A multi-dimensional integrative scoring framework for predicting functional variants in the human genome.

ABSTRACT: Attempts to identify and prioritize functional DNA elements in coding and non-coding regions, particularly through use of in silico functional annotation data, continue to increase in popularity. However, specific functional roles can vary widely from one variant to another, making it challenging to summarize different aspects of variant function with a one-dimensional rating. Here we propose multi-dimensional annotation-class integrative estimation (MACIE), an unsupervised multivariate mixed-model framework capable of integrating annotations of diverse origin to assess multi-dimensional functional roles for both coding and non-coding variants. Unlike existing one-dimensional scoring methods, MACIE views variant functionality as a composite attribute encompassing multiple characteristics a

SUBMITTER: Li X 

PROVIDER: S-EPMC8948160 | biostudies-literature | 2022 Mar

REPOSITORIES: biostudies-literature