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In silico methods for predicting functional synonymous variants.

Brian C Lin1, Upendra Katneni1, Katarzyna I Jankowska1

  • 1Hemostasis 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.

Genome Biology
|May 22, 2023
PubMed
Summary
This summary is machine-generated.

Synonymous single nucleotide variants (SNVs) impact RNA and proteins, influencing human diseases. This review highlights computational tools essential for researching these functional synonymous SNVs.

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Area of Science:

  • Genomics
  • Molecular Biology
  • Bioinformatics

Background:

  • Single nucleotide variants (SNVs) are key to human genomic diversity.
  • Synonymous SNVs, once thought silent, are now known to alter RNA and protein functions.
  • These variants are implicated in over 85 human diseases and cancers.

Purpose of the Study:

  • To review computational tools for investigating synonymous variants.
  • To guide researchers in selecting appropriate tools for synonymous SNV analysis.
  • To showcase how these tools facilitate discoveries in functional synonymous SNVs.

Main Methods:

  • Literature review of computational tools for synonymous SNV analysis.
  • Analysis of machine-learning platforms relevant to variant research.
  • Case study examples from seminal research.

Main Results:

  • Numerous machine-learning tools are available for synonymous SNV research.
  • These tools have advanced the understanding of functional synonymous SNVs.
  • Specific examples demonstrate the impact of these tools on discovery.

Conclusions:

  • Computational tools are crucial for advancing synonymous SNV research.
  • Understanding functional synonymous SNVs is vital for disease and cancer research.
  • The reviewed tools offer powerful approaches to investigate these variants.