Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Parallel Processing

Parallel Processing

The brain processes sensory information rapidly due to parallel processing, which involves sending data across multiple neural pathways at the same time. This method allows the brain to manage various sensory qualities, such as shapes, colors, movements, and locations, all concurrently. For instance, when observing a forest landscape, the brain simultaneously processes the movement of leaves, the shapes of trees, the depth between them, and the various shades of green. This enables a quick and...

Multi-species Conserved Sequences

Multi-species Conserved Sequences

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 DNA...

Maxam-Gilbert Sequencing

Maxam-Gilbert Sequencing

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...

You might also read

Related Articles

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

Sort by

Same author

Comparative Multi-Marker Environmental DNA Metabarcoding of Marine Metazoan Communities: Water vs. Sediment.

Molecular ecology resources·2026

Same author

Paired Duodenal and Salivary Microbiome Analysis in Pancreatic Cancer Without Duct Obstruction.

United European gastroenterology journal·2026

Same author

Protective Effects of Grapeseed Proanthocyanidins in Ulcerative Colitis: A Pilot Study Evaluating a Potential Therapeutic Strategy.

Journal of clinical medicine·2026

Same author

Editorial: Identification of phenotypically important genomic variants.

Frontiers in bioinformatics·2023

Same author

Longitudinal flux balance analyses of a patient with episodic colonic inflammation reveals microbiome metabolic dynamics.

Gut microbes·2023

Same author

CK2β-regulated signaling controls B cell differentiation and function.

Frontiers in immunology·2023

Search research articles

Related Experiment Video

Updated: Jul 6, 2026

Demonstration of the Sequence Alignment to Predict Across Species Susceptibility Tool for Rapid Assessment of Protein Conservation

Demonstration of the Sequence Alignment to Predict Across Species Susceptibility Tool for Rapid Assessment of Protein Conservation

Published on: February 10, 2023

CUDA compatible GPU cards as efficient hardware accelerators for Smith-Waterman sequence alignment.

Svetlin A Manavski¹, Giorgio Valle

¹CRIBI, University of Padova, Padova, Italy. svetlin.manavski@cribi.unipd.it

BMC Bioinformatics

|April 18, 2008

Summary

Accelerating biological sequence alignment, this study presents the fastest exact Smith-Waterman algorithm on graphics processing units (GPUs). This high-performance solution enables large-scale sequence similarity searches at low cost.

More Related Videos

Novel 3D/VR Interactive Environment for MD Simulations, Visualization and Analysis

Novel 3D/VR Interactive Environment for MD Simulations, Visualization and Analysis

Published on: December 18, 2014

Related Experiment Videos

Last Updated: Jul 6, 2026

Demonstration of the Sequence Alignment to Predict Across Species Susceptibility Tool for Rapid Assessment of Protein Conservation

Demonstration of the Sequence Alignment to Predict Across Species Susceptibility Tool for Rapid Assessment of Protein Conservation

Published on: February 10, 2023

Novel 3D/VR Interactive Environment for MD Simulations, Visualization and Analysis

Novel 3D/VR Interactive Environment for MD Simulations, Visualization and Analysis

Published on: December 18, 2014

Area of Science:

Bioinformatics
Computational Biology
Genomics

Background:

Protein and DNA similarity searching is crucial in molecular biology.
The Smith-Waterman algorithm provides optimal local alignments but is computationally intensive.
Large databases necessitate faster, albeit less sensitive, heuristic methods like BLAST and FASTA.

Purpose of the Study:

To leverage the computational power of graphics processing units (GPUs) for high-performance sequence alignment.
To develop a fast and exact implementation of the Smith-Waterman algorithm.

Main Methods:

Implementation of the Smith-Waterman algorithm using NVidia's CUDA programming environment.
Utilizing the G80 Graphics Processing Unit (GPU) for direct hardware access.
Performance benchmarking against established tools (SSEARCH, BLAST) and other GPU/SIMD implementations.

Main Results:

Achieved speeds exceeding 3.5 GCUPS (Giga Cell Updates Per Second) on commodity hardware (dual GeForce 8800 GTX).
Outperformed existing GPU and SIMD implementations by 2 to 30 times.
Demonstrated the feasibility of exact Smith-Waterman on commodity hardware.

Conclusions:

Modern GPUs are effective hardware accelerators for sequence alignment.
The developed solution offers superior performance compared to other commodity hardware approaches.
Enables cost-effective, large-scale sequence alignments using the exact Smith-Waterman algorithm.