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With the ubiquitous presence of next-generation sequencing in modern biological, genetic, pharmaceutical and medical research, not everyone pays attention to the underlying computational methods. Even fewer researchers know what were the origins of the current models for DNA assembly. We present original graph models used in DNA sequencing by hybridization, discuss their properties and connections between them. We also explain how these graph models evolved to adapt to the characteristics of next-generation sequencing. Moreover, we present a practical comparison of state-of-the-art DNA de novo assembly tools representing these transformed models, i.e. overlap and decomposition-based graphs. Even though the competition is tough, some assemblers perform better and certainly large differences may be observed in hardware resources utilization. Finally, we outline the most important trends in the sequencing field, and try to predict their impact on the computational models in the future.

Computational genomics is an emerging field that is enabling us to reveal the origins of life and the genetic basis of diseases such as cancer. Next Generation Sequencing (NGS) technologies have unleashed a wealth of genomic information by producing immense amounts of raw data. Before any functional analysis can be applied to this data, read alignment is applied to find the genomic coordinates of the produced sequences. Alignment algorithms have evolved rapidly with the advancement in sequencing technology, striving to achieve biological accuracy at the expense of increasing space and time complexities. Hardware approaches have been proposed to accelerate the computational bottlenecks created by the alignment process. Although several hardware approaches have achieved remarkable speedups, most have overlooked important biological features, which have hampered their widespread adoption by the genomics community. In this paper, we provide a brief biological introduction to genomics and NGS. We discuss the most popular next generation read alignment tools and algorithms. Furthermore, we provide a comprehensive survey of the hardware implementations used to accelerate these algorithms.

Rapid advances in sequencing technologies of second- and even third-generation made the whole genome sequencing a routine procedure. However, the methods for assembling of the obtained sequences and its results require special consideration. Modern assemblers are based on heuristic algorithms, which lead to fragmented genome assembly composed of scaffolds and contigs of different lengths, the order of which along the chromosome and belonging to a particular chromosome often remain unknown. In this regard, the resulting genome sequence can only be considered as a draft assembly. The principal improvement in the quality and reliability of a draft assembly can be achieved by targeted sequencing of the genome elements of different size, e.g., chromosomes, chromosomal regions, and DNA fragments cloned in different vectors, as well as using reference genome, optical mapping, and Hi-C technology. This approach, in addition to simplifying the assembly of the genome draft, will more accurately identify numerical and structural chromosomal variations and abnormalities of the genomes of the studied species. In this review, we discuss the key technologies for the genome sequencing and the de novo assembly, as well as different approaches to improve the quality of existing drafts of genome sequences.

The initial sequencing of the human genome took nearly a decade to complete. Today, the same can be accomplished in hours using next-generation sequencing (NGS). Before the invention of NGS, genetic testing was accomplished by traditional Sanger sequencing. In cardiovascular medicine, this approach typically involved the sequencing of a region of interest in a single affected patient’s DNA and then comparison of that sequence to the human genome reference sequence. Variants found were then compared to a panel of sequences from unaffected people, often anonymous blood donors. This strategy has been the mainstay of genetic testing to assess small numbers of disease-related genes across cardiovascular and noncardiovascular diseases. However, this approach is limited in its scalability. NGS builds on these basic principles, extending them to allow billions of DNA molecules to be sequenced simultaneously. This multiplex technology yields billions of small polynucleotide reads, called “short reads.” Mapping and assembly of these short reads to a reference genome then allows discovery of genetic variants at scale (Figure 1), increasing the speed and reducing the cost of sequencing by orders of magnitude.1 Thus, NGS can easily be applied to panels of genes, every gene, or the whole genome in clinical settings. This scientific leap in sequencing technology has occurred over just 15 years and has brought the genetic code to the forefront of diagnosis and therapy, making it increasingly important for clinicians to be aware of its utility and limitations in practice. Figure 1. Comparison of traditional sequencing and next-generation sequencing (NGS). In traditional sequencing, DNA is replicated in the presence of fluorescently labeled bases, yielding differently sized strands with different terminal bases. These terminal bases are identified to assemble the entire sequence and compare it …

Vast improvements in sequencing technology have made it practical to simultaneously sequence millions of nucleotides distributed across the genome, opening the door for genomic studies in virtually any species. Ornithological research stands to benefit in three substantial ways. First, genomic methods enhance our ability to parse and simultaneously analyze both neutral and non-neutral genomic regions, thus providing insight into adaptive evolution and divergence. Second, the sheer quantity of sequence data generated by current sequencing platforms allows increased precision and resolution in analyses. Third, high-throughput sequencing can benefit applications that focus on a small number of loci that are otherwise prohibitively expensive, time-consuming, and technically difficult using traditional sequencing methods. These advances have improved our ability to understand evolutionary processes like speciation and local adaptation, but they also offer many practical applications in the fields of p...

Reference is regularly made to the power of new genomic sequencing approaches. Using powerful technology, however, is not the same as having the necessary power to address a research question with statistical robustness. In the rush to adopt new and improved genomic research methods, limitations of technology and experimental design may be initially neglected. Here, we review these issues with regard to RNA sequencing (RNA-seq). RNA-seq adds large-scale transcriptomics to the toolkit of ecological and evolutionary biologists, enabling differential gene expression (DE) studies in nonmodel species without the need for prior genomic resources. High biological variance is typical of field-based gene expression studies and means that larger sample sizes are often needed to achieve the same degree of statistical power as clinical studies based on data from cell lines or inbred animal models. Sequencing costs have plummeted, yet RNA-seq studies still underutilize biological replication. Finite research budgets force a trade-off between sequencing effort and replication in RNA-seq experimental design. However, clear guidelines for negotiating this trade-off, while taking into account study-specific factors affecting power, are currently lacking. Study designs that prioritize sequencing depth over replication fail to capitalize on the power of RNA-seq technology for DE inference. Significant recent research effort has gone into developing statistical frameworks and software tools for power analysis and sample size calculation in the context of RNA-seq DE analysis. We synthesize progress in this area and derive an accessible rule-of-thumb guide for designing powerful RNA-seq experiments relevant in eco-evolutionary and clinical settings alike.

