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Transcription Initiation

Transcription Initiation

Initiation is the first step of transcription in eukaryotes. Prokaryotic RNA Polymerase (RNAP) can bind to the template DNA and start transcribing. On the other hand, transcription in eukaryotes requires additional proteins, called transcription factors, to first bind to the promoter region in the DNA template. This binding helps recruit the specific RNAP that can assemble on the DNA and start transcription.
The promoters and enhancers and their accessory proteins allow tight regulation of...

Bacterial Transcription

Bacterial Transcription

RNA polymerase (RNAP) carries out DNA-dependent RNA synthesis in both bacteria and eukaryotes. Bacteria do not have a membrane-bound nucleus. So, transcription and translation occur simultaneously, on the same DNA template.
Transcription can be divided into three main stages, each involving distinct DNA sequences to guide the polymerase. These are:

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...

Transcription

Transcription

Transcription is the synthesis of 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 correctly synthesizing messenger RNA (mRNA). Transcriptional regulation is responsible for the differentiation of different types of cells and often for the proper cellular response to environmental signals.
Transcription Can Produce Different Kinds of RNA Molecules
In eukaryotes,...

Master Transcription Regulators

Master Transcription Regulators

Transcription Factors

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: Jul 26, 2025

Transcription Start Site Mapping Using Super-low Input Carrier-CAGE

Transcription Start Site Mapping Using Super-low Input Carrier-CAGE

Published on: June 26, 2019

Deep learning and support vector machines for transcription start site identification.

José A Barbero-Aparicio¹, Alicia Olivares-Gil¹, José F Díez-Pastor¹

¹Departamento de Ingeniería Informática, Universidad de Burgos, Burgos, Spain.

Peerj. Computer Science

|June 22, 2023

Summary

Deep learning methods, particularly Long Short-Term Memory networks (LSTMs), outperform Support Vector Machines (SVMs) for identifying transcription start sites (TSS). These advanced models are more efficient and better handle the large datasets required for gene identification.

Keywords:

Bioinformatics Convolutional neural network Deep learning Long short-term memory Machine learning Support vector machine Transcription start site

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Describing a Transcription Factor Dependent Regulation of the MicroRNA Transcriptome

Describing a Transcription Factor Dependent Regulation of the MicroRNA Transcriptome

Published on: June 15, 2016

Area of Science:

Genomics
Bioinformatics
Computational Biology

Background:

Accurate identification of transcription start sites (TSS) is crucial for gene identification and understanding gene regulation.
While machine learning, especially deep learning, shows promise, its application to TSS identification remains underexplored.
Existing studies often lack comparisons with established methods like Support Vector Machines (SVMs) and curated datasets.

Purpose of the Study:

To compare the performance of deep learning models, specifically Long Short-Term Memory (LSTM) networks, against Support Vector Machines (SVMs) for TSS prediction.
To investigate data processing strategies, including training example generation and handling data imbalance.
To develop a method for generating comprehensive TSS datasets applicable across species.

Main Methods:

Utilized the human genome reference GRCh38 for model training and evaluation.
Implemented and compared deep learning architectures (including LSTMs) with SVMs for TSS prediction.
Assessed model generalization using the mouse genome.
Developed a novel method for creating curated TSS datasets with negative instances.

Main Results:

Deep learning methods, particularly LSTMs, demonstrated superior performance and efficiency compared to SVMs for TSS identification.
SVMs were found to be computationally slower than deep learning approaches.
LSTM networks showed strong generalization capabilities on the mouse genome.
A robust method for generating species-agnostic TSS datasets was established.

Conclusions:

Deep learning, especially LSTMs, is better suited for TSS identification due to its efficiency and ability to process long sequences and large datasets.
The developed dataset generation method facilitates further research in TSS prediction across various species.
This study provides valuable insights into optimal deep learning architectures for TSS identification.