Generic placeholder image

Current Pharmaceutical Analysis

Editor-in-Chief

ISSN (Print): 1573-4129
ISSN (Online): 1875-676X

Mini-Review Article

Survey on Multi-omics, and Multi-omics Data Analysis, Integration and Application

Author(s): Mohamad Hesam Shahrajabian and Wenli Sun*

Volume 19, Issue 4, 2023

Published on: 10 April, 2023

Page: [267 - 281] Pages: 15

DOI: 10.2174/1573412919666230406100948

Price: $65

Abstract

Multi-omics approaches have developed as a profitable technique for plant systems, a popular method in medical and biological sciences underlining the necessity to outline new integrative technology and functions to facilitate the multi-scale depiction of biological systems. Understanding a biological system through various omics layers reveals supplementary sources of variability and probably inferring the sequence of cases leading to a definitive process. Manuscripts and reviews were searched on PubMed with the keywords of multi-omics, data analysis, omics, data analysis, data integration, deep learning multi-omics, and multi-omics integration. Articles that were published after 2010 were prioritized. The authors focused mainly on popular publications developing new approaches. Omics reveal interesting tools to produce behavioral and interactions data in microbial communities, and integrating omics details into microbial risk assessment will have an impact on food safety, and also on relevant spoilage control procedures. Omics datasets, comprehensively characterizing biological cases at a molecular level, are continually increasing in both dimensionality and complexity. Multi-omics data analysis is appropriate for treatment optimization, molecular testing and disease prognosis, and to achieve mechanistic understandings of diseases. New effective solutions for multi-omics data analysis together with well-designed components are recommended for many trials. The goal of this mini-review article is to introduce multi-omics technologies considering different multi-omics analyses.

