Journal of Genome Biotechnology and Genetics doi: 10.3390/jgbg1010004

Authors: Paytin Knebel Jacob Harris Isaac Steveson Bridger Kearns Andrew S. Todeschini Lindsay Perrett DeLaney Anderson Erick Beltran Bryson Leary Jonah Settle Isaac Carlson Hudson Christensen Alberto Trujano Abraham B. Alton Ken Dixon Jared J. Barrott

Intratumoral heterogeneity is a defining feature of cancer, yet standard sequencing and reporting practices often overlook somatic variants present at low variant allele frequencies (VAFs), commonly below 5%. Increasing evidence indicates that these rare alleles can represent clinically meaningful subclones involved in tumor evolution, therapeutic resistance, minimal residual disease, and metastatic dissemination. However, detecting and interpreting low-VAF variants is technically and analytically challenging because background error rates, library artifacts, genomic context, and caller assumptions increasingly overlap with true signal as allele fraction decreases. In this review, we integrate biological and clinical evidence supporting the relevance of low-VAFs and evaluate constraints across sequencing strategies, including whole genome and whole exome approaches and deep targeted panels. We discuss why detectability depends strongly on variant class and genome architecture, with SNVs generally more tractable than indels and structural variants. We then summarize practical approaches that improve sensitivity and specificity beyond increasing depth, including proper tissue handling, molecular enrichment, unique molecular identifiers, duplex-consensus methods, advanced error modeling, and orthogonal validation. Finally, we highlight emerging single-cell, spatial, and multiomic technologies that resolve rare variants in a cellular context. Collectively, these advances support incorporating low-VAF detection into precision oncology frameworks.

Journal of Genome Biotechnology and Genetics doi: 10.3390/jgbg1010003

Authors: Karima Bahmane Sambit Bhattacharya My Abdelmajid Kassem

Missense single nucleotide variants (SNVs) represent one of the most common forms of genetic variation and account for a substantial proportion of variants of uncertain significance in clinical databases. Accurate computational classification of these variants remains an important challenge in precision medicine and genomic research. In this study, we present PathoPredictor, an interpretable machine-learning framework designed to distinguish pathogenic from benign missense variants using curated clinical variant data and functional annotations. High-confidence variants were obtained from the November 2023 ClinVar release and annotated using dbNSFP v5.1 (GRCh37). After data filtering, imputation, and normalization, 59,302 expert-reviewed missense variants were retained for model development. Six machine-learning algorithms were evaluated under identical cross-validation conditions applied to the training set. Among the evaluated models, LightGBM demonstrated the strongest overall performance and was selected as the final PathoPredictor classifier, achieving a mean ROC–AUC of 0.93 ± 0.004, accuracy of 0.90 ± 0.006, and Matthew’s correlation coefficient of 0.80 ± 0.008 across five cross-validation folds. Model interpretability was examined using SHAP (SHapley Additive exPlanations), enabling both global feature ranking and variant-level explanation of predictions. Temporal validation using ClinVar variants submitted after November 2023 showed consistent predictive performance on previously unseen submissions within the same database ecosystem (ROC–AUC = 0.91). While the framework demonstrates strong discrimination and structured interpretability, potential limitations include training data bias and partial circularity associated with the inclusion of existing meta-predictors. Overall, PathoPredictor provides a reproducible and interpretable computational framework for integrating functional annotations in missense variant prioritization, supporting research and genomic analysis workflows.

Journal of Genome Biotechnology and Genetics doi: 10.3390/jgbg1010002

Authors: Mohamed Zarid

Fruit quality in melon (Cucumis melo L.) is determined by complex traits such as texture and aroma, which are shaped by both genetic factors and environmental conditions. In this study, we applied an integrated physiology–metabolomics–transcriptomics approach to examine the genetic and seasonal regulation of these traits in the near-isogenic line SC10-2, carrying a defined introgression on linkage group X (LG X), in comparison with its recurrent parent ‘Piel de Sapo’ (PS). Fruit firmness, juiciness, respiration, ethylene production, and volatile organic compounds (VOCs) were evaluated over postharvest ripening across two growing seasons. SC10-2 consistently exhibited firmer flesh, reduced juiciness, and distinct VOC profiles relative to PS, although the magnitude of these differences varied between seasons. Transcriptomic analysis identified 2954 differentially expressed genes genome-wide, including 909 genes located within the LG X introgression, among which candidate genes such as CmTrpD, CmHK4-like, and CmNAC18 showed expression patterns associated with texture- and aroma-related traits. Seasonal comparisons indicated that VOC composition was particularly sensitive to environmental variation, underscoring the contribution of genotype × season interactions to fruit quality expression. Together, these results refine the phenotypic and molecular characterization of the LG X introgression in SC10-2 and provide testable candidate genes and hypotheses for understanding the genetic basis of melon texture and aroma under the studied conditions.

Journal of Genome Biotechnology and Genetics doi: 10.3390/jgbg1010001

Authors: Khalid Meksem

As Editor-in-Chief, I am excited to introduce the Journal of Genome Biotechnology and Genetics during a period of unprecedented innovation in the field of genomics and genetics [...]