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

Gas Chromatography–Mass Spectrometry (GC–MS)01:14

Gas Chromatography–Mass Spectrometry (GC–MS)

6.6K
Gas chromatography–mass spectrometry (GC–MS) is the combination of analytical techniques of gas chromatography and mass spectrometry in a single instrument for analyzing a mixture of compounds. The gas chromatograph separates the compounds in the mixture, and the mass spectrometer analyzes each compound separately to determine the molecular masses and molecular structures.
A gas chromatograph consists of a long, narrow capillary column with a polysiloxane coating on the inner wall....
6.6K
How Data are Classified: Numerical Data00:59

How Data are Classified: Numerical Data

37.1K
Data that are countable or measurable in specific units are called numerical or quantitative data. Quantitative data are always numbers. Quantitative data are the result of counting or measuring the attributes of a population. Amount of money, pulse rate, weight, number of people living in a town, and number of students who opt for statistics are examples of quantitative data.
Quantitative data may be either discrete or continuous. All quantitative data that take on only specific numerical...
37.1K
How Data are Classified: Categorical Data01:11

How Data are Classified: Categorical Data

43.1K
A variable, usually notated by capital letters such as X and Y, is a characteristic or measurement that can be determined for each member of a population. Data are the actual values of variables. They may be numbers, or they may be words. Datum is a single value.
Data are classified based on whether they are measurable or not. Categorical data cannot be measured; instead, it can be divided into categories. For example, if Y denotes a person's party affiliation, some examples of Y include...
43.1K
Data Reporting and Recording01:24

Data Reporting and Recording

5.4K
Reporting and recording are crucial in data documentation. The timely, thorough, and accurate documentation of facts is essential when recording patient data. Failure to record findings during an assessment or interpretation of a problem will result in loss of information and make the patient document unreliable. The reader is left with general impressions if the information is not specific. A recording is documenting data of the individual's health information in a traceable, secure, and...
5.4K
Data Collection I01:30

Data Collection I

7.9K
Data collection gathers information needed to make accurate judgments about a patient's present condition. During a health history interview, subjective data is collected from the patient, their caregivers, or family members, and objective data is collected through observations and physical assessment. Patients are the primary source of subjective data. Thus information gathered from patients through interviews, observations, and physical examination is primary data. Secondary sources of...
7.9K
Data Validation01:03

Data Validation

6.4K
Data validation is an essential part of a comprehensive assessment. Validation is confirming or verifying and opening the door to gathering more assessment data as it clarifies vague or unclear data. The process of checking and verifying the collected information is called data validation. The primary purpose of data validation is to ensure data is as free from error, bias, and misinterpretation as possible.
Nursing assessment guides are generally based on holistic models rather than medical...
6.4K

You might also read

Related Articles

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

Sort by
Same author

Correction to "Multi-Laboratory Assessment Reveals Variable Ion Species Profiles in Electrospray Ionization Mass Spectrometer".

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

Untargeted metabolomics of mature sorghum [<i>Sorghum bicolor</i> (L.) Moench] grain reveals metabolites associated with antimicrobial activity against <i>Clostridium perfringens</i>.

Frontiers in plant science·2026
Same author

Evaluation of equation as a classifier for kidney function in domestic cats: a proof-of-concept study.

npj aging·2026
Same author

Multi-Laboratory Assessment Reveals Variable Ion Species Profiles in Electrospray Ionization Mass Spectrometry.

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

Best Practices in GC-MS and GC × GC-MS-Based Metabolomics and Volatile Analyses: An International Survey.

Analytical chemistry·2026
Same author

Comparison of Extraction Methods for the Quantification of Phytohormones from Tomato Fruits and Leaves by LC-MS/MS.

bioRxiv : the preprint server for biology·2026
Same journal

Tracking Synthetic Adhesins on Bacterial Surfaces with Immunofluorescence Microscopy.

Methods in molecular biology (Clifton, N.J.)·2026
Same journal

Post-Selection Methods for Analyzing mRNA Display Selections and Optimization of Hits.

Methods in molecular biology (Clifton, N.J.)·2026
Same journal

High-Performance Computing in Tandem Mass Spectrometry (MS/MS) Peptide Identification.

