Era Of Next-generation Sequencing Research Articles

RepARK—de novo creation of repeat libraries from whole-genome NGS reads

Generation of repeat libraries is a critical step for analysis of complex genomes. In the era of next-generation sequencing (NGS), such libraries are usually produced using a whole-genome shotgun (WGS) derived reference sequence whose completeness greatly influences the quality of derived repeat libraries. We describe here a de novo repeat assembly method—RepARK (Repetitive motif detection by Assembly of Repetitive K-mers)—which avoids potential biases by using abundant k-mers of NGS WGS reads without requiring a reference genome. For validation, repeat consensuses derived from simulated and real Drosophila melanogaster NGS WGS reads were compared to repeat libraries generated by four established methods. RepARK is orders of magnitude faster than the other methods and generates libraries that are: (i) composed almost entirely of repetitive motifs, (ii) more comprehensive and (iii) almost completely annotated by TEclass. Additionally, we show that the RepARK method is applicable to complex genomes like human and can even serve as a diagnostic tool to identify repetitive sequences contaminating NGS datasets.

Clinical Implementation of Germ Line Cancer Pharmacogenetic Variants During the Next-Generation Sequencing Era

Clinical Implementation of Germ Line Cancer Pharmacogenetic Variants During the Next-Generation Sequencing Era

An updated review of gastric cancer in the next-generation sequencing era: insights from bench to bedside and vice versa.

Gastric cancer (GC) is one of the most common malignancies and remains the second leading cause of cancer-related death worldwide. There is an increasing understanding of the roles that genetic and epigenetic alterations play in GCs. Recent studies using next-generation sequencing (NGS) have revealed a number of potential cancer-driving genes in GC. Whole-exome sequencing of GC has identified recurrent somatic mutations in the chromatin remodeling gene ARID1A and alterations in the cell adhesion gene FAT4, a member of the cadherin gene family. Mutations in chromatin remodeling genes (ARID1A, MLL3 and MLL) have been found in 47% of GCs. Whole-genome sequencing and whole-transcriptome sequencing analyses have also discovered novel alterations in GC. Recent studies of cancer epigenetics have revealed widespread alterations in genes involved in the epigenetic machinery, such as DNA methylation, histone modifications, nucleosome positioning, noncoding RNAs and microRNAs. Recent advances in molecular research on GC have resulted in the introduction of new diagnostic and therapeutic strategies into clinical settings. The anti-human epidermal growth receptor 2 (HER2) antibody trastuzumab has led to an era of personalized therapy in GC. In addition, ramucirumab, a monoclonal antibody targeting vascular endothelial growth factor receptor (VEGFR)-2, is the first biological treatment that showed survival benefits as a single-agent therapy in patients with advanced GC who progressed after first-line chemotherapy. Using NGS to systematically identify gene alterations in GC is a promising approach with remarkable potential for investigating the pathogenesis of GC and identifying novel therapeutic targets, as well as useful biomarkers. In this review, we will summarize the recent advances in the understanding of the molecular pathogenesis of GC, focusing on the potential use of these genetic and epigenetic alterations as diagnostic biomarkers and novel therapeutic targets.

Dealing with pseudogenes in molecular diagnostics in the next-generation sequencing era.

In this chapter, we focus on issues related to the application of next-generation sequencing (NGS) strategies for the analysis of genes with pseudogenes in a clinical setting. Hereby, target enrichment and mapping strategies are crucial factors to avoid "contaminating" pseudogene sequences, which are characterized by higher mutation rates than their functional parental genes. For the target enrichment strategies, we describe advantages and disadvantages of PCR- and capture-based enrichment methodologies. For the mapping strategies, we discuss crucial parameters that need to be considered to accurately distinguish sequences of functional genes from pseudogenic sequences. Finally, we discuss some concrete examples of genes with known pseudogenes and associated with Mendelian disorders that were analyzed by NGS on various platforms and starting from different library preparations.

First-line genetic testing for mitochondrial disorders in the next-generation sequencing era: Comprehensive known disease gene panel or exome sequencing?

First-line genetic testing for mitochondrial disorders in the next-generation sequencing era: Comprehensive known disease gene panel or exome sequencing?

Clinical implementation of germ line cancer pharmacogenetic variants during the next-generation sequencing era.

