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      Systems Biology and Multi-Omics Integration: Viewpoints from the Metabolomics Research Community

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          Abstract

          The use of multiple omics techniques (i.e., genomics, transcriptomics, proteomics, and metabolomics) is becoming increasingly popular in all facets of life science. Omics techniques provide a more holistic molecular perspective of studied biological systems compared to traditional approaches. However, due to their inherent data differences, integrating multiple omics platforms remains an ongoing challenge for many researchers. As metabolites represent the downstream products of multiple interactions between genes, transcripts, and proteins, metabolomics, the tools and approaches routinely used in this field could assist with the integration of these complex multi-omics data sets. The question is, how? Here we provide some answers (in terms of methods, software tools and databases) along with a variety of recommendations and a list of continuing challenges as identified during a peer session on multi-omics integration that was held at the recent ‘Australian and New Zealand Metabolomics Conference’ (ANZMET 2018) in Auckland, New Zealand (Sept. 2018). We envisage that this document will serve as a guide to metabolomics researchers and other members of the community wishing to perform multi-omics studies. We also believe that these ideas may allow the full promise of integrated multi-omics research and, ultimately, of systems biology to be realized.

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          The FAIR Guiding Principles for scientific data management and stewardship

          There is an urgent need to improve the infrastructure supporting the reuse of scholarly data. A diverse set of stakeholders—representing academia, industry, funding agencies, and scholarly publishers—have come together to design and jointly endorse a concise and measureable set of principles that we refer to as the FAIR Data Principles. The intent is that these may act as a guideline for those wishing to enhance the reusability of their data holdings. Distinct from peer initiatives that focus on the human scholar, the FAIR Principles put specific emphasis on enhancing the ability of machines to automatically find and use the data, in addition to supporting its reuse by individuals. This Comment is the first formal publication of the FAIR Principles, and includes the rationale behind them, and some exemplar implementations in the community.
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            Causal analysis approaches in Ingenuity Pathway Analysis

            Motivation: Prior biological knowledge greatly facilitates the meaningful interpretation of gene-expression data. Causal networks constructed from individual relationships curated from the literature are particularly suited for this task, since they create mechanistic hypotheses that explain the expression changes observed in datasets. Results: We present and discuss a suite of algorithms and tools for inferring and scoring regulator networks upstream of gene-expression data based on a large-scale causal network derived from the Ingenuity Knowledge Base. We extend the method to predict downstream effects on biological functions and diseases and demonstrate the validity of our approach by applying it to example datasets. Availability: The causal analytics tools ‘Upstream Regulator Analysis', ‘Mechanistic Networks', ‘Causal Network Analysis' and ‘Downstream Effects Analysis' are implemented and available within Ingenuity Pathway Analysis (IPA, http://www.ingenuity.com). Supplementary information: Supplementary material is available at Bioinformatics online.
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              RNA-Seq: a revolutionary tool for transcriptomics.

              RNA-Seq is a recently developed approach to transcriptome profiling that uses deep-sequencing technologies. Studies using this method have already altered our view of the extent and complexity of eukaryotic transcriptomes. RNA-Seq also provides a far more precise measurement of levels of transcripts and their isoforms than other methods. This article describes the RNA-Seq approach, the challenges associated with its application, and the advances made so far in characterizing several eukaryote transcriptomes.
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                Author and article information

                Journal
                Metabolites
                Metabolites
                metabolites
                Metabolites
                MDPI
                2218-1989
                18 April 2019
                April 2019
                : 9
                : 4
                : 76
                Affiliations
                [1 ]The New Zealand Institute for Plant and Food Research Limited, Private Bag 92169, Auckland 1142, New Zealand
                [2 ]Land and Water, Commonwealth Scientific and Industrial Research Organization (CSIRO), Ecosciences Precinct, Dutton Park, Dutton Park, QLD 4102, Australia; david.beale@ 123456csiro.au
                [3 ]Land and Water, Commonwealth Scientific and Industrial Research Organization (CSIRO), Research and Innovation Park, Acton, ACT 2601, Australia; amy.paten@ 123456csiro.au
                [4 ]Trajan Scientific and Medical, Ringwood, VIC 3134, Australia; kkouremenos@ 123456trajanscimed.com
                [5 ]Bio21 Institute, The University of Melbourne, Parkville, VIC 3010, Australia
                [6 ]Department of Biological Sciences, National University of Singapore, Singapore 117411, Singapore; sanjay@ 123456nus.edu.sg
                [7 ]Centre for Advanced Imaging, The University of Queensland, St Lucia, QLD 4072, Australia; h.schirra@ 123456uq.edu.au
                [8 ]Department of Biological Sciences, University of Alberta, Edmonton, AB T6G 2E8, Canada; dwishart@ 123456ualberta.ca
                [9 ]Department of Computing Science, University of Alberta, Edmonton, AB T6G 2E8, Canada
                Author notes
                [* ]Correspondence: farhana.pinu@ 123456plantandfood.co.nz ; Tel.: +64-9-926-3565
                Author information
                https://orcid.org/0000-0002-0180-9341
                https://orcid.org/0000-0002-9948-9197
                https://orcid.org/0000-0003-0420-2155
                https://orcid.org/0000-0002-7541-246X
                https://orcid.org/0000-0002-3207-2434
                Article
                metabolites-09-00076
                10.3390/metabo9040076
                6523452
                31003499
                2f418e9c-03ae-4acb-b19a-c993f0b99da5
                © 2019 by the authors.

                Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ( http://creativecommons.org/licenses/by/4.0/).

                History
                : 25 March 2019
                : 16 April 2019
                Categories
                Perspective

                mathematical modeling,data analysis,data integration,experimental design,quantitative omics,databases,translational metabolomics,pathway analysis,metabolic networks

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