This release reflects the code used for the analyses as published in the State of the Field in Multi-Omics Research: From Computational Needs to Data Mining and Sharing article.
For more detail please refer to:
Krassowski M, Das V, Sahu SK and Misra BB (2020) State of the Field in Multi-Omics Research: From Computational Needs to Data Mining and Sharing. Front. Genet. 11:610798. https://doi.org/10.3389/fgene.2020.610798
Abstract:
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. Multi-omics efforts have taken center stage in biomedical research leading to the development of new insights into biological events and processes. However, the mushrooming of a myriad of tools, datasets, and approaches tends to inundate the literature and overwhelm researchers new to the field. 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. 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. 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.