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Results for “Next-generation sequencing”

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PubMed2026

Proteome-wide curation of experimentally validated HPV T-cell epitopes identifies key gaps in our understanding of cellular immunity to HPV and informs vaccine design.

BACKGROUND: Human papillomavirus (HPV) drives both malignant and benign tumours. Current prophylactic vaccines are type-restricted, not optimised for T-cell induction, and lack therapeutic efficacy. Although T-cells are critical for both preventing and clearing HPV infection, experimentally validated HPV T-cell epitopes remain fragmented across the literature, limiting systematic evaluation of cellular immune targets. METHODS: We curated experimentally validated HPV T-cell epitopes from the Immune Epitope Database (IEDB). Epitopes were mapped across HPV proteins and genotypes, and analysed for response rate, sequence conservation across 454 representative HPV genomes, and HLA restriction patterns. RESULTS: 485 unique experimentally validated HPV epitopes have been described (133 studies; 1,494 functional assays). Consistent with research focus and viral biology, E6 and E7 proteins account for >60% of known HPV epitopes despite accounting for ~10% of the viral proteome. High-risk HPV types, especially HPV16 and HPV18, were the most studied (p <.001) and were enriched for CD8+ epitopes (p <.001). We identified major knowledge gaps, including: underrepresentation of structural proteins such as L2; limited epitope coverage for low-prevalence HPV genotypes; a bias towards common HLA alleles. In silico analysis indicated greater conservation of epitopes in L1/L2 and across high-risk HPV types. Conserved, commonly detected, and HLA-promiscuous epitopes were highlighted and we provide panels of candidate epitopes for consideration in immune monitoring, broad-spectrum prophylactic vaccines, and high-risk targeted therapeutic vaccines. CONCLUSION: This study provides the first comprehensive atlas of experimentally validated HPV T-cell epitopes and ranked epitope candidates for translational application. We demonstrate that our understanding of HPV T-cell immunity is constrained by biases in antigen, genotype and HLA focus and by incomplete epitope mapping. Addressing these gaps will be essential for a comprehensive assessment of cellular immunity and for utilising T-cells in next-generation vaccines.

Epitopes, T-Lymphocyte↗

PubMed2005

Efficient decoding algorithms for generalized hidden Markov model gene finders.

BACKGROUND: The Generalized Hidden Markov Model (GHMM) has proven a useful framework for the task of computational gene prediction in eukaryotic genomes, due to its flexibility and probabilistic underpinnings. As the focus of the gene finding community shifts toward the use of homology information to improve prediction accuracy, extensions to the basic GHMM model are being explored as possible ways to integrate this homology information into the prediction process. Particularly prominent among these extensions are those techniques which call for the simultaneous prediction of genes in two or more genomes at once, thereby increasing significantly the computational cost of prediction and highlighting the importance of speed and memory efficiency in the implementation of the underlying GHMM algorithms. Unfortunately, the task of implementing an efficient GHMM-based gene finder is already a nontrivial one, and it can be expected that this task will only grow more onerous as our models increase in complexity. RESULTS: As a first step toward addressing the implementation challenges of these next-generation systems, we describe in detail two software architectures for GHMM-based gene finders, one comprising the common array-based approach, and the other a highly optimized algorithm which requires significantly less memory while achieving virtually identical speed. We then show how both of these architectures can be accelerated by a factor of two by optimizing their content sensors. We finish with a brief illustration of the impact these optimizations have had on the feasibility of our new homology-based gene finder, TWAIN. CONCLUSIONS: In describing a number of optimizations for GHMM-based gene finders and making available two complete open-source software systems embodying these methods, it is our hope that others will be more enabled to explore promising extensions to the GHMM framework, thereby improving the state-of-the-art in gene prediction techniques.

Algorithms↗

PubMed2026

Engineering extracellular vesicles for targeted siRNA delivery: Advances, therapeutic applications, and clinical translation.

