Search

Background: Single-arm trials are common in precision oncology. Owing to the lack of randomized counterfactual, resultant data are not amenable to comparative outcomes analyses. Difference-in-difference (DID) methods present an opportunity to generate causal estimates of time-varying treatment outcomes. Using DID, our study estimates within-cohort effects of genomics-informed treatment versus standard care on clinical and cost outcomes., Methods: We focus on adults with advanced cancers enrolled in the single-arm BC Cancer Personalized OncoGenomics program between 2012 and 2017. All individuals had a minimum of 1-year follow up. Logistic regression explored baseline differences across patients who received a genomics-informed treatment versus a standard care treatment after genomic sequencing. DID estimated the incremental effects of genomics-informed treatment on time to treatment discontinuation (TTD), time to next treatment (TTNT), and costs. TTD and TTNT correlate with improved response and survival., Results: Our study cohort included 346 patients, of whom 140 (40%) received genomics-informed treatment after sequencing and 206 (60%) received standard care treatment. No significant differences in baseline characteristics were detected across treatment groups. DID estimated that the incremental effect of genomics-informed versus standard care treatment was 102 days (95% CI: 35, 167) on TTD, 91 days (95% CI: -9, 175) on TTNT, and CAD$91,098 (95% CI: $46,848, $176,598) on costs. Effects were most pronounced in gastrointestinal cancer patients., Conclusions: Genomics-informed treatment had a statistically significant effect on TTD compared to standard care treatment, but at increased treatment costs. Within-cohort evidence generated through this single-arm study informs the early-stage comparative effectiveness of precision oncology., (© 2021 The Authors. Cancer Medicine published by John Wiley & Sons Ltd.)

Background: A high-quality genome sequence of any model organism is an essential starting point for genetic and other studies. Older clone-based methods are slow and expensive, whereas faster, cheaper short-read-only assemblies can be incomplete and highly fragmented, which minimizes their usefulness. The last few years have seen the introduction of many new technologies for genome assembly. These new technologies and associated new algorithms are typically benchmarked on microbial genomes or, if they scale appropriately, on larger (e.g., human) genomes. However, plant genomes can be much more repetitive and larger than the human genome, and plant biochemistry often makes obtaining high-quality DNA that is free from contaminants difficult. Reflecting their challenging nature, we observe that plant genome assembly statistics are typically poorer than for vertebrates., Results: Here, we compare Illumina short read, Pacific Biosciences long read, 10x Genomics linked reads, Dovetail Hi-C, and BioNano Genomics optical maps, singly and combined, in producing high-quality long-range genome assemblies of the potato species Solanum verrucosum. We benchmark the assemblies for completeness and accuracy, as well as DNA compute requirements and sequencing costs., Conclusions: The field of genome sequencing and assembly is reaching maturity, and the differences we observe between assemblies are surprisingly small. We expect that our results will be helpful to other genome projects, and that these datasets will be used in benchmarking by assembly algorithm developers., (© The Author(s) 2019. Published by Oxford University Press.)

Obtaining high-quality samples from wild animals is a major obstacle for genomic studies of many taxa, particularly at the population level, as collection methods for such samples are typically invasive. DNA from feces is easy to obtain noninvasively, but is dominated by bacterial and other non-host DNA. The high proportion of non-host DNA drastically reduces the efficiency of high-throughput sequencing for host animal genomics. To address this issue, we developed an inexpensive capture method for enriching host DNA from noninvasive fecal samples. Our method exploits natural differences in CpG-methylation density between vertebrate and bacterial genomes to preferentially bind and isolate host DNA from majority-bacterial samples. We demonstrate that the enrichment is robust, efficient, and compatible with downstream library preparation methods useful for population studies (e.g., RADseq). Compared to other enrichment strategies, our method is quick and inexpensive, adding only a negligible cost to sample preparation. In combination with downstream methods such as RADseq, our approach allows for cost-effective and customizable genomic-scale genotyping that was previously feasible in practice only with invasive samples. Because feces are widely available and convenient to collect, our method empowers researchers to explore genomic-scale population-level questions in organisms for which invasive sampling is challenging or undesirable.

