Evidence map›Paper›PMID 38843301›Full record

ArticlePLoS computational biology2024

Common data models to streamline metabolomics processing and annotation, and implementation in a Python pipeline.

Joshua M Mitchell, Yuanye Chi, Maheshwor Thapa, Zhiqiang Pang, Jianguo Xia, Shuzhao Li

Abstract read
In one paragraph

Article in PLoS computational biology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

10 citing papers in PubMed.

  1. Assessing the metabolomics "dark matter" by a detectable khipu model.Metabolomics : Official journal of the Metabolomic Society · 2026
    Article
  2. Article
  3. Article
  4. Review
  5. Practicing Data Science in Interactive Notebooks.Methods in molecular biology (Clifton, N.J.) · 2026
    Review
  6. Metabolomics Data Processing Using the Asari Toolkit.Methods in molecular biology (Clifton, N.J.) · 2026
    Article
  7. Constructing a consensus serum metabolome.bioRxiv : the preprint server for biology · 2025
    Article
  8. Review
  9. Annotation of Metabolites in Stable Isotope Tracing Untargeted Metabolomics via Khipu-web.Journal of the American Society for Mass Spectrometry · 2024
    Article
  10. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

6 authors.

Joshua M MitchellThe Jackson Laboratory for Genomic Medicine, Farmington, Connecticut, United States of America.ORCID 0000-0003-1598-1596
Yuanye ChiThe Jackson Laboratory for Genomic Medicine, Farmington, Connecticut, United States of America.ORCID 0009-0001-2139-8172
Maheshwor ThapaThe Jackson Laboratory for Genomic Medicine, Farmington, Connecticut, United States of America.ORCID 0000-0002-8906-1584
Zhiqiang PangInstitute of Parasitology, McGill University, Montreal, Quebec, Canada.
Jianguo XiaInstitute of Parasitology, McGill University, Montreal, Quebec, Canada.ORCID 0000-0003-2040-2624
Shuzhao LiThe Jackson Laboratory for Genomic Medicine, Farmington, Connecticut, United States of America.ORCID 0000-0002-7386-2539

Funding

JAX MorPhiC Data Production CenterUM1HG012651 · NHGRI · JACKSON LABORATORY · PI Paul Robson, William Carl Skarnes · 2022 to 2026
$9.6M
TBDR01AI149746 · NIAID · JACKSON LABORATORY · PI LI, SHUZHAO · 2020 to 2024
$2.6M
Mummichog 3, aligning mass spectrometry data to biological networks - Neutral LossU01CA235493 · NCI · JACKSON LABORATORY · PI LI, SHUZHAO, SIUZDAK, GARY E · 2018 to 2021
$2.0M
NCI NIH HHS U01 CA235493NHGRI NIH HHS UM1 HG012651NIAID NIH HHS R01 AI149746
6 · The paper itself

Abstract

To standardize metabolomics data analysis and facilitate future computational developments, it is essential to have a set of well-defined templates for common data structures. Here we describe a collection of data structures involved in metabolomics data processing and illustrate how they are utilized in a full-featured Python-centric pipeline. We demonstrate the performance of the pipeline, and the details in annotation and quality control using large-scale LC-MS metabolomics and lipidomics data and LC-MS/MS data. Multiple previously published datasets are also reanalyzed to showcase its utility in biological data analysis. This pipeline allows users to streamline data processing, quality control, annotation, and standardization in an efficient and transparent manner. This work fills a major gap in the Python ecosystem for computational metabolomics.

Indexed as

MetabolomicsSoftwareChromatography, LiquidComputational BiologyHumansLipidomicsProgramming LanguagesTandem Mass Spectrometry

Identifiers

PMID38843301
PMCPMC11185459

What Socratic holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.