An ensemble strategy that significantly improves de novo assembly of microbial genomes from metagenomic next-generation sequencing data. Issue 7 (13th January 2015)

• Record Type: Journal Article
• Authors: Deng, Xutao; Naccache, Samia N.; Ng, Terry; Federman, Scot; Li, Linlin; Chiu, Charles Y.; Delwart, Eric L.
• Is Part Of: Nucleic acids research. Volume 43:Issue 7(2015)
• Page Start: e46
• Page End: e46
• Publication Date: 2015-01-13
• Subjects: Nucleic acids -- Periodicals; Molecular biology -- Periodicals; 572.805
• DOI: 10.1093/nar/gkv002
• Languages: English
• ISSNs: 0305-1048

Abstract

Abstract: Next-generation sequencing (NGS) approaches rapidly produce millions to billions of short reads, which allow pathogen detection and discovery in human clinical, animal and environmental samples. A major limitation of sequence homology-based identification for highly divergent microorganisms is the short length of reads generated by most highly parallel sequencing technologies. Short reads require a high level of sequence similarities to annotated genes to confidently predict gene function or homology. Such recognition of highly divergent homologues can be improved by reference-free ( de novo ) assembly of short overlapping sequence reads into larger contigs. We describe an ensemble strategy that integrates the sequential use of various de Bruijn graph and overlap-layout-consensus assemblers with a novel partitioned sub-assembly approach. We also proposed new quality metrics that are suitable for evaluating metagenome de novo assembly. We demonstrate that this new ensemble strategy tested using in silico spike-in, clinical and environmental NGS datasets achieved significantly better contigs than current approaches.