More generally, performing multiple omics research often means having datasets with very different data modalities originating from varied assay types and increased dimensionality. These studies generate a plethora of data, which, with careful integration under a suitable statistical and mathematical framework, can help to solve broader queries pertaining to basic and applied areas of biology. In the last decade, the application of different individual omic studies (e.g., genomics, epigenomics, transcriptomics, proteomics, metagenomics) that aimed at understanding a particular problem in human disease ( Karczewski and Snyder, 2018), agriculture ( Ichihashi et al., 2020), plant science ( Liu et al., 2016), microbiology ( Quinn et al., 2016), and the environment have been successful to a great extent. We conclude with practical advice and recommendations on software engineering and reproducibility practices to share a comprehensive awareness with new researchers in multi-omics for end-to-end workflow. Thus, we provide guidance to interested users of the domain by addressing challenges of the underlying biology, giving an overview of the available toolset, addressing common pitfalls, and acknowledging current methods’ limitations. The aims of this review are to provide an overview of the current state of the field, inform on available reliable resources, discuss the application of statistics and machine/deep learning in multi-omics analyses, discuss findable, accessible, interoperable, reusable (FAIR) research, and point to best practices in benchmarking. However, the mushrooming of a myriad of tools, datasets, and approaches tends to inundate the literature and overwhelm researchers new to the field. Multi-omics efforts have taken center stage in biomedical research leading to the development of new insights into biological events and processes. Multi-omics, variously called integrated omics, pan-omics, and trans-omics, aims to combine two or more omics data sets to aid in data analysis, visualization and interpretation to determine the mechanism of a biological process. 4Independent Researcher, Namburu, India.3Independent Researcher, Bengaluru, India.2Novo Nordisk Research Center Seattle, Inc, Seattle, WA, United States.1Nuffield Department of Women’s & Reproductive Health, University of Oxford, Oxford, United Kingdom.Michal Krassowski 1† Vivek Das 2† Sangram K.

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