Benefits and Challenges with Applying Unique Molecular Identifiers in Next Generation Sequencing to Detect Low Frequency Mutations

Indexing individual template molecules with a unique identifier (UID) before PCR and deep sequencing is promising for detecting low frequency mutations, as true mutations could be distinguished from PCR errors or sequencing errors based on consensus among reads sharing same index. In an effort to develop a robust assay to detect from urine low-abundant bladder cancer cells carrying well-documented mutations, we have tested the idea first on a set of mock templates, with wild type and known mutants mixed at defined ratios. We have measured the combined error rate for PCR and Illumina sequencing at each nucleotide position of three exons, and demonstrated the power of a UID in distinguishing and correcting errors. In addition, we have demonstrated that PCR sampling bias, rather than PCR errors, challenges the UID-deep sequencing method in faithfully detecting low frequency mutation.     

Validation of Next-Generation Sequencing of Entire Mitochondrial Genomes and the Diversity of Mitochondrial DNA Mutations in Oral Squamous Cell Carcinoma

Background

Oral squamous cell carcinoma (OSCC) is mainly caused by smoking and alcohol abuse and shows a five-year survival rate of ~50%. We aimed to explore the variation of somatic mitochondrial DNA (mtDNA) mutations in primary oral tumors, recurrences and metastases.

Methods

We performed an in-depth validation of mtDNA next-generation sequencing (NGS) on an Illumina HiSeq 2500 platform for its application to cancer tissues, with the goal to detect low-level heteroplasmies and to avoid artifacts. Therefore we genotyped the mitochondrial genome (16.6 kb) from 85 tissue samples (tumors, recurrences, resection edges, metastases and blood) collected from 28 prospectively recruited OSCC patients applying both Sanger sequencing and high-coverage NGS (~35,000 reads per base).

Results

We observed a strong correlation between Sanger sequencing and NGS in estimating the mixture ratio of heteroplasmies (r = 0.99; p<0.001). Non-synonymous heteroplasmic variants were enriched among cancerous tissues. The proportions of somatic and inherited variants in a given gene region were strongly correlated (r = 0.85; p<0.001). Half of the patients shared mutations between benign and cancerous tissue samples. Low level heteroplasmies (<10%) were more frequent in benign samples compared to tumor samples, where heteroplasmies >10% were predominant. Four out of six patients who developed a local tumor recurrence showed mutations in the recurrence that had also been observed in the primary tumor. Three out of five patients, who had tumor metastases in the lymph nodes of their necks, shared mtDNA mutations between primary tumors and lymph node metastases. The percentage of mutation heteroplasmy increased from the primary tumor to lymph node metastases.

Conclusions

We conclude that Sanger sequencing is valid for heteroplasmy quantification for heteroplasmies ≥10% and that NGS is capable of reliably detecting and quantifying heteroplasmies down to the 1%-level. The finding of shared mutations between primary tumors, recurrences and metastasis indicates a clonal origin of malignant cells in oral cancer.     

BreaKmer: detection of structural variation in targeted massively parallel sequencing data using kmers

Genomic structural variation (SV), a common hallmark of cancer, has important predictive and therapeutic implications. However, accurately detecting SV using high-throughput sequencing data remains challenging, especially for ‘targeted’ resequencing efforts. This is critically important in the clinical setting where targeted resequencing is frequently being applied to rapidly assess clinically actionable mutations in tumor biopsies in a cost-effective manner. We present BreaKmer, a novel approach that uses a ‘kmer’ strategy to assemble misaligned sequence reads for predicting insertions, deletions, inversions, tandem duplications and translocations at base-pair resolution in targeted resequencing data. Variants are predicted by realigning an assembled consensus sequence created from sequence reads that were abnormally aligned to the reference genome. Using targeted resequencing data from tumor specimens with orthogonally validated SV, non-tumor samples and whole-genome sequencing data, BreaKmer had a 97.4% overall sensitivity for known events and predicted 17 positively validated, novel variants. Relative to four publically available algorithms, BreaKmer detected SV with increased sensitivity and limited calls in non-tumor samples, key features for variant analysis of tumor specimens in both the clinical and research settings.     

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.     

