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

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...

Evolutionary Relationships through Genome Comparisons

Evolutionary Relationships through Genome Comparisons

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...

You might also read

Related Articles

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

Sort by

Same author

Structural motif search across the protein universe with Folddisco.

Nature biotechnology·2026

Same author

A novel method to select Reference Proteomes in UniProt.

bioRxiv : the preprint server for biology·2026

Same author

Organizing across disciplines to tackle shared computational challenges.

Patterns (New York, N.Y.)·2026

Same author

Simple and Thorough Detection of Related Sequences with Position-Varying Probabilities of Substitutions, Insertions, and Deletions.

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

Same author

Multiple protein structure alignment at scale with FoldMason.

Science (New York, N.Y.)·2026

Same author

gpuPairHMM: High-Speed Pair-HMM Forward Algorithm for DNA Variant Calling on GPUs.

IEEE transactions on computational biology and bioinformatics·2026

Same journal

RNAbpFlow: base pair-augmented SE(3) flow matching for conditional RNA 3D structure generation.

Nature methods·2026

Same journal

Spatio-DARLIN enables robust and efficient in situ lineage tracing in mice at single-cell resolution.

Nature methods·2026

Same journal

EasyGrid: a versatile platform for automated cryo-EM sample preparation and quality control.

Nature methods·2026

Same journal

Cloud-based microscope enables live neuroimaging for 24 h and beyond with worldwide access.

Nature methods·2026

Same journal

Deep molecular profiling in three dimensions.

Nature methods·2026

Same journal

3D pathology-guided microdissection.

Nature methods·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Jan 17, 2026

Single Cell Multiplex Reverse Transcription Polymerase Chain Reaction After Patch-clamp

Single Cell Multiplex Reverse Transcription Polymerase Chain Reaction After Patch-clamp

Published on: June 20, 2018

GPU-accelerated homology search with MMseqs2.

Felix Kallenborn¹, Alejandro Chacon², Christian Hundt²

¹Department of Computer Science, Johannes Gutenberg University Mainz, Mainz, Germany.

|September 19, 2025

Summary

This summary is machine-generated.

Graphics processing unit (GPU)-accelerated MMseqs2 significantly speeds up protein database searches and structure prediction. This tool offers a cost-effective solution for large-scale biological data analysis, enhancing research efficiency.

More Related Videos

An Integrated Approach for Microprotein Identification and Sequence Analysis

An Integrated Approach for Microprotein Identification and Sequence Analysis

Published on: July 12, 2022

A Computational Pipeline for Intergenic/Intragenic Enhancer RNA Quantification in Mouse Embryonic Stem Cells

A Computational Pipeline for Intergenic/Intragenic Enhancer RNA Quantification in Mouse Embryonic Stem Cells

Published on: October 28, 2025

Related Experiment Videos

Last Updated: Jan 17, 2026

Single Cell Multiplex Reverse Transcription Polymerase Chain Reaction After Patch-clamp

Single Cell Multiplex Reverse Transcription Polymerase Chain Reaction After Patch-clamp

Published on: June 20, 2018

An Integrated Approach for Microprotein Identification and Sequence Analysis

An Integrated Approach for Microprotein Identification and Sequence Analysis

Published on: July 12, 2022

A Computational Pipeline for Intergenic/Intragenic Enhancer RNA Quantification in Mouse Embryonic Stem Cells

A Computational Pipeline for Intergenic/Intragenic Enhancer RNA Quantification in Mouse Embryonic Stem Cells

Published on: October 28, 2025

Area of Science:

Bioinformatics
Computational Biology
Structural Biology

Background:

Protein databases are expanding rapidly, necessitating faster and more sensitive search tools.
Current computational methods struggle to keep pace with the growing volume of biological data.

Purpose of the Study:

To introduce and evaluate the performance of the graphics processing unit (GPU)-accelerated MMseqs2 tool.
To demonstrate the efficiency gains of MMseqs2-GPU for protein sequence and structure analysis.

Main Methods:

Benchmarking MMseqs2-GPU against CPU-based methods for single-protein searches.
Evaluating MMseqs2-GPU for large query batch searches using multiple GPUs.
Assessing the impact of MMseqs2-GPU on protein structure prediction (ColabFold) and structure search (Foldseek) pipelines.

Main Results:

MMseqs2-GPU achieves 6x faster single-protein searches compared to CPU methods.
For large batches, MMseqs2-GPU is 2.4x faster than the leading alternative using eight GPUs.
Accelerates ColabFold by 31.8x and Foldseek by 4-27x.

Conclusions:

GPU-accelerated MMseqs2 provides a substantial speedup for protein database searching and analysis.
This tool enhances the efficiency of critical bioinformatics tasks like structure prediction and search.
MMseqs2-GPU offers a cost-effective and powerful solution for modern biological research.