Next »
Graphical Abstract

[1]
Goh, M.S.; Lam, S.D.; Yang, Y.; Naqiuddin, M.; Addis, S.N.K.; Yong, W.T.L.; Luang-In, V.; Sonne, C.; Ma, N.L. Omics technologies used in pesticide residue detection and mitigation in crop. J. Hazard. Mater., 2021, 420, 126624.
[http://dx.doi.org/10.1016/j.jhazmat.2021.126624] [PMID: 34329083]
[2]
Qian, Y.; Li, L.; Sun, Z.; Liu, J.; Yuan, W.; Wang, Z. A multi-omics view of the complex mechanism of vascular calcification. Biomed. Pharmacother., 2021, 135, 111192.
[http://dx.doi.org/10.1016/j.biopha.2020.111192] [PMID: 33401220]
[3]
Zogli, P.; Pingault, L.; Grover, S.; Louis, J. Ento(o)mics: The intersection of ‘omic’ approaches to decipher plant defense against sap-sucking insect pests. Curr. Opin. Plant Biol., 2020, 56, 153-161.
[http://dx.doi.org/10.1016/j.pbi.2020.06.002] [PMID: 32721874]
[4]
Van Assche, R.; Broeckx, V.; Boonen, K.; Maes, E.; De Haes, W.; Schoofs, L.; Temmerman, L. Integrating -Omics: Systems biology as explored through C. elegans research. J. Mol. Biol., 2015, 427(21), 3441-3451.
[http://dx.doi.org/10.1016/j.jmb.2015.03.015] [PMID: 25839106]
[5]
Alotaibi, F.; Alharbi, S.; Alotaibi, M.; Al Mosallam, M.; Motawei, M.; Alrajhi, A. Wheat omics: Classical breeding to new breeding technologies. Saudi J. Biol. Sci., 2021, 28(2), 1433-1444.
[http://dx.doi.org/10.1016/j.sjbs.2020.11.083] [PMID: 33613071]
[6]
Shahrajabian, M.H. Medicinal herbs with anti-inflammatory activities for natural and organic healing. Curr. Org. Chem., 2021, 25(23), 2885-2901.
[http://dx.doi.org/10.2174/1385272825666211110115656]
[7]
Shahrajabian, M.H.; Sun, W.; Cheng, Q. The importance of flavonoids and phytochemicals of medicinal plants with antiviral activities. Mini Rev. Org. Chem., 2021, 18, 1-26.
[http://dx.doi.org/10.2174/1570178618666210707161025]
[8]
Picariello, G.; Sciammaro, L.P.; Puppo, M.C.; Mamone, G. Chapter 18 - Omic sciences for analysis of different Prosopis species. In: Prosopis as a Heat Tolerant Nitrogen Fixing Desert Food Legume; , 2022; 2022, pp. 263-273.
[http://dx.doi.org/10.1016/B978-0-12-823320-7.00007-9]
[9]
Simats, A.; Ramiro, L.; García-Berrocoso, T.; Briansó, F.; Gonzalo, R.; Martín, L.; Sabé, A.; Gill, N.; Penalba, A.; Colomé, N.; Sánchez, A.; Canals, F.; Bustamante, A.; Rosell, A.; Montaner, J. A mouse brain-based multi-omics integrative approach reveals potential blood biomarkers for ischemic stroke. Mol. Cell. Proteomics, 2020, 19(12), 1921-1936.
[http://dx.doi.org/10.1074/mcp.RA120.002283] [PMID: 32868372]
[10]
Cocolin, L.; Mataragas, M.; Bourdichon, F.; Doulgeraki, A.; Pilet, M.F.; Jagadeesan, B.; Rantsiou, K.; Phister, T. Next generation microbiological risk assessment meta-omics: The next need for integration. Int. J. Food Microbiol., 2018, 287, 10-17.
[http://dx.doi.org/10.1016/j.ijfoodmicro.2017.11.008] [PMID: 29157743]
[11]
Judes, G.; Rifaï, K.; Daures, M.; Dubois, L.; Bignon, Y.J.; Penault-Llorca, F.; Bernard-Gallon, D. High-throughput «Omics» technologies: New tools for the study of triple-negative breast cancer. Cancer Lett., 2016, 382(1), 77-85.
[http://dx.doi.org/10.1016/j.canlet.2016.03.001] [PMID: 26965997]
[12]
Calciolari, E.; Donos, N. The use of omics profiling to improve outcomes of bone regeneration and osseointegration. How far are we from personalized medicine in dentistry? J. Proteomics, 2018, 188, 85-96.
[http://dx.doi.org/10.1016/j.jprot.2018.01.017] [PMID: 29410240]
[13]
Horgan, R.P.; Kenny, L.C. ‘Omic’ technologies: genomics, transcriptomics, proteomics and metabolomics. Obstet. Gynaecol., 2011, 13(3), 189-195.
[http://dx.doi.org/10.1576/toag.13.3.189.27672]
[14]
Sun, Y.V.; Hu, Y.J. Integrative analysis of multi-omics data for discovery and functional studies of complex human diseases HHS public access. Adv. Genet., 2016, 93, 147-190.
[http://dx.doi.org/10.1016/bs.adgen.2015.11.004] [PMID: 26915271]
[15]
Arora, N.; Philippidis, G.P. Unraveling metabolic alterations in Chlorella vulgaris cultivated on renewable sugars using time resolved multi-omics. Sci. Total Environ., 2021, 800, 149504.
[http://dx.doi.org/10.1016/j.scitotenv.2021.149504] [PMID: 34426316]
[16]
Tyler, S.R.; Bunyavanich, S. Leveraging -omics for asthma endotyping. J. Allergy Clin. Immunol., 2019, 144(1), 13-23.
[http://dx.doi.org/10.1016/j.jaci.2019.05.015] [PMID: 31277743]
[17]
Nguyen, T.V.; Alfaro, A.C.; Mundy, C.; Petersen, J.; Ragg, N.L.C. Omics research on abalone (Haliotis spp.): Current state and perspectives. Aquaculture, 2022, 547, 737438.
[http://dx.doi.org/10.1016/j.aquaculture.2021.737438]
[18]
Ricard-Blum, S.; Miele, A.E. Omic approaches to decipher the molecular mechanisms of fibrosis, and design new anti-fibrotic strategies. Semin. Cell Dev. Biol., 2020, 101, 161-169.
[http://dx.doi.org/10.1016/j.semcdb.2019.12.009] [PMID: 31883993]
[19]
Charkoftaki, G.; Thompson, D.C.; Golla, J.P.; Garcia-Milian, R.; Lam, T.T.; Engel, J.; Vasiliou, V. Integrated multi-omics approach reveals a role of ALDH1A1 in lipid metabolism in human colon cancer cells. Chem. Biol. Interact., 2019, 304, 88-96.
[http://dx.doi.org/10.1016/j.cbi.2019.02.030] [PMID: 30851239]
[20]
Gu, F.; Liang, S.; Zhu, S.; Liu, J.; Sun, H.Z. Multi-omics revealed the effects of rumen-protected methionine on the nutrient profile of milk in dairy cows. Food Res. Int., 2021, 149, 110682.
[http://dx.doi.org/10.1016/j.foodres.2021.110682] [PMID: 34600684]
[21]
Böhme, K.; Calo-Mata, P.; Barros-Velázquez, J.; Ortea, I. Recent applications of omics-based technologies to main topics in food authentication. Trends Analyt. Chem., 2019, 110, 221-232.
[http://dx.doi.org/10.1016/j.trac.2018.11.005]
[22]
Virkud, Y.V.; Kelly, R.S.; Wood, C.; Lasky-Su, J.A. The nuts and bolts of omics for the clinical allergist. Ann. Allergy Asthma Immunol., 2019, 123(6), 558-563.
[http://dx.doi.org/10.1016/j.anai.2019.09.017] [PMID: 31562939]
[23]
Raghow, R. An Omics perspective on cardiomyopathies and heart failure. Trends Mol. Med., 2016, 22(9), 813-827.
[http://dx.doi.org/10.1016/j.molmed.2016.07.007] [PMID: 27499035]
[24]
Kim, B.M.; Kim, J.; Choi, I.Y.; Raisuddin, S.; Au, D.W.T.; Leung, K.M.Y.; Wu, R.S.S.; Rhee, J.S.; Lee, J.S. Omics of the marine medaka (Oryzias melastigma) and its relevance to marine environmental research. Mar. Environ. Res., 2016, 113, 141-152.
[http://dx.doi.org/10.1016/j.marenvres.2015.12.004] [PMID: 26716363]
[25]
Marie, B. Disentangling of the ecotoxicological signal using “omics” analyses, a lesson from the survey of the impact of cyanobacterial proliferations on fishes. Sci. Total Environ., 2020, 736, 139701.
[http://dx.doi.org/10.1016/j.scitotenv.2020.139701] [PMID: 32497891]
[26]
Lancaster, S.M.; Sanghi, A.; Wu, S.; Snyder, M.P. A customizable analysis of flow in integrative multi-omics. Biomolecules, 2020, 10(12), 1606.
[http://dx.doi.org/10.3390/biom10121606] [PMID: 33260881]
[27]
Dalal, N.; Jalandra, R.; Sharma, M.; Prakash, H.; Makharia, G.K.; Solanki, P.R.; Singh, R.; Kumar, A. Omics technologies for improved diagnosis and treatment of colorectal cancer: Technical advancement and major perspectives. Biomed. Pharmacother., 2020, 131, 110648.
[http://dx.doi.org/10.1016/j.biopha.2020.110648] [PMID: 33152902]
[28]
Douglas, A.E. Omics and the metabolic function of insect–microbial symbioses. Curr. Opin. Insect Sci., 2018, 29, 1-6.
[http://dx.doi.org/10.1016/j.cois.2018.05.012] [PMID: 30551814]
