Cost-effective Sequencing Research Articles

Full-Length Mitochondrial-DNA Sequencing on the PacBio RSII.

Conventional mitochondrial-DNA (MT DNA) sequencing approaches use Sanger sequencing of 20-40 partially overlapping PCR fragments per individual, which is a time- and resource-consuming process. We have developed a high-throughput, accurate, fast, and cost-effective human MT DNA sequencing approach. In this setup we first generate long-range PCR products for two partially overlapping 7.7 and 9.2 kb MT DNA-specific amplicons, add sample-specific barcodes, and sequence these on the PacBio RSII system to obtain full-length MT DNA sequences for genotyping/haplotyping purposes.

Efficient Enrichment of Bacterial mRNA from Host-Bacteria Total RNA Samples.

Despite numerous advances in genomics and bioinformatics, technological hurdles remain to examine host-microbe transcriptomics. Sometimes the transcriptome of either or both can be ascertained merely by generating more sequencing reads. However, many cases exist where bacterial mRNA needs to be enriched further to enable cost-effective sequencing of the pathogen or endosymbiont. While a suitable method is commercially available for mammalian samples of this type, development of such methods has languished for invertebrate samples. Furthermore, a common method across multiple taxa would facilitate comparisons between bacteria in invertebrate vectors and their vertebrate hosts. Here, a method is described to concurrently remove polyadenylated transcripts, prokaryotic rRNA, and eukaryotic rRNA, including those with low amounts of starting material (e.g. 100 ng). In a Wolbachia-Drosophila system, this bacterial mRNA enrichment yielded a 3-fold increase in Wolbachia mRNA abundance and a concomitant 3.3-fold increase in the percentage of transcripts detected. More specifically, 70% of the genome could be recovered by transcriptome sequencing compared to 21% in the total RNA. Sequencing of similar bacterial mRNA-enriched samples generated from Ehrlichia-infected canine cells covers 93% of the Ehrlichia genome, suggesting ubiquitous transcription across the entire Ehrlichia chaffeensis genome. This technique can potentially be used to enrich bacterial mRNA in many studies of host-microbe interactions.

Using a constraint-based regression method for relative quantification of somatic mutations in pyrosequencing signals: a case for NRAS analysis.

BackgroundPyrosequencing Allele Quantification (AQ) is a cost-effective DNA sequencing method that can be used for detecting somatic mutations in formalin-fixed paraffin-embedded (FFPE) samples. The method displays a low turnaround time and a high sensitivity. Pyrosequencing suffers however from two main drawbacks including (i) low specificity and (ii) difficult signal interpretation when multiple mutations are reported in a hotspot genomic region.ResultsUsing a constraint-based regression method, the new AdvISER-PYRO-SMQ algorithm was developed in the current study and implemented into an R package. As a proof-of-concept, AdvISER-PYRO-SMQ was used to identify a set of 9 distinct point mutations affecting codon 61 of the NRAS oncogene. In parallel, a pyrosequencing assay using the Qiagen software and its AQ module was used to assess selectively the presence of a single point mutation (NRASc.182A>G - Q61R-1) among the set of codon 61 mutations, and to analyze related pyrosequencing signals. AdvISER-PYRO-SMQ produced a lower limit of blank (0 %) than the AQ module of Qiagen software (5.1 %) and similar limit of detection were obtained for both software (5.6 vs 4.8 %). AdvISER-PYRO-SMQ was able to screen for the presence of 9 distinct mutations with a single pyrosequencing reaction whereas the AQ module was limited to screen a single mutation per reaction.ConclusionUsing a constraint-based regression method enables to analyze pyrosequencing signal and to detect multiple mutations within a hotspot genomic region with an optimal compromise between sensitivity and specificity. The AdvISER-PYRO-SMQ R package provides a generic tool which can be applied on a wide range of somatic mutations. Its implementation in a Shiny web interactive application (available at https://ucl-irec-ctma.shinyapps.io/Pyrosequencing-NRAS-61/) enables its use in research or clinical routine applications.Electronic supplementary materialThe online version of this article (doi:10.1186/s13015-016-0086-4) contains supplementary material, which is available to authorized users.