Simple sequence repeats (SSRs) are becoming a choice of markers in fish genetic research due to their abundance in the genome, co-dominant nature, high polymorphism and ability to reproduce. Thus, in this review, we have discussed regarding SSRs markers developed in fishes using different techniques. These markers have been used for revealing genetic variability, strain and species identification, genetic linkage map construction and parentage assignment in fish genetic research. Recently, high-throughput sequencing platform has been widely used in non-model fishes for genome/transcriptome sequencing to understand genomic information. The rapid progress in fish genomic research has been made due to sequencing platform along with their low cost for sequencing and use of the advanced computational tools for generated data analysis. We have shown that different next-generation sequencing platforms have been applied in the genomic studies for SSRs markers identification in fishes with evidence. We have depicted the use of various computational tools/algorithms for the SSRs identification from genome/transcriptome data. However, we also highlighted existing challenges in high-throughput sequencing data analysis as well as the current need of computationally deep analysis software/tools/expertise. The purpose of this review is to get envisage on the various possibilities, which can be harnessed via these new technologies and advanced computational tools for SSRs marker development via genome/transcriptome sequencing of aquaculture species.

Recent advances in single-cell sequencing hold great potential for exploring biological systems with unprecedented resolution. Sequencing the genome of individual cells can reveal somatic mutations and allows the investigation of clonal dynamics. Single-cell transcriptome sequencing can elucidate the cell type composition of a sample. However, single-cell sequencing comes with major technical challenges and yields complex data output. In this Primer, we provide an overview of available methods and discuss experimental design and single-cell data analysis. We hope that these guidelines will enable a growing number of researchers to leverage the power of single-cell sequencing.

The availability of reference genome sequences, especially the human reference, has revolutionized the study of biology. However, whilst the genomes of some species have been fully sequenced, a wide range of biological problems still cannot be effectively studied for lack of genome sequence information. Here, I identify neglected areas of biology and describe how both targeted species sequencing and more broad taxonomic surveys of the tree of life can address important biological questions. I enumerate the significant benefits that would accrue from sequencing a broader range of taxa, as well as discuss the technical advances in sequencing and assembly methods that would allow for wide-ranging application of whole-genome analysis. Finally, I suggest that in addition to “Big Science” survey initiatives to sequence the tree of life, a modified infrastructure-funding paradigm would better support reference genome sequence generation for research communities most in need.

Characterizing the errors generated by common high-throughput sequencing platforms and telling true genetic variation from technical artefacts are two interdependent steps, essential to many analyses such as single nucleotide variant calling, haplotype inference, sequence assembly and evolutionary studies. Both random and systematic errors can show a specific occurrence profile for each of the six prominent sequencing platforms surveyed here: 454 pyrosequencing, Complete Genomics DNA nanoball sequencing, Illumina sequencing by synthesis, Ion Torrent semiconductor sequencing, Pacific Biosciences single-molecule real-time sequencing and Oxford Nanopore sequencing. There is a large variety of programs available for error removal in sequencing read data, which differ in the error models and statistical techniques they use, the features of the data they analyse, the parameters they determine from them and the data structures and algorithms they use. We highlight the assumptions they make and for which data types these hold, providing guidance which tools to consider for benchmarking with regard to the data properties. While no benchmarking results are included here, such specific benchmarks would greatly inform tool choices and future software development. The development of stand-alone error correctors, as well as single nucleotide variant and haplotype callers, could also benefit from using more of the knowledge about error profiles and from (re)combining ideas from the existing approaches presented here.

The analysis of next-generation sequencing data is computationally and statistically challenging because of the massive volume of data and imperfect data quality. We present GotCloud, a pipeline for efficiently detecting and genotyping high-quality variants from large-scale sequencing data. GotCloud automates sequence alignment, sample-level quality control, variant calling, filtering of likely artifacts using machine-learning techniques, and genotype refinement using haplotype information. The pipeline can process thousands of samples in parallel and requires less computational resources than current alternatives. Experiments with whole-genome and exome-targeted sequence data generated by the 1000 Genomes Project show that the pipeline provides effective filtering against false positive variants and high power to detect true variants. Our pipeline has already contributed to variant detection and genotyping in several large-scale sequencing projects, including the 1000 Genomes Project and the NHLBI Exome Sequencing Project. We hope it will now prove useful to many medical sequencing studies.

High-throughput sequencing (HTS) has radically altered approaches to human evolutionary research. Recent contributions highlight that HTS is able to reach depths of the human lineage previously thought to be impossible. In this paper, we outline the methodological advances afforded by recent developments in DNA recovery, data output, scalability, speed, and resolution of the current sequencing technology. We review and critically evaluate the 'DNA pipeline' for ancient samples: from DNA extraction, to constructing immortalized sequence libraries, to enrichment strategies (e.g., polymerase chain reaction [PCR] and hybridization capture), and finally, to bioinformatic analyses of sequence data. We argue that continued evaluations and improvements to this process are essential to ensure sequence data validity. Also, we highlight the role of contamination and authentication in ancient DNA-HTS, which is particularly relevant to ancient human genomics, since sequencing the genomes of hominins such as Homo erectus and Homo heidelbergensis may soon be within the realm of possibility.

Over the last 10 years, next-generation sequencing (NGS) has transformed genomic research through substantial advances in technology and reduction in the cost of sequencing, and also in the systems required for analysis of these large volumes of data. This technology is now being used as a standard molecular diagnostic test in some clinical settings. The advances in sequencing have come so rapidly that the major bottleneck in identification of causal variants is no longer the sequencing or analysis (given access to appropriate tools), but rather clinical interpretation. Interpretation of genetic findings in a complex and ever changing clinical setting is scarcely a new challenge, but the task is increasingly complex in clinical genome-wide sequencing given the dramatic increase in dataset size and complexity. This increase requires application of appropriate interpretation tools, as well as development and application of appropriate methodologies and standard procedures. This unit provides an overview of these items. Specific challenges related to implementation of genome-wide sequencing in a clinical setting are discussed. © 2017 by John Wiley & Sons, Inc.

Motivation Structural variation, including large deletions, duplications, inversions, translocations and other rearrangements, is common in human and cancer genomes. A number of methods have been developed to identify structural variants from Illumina short-read sequencing data. However, reliable identification of structural variants remains challenging because many variants have breakpoints in repetitive regions of the genome and thus are difficult to identify with short reads. The recently developed linked-read sequencing technology from 10X Genomics combines a novel barcoding strategy with Illumina sequencing. This technology labels all reads that originate from a small number (∼5 to 10) DNA molecules ∼50 Kbp in length with the same molecular barcode. These barcoded reads contain long-range sequence information that is advantageous for identification of structural variants. Results We present Novel Adjacency Identification with Barcoded Reads (NAIBR), an algorithm to identify structural variants in linked-read sequencing data. NAIBR predicts novel adjacencies in an individual genome resulting from structural variants using a probabilistic model that combines multiple signals in barcoded reads. We show that NAIBR outperforms several existing methods for structural variant identification—including two recent methods that also analyze linked-reads—on simulated sequencing data and 10X whole-genome sequencing data from the NA12878 human genome and the HCC1954 breast cancer cell line. Several of the novel somatic structural variants identified in HCC1954 overlap known cancer genes. Availability and implementation Software is available at compbio.cs.brown.edu/software. Supplementary information Supplementary data are available at Bioinformatics online.

BackgroundChromosome conformation capture (3C) and HiC DNA sequencing methods have rapidly advanced our understanding of the spatial organization of genomes and metagenomes. Many variants of these protocols have been developed, each with their own strengths. Currently there is no systematic means for simulating sequence data from this family of sequencing protocols.FindingsWe describe a computational simulator that, given reference genome sequences and some basic parameters, will simulate HiC sequencing on those sequences. The simulator models the basic spatial structure in genomes that is commonly observed in HiC and 3C datasets, including the distance-decay relationship in proximity ligation, differences in the frequency of interaction within and across chromosomes, and the structure imposed by cells. A means to model the 3D structure of topologically associating domains (TADs) is provided. The simulator also models several sources of error common to 3C and HiC library preparation and sequencing methods, including spurious proximity ligation events and sequencing error.ConclusionsWe have introduced the first comprehensive simulator for 3C and HiC sequencing protocols. We expect the simulator to have use in testing of HiC data analysis algorithms, as well as more general value for experimental design, where questions such as the required depth of sequencing, enzyme choice, and other decisions must be made in advance in order to ensure adequate statistical power to test the relevant hypotheses.

Computational genomics refers to the use of numerical and statistical analysis to extract data from genetic materials in organisms. In recent years, studies in bioinformatics have focused on understanding genomes and their functions using genome sequences. The huge demand for low-cost sequencing forces the development of high-efficiency sequencing technologies. One of these is the Next-Generation Sequencing (NGS), which can produce millions and billons of sequences. This big data produced by NGS must be processed efficiently in terms of both time and cost. In this study, a high performance method and its application are presented for correcting errors in NGS-read metagenome sequences. Since the number of sequences are in billions, it takes quite a long time to achieve computations with conventional methods.

BackgroundThe persistent decrease in cost and difficulty of whole genome sequencing of microbial organisms has led to a dramatic increase in the number of species and strains characterized from a wide variety of environments. Microbial genome sequencing can now be conducted by small laboratories and as part of undergraduate curriculum. While sequencing is routine in microbiology, assembly, annotation and downstream analyses still require computational resources and expertise, often necessitating familiarity with programming languages. To address this problem, we have created a light-weight, user-friendly tool for the assembly and annotation of microbial sequencing projects.ResultsThe Prokaryotic Assembly and Annotation Tool, Peasant, automates the processes of read quality control, genome assembly, and annotation for microbial sequencing projects. High-quality assemblies and annotations can be generated by Peasant without the need of programming expertise or high-performance computing resources. Furthermore, statistics are calculated so that users can evaluate their sequencing project. To illustrate the computational speed and accuracy of Peasant, the SRA records of 322 Illumina platform whole genome sequencing assays for Bacillus species were retrieved from NCBI, assembled and annotated on a single desktop computer. From the assemblies and annotations produced, a comprehensive analysis of the diversity of over 200 high-quality samples was conducted, looking at both the 16S rRNA phylogenetic marker as well as the Bacillus core genome.ConclusionsPeasant provides an intuitive solution for high-quality whole genome sequence assembly and annotation for users with limited programing experience and/or computational resources. The analysis of the Bacillus whole genome sequencing projects exemplifies the utility of this tool. Furthermore, the study conducted here provides insight into the diversity of the species, the largest such comparison conducted to date.

