Commercial high-throughput sequencing and its applications in DNA analysis

DNA sequences can be used for the analysis of genetic variation and gene function. The high-throughput sequencing techniques that have been developed over the past three years can read as many as one billion bases per run, and are far less expensive than the traditional Sanger sequencing method. Therefore, the high-throughput sequencing has been applied extensively to genomic analyses, such as screening for mutations, construction of genomic methylation maps, and the study of DNA-protein interactions. Although they have only been available for a short period, high-throughput sequencing techniques are profoundly affecting many of the life sciences, and are opening out new potential avenues of research. With the highly-developed commercial high-throughput sequencing platforms, each laboratory has the opportunity to explore this research field. Therefore, in this paper, we have focused on commercially-popular high-throughput sequencing techniques and the ways in which they have been applied over the past three years.     

Transposome Insertional Mutagenesis and Direct Sequencing of Microbial Genomes

Preformed transposase-transposon complexes called Transposomes have been electroporated into bacterial cells. The magnesium dependent process of insertion of the transposable element into bacterial chromosomal DNA occurs in vivo. The transposition efficiency of a Transposome containing a kanamycin marker was between 1.0×10⁴and 1.0×10⁷kanamycin resistant clones per microgram of transposon DNA in three gram-negative enteric bacterial species. Transposon integration sites were examined by direct genome sequencing of chromosomal DNA. Genomic DNA was isolated from transposition clones and directly cycle sequenced with primers specific for the ends of the transposon. The precise location of genome interruption for a transposition clone was identified by homology to known genes or sequences. Mutant phenotypes were rapidly correlated with genomic insertions sites.     

PacBio-LITS: a large-insert targeted sequencing method for characterization of human disease-associated chromosomal structural variations

Background

Generation of long (>5 Kb) DNA sequencing reads provides an approach for interrogation of complex regions in the human genome. Currently, large-insert whole genome sequencing (WGS) technologies from Pacific Biosciences (PacBio) enable analysis of chromosomal structural variations (SVs), but the cost to achieve the required sequence coverage across the entire human genome is high.

Results

We developed a method (termed PacBio-LITS) that combines oligonucleotide-based DNA target-capture enrichment technologies with PacBio large-insert library preparation to facilitate SV studies at specific chromosomal regions. PacBio-LITS provides deep sequence coverage at the specified sites at substantially reduced cost compared with PacBio WGS. The efficacy of PacBio-LITS is illustrated by delineating the breakpoint junctions of low copy repeat (LCR)-associated complex structural rearrangements on chr17p11.2 in patients diagnosed with Potocki–Lupski syndrome (PTLS; MIM#610883). We successfully identified previously determined breakpoint junctions in three PTLS cases, and also were able to discover novel junctions in repetitive sequences, including LCR-mediated breakpoints. The new information has enabled us to propose mechanisms for formation of these structural variants.

Conclusions

The new method leverages the cost efficiency of targeted capture-sequencing as well as the mappability and scaffolding capabilities of long sequencing reads generated by the PacBio platform. It is therefore suitable for studying complex SVs, especially those involving LCRs, inversions, and the generation of chimeric Alu elements at the breakpoints. Other genomic research applications, such as haplotype phasing and small insertion and deletion validation could also benefit from this technology.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-015-1370-2) contains supplementary material, which is available to authorized users.     

Shotgun法测序制备高纯度水稻基因组DNA的方法探讨(英)

