MetaVelvet: an extension of Velvet assembler to de novo metagenome assembly from short sequence reads

By Toshiaki Namiki, Tsuyoshi Hachiya, Hideaki Tanaka, Yasubumi Sakakibara. ACM International Conference on Bioinformatics, Computational Biology and Biomedicine, 2011

642 citations recorded by the source corpus.

Motivation: An important step of "metagenomics" analysis is the assembly of multiple genomes from mixed sequence reads of multiple species in a microbial community. Most conventional pipelines employ a single-genome assembler with carefully optimized parameters and post-process the resulting scaffolds to correct assembly errors. Limitations of the use of a single-genome assembler for de novo metagenome assembly are that highly conserved sequences shared between different species often causes chimera contigs, and sequences of highly abundant species are likely mis-identified as repeats in a single genome, resulting in a number of small fragmented scaffolds. The metagenome assembly problem becomes harder when assembling from very short sequence reads. Method: We modified and extended a single-genome and de Bruijn-graph based assembler, known as "Velvet" [27], for short reads to metagenome assembly, called "MetaVelvet", for mixed short reads of multiple species. Our fundamental ideas are first decomposing de Bruijn graph constructed from mixed short reads into individual sub-graphs and second building scaffolds based on every decomposed de Bruijn sub-graph as isolate species genome. We make use of two features, graph connectivity and coverage (abundance) difference, for the decomposition of de Bruijn graph. Results: On simulated datasets, MetaVelvet succeeded to generate higher N50 scores and smaller chimeric scaffolds than any compared single-genome assemblers, produce high-quality scaffolds as well as the separate assembly using Velvet from isolated species sequence reads, and MetaVelvet reconstructed even relatively low-coverage genome sequences as scaffolds. On a real dataset of Human Gut microbial read data, MetaVelvet produced longer scaffolds, increased the number of predicted genes, and improved the assignments of a phylum-level taxonomy in the sense that the rate of predicted genes that cannot be assigned to any tanoxomy is reduced. Availability: The source code of MetaVelvet is freely available at http://metavelvet.dna.bio.keio.ac.jp under the GNU General Public License.

Subfields: Ecology, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/0de503f5ba1ca63c8b559eccb2d7dc3d8a41f862

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Citations in the corpus

A review of methods and databases for metagenomic classification and assembly (2019, neutral): “MetaVelvet [106] and Ray Meta [104] are single k-mer de Bruijn graph assemblers for metagenomics data.”
Improvements to PATRIC, the all-bacterial Bioinformatics Database and Analysis Resource Center (2016, positive): “PATRIC provides several assembly strategies (Figure 3) that include algorithms for base calling correction [BayesHammer (12) and Kmer-genie (13)], and assemblers [Velvet (14), IDBA (15), Spades (16), MEGAHIT (17) and MiniASM (18)].”
Assembling metagenomes, one community at a time (2017, neutral): “2.02 [15], SPAdes v3.”
The Road to Metagenomics: From Microbiology to DNA Sequencing Technologies and Bioinformatics (2015, neutral): “Nowadays, there are ad hoc assemblers for metagenome reads ( Table 2 ) such as IDBA-UD (Peng et al., 2012) and MetaVelvet (Namiki et al., 2012).”
Characterization of the Gut Microbiome Using 16S or Shotgun Metagenomics (2016, neutral): “Sequences that surpass a dissimilarity threshold (bit-score) are progressively placed on higher taxonomy levels.”
Sequence assembly demystified (2013, positive): “To mitigate the effect of cross-genome repeats, scientists have relied on careful analysis of the assembly graph and the use of mate-pair information 3 and have also developed approaches for separating out individual genomes, which can then be assembled with traditional tools 4 .”
MaxBin: an automated binning method to recover individual genomes from metagenomes using an expectation-maximization algorithm (2014, neutral): “Recent developments in assembling metagenomic sequences [54-56] and uneven coverage single cell sequences [57] should improve the binning performance of MaxBin and other binning algorithms.”
Metagenomics: Tools and Insights for Analyzing Next-Generation Sequencing Data Derived from Biodiversity Studies (2015, positive): “The next generation of assembly tools, such as MetaVelvet and very recently MetaVelvet-SL 45,46 and Meta-IDBA, 47 was developed to address these issues.”