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

Multi-species Conserved Sequences02:51

Multi-species Conserved Sequences

3.3K
Next-generation sequencing technologies have created large genomic databases of a variety of animals and plants. Ever since the human genome project was completed, scientists studied the genome of primates, mammals, and other phylogenetically distant living beings. Such large-scale  studies have provided new insights into the evolutionary relationship between organisms.
Although the genome of each species varies greatly from each other, a few sequences are highly conserved. Such conserved...
3.3K
Conservation of Protein Domains Over Different Proteins02:26

Conservation of Protein Domains Over Different Proteins

11.7K
Protein domains are small structurally independent units that are part of a single amino acid chain.  Although these domains are often structurally independent, they may rely on synergistic effects to perform their functions as part of a larger protein. Protein domains may be conserved within the same organism, as well as across different organisms.
A limited set of protein domains often duplicate and recombine during evolution. These domains can be organized in different combinations to...
11.7K
Modern Molecular Taxonomy01:29

Modern Molecular Taxonomy

828
Advancements in molecular biology have revolutionized the identification and characterization of bacteria, with multiple methods leveraging DNA sequencing for enhanced precision. As sequencing technologies improve and costs decline, these approaches are increasingly used in clinical, environmental, and evolutionary studies.Multilocus Sequence Typing (MLST) examines several housekeeping genes, essential chromosomal genes encoding cellular functions, to distinguish strains. Approximately...
828
Conservation of Protein Domains02:26

Conservation of Protein Domains

2.9K
2.9K
Maxam-Gilbert Sequencing01:05

Maxam-Gilbert Sequencing

10.3K
In the same year as the discovery of the Sanger sequencing method, another group of scientists, Allan Maxam and Walter Gilbert, demonstrated their chemical-cleavage method for DNA sequencing. The Maxam-Gilbert method relies on using different chemicals that can cleave the DNA sequence at specific sites, the separation of resulting DNA fragments of variable size using electrophoresis, and deciphering the DNA sequence from the resulting gel bands.
Challenges of the Maxam-Gilbert Method
The...
10.3K
DNA Microarrays02:34

DNA Microarrays

16.5K
Microarrays are high-throughput and relatively inexpensive assays that can be automated to analyze large quantities of data at a time. They are used in genome-wide studies to compare gene or protein expression under two varied conditions, such as healthy and diseased states. Microarrays consist of glass or silica slides on which probe molecules are covalently attached through surface functionalization. Most commonly, the slides are prepared through the chemisorption of silanes to silica...
16.5K

You might also read

Related Articles

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

Sort by
Same author

<i>Special Issue:</i> 13th International Conference on Computational Advances in Bio and Medical Sciences.

Journal of computational biology : a journal of computational molecular cell biology·2026
Same author

Fast Algorithms for Computing Jaro Similarity.

Journal of computational biology : a journal of computational molecular cell biology·2026
Same author

<i>Special Section:</i> 12th International Computational Advances in Bio and Medical Sciences (ICCABS 2023).

Journal of computational biology : a journal of computational molecular cell biology·2025
Same author

Randomized feature selection based semi-supervised latent Dirichlet allocation for microbiome analysis.

Scientific reports·2024
Same author

KE: A Knowledge Enhancing Framework for Machine Learning Models.

The journal of physical chemistry. A·2023
Same author

Special Issue: 11th International Computational Advances in Bio and Medical Sciences (ICCABS 2021).

Journal of computational biology : a journal of computational molecular cell biology·2023
Same journal

Ensemble Modeling of Multiple Physical Indicators to Dynamically Phenotype Autism Spectrum Disorder.

Algorithms·2026
Same journal

Multimodal LLM vs. Human-Measured Features for AI Predictions of Autism in Home Videos.

Algorithms·2026
Same journal

Inferring the Timing of Antiretroviral Therapy by Zero-Inflated Random Change Point Models Using Longitudinal Data Subject to Left-Censoring.

Algorithms·2026
Same journal

Synthesizing Explainability Across Multiple ML Models for Structured Data.

Algorithms·2026
Same journal

Closest Farthest Widest.

Algorithms·2025
Same journal

Algorithmic Design of Geometric Data for Molecular Potential Energy Surfaces.

Algorithms·2025
See all related articles

Related Experiment Video

Updated: Apr 22, 2026

Using SCOPE to Identify Potential Regulatory Motifs in Coregulated Genes
07:55

Using SCOPE to Identify Potential Regulatory Motifs in Coregulated Genes

Published on: May 31, 2011

10.6K

PMS6MC: A Multicore Algorithm for Motif Discovery.

Shibdas Bandyopadhyay1, Sartaj Sahni2, Sanguthevar Rajasekaran3

  • 1VMware Inc, Palo Alto,CA 94304, sbandyopadhyay@vmware.com.

Algorithms
|October 14, 2014
PubMed
Summary
This summary is machine-generated.

We created PMS6MC, an efficient multicore algorithm for (l, d)-motif discovery. This new algorithm significantly speeds up finding motifs across multiple DNA sequences, outperforming existing parallel methods.

Keywords:
Planted motif searchmulti-core algorithmsparallel string algorithms

More Related Videos

Peptide-based Identification of Functional Motifs and their Binding Partners
14:28

Peptide-based Identification of Functional Motifs and their Binding Partners

Published on: June 30, 2013

11.9K
Optimization of Synthetic Proteins: Identification of Interpositional Dependencies Indicating Structurally and/or Functionally Linked Residues
07:08

Optimization of Synthetic Proteins: Identification of Interpositional Dependencies Indicating Structurally and/or Functionally Linked Residues

Published on: July 14, 2015

9.6K

Related Experiment Videos

Last Updated: Apr 22, 2026

Using SCOPE to Identify Potential Regulatory Motifs in Coregulated Genes
07:55

Using SCOPE to Identify Potential Regulatory Motifs in Coregulated Genes

Published on: May 31, 2011

10.6K
Peptide-based Identification of Functional Motifs and their Binding Partners
14:28

Peptide-based Identification of Functional Motifs and their Binding Partners

Published on: June 30, 2013

11.9K
Optimization of Synthetic Proteins: Identification of Interpositional Dependencies Indicating Structurally and/or Functionally Linked Residues
07:08

Optimization of Synthetic Proteins: Identification of Interpositional Dependencies Indicating Structurally and/or Functionally Linked Residues

Published on: July 14, 2015

9.6K

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Algorithm Design

Background:

  • Motif discovery is crucial for understanding biological sequences.
  • Existing single-core algorithms struggle with large biological datasets.
  • Parallel processing offers potential for faster motif discovery.

Purpose of the Study:

  • To develop an efficient multicore algorithm for the (l, d)-motif discovery problem.
  • To enhance the speed of motif discovery in large biological sequence sets.
  • To provide a faster alternative to existing single-core and parallel motif search algorithms.

Main Methods:

  • Developed PMS6MC, a multicore algorithm based on the PMS6 single-core algorithm.
  • Implemented and tested PMS6MC on challenging (l, d)-motif instances.
  • Evaluated performance on an Intel 6-core system, measuring speedup relative to PMS6.

Main Results:

  • PMS6MC achieved significant speedups over the single-core PMS6 algorithm.
  • Speedups ranged from 2.75x for (13,4) instances to 6.62x for (17,6) instances.
  • Estimated PMS6MC to be 2-4 times faster than other parallel motif search algorithms.

Conclusions:

  • PMS6MC is an efficient and effective multicore algorithm for (l, d)-motif discovery.
  • The algorithm offers substantial performance improvements for large-scale biological sequence analysis.
  • PMS6MC represents a significant advancement in computational motif discovery.