Genomics (Quantitative Biology) Papers of the Week

We sequenced and characterized influenza viruses circulating in Southern Greece (S. Greece) during two consecutive influenza seasons, 2024–2025 and 2025–2026, using Oxford Nanopore Technologies (ONT) sequencing.

In this study, we established high glucose-induced cell models and diabetic rat models to investigate the role and molecular mechanisms of ADSC-exos in mitigating OS in DBD.

Here, we describe novel genomic alterations responsible for therapeutic resistance at a subtype level in a clinical trial (AALL1331) which randomized patients to blinatumomab and chemotherapy versus chemotherapy alone.

Methods We integrated transcriptomic data from three IDD cohorts (GSE167199, GSE176205, GSE186542) and performed differential expression analysis, WGCNA, consensus clustering, and CIBERSORT to identify oxidative stress-related genes, define molecular subtypes, and evaluate immune infiltration.

All findings in this study are derived from computational virtual screening and molecular docking simulations and require further experimental validation.

Results In the present study, a pan-genome analysis of the OsGRF gene family was carried out using 150 high-quality rice ( Oryza sativa L.) genomes of Geng ( Japonica ), Xian ( Indica ), Aus , Bas ( Basmati ), and Or ( Oryza rufipogon ) accessions.

But successful design requires capabilities that current benchmarks do not measure. Here we introduce BEND 2.0 (BENchmark for DNA language models), which tests how well models predict the effects of DNA changes and whether these predictions can guide sequence design.

In this paper, A Location-wise Proteome/transcriptome Abundance Comparative Analyzer (ALPACA) is introduced, a web-based visual data mining system for subcellular location-specific knowledge mining from multi-omics data.

Here, we examined how AlphaFold 3 (AF3) scaffold choice and conformational sampling affect Δ Δ G -based prioritization of TCR–pMHC complementarity-determining region (CDR) mutations.

Methods Paired differential expression analysis of GSE43292 (32 plaque vs. 32 paired macroscopically intact carotid samples), combined with weighted gene co-expression network analysis, defined a plaque-associated module that was intersected with 4,922 mitophagy-associated genes (GeneCards) to identify mitophagy-related DEGs.

Methods: We identified the potential lactylation-related biomarkers for heart failure (HF) by integrating bioinformatics analysis with machine learning algorithms.