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Sci Data. 2019 Oct 31;6(1):256. doi: 10.1038/s41597-019-0202-7.

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

Author information

1
Navarrabiomed, Complejo Hospitalario de Navarra (CHN), Universidad Pública de Navarra (UPNA), IdiSNA, Pamplona, Spain.
2
Unit of Computational Medicine, Department of Medicine, Solna, Center for Molecular Medicine, Karolinska Institutet, Stockholm, Sweden.
3
Science for Life Laboratory, Solna, Sweden.
4
Department of Applied Statistics, Operations Research and Quality, Universitat Politècnica de València, Valencia, Spain.
5
MRC London Institute of Medical Sciences, Institute of Clinical Sciences, Faculty of Medicine, Imperial College London, Du Cane Road, London, W12 0NN, UK.
6
Department of Developmental and Cell Biology and Center for Complex Biological Systems, University of California, Irvine, CA, USA.
7
Chromatin and Disease Group, Cancer Epigenetics and Biology Programme (PEBC), Bellvitge Biomedical Research Institute (IDIBELL), 08908 L'Hospitalet de Llobregat, Barcelona, Spain.
8
Protein Analysis Unit, Biomedical Center, Ludwig Maximilian University of Munich, Munich, Germany.
9
Division of Analytical Biosciences, Leiden Academic Center for Drug Research, Leiden University, Leiden, The Netherlands.
10
Biomax Informatics AG, Planegg, Germany.
11
Centre for Human Metabolomics, Faculty of Natural Sciences, North-West University (Potchefstroom Campus), Potchefstroom, South Africa.
12
Microbiology and Cell Science Department, Institute for Food and Agricultural Research, Genetics Institute, University of Florida, Gainesville, Florida, USA.
13
Computer Science Department, University of Crete, Heraklion, Greece.
14
Institute of Chemical Biology, Ilia State University, Tbilisi, Georgia, United States.
15
Gnosis Data Analysis PC, Heraklion, Greece.
16
QIAGEN Aarhus A/S, Silkeborgvej 2, 8000, Aarhus, Denmark.
17
Swammerdam Institute for Life Sciences, University of Amsterdam, Amsterdam, The Netherlands.
18
MRC London Institute of Medical Sciences, Institute of Clinical Sciences, Faculty of Medicine, Imperial College London, Du Cane Road, London, W12 0NN, UK. matthias.merkenschlager@lms.mrc.ac.uk.
19
Unit of Computational Medicine, Department of Medicine, Solna, Center for Molecular Medicine, Karolinska Institutet, Stockholm, Sweden. jesper.tegner@kaust.edu.sa.
20
Science for Life Laboratory, Solna, Sweden. jesper.tegner@kaust.edu.sa.
21
Biological and Environmental Sciences and Engineering Division, Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia. jesper.tegner@kaust.edu.sa.
22
Microbiology and Cell Science Department, Institute for Food and Agricultural Research, Genetics Institute, University of Florida, Gainesville, Florida, USA. aconesa@ufl.edu.

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.

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