Enhancing the detection of barcoded reads in high throughput DNA sequencing data by controlling the false discovery rate

Background

DNA barcodes are short unique sequences used to label DNA or RNA-derived samples in multiplexed deep sequencing experiments. During the demultiplexing step, barcodes must be detected and their position identified. In some cases (e.g., with PacBio SMRT), the position of the barcode and DNA context is not well defined. Many reads start inside the genomic insert so that adjacent primers might be missed. The matter is further complicated by coincidental similarities between barcode sequences and reference DNA. Therefore, a robust strategy is required in order to detect barcoded reads and avoid a large number of false positives or negatives.For mass inference problems such as this one, false discovery rate (FDR) methods are powerful and balanced solutions. Since existing FDR methods cannot be applied to this particular problem, we present an adapted FDR method that is suitable for the detection of barcoded reads as well as suggest possible improvements.

Results

In our analysis, barcode sequences showed high rates of coincidental similarities with the Mus musculus reference DNA. This problem became more acute when the length of the barcode sequence decreased and the number of barcodes in the set increased. The method presented in this paper controls the tail area-based false discovery rate to distinguish between barcoded and unbarcoded reads. This method helps to establish the highest acceptable minimal distance between reads and barcode sequences. In a proof of concept experiment we correctly detected barcodes in 83% of the reads with a precision of 89%. Sensitivity improved to 99% at 99% precision when the adjacent primer sequence was incorporated in the analysis. The analysis was further improved using a paired end strategy. Following an analysis of the data for sequence variants induced in the Atp1a1 gene of C57BL/6 murine melanocytes by ultraviolet light and conferring resistance to ouabain, we found no evidence of cross-contamination of DNA material between samples.

Conclusion

Our method offers a proper quantitative treatment of the problem of detecting barcoded reads in a noisy sequencing environment. It is based on the false discovery rate statistics that allows a proper trade-off between sensitivity and precision to be chosen.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2105-15-264) contains supplementary material, which is available to authorized users.     

Occupancy Modeling,Maximum Contig Size Probabilities and Designing Metagenomics Experiments

Mathematical aspects of coverage and gaps in genome assembly have received substantial attention by bioinformaticians. Typical problems under consideration suppose that reads can be experimentally obtained from a single genome and that the number of reads will be set to cover a large percentage of that genome at a desired depth. In metagenomics experiments genomes from multiple species are simultaneously analyzed and obtaining large numbers of reads per genome is unlikely. We propose the probability of obtaining at least one contig of a desired minimum size from each novel genome in the pool without restriction based on depth of coverage as a metric for metagenomic experimental design. We derive an approximation to the distribution of maximum contig size for single genome assemblies using relatively few reads. This approximation is verified in simulation studies and applied to a number of different metagenomic experimental design problems, ranging in difficulty from detecting a single novel genome in a pool of known species to detecting each of a random number of novel genomes collectively sized and with abundances corresponding to given distributions in a single pool.     

Improving the sensitivity of long read overlap detection using grouped short k-mer matches

Background

Single-molecule, real-time sequencing (SMRT) developed by Pacific BioSciences produces longer reads than second-generation sequencing technologies such as Illumina. The increased read length enables PacBio sequencing to close gaps in genome assembly, reveal structural variations, and characterize the intra-species variations. It also holds the promise to decipher the community structure in complex microbial communities because long reads help metagenomic assembly. One key step in genome assembly using long reads is to quickly identify reads forming overlaps. Because PacBio data has higher sequencing error rate and lower coverage than popular short read sequencing technologies (such as Illumina), efficient detection of true overlaps requires specially designed algorithms. In particular, there is still a need to improve the sensitivity of detecting small overlaps or overlaps with high error rates in both reads. Addressing this need will enable better assembly for metagenomic data produced by third-generation sequencing technologies.

Results

In this work, we designed and implemented an overlap detection program named GroupK, for third-generation sequencing reads based on grouped k-mer hits. While using k-mer hits for detecting reads’ overlaps has been adopted by several existing programs, our method uses a group of short k-mer hits satisfying statistically derived distance constraints to increase the sensitivity of small overlap detection. Grouped k-mer hit was originally designed for homology search. We are the first to apply group hit for long read overlap detection. The experimental results of applying our pipeline to both simulated and real third-generation sequencing data showed that GroupK enables more sensitive overlap detection, especially for datasets of low sequencing coverage.

Conclusions

GroupK is best used for detecting small overlaps for third-generation sequencing data. It provides a useful supplementary tool to existing ones for more sensitive and accurate overlap detection. The source code is freely available at https://github.com/Strideradu/GroupK.