Today's short-read sequencing instruments can generate read lengths between 50 bp and 700 bp depending on the specific instrument. These high-throughput sequencing approaches have revolutionized genomic science, allowing hundreds of thousands of full genomes to be sequenced, and have become indispensable tools for many researchers. With greater insight has come the revelation that many genomes are much more complicated than originally thought and include many rearrangements and copy-number variations. Unfortunately, short-read sequencing technologies are not well suited for identifying many of these types of events. Long-read sequencing technologies can read contiguous fragments of DNA in excess of 10 kb and are much better suited for detecting large structural events. The newest long-read sequencing instrument is the MinION device from Oxford Nanopore. The rapid sequencing speed and low upfront instrument cost are features drawing interest in this device from the genomics community. This unit provides a representative protocol for carrying out human genome sequencing on the Oxford Nanopore MinION. © 2017 by John Wiley & Sons, Inc., (Copyright © 2017 John Wiley & Sons, Inc.)

The progress of next-generation sequencing has a major impact on medical and genomic research. This high-throughput technology can now produce billions of short DNA or RNA fragments in excess of a few terabytes of data in a single run. This leads to massive datasets used by a wide range of applications including personalized cancer treatment and precision medicine. In addition to the hugely increased throughput, the cost of using high-throughput technologies has been dramatically decreasing. A low sequencing cost of around US$1000 per genome has now rendered large population-scale projects feasible. However, to make effective use of the produced data, the design of big data algorithms and their efficient implementation on modern high performance computing systems is required., (Copyright © 2017 Elsevier Ltd. All rights reserved.)

The rapidly reducing cost of bacterial genome sequencing has lead to its routine use in large-scale microbial analysis. Though mapping approaches can be used to find differences relative to the reference, many bacteria are subject to constant evolutionary pressures resulting in events such as the loss and gain of mobile genetic elements, horizontal gene transfer through recombination and genomic rearrangements. De novo assembly is the reconstruction of the underlying genome sequence, an essential step to understanding bacterial genome diversity. Here we present a high-throughput bacterial assembly and improvement pipeline that has been used to generate nearly 20 000 annotated draft genome assemblies in public databases. We demonstrate its performance on a public data set of 9404 genomes. We find all the genes used in multi-locus sequence typing schema present in 99.6 % of assembled genomes. When tested on low-, neutral- and high-GC organisms, more than 94 % of genes were present and completely intact. The pipeline has been proven to be scalable and robust with a wide variety of datasets without requiring human intervention. All of the software is available on GitHub under the GNU GPL open source license.

The increasing use of advanced nucleic acid sequencing technologies for clinical diagnostics and therapeutics has made vital understanding the costs of performing these procedures and their value to patients, providers, and payers. The Association for Molecular Pathology invested in a cost and value analysis of specific genomic sequencing procedures (GSPs) newly coded by the American Medical Association Current Procedural Terminology Editorial Panel. Cost data and work effort, including the development and use of data analysis pipelines, were gathered from representative laboratories currently performing these GSPs. Results were aggregated to generate representative cost ranges given the complexity and variability of performing the tests. Cost-impact models for three clinical scenarios were generated with assistance from key opinion leaders: impact of using a targeted gene panel in optimizing care for patients with advanced non-small-cell lung cancer, use of a targeted gene panel in the diagnosis and management of patients with sensorineural hearing loss, and exome sequencing in the diagnosis and management of children with neurodevelopmental disorders of unknown genetic etiology. Each model demonstrated value by either reducing health care costs or identifying appropriate care pathways. The templates generated will aid laboratories in assessing their individual costs, considering the value structure in their own patient populations, and contributing their data to the ongoing dialogue regarding the impact of GSPs on improving patient care., (Copyright © 2016 American Society for Investigative Pathology and the Association for Molecular Pathology. Published by Elsevier Inc. All rights reserved.)

