State of the Field in Multi-Omics Research: From Computational Needs to Data Mining and Sharing
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 lea...
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| Published in: | Frontiers in genetics Vol. 11; p. 610798 |
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| Main Authors: | , , , |
| Format: | Journal Article |
| Language: | English |
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Switzerland
Frontiers Media S.A
10.12.2020
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| ISSN: | 1664-8021, 1664-8021 |
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| 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. |
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| AbstractList | 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. 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.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. |
| Author | Sahu, Sangram K. Misra, Biswapriya B. Krassowski, Michal Das, Vivek |
| AuthorAffiliation | 3 Independent Researcher , Bengaluru , India 1 Nuffield Department of Women’s & Reproductive Health, University of Oxford , Oxford , United Kingdom 2 Novo Nordisk Research Center Seattle, Inc , Seattle, WA , United States 4 Independent Researcher , Namburu , India |
| AuthorAffiliation_xml | – name: 1 Nuffield Department of Women’s & Reproductive Health, University of Oxford , Oxford , United Kingdom – name: 4 Independent Researcher , Namburu , India – name: 2 Novo Nordisk Research Center Seattle, Inc , Seattle, WA , United States – name: 3 Independent Researcher , Bengaluru , India |
| Author_xml | – sequence: 1 givenname: Michal surname: Krassowski fullname: Krassowski, Michal – sequence: 2 givenname: Vivek surname: Das fullname: Das, Vivek – sequence: 3 givenname: Sangram K. surname: Sahu fullname: Sahu, Sangram K. – sequence: 4 givenname: Biswapriya B. surname: Misra fullname: Misra, Biswapriya B. |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/33362867$$D View this record in MEDLINE/PubMed |
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| Copyright | Copyright © 2020 Krassowski, Das, Sahu and Misra. Copyright © 2020 Krassowski, Das, Sahu and Misra. 2020 Krassowski, Das, Sahu and Misra |
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| Keywords | visualization FAIR benchmarking data heterogeneity reproducibility multi-omics integrated omics machine learning |
| Language | English |
| License | Copyright © 2020 Krassowski, Das, Sahu and Misra. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
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