Methods in molecular biology (Clifton, N.J.)·2026
Same journal

Engineering and Adapting Disulfide-Containing Proteins to Enable Intracellular Functionality.

Methods in molecular biology (Clifton, N.J.)·2026
Same journal

AI-Driven Protein Research: From Prediction to Design.

Methods in molecular biology (Clifton, N.J.)·2026
Same journal

Methods for the In Vitro Selection of Protein and Peptide Libraries Using mRNA Display.

Methods in molecular biology (Clifton, N.J.)·2026
See all related articles

Related Experiment Video

Updated: Jan 24, 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

4.2K

Data Processing for GC-MS- and LC-MS-Based Untargeted Metabolomics.

Linxing Yao1, Amy M Sheflin1, Corey D Broeckling2

  • 1Proteomics and Metabolomics Facility, Colorado State University, Fort Collins, CO, USA.

Methods in Molecular Biology (Clifton, N.J.)
|May 24, 2019
PubMed
Summary
This summary is machine-generated.

This study presents a high-throughput data processing workflow for untargeted metabolomics. The method enables efficient feature detection, alignment, and annotation for complex biological samples.

Keywords:
GC-MSLC-MSRAMClustRRAMSearchUntargeted metabolomicsXCMS

More Related Videos

Untargeted Liquid Chromatography-Mass Spectrometry-Based Metabolomics Analysis of Wheat Grain
07:10

Untargeted Liquid Chromatography-Mass Spectrometry-Based Metabolomics Analysis of Wheat Grain

Published on: March 13, 2020

10.6K
Author Spotlight: Emerging Technologies and Advanced Tools for Decoding Metabolomics Data Analysis
07:11

Author Spotlight: Emerging Technologies and Advanced Tools for Decoding Metabolomics Data Analysis

Published on: November 10, 2023

3.3K

Related Experiment Videos

Last Updated: Jan 24, 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

4.2K
Untargeted Liquid Chromatography-Mass Spectrometry-Based Metabolomics Analysis of Wheat Grain
07:10

Untargeted Liquid Chromatography-Mass Spectrometry-Based Metabolomics Analysis of Wheat Grain

Published on: March 13, 2020

10.6K
Author Spotlight: Emerging Technologies and Advanced Tools for Decoding Metabolomics Data Analysis
07:11

Author Spotlight: Emerging Technologies and Advanced Tools for Decoding Metabolomics Data Analysis

Published on: November 10, 2023

3.3K

Area of Science:

  • Metabolomics
  • Analytical Chemistry
  • Bioinformatics

Background:

  • Untargeted metabolomics utilizes gas chromatography and liquid chromatography coupled to mass spectrometry for profiling small metabolites in biological samples.
  • Raw data from untargeted metabolomics studies are complex and necessitate robust processing for statistical analysis and interpretation.
  • Effective data processing is crucial for extracting meaningful biological insights from metabolomic datasets.

Purpose of the Study:

  • To describe a high-throughput, semiautomated data processing workflow for untargeted metabolomics.
  • To outline key steps including feature detection, alignment, data reduction, and spectral-matching-based annotation.
  • To present a workflow adaptable to various instrument vendors and accessible through freely available tools.

Main Methods:

  • Gas chromatography-mass spectrometry (GC-MS) and liquid chromatography-mass spectrometry (LC-MS) based untargeted metabolomics.
  • Development and implementation of a semiautomated data processing pipeline.
  • Utilizing vendor-neutral data file formats and open-source software for data analysis.

Main Results:

  • Successful implementation of a high-throughput data processing workflow for untargeted metabolomics.
  • Demonstrated capability for feature detection, alignment, and spectral-matching-based annotation.
  • The workflow effectively reduces data complexity for subsequent statistical analysis.

Conclusions:

  • The described semiautomated workflow provides an efficient and accessible method for processing untargeted metabolomics data.
  • Its reliance on vendor-neutral formats and free tools ensures broad applicability across different laboratories and instrument platforms.
  • This approach facilitates robust and reproducible metabolomic data analysis, aiding biological discovery.