The line list is the primary tool used by epidemiologists to collect and organize preliminary information on cases under investigation. IRIDA enables dynamic line list generation based on uploaded or entered metadata and other contextual information. IRIDA enables epidemiologists to analyze and integrate contextual information with the results of genomic sequence analyses and other laboratory information, relieving them of cumbersome and error-prone collation of data from multiple sources, such as FAX, e-mail and spreadsheets. Automated line list generation and genomic data correlation is key to effective disease surveillance and outbreak investigations, and rapid intervention that serves to protect public health.

Metadata can be uploaded to the IRIDA platform as csv files, or can be added directly to individual Sample tables via the web interface. Metadata can be searched, and fields of data can be included or excluded from analysis using customizable filters. Data can also be re-ordered within the line list using the Toggle features.

 

IRIDA’s Advanced Visualization system allows users to view tree images and associated metadata together and is designed to show the clustering concordance of the genomic data and associated metadata. Metadata columns can be exposed or hidden as appropriate for a given analysis. Metadata fields are coloured by value to help visualize their grouping patterns. By integrating line lists with the phylogenetic tree data, epidemiologists can better characterize isolate clusters providing insight into potential sources of exposure and transmission. This feature enhances decision-making and can be used to identify isolate clusters fitting the criteria for triggering outbreak investigation and response protocols.

Trees built by IRIDA’s phylogenomics pipeline SNVPhyl can be viewed using a modified version of PhyloCanvas (http://phylocanvas.org/) that runs in the web browser and can support real-time visualization of trees with hundreds of taxa. IRIDA also supports the display of tree images with epidemiological metadata. The integration of genomic relatedness information with epidemiological data can assist in outbreak investigations and enhance decision-making by providing insight into disease transmission and source attribution.