Multiomics

A branch of the field of multiomics is the analysis of multilevel single-cell data, called single-cell multiomics.[6][7] 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.[6]

Methods for parallel single-cell genomic and transcriptomic analysis can be based on simultaneous amplification[8] or physical separation of RNA and genomic DNA.[9][10] 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[11][12] to single cell RNA-Seq.[13] Other techniques to query the epigenome, as single-cell ATAC-Seq[14] and single-cell Hi-C[15] also exist.

A different, but related, challenge is the integration of proteomic and transcriptomic data.[16][17] One approach to perform such measurement is to physically separate single-cell lysates in two, processing half for RNA, and half for proteins.[16] The protein content of lysates can be measured by proximity extension assays (PEA), for example, which use DNA-barcoded antibodies.[18] A different approach uses a combination of heavy-metal RNA probes and protein antibiodies to adapt mass cytometry for multiomic analysis.[17]

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.[19][20][21] For example, one of the methods of the mixOmics project implements a method based on sparse Partial Least Squares regression for selection of features (putative biomarkers).[22]

Overview of phases 1 and 2 of the human microbiome project.

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.[23] The applications range from understanding host-pathogen interactions and infectious diseases[24][25] to understanding better chronic and complex non-communicable diseases[26] and improving personalized medicine.[27]

The Bioconductor project curates a variety of R packages aimed at integrating omic data:

• omicade4, for multiple co-inertia analysis of multi omic datasets[38]
• MultiAssayExperiment, offering a bioconductor interface for overlapping samples[39]
• IMAS, a package focused on using multi omic data for evaluating alternative splicing[40]
• bioCancer, a package for visualization of multiomic cancer data[41]
• mixOmics, a suite of multivariate methods for data integration[22]
• MultiDataSet, a package for encapsulating multiple data sets[42]

The OmicTools[5] database further highlights R packages and other tools for multi omic data analysis:

• PaintOmics, a web resource for visualization of multi-omics datasets[43][44]
• SIGMA, a Java program focused on integrated analysis of cancer datasets[45]
• iOmicsPASS, a tool in C++ for multiomic-based phenotype prediction[46]
• Grimon, an R graphical interface for visualization of multiomic data[47]
• Omics Pipe, a framework in Python for reproducibly automating multiomic data analysis[48]

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),[49] integrating diverse animal models,
• The Pancreatic Expression Database, integrating data related to pancreatic tissue,
• LinkedOmics,[50][51] connecting data from TCGA cancer datasets,
• OASIS,[52] a web-based resource for general cancer studies,
• BCIP,[53] a platform for breast cancer studies,
• C/VDdb,[54] connecting data from several cardiovascular disease studies,
• ZikaVR,[55] a multiomic resource for Zika virus data
• Ecomics,[56] a normalized multi-omic database for Escherichia coli data,
• GourdBase,[57] integrating data from studies with gourd,
• MODEM,[58] a database for multilevel maize data,
• SoyKB,[59] a database for multilevel soybean data,
• ProteomicsDB[60], a multi-omics and multi-organism resource for life science research

• Pangenomics
• Hologenomics
• DisGeNET
• Systems Biology
• Network Medicine