Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Transcription Factors02:16

Transcription Factors

70.5K
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...
70.5K
Transcription Factors02:16

Transcription Factors

21.4K
21.4K
General Transcription Factors01:30

General Transcription Factors

5.9K
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...
5.9K
Cooperative Binding of Transcription Regulators02:13

Cooperative Binding of Transcription Regulators

6.0K
Transcriptional regulators bind to specific cis-regulatory sequences in the DNA to regulate gene transcription. These cis-regulatory sequences are very short, usually less than ten nucleotide pairs in length. The short length means that there is a high probability of the exact same sequence randomly occurring throughout the genome.  Since regulators can also bind to groups of similar sequences, this further increases the chances of random binding. Transcriptional regulators form...
6.0K
Ribosome Profiling02:24

Ribosome Profiling

3.2K
Ribosome profiling or ribo-sequencing is a deep sequencing technique that produces a snapshot of active translation in a cell. It selectively sequences the mRNAs protected by ribosomes to get an insight into a cell’s translation landscape at any given point in time.
Applications of ribosome profiling
Ribosome profiling has many applications, including in vivo monitoring of translation inside a particular organ or tissue type and quantifying new protein synthesis levels.
The technique...
3.2K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Missing value replacement in strings and applications.

Data mining and knowledge discovery·2025
Same author

Pangenome comparison via ED strings.

Frontiers in bioinformatics·2024
Same author

Seedability: optimizing alignment parameters for sensitive sequence comparison.

Bioinformatics advances·2023
Same author

Palidis: fast discovery of novel insertion sequences.

Microbial genomics·2023
Same author

Clustering Demographics and Sequences of Diagnosis Codes.

IEEE journal of biomedical and health informatics·2021
Same author

IUPACpal: efficient identification of inverted repeats in IUPAC-encoded DNA sequences.

BMC bioinformatics·2021

Related Experiment Video

Updated: Apr 27, 2026

Mapping the Structure-Function Relationships of Disordered Oncogenic Transcription Factors Using Transcriptomic Analysis
09:58

Mapping the Structure-Function Relationships of Disordered Oncogenic Transcription Factors Using Transcriptomic Analysis

Published on: June 27, 2020

3.9K

MoTeX-II: structured MoTif eXtraction from large-scale datasets.

Solon P Pissis1

  • 1Department of Informatics, King's College London, The Strand, WC2R 2LS London, UK. solon.pissis@kcl.ac.uk.

BMC Bioinformatics
|July 10, 2014
PubMed
Summary
This summary is machine-generated.

MoTeX-II is a new high-performance computing tool for motif extraction from large biological datasets. It efficiently identifies structured motifs, outperforming existing tools in speed and handling large-scale data.

More Related Videos

Enhanced Yeast One-hybrid Screens To Identify Transcription Factor Binding To Human DNA Sequences
11:25

Enhanced Yeast One-hybrid Screens To Identify Transcription Factor Binding To Human DNA Sequences

Published on: February 11, 2019

7.7K
Real-time Analysis of Transcription Factor Binding, Transcription, Translation, and Turnover to Display Global Events During Cellular Activation
12:54

Real-time Analysis of Transcription Factor Binding, Transcription, Translation, and Turnover to Display Global Events During Cellular Activation

Published on: March 7, 2018

15.0K

Related Experiment Videos

Last Updated: Apr 27, 2026

Mapping the Structure-Function Relationships of Disordered Oncogenic Transcription Factors Using Transcriptomic Analysis
09:58

Mapping the Structure-Function Relationships of Disordered Oncogenic Transcription Factors Using Transcriptomic Analysis

Published on: June 27, 2020

3.9K
Enhanced Yeast One-hybrid Screens To Identify Transcription Factor Binding To Human DNA Sequences
11:25

Enhanced Yeast One-hybrid Screens To Identify Transcription Factor Binding To Human DNA Sequences

Published on: February 11, 2019

7.7K
Real-time Analysis of Transcription Factor Binding, Transcription, Translation, and Turnover to Display Global Events During Cellular Activation
12:54

Real-time Analysis of Transcription Factor Binding, Transcription, Translation, and Turnover to Display Global Events During Cellular Activation

Published on: March 7, 2018

15.0K

Area of Science:

  • Computer Science
  • Computational Biology
  • Bioinformatics

Background:

  • Motif extraction is crucial for understanding gene expression regulation.
  • Current tools struggle with large-scale datasets and structured motifs.
  • Existing methods face limitations in time and space complexity for motif identification.

Purpose of the Study:

  • Introduce MoTeX-II, a high-performance computing tool for structured motif extraction.
  • Address limitations of current motif extraction tools for large-scale biological data.
  • Enable efficient processing of full-length biological datasets for motif discovery.

Main Methods:

  • MoTeX-II utilizes word-based algorithms for approximate string matching.
  • The tool is available in CPU, OpenMP, and MPI versions for scalability.
  • Efficient merging of single motif occurrences optimizes runtime.

Main Results:

  • MoTeX-II achieves comparable accuracy to state-of-the-art structured motif extraction tools.
  • Demonstrates superior runtime efficiency, processing human genes in hours vs. weeks.
  • Successfully extracts known composite transcription factor binding sites from real datasets.

Conclusions:

  • MoTeX-II facilitates reliable information derivation from large biological datasets.
  • Enables motif extraction (single and structured) with various parameters in reasonable time.
  • The open-source MoTeX-II tool is available for research use.