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

Phosphoethanolamine modification of lipid A found in five distinct <i>Akkermansia</i> species.

Microbiology spectrum·2026
Same author

Association of Fontan Circulation With Gut Microbiome Derived Straight and Branched Short Chain Fatty Acids.

Journal of gastroenterology and hepatology·2026
Same author

DMEM and EMEM as alternate growth media for pathogenic Leptospira.

PLoS neglected tropical diseases·2026
Same author

A Culture-Free Lipidomics-Based Screening Test for Uropathogens.

Clinical chemistry·2026
Same author

Benefits of Field Asymmetric Ion Mobility Spectrometry and Kendrick Mass Defect Plots in Lipid A Analysis.

Journal of the American Society for Mass Spectrometry·2026
Same author

Proteomic Adaptations of <i>Escherichia coli</i> in Urinary Tract Infection Patients.

Journal of proteome research·2026

Related Experiment Video

Updated: Jun 13, 2026

Area-based Image Analysis Algorithm for Quantification of Macrophage-fibroblast Cocultures
07:05

Area-based Image Analysis Algorithm for Quantification of Macrophage-fibroblast Cocultures

Published on: February 15, 2022

WaveletQuant, an improved quantification software based on wavelet signal threshold de-noising for labeled

Fan Mo1, Qun Mo, Yuanyuan Chen

  • 1Zhejiang-California Nanosystems Institute, Zhejiang University, Hangzhou, China.

BMC Bioinformatics
|May 1, 2010
PubMed
Summary
This summary is machine-generated.

WaveletQuant, a new program using wavelet theory, improves mass spectrometry-based proteomic quantification by reducing noise and identifying true peaks. This novel approach enhances protein quantification accuracy and identifies more proteins compared to existing methods.

More Related Videos

Deep Proteome Profiling by Isobaric Labeling, Extensive Liquid Chromatography, Mass Spectrometry, and Software-assisted Quantification
10:37

Deep Proteome Profiling by Isobaric Labeling, Extensive Liquid Chromatography, Mass Spectrometry, and Software-assisted Quantification

Published on: November 15, 2017

Phosphopeptide Enrichment Coupled with Label-free Quantitative Mass Spectrometry to Investigate the Phosphoproteome in Prostate Cancer
12:23

Phosphopeptide Enrichment Coupled with Label-free Quantitative Mass Spectrometry to Investigate the Phosphoproteome in Prostate Cancer

Published on: August 2, 2018

Related Experiment Videos

Last Updated: Jun 13, 2026

Area-based Image Analysis Algorithm for Quantification of Macrophage-fibroblast Cocultures
07:05

Area-based Image Analysis Algorithm for Quantification of Macrophage-fibroblast Cocultures

Published on: February 15, 2022

Deep Proteome Profiling by Isobaric Labeling, Extensive Liquid Chromatography, Mass Spectrometry, and Software-assisted Quantification
10:37

Deep Proteome Profiling by Isobaric Labeling, Extensive Liquid Chromatography, Mass Spectrometry, and Software-assisted Quantification

Published on: November 15, 2017

Phosphopeptide Enrichment Coupled with Label-free Quantitative Mass Spectrometry to Investigate the Phosphoproteome in Prostate Cancer
12:23

Phosphopeptide Enrichment Coupled with Label-free Quantitative Mass Spectrometry to Investigate the Phosphoproteome in Prostate Cancer

Published on: August 2, 2018

Area of Science:

  • Proteomics
  • Biotechnology
  • Computational Biology

Background:

  • Quantitative proteomics identifies and quantifies proteins in complex samples.
  • Mass spectrometry (MS) based peptide quantification is susceptible to noise, requiring effective smoothing filters.
  • Traditional filters like moving average and Savitzky-Golay have limitations in accurate peptide quantification.

Purpose of the Study:

  • To present WaveletQuant, a novel program for improved MS-based proteomic quantification.
  • To address limitations of traditional noise reduction filters in mass spectrometry data.
  • To offer an alternative or superior method for accurate peptide quantification.

Main Methods:

  • Developed a novel discrete wavelet transform (DWT) and a 'Spatial Adaptive Algorithm' for noise removal and peak identification.
  • Programmed and compiled WaveletQuant using Visual C++ 2005 Express Edition.
  • Integrated WaveletQuant into the Trans-Proteomic Pipeline (TPP) for proteomics analysis.

Main Results:

  • WaveletQuant demonstrated superior performance in protein quantification compared to ASAPRatio within the TPP.
  • The program successfully quantified more proteins with higher accuracy using both yeast extract and ovarian cancer cell lysate datasets.
  • Noise reduction and true peak identification were significantly improved by the wavelet-based approach.

Conclusions:

  • WaveletQuant offers enhanced accuracy and scope in MS-based proteomic quantification.
  • The program provides a valuable alternative to existing quantification methods, particularly in noisy datasets.
  • WaveletQuant is available for download, facilitating its adoption in proteomics research.