Gomez Cabrero, DavidTarazona, SoniaFerreirós Vidal, IsabelRamirez, Ricardo N.Company, CarlosSchmidt, AndreasReijmers, Theovon Saint Paul, VeronicaMarabita, FrancescoRodríguez Ubreva, JavierGarcía Gómez, AntonioCarroll, TomCooper, LeeLiang, ZiweiDharmalingam, Gopurajavan der Kloet, FransHarms, Amy C.Balzano Nogueira, LeandroLagani, VincenzoTsamardinos, IoannisLappe, MichaelMaier, DieterWesterhuis, Johan A.Hankemeier, ThomasImhof, AxelBallestar Tarín, EstebanMortazavi, AliMerkenschlager, MatthiasEgner, JesperConesa, Ana2020-07-172020-07-172019-10-31https://hdl.handle.net/2445/168977Multi-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 STAT egra 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 includes15 p.application/pdfengcc by (c) Gomez Cabrero et al., 2019http://creativecommons.org/licenses/by/3.0/es/Fisiologia cel·lularCèl·lules BRatolins (Animals de laboratori)Cell physiologyB cellsMice (Laboratory animals)info:eu-repo/semantics/article2020-07-17info:eu-repo/semantics/openAccess31672995