Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Leaky Scanning02:28

Leaky Scanning

5.3K
During most eukaryotic translation processes, the small 40S ribosome subunit scans an mRNA from its 5' end until it encounters the first start AUG codon. The large 60S ribosomal subunit then joins the smaller one to initiate protein synthesis. The location of the translation initiation is largely determined by the nucleotides near the start codon as there may be multiple translation initiation sites present on the mRNA.  Marilyn Kozak discovered that the sequence RCCAUGG (where R...
5.3K
Comparing Copy Number Variations and SNPs02:26

Comparing Copy Number Variations and SNPs

18.1K
Sequencing of the human genome has opened up several best-kept secrets of the genome. Scientists have identified thousands of genome variations that exist within a population. These variations can be a single nucleotide or a larger chromosomal variation.
Copy number variations or CNVs are the structural variations that cover more than 1kb of DNA sequence. The single nucleotide polymorphism (SNP), on the other hand, is a single nucleotide change or a point mutation that is found in more than 1%...
18.1K
Single Nucleotide Polymorphisms-SNPs01:05

Single Nucleotide Polymorphisms-SNPs

16.8K
A single nucleotide polymorphism or SNP is a single nucleotide variation at a specific genomic position in a large population. It is the most prevalent type of sequence variation found in the human genome. Point mutations that occur in more than 1% of the population qualify as SNPs. These are present once every 1000 nucleotides on an average in the human genome. Replacement of a purine with another purine (A/G) or a pyrimidine with another pyrimidine (C/T) is known as a transition. In contrast,...
16.8K
Genome-wide Association Studies-GWAS01:11

Genome-wide Association Studies-GWAS

14.7K
Genome-wide association studies or GWAS are used to identify whether common SNPs are associated with certain diseases. Suppose specific SNPs are more frequently observed in individuals with a particular disease than those without the disease. In that case, those SNPs are said to be associated with the disease. Chi-square analysis is performed to check the probability of the allele likely to be associated with the disease.
GWAS does not require the identification of the target gene involved in...
14.7K
Translation01:31

Translation

16.3K
Translation is the process of synthesizing proteins from the genetic information carried by messenger RNA (mRNA). Following transcription, it constitutes the final step in the expression of genes. This process is carried out by ribosomes, complexes of protein and specialized RNA molecules. Ribosomes, transfer RNA (tRNA), and other proteins produce a chain of amino acids—the polypeptide—as the end product of translation.
Translation Produces the Building Blocks of Life
Proteins are...
16.3K
Nonsense-mediated mRNA Decay02:27

Nonsense-mediated mRNA Decay

11.0K
The Upf proteins that carry out nonsense-mediated decay (NMD) are found in all eukaryotic organisms, including humans. Each protein has an individual role, but they need to work in collaboration. Upf1 is an ATP-dependent RNA helicase that unwinds the RNA helix. Because Upf1 can unwind any RNA, Upf2 and Upf3 are required to help Upf1 discriminate between nonsense and normal mRNAs.
Usually, Upf3 binds to an Exon Junction Complex (EJC) at mRNA splice sites. If a ribosome fully translates the mRNA,...
11.0K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Whole metagenome sequencing: not deep enough for complete microbial function recovery.

Microbiome·2026
Same author

Nationwide spread of multidrug resistant Klebsiella pneumoniae across US communities.

Nature communications·2026
Same author

16S rRNA sequence captures microbial functional potential.

bioRxiv : the preprint server for biology·2026
Same author

Quantifying uncertainty in protein representations across models and tasks.

Nature methods·2026
Same author

Whole Metagenome Sequencing: not Deep Enough for Complete Microbial Function Recovery.

bioRxiv : the preprint server for biology·2025
Same author

The CoREST Complex Regulates Alternative Splicing by the Transcriptional Regulation of RNA Processing Genes in Melanoma Cells.

Cells·2025

Related Experiment Video

Updated: Oct 11, 2025

Determining the Likelihood of Variant Pathogenicity Using Amino Acid-level Signal-to-Noise Analysis of Genetic Variation
07:15

Determining the Likelihood of Variant Pathogenicity Using Amino Acid-level Signal-to-Noise Analysis of Genetic Variation

Published on: January 16, 2019

11.1K

Decoding the effects of synonymous variants.

