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

Real Time RT-PCR02:57

Real Time RT-PCR

Real-time reverse transcription-polymerase chain reaction, or Real-time RT-PCR, is an analytical tool used to determine the expression level of target genes. The method involves converting mRNA to complementary DNA with the help of an enzyme known as reverse transcriptase, followed by the PCR amplification of the cDNA. These two processes can be performed simultaneously in a single tube or separately as a two-step reaction.
The real-time quantification of the number of amplified products is...

You might also read

Related Articles

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

Sort by
Same author

RAmpSim: a thermodynamic simulator for hybridization capture in metagenomic sequencing.

Bioinformatics (Oxford, England)·2026
Same author

Functionalized MOFs for Subnanometric Control of Pd Speciation for Selective Hydrogenation of Butadiene.

ACS applied nano materials·2026
Same author

Primer Design through Submodular Function Estimation.

Bioinformatics (Oxford, England)·2026
Same author

Accelerating String Comparison in RLZ Compressed Sequences via LCE Jumps.

bioRxiv : the preprint server for biology·2026
Same author

Building genomic data structures from compressed representations using prefix-free parsing.

Genome research·2026
Same author

Monocyte-derived macrophages drive neurological tissue damage through mitochondrial reactive oxygen species.

Science immunology·2026

Related Experiment Video

Updated: Jun 16, 2026

Automated Multimodal Stimulation and Simultaneous Neuronal Recording from Multiple Small Organisms
08:28

Automated Multimodal Stimulation and Simultaneous Neuronal Recording from Multiple Small Organisms

Published on: March 3, 2023

Fast motif recognition via application of statistical thresholds.

Christina Boucher1, James King

  • 1David R, Cheriton School of Computer Science, University of Waterloo, Waterloo, Ontario, Canada. cabouche@cs.uwaterloo.ca

BMC Bioinformatics
|February 4, 2010
PubMed
Summary

A new heuristic significantly speeds up motif recognition for detecting transcription factor binding sites. The developed program, sMCL-WMR, offers improved accuracy and efficiency in analyzing genomic data.

More Related Videos

Using Three-color Single-molecule FRET to Study the Correlation of Protein Interactions
11:22

Using Three-color Single-molecule FRET to Study the Correlation of Protein Interactions

Published on: January 30, 2018

Related Experiment Videos

Last Updated: Jun 16, 2026

Automated Multimodal Stimulation and Simultaneous Neuronal Recording from Multiple Small Organisms
08:28

Automated Multimodal Stimulation and Simultaneous Neuronal Recording from Multiple Small Organisms

Published on: March 3, 2023

Using Three-color Single-molecule FRET to Study the Correlation of Protein Interactions
11:22

Using Three-color Single-molecule FRET to Study the Correlation of Protein Interactions

Published on: January 30, 2018

Area of Science:

  • Computational biology
  • Bioinformatics

Background:

  • Motif recognition is crucial for identifying transcription factor binding sites in genomic data.
  • The CONSENSUS STRING problem, determining if a consensus string exists within a Hamming distance threshold for a set of strings, is NP-complete.
  • This computational challenge is a bottleneck in existing motif recognition programs like MCL-WMR.

Purpose of the Study:

  • To develop an efficient heuristic for the CONSENSUS STRING problem with a low error rate.
  • To introduce a novel motif recognition program, sMCL-WMR, based on this heuristic.
  • To evaluate the performance of sMCL-WMR in detecting weak motifs and transcription factor binding sites.

Main Methods:

  • Development of a heuristic algorithm to approximate the solution to the CONSENSUS STRING problem.
  • Application of the heuristic to create the sMCL-WMR motif recognition program.
  • Benchmarking sMCL-WMR against leading motif recognition programs using synthetic and real genomic datasets.

Main Results:

  • The sMCL-WMR program demonstrates impressive accuracy and efficiency in motif recognition.
  • sMCL-WMR successfully detects weak motifs in large datasets and real genomic data.
  • The new heuristic provides insights into sampling pairwise bounded sets for motif recognition.

Conclusions:

  • The novel heuristic enables sMCL-WMR, a state-of-the-art program for detecting weak motifs in large datasets.
  • sMCL-WMR is significantly faster (orders of magnitude) than its predecessor, MCL-WMR.
  • sMCL-WMR accurately identifies transcription factor binding sites, solving previously intractable problems.