metaSPAdes: a new versatile metagenomic assembler.

By Sergey Nurk, Dmitry Meleshko, Anton I. Korobeynikov, Pavel A. Pevzner. 2017

3,820 citations recorded by the source corpus.

While metagenomics has emerged as a technology of choice for analyzing bacterial populations, the assembly of metagenomic data remains challenging, thus stifling biological discoveries. Moreover, recent studies revealed that complex bacterial populations may be composed from dozens of related strains, thus further amplifying the challenge of metagenomic assembly. metaSPAdes addresses various challenges of metagenomic assembly by capitalizing on computational ideas that proved to be useful in assemblies of single cells and highly polymorphic diploid genomes. We benchmark metaSPAdes against other state-of-the-art metagenome assemblers and demonstrate that it results in high-quality assemblies across diverse data sets.

Subfields: Ecology, Molecular Biology

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Citations in the corpus

MGnify: the microbiome analysis resource in 2020 (2019, background): “Assembly is a computationally expensive process, frequently requiring terabytes of memory to accomplish when using metaS-PAdes (20), which is our primary assembly algorithm, as the ensuing assemblies are typically of better quality than those produced using other assemblers (21).”
A practical guide to amplicon and metagenomic analysis of microbiome data (2020, methodology): “MEGAHIT is used to assemble large, complex metagenome datasets quickly using little computer memory (Li et al., 2015), while metaSPAdes can generate longer contigs but requires more computational resources (Nurk et al., 2017).”
Petabase-scale sequence alignment catalyses viral discovery (2020, methodology): “…rnaviralSPAdes and the more specialized variant, coronaSPAdes, combines algorithms and methods from several previous approaches based on metaSPAdes 60 , rnaSPAdes 61 and metaviralSPAdes 62 with a HMMPathExtension step. coronaSPAdes constructs an assembly graph from an RNA-seq dataset…”
DNA Sequencing as a Tool to Monitor Marine Ecological Status (2017, background): “A growing number of assemblers (Boisvert et al., 2012; Peng et al., 2012) and binning tools (Alneberg et al., 2014; Imelfort et al., 2014; Nurk et al., 2017) are available that assemble short sequencing reads into larger contigs and subsequently combine them into bins based on, for example,…”
Next-Generation Sequencing Technology: Current Trends and Advancements (2023, background): “However, a close monitoring and comprehensive understanding of the host–microbiome and microbiome–intercommunity interactions are vital to healthy survival.”
Hi-C deconvolution of a human gut microbiome yields high-quality draft genomes and reveals plasmid-genome interactions (2017, methodology): “We then assembled these reads using metaSPAdes (Nurk et al., 2017), generating 506,596 contigs containing a total of 677 Mb sequence with a contig N50 of 9,168 bp (see Methods and Table S1).”