A hybrid approach for the automated finishing of bacterial genomes

Bashir, Ali Kashif; Klammer, Aaron A.; Robins, William; Chin, Chen-Shan; Webster, Dale R.; Paxinos, Ellen E.; Hsu, David; Ashby, Meredith; Wang, Susana; Peluso, Paul; Sebra, Robert; Sorenson, Jon M.; Bullard, James W.; Yen, Jackie; Valdovino, Marie; Mollova, Emilia T.; Luong, Khai; Lin, Steven; Lamay, Brianna; Joshi, Anirudh; Rowe, Lori A.; Frace, Michael; Tarr, Cheryl L.; Turnsek, Maryann; Davis, Brigid M.; Kasarskis, Andrew; Mekalanos, John J.; Waldor, Matthew K.; Schadt, Eric E.

doi:10.1038/nbt.2288

Cited by 179 publications

(156 citation statements)

References 47 publications

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“…However, many other detections were made outside of the GATC context (referred to here as offtarget effects), indicating modifications to bases outside of the expected target sites (Table 1). We note that the off-target effects that we observed are highly unlikely to result from sequencing errors given the random nature of the errors on the PacBio RS platform and given the high-degree of consensus sequence accuracy achieved on this platform (>99.99%) (Rasko et al 2011;Bashir et al 2012). …”

Section: Detection Of Kinetic Variation Events In Plasmids Modified Bmentioning

confidence: 97%

Modeling kinetic rate variation in third generation DNA sequencing data to detect putative modifications to DNA bases

et al. 2012

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Current generation DNA sequencing instruments are moving closer to seamlessly sequencing genomes of entire populations as a routine part of scientific investigation. However, while significant inroads have been made identifying small nucleotide variation and structural variations in DNA that impact phenotypes of interest, progress has not been as dramatic regarding epigenetic changes and base-level damage to DNA, largely due to technological limitations in assaying all known and unknown types of modifications at genome scale. Recently, single-molecule real time (SMRT) sequencing has been reported to identify kinetic variation (KV) events that have been demonstrated to reflect epigenetic changes of every known type, providing a path forward for detecting base modifications as a routine part of sequencing. However, to date no statistical framework has been proposed to enhance the power to detect these events while also controlling for false-positive events. By modeling enzyme kinetics in the neighborhood of an arbitrary location in a genomic region of interest as a conditional random field, we provide a statistical framework for incorporating kinetic information at a test position of interest as well as at neighboring sites that help enhance the power to detect KV events. The performance of this and related models is explored, with the best-performing model applied to plasmid DNA isolated from Escherichia coli and mitochondrial DNA isolated from human brain tissue. We highlight widespread kinetic variation events, some of which strongly associate with known modification events, while others represent putative chemically modified sites of unknown types. [Supplemental material is available for this article.] DNA sequencing carried out using current first and second (or next) generation sequencing (NGS) instruments has achieved significant success in providing DNA sequences in which the A's, G's, C's, and T's comprising any given DNA template of interest are very consistently called at accuracies that exceed 99%, enabling an explosion of whole-genome sequencing applications that promise to transform our understanding of living systems and usher in the era of personalized genomics (Shendure and Ji 2008; Wheeler et al.

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Section: Detection Of Kinetic Variation Events In Plasmids Modified Bmentioning

confidence: 97%

Modeling kinetic rate variation in third generation DNA sequencing data to detect putative modifications to DNA bases

et al. 2012

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“…Although the long read lengths produced by PacBio sequencing are particularly advantageous in performing de novo genome assembly, the platform produces higher sequencing error rates than Illumina sequencing. While newer developments in PacBio chemistry have allowed generation of accurate microbial genome assemblies (66), many researchers have employed a hybrid approach utilizing both Illumina and PacBio platforms to generate the most complete and accurate reference genomes (67,68). PacBio sequencing can reach Ͼ99% accuracy via circular consensus sequencing (CCS), whereby the same DNA template is read by DNA polymerase multiple times (69).…”

