Multiomics, multi-omics or integrative omics is a biological analysis approach in which the data sets are multiple "omes", such as the genome, proteome, transcriptome, epigenome, metabolome, and microbiome ; in other words, the use of multiple omics technologies to study life in a concerted way. By combining these "omes", scientists can analyze complex biological big data to find novel associations between biological entities, pinpoint relevant biomarkers and build elaborate markers of disease and physiology. In doing so, multiomics integrates diverse omics data to find a coherently matching geno-pheno-envirotype relationship or association. The OmicTools service lists more than 99 softwares related to multiomic datanalysis, as well as more than 99 databases on the topic.
Single-cell multiomics
A branch of the field of multiomics is the analysis of multilevel single-cell data, called single-cell multiomics. This approach gives us an unprecedent resolution to look at multilevel transitions in health and disease at the single cell level. An advantage in relation to bulk analysis is to mitigate confounding factors derived from cell to cell variation, allowing the uncovering of heterogeneous tissue architectures. Methods for parallel single-cell genomic and transcriptomic analysis can be based on simultaneous amplification or physical separation of RNA and genomic DNA. They allow insights that cannot be gathered solely from transcriptomic analysis, as RNA data do not contain non-coding genomic regions and information regarding copy-number variation, for example. An extension of this methodology is the integration of single-cell transcriptomes to single-cell methylomes, combining single-cell bisulfite sequencing to single cell RNA-Seq. Other techniques to query the epigenome, as single-cell ATAC-Seq and single-cell Hi-C also exist. A different, but related, challenge is the integration of proteomic and transcriptomic data. One approach to perform such measurement is to physically separate single-cell lysates in two, processing half for RNA, and half for proteins. The protein content of lysates can be measured by proximity extension assays, for example, which use DNA-barcoded antibodies. A different approach uses a combination of heavy-metal RNA probes and protein antibiodies to adapt mass cytometry for multiomic analysis.
In parallel to the advances in highthroughput biology, machine learning applications to biomedical data analysis are flourishing. The integration of multi-omics data analysis and machine learning has led to the discovery of new biomarkers. For example, one of the methods of the project implements a method based on sparse Partial Least Squares regression for selection of features.
Multiomics in health and disease
Multiomics currently holds a promise to fill gaps in the understanding of human health and disease, and many researchers are working on ways to generate and analyze disease-related data. The applications range from understanding host-pathogen interactions and infectious diseases to understanding better chronic and complex non-communicable diseases and improving personalized medicine.
The second phase of the $170 million Human Microbiome Project was focused on integrating patient data to different omic datasets, considering host genetics, clinical information and microbiome composition The phase one focused on characterization of communities in different body sites. Phase 2 focused in the integration of multiomic data from host & microbiome to human diseases. Specifically, the project used multiomics to improve the understanding of the interplay of gut and nasal microbiomes with type 2 diabetes, gut microbiomes and inflammatory bowel disease and vaginal microbiomes and pre-term birth.
Systems Immunology
The complexity of interactions in the human immune system has prompted the generation of a wealth of immunology-related multi-scale omic data. Multi-omic data analysis has been employed to gather novel insights about the immune response to infectious diseases, such as pediatric chikungunya, as well as noncommunicable autoimmune diseases. Integrative omics has also been employed strongly to understand effectiveness and side effects of vaccines, a field called systems vaccinology. For example, multiomics was essential to uncover the association of changes in plasma metabolites and immune system transcriptome on response to vaccination against herpes zoster.
, a framework in Python for reproducibly automating multiomic data analysis
Multiomic Databases
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. With the advent of high-throughput biology, it is becoming increasingly affordable to make multiple measurements, allowing transdomain 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, integrating diverse animal models,
The Pancreatic Expression Database, integrating data related to pancreatic tissue,
