TY - JOUR
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.
UR - http://hdl.handle.net/10754/659954
UR - http://www.nature.com/articles/s41597-019-0202-7
UR - http://www.scopus.com/inward/record.url?scp=85074443258&partnerID=8YFLogxK
U2 - 10.1038/s41597-019-0202-7
DO - 10.1038/s41597-019-0202-7
M3 - Article
C2 - 31672995
VL - 6
JO - Scientific data
JF - Scientific data
SN - 2052-4463
IS - 1
ER -