The dynamics of genome replication using deep sequencing

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Legislative and Administrative Processes. By Hans A. Linde and George Bunn; Introduction to the American Public Law System: Cases and Materials. By Jerry L. Mashaw and Richard A. Merrill

Background: Genome and transcriptome sequencing applications that rely on variation in sequence depth can be negatively affected if there are systematic biases in coverage. We have investigated patterns of local variation in sequencing coverage by utilising ultra-deep sequencing (>100,000X) of mtDNA obtained during sequencing of two vertebrate genomes, wolverine (Gulo gulo) and collared flycatcher (Ficedula albicollis). With such extreme depth, stochastic variation in coverage should be negligible, which allows us to provide a very detailed, fine-scale picture of sequence dependent coverage variation and sequencing error rates. Results: Sequencing coverage showed up to six-fold variation across the complete mtDNA and this variation was highly repeatable in sequencing of multiple individuals of the same species. Moreover, coverage in orthologous regions was correlated between the two species and was negatively correlated with GC content. We also found a negative correlation between the site-specific sequencing error rate and coverage, with certain sequence motifs "CCNGCC" being particularly prone to high rates of error and low coverage. Conclusions: Our results demonstrate that inherent sequence characteristics govern variation in coverage and suggest that some of this variation, like GC content, should be controlled for in, for example, RNA-Seq and detection of copy number variation

Metagenomic deep sequencing of aqueous fluid detects intraocular lymphomas.

IntroductionCurrently, the detection of pathogens or mutations associated with intraocular lymphomas heavily relies on prespecified, directed PCRs. With metagenomic deep sequencing (MDS), an unbiased high-throughput sequencing approach, all pathogens as well as all mutations present in the host's genome can be detected in the same small amount of ocular fluid.MethodsIn this cross-sectional case series, aqueous fluid samples from two patients were submitted to MDS to identify pathogens as well as common and rare cancer mutations.ResultsMDS of aqueous fluid from the first patient with vitreal lymphoma revealed the presence of both Epstein-Barr virus (HHV-4/EBV) and human herpes virus 8 (HHV-8) RNA. Aqueous fluid from the second patient with intraocular B-cell lymphoma demonstrated a less common mutation in the MYD88 gene associated with B-cell lymphoma.ConclusionMDS detects pathogens that, in some instances, may drive the development of intraocular lymphomas. Moreover, MDS is able to identify both common and rare mutations associated with lymphomas

VarDict: a novel and versatile variant caller for next-generation sequencing in cancer research

Accurate variant calling in next generation sequencing (NGS) is critical to understand cancer genomes better. Here we present VarDict, a novel and versatile variant caller for both DNA- and RNA-sequencing data. VarDict simultaneously calls SNV, MNV, InDels, complex and structural variants, expanding the detected genetic driver landscape of tumors. It performs local realignments on the fly for more accurate allele frequency estimation. VarDict performance scales linearly to sequencing depth, enabling ultra-deep sequencing used to explore tumor evolution or detect tumor DNA circulating in blood. In addition, VarDict performs amplicon aware variant calling for polymerase chain reaction (PCR)-based targeted sequencing often used in diagnostic settings, and is able to detect PCR artifacts. Finally, VarDict also detects differences in somatic and loss of heterozygosity variants between paired samples. VarDict reprocessing of The Cancer Genome Atlas (TCGA) Lung Adenocarcinoma dataset called known driver mutations in KRAS, EGFR, BRAF, PIK3CA and MET in 16% more patients than previously published variant calls. We believe VarDict will greatly facilitate application of NGS in clinical cancer research

QQ-SNV: single nucleotide variant detection at low frequency by comparing the quality quantiles