Analyses of genome-wide association studies (GWAS) for complex disorders usually identify common variants with a relatively small effect size that only explain a small proportion of phenotypic heritability. Several studies have suggested that a significant fraction of heritability may be explained by low-frequency (minor allele frequency (MAF) of 1-5 %) and rare-variants that are not contained in the commercial GWAS genotyping arrays (Schork et al., Curr Opin Genet Dev 19:212, 2009). Rare variants can also have relatively large effects on risk for developing human diseases or disease phenotype (Cruchaga et al., PLoS One 7:e31039, 2012). However, it is necessary to perform next-generation sequencing (NGS) studies in a large population (>4,000 samples) to detect a significant rare-variant association. Several NGS methods, such as custom capture sequencing and amplicon-based sequencing, are designed to screen a small proportion of the genome, but most of these methods are limited in the number of samples that can be multiplexed (i.e. most sequencing kits only provide 96 distinct index). Additionally, the sequencing library preparation for 4,000 samples remains expensive and thus conducting NGS studies with the aforementioned methods are not feasible for most research laboratories.The need for low-cost large scale rare-variant detection makes pooled-DNA sequencing an ideally efficient and cost-effective technique to identify rare variants in target regions by sequencing hundreds to thousands of samples. Our recent work has demonstrated that pooled-DNA sequencing can accurately detect rare variants in targeted regions in multiple DNA samples with high sensitivity and specificity (Jin et al., Alzheimers Res Ther 4:34, 2012). In these studies we used a well-established pooled-DNA sequencing approach and a computational package, SPLINTER (short indel prediction by large deviation inference and nonlinear true frequency estimation by recursion) (Vallania et al., Genome Res 20:1711, 2010), for accurate identification of rare variants in large DNA pools. Given an average sequencing coverage of 30× per haploid genome, SPLINTER can detect rare variants and short indels up to 4 base pairs (bp) with high sensitivity and specificity (up to 1 haploid allele in a pool as large as 500 individuals). Step-by-step instructions on how to conduct pooled-DNA sequencing experiments and data analyses are described in this chapter.

Genomics has recently celebrated reaching the $1000 genome milestone, making affordable DNA sequencing a reality. With this goal successfully completed, the next goal of the sequencing revolution can be sequencing sensors--miniaturized sequencing devices that are manufactured for real-time applications and deployed in large quantities at low costs. The first part of this manuscript envisions applications that will benefit from moving the sequencers to the samples in a range of domains. In the second part, the manuscript outlines the critical barriers that need to be addressed in order to reach the goal of ubiquitous sequencing sensors., (© 2015 Erlich; Published by Cold Spring Harbor Laboratory Press.)

Optical mapping by direct visualization of individual DNA molecules, stretched in nanochannels with sequence-specific fluorescent labeling, represents a promising tool for disease diagnostics and genomics. An important challenge for this technique is thermal motion of the DNA as it undergoes imaging; this blurs fluorescent patterns along the DNA and results in information loss. Correcting for this effect (a process referred to as kymograph alignment) is a common preprocessing step in nanochannel-based optical mapping workflows, and we present here a highly efficient algorithm to accomplish this via pattern recognition. We compare our method with the one previous approach, and we find that our method is orders of magnitude faster while producing data of similar quality. We demonstrate proof of principle of our approach on experimental data consisting of melt mapped bacteriophage DNA.

Reduced representation genomics approaches, of which RADseq is currently the most popular form, offer the possibility to produce genome wide data from potentially any species, without previous genomic information. The application of RADseq to highly multiplexed libraries (including numerous specimens, and potentially numerous different species) is however limited by technical constraints. First, the cost of synthesis of Illumina adaptors including molecular identifiers (MIDs) becomes excessive when numerous specimens are to be multiplexed. Second, the necessity to empirically adjust the ratio of adaptors to genomic DNA concentration impedes the high throughput application of RADseq to heterogeneous samples, of variable DNA concentration and quality. In an attempt to solve these problems, we propose here some adjustments regarding the adaptor synthesis. First, we show that the common and unique (MID) parts of adaptors can be synthesized separately and subsequently ligated, which drastically reduces the synthesis cost, and thus allows multiplexing hundreds of specimens. Second, we show that self-ligation of adaptors, which makes the adaptor concentration so critical, can be simply prevented by using unphosphorylated adaptors, which significantly improves the ligation and sequencing yield.

