QC4Metabolomics: Real-Time and Retrospective Quality Control of Metabolomics Data.

Jan Stanstrup, Lars Ove Dragsted

September 2025 Anal Chem

Synopsis of Social media discussions

The group generally agrees that QC4Metabolomics is an impactful advancement, as shown by phrases like 'improving data reliability' and 'early detection of issues.' Their interest is evident from mentions of features like real-time monitoring and open-source ease-of-use, while engagement is indicated by detailed references to diagnostic metrics and deployment methods, underscoring a meaningful dialogue about its application.

A
Agreement
 Moderate agreement

Most discussions align positively, emphasizing the importance and usefulness of the QC4Metabolomics tool for improving data quality.

I
Interest
 High level of interest

The discussions show high interest due to references to real-time monitoring and practical benefits, indicating enthusiasm among the community.

E
Engagement
 Moderate level of engagement

Participants engage by discussing specific features like diagnostic information and deployment, suggesting a moderate level of deep involvement.

I
Impact
 Moderate level of impact

The overall tone highlights the potential for significant improvements in metabolomics research, though some posts focus on technological aspects rather than broad implications.

Social Mentions

YouTube

2 Videos

Bluesky

3 Posts

Twitter

2 Posts

Metrics

Video Views

90

Total Likes

8

Extended Reach

19,406

Social Features

7

Timeline: Posts about article

Top Social Media Posts

Posts referencing the article

QC4Metabolomics for Real-Time and Retrospective Data Quality Control

QC4Metabolomics for Real-Time and Retrospective Data Quality Control

QC4Metabolomics is a software tool for monitoring data quality in untargeted metabolomics, tracking compounds and contaminants to detect issues early. It facilitates real-time and retrospective quality control, improving data reliability.

Jan Stanstrup

September 3, 2025

60 views

Real-Time and Retrospective Quality Control in Metabolomics Data Management

Real-Time and Retrospective Quality Control in Metabolomics Data Management

QC4Metabolomics is a software tool designed for real-time and retrospective quality control in untargeted metabolomics, helping researchers monitor data quality during and after acquisition.

Jan Stanstrup

September 3, 2025

30 views

  • Kozo Nishida | 西田孝三
    @kozo2 (Twitter)

    QC4Metabolomics: Real-time and Retrospective Quality Control of Metabolomics Data https://t.co/uRL1Og5VkJ
    view full post

    January 2, 2025

  • Kermit Murray
    @kkmurray.bsky.social (Bluesky)

    (BioRxiv All) QC4Metabolomics: Real-time and Retrospective Quality Control of Metabolomics Data: Motivation: The ability to answer complex biological questions in metabolomics relies on the acquisition of high-quality data. However, due to the complex… http://dlvr.it/TH3xGd #BioRxiv #MassSpecRSS
    view full post

    December 29, 2024

    2

  • bioRxivpreprint
    @biorxivpreprint.bsky.social (Bluesky)

    QC4Metabolomics: Real-time and Retrospective Quality Control of Metabolomics Data https://www.biorxiv.org/content/10.1101/2024.12.29.630653v1
    view full post

    December 29, 2024

    2

  • bioRxiv Bioinfo
    @biorxiv-bioinfo.bsky.social (Bluesky)

    QC4Metabolomics: Real-time and Retrospective Quality Control of Metabolomics Data https://www.biorxiv.org/content/10.1101/2024.12.29.630653v1
    view full post

    December 29, 2024

    2

  • bioRxiv Bioinfo
    @biorxiv_bioinfo (Twitter)

    QC4Metabolomics: Real-time and Retrospective Quality Control of Metabolomics Data https://t.co/bLEMFh5eeo #biorxiv_bioinfo
    view full post

    December 29, 2024

    2

Abstract Synopsis

  • QC4Metabolomics is a software tool designed for real-time and retrospective quality control in untargeted metabolomics, helping researchers monitor data quality during and after acquisition.
  • It tracks specific compounds and contaminants, providing diagnostic information like retention time, intensity, and peak shape through a web dashboard, enabling early detection of issues such as calibration drift or ion suppression.
  • The tool is open-source, easy to deploy with Docker, and illustrated with real-world examples where it revealed analytical problems that could have been fixed immediately, improving data reliability and reducing reanalysis costs.]