Recent advances in high-throughput genome sequencing technologies have enabled the systematic study of various genomes by making whole genome sequencing affordable. Modern sequencers generate a huge number of small sequence fragments called reads, where the read length and the per-base sequencing cost depend on the technology used. To date, many hybrid genome assembly algorithms have been developed that can take reads from multiple read sources to reconstruct the original genome. However, rigorous investigation of the feasibility conditions for complete genome reconstruction and the optimal sequencing strategy for minimizing the sequencing cost has been conspicuously missing. An important aspect of hybrid sequencing and assembly is that the feasibility conditions for genome reconstruction can be satisfied by different combinations of the available read sources, opening up the possibility of optimally combining the sources to minimize the sequencing cost while ensuring accurate genome reconstruction. In this paper, we derive the conditions for whole genome reconstruction from multiple read sources at a given confidence level and also introduce the optimal strategy for combining reads from different sources to minimize the overall sequencing cost. We show that the optimal read set, which simultaneously satisfies the feasibility conditions for genome reconstruction and minimizes the sequencing cost, can be effectively predicted through constrained discrete optimization. Through extensive evaluations based on several genomes and different read sets, we verify the derived feasibility conditions and demonstrate the performance of the proposed optimal hybrid sequencing and assembly strategy.

Recently, the requirement for fast, low-cost, and highly reliable methods has triggered the development of next-generation DNA sequencing technologies. In addition to overcoming the restrictions in Sanger sequencing methods, large-scale genetic data have provided the development of different analysis methods. In this paper, widely used next-generation sequencing platforms, the analyses of the data obtained, and the applications of these technologies are discussed.

Examining patterns of molecular genetic variation in both modern-day and ancient humans has proved to be a powerful approach to learn about our origins. Rapid advances in DNA sequencing technology have allowed us to characterize increasing amounts of genomic information. Although this clearly provides unprecedented power for inference, it also introduces more complexity into the way we use and interpret such data. Here, we review ongoing debates that have been influenced by improvements in our ability to sequence DNA and discuss some of the analytical challenges that need to be overcome in order to fully exploit the rich historical information that is contained in the entirety of the human genome.

A major limitation of high-throughput DNA sequencing is the high rate of erroneous base calls produced. For instance, Illumina sequencing machines produce errors at a rate of ~0.1-1 × 10(-2) per base sequenced. These technologies typically produce billions of base calls per experiment, translating to millions of errors. We have developed a unique library preparation strategy, "circle sequencing," which allows for robust downstream computational correction of these errors. In this strategy, DNA templates are circularized, copied multiple times in tandem with a rolling circle polymerase, and then sequenced on any high-throughput sequencing machine. Each read produced is computationally processed to obtain a consensus sequence of all linked copies of the original molecule. Physically linking the copies ensures that each copy is independently derived from the original molecule and allows for efficient formation of consensus sequences. The circle-sequencing protocol precedes standard library preparations and is therefore suitable for a broad range of sequencing applications. We tested our method using the Illumina MiSeq platform and obtained errors in our processed sequencing reads at a rate as low as 7.6 × 10(-6) per base sequenced, dramatically improving the error rate of Illumina sequencing and putting error on par with low-throughput, but highly accurate, Sanger sequencing. Circle sequencing also had substantially higher efficiency and lower cost than existing barcode-based schemes for correcting sequencing errors.

In language of genetics and biochemistry, sequencing is the determination of an unbranched biopolymer's primary structure. A sequence is a symbolic linear depiction, result of sequencing. This sequence is a succinct summary of the most of the sequenced molecule's atomic-level structure. (Most known is DNA-sequencing, RNA-sequencing, Protein-sequencing and Next-Generation-sequencing)

The technique that allows for researchers to read and convert the genetic information found in the DNA of any organisms is called Genome Sequencing. Genome Sequencing involves determining the order of the nucleotide subunits found in DNA, which consists of a small number of bases called short reads. The human genome is approximately 3 billion bases in length, which would take months or years to be processed on a single machine. So large numbers of short reads are available in such sequencing. In these cases, the first step in the data analysis pipeline is the short read mapping problem. Speed is becoming significantly important and challenging due to the huge volume of data. In this paper, we have proposed an approach that will take a dataset of DNA sequencing as an input and split them across the cluster machine by applying MapReduce implementation of Hadoop to make the search efficient for large scale genome sequencing applications.

The scale of genomic sequencing data and the complexity of bioinformatic algorithms make it difficult for students to develop a concrete understanding of assembling complete genomes from millions of short DNA sequences. We present a hands-on activity where students explore the genome assembly process using short DNA sequences printed on paper. Topics highlighted during the lesson include overlap identification, reference sequences, and the challenges arising from sequencing errors, low-frequency mutations, and repetitive regions. Sample materials provide reads and solutions for assembling clinically relevant regions of the S. gordonii penicillin binding protein and the human HTT gene. An online tool allows instructors to generate custom read sets from other DNA sequences.

Next-generation (or massively parallel) sequencing has rapidly supplanted traditional Sanger sequencing as the preferred methodology for acquiring clinical sequence data. Massively parallel sequencing offers numerous advantages including high sensitivity and specificity for genetic variants, the ability to consolidate numerous molecular tests to a single platform, and unprecedented sequencing throughput. However, reaping the full benefit of this new technology is a complicated, collaborative undertaking, and relies upon the interdependency of three disciplines: chemistry, computer science, and clinical medicine. Specialized sequencing instruments can simultaneously sequence a host of genetic targets in numerous patient samples, generating millions of sequence reads per reaction. Thus, informatics tools are necessary for searching these data and structuring information in a format that can be understood by a molecular pathologist. Finally, clinical understanding of the impact of genetic variants on disease diagnosis, treatment, and prognosis is used to generate a relevant clinical report.

With the introduction of massively parallel sequencing (including whole exome sequencing and whole genome sequencing), the field of molecular diagnostics is moving incredibly rapidly toward platforms that will interrogate many or all genes simultaneously. These approaches have many benefits and limitations that are being identified and overcome simultaneously with their clinical deployment. Variant interpretation is and will likely continue to be a challenge for the foreseeable future, particularly as we begin to consider the clinical relevance of noncoding variants as part of the scope of clinical diagnostic evaluations. This chapter takes a broad look at the scope of whole genome sequencing and how it may be incorporated into molecular diagnostic testing.

In this paper, an algorithmic approach is developed to solve the combinatorial part of DNA sequencing by hybridization using graph theoretical concepts. It is assumed here, that the spectrum is ideal one and fragments are of equal length. The goal is to reconstruct the DNA molecule based on the fragments overlap. Suitable examples are also provided to justify the algorithm.

Background Variations in DNA copy number have an important contribution to the development of several diseases, including autism, schizophrenia and cancer. Single-cell sequencing technology allows the dissection of genomic heterogeneity at the single-cell level, thereby providing important evolutionary information about cancer cells. In contrast to traditional bulk sequencing, single-cell sequencing requires the amplification of the whole genome of a single cell to accumulate enough samples for sequencing. However, the amplification process inevitably introduces amplification bias, resulting in an over-dispersing portion of the sequencing data. Recent study has manifested that the over-dispersed portion of the single-cell sequencing data could be well modelled by negative binomial distributions. Results We developed a read-depth based method, nbCNV to detect the copy number variants (CNVs). The nbCNV method uses two constraints-sparsity and smoothness to fit the CNV patterns under the assumption that the read signals are negatively binomially distributed. The problem of CNV detection was formulated as a quadratic optimization problem, and was solved by an efficient numerical solution based on the classical alternating direction minimization method. Conclusions Extensive experiments to compare nbCNV with existing benchmark models were conducted on both simulated data and empirical single-cell sequencing data. The results of those experiments demonstrate that nbCNV achieves superior performance and high robustness for the detection of CNVs in single-cell sequencing data. Electronic supplementary material The online version of this article (doi:10.1186/s12859-016-1239-7) contains supplementary material, which is available to authorized users.

The human genome holds an extraordinary trove of information about human development, physiology, medicine and evolution. Here we report the results of an international collaboration to produce and make freely available a draft sequence of the human genome. We also present an initial analysis of the data, describing some of the insights that can be gleaned from the sequence.

Computer simulation of genomic data has become increasingly popular for assessing and validating biological models or for gaining an understanding of specific data sets. Several computational tools for the simulation of next-generation sequencing (NGS) data have been developed in recent years, which could be used to compare existing and new NGS analytical pipelines. Here we review 23 of these tools, highlighting their distinct functionality, requirements and potential applications. We also provide a decision tree for the informed selection of an appropriate NGS simulation tool for the specific question at hand.

Third-generation long-range DNA sequencing and mapping technologies are creating a renaissance in high-quality genome sequencing. Unlike second-generation sequencing, which produces short reads a few hundred base-pairs long, third-generation single-molecule technologies generate over 10,000 bp reads or map over 100,000 bp molecules. We analyze how increased read lengths can be used to address longstanding problems in de novo genome assembly, structural variation analysis and haplotype phasing.

