A Streamlined Method for Detecting Structural Variants in Cancer Genomes by Short Read Paired-End Sequencing

Defining the architecture of a specific cancer genome, including its structural variants, is essential for understanding tumor biology, mechanisms of oncogenesis, and for designing effective personalized therapies. Short read paired-end sequencing is currently the most sensitive method for detecting somatic mutations that arise during tumor development. However, mapping structural variants using this method leads to a large number of false positive calls, mostly due to the repetitive nature of the genome and the difficulty of assigning correct mapping positions to short reads. This study describes a method to efficiently identify large tumor-specific deletions, inversions, duplications and translocations from low coverage data using SVDetect or BreakDancer software and a set of novel filtering procedures designed to reduce false positive calls. Applying our method to a spontaneous T cell lymphoma arising in a core RAG2/p53-deficient mouse, we identified 40 validated tumor-specific structural rearrangements supported by as few as 2 independent read pairs.     

一个RNAi载体上反向重复片段的测序策略

相邻的反向重复DNA片段有形成单链内二级结构的倾向,属于一种测序困难的DNA模板。解决RNAi载体插入的反向重复片段的测序问题,为该类载体正确性的测序验证奠定基础。采用常规分子克隆方法构建表达小麦TaATG2串联反向重复片段的RNAi载体,设计2种策略对经菌落PCR初步鉴定的载体进行测序验证:一种是以完整的载体质粒为模板进行测序;另一种是先对载体进行酶切处理,切除反向重复片段中的一个后对保留另一个片段的线性载体进行测序。结果表明,第一种测序策略受到串联反向重复片段形成的单链内部二级结构的影响,测序信号在反向重复片段处出现衰减或乱峰,无法读取序列。第二种测序策略排除了2个反向重复片段之间的干扰,保留在载体上的片段测序信号清晰,序列准确。采用酶切切除一个片段后进行测序的方法,经过2次酶切和2次测序可以有效地对载体上的2个反向重复片段分别进行序列测定,进而确认构建载体的正确性。     

一种基于单分子纳米操纵的有序化测序策略

尽管包括人类在内的许多生物物种的基因组测序工作已经完成,但由于现有测序技术的限制,大部分复杂基因组还存在很多大大小小的缺口.缺口的填补以及对其他重复序列区域的测序迫切需要全新的思路和技术.基于在DNA单分子定位切割和拾取方面的实验进展,提出了一种基于原子力显微镜纳米操纵技术的单分子有序化测序策略.计算机模拟的结果表明,这一方法和策略是可行的,有助于解决目前测序工作中所遇到的一些棘手问题.     

Random Whole Metagenomic Sequencing for Forensic Discrimination of Soils

Here we assess the ability of random whole metagenomic sequencing approaches to discriminate between similar soils from two geographically distinct urban sites for application in forensic science. Repeat samples from two parklands in residential areas separated by approximately 3 km were collected and the DNA was extracted. Shotgun, whole genome amplification (WGA) and single arbitrarily primed DNA amplification (AP-PCR) based sequencing techniques were then used to generate soil metagenomic profiles. Full and subsampled metagenomic datasets were then annotated against M5NR/M5RNA (taxonomic classification) and SEED Subsystems (metabolic classification) databases. Further comparative analyses were performed using a number of statistical tools including: hierarchical agglomerative clustering (CLUSTER); similarity profile analysis (SIMPROF); non-metric multidimensional scaling (NMDS); and canonical analysis of principal coordinates (CAP) at all major levels of taxonomic and metabolic classification. Our data showed that shotgun and WGA-based approaches generated highly similar metagenomic profiles for the soil samples such that the soil samples could not be distinguished accurately. An AP-PCR based approach was shown to be successful at obtaining reproducible site-specific metagenomic DNA profiles, which in turn were employed for successful discrimination of visually similar soil samples collected from two different locations.     

Strategy for Modular Tagged High-Throughput Amplicon Sequencing

The use and validation of a strategy that allows a universal set of bar-coded sequencing primers to be appended to an amplified PCR product is described. The strategy allows a modular approach, in that the same bar code can be used with two or more target-specific primer sets, even simultaneously.     

