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

Mass Spectrometry: Complex Analysis01:21

Mass Spectrometry: Complex Analysis

Mass spectrometry is an important technique for the identification of pure compounds. However, it has some limitations for the analysis of complex mixtures, often due to excessive fragmentation making the spectrum too complicated to decipher. Mass spectrometry can be combined with suitable separation methods in sequence, forming hyphenated methods, which are useful in the analysis of complex mixtures.
GC–MS is a powerful hyphenated method commonly used in forensics and environmental...
Tandem Mass Spectrometry01:21

Tandem Mass Spectrometry

Tandem mass spectrometry is a technique that uses multiple mass analyzers in series to obtain a higher selectivity and reduce chemical noise during analyte detection. Instruments with multiple analyzers separated by an interaction cell enable secondary fragmentation and selected study of the fragment ions.Secondary fragmentations occur in the interaction cell and can be induced by various factors. Fragmentation induced by collision with inert gases, such as N2, Ar, He, etc., is called...
MALDI-TOF Mass Spectrometry01:19

MALDI-TOF Mass Spectrometry

Mass spectrometry is a powerful characterization technique that can identify and separate a wide variety of compounds ranging from chemical to biological entities, based on their mass-to-charge ratio (m/z). The instruments that allow this detection, known as mass spectrometers, have three components: an ion source, a mass analyzer, and a detector. These spectrometers differ based on the nature of their ion source and analyzers.Matrix-assisted laser desorption ionization (MALDI) is a commonly...
Mass Spectrometry: Overview01:19

Mass Spectrometry: Overview

Mass spectrometry is an analytical technique used to determine the molecular mass and molecular formula of a compound. The basic principle of mass spectrometry is to generate ions from the analyte molecule and measure these ion abundances against their molecular mass. One common type of ionization, known as electron ionization or EI, bombards the analyte molecules in the gas phase with high-energy electron beams. The electron beams displace an electron from the molecule and leave behind a...
Peptide Identification Using Tandem Mass Spectrometry01:33

Peptide Identification Using Tandem Mass Spectrometry

Tandem mass spectrometry, also known as MS/MS or MS2, is an analytical technique that employs two mass analyzers. Essentially it is a series of mass spectrometers that helps isolate a particular biomolecule and then helps study its chemical properties.
This technique helps gather information regarding the protein from which the peptide was obtained and to study the peptides’ amino acid sequence. Identifying peptides from a complex mixture is an important component of the growing field of...
High-Resolution Mass Spectrometry (HRMS)01:15

High-Resolution Mass Spectrometry (HRMS)

The resolution of a mass spectrometer depends on the efficiency of separating ions with different ion masses. The mass of an atom is approximated to the sum of the masses of protons and neutrons inside, considering the masses of protons and neutrons as equal. However, the masses of the proton (1.6726 × 10−24 g) and neutron (1.6749 × 10−24 g) are not truly equal. There is a minor error in the expression of atomic masses relative to the simplest atom of hydrogen. For example, the mass of helium...

You might also read

Related Articles

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

Sort by
Same author

DeepER-Med: Advancing Deep Evidence-Based Research in Medicine Through Agentic AI.

ArXiv·2026
Same author

Habenula alterations in resting state functional connectivity among autistic individuals.

Biological psychiatry. Cognitive neuroscience and neuroimaging·2026
Same author

Predicting progression of Alzheimer's disease using blood-based multi-omics data.

Bioinformatics advances·2026
Same author

Executive Summary of ASN Kidney Health Guidance on Conservative Management in People with Kidney Failure.

Journal of the American Society of Nephrology : JASN·2026
Same author

ASN Kidney Health Guidance on Conservative Management in People with Kidney Failure.

Journal of the American Society of Nephrology : JASN·2026
Same author

End-to-end deep attention-based multitask pipeline for predicting uncertainty-quantified peptide properties from mass spectrometry data.

Scientific reports·2026

Related Experiment Video

Updated: May 13, 2026

Analyzing Large Protein Complexes by Structural Mass Spectrometry
15:35

Analyzing Large Protein Complexes by Structural Mass Spectrometry

Published on: June 19, 2010

An Efficient Algorithm for Clustering of Large-Scale Mass Spectrometry Data.

Fahad Saeed1, Trairak Pisitkun, Mark A Knepper

  • 1Epithelial Systems Biology Laboratory, National Heart Lung and Blood Institute (NHLBI), National Institutes of Health (NIH), Bethesda, Maryland USA.

Proceedings. IEEE International Conference on Bioinformatics and Biomedicine
|March 9, 2013
PubMed
Summary
This summary is machine-generated.

This study introduces the Clustering Algorithm for Mass Spectra (CAMS), an efficient method for analyzing large spectral datasets. CAMS enhances spectral assignment accuracy and confidence by effectively clustering redundant peptide data.

Keywords:
ClusteringEfficient AlgorithmsGraph TheoryMass spectrometry

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

Related Experiment Videos

Last Updated: May 13, 2026

Analyzing Large Protein Complexes by Structural Mass Spectrometry
15:35

Analyzing Large Protein Complexes by Structural Mass Spectrometry

Published on: June 19, 2010

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

Area of Science:

  • Proteomics
  • Bioinformatics
  • Analytical Chemistry

Background:

  • High-throughput spectrometers generate massive datasets with redundant peptide spectra from LC-MS/MS experiments.
  • Efficient data processing is crucial for accurate spectral assignment and interpretation in proteomics.

Purpose of the Study:

  • To present an efficient algorithm, CAMS (Clustering Algorithm for Mass Spectra), for clustering mass spectrometry data.
  • To improve the sensitivity and confidence of spectral assignment in large-scale proteomics studies.

Main Methods:

  • Development of the Clustering Algorithm for Mass Spectra (CAMS).
  • Utilization of a novel F-set metric for identifying similar spectra.
  • Application of a graph theoretic framework for efficient clustering.
  • Testing on real HCD and CID data sets with varying peptide complexities.

Main Results:

  • CAMS demonstrates very high accuracy in clustering spectra from large datasets.
  • The algorithm achieves this accuracy within a reasonable computational time.
  • CAMS effectively reduces computational time by compressing data and increases throughput by interpreting low signal-to-noise spectra.

Conclusions:

  • CAMS provides an efficient and accurate solution for clustering mass spectrometry data.
  • The algorithm enhances the analysis of large spectral datasets, improving proteomics research.
  • CAMS facilitates faster and more reliable interpretation of complex biological samples.