     

Discovering single nucleotide variants and indels from bulk and single-cell ATAC-seq

Genetic variants and de novo mutations in regulatory regions of the genome are typically discovered by whole-genome sequencing (WGS), however WGS is expensive and most WGS reads come from non-regulatory regions. The Assay for Transposase-Accessible Chromatin (ATAC-seq) generates reads from regulatory sequences and could potentially be used as a low-cost ‘capture’ method for regulatory variant discovery, but its use for this purpose has not been systematically evaluated. Here we apply seven variant callers to bulk and single-cell ATAC-seq data and evaluate their ability to identify single nucleotide variants (SNVs) and insertions/deletions (indels). In addition, we develop an ensemble classifier, VarCA, which combines features from individual variant callers to predict variants. The Genome Analysis Toolkit (GATK) is the best-performing individual caller with precision/recall on a bulk ATAC test dataset of 0.92/0.97 for SNVs and 0.87/0.82 for indels within ATAC-seq peak regions with at least 10 reads. On bulk ATAC-seq reads, VarCA achieves superior performance with precision/recall of 0.99/0.95 for SNVs and 0.93/0.80 for indels. On single-cell ATAC-seq reads, VarCA attains precision/recall of 0.98/0.94 for SNVs and 0.82/0.82 for indels. In summary, ATAC-seq reads can be used to accurately discover non-coding regulatory variants in the absence of whole-genome sequencing data and our ensemble method, VarCA, has the best overall performance.     

Detection of the most common G6PD gene mutations in Chinese using amplification refractory mutation system.

Glucose-6-phosphate dehydrogenase (G6PD) is the most common human enzymopathy. To date more than 122 mutations in the G6PD gene have been discovered, among which 12 point mutations are found in the Chinese. The 2 most common mutations, G1388A and G1376T, account for more than 50% of mutations representing various regions and ethnic groups in China. Setting up a simple and accurate method for detecting these mutations is not only useful for studying the frequency of the G6PD genotypes, but also for finding new mutations. The purpose of this study was to find a simple, inexpensive and accurate method for detecting these common mutations. The amplification refractory mutation system (ARMS) method was used in this study. Samples from 28 G6PD-deficient males were investigated. The natural and mismatched amplification and restriction enzyme digestion method was used as a standard method to evaluate the nature of the point mutations. Sixteen cases were found carrying the G1388A mutation and 12 the G1376T mutation. Fourteen cases of G1388A and 10 cases of G1376T were confirmed by ARMS. Four cases were not in concordance with the results obtained by the mismatched amplification-restriction enzyme digestion. These 4 cases were then judged by direct PCR sequencing at exon 12. The DNA sequencing data supported the results obtained by ARMS. Thus we concluded that the ARMS is a rapid, simple, inexpensive and accurate method for detecting the most common G6PD gene mutations among the Chinese.     

SAP-A Sequence Mapping and Analyzing Program for Long Sequence Reads Alignment and Accurate Variants Discovery

The third-generation of sequencing technologies produces sequence reads of 1000 bp or more that may contain high polymorphism information. However, most currently available sequence analysis tools are developed specifically for analyzing short sequence reads. While the traditional Smith-Waterman (SW) algorithm can be used to map long sequence reads, its naive implementation is computationally infeasible. We have developed a new Sequence mapping and Analyzing Program (SAP) that implements a modified version of SW to speed up the alignment process. In benchmarks with simulated and real exon sequencing data and a real E. coli genome sequence data generated by the third-generation sequencing technologies, SAP outperforms currently available tools for mapping short and long sequence reads in both speed and proportion of captured reads. In addition, it achieves high accuracy in detecting SNPs and InDels in the simulated data. SAP is available at https://github.com/davidsun/SAP.     

A sensitive mutation screening method supporting cell line development for biotherapeutics

Random genetic mutations, which can occur during cell line development, can lead to sequence variants that comprise pharmaceutical product quality generated by recombinant technology. Mutation screening can minimize the probability of selecting clones harboring sequence variants. Here we report a polymerase chain reaction (PCR)-based mutation screening approach using high-resolution melting (HRM) analysis combined with a mutation enrichment step using limiting dilution to detect low-level mutations at 0.5%. The method allows unknown mutation discovery regardless of its location in a transgene as well as independent of its position in an HRM fragment, ranging from approximately 200 to 300 bp in size.     

