Multimodal learning of noncoding variant effects using genome sequence and chromatin structure.

ABSTRACT:

Motivation

A growing amount of noncoding genetic variants, including single-nucleotide polymorphisms, are found to be associated with complex human traits and diseases. Their mechanistic interpretation is relatively limited and can use the help from computational prediction of their effects on epigenetic profiles. However, current models often focus on local, 1D genome sequence determinants and disregard global, 3D chromatin structure that critically affects epigenetic events.

Results

We find that noncoding variants of unexpected high similarity in epigenetic profiles, with regards to their relatively low similarity in local sequences, can be largely attributed to their proximity in chromatin structure. Accordingly, we have developed a multimodal deep learning scheme that in

SUBMITTER: Tan W 

PROVIDER: S-EPMC10502240 | biostudies-literature | 2023 Sep

REPOSITORIES: biostudies-literature