Independent study validates Solu as fastest for genomic epidemiology analysis

A new peer-reviewed study from the Central Texas Veterans Healthcare System (CTVHCS), published in Antimicrobial Stewardship & Healthcare Epidemiology*, independently benchmarks Solu alongside two other cloud-based platforms on 90 clinical bacterial isolates. Solu delivered the fastest turnaround times and detected the most epidemiological clusters in 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 identified the most epidemiological clusters (12), supporting its use for outbreak detection and infection prevention.
• Solu correctly identified 87 of 90 isolates at the species level, with no predefined organism list required.
• The study reinforces that clinical microbiology labs can adopt WGS for routine use without building in-house bioinformatics capacity.

Why this study matters

WGS is one of the most powerful tools available to clinical microbiology and infection prevention teams, but adoption has been held back by one stubborn bottleneck: data analysis. Traditional pipelines require bioinformatics expertise, coding knowledge, and significant compute: resources most clinical labs don't have.

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

The study design

Hwang and colleagues collected 90 bacterial isolates from inpatient units at CTVHCS between August 2024 and March 2025. The isolates spanned clinically important 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 median coverage depth of 113.9x. The same FASTQ dataset was then uploaded to each of the three cloud platforms evaluated. The researchers assessed usability, turnaround time, cost, and analytical performance across species identification, AMR gene detection, and epidemiological clustering.

How Solu performed

Turnaround time. Solu was by a wide margin the fastest platform in the study. Analysis began as soon as the first sample was uploaded, with individual samples processed in approximately 10 minutes and a batch of 50 samples completing in about one hour. The other platforms evaluated took 3 to 4 hours. In outbreak investigations, where every hour of delay is an hour of potential onward transmission, this difference is operationally meaningful.

Epidemiological clustering. Solu detected the most clusters of any platform in the study: 12 in total, spanning E. coli (4), K. pneumoniae (3), P. aeruginosa (2), S. aureus (2), and E. faecalis (1). Solu uses SNP-based clustering with a 20-SNP threshold, the methodology most widely used in the hospital epidemiology literature. For infection prevention teams, a missed cluster is a missed transmission event.

Species identification. Solu correctly identified 87 of 90 isolates. Unlike platforms that restrict analysis to a predetermined list of organisms, Solu processes any species the user uploads.

Usability and output. The researchers highlighted several features of the Solu interface that matter in day-to-day use: automatic species grouping, real-time analysis progress, customizable phylogenetic trees, resistome heatmaps that display AMR gene predictions alongside predicted genomic location (chromosome vs. plasmid), and a "Similar Public Genomes" tool that assesses relatedness against global reference databases. The authors called out the latter as a standout feature.

"Cloud-based platforms may ease barriers for adopting WGS for clinical and infection prevention purposes."Hwang et al., 2026

What this means for clinical labs considering WGS

The technology to sequence pathogens has become accessible, but the analysis layer has lagged behind. When a clinical microbiology lab can upload FASTQ files and receive interpreted results (species ID, AMR profile, clustering, plasmid detection) in minutes rather than hours or days, WGS becomes viable for routine use, not just research projects or reference-laboratory workflows.

This independent evaluation adds to a growing body of peer-reviewed evidence supporting Solu's use in real clinical and public health settings, alongside our BMC Bioinformatics publication [2] and the ESR and Awanui Labs work in New Zealand demonstrating same-day outbreak investigation [3].

The bottom line

In this independent study, Solu was the fastest platform tested, detected the most epidemiological clusters, and accurately identified species across a diverse clinical panel, while being recognized by the researchers as user-friendly enough to use without 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 to discuss implementation in your lab.

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. White et al. (2025). Preprint on medRxiv. https://www.medrxiv.org/content/10.1101/2025.02.04.25321496v1

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