The NIH has launched a 2-year program to provide exome sequencing and analysis for NIH investigators. Successful applicants will receive, at no cost, sequencing for 50 to 300 exomes at the NIH Intramural Sequencing Center. The program will sequence and analyze 1,000 exomes in total., (©2014 American Association for Cancer Research.)

It is over 10 years since the genome sequence of the first crop was published. Since then, the number of crop genomes sequenced each year has increased steadily. The amazing pace at which genome sequences are becoming available is largely due to the improvement in sequencing technologies both in terms of cost and speed. Modern sequencing technologies allow the sequencing of multiple cultivars of smaller crop genomes at a reasonable cost. Though many of the published genomes are considered incomplete, they nevertheless have proved a valuable tool to understand important crop traits such as fruit ripening, grain traits and flowering time adaptation., (Copyright © 2013 Elsevier Ltd. All rights reserved.)

Background: There is tremendous potential for genome sequencing to improve clinical diagnosis and care once it becomes routinely accessible, but this will require formalizing research methods into clinical best practices in the areas of sequence data generation, analysis, interpretation and reporting. The CLARITY Challenge was designed to spur convergence in methods for diagnosing genetic disease starting from clinical case history and genome sequencing data. DNA samples were obtained from three families with heritable genetic disorders and genomic sequence data were donated by sequencing platform vendors. The challenge was to analyze and interpret these data with the goals of identifying disease-causing variants and reporting the findings in a clinically useful format. Participating contestant groups were solicited broadly, and an independent panel of judges evaluated their performance., Results: A total of 30 international groups were engaged. The entries reveal a general convergence of practices on most elements of the analysis and interpretation process. However, even given this commonality of approach, only two groups identified the consensus candidate variants in all disease cases, demonstrating a need for consistent fine-tuning of the generally accepted methods. There was greater diversity of the final clinical report content and in the patient consenting process, demonstrating that these areas require additional exploration and standardization., Conclusions: The CLARITY Challenge provides a comprehensive assessment of current practices for using genome sequencing to diagnose and report genetic diseases. There is remarkable convergence in bioinformatic techniques, but medical interpretation and reporting are areas that require further development by many groups.

The cost of whole genome sequencing is dropping rapidly. There has been a great deal of enthusiasm about the potential for this technological advance to transform clinical care. Given the interest and significant investment in genomics, this seems an ideal time to consider what the evidence tells us about potential benefits and harms, particularly in the context of health care policy. The scale and pace of adoption of this powerful new technology should be driven by clinical need, clinical evidence, and a commitment to put patients at the centre of health care policy., Competing Interests: The authors have declared that no competing interests exist.

Genetics is an immense science and the current developments in its methods and techniques as well as the fast emerging tools make it one of the most powerful biological sciences. Indeed, from taxonomy and ecology to physiology and molecular biology, every biological science makes use of genetics techniques and methods at one time or another. In fact, development in genetics is such that it is now possible to characterize and analyze the expression of the whole set of genes of virtually every living organism, even if it is a non-model one. Locusts are notorious for the damage they cause to the ecosystems and economies of the areas affected by their recurrent population outbreaks. To prevent and deal with these outbreaks, we now count on both biological as well as chemical agents that are proving to be successful in reducing the damage that otherwise locust population outbreaks might cause. However, a better, efficient and environmentally friendly solution is still a hoped-for target. In my opinion, the ideal future pesticide should be both environmentally friendly, risk free and species-specific. To reach the knowledge needed for the development of such species-specific anti-locust agent, deep and accurate knowledge of the locusts' genetics and molecular biology is a must. Since genes and their expression levels lie at the bottom of every biological phenomenon, any species-specific solution to the locust problem requires a good knowledge of these organisms' genes as well as the quantitative and spatio-temporal dynamics of their expression. To reach such knowledge, collaborative work is needed as well as a clear workflow that, given the fast development in the genetics tools, is not always clear to all research groups. For this reason, here I describe a genetics workflow that should allow taking advantage of the most recent genetics tools and techniques to answer question relating to locust biology. My hope is that the adoption of this and other work strategies by different research groups, especially when the work is a collaborative one, would provide precious information on the biology and the biological phenomena that these economically important organisms exhibit., (Copyright © 2013 Académie des sciences. Published by Elsevier SAS. All rights reserved.)

