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 Bossche; Veit 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

doi:10.1038/s41467-021-26111-3

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

Professur für Computational Mass Spectrometry

Publikation: Beitrag in Fachzeitschrift › Übersichtsartikel › Begutachtung

39 Zitate (Scopus)

Abstract

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.

Originalsprache	Englisch
Aufsatznummer	5854
Fachzeitschrift	Nature Communications
Jahrgang	12
Ausgabenummer	1
DOIs	https://doi.org/10.1038/s41467-021-26111-3
Publikationsstatus	Veröffentlicht - 1 Dez. 2021

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10.1038/s41467-021-26111-3

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Dai, C., Füllgrabe, A., Pfeuffer, J., Solovyeva, E. M., Deng, J., Moreno, P., Kamatchinathan, S., Kundu, D. J., George, N., Fexova, S., Grüning, B., Föll, M. C., Griss, J., Vaudel, M., Audain, E., Locard-Paulet, M., Turewicz, M., Eisenacher, M., Uszkoreit, J., ... Perez-Riverol, Y. (2021). A proteomics sample metadata representation for multiomics integration and big data analysis. Nature Communications, 12(1), Artikel 5854. https://doi.org/10.1038/s41467-021-26111-3

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title = "A proteomics sample metadata representation for multiomics integration and big data analysis",

abstract = "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.",

author = "Chengxin Dai and Anja F{\"u}llgrabe and Julianus Pfeuffer and Solovyeva, {Elizaveta M.} and Jingwen Deng and Pablo Moreno and Selvakumar Kamatchinathan and Kundu, {Deepti Jaiswal} and Nancy George and Silvie Fexova and Bj{\"o}rn Gr{\"u}ning and F{\"o}ll, {Melanie Christine} and Johannes Griss and Marc Vaudel and Enrique Audain and Marie Locard-Paulet and Michael Turewicz and Martin Eisenacher and Julian Uszkoreit and {Van Den Bossche}, Tim and Veit Schw{\"a}mmle and Henry Webel and Stefan Schulze and David Bouyssi{\'e} and Savita Jayaram and Duggineni, {Vinay Kumar} and Patroklos Samaras and Mathias Wilhelm and Meena Choi and Mingxun Wang and Oliver Kohlbacher and Alvis Brazma and Irene Papatheodorou and Nuno Bandeira and Deutsch, {Eric W.} and Vizca{\'i}no, {Juan Antonio} and Mingze Bai and Timo Sachsenberg and Levitsky, {Lev I.} and Yasset Perez-Riverol",

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year = "2021",

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day = "1",

doi = "10.1038/s41467-021-26111-3",

language = "English",

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Dai, C, Füllgrabe, A, Pfeuffer, J, Solovyeva, EM, Deng, J, Moreno, P, Kamatchinathan, S, Kundu, DJ, George, N, Fexova, S, Grüning, B, Föll, MC, Griss, J, Vaudel, M, Audain, E, Locard-Paulet, M, Turewicz, M, Eisenacher, M, Uszkoreit, J, Van Den Bossche, T, Schwämmle, V, Webel, H, Schulze, S, Bouyssié, D, Jayaram, S, Duggineni, VK, Samaras, P, Wilhelm, M, Choi, M, Wang, M, Kohlbacher, O, Brazma, A, Papatheodorou, I, Bandeira, N, Deutsch, EW, Vizcaíno, JA, Bai, M, Sachsenberg, T, Levitsky, LI & Perez-Riverol, Y 2021, 'A proteomics sample metadata representation for multiomics integration and big data analysis', Nature Communications, Jg. 12, Nr. 1, 5854. https://doi.org/10.1038/s41467-021-26111-3

TY - JOUR

T1 - A proteomics sample metadata representation for multiomics integration and big data analysis

AU - Dai, Chengxin

AU - Füllgrabe, Anja

AU - Pfeuffer, Julianus

AU - Solovyeva, Elizaveta M.

AU - Deng, Jingwen

AU - Moreno, Pablo

AU - Kamatchinathan, Selvakumar

AU - Kundu, Deepti Jaiswal

AU - George, Nancy

AU - Fexova, Silvie

AU - Grüning, Björn

AU - Föll, Melanie Christine

AU - Griss, Johannes

AU - Vaudel, Marc

AU - Audain, Enrique

AU - Locard-Paulet, Marie

AU - Turewicz, Michael

AU - Eisenacher, Martin

AU - Uszkoreit, Julian

AU - Van Den Bossche, Tim

AU - Schwämmle, Veit

AU - Webel, Henry

AU - Schulze, Stefan

AU - Bouyssié, David

AU - Jayaram, Savita

AU - Duggineni, Vinay Kumar

AU - Samaras, Patroklos

AU - Wilhelm, Mathias

AU - Choi, Meena

AU - Wang, Mingxun

AU - Kohlbacher, Oliver

AU - Brazma, Alvis

AU - Papatheodorou, Irene

AU - Bandeira, Nuno

AU - Deutsch, Eric W.

AU - Vizcaíno, Juan Antonio

AU - Bai, Mingze

AU - Sachsenberg, Timo

AU - Levitsky, Lev I.

AU - Perez-Riverol, Yasset

PY - 2021/12/1

Y1 - 2021/12/1

N2 - 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.

AB - 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.

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U2 - 10.1038/s41467-021-26111-3

DO - 10.1038/s41467-021-26111-3

M3 - Review article

C2 - 34615866

AN - SCOPUS:85116400934

SN - 2041-1723

VL - 12

JO - Nature Communications

JF - Nature Communications

IS - 1

M1 - 5854

ER -

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

Abstract

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