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

Conservation of Protein Domains Over Different Proteins02:26

Conservation of Protein Domains Over Different Proteins

13.8K
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...
13.8K
Evolutionary Relationships through Genome Comparisons02:54

Evolutionary Relationships through Genome Comparisons

6.7K
Genome comparison is one of the excellent ways to interpret the evolutionary relationships between organisms. The basic principle of genome comparison is that if two species share a common feature, it is likely encoded by the DNA sequence conserved between both species. The advent of genome sequencing technologies in the late 20th century enabled scientists to understand the concept of conservation of domains between species and helped them to deduce evolutionary relationships across diverse...
6.7K
Conservation of Protein Domains02:26

Conservation of Protein Domains

3.8K
3.8K
Multi-species Conserved Sequences02:51

Multi-species Conserved Sequences

4.5K
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...
4.5K
Genome Annotation and Assembly03:36

Genome Annotation and Assembly

20.1K
The genome refers to all of the genetic material in an organism. It can range from a few million base pairs in microbial cells to several billion base pairs in many eukaryotic organisms. Genome assembly refers to the process of taking the DNA sequencing data and putting it all back together in a correct order to create a close representation of the original genome. This is followed by the identification of functional elements on the newly assembled genome, a process called genome annotation.
20.1K

You might also read

Related Articles

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

Sort by
Same author

Spatial Immune Model of Alveolar Lung Infection (SIMALI) Identifies Structural Determinants of Lung Inflammation.

Research square·2026
Same author

Stochastic<i>GW</i>-GPU: Rapid Quasi-Particle Energies for Molecules beyond 10,000 Atoms.

Journal of chemical theory and computation·2026
Same author

nf-core/proteinfamilies: a scalable pipeline for the generation of protein families.

GigaScience·2026
Same author

metagRoot: a comprehensive database of protein families associated with plant root microbiomes.

Nucleic acids research·2025
Same author

GenomeOcean: An Efficient Genome Foundation Model Trained on Large-Scale Metagenomic Assemblies.

bioRxiv : the preprint server for biology·2025
Same author

High-performance computing at a crossroads.

Science (New York, N.Y.)·2025

Related Experiment Video

Updated: Dec 8, 2025

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

7.6K

ADEPT: a domain independent sequence alignment strategy for gpu architectures.

Muaaz G Awan1, Jack Deslippe2, Aydin Buluc2

  • 1Lawrence Berkeley National Laboratory, 1 Cyclotron Road, Berkeley, USA. mgawan@lbl.gov.

BMC Bioinformatics
|September 16, 2020
PubMed
Summary

We developed ADEPT, a new GPU-accelerated strategy for sequence alignment, significantly speeding up bioinformatics workflows. ADEPT is domain-independent, outperforming existing CPU and GPU methods for genome and protein analysis.

Keywords:
AlignmentBioinformaticsDNAGPUProtein

More Related Videos

An Integrated Approach for Microprotein Identification and Sequence Analysis
09:37

An Integrated Approach for Microprotein Identification and Sequence Analysis

Published on: July 12, 2022

3.8K
Creating and Applying a Reference to Facilitate the Discussion and Classification of Proteins in a Diverse Group
07:49

Creating and Applying a Reference to Facilitate the Discussion and Classification of Proteins in a Diverse Group

Published on: August 16, 2017

7.3K

Related Experiment Videos

Last Updated: Dec 8, 2025

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

7.6K
An Integrated Approach for Microprotein Identification and Sequence Analysis
09:37

An Integrated Approach for Microprotein Identification and Sequence Analysis

Published on: July 12, 2022

3.8K
Creating and Applying a Reference to Facilitate the Discussion and Classification of Proteins in a Diverse Group
07:49

Creating and Applying a Reference to Facilitate the Discussion and Classification of Proteins in a Diverse Group

Published on: August 16, 2017

7.3K

Area of Science:

  • Bioinformatics
  • High-Performance Computing (HPC)
  • Computational Biology

Background:

  • Bioinformatic workflows rely on sequence alignment tools like the Smith-Waterman algorithm.
  • Increasing data sizes necessitate faster alignment methods, especially on GPU architectures.
  • Existing GPU strategies are often domain-specific or limited in application scope.

Purpose of the Study:

  • To present ADEPT, a novel, domain-independent GPU sequence alignment strategy.
  • To accelerate the Smith-Waterman algorithm for both genomic and proteomic data.
  • To enable scalable integration into existing bioinformatics pipelines.

Main Methods:

  • Developed ADEPT, a GPU-specific optimization strategy for sequence alignment.
  • Implemented the Smith-Waterman algorithm using ADEPT.
  • Benchmarked against CPU and existing GPU methods on diverse datasets.
  • Integrated ADEPT into metagenome assemblers and protein similarity pipelines.

Main Results:

  • ADEPT achieves peak performance of 360 GCUPS (proteins) and 497 GCUPs (DNA) on a single GPU node.
  • Demonstrates a 10x performance improvement over SIMD CPU implementations.
  • Achieved 10% and 30% performance boosts when integrated into MetaHipMer and PASTIS, respectively.

Conclusions:

  • ADEPT offers competitive or superior performance compared to existing GPU strategies.
  • The domain-independent nature of ADEPT enhances its applicability across bioinformatics.
  • ADEPT successfully accelerates key bioinformatics software, demonstrating practical utility.