Benchmarking and optimization of a high-throughput sequencing based method for transgene sequence variant analysis in biotherapeutic cell line development

In recent years, High-Throughput Sequencing (HTS) based methods to detect mutations in biotherapeutic transgene products have become a key quality step deployed during the development of manufacturing cell line clones. Previously we reported on a higher throughput, rapid mutation detection method based on amplicon sequencing (targeting transgene RNA) and detailed its implementation to facilitate cell line clone selection. By gaining experience with our assay in a diverse set of cell line development programs, we improved the computational analysis as well as experimental protocols. Here we report on these improvements as well as on a comprehensive benchmarking of our assay. We evaluated assay performance by mixing amplicon samples of a verified mutated antibody clone with a non-mutated antibody clone to generate spike-in mutations from ∼60% down to ∼0.3% frequencies. We subsequently tested the effect of 16 different sample and HTS library preparation protocols on the assay's ability to quantify mutations and on the occurrence of false-positive background error mutations (artifacts). Our evaluation confirmed assay robustness, established a high confidence limit of detection of ∼0.6%, and identified protocols that reduce error levels thereby significantly reducing a source of false positives that bottlenecked the identification of low-level true mutations.     

结核分枝杆菌katG基因突变的研究

探讨编码过氧化氢-过氧化物酶的katG基因突变与结核分枝杆菌异烟肼(INH)耐药性的相关关系。根据结核分枝杆菌GenBank中的katG序列,自行设计特异性寡聚核苷酸引物,采用聚合酶链反应-单链构象多态性(PCR-SSCP)分析和直接测序法(DS)分析结核分枝杆菌中katG基因突变情况。以HR37Rv标准株为对照。所有23株敏感菌均未有SSCP结果异常;35株耐药菌中,有2株(5．7％)katG基因扩增阴性,且发生在高度耐药菌中。进一步分析发现,SSCP法突变检出23株(65．7％),测序法突变检出24株(68．6％),符合率为95．8％(23／24)。参照测序法对耐药菌突变序列的分析结果,PCR—SSCP敏感、特异,可快速检测结核分枝杆菌katG耐药基因突变,有利于耐药结核分枝杆菌耐药性的快速检测。     

GPU-BSM: A GPU-Based Tool to Map Bisulfite-Treated Reads

Cytosine DNA methylation is an epigenetic mark implicated in several biological processes. Bisulfite treatment of DNA is acknowledged as the gold standard technique to study methylation. This technique introduces changes in the genomic DNA by converting cytosines to uracils while 5-methylcytosines remain nonreactive. During PCR amplification 5-methylcytosines are amplified as cytosine, whereas uracils and thymines as thymine. To detect the methylation levels, reads treated with the bisulfite must be aligned against a reference genome. Mapping these reads to a reference genome represents a significant computational challenge mainly due to the increased search space and the loss of information introduced by the treatment. To deal with this computational challenge we devised GPU-BSM, a tool based on modern Graphics Processing Units. Graphics Processing Units are hardware accelerators that are increasingly being used successfully to accelerate general-purpose scientific applications. GPU-BSM is a tool able to map bisulfite-treated reads from whole genome bisulfite sequencing and reduced representation bisulfite sequencing, and to estimate methylation levels, with the goal of detecting methylation. Due to the massive parallelization obtained by exploiting graphics cards, GPU-BSM aligns bisulfite-treated reads faster than other cutting-edge solutions, while outperforming most of them in terms of unique mapped reads.     

Correction of sequence-dependent ambiguous bases (Ns) from the 454 pyrosequencing system

Pyrosequencing of the 16S ribosomal RNA gene (16S) has become one of the most popular methods to assess microbial diversity. Pyrosequencing reads containing ambiguous bases (Ns) are generally discarded based on the assumptions of their non-sequence-dependent formation and high error rates. However, taxonomic composition differed by removal of reads with Ns. We determined whether Ns from pyrosequencing occur in a sequence-dependent manner. Our reads and the corresponding flow value data revealed occurrence of sequence-specific N errors with a common sequential pattern (a homopolymer + a few nucleotides with bases other than the homopolymer + N) and revealed that the nucleotide base of the homopolymer is the true base for the following N. Using an algorithm reflecting this sequence-dependent pattern, we corrected the Ns in the 16S (86.54%), bphD (81.37%) and nifH (81.55%) amplicon reads from a mock community with high precisions of 95.4, 96.9 and 100%, respectively. The new N correction method was applicable for determining most of Ns in amplicon reads from a soil sample, resulting in reducing taxonomic biases associated with N errors and in shotgun sequencing reads from public metagenome data. The method improves the accuracy and precision of microbial community analysis and genome sequencing using 454 pyrosequencing.     

