In silico methods for predicting functional synonymous variants

Brian C Lin; Upendra Katneni; Katarzyna I Jankowska; Douglas Meyer; Chava Kimchi-Sarfaty

doi:10.1186/s13059-023-02966-1

In silico methods for predicting functional synonymous variants

Genome Biol. 2023 May 22;24(1):126. doi: 10.1186/s13059-023-02966-1.

Authors

Brian C Lin¹, Upendra Katneni¹, Katarzyna I Jankowska¹, Douglas Meyer¹, Chava Kimchi-Sarfaty²

Affiliations

¹ Hemostasis Branch 1, Division of Hemostasis, Office of Plasma Protein Therapeutics CMC, Office of Therapeutic Products, Center for Biologics Evaluation and Research, US FDA, Silver Spring, MD, USA.
² Hemostasis Branch 1, Division of Hemostasis, Office of Plasma Protein Therapeutics CMC, Office of Therapeutic Products, Center for Biologics Evaluation and Research, US FDA, Silver Spring, MD, USA. Chava.Kimchi-Sarfaty@fda.hhs.gov.

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.

Publication types

Review
Research Support, Non-U.S. Gov't

MeSH terms

Humans
Machine Learning
Neoplasms*
Polymorphism, Single Nucleotide*
RNA

Substances

RNA