Section: Single-molecule Real-time Sequencingmentioning

confidence: 99%

Implementation and Data Analysis of Tn-seq, Whole-Genome Resequencing, and Single-Molecule Real-Time Sequencing for Bacterial Genetics

et al. 2017

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Few discoveries have been more transformative to the biological sciences than the development of DNA sequencing technologies. The rapid advancement of sequencing and bioinformatics tools has revolutionized bacterial genetics, deepening our understanding of model and clinically relevant organisms. Although application of newer sequencing technologies to studies in bacterial genetics is increasing, the implementation of DNA sequencing technologies and development of the bioinformatics tools required for analyzing the large data sets generated remain a challenge for many. In this minireview, we have chosen to summarize three sequencing approaches that are particularly useful for bacterial genetics. We provide resources for scientists new to and interested in their application. Here, we discuss the analysis of data from transposon mutagenesis followed by deep sequencing (Tnseq) to determine gene disruptions differentially represented in a mutant population and Illumina sequencing for identification of suppressor or other mutations, and we summarize single-molecule real-time (SMRT) sequencing for de novo genome assembly and the use of the output data for detection of DNA base modifications.KEYWORDS High-throughput, SMRT, Tn-seq, sequencing M any important advances in sequencing technologies can be attributed to studies in microbiology. The first sequenced genome of bacteriophage X174 was completed by Sanger in 1978 (1), followed by the shotgun sequencing of bacteriophage lambda (2) and then the 1.8-MB genome sequence for Haemophilus influenzae in 1995 (3). Subsequent efforts led to the development of next-and third-generation sequencing platforms (4). Recent advances have provided powerful tools for novel research in basic and translational bacteriology. Given the diversity and complexity of sequencing applications, the initial implementation of microbial genomics and subsequent data analysis may prove arduous for researchers new to genomics.In our experience, many laboratories unfamiliar with sequencing approaches or subsequent data analysis have become interested in implementing sequencing to enrich discovery in their studies. This minireview is not intended to be comprehensive or written for an expert. The goal of this minireview is to provide an introductory explanation, tools, and resources for bacteriologists new to sequencing approaches. Therefore, we have chosen to discuss the application of three popular sequencing approaches followed by highlighting a few examples from the literature. First, we discuss methods for assessing fitness subsequent to transposon mutagenesis followed by deep sequencing (Tn-seq). Tn-seq analysis can be used as a genome-wide gene discovery method in a broad range of microorganisms. Next, we discuss the bioinformatics tools available for applications in resequencing utilizing high-throughput sequencing. One application of resequencing is to rapidly identify suppressor mutations

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“…In theory with enough sequencing, whole genomes of novel species can be assembled [70][71][72]; however practically, de novo assembly tends to produces numerous short contigs (contiguous sequences). Producing high-quality drafts of microbial genomes will likely require borrowing techniques from eukaryotic genome assembly with longer read lengths and read-pair scaffolding [73,74], or possibly by utilizing single-cell sequencing technology [75,76]. However with the intense focus on the human microbiome, the number of fully assembled bacteria genomes is rapidly increasing, so the day may soon approach where the assembly step can be skipped in place of direct alignment to genome databases, and de novo assembly is reserved for the more diverse soil and marine ecologies.…”

Section: Metagenomicsmentioning

confidence: 99%

Computational Studies of the Intestinal Host-Microbiota Interactome

Christley

Cockrell

2015

Computation

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Abstract:A large and growing body of research implicates aberrant immune response and compositional shifts of the intestinal microbiota in the pathogenesis of many intestinal disorders. The molecular and physical interaction between the host and the microbiota, known as the host-microbiota interactome, is one of the key drivers in the pathophysiology of many of these disorders. This host-microbiota interactome is a set of dynamic and complex processes, and needs to be treated as a distinct entity and subject for study. Disentangling this complex web of interactions will require novel approaches, using a combination of data-driven bioinformatics with knowledge-driven computational modeling. This review describes the computational approaches for investigating the host-microbiota interactome, with emphasis on the human intestinal tract and innate immunity, and highlights open challenges and existing gaps in the computation methodology for advancing our knowledge about this important facet of human health.

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A hybrid approach for the automated finishing of bacterial genomes

Cited by 179 publications

References 47 publications

Modeling kinetic rate variation in third generation DNA sequencing data to detect putative modifications to DNA bases

Modeling kinetic rate variation in third generation DNA sequencing data to detect putative modifications to DNA bases

Implementation and Data Analysis of Tn-seq, Whole-Genome Resequencing, and Single-Molecule Real-Time Sequencing for Bacterial Genetics

Computational Studies of the Intestinal Host-Microbiota Interactome

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