Genome data are useful for both basic and applied research; however, it is difficult to carry out large-scale genome analyses using species with limited genetic or genomic resources. Here, we describe a cost-effective method to analyze the genome of a non-model species, using the cabbage moth, Mamestra brassicae (Lepidoptera: Noctuidae). First, we conducted expression sequence tag (EST) analysis. In this analysis, we performed PCR-based prescreening of a non-normalized embryonic cDNA library to eliminate already sequenced cDNAs from further sequencing, which significantly increased the percentage of unique genes. Next, we constructed a fosmid library of M. brassicae and isolated 120 clones containing 119 putative single copy genes by PCR-based screening with primer sets designed from the ESTs. Finally, we showed that the isolated fosmid clones could be used as probes for multicolor fluorescence in situ hybridization (FISH) analysis against an M. brassicae chromosome and confirmed conserved gene order between M. brassicae and the silkworm, Bombyx mori. Thus, we developed new genomic resources for comparative genome analysis in M. brassicae using robust and relatively low cost methods that can be applied to any non-model organism.

Next-Generation Sequencing (NGS) methods have revolutionized various aspects of genomics including transcriptome analysis. Digital expression analysis is all set to replace analog expression analysis that uses microarray chips through their cost-effectiveness, reproducibility, accuracy, and speed. The last 2 years have seen a surge in the development of statistical methods and software tools for analysis and visualization of NGS data. Large amounts of NGS data are available for pathogenic fungi and oomycetes. As the analysis results start pouring in, it brings about a paradigm shift in the understanding of host pathogen interactions with discovery of new transcripts, splice variants, mutations, regulatory elements, and epigenetic controls. Here we describe the core technology of the new sequencing platforms, the methodology of data analysis, and different aspects of applications.

Today, resequencing of a human genome can be performed in approximately a week using a single instrument. Thanks to a steady logarithmic rate of increase in performance for DNA sequencing platforms over the past seven years, DNA sequencing is one of the fastest developing technology fields. As the process becomes faster, it opens up possibilities within health care, diagnostics, and entirely new fields of research. Immediate genetic characterization of contagious outbreaks has been exemplified, and with such applications for the direct benefit of human health, expectations of future sensitive, rapid, high-throughput, and cost-effective technologies are steadily growing. Simultaneously, some of the limitations of a rapidly growing field have become apparent, and questions regarding the quality of some of the data deposited into databases have been raised. A human genome sequenced in only an hour is likely to become a reality in the future, but its definition may not be as certain.

Advances in sequencing technology have led to a sharp decrease in the cost of 'data generation'. But is this sufficient to ensure cost-effective and efficient 'knowledge generation'?

The present paper describes a feasible protocol enabling a merge of the classical Sanger sequencing method with the sequencing by DNA synthesis: The cycled proofreading sequencing. This new protocol involves partial chain termination and replacing extension. The first step is done with mixed ddNTP/dNTP targeting one type of nucleotide per subcycle and its relevant replacing extension with unlabeled phosphorothioate-modified dNTP of the same nucleotide type used in partial chain termination. Replacing extension serves as the key step to eliminate fluorescent signals integrated at the previous step, proofread errors, reactivate and synchronize the 3' termini of extended DNA molecules. This microchip-based sequencing method thus inherits advantages from "sequencing-by-termination" and "sequencing-by-extension"; ddNTP is used and reactions are cycled. This microchip-based sequencing method is applicable for reaching the $1000-genome project goal.

This article reviews basic concepts, general applications, and the potential impact of next-generation sequencing (NGS) technologies on genomics, with particular reference to currently available and possible future platforms and bioinformatics. NGS technologies have demonstrated the capacity to sequence DNA at unprecedented speed, thereby enabling previously unimaginable scientific achievements and novel biological applications. But, the massive data produced by NGS also presents a significant challenge for data storage, analyses, and management solutions. Advanced bioinformatic tools are essential for the successful application of NGS technology. As evidenced throughout this review, NGS technologies will have a striking impact on genomic research and the entire biological field. With its ability to tackle the unsolved challenges unconquered by previous genomic technologies, NGS is likely to unravel the complexity of the human genome in terms of genetic variations, some of which may be confined to susceptible loci for some common human conditions. The impact of NGS technologies on genomics will be far reaching and likely change the field for years to come., (Copyright © 2011. Published by Elsevier Ltd.)

