STATegra, a comprehensive multi-omics dataset of B-cell differentiation in mouse

David Gomez-Cabrero; Sonia Tarazona; Isabel Ferreirós-Vidal; Ricardo N. Ramirez; Carlos Company; Andreas Schmidt; Theo Reijmers; Veronica von Saint Paul; Francesco Marabita; Javier Rodríguez-Ubreva; Antonio Garcia-Gomez; Thomas Carroll; Lee Cooper; Ziwei Liang; Gopuraja Dharmalingam; Frans van der Kloet; Amy C. Harms; Leandro Balzano-Nogueira; Vincenzo Lagani; Ioannis Tsamardinos; Michael Lappe; Dieter Maier; Johan A. Westerhuis; Thomas Hankemeier; Axel Imhof; Esteban Ballestar; Ali Mortazavi; Matthias Merkenschlager; Jesper Tegner; Ana Conesa

doi:10.1038/s41597-019-0202-7

STATegra, a comprehensive multi-omics dataset of B-cell differentiation in mouse

David Gomez-Cabrero, Sonia Tarazona, Isabel Ferreirós-Vidal, Ricardo N. Ramirez, Carlos Company, Andreas Schmidt, Theo Reijmers, Veronica von Saint Paul, Francesco Marabita, Javier Rodríguez-Ubreva, Antonio Garcia-Gomez, Thomas Carroll, Lee Cooper, Ziwei Liang, Gopuraja Dharmalingam, Frans van der Kloet, Amy C. Harms, Leandro Balzano-Nogueira, Vincenzo Lagani, Ioannis TsamardinosMichael Lappe, Dieter Maier, Johan A. Westerhuis, Thomas Hankemeier, Axel Imhof, Esteban Ballestar, Ali Mortazavi, Matthias Merkenschlager, Jesper Tegner, Ana Conesa

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20 Scopus citations

Abstract

Multi-omics approaches use a diversity of high-throughput technologies to profile the different molecular layers of living cells. Ideally, the integration of this information should result in comprehensive systems models of cellular physiology and regulation. However, most multi-omics projects still include a limited number of molecular assays and there have been very few multi-omic studies that evaluate dynamic processes such as cellular growth, development and adaptation. Hence, we lack formal analysis methods and comprehensive multi-omics datasets that can be leveraged to develop true multi-layered models for dynamic cellular systems. Here we present the STATegra multi-omics dataset that combines measurements from up to 10 different omics technologies applied to the same biological system, namely the well-studied mouse pre-B-cell differentiation. STATegra includes high-throughput measurements of chromatin structure, gene expression, proteomics and metabolomics, and it is complemented with single-cell data. To our knowledge, the STATegra collection is the most diverse multi-omics dataset describing a dynamic biological system.

Original language	English (US)
Journal	Scientific data
Volume	6
Issue number	1
DOIs	https://doi.org/10.1038/s41597-019-0202-7
State	Published - Oct 31 2019

Bibliographical note

KAUST Repository Item: Exported on 2020-10-01
Acknowledgements: This work has been funded by the European Union Seventh Framework Programme [FP7/2007–2013] under the grant agreement 306000-STATegra. We thank all members of the STATegra consortium for their contributions to this work.

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10.1038/s41597-019-0202-7

https://repository.kaust.edu.sa/bitstream/10754/659954/1/s41597-019-0202-7.pdf

Cite this

Gomez-Cabrero, D., Tarazona, S., Ferreirós-Vidal, I., Ramirez, R. N., Company, C., Schmidt, A., Reijmers, T., Paul, V. V. S., Marabita, F., Rodríguez-Ubreva, J., Garcia-Gomez, A., Carroll, T., Cooper, L., Liang, Z., Dharmalingam, G., van der Kloet, F., Harms, A. C., Balzano-Nogueira, L., Lagani, V., ... Conesa, A. (2019). STATegra, a comprehensive multi-omics dataset of B-cell differentiation in mouse. Scientific data, 6(1). https://doi.org/10.1038/s41597-019-0202-7

@article{aa6657da14df415cab137edd808a8c86,

title = "STATegra, a comprehensive multi-omics dataset of B-cell differentiation in mouse",

abstract = "Multi-omics approaches use a diversity of high-throughput technologies to profile the different molecular layers of living cells. Ideally, the integration of this information should result in comprehensive systems models of cellular physiology and regulation. However, most multi-omics projects still include a limited number of molecular assays and there have been very few multi-omic studies that evaluate dynamic processes such as cellular growth, development and adaptation. Hence, we lack formal analysis methods and comprehensive multi-omics datasets that can be leveraged to develop true multi-layered models for dynamic cellular systems. Here we present the STATegra multi-omics dataset that combines measurements from up to 10 different omics technologies applied to the same biological system, namely the well-studied mouse pre-B-cell differentiation. STATegra includes high-throughput measurements of chromatin structure, gene expression, proteomics and metabolomics, and it is complemented with single-cell data. To our knowledge, the STATegra collection is the most diverse multi-omics dataset describing a dynamic biological system.",

author = "David Gomez-Cabrero and Sonia Tarazona and Isabel Ferreir{\'o}s-Vidal and Ramirez, {Ricardo N.} and Carlos Company and Andreas Schmidt and Theo Reijmers and Paul, {Veronica von Saint} and Francesco Marabita and Javier Rodr{\'i}guez-Ubreva and Antonio Garcia-Gomez and Thomas Carroll and Lee Cooper and Ziwei Liang and Gopuraja Dharmalingam and {van der Kloet}, Frans and Harms, {Amy C.} and Leandro Balzano-Nogueira and Vincenzo Lagani and Ioannis Tsamardinos and Michael Lappe and Dieter Maier and Westerhuis, {Johan A.} and Thomas Hankemeier and Axel Imhof and Esteban Ballestar and Ali Mortazavi and Matthias Merkenschlager and Jesper Tegner and Ana Conesa",

note = "KAUST Repository Item: Exported on 2020-10-01 Acknowledgements: This work has been funded by the European Union Seventh Framework Programme [FP7/2007–2013] under the grant agreement 306000-STATegra. We thank all members of the STATegra consortium for their contributions to this work.",

year = "2019",

month = oct,

day = "31",

doi = "10.1038/s41597-019-0202-7",

language = "English (US)",

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Gomez-Cabrero, D, Tarazona, S, Ferreirós-Vidal, I, Ramirez, RN, Company, C, Schmidt, A, Reijmers, T, Paul, VVS, Marabita, F, Rodríguez-Ubreva, J, Garcia-Gomez, A, Carroll, T, Cooper, L, Liang, Z, Dharmalingam, G, van der Kloet, F, Harms, AC, Balzano-Nogueira, L, Lagani, V, Tsamardinos, I, Lappe, M, Maier, D, Westerhuis, JA, Hankemeier, T, Imhof, A, Ballestar, E, Mortazavi, A, Merkenschlager, M, Tegner, J & Conesa, A 2019, 'STATegra, a comprehensive multi-omics dataset of B-cell differentiation in mouse', Scientific data, vol. 6, no. 1. https://doi.org/10.1038/s41597-019-0202-7

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T1 - STATegra, a comprehensive multi-omics dataset of B-cell differentiation in mouse

AU - Gomez-Cabrero, David

AU - Tarazona, Sonia

AU - Ferreirós-Vidal, Isabel

AU - Ramirez, Ricardo N.

AU - Company, Carlos

AU - Schmidt, Andreas

AU - Reijmers, Theo

AU - Paul, Veronica von Saint

AU - Marabita, Francesco

AU - Rodríguez-Ubreva, Javier

AU - Garcia-Gomez, Antonio

AU - Carroll, Thomas

AU - Cooper, Lee

AU - Liang, Ziwei

AU - Dharmalingam, Gopuraja

AU - van der Kloet, Frans

AU - Harms, Amy C.

AU - Balzano-Nogueira, Leandro

AU - Lagani, Vincenzo

AU - Tsamardinos, Ioannis

AU - Lappe, Michael

AU - Maier, Dieter

AU - Westerhuis, Johan A.

AU - Hankemeier, Thomas

AU - Imhof, Axel

AU - Ballestar, Esteban

AU - Mortazavi, Ali

AU - Merkenschlager, Matthias

AU - Tegner, Jesper

AU - Conesa, Ana

N1 - KAUST Repository Item: Exported on 2020-10-01 Acknowledgements: This work has been funded by the European Union Seventh Framework Programme [FP7/2007–2013] under the grant agreement 306000-STATegra. We thank all members of the STATegra consortium for their contributions to this work.

PY - 2019/10/31

Y1 - 2019/10/31

N2 - Multi-omics approaches use a diversity of high-throughput technologies to profile the different molecular layers of living cells. Ideally, the integration of this information should result in comprehensive systems models of cellular physiology and regulation. However, most multi-omics projects still include a limited number of molecular assays and there have been very few multi-omic studies that evaluate dynamic processes such as cellular growth, development and adaptation. Hence, we lack formal analysis methods and comprehensive multi-omics datasets that can be leveraged to develop true multi-layered models for dynamic cellular systems. Here we present the STATegra multi-omics dataset that combines measurements from up to 10 different omics technologies applied to the same biological system, namely the well-studied mouse pre-B-cell differentiation. STATegra includes high-throughput measurements of chromatin structure, gene expression, proteomics and metabolomics, and it is complemented with single-cell data. To our knowledge, the STATegra collection is the most diverse multi-omics dataset describing a dynamic biological system.

AB - Multi-omics approaches use a diversity of high-throughput technologies to profile the different molecular layers of living cells. Ideally, the integration of this information should result in comprehensive systems models of cellular physiology and regulation. However, most multi-omics projects still include a limited number of molecular assays and there have been very few multi-omic studies that evaluate dynamic processes such as cellular growth, development and adaptation. Hence, we lack formal analysis methods and comprehensive multi-omics datasets that can be leveraged to develop true multi-layered models for dynamic cellular systems. Here we present the STATegra multi-omics dataset that combines measurements from up to 10 different omics technologies applied to the same biological system, namely the well-studied mouse pre-B-cell differentiation. STATegra includes high-throughput measurements of chromatin structure, gene expression, proteomics and metabolomics, and it is complemented with single-cell data. To our knowledge, the STATegra collection is the most diverse multi-omics dataset describing a dynamic biological system.

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JF - Scientific data

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ER -

STATegra, a comprehensive multi-omics dataset of B-cell differentiation in mouse

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STATegra, a comprehensive multi-omics dataset of B-cell differentiation in mouse

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