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PubMed2026

Performance comparison of rapid and native barcoding methods for Oxford Nanopore sequencing of Poliovirus Viral Protein 1 (VP1) amplicons.

Accurate and timely sequencing of poliovirus is critical for global eradication efforts, particularly for molecular epidemiology based on the typing region of the genome, viral protein 1 (VP1). While Oxford Nanopore Technologies (ONT) sequencing has expanded capabilities for poliovirus surveillance, the relative performance of different ONT library preparation methods, including ligation-based (Native Barcoding) and transposase-based (Rapid Barcoding) approaches, has not been systematically evaluated. In this study, we compared rapid barcoding and native barcoding workflows for sequencing VP1 amplicons from 17 type 2 poliovirus-positive samples, each processed in triplicate. Native barcoding generated significantly more sequencing output, producing approximately 2.3-fold greater total read yield than rapid barcoding, and demonstrated higher run-to-run reproducibility (R2 = 0.979-0.998 vs. 0.847-0.929, respectively; p&#x202f;<&#x202f;0.001). In addition, native barcoding generated 80% of the total yield achieved by rapid barcoding within approximately 7&#x202f;h, whereas rapid barcoding required approximately 40&#x202f;h to reach the same output. Despite these differences, both methods produced identical VP1 consensus sequences across all samples, with comparable read quality (median per-base Q-scores of approximately Q17-Q18). Rapid barcoding provided substantial practical advantages, reducing hands-on library preparation time (55 vs. 200&#x202f;min) and per-sample cost ($12.82 vs. $16.54), while simplifying workflow and reducing technical complexity. These findings indicate that sequencing yield may not be a determinant of downstream analytical outcomes for poliovirus VP1 ONT sequencing. Rapid barcoding therefore represents a cost-effective and efficient approach for routine poliovirus surveillance, whereas native barcoding remains advantageous in applications requiring rapid data generation or maximal sequencing depth.

Poliovirus↗
PubMed2024

TGIRT-seq to profile tRNA-derived RNAs and associated RNA modifications.

RNA modifications are key regulators for RNA processes. tRNA-derived RNAs are small RNAs with size between 15 and 50 bases long that are processed from mature or precursor tRNAs. Despite their more recent discovery, tRNA-derived RNAs have been found to play regulatory roles in many cellular processes including gene silencing, protein synthesis, stress response, and transgenerational inheritance. Furthermore, tRNA-derived RNAs are highly abundant in bodily fluids, posing as potential biomarkers. A unique feature of tRNA-derived RNAs is that they are rich in RNA modifications. Many of the RNA modifications on tRNA-derived RNAs disrupt Watson-Crick base pairing and will thus stall reverse transcriptase, such as N1-methyladenosine (m1A), N1-methylguanosine (m1G) and N2, N2-dimethylguanosine (m22G). These RNA modifications add another layer of regulation onto tRNA-derived RNAs' functions and are of interests for future research. However, these RNA modifications could also lead to lower detection of modification-containing RNAs in genome-wide small RNA sequencing analysis due to reverse transcriptase stall. To circumvent this bias, TGIRT (Thermostable Group II Intron Reverse Transcriptase) has been used to readthrough RNA modifications inserting mismatches. These mismatch signatures can then be used to precisely map the modification sites at base resolution. Here we describe the step-by-step experimental protocol to start with purified RNAs from cells or tissues and use TGIRT to make small RNA sequencing library for Illumina sequencing to profile the abundance of tRNA-derived RNAs and the associated RNA modifications.

RNA, Transfer↗
PubMed2026

Reference-Free Microsatellite Instability Detection from Tumor Sequencing Using Intrasample Variability Modeling.

Microsatellite instability (MSI) is a predictive biomarker in several tumor types. However, many next-generation sequencing-based callers require matched normal samples, reference panels, or pretrained models, limiting their portability across assays and sequencing centers. We developed PROMIS (PROfiling of Microsatellite InStability), a tumor-only, reference-free pipeline that uses a discrete mixture model to characterize intrasample repeat-length distributions at predefined microsatellite loci. Locus-level classifications are then aggregated into a continuous MSI score. We benchmarked PROMIS in colorectal (CRC), endometrial (UCEC), and gastric (STAD) cancers from The Cancer Genome Atlas. PROMIS achieved an overall area under the receiver operating characteristic curve (AUC) of 0.995 and cohort-specific AUCs of 1.00 in CRC and stomach adenocarcinoma and 0.999 in uterine corpus endometrial carcinoma, comparable to established tools despite not using matched normals or pretrained models. Subsampling demonstrated robust performance with substantially fewer loci. In silico dilution showed progressively reduced MSI-microsatellite-stable discrimination, with the pooled AUC declining from 0.83 at 10% tumor fraction to 0.53 at 1%. At low tumor fractions, tumor-type-specific baseline microsatellite variability increasingly influenced PROMIS scores. Finally, in prostate and CRC cell-free DNA cohorts, including Illumina TSO500 data and an 18-gene panel, PROMIS yielded MSI scores concordant with orthogonal tissue- and panel-based classifications across the evaluated Illumina-based sequencing contexts. Accordingly, the present validation should be considered limited to Illumina-based sequencing platforms. PROMIS is intended to complement existing genomic profiling workflows by enabling MSI assessment from sequencing data already generated for broader molecular analyses. Prospective clinical validation remains necessary before clinical implementation.

Journal Article↗
PubMed2025

HIV-phyloTSI: subtype-independent estimation of time since HIV-1 infection for cross-sectional measures of population incidence using deep sequence data.

BACKGROUND: Estimating the time since HIV infection (TSI) at population level is essential for tracking changes in the global HIV epidemic. Most methods for determining TSI give a binary classification of infections as recent or non-recent within a window of several months, and cannot assess the cumulative impact of an intervention. RESULTS: We developed a Random Forest Regression model, HIV-phyloTSI, which combines measures of within-host diversity and divergence to generate continuous TSI estimates directly from viral deep-sequencing data, with no need for additional variables. HIV-phyloTSI provides a continuous measure of TSI up to 9&#xa0;years, with a mean absolute error of less than 12&#xa0;months overall and less than 5&#xa0;months for infections with a TSI of up to a year. It performs equally well for all major HIV subtypes based on data from African and European cohorts. CONCLUSIONS: We demonstrate how HIV-phyloTSI can be used for incidence estimates on a population level.

HIV Infections↗