High-throughput sequencing technologies promise to transform the fields of genetics and comparative biology by delivering tens of thousands of genomes in the near future. Although it is feasible to construct de novo genome assemblies in a few months, there has been relatively little attention to what is lost by sole application of short sequence reads. We compared the recent de novo assemblies using the short oligonucleotide analysis package (SOAP), generated from the genomes of a Han Chinese individual and a Yoruban individual, to experimentally validated genomic features. We found that de novo assemblies were 16.2% shorter than the reference genome and that 420.2 megabase pairs of common repeats and 99.1% of validated duplicated sequences were missing from the genome. Consequently, over 2,377 coding exons were completely missing. We conclude that high-quality sequencing approaches must be considered in conjunction with high-throughput sequencing for comparative genomics analyses and studies of genome evolution.

Several methods have been developed to enrich DNA for subsets of the genome prior to next-generation sequencing. These front-end enrichment strategies provide powerful and cost-effective tools for researchers interested in collecting large-scale genomic sequence data. In this review, I provide an overview of both general and targeted reduced representation enrichment strategies that are commonly used in tandem with next-generation sequencing. I focus on several key issues that are likely to be important when deciding which enrichment strategy is most appropriate for a given experiment. Overall, these techniques can enable the collection of large-scale genomic data in diverse species, providing a powerful tool for the study of evolutionary biology.

The ability to assay genome-scale methylation patterns using high-throughput sequencing makes it possible to carry out association studies to determine the relationship between epigenetic variation and phenotype. While bisulfite sequencing can determine a methylome at high resolution, cost inhibits its use in comparative and population studies. MethylSeq, based on sequencing of fragment ends produced by a methylation-sensitive restriction enzyme, is a method for methyltyping (survey of methylation states) and is a site-specific and cost-effective alternative to whole-genome bisulfite sequencing. Despite its advantages, the use of MethylSeq has been restricted by biases in MethylSeq data that complicate the determination of methyltypes. Here we introduce a statistical method, MetMap, that produces corrected site-specific methylation states from MethylSeq experiments and annotates unmethylated islands across the genome. MetMap integrates genome sequence information with experimental data, in a statistically sound and cohesive Bayesian Network. It infers the extent of methylation at individual CGs and across regions, and serves as a framework for comparative methylation analysis within and among species. We validated MetMap's inferences with direct bisulfite sequencing, showing that the methylation status of sites and islands is accurately inferred. We used MetMap to analyze MethylSeq data from four human neutrophil samples, identifying novel, highly unmethylated islands that are invisible to sequence-based annotation strategies. The combination of MethylSeq and MetMap is a powerful and cost-effective tool for determining genome-scale methyltypes suitable for comparative and association studies.

DNA sequence information underpins genetic research, enabling discoveries of important biological or medical benefit. Sequencing projects have traditionally used long (400-800 base pair) reads, but the existence of reference sequences for the human and many other genomes makes it possible to develop new, fast approaches to re-sequencing, whereby shorter reads are compared to a reference to identify intraspecies genetic variation. Here we report an approach that generates several billion bases of accurate nucleotide sequence per experiment at low cost. Single molecules of DNA are attached to a flat surface, amplified in situ and used as templates for synthetic sequencing with fluorescent reversible terminator deoxyribonucleotides. Images of the surface are analysed to generate high-quality sequence. We demonstrate application of this approach to human genome sequencing on flow-sorted X chromosomes and then scale the approach to determine the genome sequence of a male Yoruba from Ibadan, Nigeria. We build an accurate consensus sequence from >30x average depth of paired 35-base reads. We characterize four million single-nucleotide polymorphisms and four hundred thousand structural variants, many of which were previously unknown. Our approach is effective for accurate, rapid and economical whole-genome re-sequencing and many other biomedical applications.