Genomes are becoming recognized as dynamic molecules which modify their structures, mediated through epigenetic mechanisms, in response to intrinsic and extrinsic stimulants, and directed by cell development in single-cell organisms and mammalian cells, examples including V(D)J recombination during B-cell and T-cell lymphocyte maturation, and subsequent class switch recombination and somatic hypermutation in the constant segment of the IgH locus to enhance antibody affinity (Shiao, 2015). Ontogeny-driven rearrangement, methylation, and transcription in mouse tandemly repeated 45S rDNA suggests that dynamic genome restructuring plays a key role in embryonic development and tissue differentiation in mammals (Shiao et al., 2011). Any mistakes accompanying this genome restructuring process can introduce mutations, examples including oncogenic fusions to immunoglobulin, T-cell receptor, and 28S rDNA genes (Kobayashi et al., 2014), and the Robertsonian translocation known to increase risk of miscarriage, infertility, and congenital abnormalities (Alfarawati et al., 2012). Whole genome sequencing becomes a critical tool to determine the extent of restructuring and to assist in deciphering the mechanisms of genome rearrangements. In this Research Topic, a collection of eight articles touches upon various aspects of whole genome sequencing, encompassing challenges to obtain 100% genome sequence coverage or reliable reference genomes, mapping and assembly of short-read sequencing data, detection of copy number variation across large genome segments, progress in long-read single molecule sequencing technologies, and clinical applications of exome and genome sequencing. One major challenge is associated with sequence reads that are insufficiently long to span long repetitive sequences, which are interspersed in the genome or can be concentrated in telomere, centromere, and acrocentric regions. These articles provide some directions that should bring whole genome sequencing closer to the goal of 100% completion. Mascher and Stein recommend a workflow for creating a genetically anchored whole-genome shot gun (WGS) assembly: (i) construction of a WGS assembly from next-generation sequencing (NGS) data in individual genomes, (ii) mapping of the sequence reads of a segregating population to the assembly to allow detection of single-nucleotide polymorphisms (SNPs), (iii) building a genetic linkage map, and (iv) integration of the contigs harboring the WGS SNPs to the linkage map. The workflow is particularly useful for non-model but economically important species, for which high-quality reference genomes are still under construction. Li and Freudenberg employ a computational model to demonstrate that the read length of genome sequencing needs to be increased exponentially to convert unmappable regions/reads to mappable, especially in highly repetitive genome regions stretching across 10,000 or more bases, such as telomeres, centromeres, short-arms of acrocentric chromosomes, and large heterochromatic regions. Jiang et al. present an approach combining conventional bacterial artificial chromosome (BAC) end sequences with physical map contig-specific sequences (PMCSS) to increase the total length of genome contigs for scaffolding. PMCSSs are obtained by restriction digestion with two enzymes, followed by tagging of digested fragments with barcoded adaptors, amplification, sequencing, and decoding of each fragment for assembly. The approach is valuable for assembly of repetitive sequences and/or complex genomes which go through multiple rounds of genome duplication. Peters et al. propose a strategy for achieving an error-free “perfect genome” from inputs of 10 to 100 cells, by distribution of fragmented long DNAs into individual nano-compartments, followed by on-site fragmentation and tagging with compartment-specific barcoded sequences, pooling of tagged DNAs, pair-end massively parallel sequencing of about 300 bases, and unbiased phased de novo assembly of sequences. The strategy is capable of resolving paternal/maternal alleles and repetitive sequences, and is applicable to analyses of in vitro fertilized embryo biopsies, circulating tumor cells, and circulating fetal cells. Glusman et al. describe a method to identify copy number variants (CNVs) of 1–100 kb in individual genomes, based on a comparison to Reference Coverage Profiles (RCP) pre-computed from over 6000 high quality (>40X) genomes. Their strategy is to compress raw genome coverage data from short-read sequencing hundreds-fold, followed by scaling of the genome coverage stratified at 1 kb-resolution into 25-type %GC ranges to the total autosomal coverage and further to average coverage from a set of genomes obtained by specific technology. Next, 1-kb resolution RCF represented by median scaled coverage is generated to serve as an estimator of diploid genome, followed by normalization of individual scaled coverage in each kb-sized region to the corresponding RCF value to obtain a normalized coverage profile (NCP). Finally, segmentation of the NCP is performed using hidden Markov model (HMMSeg) for ploidy calling, followed by computation of CNVs frequencies. Wang et al. overview progress in protein and solid-state nanopore sequencing technologies, and offer many tips for improving such technologies. The areas to be improved include detection of ionic current blockage, reduction of signal interference from neighboring bases, discrimination of electronic tunneling signal by an electrode which can be further modified to enhance its interaction with bases, and transistor-based sequencing devices. Alvarez et al. introduce a statistical method to account for correlations between currents to reduce call error of transverse current-based DNA/RNA nanopore sequencing. The error comes from intrinsic noise in the current arising from interaction of molecular orbitals of neighboring bases along the chain of the DNA or RNA molecule. They also demonstrate that aluminum and graphene electrodes produce near 100% fidelity of base calling in 200-bp random sequences, human insulin and BRCA1 genes, and homopolymers after applying the statistical method. Shen et al. review clinical applications of massively parallel next-generation sequencing of exomes and/or genomes in cancer diagnosis and treatment. They provide examples of clinically relevant somatic mutations and structural rearrangements in several cancer types and compile a list of commercial laboratories which offer such exome- or genome-based sequencing service for clinical uses. These clinical applications will surely assist in development of personalized or precision medicine. Recently, reports of read length of over 50 kb using a nanopore sequencer in conjunction with short-read sequencing provide proof of the principle that nanopore sequencing has the potential to directly sequence entire regions containing long repetitive sequences (Goodwin et al., 2015; Madoui et al., 2015). The high error rate of the nanopore technology still represents a bottleneck for extending the read length. In sum, major progress in marching toward 100% genome sequencing has been made, but many challenges remain. These challenges include routinely obtaining long reads (preferably 100 kb or more) with high fidelity to encompass large repetitive sequences and CNVs, being able to handle the low quantities of native DNA/RNA typically employed for clinical or cell-specific diagnoses, and developing robust algorithms to assist in sequence call, genome assembly, and clinical interpretation from the torrents of sequence data that are increasingly becoming available. Novel technologies, strategies, and ingenuity are needed for further improvement of whole genome sequencing.

Analysis of the evolutionary conservation of amino acids is useful for the analysis of sequence variants detected in individuals with regard to possible impact on protein function. Rapid advances in DNA sequencing technologies are enabling affordable access to SNPs, exome sequencing, and whole genome shotgun sequencing. An understanding of the biological processes that have shaped diverging protein sequences aids the interpretation of sequence variants. The divergence model of protein evolution is presented as a general framework for interpreting information available from comparative analysis of protein sequences.

Genome sequencing has been restricted to species with a small genome size. With the advent of second- and third-generation sequencing technologies, the potential to sequence genomes of all sizes is becoming a reality. As the field of whole genome sequencing has developed, there has been a growing appreciation of the need to better represent the major lineages of the plant tree of life, rather than just those that contain economically important taxa. We argue that as well as accounting for phylogenetic diversity when selecting species to analyse, in order to gain a comprehensive understanding of genome evolution, large-scale sequencing projects also need to reflect the diversity of genome sizes in plants. In this article we briefly outline evidence from the literature to support this view.

Recent reviews are setting the stage for the use of next-generation sequencing technologies in phylogenetic applications. However, the processes for developing new markers for phylogenetic analyses remain difficult to navigate for many researchers in plant systematics. We review several experimental approaches and practical considerations for developing new phylogenetic markers with next-generation sequencing technologies. We also outline a flexible framework for data acquisition that is readily adaptable to the needs of individual researchers and carefully consider cost-related issues that may be of concern to many laboratories in evolutionary biology. The next-generation and targeted sequencing approaches presented here offer considerable savings of time and money over the traditional PCR and Sanger sequencing approaches currently used in plant systematic research, particularly in cases involving large numbers of taxa and phylogenetic markers. Even with a limited research budget, next-generation sequenc...

