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This preprint describes TITAN-RNA, a hybrid-capture next-generation sequencing panel designed to detect known and potentially divergent or previously uncharacterized Flaviviridae and other tick-borne viral pathogens for diagnostic and vector-surveillance applications. The study reports bench validation of the assay using simulated novel viruses and field samples, demonstrating tolerance to sequence divergence, a low extrapolated limit of detection in blood, strong linearity, and the identification of two putatively novel segmented Flavi-like viruses from Haemaphysalis longicornis ticks in New York State.

The work moves the field forward by addressing an important limitation of PCR-based diagnostics: high target specificity can reduce detection of divergent or evolving viral sequences. By combining hybrid-capture enrichment, sequencing, and phylogenetic analysis, this approach provides a more flexible framework for pathogen detection, viral subtyping, and real-time surveillance of viral evolution. The study is particularly valuable for public health surveillance because it suggests that targeted sequencing panels can bridge the gap between highly specific PCR assays and broader metagenomic approaches, while remaining compatible with standard molecular biology workflows and benchtop sequencing platforms.

Major issues

• Clarify the intended diagnostic scope of TITAN-RNA. The title emphasizes known and unknown Flaviviridae, while the abstract states that the panel was developed for “all known viral tick-borne pathogens.” The authors should clearly define whether TITAN-RNA is intended as a Flaviviridae-focused assay, a broader tick-borne viral panel, or a hybrid panel with different levels of validation across viral families. A table listing all targeted viruses, genome regions, probe coverage, and expected detection limits by viral group would strengthen the manuscript.

• Provide more detail on analytical validation design. The reported extrapolated limit of detection of 19.1 genome copies in blood is impressive, but the manuscript should clearly report the number of replicates, dilution series design, input material, extraction method, sample matrix, confidence intervals, and acceptance criteria used for LoD determination. Because hybrid-capture sequencing can show variability at very low input levels, more information on stochastic dropout, reproducibility, and near-LoD performance would improve confidence in the diagnostic claims.

• Expand reproducibility and robustness assessment. For diagnostic or surveillance use, the authors should clarify whether assay performance was evaluated across different runs, operators, extraction batches, library-preparation batches, capture batches, and sequencing runs. Inter-run and intra-run reproducibility data would be especially important if the assay is proposed for public health or clinical diagnostic implementation.

• Clarify how mutation-tolerance performance was validated. The study reports tolerance of 10% evenly distributed mutations and 27% naturally occurring viral divergence. This is an important finding, but the authors should explain how these thresholds were derived, how many simulated and naturally divergent targets were tested, and whether detection varied by genome region, GC content, probe density, viral load, or mutation clustering. A coverage-depth comparison across divergence levels would make the claim more transparent.

• Strengthen evidence supporting the two putatively novel Flavi-like viruses. The identification of two putatively novel segmented Flavi-like viruses is a significant finding. The authors should provide clear evidence that these signals are not due to index hopping, environmental contamination, assembly artifacts, reagent contamination, or low-level cross-sample carryover. Useful supporting data would include negative controls, independent extraction or library confirmation, genome coverage plots, read-pair support, assembly statistics, phylogenetic placement, and preferably orthogonal confirmation by RT-PCR or targeted amplicon sequencing.

• Clarify quantitative interpretation of hybrid-capture sequencing data. The reported linearity performance is encouraging, but hybrid-capture workflows may introduce enrichment bias depending on probe design, target divergence, input concentration, and capture efficiency. The authors should clarify whether the assay is intended to provide quantitative viral load estimates or semi-quantitative detection trends. If quantitative use is proposed, additional validation across matrices and viral targets would be important.

• Add more detail on the bioinformatics and reporting pipeline. Since diagnostic and surveillance interpretation depends heavily on analysis, the manuscript should describe the pipeline in more detail, including read trimming, host/background subtraction, alignment or assembly methods, taxonomic classification, phylogenetic analysis, minimum read-depth thresholds, breadth-of-coverage requirements, contamination controls, and criteria for calling a positive, divergent, or novel virus.

Minor issues

• Define TITAN-RNA early in the manuscript. The acronym should be expanded clearly at first use if not already done.

• Improve consistency between “diagnostics” and “surveillance” language. The manuscript would benefit from separating claims related to clinical diagnostic testing from claims related to vector surveillance, because the validation expectations and sample matrices may differ.

• Clarify sample types and matrices. The abstract mentions blood and field samples. The manuscript should clearly list all tested matrices, including blood, tick homogenates, environmental/vector samples, and any contrived samples.

• Include a concise workflow figure. A figure showing sample input → extraction → library preparation → hybrid capture → sequencing → bioinformatics → interpretation/reporting would improve readability.

• Provide probe-design details in a table or supplement. The authors should summarize probe design criteria, targeted viral groups, genome regions covered, expected mismatch tolerance, and any regions intentionally excluded.

• Clarify “known” versus “unknown” detection. The manuscript should define what qualifies as “unknown” detection: divergent strain detection, novel species detection, novel genus-level detection, or discovery of highly divergent Flavi-like viruses.

• Report confidence intervals where possible. Confidence intervals for LoD, linearity, and detection performance would make the validation metrics more interpretable.

• Make data availability clear. Sequence data, consensus genomes, probe-design resources where appropriate, and analysis scripts should be made available or clearly referenced to support reproducibility.

• Clarify whether orthogonal confirmation was performed. If RT-PCR, Sanger sequencing, amplicon sequencing, or independent library preparation was used to confirm key findings, this should be highlighted more clearly.

• Consider a limitations paragraph. The authors should explicitly discuss limitations such as probe-bias, reduced performance for highly divergent viruses, matrix-specific sensitivity, contamination risk in low-input sequencing, and requirements for bioinformatics expertise.

Overall assessment

This is a timely and valuable study with clear relevance to sequencing-based diagnostics, pathogen surveillance, and public health monitoring of viral evolution. The hybrid-capture approach appears promising because it maintains targeted enrichment while allowing broader detection than traditional PCR assays. The strongest aspects of the work are the focus on divergent-virus detection, the reported analytical sensitivity and linearity, and the application to field surveillance samples. The main areas needing clarification are the exact validated scope of the panel, reproducibility across runs and sample types, detailed criteria for novel-virus calls, and the extent to which the assay can support quantitative diagnostic interpretation. With these additions, the manuscript would provide a stronger and more implementable framework for hybrid-capture viral sequencing in diagnostic and surveillance settings.

Competing interests

The author declares that they have no competing interests.

Use of Artificial Intelligence (AI)

The author declares that they used generative AI to come up with new ideas for their review.