[29]
Palazzotto, E.; Weber, T. Omics and multi-omics approaches to study the biosynthesis of secondary metabolites in microorganisms. Curr. Opin. Microbiol., 2018, 45, 109-116.
[http://dx.doi.org/10.1016/j.mib.2018.03.004] [PMID: 29656009]
[30]
Wani, N.; Raza, K. Integrative approaches to reconstruct regulatory networks from multi-omics data: A review of state-of-the-art methods. Comput. Biol. Chem., 2019, 83, 107120.
[http://dx.doi.org/10.1016/j.compbiolchem.2019.107120] [PMID: 31499298]
[31]
Benedetto, A.; Pezzolato, M.; Biasibetti, E.; Bozzetta, E. Omics applications in the fight against abuse of anabolic substances in cattle: challenges, perspectives and opportunities. Curr. Opin. Food Sci., 2021, 40, 112-120.
[http://dx.doi.org/10.1016/j.cofs.2021.03.001]
[32]
Wang, D.; Zhang, S.; Zhang, H.; Lin, S. Omics study of harmful algal blooms in China: Current status, challenges, and future perspectives. Harmful Algae, 2021, 107, 102079.
[http://dx.doi.org/10.1016/j.hal.2021.102079] [PMID: 34456014]
[33]
Buriani, A.; Garcia-Bermejo, M.L.; Bosisio, E.; Xu, Q.; Li, H.; Dong, X.; Simmonds, M.S.J.; Carrara, M.; Tejedor, N.; Lucio-Cazana, J.; Hylands, P.J. Omic techniques in systems biology approaches to traditional Chinese medicine research: Present and future. J. Ethnopharmacol., 2012, 140(3), 535-544.
[http://dx.doi.org/10.1016/j.jep.2012.01.055] [PMID: 22342380]
[34]
Volonté, C.; Morello, G.; Spampinato, A.G.; Amadio, S.; Apolloni, S.; D’Agata, V.; Cavallaro, S. Omics-based exploration and functional validation of neurotrophic factors and histamine as therapeutic targets in ALS. Ageing Res. Rev., 2020, 62, 101121.
[http://dx.doi.org/10.1016/j.arr.2020.101121] [PMID: 32653439]
[35]
Nwokwu, C.D.; Ishraq Bari, S.M.; Hutson, K.H.; Brausell, C.; Nestorova, G.G. ExoPRIME: Solid-phase immunoisolation and OMICS analysis of surface-marker-specific exosomal subpopulations. Talanta, 2022, 236, 122870.
[http://dx.doi.org/10.1016/j.talanta.2021.122870] [PMID: 34635251]
[36]
Mun, J.; Choi, G.; Lim, B. A guide for bioinformaticians: ‘omics-based drug discovery for precision oncology. Drug Discov. Today, 2020, 25(11), 1897-1904.
[http://dx.doi.org/10.1016/j.drudis.2020.08.004] [PMID: 32828947]
[37]
Peinado, R.S.; Eberle, R.J.; Pacca, C.C.; Arni, R.K.; Coronado, M.A. Review of -omics studies on mosquito-borne viruses of the Flavivirus genus. Virus Res., 2022, 307, 198610.
[http://dx.doi.org/10.1016/j.virusres.2021.198610] [PMID: 34718046]
[38]
Volkova, P.Y.; Geras’kin, S.A. ‘Omic’ technologies as a helpful tool in radioecological research. J. Environ. Radioact., 2018, 189, 156-167.
[http://dx.doi.org/10.1016/j.jenvrad.2018.04.011] [PMID: 29677564]
[39]
Simões, T.; Novais, S.C.; Natal-da-Luz, T.; Devreese, B.; de Boer, T.; Roelofs, D.; Sousa, J.P.; van Straalen, N.M.; Lemos, M.F.L. Using time-lapse omics correlations to integrate toxicological pathways of a formulated fungicide in a soil invertebrate. Environ. Pollut., 2019, 246, 845-854.
[http://dx.doi.org/10.1016/j.envpol.2018.12.069] [PMID: 30623841]
[40]
Hernandez, E.P.; Talactac, M.R.; Fujisaki, K.; Tanaka, T. The case for oxidative stress molecule involvement in the tick-pathogen interactions -an omics approach. Dev. Comp. Immunol., 2019, 100, 103409.
[http://dx.doi.org/10.1016/j.dci.2019.103409] [PMID: 31200008]
[41]
Kok, E.; van Dijk, J.; Voorhuijzen, M.; Staats, M.; Slot, M.; Lommen, A.; Venema, D.; Pla, M.; Corujo, M.; Barros, E.; Hutten, R.; Jansen, J.; van der Voet, H. Omics analyses of potato plant materials using an improved one-class classification tool to identify aberrant compositional profiles in risk assessment procedures. Food Chem., 2019, 292, 350-358.
[http://dx.doi.org/10.1016/j.foodchem.2018.07.224] [PMID: 31054687]
[42]
Mishra, A.; Medhi, K.; Malaviya, P.; Thakur, I.S. Omics approaches for microalgal applications: Prospects and challenges. Bioresour. Technol., 2019, 291, 121890.
[http://dx.doi.org/10.1016/j.biortech.2019.121890] [PMID: 31378447]
[43]
Grivas, A.; Fragoulis, G.; Garantziotis, P.; Banos, A.; Nikiphorou, E.; Boumpas, D. Unraveling the complexities of psoriatic arthritis by the use of -Omics and their relevance for clinical care. Autoimmun. Rev., 2021, 20(11), 102949.
[http://dx.doi.org/10.1016/j.autrev.2021.102949] [PMID: 34509654]
[44]
Pelkonen, O.; Pasanen, M.; Lindon, J.C.; Chan, K.; Zhao, L.; Deal, G.; Xu, Q.; Fan, T.P. Omics and its potential impact on R&D and regulation of complex herbal products. J. Ethnopharmacol., 2012, 140(3), 587-593.
[http://dx.doi.org/10.1016/j.jep.2012.01.035] [PMID: 22313626]
[45]
Liu, Y.; Xue, M.; Cao, D.; Qin, L.; Wang, Y.; Miao, Z.; Wang, P.; Hu, X.; Shen, J.; Xiong, B. Multi-omics characterization of WNT pathway reactivation to ameliorate BET inhibitor resistance in liver cancer cells. Genomics, 2021, 113(3), 1057-1069.
[http://dx.doi.org/10.1016/j.ygeno.2021.02.017] [PMID: 33667649]
[46]
Gomes de Oliveira Dal’Molin, C.; Nielsen, L.K. Plant genome-scale reconstruction: from single cell to multi-tissue modelling and omics analyses. Curr. Opin. Biotechnol., 2018, 49, 42-48.
[http://dx.doi.org/10.1016/j.copbio.2017.07.009] [PMID: 28806583]
[47]
Jamla, M.; Khare, T.; Joshi, S.; Patil, S.; Penna, S.; Kumar, V. Omics approaches for understanding heavy metal responses and tolerance in plants. Curr. Plant Biol., 2021, 27, 100213.
[http://dx.doi.org/10.1016/j.cpb.2021.100213]
[48]
Adossa, N.; Khan, S.; Rytkönen, K.T.; Elo, L.L. Computational strategies for single-cell multi-omics integration. Comput. Struct. Biotechnol. J., 2021, 19, 2588-2596.
[http://dx.doi.org/10.1016/j.csbj.2021.04.060] [PMID: 34025945]
[49]
Dey, S.S.; Kester, L.; Spanjaard, B.; Bienko, M.; van Oudenaarden, A. Integrated genome and transcriptome sequencing of the same cell. Nat. Biotechnol., 2015, 33(3), 285-289.
[http://dx.doi.org/10.1038/nbt.3129] [PMID: 25599178]
[50]
Angermueller, C.; Clark, S.J.; Lee, H.J.; Macaulay, I.C.; Teng, M.J.; Hu, T.X.; Krueger, F.; Smallwood, S.A.; Ponting, C.P.; Voet, T.; Kelsey, G.; Stegle, O.; Reik, W. Parallel single-cell sequencing links transcriptional and epigenetic heterogeneity. Nat. Methods, 2016, 13(3), 229-232.
[http://dx.doi.org/10.1038/nmeth.3728] [PMID: 26752769]
[51]
Zhu, C.; Preissl, S.; Ren, B. Single-cell multimodal omics: the power of many. Nat. Methods, 2020, 17(1), 11-14.
[http://dx.doi.org/10.1038/s41592-019-0691-5] [PMID: 31907462]
[52]
Macaulay, I.C.; Ponting, C.P.; Voet, T. Single-cell multiomics: Multiple measurements from single cells. Trends Genet., 2017, 33(2), 155-168.
[http://dx.doi.org/10.1016/j.tig.2016.12.003] [PMID: 28089370]
[53]
Ribeiro, D.M.; Salama, A.A.K.; Vitor, A.C.M.; Argüello, A.; Moncau, C.T.; Santos, E.M.; Caja, G.; de Oliveira, J.S.; Balieiro, J.C.C.; Hernández-Castellano, L.E.; Zachut, M.; Poleti, M.D.; Castro, N.; Alves, S.P.; Almeida, A.M. The application of omics in ruminant production: a review in the tropical and sub-tropical animal production context. J. Proteomics, 2020, 227, 103905.
[http://dx.doi.org/10.1016/j.jprot.2020.103905] [PMID: 32712373]
[54]
Yoon, S.J.; Lee, C.B.; Chae, S.U.; Jo, S.J.; Bae, S.K. The comprehensive omics approach from metabolomics to advanced omics for development of immune checkpoint inhibitors: potential strategies for next generation of cancer immunotherapy. Int. J. Mol. Sci., 2021, 22(13), 6932.
