Machine Learning Approaches for the Prioritization of Genomic Variants Impacting Pre-mRNA Splicing.

ABSTRACT: Defects in pre-mRNA splicing are frequently a cause of Mendelian disease. Despite the advent of next-generation sequencing, allowing a deeper insight into a patient's variant landscape, the ability to characterize variants causing splicing defects has not progressed with the same speed. To address this, recent years have seen a sharp spike in the number of splice prediction tools leveraging machine learning approaches, leaving clinical geneticists with a plethora of choices for in silico analysis. In this review, some basic principles of machine learning are introduced in the context of genomics and splicing analysis. A critical comparative approach is then used to describe seven recent machine learning-based splice prediction tools, revealing highly diverse approaches and common caveats.

SUBMITTER: Rowlands CF 

PROVIDER: S-EPMC6953098 | biostudies-literature | 2019 Nov

REPOSITORIES: biostudies-literature