Bridging Genomics to Phenomics at Atomic Resolution through Variation Spatial Profiling.

ABSTRACT: To understand the impact of genome sequence variation (the genotype) responsible for biological diversity and human health (the phenotype) including cystic fibrosis and Alzheimer's disease, we developed a Gaussian-process-based machine learning (ML) approach, variation spatial profiling (VSP). VSP uses a sparse collection of known variants found in the population that perturb the protein fold to define unknown variant function based on the emergent general principle of spatial covariance (SCV). SCV quantitatively captures the role of proximity in genotype-to-phenotype spatial-temporal relationships. Phenotype landscapes generated through SCV provide a platform that can be used to describe the functional properties that drive sequence-to-function-to-structure design of the polypeptide fold

SUBMITTER: Wang C 

PROVIDER: S-EPMC6261431 | biostudies-literature | 2018 Aug

REPOSITORIES: biostudies-literature