Background/Objectives: Massively parallel sequencing technologies have advanced chronic lymphocytic leukemia (CLL) diagnostics and precision oncology. Illumina platforms, while offering robust performance, require substantial infrastructure investment and a large number of samples for cost-efficiency. Conversely, third-generation long-read nanopore sequencing from Oxford Nanopore Technologies (ONT) can significantly reduce sequencing costs, making it a valuable tool in resource-limited settings. However, nanopore sequencing faces challenges with lower accuracy and throughput than Illumina platforms, necessitating additional computational strategies. In this paper, we demonstrate that integrating publicly available short-read data with in-house generated ONT data, along with the application of machine learning approaches, enables the characterization of the CLL transcriptome landscape, the identification of clinically relevant molecular subtypes, and the assignment of these subtypes to nanopore-sequenced samples. Methods: Public Illumina RNA sequencing data for 608 CLL samples were obtained from the CLL-Map Portal. CLL transcriptome analysis, gene module identification, and transcriptomic subtype classification were performed using the oposSOM R package for high-dimensional data visualization with self-organizing maps. Eight CLL patients were recruited from the Hematology Center After Prof. R. Yeolyan (Yerevan, Armenia). Sequencing libraries were prepared from blood total RNA using the PCR-cDNA sequencing-barcoding kit (SQK-PCB109) following the manufacturer’s protocol and sequenced on an R9.4.1 flow cell for 24–48 h. Raw reads were converted to TPM values. These data were projected into the SOMs space using the supervised SOMs portrayal (supSOM) approach to predict the SOMs portrait of new samples using support vector machine regression. Results: The CLL transcriptomic landscape reveals disruptions in gene modules (spots) associated with T cell cytotoxicity, B and T cell activation, inflammation, cell cycle, DNA repair, proliferation, and splicing. A specific gene module contained genes associated with poor prognosis in CLL. Accordingly, CLL samples were classified into T-cell cytotoxic, immune, proliferative, splicing, and three mixed types: proliferative–immune, proliferative–splicing, and proliferative–immune–splicing. These transcriptomic subtypes were associated with survival orthogonal to gender and mutation status. Using supervised machine learning approaches, transcriptomic subtypes were assigned to patient samples sequenced with nanopore sequencing. Conclusions: This study demonstrates that the CLL transcriptome landscape can be parsed into functional modules, revealing distinct molecular subtypes based on proliferative and immune activity, with important implications for prognosis and treatment that are orthogonal to other molecular classifications. Additionally, the integration of nanopore sequencing with public datasets and machine learning offers a cost-effective approach to molecular subtyping and prognostic prediction, facilitating more accessible and personalized CLL care.

Grape phylloxera (Daktulosphaira vitifoliae Fitch) is a major global pest of cultivated grapevine. Armenia presents an unusual viticultural system, with 85-90% of vineyards remaining own-rooted despite the established presence of phylloxera. We investigated the genetic diversity and population structure of 370 root-feeding individuals collected from three localities in the Ararat Valley and Tavush Province using seven polymorphic simple sequence repeat markers. The seven loci yielded 48 alleles, with mean expected and observed heterozygosities of 0.570 and 0.933, re-spectively. Analysis of molecular variance revealed significant regional differentiation (Fst = 0.127, P = 0.001), driven primarily by differentiation between Tavush and the Ararat Valley popu-lations. The 370 individuals comprised 55 multilocus genotypes (MLGs), with two dominant MLGs represented by 171 and 116 individuals, together accounting for 77.6% of the sample. A population-aware, clone-corrected analysis of 59 MLG-by-population observations confirmed the same regional pattern and yielded stronger differentiation (Fst = 0.163, P = 0.001). Concordant separation of Tavush from the Ararat Valley was observed in principal coordinates analysis and discriminant analysis of principal components. The predominance of repeated MLGs is consistent with substantial clonal amplification. This study provides the first molecular baseline for D. vitifoliae in Armenia and establishes a foundation for monitoring genotype persistence, dispersal, and regional differentiation.