Independent study validates Solu as fastest for genomic epidemiology analysis

A new peer-reviewed study from the Central Texas Veterans Health Care System (CTVHCS), published in Antimicrobial Stewardship & Healthcare Epidemiology, benchmarks Solu alongside two other cloud-based platforms on 90 clinical bacterial isolates. Solu delivered the fastest turnaround times and was the only platform to detect all 12 clusters of closely related isolates found across the study [1].

Key takeaways

• Solu analyzed samples in ~10 minutes each and completed a batch of 50 samples in ~1 hour, roughly 3–4x faster than the alternatives evaluated.
• Solu was the only platform to detect all 12 clusters of closely related isolates found across the study.
• Solu detects the species from the sequencing data and groups samples automatically, with no predefined organism list required.
• The authors found Solu requires minimal to no bioinformatics expertise to use WGS data and perform complex analysis.

Why this study matters

WGS is one of the most powerful tools available for genomic surveillance and epidemiology, but adoption has been held back by one stubborn bottleneck: data analysis. Traditional pipelines require bioinformatics expertise, coding knowledge and significant compute, resources many labs don't have.

Cloud-based platforms like Solu are built to close this gap. Because a claim like "no bioinformatics expertise needed" is easy to make but harder to verify, head-to-head benchmarking studies are valuable. They let prospective users see how platforms actually perform on real data, evaluated by independent researchers.

The study design

Hwang and colleagues collected 90 bacterial isolates from inpatient units at CTVHCS between August 2024 and March 2025. The isolates spanned common healthcare-associated species including E. coli, K. pneumoniae, P. aeruginosa, S. aureus, E. faecalis and A. baumannii.

All isolates were sequenced on an Illumina NextSeq 550 at a median coverage depth of 113.9x. The same FASTQ dataset was then uploaded to each of the three cloud platforms. The researchers assessed usability, turnaround time, cost and analysis output.

How Solu performed

• Turnaround time. Solu was the fastest platform in the study by a wide margin. Analysis began as soon as the first sample was uploaded, with individual samples processed in about 10 minutes and a batch of 50 completing in about an hour. The other platforms evaluated took 3 to 4 hours.
• Clustering. Solu was the only platform to detect all 12 clusters of closely related isolates found across the study: E. coli (4), K. pneumoniae (3), P. aeruginosa (2), S. aureus (2) and E. faecalis (1). SNP clustering runs automatically across all uploaded species, using a 20-SNP threshold.
• Species detection. Solu detects the species directly from the sequencing data and groups samples automatically. Unlike platforms that restrict analysis to a predefined list of organisms, Solu processes any species the user uploads.
• Usability. The researchers described Solu as user-friendly and practical for outbreak investigations, and highlighted several features that support day-to-day use: automatic SNP clustering across uploaded species, real-time display of analysis progress, customizable phylogenetic trees exportable as PNG, resistome heatmaps showing AMR gene predictions, match strength and chromosome vs. plasmid location, and Similar Public Genomes, which assesses relatedness against global reference databases. The authors singled out Similar Public Genomes as a standout feature.

“Solu requires minimal to no bioinformatics expertise to utilize WGS data and perform complex analysis.”
Hwang et al., 2026

What this means for labs considering WGS

Sequencing pathogens has become accessible, but the analysis step has lagged behind. When a lab can upload FASTQ files and get species ID, AMR profiles, clustering and plasmid detection back in minutes rather than days, WGS becomes practical for routine surveillance, not just research projects or reference-laboratory workflows.

This independent evaluation adds to a growing body of peer-reviewed evidence for Solu, alongside our BMC Bioinformatics publication [2] and the work by PHF Science and Awanui Labs Wellington in New Zealand on ultra-rapid, high-resolution analysis in a front-line hospital laboratory [3].

The bottom line

In this independent study, Solu was the fastest platform tested and the only one to detect all 12 clusters of closely related isolates. The researchers also found it requires minimal to no bioinformatics expertise.

If you're evaluating cloud-based WGS analysis for your lab, we'd encourage you to read the full paper.

Ready to see how Solu performs on your own sequencing data? Start analyzing samples for free or contact our team.

References

1. Hwang, M., Choi, H., Chatterjee, P., & Jinadatha, C. (2026). A comparative review of three cloud-based platforms for microbial whole genome sequencing analysis. Antimicrobial Stewardship & Healthcare Epidemiology, 6, e79. https://doi.org/10.1017/ash.2026.10316
2. Saratto, T., Visuri, K., Lehtinen, J., Ortega-Sanz, I., Steenwyk, J.L., & Sihvonen, S. (2025). Solu: a cloud platform for real-time genomic pathogen surveillance. BMC Bioinformatics, 26, 12. https://doi.org/10.1186/s12859-024-06005-z
3. Bloomfield, M. et al. (2026). The need for speed: ultra-rapid high-resolution outbreak analysis in a front-line hospital microbiology laboratory. Journal of Hospital Infection, 168, 48–57. https://doi.org/10.1016/j.jhin.2025.11.020