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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.

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...

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.

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.

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.

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.

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.

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.

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)

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.

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.

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.

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.

Motivation: The enormous amount of short reads generated by the new DNA sequencing technologies call for the development of fast and accurate read alignment programs. A first generation of hash table-based methods has been developed, including MAQ, which is accurate, feature rich and fast enough to align short reads from a single individual. However, MAQ does not support gapped alignment for single-end reads, which makes it unsuitable for alignment of longer reads where indels may occur frequently. The speed of MAQ is also a concern when the alignment is scaled up to the resequencing of hundreds of individuals. Results: We implemented Burrows-Wheeler Alignment tool (BWA), a new read alignment package that is based on backward search with Burrows–Wheeler Transform (BWT), to efficiently align short sequencing reads against a large reference sequence such as the human genome, allowing mismatches and gaps. BWA supports both base space reads, e.g. from Illumina sequencing machines, and color space reads from AB SOLiD machines. Evaluations on both simulated and real data suggest that BWA is ∼10–20× faster than MAQ, while achieving similar accuracy. In addition, BWA outputs alignment in the new standard SAM (Sequence Alignment/Map) format. Variant calling and other downstream analyses after the alignment can be achieved with the open source SAMtools software package. Availability: http://maq.sourceforge.net Contact: rd@sanger.ac.uk

Next-generation sequencing technologies are revolutionizing biology by allowing for genome-wide transcription factor binding-site profiling, transcriptome sequencing, and more recently, whole-genome resequencing. While it is currently not possible to generate complete de novo assemblies of higher-vertebrate genomes using next-generation sequencing, improvements in sequence read lengths and throughput, coupled with new assembly algorithms for large data sets, will soon make this a reality. These developments will in turn spawn a revolution in how genomic data are used to understand genetics and how model organisms are used for disease gene discovery. This review provides an overview of the current next-generation sequencing platforms and the newest computational tools for the analysis of next-generation sequencing data. We also describe how next-generation sequencing may be applied in the context of vertebrate model organism genetics.

Background: For the past 30 years, the Sanger method has been the dominant approach and gold standard for DNA sequencing. The commercial launch of the first massively parallel pyrosequencing platform in 2005 ushered in the new era of high-throughput genomic analysis now referred to as next-generation sequencing (NGS).Content: This review describes fundamental principles of commercially available NGS platforms. Although the platforms differ in their engineering configurations and sequencing chemistries, they share a technical paradigm in that sequencing of spatially separated, clonally amplified DNA templates or single DNA molecules is performed in a flow cell in a massively parallel manner. Through iterative cycles of polymerase-mediated nucleotide extensions or, in one approach, through successive oligonucleotide ligations, sequence outputs in the range of hundreds of megabases to gigabases are now obtained routinely. Highlighted in this review are the impact of NGS on basic research, bioinformatics considerations, and translation of this technology into clinical diagnostics. Also presented is a view into future technologies, including real-time single-molecule DNA sequencing and nanopore-based sequencing.Summary: In the relatively short time frame since 2005, NGS has fundamentally altered genomics research and allowed investigators to conduct experiments that were previously not technically feasible or affordable. The various technologies that constitute this new paradigm continue to evolve, and further improvements in technology robustness and process streamlining will pave the path for translation into clinical diagnostics.

The speed, accuracy, efficiency, and cost-effectiveness of DNA sequencing have been improving continuously since the initial derivation of the technique in the mid-1970s. With the advent of massively parallel sequencing technologies, DNA sequencing costs have been dramatically reduced. No longer is it unthinkable to sequence hundreds or even thousands of genes in a single individual with a suspected genetic disease or complex disease predisposition. Along with the benefits offered by these technologies come a number of challenges that must be addressed before wide-scale sequencing becomes accepted medical practice. Molecular diagnosticians will need to become comfortable with, and gain confidence in, these new platforms, which are based on radically different technologies compared to the standard DNA sequencers in routine use today. Experience will determine whether these instruments are best applied to sequencing versus resequencing. Perhaps most importantly, along with increasing read lengths inevitably comes increased ascertainment of novel sequence variants of uncertain clinical significance, the postanalytical aspects of which could bog down the entire field. But despite these obstacles, and as a direct result of the promises these sequencing advances present, it will likely not be long before next-generation sequencing begins to make an impact in molecular medicine. In this review, technical issues are discussed, in addition to the practical considerations that will need to be addressed as advances push toward personal genome sequencing.