A Strategy for Direct Mapping and Identification of Mutations by Whole-Genome Sequencing

Mutant screens have proven powerful for genetic dissection of a myriad of biological processes, but subsequent identification and isolation of the causative mutations are usually complex and time consuming. We have made the process easier by establishing a novel strategy that employs whole-genome sequencing to simultaneously map and identify mutations without the need for any prior genetic mapping.THE challenges posed by the identification of a causal mutation in a mutant of interest have in effect restricted the use of forward genetics to those organisms benefiting from a solid genetic toolbox. Whole-genome sequencing (WGS) is promising to revolutionize the way phenotypic traits are assigned to genes. However, current strategies to identify causal mutations using WGS require first the identification of an approximate genomic location containing the mutation of interest (Sarin et al. 2008; Smith et al. 2008; Srivatsan et al. 2008; Blumenstiel et al. 2009; Irvine et al. 2009). This is because genomes contain many natural sequence variations (Denver et al. 2004; Hillier et al. 2008; Sarin et al. 2010), which, along with mutagen-induced ones, complicate the identification of the causal mutation when an approximate genomic location has not been previously identified. Mapping has previously been achieved with time-consuming and laborious techniques that, in addition, rely on an organism''s single-nucleotide polymorphism (SNP) map and established variant strains. For example, traditional SNP-based mapping (Wicks et al. 2001; Davis et al. 2005) has previously been used in Caenorhabditis elegans to narrow down the genomic region containing the mutation of interest, prior to conducting WGS (Sarin et al. 2008). In Arabidopsis, simultaneous SNP mapping and mutation identification has been achieved with WGS, but this requires the generation of a mapping population of up to 500 F₂ progeny to identify only one allele (Schneeberger et al. 2009). This is a challenging prospect for many model systems. Indeed, if the mutant phenotype is subtle, the isolation of such numbers of recombinants is very tedious. Furthermore, it is not applicable in those organisms where a mapping population cannot be generated, simply because of a lack of intercrossable variants or because of life cycles (parasitic organisms, for example) that would make it extremely difficult to follow and isolate many recombinant individuals.Here, we describe a strategy to simultaneously and rapidly locate and identify multiple mutations from a mutagenesis screen with WGS that circumvents these limitations. This powerful and straightforward method directly uses mutagen-induced nucleotide changes that are linked to the causal mutation to identify its specific genomic location, thus negating the construction of genetic mapping populations and subsequent mapping.Treatment of organisms with a chemical mutagen induces nucleotide changes throughout the genome. Following mutagenesis, backcrossing or outcrossing of the mutagenized organism to unmutagenized counterparts is performed to eliminate mutagen-induced mutations (Figure 1A; supporting information, File S2). The phenotype-causing mutation remains as only backcrossed individuals showing the phenotype of interest are retained. In addition, mutagen-induced nucleotide changes that are genetically linked to the causal mutation and physically surround it on the chromosome will remain, in contrast to unlinked nucleotide changes (Figure 1A). As a result of this genetic linkage, a high-density cluster of typical mutagen-induced variants is visualized from sequence data obtained by WGS, which is positioned around the causal mutation. By locating such high-density regions, one maps the approximate genomic location of the causal mutation and subsequently identifies the affected gene within this region.Open in a separate windowFigure 1.—Mapping mutations on the basis of density of mutagen-induced DNA damage across the genome. (A) Visual representation of our WGS cloning strategy. Mutagen treatment induces point mutations throughout the genome (red asterisks). Backcrossing to the original unmutated parent strain removes much of the mutagen-induced nucleotide changes except for the causal mutation (green asterisk) and those genetically linked to it. WGS sequencing can be used to detect canonical mutagen-induced point mutations, thus revealing a physical position for the causal mutation. Shared background variants (yellow crosses) are filtered out from WGS data by comparing the sequences of mutants sequenced side-by-side, revealing a high-density variant cluster in only one genomic region. Importantly, genomic sequences of mutants derived from the same starting strain must be compared, to allow subtraction of nucleotide variants that are common to this particular strain, through sequence comparison. (B) Physical map of total nucleotide variations per megabase across the genome compared to the wild-type reference genome for each mutant (fp6, fp9, and fp12) after WGS. (C) After sequence quality filtering, subtraction of common variants between the 3 mutants, and filtering out noncanonical EMS nucleotide changes, high-density variant peaks are obtained in one genomic location for each mutant (red boxes). Steps 1 and 3 are essential for clear visualization of the high-density peaks whereas step 2 improves visualization. (D) Close-up of variants on chromosome III for fp6. Within this peak we identified only 6 candidate mutations that could potentially affect a protein sequence. We confirmed that the missense