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

Genome Annotation and Assembly03:36

Genome Annotation and Assembly

18.9K
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.
18.9K
Improving Translational Accuracy02:07

Improving Translational Accuracy

2.6K
2.6K
Protein Complex Assembly02:41

Protein Complex Assembly

2.1K
2.1K
Transfer RNA Synthesis02:35

Transfer RNA Synthesis

2.8K
2.8K
RNA Editing02:23

RNA Editing

9.0K
RNA editing is a post-transcriptional modification where a precursor mRNA (pre-mRNA) nucleotide sequence is changed by base insertion, deletion, or modification. The extent of RNA editing varies from a few hundred bases, in mitochondrial DNA of trypanosomes, to a just single base, in nuclear genes of mammals. Even a single base change in the pre-mRNA can convert a codon for one amino acid into the codon for another amino acid or a stop codon. This type of re-coding can significantly affect the...
9.0K
mRNA Stability and Gene Expression02:51

mRNA Stability and Gene Expression

2.8K
2.8K

You might also read

Related Articles

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

Sort by
Same author

Augmenting transcriptome annotations through the lens of splicing evolution.

Genome research·2026
Same author

Hash functions in nucleotide sequence analysis.

Genome research·2026
Same author

Minimum flow decomposition guided by saturating subflows.

bioRxiv : the preprint server for biology·2025
Same author

MELO-ED: learning locality-sensitive multi-embeddings for edit distance.

bioRxiv : the preprint server for biology·2025
Same author

Amaranth: Enhanced Single-Cell Transcript Assembly via Discriminative Modeling of UMI Reads and Internal Reads.

bioRxiv : the preprint server for biology·2025
Same author

Accurate Reconstruction of Circular RNAs from Complex Rolling Circular Long Reads with CircPlex.

bioRxiv : the preprint server for biology·2025
Same journal

Detection, communication, and individual identification with deep audio embeddings: A case study with North Atlantic right whales.

PLoS computational biology·2026
Same journal

Exploring the structural lexicon of the Proteome via Metric Geometry.

PLoS computational biology·2026
Same journal

Linking retinal sampling in neural encoding models to temporal profiles of visual processing in humans.

PLoS computational biology·2026
Same journal

CAdir: Joint clustering of cells and genes for single-cell transcriptomics with visualization-driven cluster quality assessment.

PLoS computational biology·2026
Same journal

Systematic design of auxotrophic strains and media conditions to probe metabolic functions in E. coli.

PLoS computational biology·2026
Same journal

Neuronal excitability and parameter variability in the Hodgkin-Huxley model.

PLoS computational biology·2026
See all related articles

Related Experiment Video

Updated: Jul 7, 2025

Genome-wide Surveillance of Transcription Errors in Eukaryotic Organisms
09:30

Genome-wide Surveillance of Transcription Errors in Eukaryotic Organisms

Published on: September 13, 2018

9.6K

Transcript assembly and annotations: Bias and adjustment.

Qimin Zhang1, Mingfu Shao1,2

  • 1Department of Computer Science and Engineering, School of Electrical Engineering and Computer Science, The Pennsylvania State University, University Park, Pennsylvania, United States of America.

Plos Computational Biology
|December 21, 2023
PubMed
Summary
This summary is machine-generated.

Transcript annotations significantly impact gene expression analysis and transcript assembly. Different annotations, especially regarding intron retention, can lead to contradictory conclusions about assembler performance. A new tool, irtool, helps generate assemblies without intron retentions.

More Related Videos

Leveraging CyVerse Resources for De Novo Comparative Transcriptomics of Underserved Non-model Organisms
10:41

Leveraging CyVerse Resources for De Novo Comparative Transcriptomics of Underserved Non-model Organisms

Published on: May 9, 2017

9.3K
Improving Small RNA-seq: Less Bias and Better Detection of 2'-O-Methyl RNAs
08:49

Improving Small RNA-seq: Less Bias and Better Detection of 2'-O-Methyl RNAs

Published on: September 16, 2019

7.7K

Related Experiment Videos

Last Updated: Jul 7, 2025

Genome-wide Surveillance of Transcription Errors in Eukaryotic Organisms
09:30

Genome-wide Surveillance of Transcription Errors in Eukaryotic Organisms

Published on: September 13, 2018

9.6K
Leveraging CyVerse Resources for De Novo Comparative Transcriptomics of Underserved Non-model Organisms
10:41

Leveraging CyVerse Resources for De Novo Comparative Transcriptomics of Underserved Non-model Organisms

Published on: May 9, 2017

9.3K
Improving Small RNA-seq: Less Bias and Better Detection of 2'-O-Methyl RNAs
08:49

Improving Small RNA-seq: Less Bias and Better Detection of 2'-O-Methyl RNAs

Published on: September 16, 2019

7.7K

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Transcript annotations are crucial for gene expression analysis and transcript assembly accuracy.
  • Discrepancies between major annotation sources (RefSeq, Ensembl/GENCODE) affect downstream analyses.
  • The influence of annotation choice on transcript assembly evaluation is not fully understood.

Purpose of the Study:

  • To investigate the impact of different transcript annotations on transcript assembly evaluation.
  • To understand the reasons behind contradictory conclusions when using different annotations.
  • To develop a method for improving transcript assembly by addressing annotation biases.

Main Methods:

  • Comparative analysis of transcript assembly results using distinct annotation sources.
  • Structural comparison of annotations at various levels, focusing on intron-chain differences.
  • Examination of biotype distribution in annotated and assembled transcripts, identifying biases.
  • Development and evaluation of a pipeline incorporating a tool (irtool) to exclude intron retention.

Main Results:

  • Different annotations can lead to opposite conclusions regarding transcript assembler performance.
  • Primary structural differences between annotations lie at the intron-chain level.
  • A significant bias exists towards annotating and assembling transcripts with intron retentions.
  • The irtool pipeline successfully generates assemblies with reduced intron retention.

Conclusions:

  • Annotation choice critically influences transcript assembly evaluation, potentially yielding conflicting results.
  • Intron retention bias in annotations and assembly explains observed discrepancies.
  • The irtool pipeline offers a solution to mitigate intron retention bias in transcript assembly.
  • Guidance is provided for selecting appropriate assembly tools based on application needs.