Huge amount of Genomics data is present with the scientist, researchers and various bioinformatics or pharmaceutical companies today. To handle these data biologists, computer scientists, analysts are developing various automated techniques. One of the major queries is Genome sequencing, also called read mapping. There are two types of the reads that needs to be handled or processed, as one short read and another one long read. So for the reads alignment various automated software tools are being developed. Read alignment is finding nucleotide sequence in reference genome. Next-Generation Sequencing (NGS) technology is playing vital role for development and advancements of various sequencing algorithms. This paper is about discussion of these techniques and advancements that are being carried out for improving the process of Genome sequencing.

Once a biochemical method has been devised to sample RNA or DNA of interest, sequencing can be used to identify the sampled molecules with high fidelity and low bias. High-throughput sequencing has therefore become the primary data acquisition method for many genomics studies and is being used more and more to address molecular biology questions. By applying principles of statistical experimental design, sequencing experiments can be made more sensitive to the effects under study as well as more biologically sound, hence more replicable.

Fifteen years elapsed between the discovery of the double helix (1953) and the first DNA sequencing (1968). Modern DNA sequencing began in 1977, with development of the chemical method of Maxam and Gilbert and the dideoxy method of Sanger, Nicklen and Coulson, and with the first complete DNA sequence (phage ϕX174), which demonstrated that sequence could give profound insights into genetic organization. Incremental improvements allowed sequencing of molecules >200 kb (human cytomegalovirus) leading to an avalanche of data that demanded computational analysis and spawned the field of bioinformatics. The US Human Genome Project spurred sequencing activity. By 1992 the first ‘sequencing factory’ was established, and others soon followed. The first complete cellular genome sequences, from bacteria, appeared in 1995 and other eubacterial, archaebacterial and eukaryotic genomes were soon sequenced. Competition between the public Human Genome Project and Celera Genomics produced working drafts of the human genome sequence, published in 2001, but refinement and analysis of the human genome sequence will continue for the foreseeable future. New ‘massively parallel’ sequencing methods are greatly increasing sequencing capacity, but further innovations are needed to achieve the ‘thousand dollar genome’ that many feel is prerequisite to personalized genomic medicine. These advances will also allow new approaches to a variety of problems in biology, evolution and the environment.

The availability of genomes across the tree of life is highly biased toward vertebrates, pathogens, human disease models, and organisms with relatively small and simple genomes. Recent progress in genomics has enabled thede novodecoding of the genome of virtually any organism, greatly expanding its potential for understanding the biology and evolution of the full spectrum of biodiversity. The increasing diversity of sequencing technologies, assays, andde novoassembly algorithms have augmented the complexity ofde novogenome sequencing projects in non-model organisms. To reduce the costs and challenges inde novogenome sequencing projects and streamline their experimental design and analysis, we developed iWGS (in silicoWholeGenomeSequencer and Analyzer), an automated pipeline for guiding the choice of appropriate sequencing strategy and assembly protocols. iWGS seamlessly integrates the four key steps of ade novogenome sequencing project: data generation (through simulation), data quality control,de novoassembly, and assembly evaluation and validation. The last three steps can also be applied to the analysis of real data. iWGS is designed to enable the user to have great flexibility in testing the range of experimental designs available for genome sequencing projects, and supports all major sequencing technologies and popular assembly tools. Three case studies illustrate how iWGS can guide the design ofde novogenome sequencing projects and evaluate the performance of a wide variety of user-specified sequencing strategies and assembly protocols on genomes of differing architectures. iWGS, along with a detailed documentation, is freely available athttps://github.com/zhouxiaofan1983/iWGS.