mutation in egl-5 was the causal mutation (Figure S2). For fp9 and fp12 we identified only 10 (9 missense and 1 3′-UTR) and 4 (2 premature stop and 2 missense) candidate mutations, respectively, within each mutant''s EMS-based mapped region. Thus, our method consistently allowed precise mapping in 3 different mutants to a region small enough to contain only a handful of candidate mutations.As a proof-of-principle, we simultaneously mapped and sequenced the causal mutations of multiple C. elegans mutants isolated from an EMS mutagenesis screen using this strategy. The mutagenesis screen itself was undertaken to identify genes that controlled the reprogramming of a single cell called Y into another cell called PDA during C. elegans development (Jarriault et al. 2008). After EMS treatment, three distinct mutant alleles (fp6, fp9, and fp12) were backcrossed to the original unmutagenized strain 4-6X. It is important to note that a backcrossing or outcrossing step is necessary for the analysis of mutants obtained from all mutagenesis screens, irrespective of the type of mutant identification strategy used or the type of mutagen or organism used (and, as such, does not represent an extra step introduced by our method). The mutants then underwent WGS side-by-side (Table S1, Table S2, Figure S1, and File S2). After alignment to the wild-type N2 reference genome using MAQgene software (Bigelow et al. 2009), the sequencing data obtained for each mutant were compared, and we subtracted common nucleotide variants that were shared between at least two of our three mutants (File S1). These shared variants, which are very unlikely to be either the causal mutation or EMS-induced mutations from the screen itself, represent strain differences between the N2 used to generate the reference genome and the PS3662 strain used here for mutagenesis. Note that this step eliminated ∼2000 point mutations as potential candidates for our causal mutation. This result strongly emphasizes the advantage of conducting WGS on two or more mutants side-by-side, as reference genomes may contain many nucleotide variations when compared to organisms sequenced from the laboratory (Denver et al. 2004; Hillier et al. 2008; Sarin et al. 2010; this study) and as such would confound mutation identification.To identify EMS-induced changes linked to the causal mutation and expose its location, we looked only at variants that matched the canonical EMS-induced G/C > A/T transitions (Drake and Baltz 1976), revealing localized peaks of high-density variation on a single chromosome for each mutant (Figure 1, B and C). These peaks correspond to regions of high mutagen-induced damage that were not removed during backcrossing and therefore are most likely genetically linked to the causal mutation. We therefore focused our attention on these physical regions to identify candidate mutations within them. We localized fp6 to a 4.29-Mb region on chromosome III, fp9 to a 7.11-Mb region on chromosome X, and fp12 to a 1.28-Mb region on a different part of chromosome X (Figure 1C).As a proof of principle, we further examined the nucleotide changes present in the interval to which fp6 was linked. Taking into consideration all variant types (point mutations and indels), we identified only six candidate mutations that potentially affected a gene''s function (Figure 1D and Table S3). One of these, affecting the egl-5/hox gene, lies almost perfectly in the middle of the predicted EMS-based mapped region. We confirmed the existence of the mutation in egl-5 by manual resequencing. Both egl-5 targeted RNAi and noncomplementation with the egl-5(n945) null allele confirmed that fp6 affected egl-5 and caused the Y-to-PDA reprogramming defect (Figure S2). fp9 and fp12 each map to distinct regions on chromosome X that also contain only a handful of candidate mutations (10 and 4, respectively) (Figure 1C). Thus, our method consistently allowed precise mapping in 3 different mutants to a region small enough to contain only a handful of candidate mutations and subsequent identification of the causal mutation.We calculated that comparison of WGS data for only two mutants of the same mutagenesis screen is sufficient to localize and sequence the causal mutation (Table S4). Thirteen times sequence coverage has been found to be sufficient to identify a mutation in a pre-SNP mapped C. elegans mutant (Shen et al. 2008). Here, we tested the sequence coverage necessary to perform simultaneous mapping and mutant identification using our strategy and found that 13× was more than enough (Table S4). In addition, by performing longer reads and/or paired-end sequencing, our method can be scaled up to bigger genomes or allow multiple mutant sequencing on each flow cell lane [for, e.g., using multiplex WGS (Cronn et al. 2008)]. Furthermore, because direct sequence comparison is ultimately made between two mutants sequenced side-by-side, the quality of an organism''s reference genome (which is used only for alignment purposes) does not have a bearing on the mapping or mutant identification outcome. Moreover, recent advances in de novo alignment of short reads generated from next generation sequencing platforms (Li et al. 2010; Nowrousian et al. 2010; Webb and Rosenthal 2010; Young et al. 2010) suggest that a reference genome may not even be required to perform mutagen-based mapping and mutant identification with WGS. We predict that technical advances in these areas will make it possible to perform mutagenesis screens on any nonsequenced and genetically uncharacterized organism and use our strategy to quickly identify the causal mutation of an interesting mutant.