[http://dx.doi.org/10.3390/ijms22136932] [PMID: 34203237]
[55]
Haddad, N.; Johnson, N.; Kathariou, S.; Métris, A.; Phister, T.; Pielaat, A.; Tassou, C.; Wells-Bennik, M.H.J.; Zwietering, M.H. Next generation microbiological risk assessment-Potential of omics data for hazard characterisation. Int. J. Food Microbiol., 2018, 287, 28-39.
[http://dx.doi.org/10.1016/j.ijfoodmicro.2018.04.015] [PMID: 29703417]
[56]
Meng, C.; Basunia, A.; Peters, B.; Gholami, A.M.; Kuster, B.; Culhane, A.C. MOGSA: Integrative single sample gene-set analysis of multiple omics data. Mol. Cell. Proteomics, 2019, 18(8)(Suppl. 1), S153-S168.
[http://dx.doi.org/10.1074/mcp.TIR118.001251] [PMID: 31243065]
[57]
Lv, D.; Zhang, X.; Liu, Q. Single-cell omics decipher tumor evolution. Medicine in Omics, 2021, 2, 100006.
[http://dx.doi.org/10.1016/j.meomic.2021.100006]
[58]
Duan, M.; Zhao, W.L.; Zhou, L.; Novák, P.; Zhu, X.; Yin, K. Omics research in vascular calcification. Clin. Chim. Acta, 2020, 511, 198-207.
[http://dx.doi.org/10.1016/j.cca.2020.10.021] [PMID: 33096032]
[59]
Sauer, U.G.; Deferme, L.; Gribaldo, L.; Hackermüller, J.; Tralau, T.; van Ravenzwaay, B.; Yauk, C.; Poole, A.; Tong, W.; Gant, T.W. The challenge of the application of 'omics technologies in chemicals risk assessment: Background and outlook. Regul. Toxicol. Pharmacol., 2017, 91(1)(Suppl. 1), S14-S26.
[http://dx.doi.org/10.1016/j.yrtph.2017.09.020] [PMID: 28927750]
[60]
Rai, V.; Mukherjee, R.; Ghosh, A.K.; Routray, A.; Chakraborty, C. “Omics” in oral cancer: New approaches for biomarker discovery. Arch. Oral Biol., 2018, 87, 15-34.
[http://dx.doi.org/10.1016/j.archoralbio.2017.12.003] [PMID: 29247855]
[61]
Ghoul, M.; Andersen, S.B.; West, S.A. Sociomics: Using omic approaches to understand social evolution. Trends Genet., 2017, 33(6), 408-419.
[http://dx.doi.org/10.1016/j.tig.2017.03.009] [PMID: 28506494]
[62]
Hayward, S.A.L. Application of functional ‘Omics’ in environmental stress physiology: insights, limitations, and future challenges. Curr. Opin. Insect Sci., 2014, 4, 35-41.
[http://dx.doi.org/10.1016/j.cois.2014.08.005] [PMID: 28043406]
[63]
Tsang, C.C.; Tang, J.Y.M.; Lau, S.K.P.; Woo, P.C.Y. Taxonomy and evolution of Aspergillus, Penicillium and Talaromyces in the omics era – Past, present and future. Comput. Struct. Biotechnol. J., 2018, 16, 197-210.
[http://dx.doi.org/10.1016/j.csbj.2018.05.003] [PMID: 30002790]
[64]
Martin, S.A.M.; Król, E. Nutrigenomics and immune function in fish: new insights from omics technologies. Dev. Comp. Immunol., 2017, 75, 86-98.
[http://dx.doi.org/10.1016/j.dci.2017.02.024] [PMID: 28254621]
[65]
Komatsu, S.; Shirasaka, N.; Sakata, K. ‘Omics’ techniques for identifying flooding–response mechanisms in soybean. J. Proteomics, 2013, 93, 169-178.
[http://dx.doi.org/10.1016/j.jprot.2012.12.016] [PMID: 23313220]
[66]
den Besten, H.M.W.; Amézquita, A.; Bover-Cid, S.; Dagnas, S.; Ellouze, M.; Guillou, S.; Nychas, G.; O’Mahony, C.; Pérez-Rodriguez, F.; Membré, J.M. Next generation of microbiological risk assessment: Potential of omics data for exposure assessment. Int. J. Food Microbiol., 2018, 287, 18-27.
[http://dx.doi.org/10.1016/j.ijfoodmicro.2017.10.006] [PMID: 29032838]
[67]
Monti, D.; Ostan, R.; Borelli, V.; Castellani, G.; Franceschi, C. Inflammaging and human longevity in the omics era. Mech Ageing Dev, 2017, 165(Part B), 129-138.
[http://dx.doi.org/10.1016/j.mad.2016.12.008]
[68]
Zainal-Abidin, R.A.; Ruhaizat-Ooi, I.H.; Harun, S. A review of omics technologies and bioinformatics to accelerate improvement of papaya traits. Agronomy (Basel), 2021, 11(7), 1356.
[http://dx.doi.org/10.3390/agronomy11071356]
[69]
McDaniel, E.A.; Wahl, S.A.; Ishii, S.; Pinto, A.; Ziels, R.; Nielsen, P.H.; McMahon, K.D.; Williams, R.B.H. Prospects for multi-omics in the microbial ecology of water engineering. Water Res., 2021, 205, 117608.
[http://dx.doi.org/10.1016/j.watres.2021.117608] [PMID: 34555741]
[70]
Shahrajabian, M.H.; Sun, W.; Cheng, Q. Different methods for molecular and rapid detection of human novel coronavirus. Curr. Pharm. Des., 2021, 27(25), 2893-2903.
[http://dx.doi.org/10.2174/1381612827666210604114411] [PMID: 34086547]
[71]
Shahrajabian, M.H.; Sun, W.; Cheng, Q. Molecular breeding and the impacts of some important genes families on agronomic traits, a review. Genet. Resour. Crop Evol., 2021, 68(5), 1709-1730.
[http://dx.doi.org/10.1007/s10722-021-01148-x]
[72]
Sun, W.; Shahrajabian, M.H.; Cheng, Q. Natural dietary and medicinal plants with anti-obesity therapeutics activities for treatment and prevention of obesity during lock down and in post-COVID-19 era. Appl. Sci., 2021, 11(17), 7889.
[http://dx.doi.org/10.3390/app11177889]
[73]
Pathania, R.; Srivastava, A.; Srivastava, S.; Shukla, P. Metabolic systems biology and multi-omics of cyanobacteria: Perspectives and future directions. Bioresour. Technol., 2022, 343, 126007.
[http://dx.doi.org/10.1016/j.biortech.2021.126007] [PMID: 34634665]
[74]
Santiago-Rodriguez, T.M.; Hollister, E.B. Multi ‘omic data integration: A review of concepts, considerations, and approaches. Semin. Perinatol., 2021, 45(6), 151456.
[http://dx.doi.org/10.1016/j.semperi.2021.151456] [PMID: 34256961]
[75]
Colás-Ruiz, N.R.; Ramirez, G.; Courant, F.; Gomez, E.; Hampel, M.; Lara-Martín, P.A. Multi-omic approach to evaluate the response of gilt-head sea bream (Sparus aurata) exposed to the UV filter sulisobenzone. Sci. Total Environ., 2022, 803, 150080.
[http://dx.doi.org/10.1016/j.scitotenv.2021.150080] [PMID: 34525742]
[76]
Nyholm, L.; Koziol, A.; Marcos, S.; Botnen, A.B.; Aizpurua, O.; Gopalakrishnan, S.; Limborg, M.T.; Gilbert, M.T.P.; Alberdi, A. Holo-omics: Integrated host-microbiota multi-omics for basic and applied biological research. iScience, 2020, 23(8), 101414.
[http://dx.doi.org/10.1016/j.isci.2020.101414] [PMID: 32777774]
[77]
Bari, S.; Vike, N.L.; Stetsiv, K.; Walter, A.; Newman, S.; Kawata, K.; Bazarian, J.J.; Papa, L.; Nauman, E.A.; Talavage, T.M.; Slobounov, S.; Breiter, H.C. Integrating multi-omics with neuroimaging and behavior: A preliminary model of dysfunction in football athletes. Neuroimage. Reports, 2021, 1(3), 100032.
[http://dx.doi.org/10.1016/j.ynirp.2021.100032]
[78]
Tian, L.; Wang, L. Multi-omics analysis reveals structure and function of biofilm microbial communities in a pre-denitrification biofilter. Sci. Total Environ., 2021, 757, 143908.
[http://dx.doi.org/10.1016/j.scitotenv.2020.143908] [PMID: 33316516]
[79]
Ferrocino, I.; Rantsiou, K.; Cocolin, L. Microbiota of milk and dairy foods: Structure and function by –omics approaches. In: Encyclopedia Dairy Sci; , 2022; pp. 313-318.
[http://dx.doi.org/10.1016/B978-0-08-100596-5.22973-9]
[80]
Khdhiri, M.; Piché-Choquette, S.; Tremblay, J.; Tringe, S.G.; Constant, P. Meta-omics survey of [NiFe]-hydrogenase genes fails to capture drastic variations in H2-oxidation activity measured in three soils exposed to H2. Soil Biol. Biochem., 2018, 125, 239-243.
[http://dx.doi.org/10.1016/j.soilbio.2018.07.020]
[81]
Puig-Castellví, F.; Jouan-Rimbaud Bouveresse, D.; Mazéas, L.; Chapleur, O.; Rutledge, D.N. Rearrangement of incomplete multi-omics datasets combined with ComDim for evaluating replicate cross-platform variability and batch influence. Chemom. Intell. Lab. Syst., 2021, 218, 104422.
[http://dx.doi.org/10.1016/j.chemolab.2021.104422]
[82]