More than 100 medications approved by the US Food and Drug Administration include pharmacogenetic biomarkers in the drug label, many with cancer indications referencing germ line DNA variations. With the advent of next-generation sequencing (NGS) and its rapidly increasing uptake into cancer research and clinical practice, an enormous amount of data to inform documented gene-drug associations will be collected that must be exploited to optimize patient benefit. This review focuses on the implementation of germ line cancer pharmacogenetics in clinical practice. Specifically, it discusses the importance of germ line variation in cancer and the role of NGS in pharmacogenetic discovery and implementation. In the context of a scenario in which massive amounts of NGS-based genetic information will be increasingly available to health stakeholders, this review explores the ongoing debate regarding the threshold of evidence necessary for implementation, provides an overview of recommendations in cancer by professional organizations and regulatory bodies, and discusses limitations of current guidelines and strategies to improve third-party coverage.

From genomics to functional markers in the era of next-generation sequencing

The availability of complete genome sequences, along with other genomic resources for Arabidopsis, rice, pigeon pea, soybean and other crops, has revolutionized our understanding of the genetic make-up of plants. Next-generation DNA sequencing (NGS) has facilitated single nucleotide polymorphism discovery in plants. Functionally-characterized sequences can be identified and functional markers (FMs) for important traits can be developed at an ever-increasing ease. FMs are derived from sequence polymorphisms found in allelic variants of a functional gene. Linkage disequilibrium-based association mapping and homologous recombinants have been developed for identification of "perfect" markers for their use in crop improvement practices. Compared with many other molecular markers, FMs derived from the functionally characterized sequence genes using NGS techniques and their use provide opportunities to develop high-yielding plant genotypes resistant to various stresses at a fast pace.

The Finnish Disease Heritage Database (FinDis) Update-A Database for the Genes Mutated in the Finnish Disease Heritage Brought to the Next-Generation Sequencing Era

The Finnish Disease Heritage Database (FinDis) (http://findis.org) was originally published in 2004 as a centralized information resource for rare monogenic diseases enriched in the Finnish population. The FinDis database originally contained 405 causative variants for 30 diseases. At the time, the FinDis database was a comprehensive collection of data, but since 1994, a large amount of new information has emerged, making the necessity to update the database evident. We collected information and updated the database to contain genes and causative variants for 35 diseases, including six more genes and more than 1,400 additional disease-causing variants. Information for causative variants for each gene is collected under the LOVD 3.0 platform, enabling easy updating. The FinDis portal provides a centralized resource and user interface to link information on each disease and gene with variant data in the LOVD 3.0 platform. The software written to achieve this has been open-sourced and made available on GitHub (http://github.com/findis-db), allowing biomedical institutions in other countries to present their national data in a similar way, and to both contribute to, and benefit from, standardized variation data. The updated FinDis portal provides a unique resource to assist patient diagnosis, research, and the development of new cures.

Rare-disease genetics in the era of next-generation sequencing: discovery to translation

Work over the past 25 years has resulted in the identification of genes responsible for ~50% of the estimated 7,000 rare monogenic diseases, and it is predicted that most of the remaining disease-causing genes will be identified by the year 2020, and probably sooner. This marked acceleration is the result of dramatic improvements in DNA-sequencing technologies and the associated analyses. We examine the rapid maturation of rare-disease genetic analysis and successful strategies for gene identification. We highlight the impact of discovering rare-disease-causing genes, from clinical diagnostics to insights gained into biological mechanisms and common diseases. Last, we explore the increasing therapeutic opportunities and challenges that the resulting expansion of the 'atlas' of human genetic pathology will bring.

Biological Intuition in Alignment-Free Methods: Response to Posada

A recent editorial in Journal of Molecular Evolution highlights opportunities and challenges facing molecular evolution in the era of next-generation sequencing. Abundant sequence data should allow more-complex models to be fit at higher confidence, making phylogenetic inference more reliable and improving our understanding of evolution at the molecular level. However, concern that approaches based on multiple sequence alignment may be computationally infeasible for large datasets is driving the development of so-called alignment-free methods for sequence comparison and phylogenetic inference. The recent editorial characterized these approaches as model-free, not based on the concept of homology, and lacking in biological intuition. We argue here that alignment-free methods have not abandoned models or homology, and can be biologically intuitive.