Background There has been an exponential growth in the number of genome sequencing projects since the introduction of next generation DNA sequencing technologies. Genome projects have increasingly involved assembly of whole genome data which produces inferior assemblies compared to traditional Sanger sequencing of genomic fragments cloned into bacterial artificial chromosomes (BACs). While whole genome shotgun sequencing using next generation sequencing (NGS) is relatively fast and inexpensive, this method is extremely challenging for highly complex genomes, where polyploidy or high repeat content confounds accurate assembly, or where a highly accurate ‘gold’ reference is required. Several attempts have been made to improve genome sequencing approaches by incorporating NGS methods, to variable success. Results We present the application of a novel BAC sequencing approach which combines indexed pools of BACs, Illumina paired read sequencing, a sequence assembler specifically designed for complex BAC assembly, and a custom bioinformatics pipeline. We demonstrate this method by sequencing and assembling BAC cloned fragments from bread wheat and sugarcane genomes. Conclusions We demonstrate that our assembly approach is accurate, robust, cost effective and scalable, with applications for complete genome sequencing in large and complex genomes. Electronic supplementary material The online version of this article (doi:10.1186/s13007-016-0107-9) contains supplementary material, which is available to authorized users.

The Sanger sequencing method produces relatively long DNA sequences of unmatched quality and has been considered for long time as the gold standard for sequencing DNA. Many improvements of the Sanger method that culminated with fluorescent dyes coupled with automated capillary electrophoresis enabled the sequencing of the first genomes. Nevertheless, using this technology to sequence whole genomes was costly, laborious and time consuming even for genomes that are relatively small in size. A major technological advance was the introduction of next-generation sequencing (NGS) pioneered by 454 Life Sciences in the early part of the 21th century. NGS allowed scientists to sequence thousands to millions of DNA molecules in a single machine run. Since then, new NGS technologies have emerged and existing NGS platforms have been improved, enabling the production of genome sequences at an unprecedented rate as well as broadening the spectrum of NGS applications. The current affordability of generating genomic information, especially with microbial samples, has resulted in a false sense of simplicity that belies the fact that many researchers still consider these technologies a black box. In this review, our objective is to identify and discuss four steps that we consider crucial to the success of any NGS-related project. These steps are: (1) the definition of the research objectives beyond sequencing and appropriate experimental planning, (2) library preparation, (3) sequencing and (4) data analysis. The goal of this review is to give an overview of the process, from sample to analysis, and discuss how to optimize your resources to achieve the most from your NGS-based research. Regardless of the evolution and improvement of the sequencing technologies, these four steps will remain relevant.

The current genomic revolution was made possible by joint advances in genome sequencing technologies and computational approaches for analyzing sequence data. The close interaction between biologists and computational scientists is perhaps most apparent in the development of approaches for sequencing entire genomes, a feat that would not be possible without sophisticated computational tools called genome assemblers (short for genome sequence assemblers). Here, we survey the key developments in algorithms for assembling genome sequences since the development of the first DNA sequencing methods more than 35 years ago.

During the past decade, DNA sequencing output has been mostly dominated by the second generation sequencing platforms which are characterized by low cost, high throughput and shorter read lengths for example, Illumina. The emergence and development of so called third generation sequencing platforms such as PacBio has permitted exceptionally long reads (over 20 kb) to be generated. Due to read length increases, algorithm improvements and hybrid assembly approaches, the concept of one chromosome, one contig and automated finishing of microbial genomes is now a realistic and achievable task for many microbial laboratories. In this paper, we describe high quality sequence datasets which span three generations of sequencing technologies, containing six types of data from four NGS platforms and originating from a single microorganism, Clostridium autoethanogenum. The dataset reported here will be useful for the scientific community to evaluate upcoming NGS platforms, enabling comparison of existing and novel bioinformatics approaches and will encourage interest in the development of innovative experimental and computational methods for NGS data. Machine-accessible metadata file describing the reported data (ISA-Tab format)

