A proteomics sample metadata representation for multiomics integration and big data analysis

Chengxin Dai, Anja Füllgrabe, Julianus Pfeuffer, Elizaveta M. Solovyeva, Jingwen Deng, Pablo Moreno, Selvakumar Kamatchinathan, Deepti Jaiswal Kundu, Nancy George, Silvie Fexova, Björn Grüning, Melanie Christine Föll, Johannes Griss, Marc Vaudel, Enrique Audain, Marie Locard-Paulet, Michael Turewicz, Martin Eisenacher, Julian Uszkoreit, Tim Van Den BosscheVeit Schwämmle, Henry Webel, Stefan Schulze, David Bouyssié, Savita Jayaram, Vinay Kumar Duggineni, Patroklos Samaras, Mathias Wilhelm, Meena Choi, Mingxun Wang, Oliver Kohlbacher, Alvis Brazma, Irene Papatheodorou, Nuno Bandeira, Eric W. Deutsch, Juan Antonio Vizcaíno, Mingze Bai*, Timo Sachsenberg*, Lev I. Levitsky*, Yasset Perez-Riverol*

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Abstrakt

The amount of public proteomics data is rapidly increasing but there is no standardized format to describe the sample metadata and their relationship with the dataset files in a way that fully supports their understanding or reanalysis. Here we propose to develop the transcriptomics data format MAGE-TAB into a standard representation for proteomics sample metadata. We implement MAGE-TAB-Proteomics in a crowdsourcing project to manually curate over 200 public datasets. We also describe tools and libraries to validate and submit sample metadata-related information to the PRIDE repository. We expect that these developments will improve the reproducibility and facilitate the reanalysis and integration of public proteomics datasets.

OriginalsprogEngelsk
Artikelnummer5854
TidsskriftNature Communications
Vol/bind12
Udgave nummer1
Antal sider8
ISSN2041-1723
DOI
StatusUdgivet - dec. 2021

Bibliografisk note

Funding Information:
YPR, SK, DJK, and JAV would like to acknowledge funding from the Wellcome Trust grant number 208391/Z/17/Z and EMBL core funding. MLP is supported financially by the Novo Nordisk Foundation (Grant agreement NNF14CC0001). MT and TS are supported by de.NBI, a project of the German Federal Ministry of Education and Research (BMBF) [grant number FKZ 031 A 534 A and FKZ 031 A 535 A]. ME and JU are members of the Center for Protein Diagnostics (PRODI), a grant from the Ministry of Innovation, Science, and Research of North-Rhine Westphalia, Germany. TVDB is supported by the Research Foundation—Flanders (SB grant 1S90918N). EWD acknowledges NIGMS grants R01GM087221, R24GM127667, and NSF grants 1933311 and 1922871. SS was supported by the NSF grant 1817518. CD and MB are supported by the National Key Research and Development Program of China (2017YFC0908404, 2017YFC0908405) and the Natural Science Foundation of Chongqing, China (cstc2018jcyjAX0225). Funds for the overall project were also made available by an ELIXIR Implementation Study. LIL and EMS are supported by the Russian Basic Science Foundation (grant #18-29-13015).

Publisher Copyright:
© 2021, The Author(s).

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