Evidence map›Paper›PMID 27571551›Full record

ArticleNature methods2016

MetaMass, a tool for meta-analysis of subcellular proteomics data.

Fridtjof Lund-Johansen, Daniel de la Rosa Carrillo, Adi Mehta, Krzysztof Sikorski, Marit Inngjerdingen, Tomas Kalina, Kjetil Røysland, Gustavo Antonio de Souza, Andrew R M Bradbury, Quentin Lecrevisse and 1 more

Abstract read
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In one paragraph

Article in Nature methods, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
23citing papers in PubMed, 1 pooled it
–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

23 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
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  4. Review
  5. Review
  6. Article
  7. Review
  8. Article
  9. Article
  10. PSL-LCCL: a resource for subcellular protein localization in liver cancer cell line SK_HEP1.Database : the journal of biological databases and curation · 2022
    Article
  11. Article
  12. Subcellular proteomics.Nature reviews. Methods primers · 2021
    Article
  13. Organellar Maps Through Proteomic Profiling - A Conceptual Guide.Molecular & cellular proteomics : MCP · 2020
    Review
  14. Article
  15. Spatial proteomics: a powerful discovery tool for cell biology.Nature reviews. Molecular cell biology · 2019
    Review
  16. Article
  17. Article
  18. Article
  19. Article
  20. 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

11 authors.

Fridtjof Lund-JohansenDepartment of Immunology, Oslo University Hospital, Oslo, Norway.ORCID 0000-0002-2445-1258
Daniel de la Rosa CarrilloDepartment of Immunology, Oslo University Hospital, Oslo, Norway.
Adi MehtaDepartment of Immunology, Oslo University Hospital, Oslo, Norway.
Krzysztof SikorskiDepartment of Immunology, Oslo University Hospital, Oslo, Norway.
Marit InngjerdingenDepartment of Immunology, Oslo University Hospital, Oslo, Norway.
Tomas KalinaChildhood Leukemia Investigation Prague (CLIP), Department of Pediatric Hematology and Oncology, 2nd Faculty of Medicine, Charles University Prague and University Hospital Motol, Prague, Czech Republic.ORCID 0000-0003-4475-2872
Kjetil RøyslandDepartment of Biostatistics, Institute of Basic Medical Sciences, Faculty of Medicine, University of Oslo, Oslo, Norway.
Gustavo Antonio de SouzaDepartment of Immunology, Oslo University Hospital, Oslo, Norway.
Andrew R M BradburyB Division, Los Alamos National Laboratory, Los Alamos, New Mexico, USA.
Quentin LecrevisseCancer Research Centre (IBMCC, CSIC-USAL), University of Salamanca, Salamanca, Spain.
Jan StuchlyChildhood Leukemia Investigation Prague (CLIP), Department of Pediatric Hematology and Oncology, 2nd Faculty of Medicine, Charles University Prague and University Hospital Motol, Prague, Czech Republic.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

We report a tool for the analysis of subcellular proteomics data, called MetaMass, based on the use of standardized lists of subcellular markers. We analyzed data from 11 studies using MetaMass, mapping the subcellular location of 5,970 proteins. Our analysis revealed large variations in the performance of subcellular fractionation protocols as well as systematic biases in protein annotation databases. The Excel and R versions of MetaMass should enhance transparency and reproducibility in subcellular proteomics.

Indexed as

Meta-Analysis as TopicAlgorithmsAnimalsBiomarkersCells, CulturedEmbryonic Stem CellsMiceProteinsProteomicsSubcellular FractionsBiomarkersProteins

Identifiers

PMID27571551

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Registered trials

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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 OpenQuestion graph.