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Related Concept Videos

Transcription Factors02:16

Transcription Factors

82.8K
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...
82.8K
Transcription Elongation Factors02:35

Transcription Elongation Factors

14.0K
Transcription elongation is a dynamic process that alters depending upon the sequence heterogeneity of the DNA being transcribed. Hence, it is not surprising that the elongation complex's composition also varies along the way while transcribing a gene.
The transcription elongation is regulated via pausing of RNA polymerase on several occasions during transcription. In bacteria, these halts are necessary because the transcription of DNA into mRNA is coupled to the translation of that mRNA...
14.0K
Transcription Elongation Factors02:35

Transcription Elongation Factors

4.8K
4.8K
General Transcription Factors01:30

General Transcription Factors

7.1K
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...
7.1K
Transcription01:10

Transcription

157.0K
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...
157.0K
Eukaryotic Transcription Inhibitors01:52

Eukaryotic Transcription Inhibitors

11.0K
Certain biochemical processes, such as embryonic development and cell growth regulation, depend on the repression of specific genes. DNA binding proteins known as eukaryotic transcription inhibitors regulate the repression of gene expression in eukaryotes. The presence of these inhibitors at the required location and time in the cell is triggered by the presence of hormones and additional signals from other cells.
Eukaryotic transcription inhibitors usually contain two distinct domains, a...
11.0K

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Describing a Transcription Factor Dependent Regulation of the MicroRNA Transcriptome
07:23

Describing a Transcription Factor Dependent Regulation of the MicroRNA Transcriptome

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Implementing a Transcription Factor Interaction Prediction System Using the GenoMetric Query Language.

Stefano Perna1, Arif Canakoglu2, Pietro Pinoli2

  • 1DEIB, Politecnico di Milano, Milano, Italy. stefano.perna@polimi.it.

Methods in Molecular Biology (Clifton, N.J.)
|July 22, 2018
PubMed
Summary
This summary is machine-generated.

The TICA web server predicts transcription factor interactions using genomic data. It efficiently analyzes large datasets, aiding biological discovery and hypothesis generation.

Keywords:
BiostatisticsChIP-seq analysisData integrationGene regulationGenomic computingTranscription factor interaction

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Area of Science:

  • Genomics and Transcriptomics
  • Computational Biology
  • Bioinformatics

Background:

  • Increasing data volume in genomics and transcriptomics necessitates advanced computational tools.
  • Biology increasingly relies on computational methods for data analysis and knowledge extraction.

Purpose of the Study:

  • To present the TICA web server, a tool for transcription factor interaction prediction.
  • To support data-driven knowledge discovery in genomics.

Main Methods:

  • Leveraging the GenoMetric Query Language for integrating large genomic datasets.
  • Employing a statistical method for detecting co-locations to infer transcription factor interactions.
  • Utilizing Apache Hadoop and Spark technologies for efficient data management.

Main Results:

  • TICA enables analysis of user-uploaded ChIP-seq data against ENCODE data or other user datasets.
  • Achieves linear computation time scaling with dataset size and density.
  • Predictions are validated against existing biological knowledge using ENCODE data.

Conclusions:

  • TICA is a reliable and efficient web server for transcription factor interaction screening.
  • Facilitates data-driven hypothesis generation in biological research.