Best Practices

The establishment of best practices for the implementation of WGS data for typing and characterization of microbial pathogens will ensure reliable, comparable, and transparent processes underlying infectious disease risk and management decisions.

Implementation of Clinical and Bioinformatics Standards

Best practices for implementing NGS data in clinical settings for diagnostics of infectious disease include the provision of documentation for validation and quality assurance, high capacity data storage, version traceability and data transfer confidentiality as essential components for meeting regulatory standards (Gargis et al, 2015; Roy et al, 2018; Gargis et al, 2016). In addition to clinical best practices, current best practices in bioinformatics for food safety also include the implementation of data standards; maintainability of software; the demonstration of data integrity, and auditability; as well as the development of frameworks for reporting and interpretation (Lambert et al, 2017).

The IRIDA project team has participated in the creation of best practices for the application of genomic data supporting regulatory food safety, and continues to work with international groups such as the International Organization for Standards (ISO) to improve food safety and industry standards. The IRIDA platform has been developed according to these best practices since its inception.

Auditability and Transparency

The methods and parameters used in WGS-based analyses can influence results and their interpretation, which can have significant implications for genomic epidemiology investigations. Such analyses generate key evidence supporting clinical and epidemiological interventions, which can have legal and medical ramifications. Therefore, it is crucial to maintain consistent, comprehensive and transparent records regarding how and when evidence was generated.

To ensure processes are carefully documented:

  • All information regarding data provenance and analytical parameters is stored within the log files of the IRIDA database
  • Output files from an analysis are archived in IRIDA’s underlying file system and cannot be deleted from IRIDA (”deleted” files are removed from user access but are still preserved on file)
  • All files used to generate analysis results are available under the Input Files tab
  • The Provenance tab shows information about all the steps and parameters used to generate any result listed in a tree format with the top entry representing the individual Galaxy tool used to generate the output file
  • If a User is added then later removed from a Project, there will be a time-stamped entry in the IRIDA database showing both instances and their author