Sequencing

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11th Annual <strong>Sequencing</strong>, 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. 93
11th Annual <strong>Sequencing</strong>, Finishing, and Analysis in the Future Meeting DETERMINATION AND CHARACTERIZATION OF CLINICALLY OBTAINED VIRAL STRAINS VIA NEXT GENERATION SEQUENCING AND POST SEQUENCING BIOINFORMATIC ANALYSIS Wednesday, 1st June 20:00 La Fonda Mezzanine (2nd Floor) Poster (PS‐2b.06) Brianna Mulligan 1 , Walter Dehority 1 , Kurt Schwalm 1 , Stephen Young 2 , Darrell Dinwiddie 1 1 University of New Mexico, 2 TriCore Reference Laboratories, Albuquerque, New Mexico Biological resource centers (BRCs) serve contemporary life sciences by collecting, archiving, updating, and integrating a variety of research data. Researchers on the individual and institutional level can then freely access that information through user‐friendly interfaces with computational analysis tools as an essential resource. In 2006, the World Data Center for Microorganisms had over 500 BRCs registered, but currently there are only 32 BRCs dedicated solely to virology. The amount of web‐accessible information available to virologists is significantly less than other fields of study, and furthermore, often there is inefficient linkage between these databases and to larger databases such as Genbank or PubMed. Accordingly, we sought to develop tools and a database that will simultaneously integrate virus sequence data to identify viral strains, and characterize and annotate genomic variation while enabling researchers to connect with additional available analytical tools, and integrate information to pertinent BRCs in rapid and high throughput manner. We are currently developing our toolset and database utilizing complete and nearly complete genomes obtained using next generation sequencing of samples from 102 patients in New Mexico with respiratory syncytial virus infections. Implemented as a web interface, our database intends to accomplish this integration to BRCs by a knuckles‐and‐nodes approach which will accommodate expansion to 30 additional respiratory viruses. These tools include viral strain differentiation through application of the Needleman–Wunsch algorithm, identity scoring, and weighting of least variable segments within the aligned sequences of the collected viruses. Our database will enable researchers and clinicians to conduct rapid and efficient genomic and epidemiologic examination of clinical viral strains, which can provide critical insight into the pathogenesis of infection and will ultimately lead to an improved clinical understanding of the disparate clinical outcomes seen in acute pediatric respiratory viral infections. 94
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Reliable solutions for focused NGS
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Sequencing

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