As a result of improvements in genome assembly algorithms and the ever decreasing costs of high-throughput sequencing technologies, new high quality draft genome sequences are published at a striking pace. With well-established methodologies, larger and more complex genomes are being tackled, including polyploid plant genomes. Given the similarity between multiple copies of a basic genome in polyploid individuals, assembly of such data usually results in collapsed contigs that represent a variable number of homoeologous genomic regions. Unfortunately, such collapse is often not ideal, as keeping contigs separate can lead both to improved assembly and also insights about how haplotypes influence phenotype. Here, we describe a first step in avoiding inappropriate collapse during assembly. In particular, we describe ConPADE (Contig Ploidy and Allele Dosage Estimation), a probabilistic method that estimates the ploidy of any given contig/scaffold based on its allele proportions. In the process, we report findings regarding errors in sequencing. The method can be used for whole genome shotgun (WGS) sequencing data. We also show applicability of the method for variant calling and allele dosage estimation. Results for simulated and real datasets are discussed and provide evidence that ConPADE performs well as long as enough sequencing coverage is available, or the true contig ploidy is low. We show that ConPADE may also be used for related applications, such as the identification of duplicated genes in fragmented assemblies, although refinements are needed., Author Summary Diploid organisms, such as human beings, have two “copies” of each chromosome, whereas polyploid organisms have multiple “copies” (we use quotes to stress that the “copies” are not identical). A key difference between diploid and polyploid organisms is that the “copies” tend to be less similar in polyploid organisms. This difference leads to important differences in the process of de novo genome assembly from short fragments of DNA. In particular, when assembling polyploid organisms, contigs corresponding to different copies of the chromosomes can be quite different, and merging them leads to loss of information. Thus, it is important to maintain distinct contigs, even though they correspond to copies of the same chromosomal region. An important step in doing so is to determine how many truly distinct copies of a chromosomal region are found in a single contig. For example, if there are 12 copies of a particular chromosome, the possible number of distinct copies could be anywhere from 1 to 12. We call this task “contig ploidy estimation”, and present a method for accomplishing it. This set of methods is useful for the de novo assembly of complex, polyploid genomes such as sugarcane, switchgrass, and wheat.

The proliferation of large-scale DNA-sequencing projects in recent years has driven a search for alternative methods to reduce time and cost. Here we describe a scalable, highly parallel sequencing system with raw throughput significantly greater than that of state-of-the-art capillary electrophoresis instruments. The apparatus uses a novel fibre-optic slide of individual wells and is able to sequence 25 million bases, at 99% or better accuracy, in one four-hour run. To achieve an approximately 100-fold increase in throughput over current Sanger sequencing technology, we have developed an emulsion method for DNA amplification and an instrument for sequencing by synthesis using a pyrosequencing protocol optimized for solid support and picolitre-scale volumes. Here we show the utility, throughput, accuracy and robustness of this system by shotgun sequencing and de novo assembly of the Mycoplasma genitalium genome with 96% coverage at 99.96% accuracy in one run of the machine.

Ultimately, genome sequencing seeks to provide an organism's complete DNA sequence. Automation of DNA sequencing allowed scientists to decode entire genomes and gave birth to genomics, the analytic and comparative study of genomes. Although genomes can include billions of nucleotides, the chemical reactions researchers use to decode the DNA are accurate for only about 600 to 700 nucleotides at a time. The DNA reads that sequencing produces must then be assembled into a complete picture of the genome. Errors and certain DNA characteristics complicate assembly. Resolving these problems entails an additional and costly finishing phase that involves extensive human intervention. Assembly programs can dramatically reduce this cost by taking into account additional information obtained during finishing. The paper considers how algorithms that can assemble millions of DNA fragments into gene sequences underlie the current revolution in biotechnology, helping researchers build the growing database of complete genomes.