Miniaturization Technologies for Efficient Single-Cell Library Preparation for Next-Generation Sequencing.

As the cost of next-generation sequencing has decreased, library preparation costs have become a more significant proportion of the total cost, especially for high-throughput applications such as single-cell RNA profiling. Here, we have applied novel technologies to scale down reaction volumes for library preparation. Our system consisted of in vitro differentiated human embryonic stem cells representing two stages of pancreatic differentiation, for which we prepared multiple biological and technical replicates. We used the Fluidigm (San Francisco, CA) C1 single-cell Autoprep System for single-cell complementary DNA (cDNA) generation and an enzyme-based tagmentation system (Nextera XT; Illumina, San Diego, CA) with a nanoliter liquid handler (mosquito HTS; TTP Labtech, Royston, UK) for library preparation, reducing the reaction volume down to 2 µL and using as little as 20 pg of input cDNA. The resulting sequencing data were bioinformatically analyzed and correlated among the different library reaction volumes. Our results showed that decreasing the reaction volume did not interfere with the quality or the reproducibility of the sequencing data, and the transcriptional data from the scaled-down libraries allowed us to distinguish between single cells. Thus, we have developed a process to enable efficient and cost-effective high-throughput single-cell transcriptome sequencing.

Pluribus-Exploring the Limits of Error Correction Using a Suffix Tree.

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Validation and Application of a Custom-Designed Targeted Next-Generation Sequencing Panel for the Diagnostic Mutational Profiling of Solid Tumors.

The inevitable switch from standard molecular methods to next-generation sequencing for the molecular profiling of tumors is challenging for most diagnostic laboratories. However, fixed validation criteria for diagnostic accreditation are not in place because of the great variability in methods and aims. Here, we describe the validation of a custom panel of hotspots in 24 genes for the detection of somatic mutations in non-small cell lung carcinoma, colorectal carcinoma and malignant melanoma starting from FFPE sections, using 14, 36 and 5 cases, respectively. The targeted hotspots were selected for their present or future clinical relevance in solid tumor types. The target regions were enriched with the TruSeq approach starting from limited amounts of DNA. Cost effective sequencing of 12 pooled libraries was done using a micro flow cell on the MiSeq and subsequent data analysis with MiSeqReporter and VariantStudio. The entire workflow was diagnostically validated showing a robust performance with maximal sensitivity and specificity using as thresholds a variant allele frequency >5% and a minimal amplicon coverage of 300. We implemented this method through the analysis of 150 routine diagnostic samples and identified clinically relevant mutations in 16 genes including KRAS (32%), TP53 (32%), BRAF (12%), APC (11%), EGFR (8%) and NRAS (5%). Importantly, the highest success rate was obtained when using also the low quality DNA samples. In conclusion, we provide a workflow for the validation of targeted NGS by a custom-designed pan-solid tumor panel in a molecular diagnostic lab and demonstrate its robustness in a clinical setting.

Will Benchtop Sequencers Resolve the Sequencing Trade-off in Plant Genetics?

Will Benchtop Sequencers Resolve the Sequencing Trade-off in Plant Genetics?

Bioinformatics tools for analysing viral genomic data.

The field of viral genomics and bioinformatics is experiencing a strong resurgence due to high-throughput sequencing (HTS) technology, which enables the rapid and cost-effective sequencing and subsequent assembly of large numbers of viral genomes. In addition, the unprecedented power of HTS technologies has enabled the analysis of intra-host viral diversity and quasispecies dynamics in relation to important biological questions on viral transmission, vaccine resistance and host jumping. HTS also enables the rapid identification of both known and potentially new viruses from field and clinical samples, thus adding new tools to the fields of viral discovery and metagenomics. Bioinformatics has been central to the rise of HTS applications because new algorithms and software tools are continually needed to process and analyse the large, complex datasets generated in this rapidly evolving area. In this paper, the authors give a brief overview of the main bioinformatics tools available for viral genomic research, with a particular emphasis on HTS technologies and their main applications. They summarise the major steps in various HTS analyses, starting with quality control of raw reads and encompassing activities ranging from consensus and de novo genome assembly to variant calling and metagenomics, as well as RNA sequencing.

