metaSPAdes: a new versatile metagenomic assembler

Nurk, Sergey; Meleshko, Dmitry; Korobeynikov, Anton; Pevzner, Pavel A.

doi:10.1101/gr.213959.116

Cited by 3,161 publications

(2,672 citation statements)

References 87 publications

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“…However, that study used whole-metagenome-assembly-based approaches to achieve strain-level taxonomic resolution of the STEC in the samples. Whole-metagenome assembly is a computationally intensive, time-consuming process, as illustrated by Nurk et al, who recently reported that metagenome assembly can take between 1.5 h and 6 h, with a memory footprint ranging from 7.3 GB to 234.5 GB, depending on the chosen assembler, for processing of a single human gut metagenomic sample (28). Thus, the application of more rapid, less intensive bioinformatic tools for strain detection is desirable.…”

Section: Discussionmentioning

confidence: 99%

Strain-Level Metagenomic Analysis of the Fermented Dairy Beverage Nunu Highlights Potential Food Safety Risks

Walsh

Crispie

Daari

et al. 2017

Appl Environ Microbiol

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The rapid detection of pathogenic strains in food products is essential for the prevention of disease outbreaks. It has already been demonstrated that whole-metagenome shotgun sequencing can be used to detect pathogens in food but, until recently, strain-level detection of pathogens has relied on wholemetagenome assembly, which is a computationally demanding process. Here we demonstrated that three short-read-alignment-based methods, i.e., MetaMLST, PanPhlAn, and StrainPhlAn, could accurately and rapidly identify pathogenic strains in spinach metagenomes that had been intentionally spiked with Shiga toxin-producing Escherichia coli in a previous study. Subsequently, we employed the methods, in combination with other metagenomics approaches, to assess the safety of nunu, a traditional Ghanaian fermented milk product that is produced by the spontaneous fermentation of raw cow milk. We showed that nunu samples were frequently contaminated with bacteria associated with the bovine gut and, worryingly, we detected putatively pathogenic E. coli and Klebsiella pneumoniae strains in a subset of nunu samples. Ultimately, our work establishes that short-read-alignmentbased bioinformatics approaches are suitable food safety tools, and we describe a real-life example of their utilization.IMPORTANCE Foodborne pathogens are responsible for millions of illnesses each year. Here we demonstrate that short-read-alignment-based bioinformatics tools can accurately and rapidly detect pathogenic strains in food products by using shotgun metagenomics data. The methods used here are considerably faster than both traditional culturing methods and alternative bioinformatics approaches that rely on metagenome assembly; therefore, they can potentially be used for more high-throughput food safety testing. Overall, our results suggest that wholemetagenome sequencing can be used as a practical food safety tool to prevent diseases or to link outbreaks to specific food products.KEYWORDS fermentation, food-borne pathogens, metagenomics I n recent years, high-throughput sequencing (HTS) has become an important tool in food microbiology (1). HTS enables in-depth characterization of food-related microbial isolates, via whole-genome sequencing (WGS), and it facilitates cultureindependent analysis of mixed microbial communities in foods, via metagenomic sequencing.WGS has provided invaluable insights into the genetics of starter cultures (2, 3), and it is routinely used in epidemiology to identify outbreak-associated foodborne pathogens isolated from clinical samples, through comparison of the single-nucleotide polymorphism (SNP) profiles of outbreak strain genomes versus nonoutbreak strain

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Section: Discussionmentioning

confidence: 99%

Strain-Level Metagenomic Analysis of the Fermented Dairy Beverage Nunu Highlights Potential Food Safety Risks

Walsh

Crispie

Daari

et al. 2017

Appl Environ Microbiol

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“…Because of the stochastic 21 sampling nature associated with WGS and the presence of sequencing errors, it is 22 necessary for the reads to cover a single gene or genome many times (coverage), 23 typically 30x-50x, to ensure high quality de novo assembly [2]. Unlike in single genome 24 sequencing projects where the majority of the genomic regions are equally represented, 25 in transcriptome and metagenome sequencing projects, different species of transcripts or 26 genomes may have very unequal representation, up to several orders of magnitude in one would sequence the population at much higher depth than single genome projects. 29 As in practice it is difficult to precisely estimate the required sequencing depth without 30 knowing the community structure, sequencing large transcriptomes and complex 31 metagenomes often generates as much data as the budget allows, producing 100-1000 32 GB of sequence data or more [15] [33].…”

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“…(MetaSPAdes [26], MEGAHIT [19], etc) or MPI to distributed on a cluster [11]. The 47 shared memory approach is very hard to scale up to exponentially increased NGS data 48 size.…”

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SpaRC: Scalable Sequence Clustering using Apache Spark

Shi¹,

Meng

Tseng

et al. 2018

Preprint

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“…Metagenomic sequence data is analyzed by an ever-increasing range of bioinformatic tools. These tools are able to perform a wide variety of applications, for example, quality control analysis of the raw sequence data 28 , overlapping of paired end reads 29 , de novo assembly of sequence reads to contigs and scaffolds 30,31 , taxonomic classification and visualization of sequence reads and assembled sequences 7,12,32,33 and the functional annotation of assembled sequences 34,35 .…”

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Metagenomic Analysis of Silage

Tennant

Sambles

Diffey

et al. 2017

JoVE

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Metagenomics is defined as the direct analysis of deoxyribonucleic acid (DNA) purified from environmental samples and enables taxonomic identification of the microbial communities present within them. Two main metagenomic approaches exist; sequencing the 16S rRNA gene coding region, which exhibits sufficient variation between taxa for identification, and shotgun sequencing, in which genomes of the organisms that are present in the sample are analyzed and ascribed to "operational taxonomic units"; species, genera or families depending on the extent of sequencing coverage.In this study, shotgun sequencing was used to analyze the microbial community present in cattle silage and, coupled with a range of bioinformatics tools to quality check and filter the DNA sequence reads, perform taxonomic classification of the microbial populations present within the sampled silage, and achieve functional annotation of the sequences. These methods were employed to identify potentially harmful bacteria that existed within the silage, an indication of silage spoilage. If spoiled silage is not remediated, then upon ingestion it could be potentially fatal to the livestock.

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metaSPAdes: a new versatile metagenomic assembler

Cited by 3,161 publications

References 87 publications

Strain-Level Metagenomic Analysis of the Fermented Dairy Beverage Nunu Highlights Potential Food Safety Risks

Strain-Level Metagenomic Analysis of the Fermented Dairy Beverage Nunu Highlights Potential Food Safety Risks

SpaRC: Scalable Sequence Clustering using Apache Spark

Metagenomic Analysis of Silage

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