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

General Transcription Factors01:30

General Transcription Factors

5.2K
Tissue-specific transcription factors contribute to diverse cellular functions in mammals. For example, the gene for beta globin, a major component of hemoglobin, is present in all cells of the body. However, it is only expressed in red blood cells because the transcription factors that can bind to the promoter sequences of the beta globin gene are only expressed in these cells. Tissue-specific transcription factors also ensure that mutations in these factors may impair only the function of...
5.2K

You might also read

Related Articles

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

Sort by
Same author

TSProm: deep learning framework to predict tissue-specific regulatory logic.

NAR genomics and bioinformatics·2026
Same author

Solid Tumors Pan Cancer Transcriptome: Tissue/Cancer specific expression groups at the Isoform-Level.

bioRxiv : the preprint server for biology·2026
Same author

HViLM: A Foundation Model for Viral Genomics Enables Multi-Task Prediction of Pathogenicity, Transmissibility, and Host Tropism.

bioRxiv : the preprint server for biology·2026
Same author

A DNABERT based deep learning framework for predicting transcription factor binding sites.

Scientific reports·2026
Same author

TSProm: Deciphering the Genomic Context of Tissue Specificity.

bioRxiv : the preprint server for biology·2025
Same author

DNABERT-S: pioneering species differentiation with species-aware DNA embeddings.

Bioinformatics (Oxford, England)·2025
Same journal

conMItion: an R package adjusting confounding factors for associations in multi-omics.

Bioinformatics (Oxford, England)·2026
Same journal

SpaMFG: a Spatial Multi-omics Integration Method based on Feature Grouping.

Bioinformatics (Oxford, England)·2026
Same journal

CSCN: Inference of Cell-Specific Causal Networks Using Single-Cell RNA-Seq Data.

Bioinformatics (Oxford, England)·2026
Same journal

Sparse CCA-Based Mediation Analysis with High-Dimensional Exposures and Mediators.

Bioinformatics (Oxford, England)·2026
Same journal

Enhancing Cross-Context Generalization in Drug Perturbation Prediction with a Multimodal Conditional Diffusion Framework.

Bioinformatics (Oxford, England)·2026
Same journal

Primer Design through Submodular Function Estimation.

Bioinformatics (Oxford, England)·2026
See all related articles

Related Experiment Video

Updated: Jun 17, 2025

IR-TEx: An Open Source Data Integration Tool for Big Data Transcriptomics Designed for the Malaria Vector Anopheles gambiae
08:22

IR-TEx: An Open Source Data Integration Tool for Big Data Transcriptomics Designed for the Malaria Vector Anopheles gambiae

Published on: January 15, 2020

6.1K

TransTEx: novel tissue-specificity scoring method for grouping human transcriptome into different expression groups.

Pallavi Surana1, Pratik Dutta1, Ramana V Davuluri1

  • 1Department of Biomedical Informatics, Stony Brook University, Stony Brook, NY 11794, USA.

Bioinformatics (Oxford, England)
|August 9, 2024
PubMed
Summary
This summary is machine-generated.

We developed TransTEx, a novel method to analyze transcript-level tissue expression, identifying tissue-specific transcripts crucial for understanding gene regulation. This approach reveals complex isoform-level expression patterns across human tissues.

More Related Videos

Identification of Key Factors Regulating Self-renewal and Differentiation in EML Hematopoietic Precursor Cells by RNA-sequencing Analysis
12:44

Identification of Key Factors Regulating Self-renewal and Differentiation in EML Hematopoietic Precursor Cells by RNA-sequencing Analysis

Published on: November 11, 2014

12.3K
Isolation and Profiling of Human Primary Mesenteric Arterial Endothelial Cells at the Transcriptome Level
09:45

Isolation and Profiling of Human Primary Mesenteric Arterial Endothelial Cells at the Transcriptome Level

Published on: March 14, 2022

2.9K

Related Experiment Videos

Last Updated: Jun 17, 2025

IR-TEx: An Open Source Data Integration Tool for Big Data Transcriptomics Designed for the Malaria Vector Anopheles gambiae
08:22

IR-TEx: An Open Source Data Integration Tool for Big Data Transcriptomics Designed for the Malaria Vector Anopheles gambiae

Published on: January 15, 2020

6.1K
Identification of Key Factors Regulating Self-renewal and Differentiation in EML Hematopoietic Precursor Cells by RNA-sequencing Analysis
12:44

Identification of Key Factors Regulating Self-renewal and Differentiation in EML Hematopoietic Precursor Cells by RNA-sequencing Analysis

Published on: November 11, 2014

12.3K
Isolation and Profiling of Human Primary Mesenteric Arterial Endothelial Cells at the Transcriptome Level
09:45

Isolation and Profiling of Human Primary Mesenteric Arterial Endothelial Cells at the Transcriptome Level

Published on: March 14, 2022

2.9K

Area of Science:

  • Genomics
  • Transcriptomics
  • Bioinformatics

Background:

  • Human tissues exhibit distinct molecular processes, but traditional gene-level analysis overlooks transcript variants and protein isoforms.
  • Changes in alternative splicing and isoform expression are linked to disease prognosis and drug resistance.

Purpose of the Study:

  • To develop and apply a novel method, TransTEx (Transcript-level Tissue Expression), for scoring and grouping transcripts based on tissue specificity.
  • To analyze human transcript expression patterns at a finer resolution than gene-level analysis.

Main Methods:

  • TransTEx applies sequential cut-offs to transcript probability estimates, P-values, and fold-change values.
  • The method was applied to GTEx mRNA-seq data, categorizing 199,166 human transcripts into tissue-specific, enhanced, widely expressed, lowly expressed, and null groups.
  • Analysis involved overlapping brain-specific transcripts with cell-type markers from the scBrainMap database.

Main Results:

  • TransTEx classified transcripts into tissue-specific (TSp), tissue-enhanced, widely expressed (Wide), lowly expressed (Low), and no expression (Null) groups.
  • Testis showed the highest number of TSp isoforms (13,466), followed by liver, brain, pituitary, and muscle.
  • Tissue specificity of alternative transcripts is largely influenced by alternate promoter usage.
  • 63% of brain-specific transcripts were enriched in nonneuronal cell types, primarily astrocytes, endothelial cells, and oligodendrocytes.
  • Identified brain-specific and testis-specific alternative transcripts linked to cell-type markers, highlighting complex isoform-level regulation.

Conclusions:

  • TransTEx provides a robust method for analyzing transcript-level tissue expression and identifying tissue-specific isoforms.
  • The findings underscore the importance of isoform-level analysis for understanding tissue-specific gene regulation and cell-type specificity.
  • TransTEx can be applied to bulk and single-cell RNA-seq data for discovering novel isoform-level gene markers.