在用散弹 (shotgun)法测定水稻 (OryzasativaL .ssp .indica)基因组全序列的过程中 ,叶绿体和线粒体DNA的污染问题非常严峻 .应用脉冲场电泳 (PFGE)技术对水稻基因组DNA进行纯化 ,结果表明它能够有效去除叶绿体和线粒体DNA ,使其污染率从 3%降低到 0 2 % .同时 ,比较了水稻绿苗和黄化苗的DNA得率 ,以及HB法和NIB法制备大分子质量(HMW)DNA的异同 .最后提出一套制备水稻基因组DNA的方法 ,包括黄化苗培养 ;细胞核的分离、包埋和裂解 ;脉冲场电泳纯化、回收聚集在低熔点 (LMP)胶中的水稻HMWDNA .用该方法所得的水稻基因组DNA ,纯度高 (无叶绿体和线粒体DNA污染 )、基因组完整 (机械剪切和降解少 )、回收率高 (操作过程中DNA损失少 ) .另外 ,首次报道在融化的低熔点(LMP)胶中对水稻HMWDNA于 38℃进行超声波处理 ,能够获得用于shotgun文库和梯度文库构建所需要的各种DNA片段(1 5～ 3kb ,3～ 12kb) ,并且效果优于在TE中进行操作     

Toward genomic prediction from whole-genome sequence data: impact of sequencing design on genotype imputation and accuracy of predictions

Genomic prediction from whole-genome sequence data is attractive, as the accuracy of genomic prediction is no longer bounded by extent of linkage disequilibrium between DNA markers and causal mutations affecting the trait, given the causal mutations are in the data set. A cost-effective strategy could be to sequence a small proportion of the population, and impute sequence data to the rest of the reference population. Here, we describe strategies for selecting individuals for sequencing, based on either pedigree relationships or haplotype diversity. Performance of these strategies (number of variants detected and accuracy of imputation) were evaluated in sequence data simulated through a real Belgian Blue cattle pedigree. A strategy (AHAP), which selected a subset of individuals for sequencing that maximized the number of unique haplotypes (from single-nucleotide polymorphism panel data) sequenced gave good performance across a range of variant minor allele frequencies. We then investigated the optimum number of individuals to sequence by fold coverage given a maximum total sequencing effort. At 600 total fold coverage (x 600), the optimum strategy was to sequence 75 individuals at eightfold coverage. Finally, we investigated the accuracy of genomic predictions that could be achieved. The advantage of using imputed sequence data compared with dense SNP array genotypes was highly dependent on the allele frequency spectrum of the causative mutations affecting the trait. When this followed a neutral distribution, the advantage of the imputed sequence data was small; however, when the causal mutations all had low minor allele frequencies, using the sequence data improved the accuracy of genomic prediction by up to 30%.     

面向新一代基因组测序技术的序列拼接算法

随着新一代测序技术的发展,新的拼接算法应运而生。介绍了目前国际上广泛认可的几种新的拼接算法的基本原理与具体步骤,分析每种算法的优缺点以及适用范围。用Helicobacter acinonychis的Illumina 1G测序数据检测SSAKE,VCAKE,SHARCGS以及velvet的性能,并对未来拼接算法的研究提出展望。     

Inexpensive Computation of the Inverse of the Genomic Relationship Matrix in Populations with Small Effective Population Size

Many computations with SNP data including genomic evaluation, parameter estimation, and genome-wide association studies use an inverse of the genomic relationship matrix. The cost of a regular inversion is cubic and is prohibitively expensive for large matrices. Recent studies in cattle demonstrated that the inverse can be computed in almost linear time by recursion on any subset of ∼10,000 individuals. The purpose of this study is to present a theory of why such a recursion works and its implication for other populations. Assume that, because of a small effective population size, the additive information in a genotyped population has a small dimensionality, even with a very large number of SNP markers. That dimensionality is visible as a limited number of effective SNP effects, independent chromosome segments, or the rank of the genomic relationship matrix. Decompose a population arbitrarily into core and noncore individuals, with the number of core individuals equal to that dimensionality. Then, breeding values of noncore individuals can be derived by recursions on breeding values of core individuals, with coefficients of the recursion computed from the genomic relationship matrix. A resulting algorithm for the inversion called “algorithm for proven and young” (APY) has a linear computing and memory cost for noncore animals. Noninfinitesimal genetic architecture can be accommodated through a trait-specific genomic relationship matrix, possibly derived from Bayesian regressions. For populations with small effective population size, the inverse of the genomic relationship matrix can be computed inexpensively for a very large number of genotyped individuals.