Automated, non-hybrid de novo genome assemblies and epigenomes of bacterial pathogens.

Author(s): Clark, Tyson A. and Luong, Khai and Chin, Chen-Shan and Boitano, Matthew and Turner, Stephen W. and Korlach, Jonas

Understanding the genetic basis of infectious diseases is critical to enacting effective treatments, and several large-scale sequencing initiatives are underway to collect this information. Sequencing bacterial samples is typically performed by mapping sequence reads against genomes of known reference strains. While such resequencing informs on the spectrum of single-nucleotide differences relative to the chosen reference, it can miss numerous other forms of variation known to influence pathogenicity: structural variations (duplications, inversions), acquisition of mobile elements (phages, plasmids), homonucleotide length variation causing phase variation, and epigenetic marks (methylation, phosphorothioation) that influence gene expression to switch bacteria from non- pathogenic to pathogenic states. Therefore, sequencing methods which provide complete, de novo genome assemblies and epigenomes are necessary to fully characterize infectious disease agents in an unbiased, hypothesis-free manner. Hybrid assembly methods have been described that combine long sequence reads from SMRT DNA Sequencing with short reads (SMRT CCS (circular consensus) or second-generation reads), wherein the short reads are used to error-correct the long reads which are then used for assembly. We have developed a new paradigm for microbial de novo assemblies in which SMRT sequencing reads from a single long insert library are used exclusively to close the genome through a hierarchical genome assembly process, thereby obviating the need for a second sample preparation, sequencing run, and data set. We have applied this method to achieve closed de novo genomes with accuracies exceeding QV50 (>99.999%) for numerous disease outbreak samples, including E. coli, Salmonella, Campylobacter, Listeria, Neisseria, and H. pylori. The kinetic information from the same SMRT Sequencing reads is utilized to determine epigenomes. Approximately 70% of all methyltransferase specificities we have determined to date represent previously unknown bacterial epigenetic signatures. With relatively short sequencing run times and automated analysis pipelines, it is possible to go from an unknown DNA sample to its complete de novo genome and epigenome in about a day.

Organization: PacBio
Year: 2013

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