Molecular subtyping and improved treatment of neurodevelopmental disease.

The next-generation sequencing revolution has substantially increased our understanding of the mutated genes that underlie complex neurodevelopmental disease. Exome sequencing has enabled us to estimate the number of genes involved in the etiology of neurodevelopmental disease, whereas targeted sequencing approaches have provided the means for quick and cost-effective sequencing of thousands of patient samples to assess the significance of individual genes. By leveraging such technologies and clinical exome sequencing, a genotype-first approach has emerged in which patients with a common genotype are first identified and then clinically reassessed as a group. This approach has proven a powerful methodology for refining disease subtypes. We propose that the molecular characterization of these genetic subtypes has important implications for diagnostics and also for future drug development. Classifying patients into subgroups with a common genetic etiology and applying treatments tailored to the specific molecular defect they carry is likely to improve management of neurodevelopmental disease in the future.

Optimal Robotic Assembly Sequence Generation Using Particle Swarm Optimization

Abstract — The optimal feasible robotic assembly sequence leads to efficient manufacturing process by minimizing the assembly cost. Assembly cost is based on the energy required to assemble the components through collision free path and robot directional changes during the assembly operations. So, the determination of a feasible assembly sequence with minimum assembly cost is vital concern for manufacturing industries. Through obtaining optimal assembly sequences taking user inputs (assembly connection matrix, precedence relations, etc.,) is less complicate, the correctness of methodology depends on the skill of the engineer who supply these inputs. The present research aims to explore PSO based methodology to determine cost effective optimal robotic assembly sequence through CAD product. The integration of PSO with CAD environment ensures the correctness and completeness of the methodology. The methods to interface with the CAD data to extract liaison data, to test for liaison predicate and feasibility predicate is presented and analyzed briefly with an example. In this methodology, each component of the assembled product is considered as the particle (bird) and mutation operation is performed to generate a new assembly sequence for each iteration. To generate optimal assembly sequence, a fitness function is generated, which is based on the energy function and robot directional changes associated with assembly sequence. The sequence which is having the best fitness value is treated as the optimal robotic assembly sequence.

Integrating Genomic Data Sets for Knowledge Discovery: An Informed Approach to Management of Captive Endangered Species.

Many endangered captive populations exhibit reduced genetic diversity resulting in health issues that impact reproductive fitness and quality of life. Numerous cost effective genomic sequencing and genotyping technologies provide unparalleled opportunity for incorporating genomics knowledge in management of endangered species. Genomic data, such as sequence data, transcriptome data, and genotyping data, provide critical information about a captive population that, when leveraged correctly, can be utilized to maximize population genetic variation while simultaneously reducing unintended introduction or propagation of undesirable phenotypes. Current approaches aimed at managing endangered captive populations utilize species survival plans (SSPs) that rely upon mean kinship estimates to maximize genetic diversity while simultaneously avoiding artificial selection in the breeding program. However, as genomic resources increase for each endangered species, the potential knowledge available for management also increases. Unlike model organisms in which considerable scientific resources are used to experimentally validate genotype-phenotype relationships, endangered species typically lack the necessary sample sizes and economic resources required for such studies. Even so, in the absence of experimentally verified genetic discoveries, genomics data still provides value. In fact, bioinformatics and comparative genomics approaches offer mechanisms for translating these raw genomics data sets into integrated knowledge that enable an informed approach to endangered species management.

Next Generation Sequencing Technologies: The Doorway to the Unexplored Genomics of Non-Model Plants.