Zishuo Zeng1, Ariel A Aptekmann1, Yana Bromberg1,2

  • 1Department of Biochemistry and Microbiology, Rutgers University, New Brunswick, NJ 08873, USA.

Nucleic Acids Research
|December 1, 2021
PubMed
Summary

Synonymous single nucleotide variants (sSNVs) are common but often missed. Our new tool, synVep, uses machine learning to accurately predict the effects of these sSNVs, improving disease variant identification.

More Related Videos

Screening for Functional Non-coding Genetic Variants Using Electrophoretic Mobility Shift Assay EMSA and DNA-affinity Precipitation Assay DAPA
11:35

Screening for Functional Non-coding Genetic Variants Using Electrophoretic Mobility Shift Assay EMSA and DNA-affinity Precipitation Assay DAPA

Published on: August 21, 2016

13.1K
Targeted Next-generation Sequencing and Bioinformatics Pipeline to Evaluate Genetic Determinants of Constitutional Disease
09:34

Targeted Next-generation Sequencing and Bioinformatics Pipeline to Evaluate Genetic Determinants of Constitutional Disease

Published on: April 4, 2018

34.1K

Related Experiment Videos

Last Updated: Oct 11, 2025

Determining the Likelihood of Variant Pathogenicity Using Amino Acid-level Signal-to-Noise Analysis of Genetic Variation
07:15

Determining the Likelihood of Variant Pathogenicity Using Amino Acid-level Signal-to-Noise Analysis of Genetic Variation

Published on: January 16, 2019

11.1K
Screening for Functional Non-coding Genetic Variants Using Electrophoretic Mobility Shift Assay EMSA and DNA-affinity Precipitation Assay DAPA
11:35

Screening for Functional Non-coding Genetic Variants Using Electrophoretic Mobility Shift Assay EMSA and DNA-affinity Precipitation Assay DAPA

Published on: August 21, 2016

13.1K
Targeted Next-generation Sequencing and Bioinformatics Pipeline to Evaluate Genetic Determinants of Constitutional Disease
09:34

Targeted Next-generation Sequencing and Bioinformatics Pipeline to Evaluate Genetic Determinants of Constitutional Disease

Published on: April 4, 2018

34.1K

Area of Science:

  • Genomics
  • Computational Biology
  • Molecular Genetics

Background:

  • Synonymous single nucleotide variants (sSNVs) are prevalent in the human genome but frequently overlooked.
  • Despite their common occurrence, sSNVs can significantly impact biological functions and contribute to disease development.
  • Current computational tools for sSNV effect prediction are limited by inadequate gold-standard data and overdependence on sequence conservation.

Purpose of the Study:

  • To develop a novel machine learning-based method, synVep, for predicting the functional impact of synonymous single nucleotide variants (sSNVs).
  • To overcome limitations of existing methods, specifically the lack of high-quality training data and reliance on conservation signals.
  • To provide an improved tool for annotating sSNVs, enabling better identification of variants with potential biological effects.

Main Methods:

  • Developed synVep, a machine learning approach utilizing a combination of observed variants from gnomAD and generated possible variants.
  • Employed positive-unlabeled learning to refine the generated variant set, removing unlikely unobservable variants.
  • Trained two sequential extreme gradient boosting models to classify variants as enriched or depleted in functional effect.

Main Results:

  • Achieved 90% precision and recall on an independent, unseen set of variants.
  • synVep scores demonstrated correlation with evolutionary distances, despite not explicitly using conservation data.
  • Successfully differentiated between pathogenic and benign variants, and between splice-site disrupting variants (SDVs) and non-SDVs.

Conclusions:

  • synVep offers a significant advancement in the annotation of synonymous single nucleotide variants (sSNVs).
  • The method effectively predicts variant effects without relying on evolutionary conservation, addressing a key limitation of prior tools.
  • This improved annotation allows researchers to better prioritize sSNVs with the highest likelihood of functional impact and disease relevance.