TABLE 1

Summary of WGS cloning strategy

A Two-Dimensional Pooling Strategy for Rare Variant Detection on Next-Generation Sequencing Platforms

We describe a method for pooling and sequencing DNA from a large number of individual samples while preserving information regarding sample identity. DNA from 576 individuals was arranged into four 12 row by 12 column matrices and then pooled by row and by column resulting in 96 total pools with 12 individuals in each pool. Pooling of DNA was carried out in a two-dimensional fashion, such that DNA from each individual is present in exactly one row pool and exactly one column pool. By considering the variants observed in the rows and columns of a matrix we are able to trace rare variants back to the specific individuals that carry them. The pooled DNA samples were enriched over a 250 kb region previously identified by GWAS to significantly predispose individuals to lung cancer. All 96 pools (12 row and 12 column pools from 4 matrices) were barcoded and sequenced on an Illumina HiSeq 2000 instrument with an average depth of coverage greater than 4,000×. Verification based on Ion PGM sequencing confirmed the presence of 91.4% of confidently classified SNVs assayed. In this way, each individual sample is sequenced in multiple pools providing more accurate variant calling than a single pool or a multiplexed approach. This provides a powerful method for rare variant detection in regions of interest at a reduced cost to the researcher.     

Effects of GC Content and Mutational Pressure on the Lengths of Exons and Coding Sequences

It has been hypothesized that the length of an exon tends to increase with the GC content because stop codons are AT-rich and should occur less frequently in GC-rich exons. This prediction assumes that mutation pressure plays a significant role in the occurrence and distribution of stop codons. However, the prediction is applicable not to all exons, but only to the last coding exon of a gene and to single-exon CDS sequences. We classified exons in multiexon genes in eight eukaryotic species into three groups-the first exon, the internal, and the last exon-and computed the Spearman correlation between the exon length and the percentage GC (%GC) for each of the three groups. In only five of the species studied is the correlation for the last coding exon greater than that for the first or internal exons. For the single-exon CDS sequences, the correlation between CDS length and %GC is mostly negative. Thus, eukaryotic genomes do not support the predicted relationship between exon length and %GC. In prokaryotic genomes, CDS length and %GC are positively correlated in each of the 68 completely sequenced prokaryotic genomes in GenBank with genomic GC contents varying from 25 to 68%, except for the wall-less Mycoplasma genitalium and the syphilis pathogen Treponema pallidum. Moreover, the average CDS length and the genomic GC content are also positively correlated. After correcting for genome size, the partial correlation between the average CDS length and the genomic GC content is 0.3217 ( p < 0.025).     

MATIN: A Random Network Coding Based Framework for High Quality Peer-to-Peer Live Video Streaming

In recent years, Random Network Coding (RNC) has emerged as a promising solution for efficient Peer-to-Peer (P2P) video multicasting over the Internet. This probably refers to this fact that RNC noticeably increases the error resiliency and throughput of the network. However, high transmission overhead arising from sending large coefficients vector as header has been the most important challenge of the RNC. Moreover, due to employing the Gauss-Jordan elimination method, considerable computational complexity can be imposed on peers in decoding the encoded blocks and checking linear dependency among the coefficients vectors. In order to address these challenges, this study introduces MATIN which is a random network coding based framework for efficient P2P video streaming. The MATIN includes a novel coefficients matrix generation method so that there is no linear dependency in the generated coefficients matrix. Using the proposed framework, each peer encapsulates one instead of n coefficients entries into the generated encoded packet which results in very low transmission overhead. It is also possible to obtain the inverted coefficients matrix using a bit number of simple arithmetic operations. In this regard, peers sustain very low computational complexities. As a result, the MATIN permits random network coding to be more efficient in P2P video streaming systems. The results obtained from simulation using OMNET++ show that it substantially outperforms the RNC which uses the Gauss-Jordan elimination method by providing better video quality on peers in terms of the four important performance metrics including video distortion, dependency distortion, End-to-End delay and Initial Startup delay.     

Statistical Inference for Nanopore Sequencing with a Biased Random Walk?Model

Nanopore sequencing promises long read-lengths and single-molecule resolution, but the stochastic motion of the DNA molecule inside the pore is, as of this writing, a barrier to high accuracy reads. We develop a method of statistical inference that explicitly accounts for this error, and demonstrate that high accuracy (>99%) sequence inference is feasible even under highly diffusive motion by using a hidden Markov model to jointly analyze multiple stochastic reads. Using this model, we place bounds on achievable inference accuracy under a range of experimental parameters.     