In just seven years, next-generation technologies have reduced the cost and increased the speed of DNA sequencing by four orders of magnitude, and experiments requiring many millions of sequencing reads are now routine. In research, sequencing is being applied not only to assemble genomes and to investigate the genetic basis of human disease, but also to explore myriad phenomena in organismic and cellular biology. In the clinic, the utility of sequence data is being intensively evaluated in diverse contexts, including reproductive medicine, oncology and infectious disease. A recurrent theme in the development of new sequencing applications is the creative 'recombination' of existing experimental building blocks. However, there remain many potentially high-impact applications of next-generation DNA sequencing that are not yet fully realized.

DNA sequencing is one of the important problems in molecular biology. One particular way to analyze this method is by using concepts from graph theory. In this paper, we will discuss the properties of DNA graphs and their application to determining the whole DNA structure and then will propose a graph theoretical approach to whole genome sequencing by building DNA graphs.

DNA sequencing has provided a wealth of information about biological systems, but thus far has focused on the four canonical bases, and 5-methylcytosine through comparison of the genomic DNA sequence to a transformed four-base sequence obtained after treatment with bisulfite. However, numerous other chemical modifications to the nucleotides are known to control fundamental life functions, influence virulence of pathogens, and are associated with many diseases. These modifications cannot be accessed with traditional sequencing methods. In this opinion, we highlight several emerging single-molecule sequencing techniques that have the potential to directly detect many types of DNA modifications as an integral part of the sequencing protocol.

Genetic mapping of mutations in model systems has facilitated the identification of genes contributing to fundamental biological processes including human diseases. However, this approach has historically required the prior characterization of informative markers. Here we report a fast and cost-effective method for genetic mapping using next-generation sequencing that combines single nucleotide polymorphism discovery, mutation localization, and potential identification of causal sequence variants. In contrast to prior approaches, we have developed a hidden Markov model to narrowly define the mutation area by inferring recombination breakpoints of chromosomes in the mutant pool. In addition, we created an interactive online software resource to facilitate automated analysis of sequencing data and demonstrate its utility in the zebrafish and mouse models. Our novel methodology and online tools will make next-generation sequencing an easily applicable resource for mutation mapping in all model systems., Harvard Stem Cell Institute (Junior Faculty Grant), National Institutes of Health (U.S.) (Grant 1R01DK090311), National Institutes of Health (U.S.) (Grant 5R01MH084676)

One of the most ambitious trends in current biomedical research is the large-scale genomic sequencing of patients. Novel high-throughput (or next-generation) sequencing technologies have redefined the way genome sequencing is performed. They are able to produce millions of short sequences (reads) in a single experiment, and with a much lower cost than previously possible. Due to this massive amount of data, efficient algorithms for mapping these sequences to a reference genome are in great demand, and recently, there has been ample work for publishing such algorithms. One important feature of these algorithms is the support of multithreaded parallel computing in order to speedup the mapping process. In this paper, we design parallel algorithms, which make use of the message-passing parallelism model, to address this problem efficiently. The proposed algorithms also take into consideration the probability scores assigned to each base for occurring in a specific position of a sequence. In particular, we present parallel algorithms for mapping short degenerate and weighted DNA sequences to a reference genome.

We offer a guide to de novo genome assembly using sequence data generated by the Illumina platform for biologists working with fungi or other organisms whose genomes are less than 100Mb in size. The guide requires no familiarity with sequencing assembly technology or associated computer programs. It defines commonly used terms in genome sequencing and assembly; provides examples of assembling short-read genome sequence data for four strains of the fungus Grosmannia clavigera using four assembly programs; gives examples of protocols and software; and presents a commented flowchart that extends from DNA preparation for submission to a sequencing center, through to processing and assembly of the raw sequence reads using freely available operating systems and software.

Recent advances in sequencing technology make it possible to comprehensively catalog genetic variation in population samples, creating a foundation for understanding human disease, ancestry and evolution. The amounts of raw data produced are prodigious, and many computational steps are required to translate this output into high-quality variant calls. We present a unified analytic framework to discover and genotype variation among multiple samples simultaneously that achieves sensitive and specific results across five sequencing technologies and three distinct, canonical experimental designs. Our process includes (i) initial read mapping; (ii) local realignment around indels; (iii) base quality score recalibration; (iv) SNP discovery and genotyping to find all potential variants; and (v) machine learning to separate true segregating variation from machine artifacts common to next-generation sequencing technologies. We here discuss the application of these tools, instantiated in the Genome Analysis Toolkit, to deep whole-genome, whole-exome capture and multi-sample low-pass (∼4×) 1000 Genomes Project datasets.

Next-gen sequencing technologies have revolutionized data collection in genetic studies and advanced genome biology to novel frontiers. However, to date, next-gen technologies have been used principally for whole genome sequencing and transcriptome sequencing. Yet many questions in population genetics and systematics rely on sequencing specific genes of known function or diversity levels. Here, we describe a targeted amplicon sequencing (TAS) approach capitalizing on next-gen capacity to sequence large numbers of targeted gene regions from a large number of samples. Our TAS approach is easily scalable, simple in execution, neither time-nor labor-intensive, relatively inexpensive, and can be applied to a broad diversity of organisms and/or genes. Our TAS approach includes a bioinformatic application, BarcodeCrucher, to take raw next-gen sequence reads and perform quality control checks and convert the data into FASTA format organized by gene and sample, ready for phylogenetic analyses. We demonstrate our approach by sequencing targeted genes of known phylogenetic utility to estimate a phylogeny for the Pancrustacea. We generated data from 44 taxa using 68 different 10-bp multiplexing identifiers. The overall quality of data produced was robust and was informative for phylogeny estimation. The potential for this method to produce copious amounts of data from a single 454 plate (e.g., 325 taxa for 24 loci) significantly reduces sequencing expenses incurred from traditional Sanger sequencing. We further discuss the advantages and disadvantages of this method, while offering suggestions to enhance the approach.