人脑胶质瘤细胞中GDNF 基因启动子1 区甲基化状态的研究

目的:观察GDNF启动子1区在人脑胶质瘤细胞中的甲基化修饰状态,以期探讨其对于GDNF在胶质瘤中高表达的影响。方法:基因测序检测10例胶质瘤与5例正常脑组织中GDNF基因序列,比较其基因是否有突变发生;重亚硫酸盐修饰后基因测序检测20例胶质瘤(10例低级别和10例高级别)与5例正常脑组织中GDNF启动子1区甲基化修饰状态。结果:GDNF启动子1区基因在胶质瘤中没有发生突变;GDNF启动子1区甲基化修饰在正常脑组织、低级别、高级别中发生率分别为72.25%、86.25%、86.75%。在胶质瘤中的甲基化修饰水平比正常脑组织明显增高(P<0.05),而高低级别之间无显著性差异。结论:在胶质瘤细胞中,GDNF启动子1区发生了高甲基化修饰,这种修饰很可能会影响GDNF基因的表达。     

Haplotype Estimation Using Sequencing Reads

High-throughput sequencing technologies produce short sequence reads that can contain phase information if they span two or more heterozygote genotypes. This information is not routinely used by current methods that infer haplotypes from genotype data. We have extended the SHAPEIT2 method to use phase-informative sequencing reads to improve phasing accuracy. Our model incorporates the read information in a probabilistic model through base quality scores within each read. The method is primarily designed for high-coverage sequence data or data sets that already have genotypes called. One important application is phasing of single samples sequenced at high coverage for use in medical sequencing and studies of rare diseases. Our method can also use existing panels of reference haplotypes. We tested the method by using a mother-father-child trio sequenced at high-coverage by Illumina together with the low-coverage sequence data from the 1000 Genomes Project (1000GP). We found that use of phase-informative reads increases the mean distance between switch errors by 22% from 274.4 kb to 328.6 kb. We also used male chromosome X haplotypes from the 1000GP samples to simulate sequencing reads with varying insert size, read length, and base error rate. When using short 100 bp paired-end reads, we found that using mixtures of insert sizes produced the best results. When using longer reads with high error rates (5–20 kb read with 4%–15% error per base), phasing performance was substantially improved.     

Analysis of the genetic diversity of influenza A viruses using next-generation DNA sequencing

Background

Influenza viruses exist as a large group of closely related viral genomes, also called quasispecies. The composition of this influenza viral quasispecies can be determined by an accurate and sensitive sequencing technique and data analysis pipeline. We compared the suitability of two benchtop next-generation sequencers for whole genome influenza A quasispecies analysis: the Illumina MiSeq sequencing-by-synthesis and the Ion Torrent PGM semiconductor sequencing technique.

Results

We first compared the accuracy and sensitivity of both sequencers using plasmid DNA and different ratios of wild type and mutant plasmid. Illumina MiSeq sequencing reads were one and a half times more accurate than those of the Ion Torrent PGM. The majority of sequencing errors were substitutions on the Illumina MiSeq and insertions and deletions, mostly in homopolymer regions, on the Ion Torrent PGM. To evaluate the suitability of the two techniques for determining the genome diversity of influenza A virus, we generated plasmid-derived PR8 virus and grew this virus in vitro. We also optimized an RT-PCR protocol to obtain uniform coverage of all eight genomic RNA segments. The sequencing reads obtained with both sequencers could successfully be assembled de novo into the segmented influenza virus genome. After mapping of the reads to the reference genome, we found that the detection limit for reliable recognition of variants in the viral genome required a frequency of 0.5% or higher. This threshold exceeds the background error rate resulting from the RT-PCR reaction and the sequencing method. Most of the variants in the PR8 virus genome were present in hemagglutinin, and these mutations were detected by both sequencers.

Conclusions

Our approach underlines the power and limitations of two commonly used next-generation sequencers for the analysis of influenza virus gene diversity. We conclude that the Illumina MiSeq platform is better suited for detecting variant sequences whereas the Ion Torrent PGM platform has a shorter turnaround time. The data analysis pipeline that we propose here will also help to standardize variant calling in small RNA genomes based on next-generation sequencing data.