A Simple Strategy for Reducing False Negatives in Calling Variants from Single-Cell Sequencing Data

Due to the growth of interest in single-cell genomics, computational methods for distinguishing true variants from artifacts are highly desirable. While special attention has been paid to false positives in variant or mutation calling from single-cell sequencing data, an equally important but often neglected issue is that of false negatives derived from allele dropout during the amplification of single cell genomes. In this paper, we propose a simple strategy to reduce the false negatives in single-cell sequencing data analysis. Simulation results show that this method is highly reliable, with an error rate of 4.94×10^-5, which is orders of magnitude lower than the expected false negative rate (~34%) estimated from a single-cell exome dataset, though the method is limited by the low SNP density in the human genome. We applied this method to analyze the exome data of a few dozen single tumor cells generated in previous studies, and extracted cell specific mutation information for a small set of sites. Interestingly, we found that there are difficulties in using the classical clonal model of tumor cell growth to explain the mutation patterns observed in some tumor cells.     

藏羚羊肌红蛋白(MGB)基因编码区的克隆与分析

为了探讨藏羚羊低氧适应的分子生物学机制,从藏羚羊(Pantholops hodgsonii)骨骼肌组织中提取总RNA,通过逆转录聚合酶联反应(RT-PCR)技术扩增出MGB基因编码区cDNA片段,并与pGEM-T Easy载体连接构建重组质粒,转化DH5α菌。扩增培养后鉴定阳性质粒并进行序列测定,结果与NCBI数据库进行比较。结果显示,MGB基因编码区由465bp组成,编码154个氨基酸。其序列与绵羊、牛、猪、人、小鼠、大鼠的同源性分别是98%、97%、90%、86%、82%、81%。推测出的氨基酸序列与绵羊、牛有98%同源性,而与猪、人、小鼠及大鼠分别有89%、85%、80%、79%的同源性。与NCBI数据库登录的绵羊的MGB cDNA序列比较发现,藏羚羊MGB基因的21位和78位密码子分别发生了突变(GGT→GAT和GAA→AAG),其对应的氨基酸分别由甘氨酸变为天冬氨酸及谷氨酸变为赖氨酸。至此,成功克隆出藏羚羊MGB基因编码区,为进一步揭示藏羚羊低氧适应的分子机制打下基础。     

Identification of Four Genes Coding for Isoforms of Murinoglobulin, the Monomeric Mouse α2-Macroglobulin: Characterization of the Exons Coding for the Bait Region

Murinoglobulins are the single chain members of the α₂-macroglobulin family of proteinase inhibitors in the mouse. DNA clones representing the genes coding for four different murinoglobulins were isolated from three independent mouse genomic DNA libraries. Sequence analysis demonstrated that in each gene two exons are coding for the bait region. This is the specific protein sequence in each α-macroglobulin, which is functionally important since it is extremely sensitive to cleavage by different proteinases. The molecular data established the existence of at least four different murinoglobulin genes. Three of these corresponded to the three cDNA clones previously identified. Sequencing of intron-exon boundaries and intron sizing allowed us to construct physical maps of the region from exon 15 to exon 25 (numbered in comparison to mouse α₂-macroglobulin) in each murinoglobulin gene. Southern blotting of genomic DNA from five different mouse strains confirmed this analysis and even suggested the possible existence of a fifth murinoglobulin gene. These data indicate that the mouse presents genetic repertoire of the α₂-macroglobulin family much more complex than originally anticipated. The bait region exon sequences showed a considerably higher degree of divergence (72 to 88% sequence identity) than that of the flanking exon sequences coding for adjacent, structural domains of the murinoglobulin proteinase inhibitors (91 to 96%). Even more surprising was that adjacent intron sequences are conserved as faithfully as the nonbait region coding exons (90 to 96%). These data demonstrate a unique property of the bait region coding sequences, as they apparently are allowed to mutate considerably. This divergency must then confer divergent proteinase inhibitory properties to the resulting proteins.     

寻找水稻DNA编码与非编码区边界方法的比较研究

在分析DNA序列复杂度、预测基因编码区和非编码的DNA边界识别等问题中,以熵为基础构造的离散量度量提供了一种强有力的工具。为改进寻找水稻基因编码与非编码区边界的效率,本文提出了两个新的离散量度量（α-KL离散量与α-Jensen-Shannon 离散量）,根据密码子的GC含量对氨基酸对应密码子构建了粗粒化向量。 比较了融合Jensen-Shannon 离散量、Jensen-Renyi 离散量、α-KL离散量和α-Jensen-Shannon 离散量等不同向量所获得的精度,结果表明,在对水稻基因编码区‘终止子’的识别效率上,构建的密码子粗粒化向量融合新引进的度量方法比Bernaola等人的方法(2000)提高了4~5倍。