A Robust Method to Analyze Copy Number Alterations of Less than 100 kb in Single Cells Using Oligonucleotide Array CGH

Comprehensive genome wide analyses of single cells became increasingly important in cancer research, but remain to be a technically challenging task. Here, we provide a protocol for array comparative genomic hybridization (aCGH) of single cells. The protocol is based on an established adapter-linker PCR (WGAM) and allowed us to detect copy number alterations as small as 56 kb in single cells. In addition we report on factors influencing the success of single cell aCGH downstream of the amplification method, including the characteristics of the reference DNA, the labeling technique, the amount of input DNA, reamplification, the aCGH resolution, and data analysis. In comparison with two other commercially available non-linear single cell amplification methods, WGAM showed a very good performance in aCGH experiments. Finally, we demonstrate that cancer cells that were processed and identified by the CellSearch® System and that were subsequently isolated from the CellSearch® cartridge as single cells by fluorescence activated cell sorting (FACS) could be successfully analyzed using our WGAM-aCGH protocol. We believe that even in the era of next-generation sequencing, our single cell aCGH protocol will be a useful and (cost-) effective approach to study copy number alterations in single cells at resolution comparable to those reported currently for single cell digital karyotyping based on next generation sequencing data.

Molecular epidemiology of viral diseases in the era of next generation sequencing

In virology, molecular epidemiology is defined as the applica-tion of molecular methods to study the causation and dynamicsof viral diseases in a population. In the study of viral diseases,this is determined by the transmission of the corresponding eti-ological agent on its host, and how this phenomenon affects thespread of the disease within the population. Molecular epidemi-ology uses information about genetic variation to investigate theevolution, transmissionandpathogenesisofviralagents.Molecularepidemiological toolshaveprovidedimportantinsightsinthestudyof viral diseases, improving significantly our understanding aboutthe history of infection, virus evolution, spread and disease trans-mission that have helped to implement public health policies.

Pitfalls encountered while investigating genetic elements by PCR

The unprecedented wealth of databases that have become available in the era of next-generation sequencing has considerably increased our knowledge of bacterial genetic elements (GEs). At the same time, the advent of single-cell based approaches has brought realization that unsuspected heterogeneity may occur in the bacterial population from a single colony. The increasing use of PCR-based techniques to study new GEs requires careful consideration of the possible different PCR targets associated with different subpopulations if incorrect or incomplete interpretations are to be avoided. In this commentary, confining ourselves to our direct experience, we illustrate some examples of PCR pitfalls that may be encountered while investigating GEs.

Microarray resources for genetic and genomic studies in chicken: a review.

Advent of microarray technologies revolutionized the nature and scope of genetic and genomic research in human and other species by allowing massively parallel analysis of thousands of genomic sites. They have been used for diverse purposes such as for transcriptome analysis, CNV detection, SNP and CNV genotyping, studying DNA-protein interaction, and detection of genome methylation. Microarrays have also made invaluable contributions to research in chicken which is an important model organism for studying embryology, immunology, oncology, virology, evolution, genetics, and genomics and also for other avian species. Despite their huge contributions in life science research, the future of microarrays is now being questioned with the advent of massively parallel next generation sequencing (NGS) technologies, which promise to overcome some of the limitations of microarray platforms. In this article we review the various microarray resources developed for chicken and their past and potential future applications. We also discuss about the future of microarrays in the NGS era particularly in the context of livestock genetics. We argue that even though NGS promises some major advantages-in particular, offers the opportunity to discover novel elements in the genome-microarrays will continue to be major tools for research and practice in the field of livestock genetics/genomics due to their affordability, high throughput nature, mature established technologies and ease of application. Moreover, with advent of new microarray technologies like capture arrays, the NGS and microarrays are expected to complement each other in future research in life science.

Consideration of plausible genetic architectures for schizophrenia and implications for analytic approaches in the era of next generation sequencing

The results of linkage and association studies imply that there are no common, dominantly active variants which have a substantial effect on the risk of schizophrenia. However, there are rare structural variants with a major effect and it is argued that results to date are not incompatible with the existence of large numbers of individually rare sequence variants with major effect, since these would not have been detected by the methods used to date. It is also argued that the epidemiology is consistent with a contribution from recessively acting variants and that likewise these might have gone undetected. It is shown that methods of analysis specifically designed to detect recessive variants through testing departure from Hardy-Weinberg equilibrium offer substantial increases of power over conventional methods. It is recommended that analytic approaches aim to detect very rare variants with major effect and that specific attempts are made to detect recessively acting loci.