Single-Cell Genomics Sequencing the genome of single cells gives insight into issues such as cell-to-cell heterogeneity and genome instability. Key to single-cell sequencing techniques are whole-genome amplification (WGA) methods that provide sufficient DNA for next-generation sequencing. Current WGA methods have been hampered by low accuracy and spatial resolution of gene copy numbers and by low amplification fidelity. Chen et al. report an improved single-cell WGA method, Linear Amplification via Transposon Insertion (LIANTI). The DNA is randomly fragmented by Tn5 transposition of a transposon that includes a T7 promoter, which allows linear amplification. The authors used the method to determine the spectrum of single-nucleotide variations in a single human cell after ultraviolet radiation. Science , this issue p. [189][1] [1]: /lookup/doi/10.1126/science.aak9787

Background De novo genome assembly of next-generation sequencing data is one of the most important current problems in bioinformatics, essential in many biological applications. In spite of significant amount of work in this area, better solutions are still very much needed. Results We present a new program, SAGE, for de novo genome assembly. As opposed to most assemblers, which are de Bruijn graph based, SAGE uses the string-overlap graph. SAGE builds upon great existing work on string-overlap graph and maximum likelihood assembly, bringing an important number of new ideas, such as the efficient computation of the transitive reduction of the string overlap graph, the use of (generalized) edge multiplicity statistics for more accurate estimation of read copy counts, and the improved use of mate pairs and min-cost flow for supporting edge merging. The assemblies produced by SAGE for several short and medium-size genomes compared favourably with those of existing leading assemblers. Conclusions SAGE benefits from innovations in almost every aspect of the assembly process: error correction of input reads, string-overlap graph construction, read copy counts estimation, overlap graph analysis and reduction, contig extraction, and scaffolding. We hope that these new ideas will help advance the current state-of-the-art in an essential area of research in genomics. Electronic supplementary material The online version of this article (doi:10.1186/1471-2105-15-302) contains supplementary material, which is available to authorized users.

The next generation sequencing technology has enabled the understanding of the whole genome of an organism at a greater coverage and reduced cost. In this study, we provide a systems biology approach to understand the functional relevance of the single nucleotide variants identified by the whole genome sequencing studies. This approach also includes a methodology for the identification of conserved modules among the family members. Our study contributes towards the much needed downstream functional analysis of the variant data, especially for the low frequency and novel variants, and provides a framework for the analysis of multiple genomes to derive relations between variants and diseases.

Genome sequencing and analysis is a field that has evolved very rapidly over the 10 years since a final draft of the human genome sequence was published in 2003. From obtaining a full genome sequence from a representative individual or strain of a small number of species, the genomics community has moved to documenting genetic diversity within species, with an emphasis on humans, and more generally by sequencing the genomes of a rapidly growing number of species. The advent of low-cost, very high throughput sequencing techniques has also made it possible to sample transcriptomes (the part of the genome transcribed into ribonucleic acid), genomic regions bound by proteins, bacterial communities, or even entire ecosystems, using sequencing approaches. This has spawned a new generation of software tools designed to handle the very large numbers of short sequences, commonly referred to as reads, produced by the new machines, and has also propelled the field of computational genomics into the realm of Big Data that require large and sophisticated computer systems for their management and analysis. Key Concepts: Rapidly evolving sequencing technologies have revolutionised the analysis of genomes. The management and analysis of billions of short sequence reads requires specialised software and access to high-end computer hardware. Clusters of commodity servers are the preferred infrastructure for genome sequencing projects, but machines with large amounts of memory are required for assembly. Cloud computing is expected to have a big impact on the field, but issues of data transfer are not fully resolved. Large distributed resources supporting a Map/Reduce programming paradigm and distributed storage, such as Apache Hadoop, will probably become standard. Keywords: computer infrastructure; hardware; networking; processors; software; genome projects; genome sequencing

The main methods used for large-scale mapping of the human and other genomes are reviewed. These methods comprise two procedures of random mapping/sequencing and an approach using linking and jumping libraries. Importantly, no method used up to now has proved efficient in comparative genome analysis. A new method is presented basing on slalom libraries. These libraries provide 10-100 times higher efficiency and may be used for mapping and sequencing whole genomes by small research groups.

