Alignment of Next-Generation Sequencing Reads

Reinert, Knut; Langmead, Ben; Weese, David; Evers, Dirk

doi:10.1146/annurev-genom-090413-025358

Cited by 108 publications

(67 citation statements)

References 71 publications

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“…WG4 previously established a proficiency testing framework, and has run two full-sized, global proficiency tests focused on assessing the quality of partner lab sequencing of bacterial isolates and of control DNA, and of performing cluster analysis on sets of bacterial genome datasets (Moran-Gilad et al, 2015b; Reinert et al, 2015); PT2016 underway at time of writing). The early trials focused on the foodborne bacterial pathogens E. coli and Salmonella enterica Serovar Typhimurium; current trials are evaluating the foodborne pathogens Listeria monocytogenes and Campylobacter spp., and antimicrobial resistant Klebsiella .…”

Section: The Global Microbial Identifier Consortiummentioning

confidence: 99%

“…Although this type of analysis can be performed on draft genome assemblies, several tools have been developed that directly compare raw sequence reads to a related reference genome sequence (Reinert et al, 2015). This process, which is referred to as variant detection by reference mapping, relies on algorithms that align each read to a reference genome and index the variation between them, also assigning confidence levels to each variant position based upon the sequence coverage and level of agreement between reads supporting the SNV (Mielczarek and Szyda, 2016).…”

Section: Modernizing Food Safety With Genomics Bioinformatics and Omentioning

confidence: 99%

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Food Safety in the Age of Next Generation Sequencing, Bioinformatics, and Open Data Access

et al. 2017

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Public health labs and food regulatory agencies globally are embracing whole genome sequencing (WGS) as a revolutionary new method that is positioned to replace numerous existing diagnostic and microbial typing technologies with a single new target: the microbial draft genome. The ability to cheaply generate large amounts of microbial genome sequence data, combined with emerging policies of food regulatory and public health institutions making their microbial sequences increasingly available and public, has served to open up the field to the general scientific community. This open data access policy shift has resulted in a proliferation of data being deposited into sequence repositories and of novel bioinformatics software designed to analyze these vast datasets. There also has been a more recent drive for improved data sharing to achieve more effective global surveillance, public health and food safety. Such developments have heightened the need for enhanced analytical systems in order to process and interpret this new type of data in a timely fashion. In this review we outline the emergence of genomics, bioinformatics and open data in the context of food safety. We also survey major efforts to translate genomics and bioinformatics technologies out of the research lab and into routine use in modern food safety labs. We conclude by discussing the challenges and opportunities that remain, including those expected to play a major role in the future of food safety science.

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Section: The Global Microbial Identifier Consortiummentioning

confidence: 99%

Section: Modernizing Food Safety With Genomics Bioinformatics and Omentioning

confidence: 99%

Food Safety in the Age of Next Generation Sequencing, Bioinformatics, and Open Data Access

et al. 2017

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“…Over the past decade, more than a hundred sophisticated alignment algorithms have been specifically designed to handle the computational challenges imposed by modern NGS platforms (see Table 3 for a summary of the characteristics of several popular NGS aligners; a technical overview is provided by Reinert et al, 2015). These algorithms are optimized for their efficiency (that is, speed), scalability (that is, storage space) and accuracy (for example, by taking specific technological biases of the different platforms and protocols into account).…”

Section: Quality Assessmentmentioning

confidence: 99%

From next-generation resequencing reads to a high-quality variant data set

Pfeifer

2016

Heredity

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Sequencing has revolutionized biology by permitting the analysis of genomic variation at an unprecedented resolution. Highthroughput sequencing is fast and inexpensive, making it accessible for a wide range of research topics. However, the produced data contain subtle but complex types of errors, biases and uncertainties that impose several statistical and computational challenges to the reliable detection of variants. To tap the full potential of high-throughput sequencing, a thorough understanding of the data produced as well as the available methodologies is required. Here, I review several commonly used methods for generating and processing next-generation resequencing data, discuss the influence of errors and biases together with their resulting implications for downstream analyses and provide general guidelines and recommendations for producing high-quality single-nucleotide polymorphism data sets from raw reads by highlighting several sophisticated reference-based methods representing the current state of the art. INTRODUCTIONSequencing has entered the scientific zeitgeist and the demand for novel sequences has never been greater, with applications spanning comparative genomics, clinical diagnostics and metagenomics, as well as agricultural, evolutionary, forensic and medical genetic studies. Several hundred species have already been completely sequenced (among them reference genomes for human and numerous major model organisms) (Pagani et al., 2012), shifting the focus of many scientific studies toward resequencing analyses in order to identify and catalog genetic variation in different individuals of a population. These population-scale studies permit insights into natural variation, inference on the demographic and selective history of a population, and variants identified in phenotypically distinct individuals can be used to dissect the relationship between genotype and phenotype.Until 2005, Sanger sequencing was the dominant technology, but it was prohibitively expensive and time consuming to routinely perform sequencing on a scale required to reach the scientific goals of many modern research projects. For these reasons, several massively parallel, high-throughput 'next-generation' sequencing (NGS) technologies have since been developed, permitting the analyses of genomes and their variation by being hundreds of times faster and over a thousand times cheaper than traditional Sanger sequencing (Metzker, 2010). Whereas the major limiting factor in the Sanger sequencing era was the experimental production of sequence data, data generation using NGS platforms is straightforward, shifting the bottleneck to downstream analyses, with computational costs often surpassing those of data production (Mardis, 2010). In fact, a multitude of bioinformatic algorithms is necessary to efficiently analyze the generated data sets and to answer biologically relevant questions. Thereby, the large amount of data with shorter read lengths, higher per-base error rates

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“…Subsequent adaptations for speed, higher-throughput, and paired-end reads were necessary to handle the large amount of data arising from NGS (22,23). The results from sequence alignment can be visualized as the original sequencing reads ordered by their best match to a position on the reference sequence (Fig.…”

Section: Current Methods In Genome Sequencingmentioning

confidence: 99%

A primer to clinical genome sequencing

Priest

2017

Current Opinion in Pediatrics

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Purpose of review Genome sequencing is now available as a clinical diagnostic test. There is a significant knowledge and translation gap for non-genetic specialists of the processes necessary to generate and interpret clinical genome sequencing. The purpose of this review is to provide a primer on contemporary clinical genome sequencing for non-genetic specialists describing the human genome project, current techniques and applications in genome sequencing, limitations of current technology, and techniques on the horizon. Recent Findings As currently implemented, genome sequencing compares short pieces of an individual’s genome to a reference sequence developed by the human genome project. Genome sequencing may be used for obtaining timely diagnostic information, cancer pharmacogenomics, or in clinical cases when previous genetic testing has not revealed a clear diagnosis. At present, the implementation of clinical genome sequencing is limited by the availability of clinicians qualified for interpretation, and current techniques in used clinical testing do not detect all types of genetic variation present in a single genome. Summary Clinicians considering a genetic diagnosis have wide array of testing choices which now includes genome sequencing. Though not a comprehensive test in its current form, genome sequencing offers more information than gene-panel or exome sequencing and has the potential to replace targeted single-gene or gene-panel testing in many clinical scenarios.

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Alignment of Next-Generation Sequencing Reads

Cited by 108 publications

References 71 publications

Food Safety in the Age of Next Generation Sequencing, Bioinformatics, and Open Data Access

Food Safety in the Age of Next Generation Sequencing, Bioinformatics, and Open Data Access

From next-generation resequencing reads to a high-quality variant data set

A primer to clinical genome sequencing

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