding exome. We set out to quantify and understand how WGS compares with the targeted capture and sequencing of the exome (exome-seq), for the specific purpose of identifying single nucleotide polymorphisms (SNPs) in exome targeted regions.

Results

We have compared polymorphism detection sensitivity and systematic biases using a set of tissue samples that have been subject to both deep exome and whole genome sequencing. The scoring of detection sensitivity was based on sequence down sampling and reference to a set of gold-standard SNP calls for each sample. Despite evidence of incremental improvements in exome capture technology over time, whole genome sequencing has greater uniformity of sequence read coverage and reduced biases in the detection of non-reference alleles than exome-seq. Exome-seq achieves 95% SNP detection sensitivity at a mean on-target depth of 40 reads, whereas WGS only requires a mean of 14 reads. Known disease causing mutations are not biased towards easy or hard to sequence areas of the genome for either exome-seq or WGS.

Conclusions

From an economic perspective, WGS is at parity with exome-seq for variant detection in the targeted coding regions. WGS offers benefits in uniformity of read coverage and more balanced allele ratio calls, both of which can in most cases be offset by deeper exome-seq, with the caveat that some exome-seq targets will never achieve sufficient mapped read depth for variant detection due to technical difficulties or probe failures. As WGS is intrinsically richer data that can provide insight into polymorphisms outside coding regions and reveal genomic rearrangements, it is likely to progressively replace exome-seq for many applications.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2105-15-247) contains supplementary material, which is available to authorized users.