Reel, P.S.; Reel, S.; Pearson, E.; Trucco, E.; Jefferson, E. Using machine learning approaches for multi-omics data analysis: A review. Biotechnol. Adv., 2021, 49, 107739.
[http://dx.doi.org/10.1016/j.biotechadv.2021.107739] [PMID: 33794304]
[83]
Liu, Q.; Cheng, B.; Jin, Y.; Hu, P. Bayesian tensor factorization-drive breast cancer subtyping by integrating multi-omics data. J. Biomed. Inform., 2022, 125, 103958.
[http://dx.doi.org/10.1016/j.jbi.2021.103958] [PMID: 34839017]
[84]
Hampel, H.; Nisticò, R.; Seyfried, N.T.; Levey, A.I.; Modeste, E.; Lemercier, P.; Baldacci, F.; Toschi, N.; Garaci, F.; Perry, G.; Emanuele, E.; Valenzuela, P.L.; Lucia, A.; Urbani, A.; Sancesario, G.M.; Mapstone, M.; Corbo, M.; Vergallo, A.; Lista, S. Omics sciences for systems biology in Alzheimer’s disease: State-of-the-art of the evidence. Ageing Res. Rev., 2021, 69, 101346.
[http://dx.doi.org/10.1016/j.arr.2021.101346] [PMID: 33915266]
[85]
Lee, H.; Sung, E.J.; Seo, S.; Min, E.K.; Lee, J.Y.; Shim, I.; Kim, P.; Kim, T.Y.; Lee, S.; Kim, K.T. Integrated multi-omics analysis reveals the underlying molecular mechanism for developmental neurotoxicity of perfluorooctanesulfonic acid in zebrafish. Environ. Int., 2021, 157, 106802.
[http://dx.doi.org/10.1016/j.envint.2021.106802] [PMID: 34358914]
[86]
Deng, Y.; Zhang, Y.; Ren, H. Multi-omic studies on the toxicity variations in effluents from different units of reclaimed water treatment. Water Res., 2022, 208, 117874.
[http://dx.doi.org/10.1016/j.watres.2021.117874] [PMID: 34814020]
[87]
Ussery, E.J.; Nielsen, K.M.; Simmons, D.; Pandelides, Z.; Mansfield, C.; Holdway, D. An ‘omics approach to investigate the growth effects of environmentally relevant concentrations of guanylurea exposure on Japanese medaka (Oryzias latipes). Aquat. Toxicol., 2021, 232, 105761.
[http://dx.doi.org/10.1016/j.aquatox.2021.105761] [PMID: 33550114]
[88]
Chai, H.; Zhou, X.; Zhang, Z.; Rao, J.; Zhao, H.; Yang, Y. Integrating multi-omics data through deep learning for accurate cancer prognosis prediction. Comput. Biol. Med., 2021, 134, 104481.
[http://dx.doi.org/10.1016/j.compbiomed.2021.104481] [PMID: 33989895]
[89]
Zhou, M.; Varol, A.; Efferth, T. Multi-omics approaches to improve malaria therapy. Pharmacol. Res., 2021, 167, 105570.
[http://dx.doi.org/10.1016/j.phrs.2021.105570] [PMID: 33766628]
[90]
Rautenstrauch, P.; Vlot, A.H.C.; Saran, S.; Ohler, U. Intricacies of single-cell multi-omics data integration. Trends Genet., 2021.
[http://dx.doi.org/10.1016/j.tig.2021.08.012] [PMID: 34561102]
[91]
Ma, S.; Shu, X.; Wang, W-X. Multi-omics reveals the regulatory mechanisms of zinc exposure on the intestine-liver axis of golden pompano Trachinotus ovatus. Sci. Total Environ., 2021.
[http://dx.doi.org/10.1016/j.scitotenv.2021.151497] [PMID: 34752869]
[92]
Peng, Z.; Yang, Q.; Yeerken, R.; Chen, J.; Liu, X.; Li, X. Multi-omics analyses reveal the mechanisms of Arsenic-induced male reproductive toxicity in mice. J. Hazard. Mater., 2022, 424(Pt C), 127548.
[http://dx.doi.org/10.1016/j.jhazmat.2021.127548] [PMID: 34741939]
[93]
Du, X.; Zhang, Q.; Jiang, Y.; Li, H.; Zhu, X.; Zhang, Y.; Liu, C.; Niu, Y.; Ji, J.; Jiang, C.; Cai, J.; Chen, R.; Kan, H. Dynamic molecular choreography induced by traffic exposure: A randomized, crossover trial using multi-omics profiling. J Hazard Mat, 2022, 424(Part A), 127359.
[http://dx.doi.org/10.1016/j.jhazmat.2021.127359]
[94]
Lin, Z.; Luo, P.; Huang, D.; Wu, Y.; Li, F.; Liu, H. Multi-omics based strategy for toxicity analysis of acrylamide in Saccharomyces cerevisiae model. Chem. Biol. Interact., 2021, 349, 109682.
[http://dx.doi.org/10.1016/j.cbi.2021.109682] [PMID: 34610338]
[95]
Afshari, R.; Pillidge, C.J.; Dias, D.A.; Osborn, A.M.; Gill, H. Biomarkers associated with cheese quality uncovered by integrative multi-omic analysis. Food Control, 2021, 123, 107752.
[http://dx.doi.org/10.1016/j.foodcont.2020.107752]
[96]
Zhang, E.; Zhang, M.; Shi, C.; Sun, L.; Shan, L.; Zhang, H.; Song, Y. An overview of advances in multi-omics analysis in prostate cancer. Life Sci., 2020, 260, 118376.
[http://dx.doi.org/10.1016/j.lfs.2020.118376] [PMID: 32898525]
[97]
Lee, H.; Gao, Y.; Ko, E.; Lee, J.; Lee, H.K.; Lee, S.; Choi, M.; Shin, S.; Park, Y.H.; Moon, H.B.; Uppal, K.; Kim, K.T. Nonmonotonic response of type 2 diabetes by low concentration organochlorine pesticide mixture: Findings from multi-omics in zebrafish. J. Hazard. Mater., 2021, 416, 125956.
[http://dx.doi.org/10.1016/j.jhazmat.2021.125956] [PMID: 34492873]
[98]
Gu, X.; Ke, S.; Wang, Q.; Zhuang, T.; Xia, C.; Xu, Y.; Yang, L.; Zhou, M. Energy metabolism in major depressive disorder: Recent advances from omics technologies and imaging. Biomed. Pharmacother., 2021, 141, 111869.
[http://dx.doi.org/10.1016/j.biopha.2021.111869] [PMID: 34225015]
[99]
Spänig, S.; Eick, L.; Nuy, J.K.; Beisser, D.; Ip, M.; Heider, D.; Boenigk, J. A multi-omics study on quantifying antimicrobial resistance in European freshwater lakes. Environ. Int., 2021, 157, 106821.
[http://dx.doi.org/10.1016/j.envint.2021.106821] [PMID: 34403881]
[100]
Zancarini, A.; Westerhuis, J.A.; Smilde, A.K.; Bouwmeester, H.J. Integration of omics data to unravel root microbiome recruitment. Curr. Opin. Biotechnol., 2021, 70, 255-261.
[http://dx.doi.org/10.1016/j.copbio.2021.06.016] [PMID: 34242993]
[101]
Liu, S.; Gui, Y.; Wang, M.S.; Zhang, L.; Xu, T.; Pan, Y.; Zhang, K.; Yu, Y.; Xiao, L.; Qiao, Y.; Bonin, C.; Hargis, G.; Huan, T.; Yu, Y.; Tao, J.; Zhang, R.; Kreutzer, D.L.; Zhou, Y.; Tian, X.J.; Wang, Y.; Fu, H.; An, X.; Liu, S.; Zhou, D. Serum integrative omics reveals the landscape of human diabetic kidney disease. Mol. Metab., 2021, 54, 101367.
[http://dx.doi.org/10.1016/j.molmet.2021.101367] [PMID: 34737094]
[102]
Egami, R.; Kokaji, T.; Hatano, A.; Yugi, K.; Eto, M.; Morita, K.; Ohno, S.; Fujii, M.; Hironaka, K.; Uematsu, S.; Terakawa, A.; Bai, Y.; Pan, Y.; Tsuchiya, T.; Ozaki, H.; Inoue, H.; Uda, S.; Kubota, H.; Suzuki, Y.; Matsumoto, M.; Nakayama, K.I.; Hirayama, A.; Soga, T.; Kuroda, S. Trans-omic analysis reveals obesity-associated dysregulation of inter-organ metabolic cycles between the liver and skeletal muscle. iScience, 2021, 24(3), 102217.
[http://dx.doi.org/10.1016/j.isci.2021.102217] [PMID: 33748705]
[103]
Jiang, L.; Hong, Y.; Xie, G.; Zhang, J.; Zhang, H.; Cai, Z. Comprehensive multi-omics approaches reveal the hepatotoxic mechanism of perfluorohexanoic acid (PFHxA) in mice. Sci. Total Environ., 2021, 790, 148160.
[http://dx.doi.org/10.1016/j.scitotenv.2021.148160] [PMID: 34380288]
[104]
Donovan, B.M.; Bastarache, L.; Turi, K.N.; Zutter, M.M.; Hartert, T.V. The current state of omics technologies in the clinical management of asthma and allergic diseases. Ann. Allergy Asthma Immunol., 2019, 123(6), 550-557.
[http://dx.doi.org/10.1016/j.anai.2019.08.460] [PMID: 31494234]
[105]
Shi, R.; Feng, Z.; Zhang, X. Integrative multi-omics landscape of non-structural protein 3 of severe acute respiratory syndrome coronaviruses. Genomics Proteomics Bioinformatics, 2021, 19(5), 707-726.
[http://dx.doi.org/10.1016/j.gpb.2021.09.007] [PMID: 34774773]
[106]