Non-model plants i.e., the species which have one or all of the characters such as long life cycle, difficulty to grow in the laboratory or poor fecundity, have been schemed out of sequencing projects earlier, due to high running cost of Sanger sequencing. Consequently, the information about their genomics and key biological processes are inadequate. However, the advent of fast and cost effective next generation sequencing (NGS) platforms in the recent past has enabled the unearthing of certain characteristic gene structures unique to these species. It has also aided in gaining insight about mechanisms underlying processes of gene expression and secondary metabolism as well as facilitated development of genomic resources for diversity characterization, evolutionary analysis and marker assisted breeding even without prior availability of genomic sequence information. In this review we explore how different Next Gen Sequencing platforms, as well as recent advances in NGS based high throughput genotyping technologies are rewarding efforts on de-novo whole genome/transcriptome sequencing, development of genome wide sequence based markers resources for improvement of non-model crops that are less costly than phenotyping.

The power of single molecule real-time sequencing technology in the de novo assembly of a eukaryotic genome.

Second-generation sequencers (SGS) have been game-changing, achieving cost-effective whole genome sequencing in many non-model organisms. However, a large portion of the genomes still remains unassembled. We reconstructed azuki bean (Vigna angularis) genome using single molecule real-time (SMRT) sequencing technology and achieved the best contiguity and coverage among currently assembled legume crops. The SMRT-based assembly produced 100 times longer contigs with 100 times smaller amount of gaps compared to the SGS-based assemblies. A detailed comparison between the assemblies revealed that the SMRT-based assembly enabled a more comprehensive gene annotation than the SGS-based assemblies where thousands of genes were missing or fragmented. A chromosome-scale assembly was generated based on the high-density genetic map, covering 86% of the azuki bean genome. We demonstrated that SMRT technology, though still needed support of SGS data, achieved a near-complete assembly of a eukaryotic genome.

The necessity for in vivo functional analysis in human medical genetics.

Approximately 50% of all congenital anomalies cannot be linked to any specific genetic etiology, but in recent years cost effective high throughput sequencing has emerged as an efficient strategy for identifying single nucleotide polymorphisms (SNPs) associated with disease. However, in many cases there is not enough evidence to determine if these SNPs underlie disease. To bridge this gap in our understanding advances in functional analyses are warranted. Several preclinical model systems are currently being utilized to provide such evidence, including the advantageous zebrafish embryo. While every system exhibits disadvantages and caveats, a new era of multidisciplinary research has evolved, which uses a broad spectrum of functional analysis tools. This approach will make it possible to identify potential therapeutic targets for both common and rare human disorders.

Rapid next-generation sequencing of dengue, EV-A71 and RSV-A viruses

Accurate characterisation of viral strains constitutes a crucial objective for the management of modern virus collections. Next-generation sequencing (NGS) provides technical solution for fast and cost-effective full genome sequencing. Here, we report protocols for rapid full-genome characterisation of RNA viruses of medical importance: dengue virus, enterovirus A71 and respiratory syncytial virus A, based on a specific amplification step followed by NGS-sequencing. A subset of full-length genome sequences representing the genetic diversity of each virus type was selected in GenBank and used to design primer sets allowing the amplification of the complete genome in 3–8 overlapping PCR fragments. The technique was used for characterising 53 strains (33 DENV, 8 EV-A71, 12 RSV-A) from various genotypes and origins. In a single assay, and in just 4 days, it provided for all strains an excellent genomic coverage (∼99% including complete ORF for all strains) and accurate sequences with high number of reads per position (250–3500 on average). The elaboration of specific PCR-based full-genome sequencing protocols for diverse virus groups is likely to revolutionise the characterisation of viral isolates in modern collection, but also to contribute in the next future to the study of RNA viruses directly from biological samples.

