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

Ribosome Profiling

Ribosome Profiling

Ribosome profiling or ribo-sequencing is a deep sequencing technique that produces a snapshot of active translation in a cell. It selectively sequences the mRNAs protected by ribosomes to get an insight into a cell’s translation landscape at any given point in time.
Applications of ribosome profiling
Ribosome profiling has many applications, including in vivo monitoring of translation inside a particular organ or tissue type and quantifying new protein synthesis levels.
The technique...

mRNA Stability and Gene Expression

mRNA Stability and Gene Expression

Cell Specific Gene Expression

Cell Specific Gene Expression

Regulated mRNA Transport

Regulated mRNA Transport

What is Gene Expression?

What is Gene Expression?

A gene is a stretch of DNA that serves as the blueprint for functional RNAs and proteins. Since DNA is comprised of nucleotides and proteins are comprised of amino acids, a mediator is required to convert the information encoded in DNA into proteins. This mediator is the messenger RNA (mRNA). mRNA copies the blueprint from DNA by a process called transcription. In eukaryotes, transcription occurs in the nucleus by complementary base-pairing with the DNA template. The mRNA is then...

Regulation of Expression at Multiple Steps

Regulation of Expression at Multiple Steps

The gene expression in cells is regulated at different stages: (i) transcription, (ii) RNA processing, (iii) RNA localization, and (iv) translation. Transcriptional regulation is mediated by regulatory proteins such as transcription factors, activators, or repressors—these control gene expression by initiating or inhibiting the transcription of genes. Once a precursor or pre-mRNA is produced, it undergoes post-transcriptional modification, including 5' capping, splicing, and the...

You might also read

Related Articles

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

Sort by

Same author

EpiATLAS - a reference for human epigenomic research.

bioRxiv : the preprint server for biology·2026

Same author

Correcting spatial transcriptomics data affected by a prevalent transcript leakage problem across platforms, species, and tissues.

bioRxiv : the preprint server for biology·2026

Same author

Spatial transcriptomics in Alzheimer's disease: technologies, challenges and discoveries.

Molecular neurodegeneration advances·2026

Same author

HIRA-mediated H3.3 deposition preserves hepatocyte cell identity during liver aging.

Research square·2026

Same author

Author Correction: The ubiquitin ligase RNF5 determines acute myeloid leukemia growth and susceptibility to histone deacetylase inhibitors.

Nature communications·2026

Same author

A Manifold-Based Measure of Transcriptional Entropy for Quantifying Aging in Single Cells.

bioRxiv : the preprint server for biology·2026

Search research articles

Related Experiment Video

Updated: Sep 12, 2025

Using the E1A Minigene Tool to Study mRNA Splicing Changes

Using the E1A Minigene Tool to Study mRNA Splicing Changes

Published on: April 22, 2021

Towards universal modeling of transcript isoform expression levels.

Savio Ho-Chit Chow¹, Christina Huan Shi¹, Aniruddha Deshpande^1,2

¹Center for Data Sciences, Sanford Burnham Prebys Medical Discovery Institute, La Jolla, CA 92037, USA.

Biorxiv : the Preprint Server for Biology

|August 8, 2025

Summary

This summary is machine-generated.

Computational biologists can now model transcript isoform expression using epigenetic data. A universal model, trained across many human tissues, accurately predicts gene expression levels, advancing the study of gene regulation.

More Related Videos

Saccharomyces cerevisiae Metabolic Labeling with 4-thiouracil and the Quantification of Newly Synthesized mRNA As a Proxy for RNA Polymerase II Activity

Saccharomyces cerevisiae Metabolic Labeling with 4-thiouracil and the Quantification of Newly Synthesized mRNA As a Proxy for RNA Polymerase II Activity

Published on: October 22, 2018

Real-time Analysis of Transcription Factor Binding, Transcription, Translation, and Turnover to Display Global Events During Cellular Activation

Real-time Analysis of Transcription Factor Binding, Transcription, Translation, and Turnover to Display Global Events During Cellular Activation

Published on: March 7, 2018

Related Experiment Videos

Last Updated: Sep 12, 2025

Using the E1A Minigene Tool to Study mRNA Splicing Changes

Using the E1A Minigene Tool to Study mRNA Splicing Changes

Published on: April 22, 2021

Saccharomyces cerevisiae Metabolic Labeling with 4-thiouracil and the Quantification of Newly Synthesized mRNA As a Proxy for RNA Polymerase II Activity

Saccharomyces cerevisiae Metabolic Labeling with 4-thiouracil and the Quantification of Newly Synthesized mRNA As a Proxy for RNA Polymerase II Activity

Published on: October 22, 2018

Real-time Analysis of Transcription Factor Binding, Transcription, Translation, and Turnover to Display Global Events During Cellular Activation

Real-time Analysis of Transcription Factor Binding, Transcription, Translation, and Turnover to Display Global Events During Cellular Activation

Published on: March 7, 2018

Area of Science:

Computational Biology
Genomics
Epigenetics

Background:

Accurate modeling of transcript expression using epigenetic features is crucial for understanding gene regulation.
Previous studies were limited by cell line models and single gene expression levels, failing to capture isoform diversity.
International Human Epigenome Consortium (IHEC) provides large-scale paired transcriptomic and epigenomic data.

Purpose of the Study:

To computationally model individual transcript isoform expression levels in human samples.
To develop a "universal" model for predicting transcript isoform expression across diverse tissue types.
To leverage graph-based methods integrating epigenomic features and gene-gene relationships.

Main Methods:

Utilized a large-scale dataset of 324 human samples from 29 tissue types from IHEC.
Employed graph-based computational models integrating location-specific epigenomic data.
Incorporated multiple types of gene-gene relationships into the modeling process.

Main Results:

Successfully modeled expression levels of individual transcript isoforms across various human tissues.
Demonstrated that models trained on data from multiple tissue types outperform tissue-specific models.
Found strong evidence supporting the feasibility of a "universal" model for transcript isoform expression inference.

Conclusions:

A universal computational model can accurately infer transcript isoform expression levels across human tissues.
Integrating diverse epigenomic and transcriptomic data enhances the predictive power of gene expression models.
This approach advances quantitative studies of gene regulation in both normal and disease states.