title: An ensemble strategy that significantly improves de novo assembly of microbial genomes from metagenomic next-generation sequencing data.
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.
authors.affiliation: Blood Systems Research Institute, San Francisco, CA 94118, USA Department of Laboratory Medicine, University of California at San Francisco, San Francisco, CA 94107, USA xdeng@bloodsystems.org.
authors.forename: Xutao
authors.initials: X
authors.lastname: Deng
authors.affiliation: Department of Laboratory Medicine, University of California at San Francisco, San Francisco, CA 94107, USA UCSF-Abbott Viral Diagnostics and Discovery Center, San Francisco, CA 94107, USA.
authors.forename: Samia N
authors.initials: SN
authors.lastname: Naccache
authors.affiliation: Blood Systems Research Institute, San Francisco, CA 94118, USA Department of Laboratory Medicine, University of California at San Francisco, San Francisco, CA 94107, USA.
authors.forename: Terry
authors.initials: T
authors.lastname: Ng
authors.affiliation: Department of Laboratory Medicine, University of California at San Francisco, San Francisco, CA 94107, USA UCSF-Abbott Viral Diagnostics and Discovery Center, San Francisco, CA 94107, USA.
authors.forename: Scot
authors.initials: S
authors.lastname: Federman
authors.affiliation: Blood Systems Research Institute, San Francisco, CA 94118, USA Department of Laboratory Medicine, University of California at San Francisco, San Francisco, CA 94107, USA.
authors.forename: Linlin
authors.initials: L
authors.lastname: Li
authors.affiliation: Department of Laboratory Medicine, University of California at San Francisco, San Francisco, CA 94107, USA UCSF-Abbott Viral Diagnostics and Discovery Center, San Francisco, CA 94107, USA Department of Medicine, Division of Infectious Diseases, UCSF, San Francisco, CA 94143, USA.
authors.forename: Charles Y
authors.initials: CY
authors.lastname: Chiu
authors.affiliation: Blood Systems Research Institute, San Francisco, CA 94118, USA Department of Laboratory Medicine, University of California at San Francisco, San Francisco, CA 94107, USA.
authors.forename: Eric L
authors.initials: EL
authors.lastname: Delwart
mesh_terms: D001419:Bacteria / Q000235:genetics*; D016680:Genome, Bacterial; D016679:Genome, Viral; D006801:Humans; D056186:Metagenomics; D017421:Sequence Analysis / Q000379:methods*; D014780:Viruses / Q000235:genetics*