A major limitation of classical omic studies is the isolation of only one level of biological complexity. For example, transcriptomic studies may provide information at the transcript level, but many different entities contribute to the biological state of the sample (genomic variants, post-translational modifications, metabolic products, interacting organisms, among others). With the advent of high-throughput biology, it is becoming increasingly affordable to make multiple measurements, allowing transdomain (e.g. RNA and protein levels) correlations and inferences. These correlations aid the construction or more complete biological networks, filling gaps in our knowledge.
Integration of data, however, is not an easy task. To facilitate the process, groups have curated database and pipelines to systematically explore multiomic data:
*Multi-Omics Profiling Expression Database (MOPED), integrating diverse animal models,
* The Pancreatic Expression Database, integrating data related to pancreatic tissue,
* [http://www.linkedomics.org/ LinkedOmics], connecting data from TCGA cancer datasets,
* OASIS, a web-based resource for general cancer studies,
* BCIP, a platform for breast cancer studies,
* C/VDdb, connecting data from several cardiovascular disease studies,
* ZikaVR, a multiomic resource for Zika virus data
* Ecomics, a normalized multi-omic database for Escherichia coli data,
* GourdBase, integrating data from studies with gourd,
* MODEM, a database for multilevel maize data,
* SoyKB,  a database for multilevel soybean data,
*[https://www.proteomicsdb.org/ ProteomicsDB], a multi-omics and multi-organism resource for life science research