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; Jan Stuchly

doi:10.1038/nmeth.3967

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

Nat Methods. 2016 Oct;13(10):837-40. doi: 10.1038/nmeth.3967. Epub 2016 Aug 29.

Authors

Fridtjof Lund-Johansen^{1

2}, Daniel de la Rosa Carrillo^{1

3}, Adi Mehta^{1

4}, Krzysztof Sikorski^{1

2}, Marit Inngjerdingen¹, Tomas Kalina⁵, Kjetil Røysland⁶, Gustavo Antonio de Souza¹, Andrew R M Bradbury⁷, Quentin Lecrevisse^{8

9

10

11}, Jan Stuchly⁵

Affiliations

¹ Department of Immunology, Oslo University Hospital, Oslo, Norway.
² K.G. Jebsen Center for Cancer Immunotherapy, University of Oslo, Oslo, Norway.
³ Department of Dermatology, Oslo University Hospital, Oslo, Norway.
⁴ K.G. Jebsen Inflammation Research Center, University of Oslo, Oslo, Norway.
⁵ Childhood Leukemia Investigation Prague (CLIP), Department of Pediatric Hematology and Oncology, 2nd Faculty of Medicine, Charles University Prague and University Hospital Motol, Prague, Czech Republic.
⁶ Department of Biostatistics, Institute of Basic Medical Sciences, Faculty of Medicine, University of Oslo, Oslo, Norway.
⁷ B Division, Los Alamos National Laboratory, Los Alamos, New Mexico, USA.
⁸ Cancer Research Centre (IBMCC, CSIC-USAL), University of Salamanca, Salamanca, Spain.
⁹ Institute of Biomedical Research of Salamanca (IBSAL), University of Salamanca, Salamanca, Spain.
¹⁰ NUCLEUS, University of Salamanca, Salamanca, Spain.
¹¹ Department of Medicine, University of Salamanca, Salamanca, Spain.

PMID: 27571551
DOI: 10.1038/nmeth.3967

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.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Algorithms
Animals
Biomarkers / metabolism
Cells, Cultured
Embryonic Stem Cells / metabolism
Meta-Analysis as Topic*
Mice
Proteins / metabolism*
Proteomics / methods*
Proteomics / statistics & numerical data
Subcellular Fractions / metabolism*

Substances

Biomarkers
Proteins