Table 2. Considerations and Challenges of Implementing Next Generation Sequencing Across Large Health Systems.
| Consideration or Challenge | Possible Mitigation or Solution |
|---|---|
| Data Generation | |
| If pre-selecting isolates for NGS workflow basedon carbapenemnase production, the CarbaNP test may miss OXA-like carbapenemases in Acinetobacter | Do not use a negative CarbaNP to downselect Acinetobacter spp.; test with PCR or perform NGS on all isolates |
| Lengthy approval processes and laborious acquisition requirements; contract awards unable to keep pace with technologic advances | Allow cooperative research agreements with operations and maintenance type of funds; employ experienced acquisitions personnel within group to work closely with contracting agency; leverage flexible or agile contracting vehicles; vendors should notify contracting officer representatives or technical supervisors of impending major advancements or new releases; allow clinical operations to be funded with research and development monies (not solely operations and maintenance monies) |
| Balancing number of full time staff to workload | 3–4 full time molecular laboratory technologists and one PhD level team lead for every 300–400 isolates sequenced per month |
| Limitations of shorter read platforms for certain types of bacterial antimicrobial resistance investigations (mobile genetic elements) | Increase access to or funding for positioning of ultra or very long read sequencing platforms at surveillance or referral laboratories |
| Limited availability of long read single molecule platforms | Wait for technologic advances to eliminate this constraint by making those platforms smaller and less expensive. |
| Compared to research laboratories, clinical laboratories are more susceptible to higher staff turnover and may not have staff with specialized training needed for preparing high quality DNA libraries | Increase and incentivize educational and training opportunities; leverage automation or robotics for library preparation |
| Data Analysis | |
| Balancing number of full time staff to workload | 5–7 full time bioinformatacists and one PhD level team lead for every 300–400 isolates sequenced per month |
| Limited access to open source and other state of the art analytic software (primarily applies to government and military organizations) | Relax .mil restrictions on computer networks for facilities involved in biomedical research and clinical support; allow use of .org or .net; expedite process and shorten approval time for obtaining Certificates of .net Worthiness |
| Data Sharing & Storage | |
| Continuous sequencing of large volumes isolates (300–400 month) of creates extraordinary burdens for sharing and storage (Petabytes over the program lifecycle) | Increase bandwidth or provide infrastructure to accommodate emailing of FASTQ / FASTA data files of 10s to 100s of isolates at once; use tiered storage; explore vendor or cloud-based solutions (but these can be prohibitively expensive for larger projects) |
| Commercial 'off-the-shelf' database for managing isolate inventory and linking clinical and antibiotic susceptibility data to sequenced genomes to does not yet exist | Adopt the structure architecture of ARMoR-D which DOD can provide at no cost to nonprofit or other government agencies |
ARMoR = Antimicrobial Resistance Monitoring and Research Program; CDC = U.S. Centers for Disease Control and Prevention; DOD = U.S. Department of Defense; NIH = U.S. National Institute of Health; FASTA/FASTQ = file format names for sequencing data