Studies of Natural Selection in the Era of Next-generation Sequencing

A deeper understanding of positive selection is of fundamental importance. Evolutionary pressures acting on the genome as a whole and the specific role of environmental pressures on divergence shape the natural selection. However, despite the intense interest in genome-wide scans, a coherent study of recent human evolutionary history has yet to emerge due to the limitations of data, models, and analytical tools. Fast and cheaper Next Generation Sequencing (NGS) technologies will generate unprecedentedly massive and highly-dimensional genomic variation data. NGS will offer unprecedented opportunities for population genetic and natural selection studies, but also raise great challenges. In the conventional population genetics, most researches have primarily focused on natural selection acting on a single locus. Little attention has been paid to determining how the natural selection acts on multiple interacted genes in response to environmental perturbation. In addition, the current paradigm for analysis of natural selection on gene-expression is to use a single value of summarizing statistic to represent gene expression level and overlook all information on expression difference in exons, genomic position and alleles. To address these critical limitations, we develop a unified framework and statistical methods for genome-wide scans for natural selection and investigation of natural selection on pathways. To explore observed expression variation in exons or in genomic position across the genes, we extend one dimensional diffusion process to multidimensional diffusion process and a single variate stochastic differential equation to multivariate stochastic differential equations. We then use the extended multidimensional diffusion processes to model the evolution of gene-expression acted by natural selection. We hope that the present new development of natural selection analysis for NGS data will open a new avenue for natural selection analysis with NGS data.

Translational bioinformatics for diagnostic and prognostic prediction of prostate cancer in the next-generation sequencing era.

The discovery of prostate cancer biomarkers has been boosted by the advent of next-generation sequencing (NGS) technologies. Nevertheless, many challenges still exist in exploiting the flood of sequence data and translating them into routine diagnostics and prognosis of prostate cancer. Here we review the recent developments in prostate cancer biomarkers by high throughput sequencing technologies. We highlight some fundamental issues of translational bioinformatics and the potential use of cloud computing in NGS data processing for the improvement of prostate cancer treatment.

Analytical Tools and Databases for Metagenomics in the Next-Generation Sequencing Era

Metagenomics has become one of the indispensable tools in microbial ecology for the last few decades, and a new revolution in metagenomic studies is now about to begin, with the help of recent advances of sequencing techniques. The massive data production and substantial cost reduction in next-generation sequencing have led to the rapid growth of metagenomic research both quantitatively and qualitatively. It is evident that metagenomics will be a standard tool for studying the diversity and function of microbes in the near future, as fingerprinting methods did previously. As the speed of data accumulation is accelerating, bioinformatic tools and associated databases for handling those datasets have become more urgent and necessary. To facilitate the bioinformatics analysis of metagenomic data, we review some recent tools and databases that are used widely in this field and give insights into the current challenges and future of metagenomics from a bioinformatics perspective.

Translational Biomedical Informatics and Computational Systems Medicine

Translational Biomedical Informatics and Computational Systems Medicine

EPD and EPDnew, high-quality promoter resources in the next-generation sequencing era

The Eukaryotic Promoter Database (EPD), available online at http://epd.vital-it.ch, is a collection of experimentally defined eukaryotic POL II promoters which has been maintained for more than 25 years. A promoter is represented by a single position in the genome, typically the major transcription start site (TSS). EPD primarily serves biologists interested in analysing the motif content, chromatin structure or DNA methylation status of co-regulated promoter subsets. Initially, promoter evidence came from TSS mapping experiments targeted at single genes and published in journal articles. Today, the TSS positions provided by EPD are inferred from next-generation sequencing data distributed in electronic form. Traditionally, EPD has been a high-quality database with low coverage. The focus of recent efforts has been to reach complete gene coverage for important model organisms. To this end, we introduced a new section called EPDnew, which is automatically assembled from multiple, carefully selected input datasets. As another novelty, we started to use chromatin signatures in addition to mRNA 5′tags to locate promoters of weekly expressed genes. Regarding user interfaces, we introduced a new promoter viewer which enables users to explore promoter-defining experimental evidence in a UCSC genome browser window.