Cited by 258Bioinformatics and Genomic NetworksMicrobial Metabolic Engineering and BioproductionGene Regulatory Network Analysis

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Trans-Omics: How To Reconstruct Biochemical Networks Across Multiple ‘Omic’ Layers

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Yugi, Katsuyuki, Kubota, Hiroyuki, Hatano, Atsushi, Kuroda, Shinya·2016·Trends in Biotechnology

DOI 10.1016/j.tibtech.2015.12.013

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Abstract

We propose 'trans-omic' analysis for reconstructing global biochemical networks across multiple omic layers by use of both multi-omic measurements and computational data integration. We introduce technologies for connecting multi-omic data based on prior knowledge of biochemical interactions and characterize a biochemical trans-omic network by concepts of a static and dynamic nature. We introduce case studies of metabolism-centric trans-omic studies to show how to reconstruct a biochemical trans-omic network by connecting multi-omic data and how to analyze it in terms of the static and dynamic nature. We propose a trans-ome-wide association study (trans-OWAS) connecting phenotypes with trans-omic networks that reflect both genetic and environmental factors, which can characterize several complex lifestyle diseases as breakdowns in the trans-omic system.

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Shannon, P., Markiel, A., Ozier, O., Baliga, N. S., Wang, J. T., Ramage, D., Amin, N., Schwikowski, B., & Ideker, T. (2003) · Genome Research · doi:10.1101/gr.1239303 · Cited by 51,888
Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources
Huang, D. W., Sherman, B. T., & Lempicki, R. A. (2009) · Nature Protocols · doi:10.1038/nprot.2008.211 · Cited by 35,890
Universal sample preparation method for proteome analysis
Wiśniewski, J. R., Zougman, A., Nagaraj, N., & Mann, M. (2009) · Nature Methods · doi:10.1038/nmeth.1322 · Cited by 8,352
The protein kinase complement of the human genome
Manning, G., Whyte, D. B., Martinez, R., Hunter, T., & Sudarsanam, S. (2002) · Science · doi:10.1126/science.1075762 · Cited by 8,145
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Qin, J., Li, Y., Cai, Z., Li, S., Zhu, J., Zhang, F., Liang, S., Zhang, W., Guan, Y., Shen, D., et al. (2012) · Nature · doi:10.1038/nature11450 · Cited by 6,762
Stable isotope labeling by amino acids in cell culture, SILAC, as a simple and accurate approach to expression proteomics
Ong, S.-E., Blagoev, B., Kratchmarova, I., Kristensen, D. B., Steen, H., Pandey, A., & Mann, M. (2002) · Molecular & Cellular Proteomics · doi:10.1074/mcp.m200025-mcp200 · Cited by 5,574
A comprehensive analysis of protein–protein interactions in Saccharomyces cerevisiae
Uetz, P., Giot, L., Cagney, G., Mansfield, T. A., Judson, R. S., Knight, J. R., Lockshon, D., Narayan, V., Srinivasan, M., Pochart, P., et al. (2000) · Nature · doi:10.1038/35001009 · Cited by 4,706
Multiplexed protein quantitation in Saccharomyces cerevisiae using amine-reactive isobaric tagging reagents
Ross, P. L., Huang, Y. N., Marchese, J. N., Williamson, B., Parker, K., Hattan, S., Khainovski, N., Pillai, S., Dey, S., Daniels, S., et al. (2004) · Molecular & Cellular Proteomics · doi:10.1074/mcp.m400129-mcp200 · Cited by 4,401
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Franceschini, A., Szklarczyk, D., Frankild, S., Kuhn, M., Simonovic, M., Roth, A., Lin, J., Minguez, P., Bork, P., von Mering, C., et al. (2012) · Nucleic Acids Research · doi:10.1093/nar/gks1094 · Cited by 4,398
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Bentley, D. R., Balasubramanian, S., Swerdlow, H. P., Smith, G. P., Milton, J., Brown, C. G., Hall, K. P., Evers, D. J., Barnes, C. L., Bignell, H. R., et al. (2008) · Nature · doi:10.1038/nature07517 · Cited by 3,684

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Authors: Yugi, K., Kubota, H., Hatano, A., & Kuroda, S.
Affiliations: Japan Science and Technology Agency (JP)
Kyushu University (JP)
The University of Tokyo (JP)
Corresponding author: Yugi, Katsuyuki
Kuroda, Shinya
ORCID: Yugi, Katsuyuki
Kubota, Hiroyuki
Hatano, Atsushi
Kuroda, Shinya
Venue: Trends in Biotechnology
Publisher: Elsevier BV
Published: 2016-04
Volume: 34
Issue: 4
Pages: 276-290
ISSN: 0167-7799 (ISSN-L)
DOI: 10.1016/j.tibtech.2015.12.013
OpenAlex: W2274879598
Cited by: 258
References: 140
Citations by year: 2026: 7 · 2025: 13 · 2024: 19 · 2023: 18 · 2022: 29 · 2021: 23 · 2020: 43 · 2019: 38 · 2018: 32 · 2017: 25 · 2016: 11
FWCI: 3.19
Type: journal article
Language: en
Open access: bronze
doi.org
Licence: www.elsevier.com/tdm/userlicense/1.0/ (tdm)
www.elsevier.com/open-access/userlicense/1.0/ (am, 356 d)
doi.org/10.15223/policy-017 (stm-asf)
doi.org/10.15223/policy-037 (stm-asf)
doi.org/10.15223/policy-012 (stm-asf)
doi.org/10.15223/policy-029 (stm-asf)
doi.org/10.15223/policy-004 (stm-asf)
Locations: Trends in biotechnology (acceptedVersion)
PubMed (publishedVersion)
Funders: Japan Society for the Promotion of Science (15H05582)
Topics: Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Microbial Metabolic Engineering and Bioproduction
Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene Regulatory Network Analysis
Keywords: trans-omics, multi-omics integration, metabolic networks, biochemical interactions, lifestyle-related diseases, genetic and environmental factors
MeSH terms: Computational Biology, Gene Regulatory Networks, Metabolic Networks and Pathways, Protein Interaction Maps

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Trans-Omics: How To Reconstruct Biochemical Networks Across Multiple ‘Omic’ Layers

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