Titz, B.; Szostak, J.; Sewer, A.; Phillips, B.; Nury, C.; Schneider, T.; Dijon, S.; Lavrynenko, O.; Elamin, A.; Guedj, E.; Tsin Wong, E.; Lebrun, S.; Vuillaume, G.; Kondylis, A.; Gubian, S.; Cano, S.; Leroy, P.; Keppler, B.; Ivanov, N.V.; Vanscheeuwijck, P.; Martin, F.; Peitsch, M.C.; Hoeng, J. Multi-omics systems toxicology study of mouse lung assessing the effects of aerosols from two heat-not-burn tobacco products and cigarette smoke. Comput. Struct. Biotechnol. J., 2020, 18, 1056-1073.
[http://dx.doi.org/10.1016/j.csbj.2020.04.011] [PMID: 32419906]
[107]
Conesa, A.; Beck, S. Making multi-omics data accessible to researchers. Sci. Data, 2019, 6(1), 251.
[http://dx.doi.org/10.1038/s41597-019-0258-4] [PMID: 31672978]
[108]
Lee, T.Y.; Huang, K.Y.; Chuang, C.H.; Lee, C.Y.; Chang, T.H. Incorporating deep learning and multi-omics autoencoding for analysis of lung adenocarcinoma prognostication. Comput. Biol. Chem., 2020, 87, 107277.
[http://dx.doi.org/10.1016/j.compbiolchem.2020.107277] [PMID: 32512487]
[109]
Djeddi, S.; Reiss, D.; Menuet, A.; Freismuth, S.; de Carvalho Neves, J.; Djerroud, S.; Massana-Muñoz, X.; Sosson, A.S.; Kretz, C.; Raffelsberger, W.; Keime, C.; Dorchies, O.M.; Thompson, J.; Laporte, J. Multi-omics comparisons of different forms of centronuclear myopathies and the effects of several therapeutic strategies. Mol. Ther., 2021, 29(8), 2514-2534.
[http://dx.doi.org/10.1016/j.ymthe.2021.04.033] [PMID: 33940157]
[110]
Krassowski, M.; Das, V.; Sahu, S.K.; Misra, B.B. State of the field in multi-omics research: from computational needs to data mining and sharing. Front. Genet., 2020, 11, 610798.
[http://dx.doi.org/10.3389/fgene.2020.610798] [PMID: 33362867]
[111]
Tortorella, S.; Servili, M.; Toschi, T.G.; Cruciani, G.; Camacho, J. Subspace discriminant index to expedite exploration of multi-class omics data. Chemom. Intell. Lab. Syst., 2020, 206, 104160.
[http://dx.doi.org/10.1016/j.chemolab.2020.104160]
[112]
Sperlea, T.; Philip Schenk, J.; Dreßler, H.; Beisser, D.; Hattab, G.; Boenigk, J.; Heider, D. Multi-omics analysis in a network context. Syst. Med. (New Rochelle), 2021, 1, 224-233.
[http://dx.doi.org/10.1101/2021.11.17.468820]
[113]
Ahmed, R.; Augustine, R.; Valera, E.; Ganguli, A.; Mesaeli, N.; Ahmad, I.S.; Bashir, R.; Hasan, A. Spatial mapping of cancer tissues by OMICS technologies. Biochim. Biophys. Acta Rev. Cancer, 2022, 1877(1), 188663.
[http://dx.doi.org/10.1016/j.bbcan.2021.188663] [PMID: 34861353]
[114]
Reska, D.; Czajkowski, M.; Jurczuk, K.; Boldak, C.; Kwedlo, W.; Bauer, W.; Koszelew, J.; Kretowski, M. Integration of solutions and services for multi-omics data analysis towards personalized medicine. Biocybern. Biomed. Eng., 2021, 41(4), 1646-1663.
[http://dx.doi.org/10.1016/j.bbe.2021.10.005]
[115]
Yan, R.; Gu, C.; You, D.; Huang, Z.; Qian, J.; Yang, Q.; Cheng, X.; Zhang, L.; Wang, H.; Wang, P.; Guo, F. Decoding dynamic epigenetic landscapes in human oocytes using single-cell multi-omics sequencing. Cell Stem Cell, 2021, 28(9), 1641-1656.e7.
[http://dx.doi.org/10.1016/j.stem.2021.04.012] [PMID: 33957080]
[116]
Solovev, I.; Shaposhnikov, M.; Moskalev, A. Multi-omics approaches to human biological age estimation. Mech. Ageing Dev., 2020, 185, 111192.
[http://dx.doi.org/10.1016/j.mad.2019.111192] [PMID: 31786174]
[117]
Brademan, D.R.; Miller, I.J.; Kwiecien, N.W.; Pagliarini, D.J.; Westphall, M.S.; Coon, J.J.; Shishkova, E. Argonaut: A web platform for collaborative multi-omics data visualization and exploration. Patterns, 2020, 1(7), 100122.
[http://dx.doi.org/10.1016/j.patter.2020.100122] [PMID: 33154995]
[118]
Song, X.; Liu, J.; Geng, N.; Shan, Y.; Zhang, B.; Zhao, B.; Ni, Y.; Liang, Z.; Chen, J.; Zhang, L.; Zhang, Y. Multi-omics analysis to reveal disorders of cell metabolism and integrin signaling pathways induced by PM2.5. J. Hazard. Mater., 2022, 424(Pt C), 127573.
[http://dx.doi.org/10.1016/j.jhazmat.2021.127573] [PMID: 34753055]
[119]
Wang, P.; Ng, Q.X.; Zhang, B.; Wei, Z.; Hassan, M.; He, Y.; Ong, C.N. Employing multi-omics to elucidate the hormetic response against oxidative stress exerted by nC60 on Daphnia pulex. Environ. Pollut., 2019, 251, 22-29.
[http://dx.doi.org/10.1016/j.envpol.2019.04.097] [PMID: 31071629]
[120]
Rawle, R.A.; Hamerly, T.; Tripet, B.P.; Giannone, R.J.; Wurch, L.; Hettich, R.L.; Podar, M.; Copié, V.; Bothner, B. Multi-omics analysis provides insight to the Ignicoccus hospitalis-Nanoarchaeum equitans association. Biochim. Biophys. Acta, Gen. Subj., 2017, 1861(9), 2218-2227.
[http://dx.doi.org/10.1016/j.bbagen.2017.06.001] [PMID: 28591626]
[121]
Beale, D.J.; Crosswell, J.; Karpe, A.V.; Ahmed, W.; Williams, M.; Morrison, P.D.; Metcalfe, S.; Staley, C.; Sadowsky, M.J.; Palombo, E.A.; Steven, A.D.L. A multi-omics based ecological analysis of coastal marine sediments from Gladstone, in Australia’s Central Queensland, and Heron Island, a nearby fringing platform reef. Sci. Total Environ., 2017, 609, 842-853.
[http://dx.doi.org/10.1016/j.scitotenv.2017.07.184] [PMID: 28768216]
[122]
Huang, S.S.Y.; Benskin, J.P.; Veldhoen, N.; Chandramouli, B.; Butler, H.; Helbing, C.C.; Cosgrove, J.R. A multi-omic approach to elucidate low-dose effects of xenobiotics in zebrafish (Danio rerio) larvae. Aquat. Toxicol., 2017, 182, 102-112.
[http://dx.doi.org/10.1016/j.aquatox.2016.11.016] [PMID: 27886581]
[123]
Lovino, M.; Randazzo, V.; Ciravegna, G.; Barbiero, P.; Ficarra, E.; Cirrincione, G. A survey on data integration for multi-omics sample clustering. Neurocomputing, 2021, 488, 494-508.
[http://dx.doi.org/10.1016/j.neucom.2021.11.094]
[124]
Tyler, S.R.; Chun, Y.; Ribeiro, V.M.; Grishina, G.; Grishin, A.; Hoffman, G.E.; Do, A.N.; Bunyavanich, S. Merged Affinity Network Association Clustering: Joint multi-omic/clinical clustering to identify disease endotypes. Cell Rep., 2021, 35(2), 108975.
[http://dx.doi.org/10.1016/j.celrep.2021.108975] [PMID: 33852839]
[125]
Wörheide, M.A.; Krumsiek, J.; Kastenmüller, G.; Arnold, M. Multi-omics integration in biomedical research - A metabolomics-centric review. Anal. Chim. Acta, 2021, 1141, 144-162.
[http://dx.doi.org/10.1016/j.aca.2020.10.038] [PMID: 33248648]
[126]
Xu, H.; Gao, L.; Huang, M.; Duan, R. A network embedding based method for partial multi-omics integration in cancer subtyping. Methods, 2021, 192, 67-76.
[http://dx.doi.org/10.1016/j.ymeth.2020.08.001] [PMID: 32805397]
[127]
Argelaguet, R.; Velten, B.; Arnol, D.; Dietrich, S.; Zenz, T.; Marioni, J.C.; Buettner, F.; Huber, W.; Stegle, O. Multi-Omics Factor Analysis-A framework for unsupervised integration of multi-omics data sets. Mol. Syst. Biol., 2018, 14(6), e8124.
[http://dx.doi.org/10.15252/msb.20178124] [PMID: 29925568]
[128]
Tan, M.S.; Cheah, P.L.; Chin, A.V.; Looi, L.M.; Chang, S.W. A review on omics-based biomarkers discovery for Alzheimer’s disease from the bioinformatics perspectives: Statistical approach vs machine learning approach. Comput. Biol. Med., 2021, 139, 104947.
[http://dx.doi.org/10.1016/j.compbiomed.2021.104947] [PMID: 34678481]
[129]
Zhang, S.; Zhang, J.; An, Y.; Zeng, X.; Qin, Z.; Zhao, Y.; Xu, H.; Liu, B. Multi-omics approaches identify SF3B3 and SIRT3 as candidate autophagic regulators and druggable targets in invasive breast carcinoma. Acta Pharm. Sin. B, 2021, 11(5), 1227-1245.
[http://dx.doi.org/10.1016/j.apsb.2020.12.013] [PMID: 34094830]
[130]
Borgmann-Winter, K.E.; Wang, K.; Bandyopadhyay, S.; Torshizi, A.D.; Blair, I.A.; Hahn, C.G. The proteome and its dynamics: A missing piece for integrative multi-omics in schizophrenia. Schizophr. Res., 2020, 217, 148-161.
