In Silico Methods for Predicting Functional Synonymous Variants

Overview

Journal Genome Biol

Specialties Biology
Genetics

Date 2023 May 22

PMID 37217943

Authors

Brian C Lin

Upendra Katneni

Katarzyna I Jankowska

Douglas Meyer

Chava Kimchi-Sarfaty

Affiliations

Soon will be listed here.

Abstract

Single nucleotide variants (SNVs) contribute to human genomic diversity. Synonymous SNVs are previously considered to be "silent," but mounting evidence has revealed that these variants can cause RNA and protein changes and are implicated in over 85 human diseases and cancers. Recent improvements in computational platforms have led to the development of numerous machine-learning tools, which can be used to advance synonymous SNV research. In this review, we discuss tools that should be used to investigate synonymous variants. We provide supportive examples from seminal studies that demonstrate how these tools have driven new discoveries of functional synonymous SNVs.

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