Cost-Effectiveness Analysis of Alternative Antiviral Strategies for the Treatment of HBeAg-Positive and HBeAg-Negative Chronic Hepatitis B in the United Kingdom

BackgroundSeven drugs are licensed for the treatment of chronic hepatitis B (CHB) in the United Kingdom. Which initial treatment, secondary therapy, and whether antivirals should be given alone or in combination are questions of considerable uncertainty. ObjectiveThe aim of this model was to undertake a comprehensive economic evaluation of all antiviral treatments for CHB to recommend the most cost-effective therapeutic sequence. MethodsWe developed a probabilistic Markov model to compare the cost-effectiveness of all clinically relevant antiviral treatment sequences for nucleos(t)ide-naive adults with hepatitis B e-antigen (HBeAg)-positive or HBeAg-negative CHB. Relative rates of HBeAg seroconversion and viral suppression were obtained from a network meta-analysis. Data on mortality, antiviral drug resistance, durability of response, adverse events, and costs were obtained from published literature. Results are reported in terms of lifetime costs, quality-adjusted life-years (QALYs), and expected net benefit. ResultsIn the base-case analysis, pegylated interferon alpha-2a (peg-IFN α-2a) followed by tenofovir disoproxil fumarate was most effective and cost-effective in HBeAg-positive patients, with a cost of £7488 per QALY gained compared with no treatment. In HBeAg-negative patients, peg-IFN α-2a followed by entecavir was most effective and cost-effective, with a cost of £6981 per QALY gained. The model was robust to a wide range of sensitivity analyses. ConclusionsPeg-IFN α-2a followed by tenofovir disoproxil fumarate or entecavir is the most effective antiviral treatment strategy for people with both variants of CHB. At a cost of less than £10,000 per QALY gained, these sequences are considered cost-effective in England and Wales. The results of this analysis were used to inform 2013 National Institute for Health and Care Excellence guideline recommendations.

Towards next generation CHO cell biology: Bioinformatics methods for RNA-Seq-based expression profiling.

High throughput, cost effective next generation sequencing (NGS) has enabled the publication of genome sequences for Cricetulus griseus and several Chinese hamster ovary (CHO) cell lines. RNA-Seq, the utilization of NGS technology to study the transcriptome, is expanding our understanding of the CHO cell biological system in areas ranging from the analysis of transcription start sites to the discovery of small noncoding RNAs. The analysis of RNA-Seq data, often comprised of several million short reads, presents a considerable challenge. If the CHO cell biology field is to fully exploit the potential of RNA-Seq, the development of robust data analysis pipelines is critical. In this manuscript, we outline bioinformatics approaches for the stages of a typical RNA-Seq expression profiling experiment including quality control, pre-processing, alignment and de novo transcriptome assembly. Algorithms for the analysis of mRNA and microRNA (miRNA) expression as well as methods for the detection of alternative splicing from RNA-Seq data are also presented. At this relatively early stage of Cricetulus griseus genome assembly and annotation, it is likely that a combination of isoform deconvolution and raw count based methods will provide the most complete picture of transcript expression patterns in CHO cell RNA-Seq experiments.

Leveraging Identity-by-Descent for Accurate Genotype Inference in Family Sequencing Data.

Sequencing family DNA samples provides an attractive alternative to population based designs to identify rare variants associated with human disease due to the enrichment of causal variants in pedigrees. Previous studies showed that genotype calling accuracy can be improved by modeling family relatedness compared to standard calling algorithms. Current family-based variant calling methods use sequencing data on single variants and ignore the identity-by-descent (IBD) sharing along the genome. In this study we describe a new computational framework to accurately estimate the IBD sharing from the sequencing data, and to utilize the inferred IBD among family members to jointly call genotypes in pedigrees. Through simulations and application to real data, we showed that IBD can be reliably estimated across the genome, even at very low coverage (e.g. 2X), and genotype accuracy can be dramatically improved. Moreover, the improvement is more pronounced for variants with low frequencies, especially at low to intermediate coverage (e.g. 10X to 20X), making our approach effective in studying rare variants in cost-effective whole genome sequencing in pedigrees. We hope that our tool is useful to the research community for identifying rare variants for human disease through family-based sequencing.

Cost-effectiveness of first-line induction and maintenance treatment sequences in non-squamous non-small cell lung cancer (NSCLC) in the U.S.

Due to the lack of direct head-to-head trials, there are limited data regarding the comparative effectiveness of induction-maintenance sequences. The objective of this study was to develop a cost-effectiveness model to compare induction-maintenance sequences in the US for the treatment of advanced non-squamous NSCLC. Decision analytic modelling was used to synthesize the treatment effect and baseline risk estimates for nine induction and maintenance treatment sequences, reflecting treatments used in the US. The model was structured using an area-under-the-curve approach and sensitivity analyses were conducted. Model validation was conducted by an independent third party. All active maintenance therapy-containing regimens, with the exception of gemcitabine+cisplatin (first-line)→erlotinib (maintenance), were more costly than induction-only regimens. Concerning treatments that may be cost effective, the incremental costs per life-year gained were $121,425, $148,994, and $191,270 for gemcitabine+cisplatin→erlotinib versus gemcitabine+cisplatin→best supportive care (BSC), pemetrexed+cisplatin→BSC versus gemcitabine+cisplatin→erlotinib, and for pemetrexed+cisplatin→pemetrexed versus pemetrexed+cisplatin→BSC, respectively. All other regimens were found to be dominated (carboplatin+paclitaxel→BSC; carboplatin+paclitaxel→erlotinib; carboplatin+paclitaxel→pemetrexed; bevacizumab+carboplatin+paclitaxel→bevacizumab) or extendedly dominated (cisplatin+gemcitabine→pemetrexed). Sensitivity analyses demonstrated stability. Depending on the specific cost-effectiveness threshold used by a decision maker, the most cost-effective treatment sequence may include the referent comparator gemcitabine+cisplatin and the studied regimens of gemcitabine+cisplatin→erlotinib, pemetrexed+cisplatin→BSC, or pemetrexed+cisplatin→pemetrexed.

Prevalence estimation for monogenic autosomal recessive diseases using population-based genetic data.

Genetic methods can complement epidemiological surveys and clinical registries in determining prevalence of monogenic autosomal recessive diseases. Several large population-based genetic databases, such as the NHLBI GO Exome Sequencing Project, are now publically available. By assuming Hardy-Weinberg equilibrium, the frequency of individuals homozygous in the general population for a particular pathogenic allele can be directly calculated from a sample of chromosomes where some harbor the pathogenic allele. Further assuming that the penetrance of the pathogenic allele(s) is known, the prevalence of recessive phenotypes can be determined. Such work can inform public health efforts for rare recessive diseases. A Bayesian estimation procedure has yet to be applied to the problem of estimating disease prevalence from large population-based genetic data. A Bayesian framework is developed to derive the posterior probability density of monogenic, autosomal recessive phenotypes. Explicit equations are presented for the credible intervals of these disease prevalence estimates. A primary impediment to performing accurate disease prevalence calculations is the determination of truly pathogenic alleles. This issue is discussed, but in many instances remains a significant barrier to investigations solely reliant on statistical interrogation--functional studies can provide important information for solidifying evidence of variant pathogenicity. We also discuss several challenges to these efforts, including the population structure in the sample of chromosomes, the treatment of allelic heterogeneity, and reduced penetrance of pathogenic variants. To illustrate the application of these methods, we utilized recently published genetic data collected on a large sample from the Schmiedeleut Hutterites. We estimate prevalence and calculate 95% credible intervals for 13 autosomal recessive diseases using these data. In addition, the Bayesian estimation procedure is applied to data from a central European study of hereditary fructose intolerance. The methods described herein show a viable path to robustly estimating both the expected prevalence of autosomal recessive phenotypes and corresponding credible intervals using population-based genetic databases that have recently become available. As these genetic databases increase in number and size with the advent of cost-effective next-generation sequencing, we anticipate that these methods and approaches may be helpful in recessive disease prevalence calculations, potentially impacting public health management, health economic analyses, and treatment of rare diseases.