Haplotype-resolved genome sequencing enables the accurate interpretation of medically relevant genetic variation, deep inferences regarding population history and non-invasive prediction of fetal genomes. We describe an approach for genome-wide haplotyping based on contiguity-preserving transposition (CPT-seq) and combinatorial indexing. Tn5 transposition is used to modify DNA with adaptor and index sequences while preserving contiguity. After DNA dilution and compartmentalization, the transposase is removed, resolving the DNA into individually indexed libraries. The libraries in each compartment, enriched for neighboring genomic elements, are further indexed via PCR. Combinatorial 96-plex indexing at both the transposition and PCR stage enables the construction of phased synthetic reads from each of the nearly 10,000 'virtual compartments'. We demonstrate the feasibility of this method by assembling >95% of the heterozygous variants in a human genome into long, accurate haplotype blocks (N50 = 1.4-2.3 Mb). The rapid, scalable and cost-effective workflow could enable haplotype resolution to become routine in human genome sequencing.

The development of parallel sequencing started a race with the aim to sequence massive amounts of DNA several orders of magnitude higher than the classical Sanger sequencing, providing the opportunity to screen large proportions of nucleic acids in a short time frame. This capability is no doubt the milestone of a new way to answer everyday questions in biology and medicine and represents a major field of research in the genomic field. This review focuses on the basic lines of next-generation sequencing, the practical approach to unravel the data generated and the diverse applications of these techniques to multiple fields in Biology and Medicine.

Due to the size of Next-Generation Sequencing data, the computational challenge of sequence alignment has been vast. Inexact alignments can take up to 90% of total CPU time in bioinformatics pipelines. High-performance Integrated Virtual Environment (HIVE), a cloud-based environment optimized for storage and analysis of extra-large data, presents an algorithmic solution: the HIVE-hexagon DNA sequence aligner. HIVE-hexagon implements novel approaches to exploit both characteristics of sequence space and CPU, RAM and Input/Output (I/O) architecture to quickly compute accurate alignments. Key components of HIVE-hexagon include non-redundification and sorting of sequences; floating diagonals of linearized dynamic programming matrices; and consideration of cross-similarity to minimize computations. Availability https://hive.biochemistry.gwu.edu/hive/

For many decades, genomic studies were based on Sanger sequencing or the dideoxy chain termination method of sequencing DNA, along with microarray and hybridization-based techniques to understand genome function. Sanger sequencing was used to sequence the genomes of many organisms, from bacteria to humans. However, in recent years ‘Next-generation’ sequencing technologies have been developed that are cheaper and far more rapid. They produce great sequencing depth, making them applicable to quantitative studies such as gene expression measurements as well. As a result, these technologies have been used extensively to study the sequence, structure, function and evolution of both eukaryotic and bacterial genomes. Here we discuss next-generation sequencing and how it has been used to study a variety of areas from gene expression and protein-DNA interactions to bacterial community function and evolution, at the scale of whole bacterial genomes. We expect that further advances in DNA sequencing technology and methods for managing and analyzing the large volumes of data produced by these approaches will help to answer many more questions in this field.

One of the most important direction of bioinformatics is the genome structural variation discovery. In this paper, firstly we explain four sequencing-based methods, i.e., Read-pair, Read-depth, Split-read and Assembly. Secondly, the advantage and disadvantage of the approaches are discussed. Thirdly, the combination of the methods is proved to be a better one. Finally, a novel combined strategy was analyzed that will be the direction of the future.

