Integrated omics: tools, advances and future approaches

Abstract

With the rapid adoption of high-throughput omic approaches to analyze biological samples such as genomics, transcriptomics, proteomics, and metabolomics, each analysis can generate tera- to peta-byte sized data files on a daily basis. These data file sizes, together with differences in nomenclature among these data types, make the integration of these multi-dimensional omics data into biologically meaningful context challenging. Variously named as integrated omics, multi-omics, poly-omics, trans-omics, pan-omics, or shortened to just 'omics', the challenges include differences in data cleaning, normalization, biomolecule identification, data dimensionality reduction, biological contextualization, statistical validation, data storage and handling, sharing, and data archiving. The ultimate goal is towards the holistic realization of a 'systems biology' understanding of the biological question in hand. Commonly used approaches in these efforts are currently limited by the 3 i's - integration, interpretation, and insights. Post integration, these very large datasets aim to yield unprecedented views of cellular systems at exquisite resolution for transformative insights into processes, events, and diseases through various computational and informatics frameworks. With the continued reduction in costs and processing time for sample analyses, and increasing types of omics datasets generated such as glycomics, lipidomics, microbiomics, and phenomics, an increasing number of scientists in this interdisciplinary domain of bioinformatics face these challenges. We discuss recent approaches, existing tools, and potential caveats in the integration of omics datasets for development of standardized analytical pipelines that could be adopted by the global omics research community.

Integrative Network Fusion: a multi-omics approach in molecular profiling

“…In the early-integration approach, also known as juxtaposition-based, the multi-omics datasets are first concatenated into one matrix. To deal with the high-dimensionality of the joint dataset, these methods generally adopt matrix factorization (68,53,55,52), statistical (46,69,70,59,57,44,71,72,73,55), and machine learning tools (74,73,55). Although the dimensionality reduction procedure is necessary and may improve the predictive performance, it can also cause the loss of key information (66).…”

Challenges and Opportunities in Cancer Metabolomics

“…This is also unlike the case of genomics where tools and methods for both de novo and reference‐guided assembly and annotations are more or less standardized. More extensive discussions on the advantages and disadvantages of ‐omics methods and workflows toward an integrated ‐omics approach are discussed elsewhere . To this end, newer large‐scale approaches, such as fishing for protein‐binding metabolites either using in vitro techniques or in silico approaches, have shown tremendous potential for the capture of the metabolite counterparts of macromolecules (i.e., proteins, and nucleic acids) and have helped pinpoint metabolite binding sites on a proteome‐wide scale.…”

Information technologies of 21st century and their impact on the society

“…In particular omics-research (genomics, proteomics, metabolomics etc.) is leading the charge to the growth of Big data [6,7]. The challenges in omics-research are data cleaning, normalization, biomolecule identification, data dimensionality reduction, biological contextualization, statistical validation, data storage and handling, sharing and data archiving.…”

“…Data analytics requirements include several tasks like those of data cleaning, normalization, biomolecule identification, data dimensionality reduction, biological contextualization, statistical validation, data storage and handling, sharing and data archiving. These tasks are required for the Big data in some of the omics datasets like genomics, transcriptomics, proteomics, metabolomics, metagenomics, phenomics [6].…”