[http://dx.doi.org/10.1016/j.schres.2019.07.025] [PMID: 31416743]
[131]
Yu, J.; Peng, J.; Chi, H. Systems immunology: Integrating multi-omics data to infer regulatory networks and hidden drivers of immunity. Curr. Opin. Syst. Biol., 2019, 15, 19-29.
[http://dx.doi.org/10.1016/j.coisb.2019.03.003] [PMID: 32789283]
[132]
Wozniak, J.M.; Mills, R.H.; Olson, J.; Caldera, J.R.; Sepich-Poore, G.D.; Carrillo-Terrazas, M.; Tsai, C.M.; Vargas, F.; Knight, R.; Dorrestein, P.C.; Liu, G.Y.; Nizet, V.; Sakoulas, G.; Rose, W.; Gonzalez, D.J. Mortality risk profiling of Staphylococcus aureus bacteremia by multi-omic serum analysis reveals early predictive and pathogenic signatures. Cell, 2020, 182(5), 1311-1327.e14.
[http://dx.doi.org/10.1016/j.cell.2020.07.040] [PMID: 32888495]
[133]
Hale, V.L.; Jeraldo, P.; Mundy, M.; Yao, J.; Keeney, G.; Scott, N.; Cheek, E.H.; Davidson, J.; Greene, M.; Martinez, C.; Lehman, J.; Pettry, C.; Reed, E.; Lyke, K.; White, B.A.; Diener, C.; Resendis-Antonio, O.; Gransee, J.; Dutta, T.; Petterson, X.M.; Boardman, L.; Larson, D.; Nelson, H.; Chia, N. Synthesis of multi-omic data and community metabolic models reveals insights into the role of hydrogen sulfide in colon cancer. Methods, 2018, 149, 59-68.
[http://dx.doi.org/10.1016/j.ymeth.2018.04.024] [PMID: 29704665]
[134]
Haas, R.; Zelezniak, A.; Iacovacci, J.; Kamrad, S.; Townsend, S.; Ralser, M. Designing and interpreting ‘multi-omic’ experiments that may change our understanding of biology. Curr. Opin. Syst. Biol., 2017, 6, 37-45.
[http://dx.doi.org/10.1016/j.coisb.2017.08.009] [PMID: 32923746]
[135]
Ma, B.; Meng, F.; Yan, G.; Yan, H.; Chai, B.; Song, F. Diagnostic classification of cancers using extreme gradient boosting algorithm and multi-omics data. Comput. Biol. Med., 2020, 121, 103761.
[http://dx.doi.org/10.1016/j.compbiomed.2020.103761] [PMID: 32339094]
[136]
Bagante, F.; Spolverato, G.; Ruzzenente, A.; Luchini, C.; Tsilimigras, D.I.; Campagnaro, T.; Conci, S.; Corbo, V.; Scarpa, A.; Guglielmi, A.; Pawlik, T.M. Artificial neural networks for multi-omics classifications of hepato-pancreato-biliary cancers: towards the clinical application of genetic data. Eur. J. Cancer, 2021, 148, 348-358.
[http://dx.doi.org/10.1016/j.ejca.2021.01.049] [PMID: 33774439]
[137]
Seal, D.B.; Das, V.; Goswami, S.; De, R.K. Estimating gene expression from DNA methylation and copy number variation: A deep learning regression model for multi-omics integration. Genomics, 2020, 112(4), 2833-2841.
[http://dx.doi.org/10.1016/j.ygeno.2020.03.021] [PMID: 32234433]
[138]
Ma, A.; McDermaid, A.; Xu, J.; Chang, Y.; Ma, Q. Integrative methods and practical challenges for single-cell multi-omics. Trends Biotechnol., 2020, 38(9), 1007-1022.
[http://dx.doi.org/10.1016/j.tibtech.2020.02.013] [PMID: 32818441]
[139]
Lee, J.W.J.; Plichta, D.; Hogstrom, L.; Borren, N.Z.; Lau, H.; Gregory, S.M.; Tan, W.; Khalili, H.; Clish, C.; Vlamakis, H.; Xavier, R.J.; Ananthakrishnan, A.N. Multi-omics reveal microbial determinants impacting responses to biologic therapies in inflammatory bowel disease. Cell Host Microbe, 2021, 29(8), 1294-1304.e4.
[http://dx.doi.org/10.1016/j.chom.2021.06.019] [PMID: 34297922]
[140]
Jiang, J.; Xing, F.; Wang, C.; Zeng, X.; Zou, Q. Investigation and development of maize fused network analysis with multi-omics. Plant Physiol. Biochem., 2019, 141, 380-387.
[http://dx.doi.org/10.1016/j.plaphy.2019.06.016] [PMID: 31220804]
[141]
Backman, M.; Flenkenthaler, F.; Blutke, A.; Dahlhoff, M.; Ländström, E.; Renner, S.; Philippou-Massier, J.; Krebs, S.; Rathkolb, B.; Prehn, C.; Grzybek, M.; Coskun, Ü.; Rothe, M.; Adamski, J.; de Angelis, M.H.; Wanke, R.; Fröhlich, T.; Arnold, G.J.; Blum, H.; Wolf, E. Multi-omics insights into functional alterations of the liver in insulin-deficient diabetes mellitus. Mol. Metab., 2019, 26, 30-44.
[http://dx.doi.org/10.1016/j.molmet.2019.05.011] [PMID: 31221621]
[142]
Lin, W.R.; Tan, S.I.; Hsiang, C.C.; Sung, P.K.; Ng, I.S. Challenges and opportunity of recent genome editing and multi-omics in cyanobacteria and microalgae for biorefinery. Bioresour. Technol., 2019, 291, 121932.
[http://dx.doi.org/10.1016/j.biortech.2019.121932] [PMID: 31387837]
[143]
Dasouki, M.; Alaiya, A.; ElAmin, T.; Shinwari, Z.; Monies, D.; Abouelhoda, M.; Jabaan, A.; Almourfi, F.; Rahbeeni, Z.; Alsohaibani, F.; Almohareb, F.; Al-Zahrani, H.; Guzmán Vega, F.J.; Arold, S.T.; Aljurf, M.; Ahmed, S.O. Comprehensive multi-omics analysis of G6PC3 deficiency-related congenital neutropenia with inflammatory bowel disease. iScience, 2021, 24(3), 102214.
[http://dx.doi.org/10.1016/j.isci.2021.102214] [PMID: 33748703]
[144]
Liu, J.; Yan, Y.; Yan, J.; Wang, J.; Wei, J.; Xiao, J.; Zeng, Y.; Feng, H. Multi-omics analysis revealed crucial genes and pathways associated with black carp antiviral innate immunity. Fish Shellfish Immunol., 2020, 106, 724-732.
[http://dx.doi.org/10.1016/j.fsi.2020.08.047] [PMID: 32871249]
[145]
Scala, G.; Kinaret, P.; Marwah, V.; Sund, J.; Fortino, V.; Greco, D. Multi-omics analysis of ten carbon nanomaterials effects highlights cell type specific patterns of molecular regulation and adaptation. NanoImpact, 2018, 11, 99-108.
[http://dx.doi.org/10.1016/j.impact.2018.05.003] [PMID: 32140619]
[146]
Kappler, L.; Lehmann, R. Mass-spectrometric multi-omics linked to function – State-of-the-art investigations of mitochondria in systems medicine. Trends Analyt. Chem., 2019, 119, 115635.
[http://dx.doi.org/10.1016/j.trac.2019.115635]
[147]
Silverbush, D.; Cristea, S.; Yanovich-Arad, G.; Geiger, T.; Beerenwinkel, N.; Sharan, R. Simultaneous integration of multi-omics data improved the identification of cancer driver modules. Cell Syst., 2019, 8(5), 456-466.e5.
[http://dx.doi.org/10.1016/j.cels.2019.04.005] [PMID: 31103572]
[148]
Hatchwell, L.; Harney, D.J.; Cielesh, M.; Young, K.; Koay, Y.C.; O’Sullivan, J.F.; Larance, M. Multi-omics analysis of the intermittent fasting response in mice identifies an unexpected role for HNF4α. Cell Rep., 2020, 30(10), 3566-3582.e4.
[http://dx.doi.org/10.1016/j.celrep.2020.02.051] [PMID: 32160557]
[149]
Deng, Y.; Ruan, Y.; Ma, B.; Timmons, M.B.; Lu, H.; Xu, X.; Zhao, H.; Yin, X. Multi-omics analysis reveals niche and fitness differences in typical denitrification microbial aggregations. Environ. Int., 2019, 132, 105085.
[http://dx.doi.org/10.1016/j.envint.2019.105085] [PMID: 31415965]
[150]
Pan, D.; Jia, D. Application of single-cell multi-omics in dissecting cancer cell plasticity and tumor heterogeneity. Front. Mol. Biosci., 2021, 8, 757024.
[http://dx.doi.org/10.3389/fmolb.2021.757024] [PMID: 34722635]
[151]
Wu, S.; Chen, D.; Snyder, M.P. Network biology bridges the gaps between quantitative genetics and multi-omics to map complex diseases. Curr. Opin. Chem. Biol., 2022, 66, 102101.
[http://dx.doi.org/10.1016/j.cbpa.2021.102101] [PMID: 34861483]
[152]
Dugourd, A.; Kuppe, C.; Sciacovelli, M.; Gjerga, E.; Gabor, A.; Emdal, K.B.; Vieira, V.; Bekker-Jensen, D.B.; Kranz, J.; Bindels, E.M.J.; Costa, A.S.H.; Sousa, A.; Beltrao, P.; Rocha, M.; Olsen, J.V.; Frezza, C.; Kramann, R.; Saez-Rodriguez, J. Causal integration of multi-omics data with prior knowledge to generate mechanistic hypotheses. Mol. Syst. Biol., 2021, 17(1), e9730.
[http://dx.doi.org/10.15252/msb.20209730] [PMID: 33502086]
[153]
