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

You might also read

Related Articles

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

Sort by
Same author

Genome editing and regeneration pipeline for engineering disease resistance in tomato using CRISPR/Cas9.

Frontiers in plant science·2026
Same author

Genome sequences of distinct genotypes of bacterial pathogen Xanthomonas euvesicatoria pv. euvesicatoria from pepper (Capsicum annuum L.) in Serbia.

Access microbiology·2026
Same author

Rapid local and systemic jasmonate signalling drives the initiation and establishment of plant systemic immunity.

Nature plants·2026
Same author

Mitochondrial ROS trigger interorganellular signaling and prime ER processes to establish enhanced plant immunity.

Science advances·2025
Same author

The <i>Arabidopsis</i> TIRome informs the design of artificial TIR (Toll/interleukin-1 receptor) domain proteins.

Proceedings of the National Academy of Sciences of the United States of America·2025
Same author

NAD(H) and NADP(H) in plants and mammals.

Molecular plant·2025
Same journal

Analysis of strength degradation of coal and rock masses and stability of mined areas under long term immersion environment.

PloS one·2026
Same journal

Biogenic Silver-Selenium nanocomposite with anticancer activity and potent efficacy against vancomycin-resistant Staphylococcus aureus.

PloS one·2026
Same journal

Preparation and physicochemical characterization of a biodegradable chitosan/carboxymethyl cellulose hydrogel synthesized in NaOH/urea medium.

PloS one·2026
Same journal

Action-guilt, survivor-guilt, and depression in combat-related PTSD.

PloS one·2026
Same journal

Explainable machine learning for predicting activities of daily living at discharge in stroke patients: A retrospective study using SHAP interpretability.

PloS one·2026
Same journal

Deep learning based two-way feature depiction model for brain tumor detection.

PloS one·2026
See all related articles

Related Experiment Video

Updated: May 20, 2026

An Integrated Workflow of Identification and Quantification on FDR Control-Based Untargeted Metabolome
05:35

An Integrated Workflow of Identification and Quantification on FDR Control-Based Untargeted Metabolome

Published on: September 20, 2022

An ultra-fast metabolite prediction algorithm.

Zheng Rong Yang1, Murray Grant

  • 1Biosciences, College of Life and Environmental Science, University of Exeter, Exeter, United Kingdom. z.r.yang@ex.ac.uk

Plos One
|June 30, 2012
PubMed
Summary
This summary is machine-generated.

Accurate spectral alignment is crucial for metabolomics data analysis. A new quicksort-based algorithm significantly improves alignment speed and accuracy for untargeted metabolomics studies.

More Related Videos

Large Scale Non-targeted Metabolomic Profiling of Serum by Ultra Performance Liquid Chromatography-Mass Spectrometry (UPLC-MS)
07:34

Large Scale Non-targeted Metabolomic Profiling of Serum by Ultra Performance Liquid Chromatography-Mass Spectrometry (UPLC-MS)

Published on: March 14, 2013

Multi-step Preparation Technique to Recover Multiple Metabolite Compound Classes for In-depth and Informative Metabolomic Analysis
11:25

Multi-step Preparation Technique to Recover Multiple Metabolite Compound Classes for In-depth and Informative Metabolomic Analysis

Published on: July 11, 2014

Related Experiment Videos

Last Updated: May 20, 2026

An Integrated Workflow of Identification and Quantification on FDR Control-Based Untargeted Metabolome
05:35

An Integrated Workflow of Identification and Quantification on FDR Control-Based Untargeted Metabolome

Published on: September 20, 2022

Large Scale Non-targeted Metabolomic Profiling of Serum by Ultra Performance Liquid Chromatography-Mass Spectrometry (UPLC-MS)
07:34

Large Scale Non-targeted Metabolomic Profiling of Serum by Ultra Performance Liquid Chromatography-Mass Spectrometry (UPLC-MS)

Published on: March 14, 2013

Multi-step Preparation Technique to Recover Multiple Metabolite Compound Classes for In-depth and Informative Metabolomic Analysis
11:25

Multi-step Preparation Technique to Recover Multiple Metabolite Compound Classes for In-depth and Informative Metabolomic Analysis

Published on: July 11, 2014

Area of Science:

  • Biochemistry
  • Analytical Chemistry
  • Bioinformatics

Background:

  • Metabolomics is vital for understanding biological processes and validating post-genomic studies.
  • Accurate metabolite identification requires robust experimental approaches, including multiple replicates and treatments.
  • Variations in mass spectra due to machine resolution and replicate differences pose challenges for feature recognition.

Purpose of the Study:

  • To address the impediment of spectral alignment in untargeted metabolomics.
  • To develop a novel algorithm for efficient and accurate spectral alignment.
  • To improve the utilization of metabolomics data for predicting metabolic changes.

Main Methods:

  • A new spectral alignment algorithm utilizing the quicksort technique was developed.
  • The algorithm was tested using both simulated and real-world metabolomics data.
  • Performance was evaluated based on alignment speed and accuracy compared to existing methods.

Main Results:

  • The quicksort-based algorithm demonstrated a significant increase in alignment speed.
  • The new algorithm also showed improvements in alignment accuracy.
  • Results indicate enhanced feature recognition across different spectra.

Conclusions:

  • The developed quicksort algorithm offers a more efficient and accurate solution for spectral alignment in metabolomics.
  • This advancement facilitates more reliable analysis of untargeted metabolomics data.
  • Improved alignment supports better prediction of metabolic dynamics and biological insights.