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Transcription

Transcription

Overview
Transcription is the process of synthesizing RNA from a DNA sequence by RNA polymerase. It is the first step in producing a protein from a gene sequence. Additionally, many other proteins and regulatory sequences are involved in the proper synthesis of messenger RNA (mRNA). Regulation of transcription is responsible for the differentiation of all the different types of cells and often for the proper cellular response to environmental signals.
Transcription Can Produce Different Kinds...

Master Transcription Regulators

Master Transcription Regulators

Master transcription regulators are regulatory proteins that are predominantly responsible for regulating the expression of multiple genes. Often these genes work in concert to drive a complex process. Activation of a master transcription regulator can lead to a cascade of transcriptional activation necessary for that outcome. These regulators can directly bind to the regulatory sequences of the various genes involved, or they can indirectly regulate transcription by binding to regulatory...

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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Related Experiment Video

Updated: Aug 22, 2025

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers

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Published on: March 1, 2024

Machine learning applications for transcription level and phenotype predictions.

Juthamard Chantaraamporn¹, Pongpannee Phumikhet¹, Sarintip Nguantad¹

¹Department of Biochemistry, Faculty of Science, Mahidol University, Bangkok, Thailand.

|November 8, 2022

Summary

Machine learning (ML) can now predict phenotypes from genomic data by analyzing gene expression. This approach helps uncover molecular mechanisms and improve predictions for synthetic biology applications.

Keywords:

artificial intelligence deep learning epigenomics machine learning phenotype prediction transcriptomics

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Area of Science:

Genomics
Molecular Biology
Bioinformatics

Background:

Predicting phenotypes from genomic variations is challenging due to environmental influences on gene expression.
Omic data and machine learning (ML) offer new opportunities to understand gene expression and phenotypes.

Purpose of the Study:

To summarize fundamental ML concepts for molecular biologists.
To highlight ML applications in transcriptomics for predicting gene expression and phenotypes.
To promote ML adoption in molecular biology and synthetic biology.

Main Methods:

Focus on transcriptomics due to data abundance and reproducibility.
Describes two ML tasks: predicting transcriptomic profiles from genomic variations and predicting phenotypes from transcriptomic profiles.
Discusses potential applications in multi-omic studies.

Main Results:

Provides a framework for using ML in molecular biology data analysis.
Demonstrates ML's utility in linking genomic variations to transcriptomic profiles and phenotypes.
Highlights the potential for improved predictions in synthetic biology.

Conclusions:

ML empowers researchers to analyze large-scale omic data.
Facilitates uncovering molecular mechanisms controlling gene expression and phenotypes.
Aims to enhance systematic predictions for synthetic biology applications.