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Regulation of Expression Occurs at Multiple Steps

Regulation of Expression Occurs at Multiple Steps

Gene expression can be regulated at almost every step from gene to protein. Transcription is the step that is most commonly regulated. This involves the binding of proteins to short regulatory sequences on the DNA. This association can either promote or inhibit the transcription of a gene associated with the respective sequence.
Transcription results in the generation of precursor (pre-mRNA) that consists of both exons and introns, which needs further processing before being translated to a...

Regulation of Expression Occurs at Multiple Steps

Regulation of Expression Occurs at Multiple Steps

Structure of a Gene

Structure of a Gene

A gene is the fundamental unit of heredity. Every individual has two copies of each gene, one inherited from each parent. Although most people contain the same genes, there is a small fraction that is slightly different amongst people. A gene with a small difference in its sequence of DNA bases forms different alleles, contributing to different phenotypes.
However, only 1% of the DNA is composed of genes that encode proteins; the rest, 99% is non-coding DNA. This non-coding DNA performs...

Cooperative Binding of Transcription Regulators

Cooperative Binding of Transcription Regulators

Transcriptional regulators bind to specific cis-regulatory sequences in the DNA to regulate gene transcription. These cis-regulatory sequences are very short, usually less than ten nucleotide pairs in length. The short length means that there is a high probability of the exact same sequence randomly occurring throughout the genome. Since regulators can also bind to groups of similar sequences, this further increases the chances of random binding. Transcriptional regulators form...

Cooperative Binding of Transcription Regulators

Cooperative Binding of Transcription Regulators

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...

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Identifying local co-regulation relationships in gene expression data.

Yonggang Pei¹, Qinghui Gao¹, Juntao Li¹

¹College of Mathematics and Information Science, Henan Normal University, Xinxiang 453007, China.

Journal of Theoretical Biology

|July 22, 2014

Summary

This study introduces a new method, Identifying Local Co-regulation Relationships (IdLCR), to find functional gene interactions within specific experimental conditions. IdLCR effectively identifies gene pairs and condition subsets, revealing novel biological relationships in gene expression data.

Keywords:

B-spline Correlation

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Published on: May 31, 2011

Area of Science:

Bioinformatics
Computational Biology
Systems Biology

Background:

Identifying gene-gene functional interactions is crucial for understanding biological processes.
Existing methods may not effectively capture context-specific co-regulation patterns.

Purpose of the Study:

To introduce a novel method, Identifying Local Co-regulation Relationships (IdLCR), for discovering context-specific gene interactions.
To identify pairwise gene-gene relationships and relevant condition subsets from gene expression data.

Main Methods:

Utilizes a regression spline model to detect functional gene-gene relationships and condition subsets.
Employs a penalized Pearson correlation to measure and rank co-regulation strengths.
Filters relationships to ensure clear biological interpretability.

Main Results:

IdLCR successfully identifies functional relationships and condition subsets in simulated data.
The method demonstrates efficacy in uncovering novel biological relationships in microarray and RNA-seq data.
Results differ from those obtained using IFGR and MINE methods.

Conclusions:

IdLCR provides a robust approach for identifying local co-regulation relationships.
The method enhances the discovery of context-specific functional gene interactions.
IdLCR offers an advancement over existing methods for gene expression data analysis.