Small interfering RNA (siRNA) therapeutics have emerged as a transformative approach for sequence-specific gene silencing, offering the potential to treat a broad spectrum of diseases by selectively suppressing disease-associated genes. However, the clinical translation of siRNA remains limited by rapid enzymatic degradation, poor cellular uptake, inadequate endosomal escape, and off-target effects, necessitating the development of efficient delivery systems. Extracellular vesicles (EVs) have gained considerable attention as natural nanocarriers owing to their excellent biocompatibility, low immunogenicity, intrinsic targeting capability, and ability to protect therapeutic cargo while traversing complex biological barriers. This review comprehensively discusses the biological characteristics of EVs, the molecular basis of RNA interference, and the major challenges associated with siRNA delivery [Fig. 1]. Recent advances in EV engineering, including cargo-loading strategies such as electroporation, sonication, extrusion, parent-cell engineering, and microfluidic approaches, together with surface functionalization using peptides, antibodies, aptamers, and hybrid nanoplatforms, are critically evaluated for improving targeting specificity and intracellular delivery. Furthermore, the therapeutic applications of engineered EV-mediated siRNA delivery in cancer, neurological disorders, liver diseases, cardiovascular diseases, inflammatory disorders, and infectious diseases are systematically summarized, highlighting their potential to enhance gene silencing while minimizing systemic toxicity. Current challenges related to large-scale manufacturing, cargo-loading efficiency, standardization, quality control, regulatory approval, and clinical translation are also discussed, together with emerging technologies involving synthetic biology, genome engineering, artificial intelligence, and multifunctional hybrid vesicles. Overall, engineered extracellular vesicles represent a highly versatile and biologically inspired platform for targeted siRNA delivery, providing a promising foundation for the development of next-generation precision RNA therapeutics and accelerating the clinical translation of gene-silencing strategies.

Extracellular vesicle engineering↗

PubMed2006

Microchip-based cell lysis and DNA extraction from sperm cells for application to forensic analysis.

The current backlog of casework is among the most significant challenges facing crime laboratories at this time. While the development of next-generation microchip-based technology for expedited forensic casework analysis offers one solution to this problem, this will require the adaptation of manual, large-volume, benchtop chemistry to small volume microfluidic devices. Analysis of evidentiary materials from rape kits where semen or sperm cells are commonly found represents a unique set of challenges for on-chip cell lysis and DNA extraction that must be addressed for successful application. The work presented here details the development of a microdevice capable of DNA extraction directly from sperm cells for application to the analysis of sexual assault evidence. A variety of chemical lysing agents are assessed for inclusion in the extraction protocol and a method for DNA purification from sperm cells is described. Suitability of the extracted DNA for short tandem repeat (STR) analysis is assessed and genetic profiles shown. Finally, on-chip cell lysis methods are evaluated, with results from fluorescence visualization of cell rupture and DNA extraction from an integrated cell lysis and purification with subsequent STR amplification presented. A method for on-chip cell lysis and DNA purification is described, with considerations toward inclusion in an integrated microdevice capable of both differential cell sorting and DNA extraction. The results of this work demonstrate the feasibility of incorporating microchip-based cell lysis and DNA extraction into forensic casework analysis.

Buffers↗

PubMed2026

Network methods for diagonal integration of unpaired single-cell multiomics data: a review.

MOTIVATION: Advances in single-cell sequencing have enabled multiomics profiling at unprecedented resolution; however, mass spectrometry-based single-cell proteomics (scMS) remains inherently destructive, precluding simultaneous transcriptomic capture. Unlike antibody-based methods such as CITE-seq, which permit paired profiling but are restricted to targeted protein panels, scMS provides unbiased, genome-scale coverage of the intracellular proteome yet necessitates post hoc integration of unpaired datasets. This diagonal integration challenge, where transcriptomes and proteomes are measured in separate cells lacking shared anchors, remains underserved by existing reviews, which focus predominantly on vertical integration strategies enabled by non-destructive assays. RESULTS: We survey the complete computational pipeline for constructing mechanistic proteogenomic networks from unpaired single-cell data, covering: (i) unimodal network inference such as knowledge-based approaches, probabilistic graphical models, temporal directionality inference, and generative and foundation model strategies that establish the transcriptomic scaffold; (ii) cross-modal integration architectures such as network propagation, graph neural networks (scMRDR, scmFormer, scCotag), and consensus frameworks designed explicitly for the unpaired proteomics setting; and (iii) benchmarking paradigms spanning network reconstruction (BEELINE, GRETA, CausalBench) and multi-task integration evaluation (scMultiBench, SCMMIB), with guidance on metric selection under network sparsity and class imbalance. We identify three principal axes of future development: generative proteomic translation from transcriptomic precursors, inductive prior embedding in next-generation architectures, and perturbation-based causal benchmarking. AVAILABILITY AND IMPLEMENTATION: This is a review article; no novel software is distributed. A curated benchmark resource table, methods starter guide, and per-method bottleneck annotations are provided in the Supplementary Material.

Multiomics↗