Background: Next generation sequencing enables studying heterogeneous populations of viral infections. When the sequencing is done at high coverage depth ("deep sequencing"), low frequency variants can be detected. Here we present QQ-SNV (http://sourceforge.net/projects/qqsnv), a logistic regression classifier model developed for the Illumina sequencing platforms that uses the quantiles of the quality scores, to distinguish true single nucleotide variants from sequencing errors based on the estimated SNV probability. To train the model, we created a dataset of an in silico mixture of five HIV-1 plasmids. Testing of our method in comparison to the existing methods LoFreq, ShoRAH, and V-Phaser 2 was performed on two HIV and four HCV plasmid mixture datasets and one influenza H1N1 clinical dataset. Results: For default application of QQ-SNV, variants were called using a SNV probability cutoff of 0.5 (QQ-SNVD). To improve the sensitivity we used a SNV probability cutoff of 0.0001 (QQ-SNVHS). To also increase specificity, SNVs called were overruled when their frequency was below the 80th percentile calculated on the distribution of error frequencies (QQ-SNVHS-P80). When comparing QQ-SNV versus the other methods on the plasmid mixture test sets, QQ-SNVD performed similarly to the existing approaches. QQ-SNVHS was more sensitive on all test sets but with more false positives. QQ-SNVHS-P80 was found to be the most accurate method over all test sets by balancing sensitivity and specificity. When applied to a paired-end HCV sequencing study, with lowest spiked-in true frequency of 0.5 %, QQ-SNVHS-P80 revealed a sensitivity of 100 % (vs. 40-60 % for the existing methods) and a specificity of 100 % (vs. 98.0-99.7 % for the existing methods). In addition, QQ-SNV required the least overall computation time to process the test sets. Finally, when testing on a clinical sample, four putative true variants with frequency below 0.5 % were consistently detected by QQ-SNVHS-P80 from different generations of Illumina sequencers. Conclusions: We developed and successfully evaluated a novel method, called QQ-SNV, for highly efficient single nucleotide variant calling on Illumina deep sequencing virology data

A Reference-Free Algorithm for Computational Normalization of Shotgun Sequencing Data

Deep shotgun sequencing and analysis of genomes, transcriptomes, amplified single-cell genomes, and metagenomes has enabled investigation of a wide range of organisms and ecosystems. However, sampling variation in short-read data sets and high sequencing error rates of modern sequencers present many new computational challenges in data interpretation. These challenges have led to the development of new classes of mapping tools and {\em de novo} assemblers. These algorithms are challenged by the continued improvement in sequencing throughput. We here describe digital normalization, a single-pass computational algorithm that systematizes coverage in shotgun sequencing data sets, thereby decreasing sampling variation, discarding redundant data, and removing the majority of errors. Digital normalization substantially reduces the size of shotgun data sets and decreases the memory and time requirements for {\em de novo} sequence assembly, all without significantly impacting content of the generated contigs. We apply digital normalization to the assembly of microbial genomic data, amplified single-cell genomic data, and transcriptomic data. Our implementation is freely available for use and modification

Polymorphism identification and improved genome annotation of Brassica rapa through Deep RNA sequencing.

The mapping and functional analysis of quantitative traits in Brassica rapa can be greatly improved with the availability of physically positioned, gene-based genetic markers and accurate genome annotation. In this study, deep transcriptome RNA sequencing (RNA-Seq) of Brassica rapa was undertaken with two objectives: SNP detection and improved transcriptome annotation. We performed SNP detection on two varieties that are parents of a mapping population to aid in development of a marker system for this population and subsequent development of high-resolution genetic map. An improved Brassica rapa transcriptome was constructed to detect novel transcripts and to improve the current genome annotation. This is useful for accurate mRNA abundance and detection of expression QTL (eQTLs) in mapping populations. Deep RNA-Seq of two Brassica rapa genotypes-R500 (var. trilocularis, Yellow Sarson) and IMB211 (a rapid cycling variety)-using eight different tissues (root, internode, leaf, petiole, apical meristem, floral meristem, silique, and seedling) grown across three different environments (growth chamber, greenhouse and field) and under two different treatments (simulated sun and simulated shade) generated 2.3 billion high-quality Illumina reads. A total of 330,995 SNPs were identified in transcribed regions between the two genotypes with an average frequency of one SNP in every 200 bases. The deep RNA-Seq reassembled Brassica rapa transcriptome identified 44,239 protein-coding genes. Compared with current gene models of B. rapa, we detected 3537 novel transcripts, 23,754 gene models had structural modifications, and 3655 annotated proteins changed. Gaps in the current genome assembly of B. rapa are highlighted by our identification of 780 unmapped transcripts. All the SNPs, annotations, and predicted transcripts can be viewed at http://phytonetworks.ucdavis.edu/