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

Aggregates Classification01:29

Aggregates Classification

1.1K
Aggregate classification is generally based on its size, petrographic characteristics, weight, and source. Size classification ranges from coarse to fine aggregates, defined by the size of the particles. Coarse aggregates are particles that do not pass through ASTM sieve No. 4, and aggregates that pass through the sieve are fine aggregates.
Petrographic classification groups aggregates based on common mineralogical characteristics. Some of the common mineral groups found in aggregates are...
1.1K
Next-generation Sequencing03:00

Next-generation Sequencing

100.0K
The first human genome sequencing project cost $2.7 billion and was declared complete in 2003, after 15 years of international cooperation and collaboration between several research teams and funding agencies. Today, with the advent of next-generation sequencing technologies, the cost and time of sequencing a human genome have dropped over 100 fold.
Next-Generation Sequencing Methods
Although all next-generation methods use different technologies, they all share a set of standard features....
100.0K
RACE - Rapid Amplification of cDNA Ends02:35

RACE - Rapid Amplification of cDNA Ends

7.5K
Rapid Amplification of cDNA Ends, or RACE, is one of the most effective methods to obtain a full-length cDNA from an mRNA sequence between a known internal region to the unknown sequence at the 5’ or 3’ end. The unknown region is cloned in the cDNA by a gene-specific primer that binds the known end, and a hybrid primer that attaches a predefined anchor sequence to the unknown end of the cDNA. The sequence in between is amplified by PCR with an anchor primer and a gene-specific...
7.5K
Parallel Processing01:20

Parallel Processing

842
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...
842
Classification of Systems-II01:31

Classification of Systems-II

540
Continuous-time systems have continuous input and output signals, with time measured continuously. These systems are generally defined by differential or algebraic equations. For instance, in an RC circuit, the relationship between input and output voltage is expressed through a differential equation derived from Ohm's law and the capacitor relation,
540
Improving Translational Accuracy02:07

Improving Translational Accuracy

15.3K
Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...
15.3K

You might also read

Related Articles

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

Sort by
Same author

New approach methodologies as first tier in an integrated approach to testing and assessment for non-genotoxic carcinogens.

ALTEX·2026
Same author

Accelign: a GPU-based library for accelerating pairwise sequence alignment.

BMC bioinformatics·2026
Same author

Suppressing dendrites <i>via</i> lateral lithium flux in Li metal solid-state batteries.

Energy & environmental science·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 author

RMapAlign3N: fast mapping of 3N-Reads.

Bioinformatics advances·2025
Same author

GPU-accelerated homology search with MMseqs2.

Nature methods·2025
Same journal

OpenIMC: an open-source platform for analyzing single-cell and spatial proteomics by imaging mass cytometry.

BMC bioinformatics·2026
Same journal

NAP: an open source pipeline for cross-domain microbiome profiling using Nanopore sequencing-derived amplicon data.

BMC bioinformatics·2026
Same journal

SurvGME: an R package for survival analysis with graphical and measurement error models.

BMC bioinformatics·2026
Same journal

SimMapNet: a Bayesian framework for gene regulatory network inference using gene ontology similarities as external hint.

BMC bioinformatics·2026
Same journal

Dual channel drug-drug interactions extraction based on cross attention.

BMC bioinformatics·2026
Same journal

FeSseqdb: a curated sequence-level database and interpretable machine learning framework for identifying iron-sulfur proteins.

BMC bioinformatics·2026
See all related articles

Related Experiment Video

Updated: Mar 9, 2026

A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data
09:34

A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data

Published on: September 25, 2021

4.6K

Accelerating metagenomic read classification on CUDA-enabled GPUs.

Robin Kobus1, Christian Hundt2, André Müller2

  • 1Institute of Computer Science, Johannes Gutenberg University Mainz, Staudingerweg 9, Mainz, 55435, Germany. rkobus@students.uni-mainz.de.

BMC Bioinformatics
|January 5, 2017
PubMed
Summary
This summary is machine-generated.

We developed cuCLARK, a GPU-accelerated tool for fast metagenomic read classification. This method significantly speeds up the assignment of taxonomic labels to sequencing reads, aiding microbiome and environmental studies.

Keywords:
CUDAExact k-mer matchingGPUsMetagenomicsTaxonomic assignment

More Related Videos

Author Spotlight: A Cost-Effective Genomic Workflow for Advancing Rabies Control in Resource-Limited Settings
10:26

Author Spotlight: A Cost-Effective Genomic Workflow for Advancing Rabies Control in Resource-Limited Settings

Published on: August 18, 2023

6.6K
Metagenomic Analysis of Silage
08:43

Metagenomic Analysis of Silage

Published on: January 13, 2017

19.2K

Related Experiment Videos

Last Updated: Mar 9, 2026

A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data
09:34

A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data

Published on: September 25, 2021

4.6K
Author Spotlight: A Cost-Effective Genomic Workflow for Advancing Rabies Control in Resource-Limited Settings
10:26

Author Spotlight: A Cost-Effective Genomic Workflow for Advancing Rabies Control in Resource-Limited Settings

Published on: August 18, 2023

6.6K
Metagenomic Analysis of Silage
08:43

Metagenomic Analysis of Silage

Published on: January 13, 2017

19.2K

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Metagenomic sequencing is crucial for studying microbiomes and diverse environments.
  • Accurate taxonomic classification of sequencing reads is a fundamental computational challenge.
  • High-throughput sequencing generates vast data, necessitating efficient classification tools.

Purpose of the Study:

  • To develop a fast and accurate computational tool for metagenomic read classification.
  • To leverage GPU acceleration for improved classification performance.

Main Methods:

  • Implemented cuCLARK, a read-level classifier utilizing CUDA-enabled GPUs.
  • Based on the CLARK method employing reduced k-mers for efficient sequence analysis.

Main Results:

  • cuCLARK achieves classification speeds of up to 50 million reads per minute on a single GPU.
  • Demonstrated significant speedups (3.2–6.6x) compared to multi-threaded CPU execution for species-level classification.

Conclusions:

  • cuCLARK offers superior speed for metagenomic read classification on CUDA-enabled GPUs.
  • The software is freely available under the GPL license.