An accurate genome sequence of a desired species is now a pre-requisite for genome research. An important step in obtaining a high-quality genome sequence is to correctly assemble short reads into longer sequences accurately representing contiguous genomic regions. Current sequencing technologies continue to offer increases in throughput, and corresponding reductions in cost and time. Unfortunately, the benefit of obtaining a large number of reads is complicated by sequencing errors, with different biases being observed with each platform. Although software are available to assemble reads for each individual system, no procedure has been proposed for high-quality simultaneous assembly based on reads from a mix of different technologies. In this paper, we describe a parallel short-read assembler, called Ray, which has been developed to assemble reads obtained from a combination of sequencing platforms. We compared its performance to other assemblers on simulated and real datasets. We used a combination of Roche/454 and Illumina reads to assemble three different genomes. We showed that mixing sequencing technologies systematically reduces the number of contigs and the number of errors. Because of its open nature, this new tool will hopefully serve as a basis to develop an assembler that can be of universal utilization (availability: http://deNovoAssembler.sf.Net/). For online Supplementary Material , see www.liebertonline.com.

Genome-wide sequencing has enabled modern biomedical research to relate more and more events in healthy as well as disease-affected cells and tissues to the genomic sequence. Now next generation sequencing (NGS) extends that reach into multiple almost complete genomes of the same species, revealing more and more details about how individual genomes as well as individual aspects of their regulation differ from each other. The inclusion of NGS-based transcriptome sequencing, chromatin-immunoprecipitation (ChIP) of transcription factor binding and epigenetic analyses (usually based on DNA methylation or histone modification ChIP) completes the picture with unprecedented resolution enabling the detection of even subtle differences such as alternative splicing of individual exons. Functional genomics aims at the elucidation of the molecular basis of biological functions and requires analyses that go far beyond the primary analysis of the reads such as mapping to a genome template sequence. The various and complex interactions between the genome, gene products and metabolites define biological function, which necessitates inclusion of results other than sequence tags in quite elaborative approaches. However, the extra efforts pay off in revealing mechanisms as well as providing the foundation for new strategies in systems biology and personalized medicine. This review emphasizes the particular contribution NGS-based technologies make to functional genomics research with a special focus on gene regulation by transcription factor binding sites.

Demand has never been greater for revolutionary technologies that deliver fast, inexpensive and accurate genome information. This challenge has catalysed the development of next-generation sequencing (NGS) technologies. The inexpensive production of large volumes of sequence data is the primary advantage over conventional methods. Here, I present a technical review of template preparation, sequencing and imaging, genome alignment and assembly approaches, and recent advances in current and near-term commercially available NGS instruments. I also outline the broad range of applications for NGS technologies, in addition to providing guidelines for platform selection to address biological questions of interest.

The most important first step in understanding next-generation sequencing data is the initial alignment or assembly that determines whether an experiment has succeeded and provides a first glimpse into the results. In parallel with the growth of new sequencing technologies, several algorithms that align or assemble the large data output of today's sequencing machines have been developed. We discuss the current algorithmic approaches and future directions of these fundamental tools and provide specific examples for some commonly used tools.

In the last several years, a number of studies have described large-scale structural variation in several genomes. Traditionally, such methods have used whole-genome array comparative genome hybridization or single-nucleotide polymorphism arrays to detect large regions subject to copy-number variation. Later techniques have been based on paired-end mapping of Sanger sequencing data, providing better resolution and accuracy. With the advent of next-generation sequencing, a new generation of methods is being developed to tackle the challenges of short reads, while taking advantage of the high coverage the new sequencing technologies provide. In this survey, we describe these methods, including their strengths and their limitations, and future research directions.

Characterization of all the functional components constituted in human genome relies in our ability to completely elucidate the genetic/epigenetic regulatory networks, chromatin states, nuclear architectures, and genome variations. Such endeavors demand for the development of robust and effective genomic technologies. In the past few years, the availability of disruptive next generation DNA sequencing technologies has offered new promise for whole genome interrogation. However, despite the massive parallel and ultra-high throughput capacity, the common nature of short read lengths found within these platforms limits their applications for many types of whole genome-based analyses. To overcome such constrain, pair end ditag (PET) based sequencing concept was conceived as an immediate solution to expand the information content and extend the linear coverage. By sequencing paired end signatures from any desired DNA fragment and mapping them to the reference genome, PET strategy allows the accurate demarcation of target DNA boundaries and defines their locations on the genomic landscape. Furthermore, the ability to delineate relationship between two ends of a DNA molecule enables the full scale discovery of unconventional gene products, genome rearrangements, and chromatin interactions. Coupling with the massively parallel and ultra-high throughput sequencing platforms, such unique features of PET strategy have the potential to revolutionize the approaches used to decipher regulatory networks in system biology, define the genome organizations, and characterize genome variations; which ultimately leads to the development of strategies for personalized medicine. Copyright © 2009 John Wiley & Sons, Inc. For further resources related to this article, please visit the WIREs website.

In recent years, a flurry of new DNA sequencing technologies have altered the landscape of genomics, providing a vast amount of sequence information at a fraction of the costs that were previously feasible. The task of assembling these sequences into a genome has, however, still remained an algorithmic challenge that is in practice answered by heuristic solutions. In order to design better assembly algorithms and exploit the characteristics of sequence data from new technologies, we need an improved understanding of the parametric complexity of the assembly problem. In this article, we provide a first theoretical study in this direction, exploring the connections between repeat complexity, read lengths, overlap lengths and coverage in determining the “hard” instances of the assembly problem. Our work suggests at least two ways in which existing assemblers can be extended in a rigorous fashion, in addition to delineating directions for future theoretical investigations.