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General Transcription Factors

General Transcription Factors

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...

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Using Human Differentially Expressed Gene Lists to Perform Downstream Pathway Enrichment Analysis and Target Prioritization

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Beyond tissueInfo: functional prediction using tissue expression profile similarity searches.

Daniel Aguilar¹, Lucy Skrabanek, Steven S Gross

¹HRH Prince Alwaleed Bin Talal Bin Abdulaziz Alsaud Institute for Computational Biomedicine, Weill Medical College of Cornell University, 1305 York Ave, New York, NY 10021, USA.

Nucleic Acids Research

|May 17, 2008

Summary

Tissue Expression Profile Similarity Searches (TEPSS) is a novel computational method for identifying gene expression patterns. This approach effectively predicts protein interactions and identifies novel gene targets without relying on sequence similarity.

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Area of Science:

Computational Biology
Bioinformatics
Genomics

Background:

Identifying gene and protein functions is crucial in molecular biology.
Existing methods often rely on sequence similarity, limiting discovery of distantly related genes.
Understanding tissue-specific gene expression is key to functional genomics.

Purpose of the Study:

To introduce and validate Tissue Expression Profile Similarity Searches (TEPSS), a computational tool.
To assess TEPSS's ability to predict protein-protein interactions and identify novel gene targets.
To demonstrate TEPSS's utility in functional prediction and gene discovery.

Main Methods:

Developed the TEPSS computational approach based on transcript tissue expression profiles.
Evaluated TEPSS by discriminating between interacting and non-interacting protein pairs using a large human protein interaction dataset.
Applied TEPSS to predict novel members of the cytosolic ribosome and S-nitrosylation targets in brain proteins.

Main Results:

TEPSS significantly enriched for known interacting protein pairs (Odds-ratio = 157.57) when ordering by TEPSS score.
The method successfully predicted non-obvious members of the cytosolic ribosome.
TEPSS identified potential S-nitrosylation targets, with some awaiting experimental validation.

Conclusions:

TEPSS is an effective and flexible computational approach for functional gene prediction.
The method's independence from sequence similarity makes it valuable for diverse gene discovery applications.
TEPSS offers a powerful tool for uncovering novel biological relationships and functions.