Next-generation sequencing (NGS) greatly promotes the research and development of whole-genome sequencing technology. Genomic sequence assemblers and algorithms dealing with high-throughput short-read data have been the cutting-edge issues in the field. This paper discusses several latest sequence assemblers and their algorithms, and conducts a comparative study of some typical de Bruijn graph-based assemblers by stimulating the sequencing and assembly of zebra fish genome data.

Sequencing genomes is one of the most fundamental problems in modern biology and has immense impact on biomedical research. De novo sequencing is computationally more challenging when compared to sequencing with a reference genome. Repeats, for instance, make genome assembly extremely difficult. Locations of reads shorter than the repeat length cannot be resolved uniquely. On the other hand the existing sequencing technology is not mature enough to identify/read the entire sequence of the genome - especially for complex organisms like mammals. However small fragments of the genome can be read with acceptable accuracy. The shotgun sequencing employed in many sequencing projects breaks the genome randomly at many places and generates a large number of small fragments (called reads) of the genome. The problem of reassembling all the fragmented reads into a sequence close to the original sequence is known as the Sequence Assembly (SA) problem. Sequence assembly is complex for various reasons including short reads, errors in sequencing, the presence of repeats, etc. In this talk we survey some of the algorithms (specifically [1, 2, 3]) that have been proposed for SA. Both de Bruijn and overlap graph based algorithms will be discussed. We also summarize a recent algorithm we have come up with for the problem of scaffolding.problem.

Automated DNA sequencing instruments embody an elegant interplay among chemistry, engineering, software, and molecular biology and have built upon Sanger's founding discovery of dideoxynucleotide sequencing to perform once-unfathomable tasks. Combined with innovative physical mapping approaches that helped to establish long-range relationships between cloned stretches of genomic DNA, fluorescent DNA sequencers produced reference genome sequences for model organisms and for the reference human genome. New types of sequencing instruments that permit amazing acceleration of data-collection rates for DNA sequencing have been developed. The ability to generate genome-scale data sets is now transforming the nature of biological inquiry. Here, I provide an historical perspective of the field, focusing on the fundamental developments that predated the advent of next-generation sequencing instruments and providing information about how these instruments work, their application to biological research, and the newest types of sequencers that can extract data from single DNA molecules.

Publisher Summary Omics refers to a field of study in biology ending in -omics, such as genomics, proteomics, or metabolomics. Sequencing is the first step in evaluating regulatory sequences as well as coding and noncoding regions. Deoxyribonucleic acid (DNA) sequences are used to reconstruct phylogenies, identify taxonomic groups, evaluate the evolution of genomes, and conduct research on population ecology and genetics. There are two original methods for sequencing DNA and in both, sequencing involves four procedures—cloning/preparing template DNA, performing sequencing reactions, gel electrophoresis of samples, and compiling and interpreting data. DNA sequencing can now be highly automated using bases labeled with fluorescent dyes. Sequences can be read by scanners directly, and sequence analysis is automated that has reduced costs and increased speed. Automated sequencing methods were used for the very large-scale sequencing required to sequence the entire Drosophila melanogaster genome. The information obtained from the Drosophila Genome Project has begun to revolutionize both fundamental and applied aspects of insect genetics. Genomics is providing insight into development, speciation, protein interactions, and evolution. Proteomics involves understanding the structure and function of the proteins encoded by genes. Transcriptomics resolves which genes are transcribed during development in specific tissues. Phenomics attempts to understand how genes and proteins interact in physiological cascades to determine the phenotype and development of organisms. Because genomics and proteomics produce huge amounts of data, biologists need to use computers and other information management tools that has generated a new discipline called bioinformatics.

BMC bioinformatics 14(Supplement 5), S1 (2013). doi:10.1186/1471-2105-14-S5-S1, Published by BioMed Central [u.a.], London