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What is Gene Expression?

What is Gene Expression?

Overview
Gene expression is the process in which DNA directs the synthesis of functional products, that is, proteins. Cells can regulate gene expression at various stages. It allows organisms to generate different cell types and enables cells to adapt to internal and external factors.
Genetic Information Flows from DNA to RNA to Protein
A gene is a stretch of DNA that serves as the blueprint for functional RNAs and proteins. Since DNA is made up of nucleotides and proteins consist of amino...

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...

Chromatin Position Affects Gene Expression

Chromatin Position Affects Gene Expression

Chromatin is the massive complex of DNA and proteins packaged inside the nucleus. The complexity of chromatin folding and how it is packaged inside the nucleus greatly influences access to genetic information. Generally, the nucleus' periphery is considered transcriptionally repressive, while the cell's interior is considered a transcriptionally active area.
Topologically Associated Domains (TADs)
The 3-dimensional positioning of chromatin in the nucleus influences the...

Cell Specific Gene Expression

Cell Specific Gene Expression

Multicellular organisms contain a variety of structurally and functionally distinct cell types, but the DNA in all the cells originated from the same parent cells. The differences in the cells can be attributed to the differential gene expression. Liver cells, whose functions include detoxification of blood, production of bile to metabolize fats, and synthesis of proteins essential for metabolism, must express a specific set of genes to perform their functions. Gene expression also varies with...

Cell Specific Gene Expression

Cell Specific Gene Expression

mRNA Stability and Gene Expression

mRNA Stability and Gene Expression

The structure and stability of mRNA molecules regulates gene expression, as mRNAs are a key step in the pathway from gene to protein. In eukaryotes, the half-life of mRNA varies from a few minutes up to several days. mRNA stability is essential in growth and development. The absence of the proteins regulating its stability, such as tristetraprolin in mice, can cause systemic issues, including bone marrow overgrowth, inflammation, and autoimmunity.
Cis-acting Elements involved in mRNA stability

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Related Experiment Video

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JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics

JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics

Published on: October 19, 2021

Tight clustering for large datasets with an application to gene expression data.

Bikram Karmakar¹, Sarmistha Das², Sohom Bhattacharya²

¹Department of Statistics, University of Pennsylvania, Philadelphia, PA, USA.

Scientific Reports

|March 1, 2019

Summary

A new scalable tight clustering algorithm efficiently handles large datasets, including gene expression data. This method identifies tight, stable clusters while effectively managing noise points for improved data analysis.

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Area of Science:

Computational biology
Data science
Bioinformatics

Background:

The tight clustering algorithm yields precise clusters but is computationally intensive.
Large datasets, such as microarray gene expression data, are common but challenging for existing algorithms.

Purpose of the Study:

To develop a practical and scalable version of the tight clustering algorithm.
To enable the analysis of very large datasets, including gene expression data.

Main Methods:

Proposing a pragmatic and scalable variant of the tight clustering method.
Deducing the theoretical properties of the enhanced algorithm.
Extensive validation through simulation and real-world data analysis.

Main Results:

The proposed algorithm demonstrates scalability for large datasets.
Effective identification of tight and stable clusters.
Successful handling of noise or scattered points.

Conclusions:

The scalable tight clustering algorithm is suitable for large-scale data analysis, particularly in gene expression studies.
This advancement overcomes previous computational limitations, making tight clustering more accessible.