Towards the genomic sequence code of DNA fragility for machine learning.

ABSTRACT: Genomic DNA breakages and the subsequent insertion and deletion mutations are important contributors to genome instability and linked diseases. Unlike the research in point mutations, the relationship between DNA sequence context and the propensity for strand breaks remains elusive. Here, by analyzing the differences and commonalities across myriads of genomic breakage datasets, we extract the sequence-linked rules and patterns behind DNA fragility. We show the overall deconvolution of the sequence influence into short-, mid- and long-range effects, and the stressor-dependent differences in defining the range and compositional effects on DNA fragility. We summarize and release our feature compendium as a library that can be seamlessly incorporated into genomic machine learning procedures,

SUBMITTER: Pflughaupt P 

PROVIDER: S-EPMC11602142 | biostudies-literature | 2024 Nov

REPOSITORIES: biostudies-literature