Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Improving Translational Accuracy

Improving Translational Accuracy

Improving Translational Accuracy

Improving Translational Accuracy

Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...

Genome Annotation and Assembly

Genome Annotation and Assembly

The genome refers to all of the genetic material in an organism. It can range from a few million base pairs in microbial cells to several billion base pairs in many eukaryotic organisms. Genome assembly refers to the process of taking the DNA sequencing data and putting it all back together in a correct order to create a close representation of the original genome. This is followed by the identification of functional elements on the newly assembled genome, a process called genome annotation.

Alternative RNA Splicing

Alternative RNA Splicing

Alternative RNA Splicing

Alternative RNA Splicing

Alternative RNA splicing is the regulated splicing of exons and introns to produce different mature mRNAs from a single pre-mRNA. Unlike in constitutive splicing where a single gene produces a single type of mRNA, alternative splicing allows an organism to produce multiple proteins from a single gene and plays an important role in protein diversity.
There are five types of alternative RNA splicing that vary in the ways the pre-mRNA segments are removed or retained in the mature mRNA. The first...

Master Transcription Regulators

Master Transcription Regulators

You might also read

Related Articles

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

Sort by

Same author

Wavelet Decomposition-Based Genomic Analysis of the Human Electrocardiogram.

medRxiv : the preprint server for health sciences·2026

Same author

Orchestrating multi-state QTL analysis with bioconductor.

BMC bioinformatics·2026

Same author

AutoCumulus: an automated mammographic density measure created using artificial intelligence.

BMC cancer·2026

Same author

Mammographic surveillance in breast cancer patients aged 50 years or older: a synopsis of the Mammo-50 RCT.

Health technology assessment (Winchester, England)·2026

Same author

Genetic association and machine learning improve the prediction of type 1 diabetes risk.

Nature genetics·2026

Same author

Functional genomics reveals mediators of beta cell survival in ER stress and type 2 diabetes risk.

bioRxiv : the preprint server for biology·2026

Search research articles

Related Experiment Video

Updated: Apr 28, 2026

Using RNA-sequencing to Detect Novel Splice Variants Related to Drug Resistance in In Vitro Cancer Models

Using RNA-sequencing to Detect Novel Splice Variants Related to Drug Resistance in In Vitro Cancer Models

Published on: December 9, 2016

Choice of transcripts and software has a large effect on variant annotation.

Davis J McCarthy¹, Peter Humburg², Alexander Kanapin²

¹Department of Statistics, University of Oxford, South Parks Road, Oxford, UK ; Wellcome Trust Centre for Human Genetics, University of Oxford, Roosevelt Drive, Oxford, UK.

Genome Medicine

|June 20, 2014

Summary

Choosing transcript sets and annotation software significantly impacts genome sequencing analysis. Discrepancies in variant annotation, especially for loss-of-function variants, highlight the need for careful tool selection in DNA variant studies.

More Related Videos

Identification of Alternative Splicing and Polyadenylation in RNA-seq Data

Identification of Alternative Splicing and Polyadenylation in RNA-seq Data

Published on: June 24, 2021

Genome-wide Surveillance of Transcription Errors in Eukaryotic Organisms

Genome-wide Surveillance of Transcription Errors in Eukaryotic Organisms

Published on: September 13, 2018

Related Experiment Videos

Last Updated: Apr 28, 2026

Using RNA-sequencing to Detect Novel Splice Variants Related to Drug Resistance in In Vitro Cancer Models

Using RNA-sequencing to Detect Novel Splice Variants Related to Drug Resistance in In Vitro Cancer Models

Published on: December 9, 2016

Identification of Alternative Splicing and Polyadenylation in RNA-seq Data

Identification of Alternative Splicing and Polyadenylation in RNA-seq Data

Published on: June 24, 2021

Genome-wide Surveillance of Transcription Errors in Eukaryotic Organisms

Genome-wide Surveillance of Transcription Errors in Eukaryotic Organisms

Published on: September 13, 2018

Area of Science:

Genomics
Bioinformatics
Computational Biology

Background:

Variant annotation is critical for genome sequencing data analysis.
Inaccurate annotations can lead to missed disease-relevant variants or an excess of false positives.
The impact of transcript set and software choice on annotation outcomes is not fully understood.

Purpose of the Study:

To quantify the extent of differences in variant annotation based on transcript sets and software.
To compare annotation results using RefSeq and Ensembl transcript sets with Annovar software.
To compare annotation results between Annovar and VEP (Variant Effect Predictor) software using Ensembl transcripts.

Main Methods:

Annotation of 80 million variants from a whole-genome sequencing study.
Comparative analysis using RefSeq and Ensembl transcript sets with Annovar.
Comparative analysis using Annovar and VEP software with Ensembl transcripts.

Main Results:

Only 44% agreement for loss-of-function variants between RefSeq and Ensembl using Annovar.
79% agreement for combined loss-of-function and nonsynonymous variants, and 83% for all exonic variants.
65% agreement for loss-of-function variants and 87% for all exonic variants between Annovar and VEP using Ensembl transcripts, with splicing variants showing the most discrepancy.

Conclusions:

Variant annotation remains a significant challenge in genomics.
The selection of transcript sets and annotation software substantially influences variant annotation outcomes.
Careful consideration and conscious choice of transcript set and software are essential for accurate genome sequencing analysis.