Integrated Omics: Tools, Advances, and Future Approaches.

By B. Misra, Carl D. Langefeld, Michael Olivier, L. Cox. Journal of Molecular Endocrinology, 2019

475 citations recorded by the source corpus.

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.

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/5502e7126d0ad962f080aed3dd50289a44c2a527

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Citations in the corpus

Machine Learning and Integrative Analysis of Biomedical Big Data (2019, background): “The problem of missing data is exacerbated in multi-omics studies as there can be more samples with missing values [108].”
The Need for Multi-Omics Biomarker Signatures in Precision Medicine (2019, methodology): “Additional tools will be needed to perform these complex integrated analyses using strict statistical models, and to help in the interpretation and biological annotation of the obtained co-expression networks or clusters of multi-omics analytes [77].”
Integration strategies of multi-omics data for machine learning analysis (2021, background): “The advent of powerful and inexpensive screening technologies [1] recently produced huge amounts of biological data that opened the way to a new era of therapeutics and personalized medicine [2].”
Systems Biology and Multi-Omics Integration: Viewpoints from the Metabolomics Research Community (2019, methodology): “[92] recently reviewed different tools available for multi-omics data integration in detail.”
Microbial Biodiversity and Bioremediation Assessment Through Omics Approaches (2020, background): “These technologies have abetted the collection exhaustive biological data about microorganisms, their cellular mechanisms, and biomolecules like enzymes associated with bioremediation (Zhang et al., 2010; Misra et al., 2018; Pandey et al., 2019).”