A Pipeline for Classifying Deleterious Coding Mutations in Agricultural Plants.

ABSTRACT: The impact of deleterious variation on both plant fitness and crop productivity is not completely understood and is a hot topic of debates. The deleterious mutations in plants have been solely predicted using sequence conservation methods rather than function-based classifiers due to lack of well-annotated mutational datasets in these organisms. Here, we developed a machine learning classifier based on a dataset of deleterious and neutral mutations in Arabidopsis thaliana by extracting 18 informative features that discriminate deleterious mutations from neutral, including 9 novel features not used in previous studies. We examined linear SVM, Gaussian SVM, and Random Forest classifiers, with the latter performing best. Random Forest classifiers exhibited a markedly higher accuracy th

SUBMITTER: Kovalev MS 

PROVIDER: S-EPMC6279870 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature