Sequencing

11th Annual Sequencing, Finishing, and Analysis in the Future Meeting
AUTOMATION AND MANAGEMENT OF INFLUENZA
NEXT-GENERATION SEQUENCING DATA ANALYSIS
AND QUALITY CONTROL PROCEDURES
Thursday, 2nd June 11:00 La Fonda Ballroom Talk (OS‐5.02)
Thomas Stark 1 , Samuel Shepard 2 , Sujatha Seenu 2 ,
Elizabeth Neuhaus 2 , John Barnes 2
1 Battelle Memorial Institute, Atlanta, GA,
2 Influenza Division, Centers for Disease Control and Prevention,
The CDC Influenza Division recently transitioned to using next generation sequencing technologies
for molecular surveillance and now routinely sequences original clinical specimens as part of its
operational pipeline. A customized Clarity LIMS workflow supports these operations and serves an
important role within this pipeline, particularly because it has been extensively configured to
streamline high throughput sequencing tasks. This overall workflow includes automated queueing
of available specimens from a central metadata store as well as auto‐assignment of samples to a
central library construction workflow. Experimental procedures are easily tracked and quality control
metrics are readily produced for each phase of the overall process, which includes monitoring of inprogress
instrumentation as well as review of post‐run analysis results. Genome assembly of the
LIMS‐staged samples is automatically scheduled on grid computing infrastructure as soon as NGS
runs complete. Downstream curation and aggregate quality control assessments are also triggered
automatically within this framework, enabling access to interpretation‐ready data as early as one
hour following the completion of a 96‐plex Illumina MiSeq run. Our custom‐configured Clarity LIMS
pipeline furnishes e‐mail status alerts and provides summary reports on a per‐run basis, two features
that serve to notify submitters of completion or failure status in real time. This overall system has
become a critical component of Influenza Genomics Team operations by minimizing clerical technical
errors within the laboratory, by maintaining consistent records of laboratory metadata associated
with these experimental procedures, by improving the timeliness of the availability of processed
data through the automation of analysis, and by providing computational scrutiny of multiplexed
data. Finally, this LIMS configuration and data management regime has been replicated in a cloudcomputing
environment in order to establish a network of procedurally identical Influenza Reference
Center satellite sites, which serve to increase national surveillance capacity, reduce turn‐around times
for the availability of genetic data, and establish readiness for pandemic preparedness.
106