Motivation: The digenic inheritance hypothesis holds the potential to enhance diagnostic yield in rare diseases. Computational approaches capable of accurately interpreting and prioritizing digenic combinations based on the proband’s phenotypic profiles and familial information can provide valuable assistance to clinicians during the diagnostic process. Results: We have developed diVas, a hypothesis-driven machine learning approach that can effectively interpret genomic variants across different gene pairs. DiVas demonstrates strong performance both in classifying and prioritizing causative pairs, consistently placing them within the top positions across 11 real cases (achieving 73% sensitivity and a median ranking of 3). Additionally, diVas exploits Explainable Artificial Intelligence (XAI) to dissect the digenic disease mechanism for predicted positive pairs. Availability and Implementation: Prediction results of the diVas method on a high-confidence, comprehensive, manually curated dataset of known digenic combinations are available at oliver.engenome.com .

Background/Objectives: DNA methylation is a key epigenetic process that regulates gene expression and is often disrupted in colorectal cancer (CRC). Aberrant methylation of promoter CpG islands can silence tumor suppressor genes and drive tumorigenesis. A subset of CRCs exhibits the CpG Island Methylator Phenotype (CIMP), characterized by widespread hypermethylation and distinct clinical outcomes. Identifying genes whose expression is epigenetically regulated by methylation is important for prioritizing candidate biomarkers and therapeutic targets in CRC. Methods: We developed and compared a series of computational approaches to identify genes whose expression is regulated by DNA methylation in The Cancer Genome Atlas (TCGA) cohort of Colon Adenocarcinoma (COAD) patients. Samples were stratified according to their CpG Island Methylator Phenotype (CIMP) level to capture distinct epigenetic subgroups. The proposed framework integrates methylation and transcriptomic data to systematically detect methylation–expression associations indicative of epigenetic regulation. Results: The best-performing method identified gene sets strongly associated with promoter methylation–expression relationships and enriched for pathways relevant to colorectal cancer progression and patient stratification. To evaluate the robustness and transferability of the approach, it was further validated on independent datasets, including Stomach Adenocarcinoma (STAD), Glioblastoma Multiforme (GBM), and Mesothelioma (MESO), supporting its robustness and potential generalizability across multiple tumor types. Conclusions: Our study highlights the potential of computational pipelines to uncover epigenetically regulated genes in colorectal cancer. The identified candidate genes provide a hypothesis-generating foundation for refining molecular stratification and guiding future studies aimed at epigenetic biomarker discovery and therapeutic hypothesis development.

In the modern era of genomic research, the scientific community is witnessing an explosive growth in the volume of published findings. While this abundance of data offers invaluable insights, it also places a pressing responsibility on genetic professionals and researchers to stay informed about the latest findings and their clinical significance. Genomic variant interpretation is currently facing a challenge in identifying the most up-to-date and relevant scientific papers, while also extracting meaningful information to accelerate the process from clinical assessment to reporting. Computer-aided literature search and summarization can play a pivotal role in this context. By synthesizing complex genomic findings into concise, interpretable summaries, this approach facilitates the translation of extensive genomic datasets into clinically relevant insights. To bridge this gap, we present VarChat (varchat.engenome.com), an innovative tool based on generative AI, developed to find and summarize the fragmented scientific literature associated with genomic variants into brief yet informative texts. VarChat provides users with a concise description of specific genetic variants, detailing their impact on related proteins and possible effects on human health. Additionally, VarChat offers direct links to related scientific trustable sources, and encourages deeper research. Availability: VarChat is freely available at varchat.engenome.com

Motivation: Standardized phenotypic descriptions are essential for accurate diagnosis, yet clinicians and researchers face challenges in manually extracting and mapping phenotypes from scientific literature or patient clinical records to the Human Phenotype Ontology. Recent advances in deep learning offer new opportunities for automation. We developed PhenoXtract, a novel phenotype extraction approach that combines Large Language Models and Knowledge Graph embedding. PhenoXtract is a multistep pipeline that takes clinical descriptions as input, extracts candidate phenotype entities using large language models, and maps them to terms from an enriched version of the Human Phenotype Ontology, processed as a knowledge graph. Results: Evaluation against expert-curated ground-truth datasets show a recall of 0.70 and precision of 0.85 for PhenoXtract, demonstrating concordance with manually extracted phenotypes, with a computation time of 10-20 seconds for each text analyzed. Moreover, PhenoXtract surpasses rule-based and deep learning-based state-of-the-art tools in two out of the three ground-truth datasets evaluated. These results suggest that hybrid approaches combining Large Language Models and Knowledge Graph embeddings represent a promising direction for automated clinical phenotyping at scale.