Evidence map›Paper›PMID 40489639›Full record

ArticleBioinformatics (Oxford, England)2025

Cell Mapping Toolkit: an end-to-end pipeline for mapping subcellular organization.

Joanna Lenkiewicz, Christopher Churas, Mengzhou Hu, Gege Qian, Mayank Jain, Maxwell Adam Levinson, Sadnan Al Manir, Yue Qin, Dylan Fong, Keiichiro Ono and 7 more

Abstract read
In one paragraph

Article in Bioinformatics (Oxford, England), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. AI-readiness Criteria for Biomedical Data.bioRxiv : the preprint server for biology · 2026
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

17 authors.

Joanna LenkiewiczDepartment of Medicine, University of California San Diego, La Jolla, CA, 92037, United States.
Christopher ChurasDepartment of Medicine, University of California San Diego, La Jolla, CA, 92037, United States.
Mengzhou HuDepartment of Medicine, University of California San Diego, La Jolla, CA, 92037, United States.ORCID 0000-0002-1571-8029
Gege QianDepartment of Medicine, University of California San Diego, La Jolla, CA, 92037, United States.
Mayank JainDepartment of Medicine, University of California San Diego, La Jolla, CA, 92037, United States.
Maxwell Adam LevinsonDepartment of Public Health Sciences (Biomedical Informatics), University of Virginia School of Medicine, Charlottesville, VA, 22903, United States.
Sadnan Al ManirDepartment of Public Health Sciences (Biomedical Informatics), University of Virginia School of Medicine, Charlottesville, VA, 22903, United States.
Yue QinDepartment of Medicine, University of California San Diego, La Jolla, CA, 92037, United States.
Dylan FongDepartment of Medicine, University of California San Diego, La Jolla, CA, 92037, United States.
Keiichiro OnoDepartment of Medicine, University of California San Diego, La Jolla, CA, 92037, United States.
Jing ChenDepartment of Medicine, University of California San Diego, La Jolla, CA, 92037, United States.
Chengzhan GaoDepartment of Medicine, University of California San Diego, La Jolla, CA, 92037, United States.
Dexter PrattDepartment of Medicine, University of California San Diego, La Jolla, CA, 92037, United States.
Jillian A ParkerDepartment of Medicine, University of California San Diego, La Jolla, CA, 92037, United States.
Timothy ClarkDepartment of Public Health Sciences (Biomedical Informatics), University of Virginia School of Medicine, Charlottesville, VA, 22903, United States.
Trey IdekerDepartment of Medicine, University of California San Diego, La Jolla, CA, 92037, United States.
Leah V SchafferDepartment of Medicine, University of California San Diego, La Jolla, CA, 92037, United States.ORCID 0000-0001-6339-9141

Funding

Bridge2AI: Cell Maps for AI (CM4AI) Data Generation ProjectOT2OD032742 · OD · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI Jean-Christophe Bélisle-Pipon, TIMOTHY W CLARK · 2022 to 2026
$21.5M
A visible machine learning system to discover targeted treatment solutions in cancerK00CA264422 · NCI · BROAD INSTITUTE, INC. · PI Yue Qin · 2023 to 2026
$395k
A visible machine learning system to discover targeted treatment solutions in cancerF99CA264422 · NCI · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI QIN, YUE · 2021 to 2022
$80k
NCI NIH HHS F99 CA264422NCI NIH HHS K00 CA264422NIH HHS OT2 OD032742
6 · The paper itself

Abstract

summaryCells are organized as a hierarchy of macromolecular assemblies, ranging from small protein complexes to entire organelles. Various technologies have been developed to elucidate subcellular architecture at different scales, such as mass spectrometry approaches for mapping protein biophysical interactions and immunofluorescence imaging for mapping protein localization. We present the Cell Mapping Toolkit, which is designed to systematically integrate data from different modalities into unified hierarchical maps of subcellular organization. The toolkit facilitates an end-to-end pipeline including processing datasets, integrating modalities, and visualizing the final cell map with rich metadata including provenance documentation at each step. The Cell Mapping Toolkit provides researchers with tools for analyzing, integrating, and visualizing diverse protein datasets in a robust and reproducible framework. AVAILABILITY AND IMPLEMENTATION: The code is freely available and is hosted on GitHub at https://github.com/idekerlab/cellmaps_pipeline. Comprehensive documentation and practical examples are provided at https://cellmaps-pipeline.readthedocs.io/.

Indexed as

Computational BiologySoftwareHumansProteinsProteins

Identifiers

PMID40489639
PMCPMC12161986

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.