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Cis-regulatory Sequences

Cis-regulatory Sequences

Cis-regulatory sequences are short fragments of non-coding DNA that are present on the same chromosomes as the genes that they regulate. These fragments serve as binding sites for transcriptional regulators, proteins that are responsible for controlling gene transcription and differential gene expression across cell types in eukaryotes. Cis-regulatory sequences can be close to the gene of interest or thousands of bases away in the DNA sequence; however, those sequences that are further away are...

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Gene families consist of groups of genes proposed to have originated from a common ancestor. Typically these arise through events in which a gene or genes are mistakenly duplicated during cell division. Unlike their parent genes (which are subject to selection pressure to maintain function), these gene copies do not need to preserve their sequences and may evolve at a relatively faster rate.
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An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
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Related Experiment Video

Updated: May 4, 2026

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline

Published on: December 7, 2021

Discovering implicit entity relation with the gene-citation-gene network.

Min Song¹, Nam-Gi Han¹, Yong-Hwan Kim¹

¹Department of Library and Information Science, Yonsei University, Seoul, Republic of Korea.

|December 21, 2013

Summary

This summary is machine-generated.

The Gene-Citation-Gene (GCG) network implicitly connects genes through citations, revealing known interactions. This novel approach aids in discovering gene interactions, with cancer-associated genes being prominent.

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Area of Science:

Bioinformatics
Computational Biology
Network Science

Background:

Traditional gene-gene networks rely on explicit co-occurrence, potentially missing implicit relationships.
Citation networks offer a novel data source for inferring gene interactions.

Purpose of the Study:

To construct and evaluate a Gene-Citation-Gene (GCG) network for implicit gene interaction discovery.
To compare the performance of the GCG network against a traditional gene-gene (GG) co-occurrence network.

Main Methods:

Constructed a GCG network using MEDLINE abstracts and citation links.
Developed a GG network based on gene co-occurrence within the same corpus.
Applied network analysis metrics including degree, centrality, and PageRank to evaluate network performance.

Main Results:

The GCG network identified 25 gene pairs, with 96% having known interactions in BioGRID.
The GCG network yielded more gene pairs but a lower initial matching rate compared to the GG network.
Combining top-ranked genes from both networks achieved a 35.53% matching rate.
Cancer emerged as the most dominant disease associated with genes in both networks.

Conclusions:

The GCG network effectively detects implicit gene interactions.
This citation-based approach complements traditional co-occurrence methods for gene interaction discovery.
The GCG network holds potential for identifying novel gene relationships and understanding disease associations.