Abstracts

Poster Abstracts
the results are presented. Analysis results are
shown in a highly accessible manner, allowing
the user to gain a quick overview as well
as permitting deep analysis. The performance
of the PAIPline was benchmarked on real and
artificial datasets of known compositions and
compared to competing tools. The results and
discussed features show that the presented approach
is a viable strategy for the identification
of pathogen sequences in NGS datasets.
n 10
SEPARATION OF FOREGROUND AND
BACKGROUND READS IN MIXED NGS
DATASETS
S. Tausch, A. Nitsche, B. Renard, P. Dabrowski;
Robert Koch Institute, Berlin, GERMANY.
NGS is a valuable technology for rapid and indepth
analysis of clinical samples, as it allows
sequencing of a pathogen’s whole genome
directly from patient material within as little
as 26 hours. However, the follow-up analysis
is severely slowed down by the abundance of
reads originating from the host. Thus, in order
to exploit the full potential of the technology
for rapid diagnostics, a method for rapid in
silico removal of host reads is necessary. Commonly,
a mapping-based approach is used to
separate reads: either reads mapping to a background
reference or reads not mapping to a
foreground reference are discarded. However,
while the former approach is highly specific
in discarding only true background reads and
the latter is highly sensitive in only keeping
foreground reads, neither offers a good balance.
Hence we have aimed at developing a
novel tool specifically geared towards both
specific and sensitive separation of foreground
and background reads. In order to determine
whether a read belongs to the foreground or
the background, we train markov chains of
an order k from 4 to 12 on user-provided sets
of foreground and background reference sequences,
where each state is a k-mer of length
k and each transition is one of the four possible
bases A, C, G and T. We then calculate the
difference of log likelihoods of each transition
observed within a read with regards to
the foreground and the background markov
chains. This difference is then used as a score
for the separation of reads, with scores smaller
than 0 indicating a background read and scores
larger than 0 indicating a foreground read.
We have tested our tool on several datasets,
including Cowpoxvirus sequenced from a
human host. In all cases, our tool was faster
than any competing tool (achieving speeds of
up to 10 Megabases/second using 4 CPUs),
including Kraken and mapping via bowtie2.
At the same time, we consistently achieved
the best F-Score of all tested tools. Our tool is
developed in python and java and available for
download from http://sourceforge.net/projects/
rambok/ We have developed a freely available,
easy to use, rapid and both highly sensitive and
specific tool for the separation of foreground
and background reads in mixed NGS datasets.
We believe that this will be highly useful as an
initial filtering step for anyone analyzing viral
sequences via NGS.
n 11
A RAPID AND SCALABLE SINGLE
NUCLEOTIDE POLYMORPHISM DISCOVERY
AND VALIDATION PIPELINE FOR OUTBREAK
INVESTIGATION OF BACTERIAL PATHOGENS
B. Rusconi 1 , A. L. Rodriguez 2 , S. S. Koenig 1 ,
M. Eppinger 1 ;
1
University of Texas at San Antonio - South
Texas Center For Emerging Infectious Diseases
(STCEID), San Antonio, TX, 2 University of
Texas at San Antonio -Computational Biology
Initiative, San Antonio, TX.
Background: Assuring a timely and effective
response in the control of bacterial outbreaks
is challenging, as discriminatory power becomes
of particular importance to distinguish
outbreak isolates that form tight clonal complexes
with only few genetic polymorphisms.
The increase of throughput and concomitant
ASM Conference on Rapid Next-Generation Sequencing and Bioinformatic
Pipelines for Enhanced Molecular Epidemiologic Investigation of Pathogens
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