Prioritising Functional Noncoding Variants via eRNA Post-transcriptional Interaction Maps in Human Samples
Benova, N.; Kuklinkova, R.; Haigh, J. L.; Boyne, J. R.; Anene, C. A.
Show abstract
Noncoding variants and mutations outnumber their coding counterparts but remain challenging to interpret functionally. We present TranCi, a method that prioritises human genetic variations by integrating enhancer RNA (eRNA) expression with eRNA-mRNA interactome maps. By linking variant-associated changes in eRNA to downstream gene regulation, TranCi captures functional effects missed by sequence-based or chromatin-centric approaches. In esophageal squamous cell cancer, TranCi identifies noncoding mutations with roles in disease initiation and progression. A personalised mode enables analysis at single-patient resolution, uncovering potential individual-specific regulatory variants. TranCi thus provides a mechanistic framework for interpreting noncoding variations and uniquely identifies their downstream targets, where standard methods often fall short. TranCi is available as a module within the eRNAkit R package (https://github.com/AneneLab/eRNAkit), leveraging its database of eRNA expression and interactions for functional variant interpretation.
Matching journals
The top 4 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- G4mer: An RNA language model for transcriptome-wide identification of G-quadruplexes and disease variants from population-scale genetic data 96%
- Integrative analysis reveals RNA G-Quadruplexes in UTRs are selectively constrained and enriched for functional associations 96%
- Single-molecule, full-length transcript isoform sequencing reveals disease mutation-associated RNA isoforms in cardiomyocytes 96%
Similar papers in this journal
- RNA allelic frequencies of somatic mutations encode substantial functional information in cancers 96%
- SVFX: a machine-learning framework to quantify the pathogenicity of structural variants 95%
- GeneWalk identifies relevant gene functions for a biological context using network representation learning 95%
Similar papers in this journal
- Genome-wide prediction of pathogenic gain- and loss-of-function variants from ensemble learning of diverse feature set 96%
- A systematic analysis of splicing variants identifies new diagnoses in the 100,000 Genomes Project. 95%
- A global cancer data integrator reveals principles of synthetic lethality, sex disparity and immunotherapy. 95%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.