Sequencing

11th Annual Sequencing, Finishing, and Analysis in the Future Meeting
VISUAL ANALYTIC TOOL FOR INVESTIGATING
UNEXPLAINED RESPIRATORY DISEASE OUTBREAKS
THROUGH THE USE OF TARGETED AMPLICON
RE-SEQUENCING
Wednesday, 1st June 20:00 La Fonda Mezzanine (2nd Floor) Poster (PS‐2b.05)
Shatavia Morrison, Heta Desai, Bernard Wolff, Alvaro Benitez, Maureen Diaz, Jonas Winchell
Centers for Disease Control and Prevention
The infectious etiology of respiratory outbreaks remains unidentified in ~50% of Unexplained
Respiratory Diseases Outbreaks (URDO) investigated by CDC, even when traditional multi‐pathogen
detection methods, such as real‐time PCR are used. The current URDO panel provides the basic
level of detection (presence vs. absence) for a known set of respiratory etiologic agents. By transforming
the foundation of this approach to a next generation sequencing‐based (NGS) method, we
are able to generate a more comprehensive data set for all targeted agents that may be present in
clinical specimens, along with the ability to identify novel or rare pathogens. A continued challenge
in the “–omics” assays is the data analysis step. Existing metagenomics visualization tools are
either too convoluted for user interpretation, non‐interactive, or lack the ability to display parent‐child
taxonomy relationships.
We present an interactive visual analytic tool that allows for analyses of multiple metagenomics
datasets simultaneously in order to identify the etiologic agent(s) during URDO outbreaks. By using
this tool with in‐house mock clinical samples, we were able to detect the seeded organisms. These
datasets were generated with the Illumina platform. After sequence read data cleansing, Kraken was
used to assign taxonomic labels using a k‐mer based approach. The Kraken output was used as
the input into the URDO visualization. This visualization has two components: (i) the back‐end
infrastructure and (ii) the front‐end web‐visualization component. The back‐end consists of a MySQL
database on a MAMP web server and PHP scripts. The MySQL database contains the parent‐child
relationships for all the bacteria and virus taxonomy lineages classified in NCBI and PHP scripts
that allow for the communication between the back‐end and front‐end visualization. The front‐end
visualization was built with HTML5 and D3.js. The D3.js is a data‐driven framework that allows for
easy manipulation of text data into visual glyphs. It allows the user to upload multiple files and
generate independent visual interpretations for each dataset as well as generate a cumulative view of
all samples to allow for inter‐level comparisons. The web page navigation is based on taxonomy
classification hierarchical schema. This allows the user to select at which taxonomy level they would
like to begin their analysis. The visualization is divided into two visual panes for easy interpretation
of parent‐child relationships. Using this approach we are able to display many‐ to‐many comparisons.
The size of the bubbles represent the number of reads associated with each organism. The user is
able to hover over the bubble glyph to retrieve data such as the percentage of reads represented in
the dataset. The summative view allows the user to highlight a specific classification unit and its
corresponding units will highlight across samples to make it easy for the user to identify any specific
classification group of interest.
This tool is useful for exploring metagenomics datasets. The framework joins scalable technologies
together to make it modular to incorporate additional characterization features such as antibiotic
resistance features, serogrouping, or receptor use. Ultimately, this tool will vastly improve URDO
investigations.
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