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September 20-22, 2017 | Toronto, Canada
10
TH
AMERICAN PEDIATRICS HEALTHCARE &
PEDIATRIC INFECTIOUS DISEASES CONGRESS
Pediatric Healthcare & Pediatric Infections 2017
H
orizontal gene transfer (HGT) and recombination leads
to the emergence of bacterial antibiotic resistance and
pathogenic traits. HGT events can be identified by comparing
a large number of fully sequenced genomes across a species
or genus, define the phylogenetic range of HGT, and find
potential sources of new resistance genes. In-depth
comparative phylogenomics can also identify subtle genome
or plasmid structural changes or mutations associated
with phenotypic changes. Comparative phylogenomics
requires that accurately sequenced, complete and properly
annotated genomes of the organism. Assembling closed
genomes requires additional mate-pair reads or “long read”
sequencing data to accompany short-read paired-end data.
To bring down the cost and time required of producing
assembled genomes and annotating genome features that
informdrugresistanceandpathogenicity,weareanalyzing the
performance for genome assembly of data from the Illumina
NextSeq, which has faster throughput than the Illumina
HiSeq (~one-two days versus ~one week), and shorter reads
(150bp paired-end versus 300bp paired end) but higher
capacity (150-400M reads per run versus ~5-15M) compared
to the Illumina MiSeq. Bioinformatics improvements are
also needed to make rapid, routine production of complete
genomes a reality. Modern assemblers such as SPAdes 3.6.0
running on a standard Linux blade are capable in a few hours
of converting mixes of reads from different library preps into
high-quality assemblies with only a few gaps. Remaining
breaks in scaffolds are generally due to repeats (e.g., rRNA
genes) are addressed by our software for gap closure
techniques, that avoid custom PCR or targeted sequencing.
Our goal is to improve the understanding of emergence of
pathogenesis using sequencing, comparative genomics, and
machine learning analysis of ~1000 pathogen genomes.
Machine learning algorithms will be used to digest the
diverse features (change in virulence genes, recombination,
horizontal gene transfer, patient diagnostics). Temporal data
and evolutionary models can thus determine whether the
origin of a particular isolate is likely to have been from the
environment (could it have evolved from previous isolates).
It can be useful for comparing differences in virulence along
or across the tree. More intriguing, it can test whether there
is a direction to virulence strength. This would open new
avenues in the prediction of uncharacterized clinical bugs and
multidrug resistance evolution and pathogen emergence.
e:
debray@sandia.govPredictive pathogen biology: Genome-based prediction of pathogenic potential and countermeasures
targets
Debjit Ray, Joseph S. Schoeniger, Kelly Williams, Corey Hudson
and
Christopher Polage
Sandia National Laboratories, Canada
University of California Davis Medical Center, Canada