Lau, E.; Cao, Q.; Lam, M.P.Y.; Wang, J.; Ng, D.C.M.; Bleakley, B.J.; Lee, J.M.; Liem, D.A.; Wang, D.; Hermjakob, H.; Ping, P. Integrated omics dissection of proteome dynamics during cardiac remodeling. Nat. Commun., 2018, 9(1), 120.
[http://dx.doi.org/10.1038/s41467-017-02467-3] [PMID: 29317621]
[154]
Reich, M.; Liefeld, T.; Gould, J.; Lerner, J.; Tamayo, P.; Mesirov, J.P. GenePattern 2.0. Nat. Genet., 2006, 38(5), 500-501.
[http://dx.doi.org/10.1038/ng0506-500] [PMID: 16642009]
[155]
Fisch, K.M.; Meißner, T.; Gioia, L.; Ducom, J.C.; Carland, T.M.; Loguercio, S.; Su, A.I. Omics Pipe: a community-based framework for reproducible multi-omics data analysis. Bioinformatics, 2015, 31(11), 1724-1728.
[http://dx.doi.org/10.1093/bioinformatics/btv061] [PMID: 25637560]
[156]
Afgan, E.; Baker, D.; Batut, B.; van den Beek, M.; Bouvier, D.; Čech, M.; Chilton, J.; Clements, D.; Coraor, N.; Grüning, B.A.; Guerler, A.; Hillman-Jackson, J.; Hiltemann, S.; Jalili, V.; Rasche, H.; Soranzo, N.; Goecks, J.; Taylor, J.; Nekrutenko, A.; Blankenberg, D. The Galaxy platform for accessible, reproducible and collaborative biomedical analyses: 2018 update. Nucleic Acids Res., 2018, 46(W1), W537-W544.
[http://dx.doi.org/10.1093/nar/gky379]
[157]
Yu, C.; Qi, X.; Lin, Y.; Li, Y.; Shen, B. iODA: An integrated tool for analysis of cancer pathway consistency from heterogeneous multi-omics data. J. Biomed. Inform., 2020, 112, 103605.
[http://dx.doi.org/10.1016/j.jbi.2020.103605] [PMID: 33096244]
[158]
Dierickx, S.; Maes, K.; Roelants, S.L.K.W.; Pomian, B.; Van Meulebroek, L.; De Maeseneire, S.L.; Vanhaecke, L.; Soetaert, W.K. A multi-omics study to boost continuous bolaform sophorolipid production. N. Biotechnol., 2022, 66, 107-115.
[http://dx.doi.org/10.1016/j.nbt.2021.11.002] [PMID: 34774786]
[159]
Xu, C.; Liu, D.; Zhang, L.; Xu, Z.; He, W.; Jiang, H.; Zheng, M.; Qiao, N. AutoOmics: New multimodal approach for multi-omics research. Artif. Intell. Life Sci., 2021, 1, 100012.
[http://dx.doi.org/10.1016/j.ailsci.2021.100012]
[160]
Menyhárt, O.; Győrffy, B. Multi-omics approaches in cancer research with applications in tumor subtyping, prognosis, and diagnosis. Comput. Struct. Biotechnol. J., 2021, 19, 949-960.
[http://dx.doi.org/10.1016/j.csbj.2021.01.009] [PMID: 33613862]
[161]
Allendes Osorio, R.S.; Nyström-Persson, J.T.; Nojima, Y.; Kosugi, Y.; Mizuguchi, K.; Natsume-Kitatani, Y. Panomicon: A web-based environment for interactive, visual analysis of multi-omics data. Heliyon, 2020, 6(8), e04618.
[http://dx.doi.org/10.1016/j.heliyon.2020.e04618] [PMID: 32904262]
[162]
Picard, M.; Scott-Boyer, M.P.; Bodein, A.; Périn, O.; Droit, A. Integration strategies of multi-omics data for machine learning analysis. Comput. Struct. Biotechnol. J., 2021, 19, 3735-3746.
[http://dx.doi.org/10.1016/j.csbj.2021.06.030] [PMID: 34285775]
[163]
Subramanian, I.; Verma, S.; Kumar, S.; Jere, A.; Anamika, K. Multi-omics data integration, interpretation, and its application. Bioinform. Biol. Insights, 2022, 14, 1-24.
[http://dx.doi.org/10.1177/1177932219899051] [PMID: 32076369]
[164]
Bingham, G.C.; Lee, F.; Naba, A.; Barker, T.H. Spatial-omics: Novel approaches to probe cell heterogeneity and extracellular matrix biology. Matrix Biol., 2020, 91-92, 152-166.
[http://dx.doi.org/10.1016/j.matbio.2020.04.004] [PMID: 32416243]
[165]
Wolters, J.E.J.; van Breda, S.G.J.; Grossmann, J.; Fortes, C.; Caiment, F.; Kleinjans, J.C.S. Integrated ‘omics analysis reveals new drug-induced mitochondrial perturbations in human hepatocytes. Toxicol. Lett., 2018, 289, 1-13.
[http://dx.doi.org/10.1016/j.toxlet.2018.02.026] [PMID: 29501571]
[166]
Corujo, M.; Pla, M.; van Dijk, J.; Voorhuijzen, M.; Staats, M.; Slot, M.; Lommen, A.; Barros, E.; Nadal, A.; Puigdomènech, P.; Paz, J.L.L.; van der Voet, H.; Kok, E. Use of omics analytical methods in the study of genetically modified maize varieties tested in 90 days feeding trials. Food Chem., 2019, 292, 359-371.
[http://dx.doi.org/10.1016/j.foodchem.2018.05.109] [PMID: 31054688]
[167]
Dihazi, H.; Asif, A.R.; Beißbarth, T.; Bohrer, R.; Feussner, K.; Feussner, I.; Jahn, O.; Lenz, C.; Majcherczyk, A.; Schmidt, B.; Schmitt, K.; Urlaub, H.; Valerius, O. Integrative omics-From data to biology. Expert Rev. Proteomics, 2018, 15(6), 463-466.
[http://dx.doi.org/10.1080/14789450.2018.1476143] [PMID: 29757692]
[168]
Leung Kwan, K.K.; Wong, T.Y.; Wu, Q.Y.; Xia Dong, T.T.; Lam, H.; Keung Tsim, K.W. Mass spectrometry-based multi-omics analysis reveals the thermogenetic regulation of herbal medicine in rat model of yeast-induced fever. J. Ethnopharmacol., 2021, 279, 114382.
[http://dx.doi.org/10.1016/j.jep.2021.114382] [PMID: 34197959]
[169]
Eisfeld, A.J.; Halfmann, P.J.; Wendler, J.P.; Kyle, J.E.; Burnum-Johnson, K.E.; Peralta, Z.; Maemura, T.; Walters, K.B.; Watanabe, T.; Fukuyama, S.; Yamashita, M.; Jacobs, J.M.; Kim, Y.M.; Casey, C.P.; Stratton, K.G.; Webb-Robertson, B.J.M.; Gritsenko, M.A.; Monroe, M.E.; Weitz, K.K.; Shukla, A.K.; Tian, M.; Neumann, G.; Reed, J.L.; van Bakel, H.; Metz, T.O.; Smith, R.D.; Waters, K.M.; N’jai, A.; Sahr, F.; Kawaoka, Y. Multi-platform omics analysis of human ebola virus disease pathogenesis. Cell Host Microbe, 2017, 22(6), 817-829.e8.
[http://dx.doi.org/10.1016/j.chom.2017.10.011] [PMID: 29154144]
[170]
Shuai, M.; Zuo, L.S.Y.; Miao, Z.; Gou, W.; Xu, F.; Jiang, Z.; Ling, C.; Fu, Y.; Xiong, F.; Chen, Y.; Zheng, J.S. Multi-omics analyses reveal relationships among dairy consumption, gut microbiota and cardiometabolic health. EBioMedicine, 2021, 66, 103284.
[http://dx.doi.org/10.1016/j.ebiom.2021.103284] [PMID: 33752125]
[171]
Kel, A.E.; Stegmaier, P.; Valeev, T.; Koschmann, J.; Poroikov, V.; Kel-Margoulis, O.V.; Wingender, E. Multi-omics “upstream analysis” of regulatory genomic regions helps identifying targets against methotrexate resistance of colon cancer. EuPA Open Proteom., 2016, 13, 1-13.
[http://dx.doi.org/10.1016/j.euprot.2016.09.002] [PMID: 29900117]
[172]
Miao, R.; Luo, H.; Zhou, H.; Li, G.; Bu, D.; Yang, X.; Zhao, X.; Zhang, H.; Liu, S.; Zhong, Y.; Zou, Z.; Zhao, Y.; Yu, K.; He, L.; Sang, X.; Zhong, S.; Huang, J.; Wu, Y.; Miksad, R.A.; Robson, S.C.; Jiang, C.; Zhao, Y.; Zhao, H. Identification of prognostic biomarkers in hepatitis B virus-related hepatocellular carcinoma and stratification by integrative multi-omics analysis. J. Hepatol., 2014, 61(4), 840-849.
[http://dx.doi.org/10.1016/j.jhep.2014.05.025] [PMID: 24859455]
[173]
Beata, G. The use of -omics tools for assessing biodeterioration of cultural heritage: A review. J. Cult. Herit., 2020, 45, 351-361.
[http://dx.doi.org/10.1016/j.culher.2020.03.006]
[174]
Porcu, M.; Solinas, C.; Mannelli, L.; Micheletti, G.; Lambertini, M.; Willard-Gallo, K.; Neri, E.; Flanders, A.E.; Saba, L. Radiomics and “radi-…omics” in cancer immunotherapy: a guide for clinicians. Crit. Rev. Oncol. Hematol., 2020, 154, 103068.
[http://dx.doi.org/10.1016/j.critrevonc.2020.103068] [PMID: 32805498]
[175]
Hasin, Y.; Seldin, M.; Lusis, A. Multi-omics approaches to disease. Genome Biol., 2017, 18(1), 83.
[http://dx.doi.org/10.1186/s13059-017-1215-1] [PMID: 28476144]
[176]
Graw, S.; Chappell, K.; Washam, C.L.; Gies, A.; Bird, J.; Robeson, M.S., II; Byrum, S.D. Multi-omics data integration considerations and study design for biological systems and disease. Mol. Omics, 2021, 17(2), 170-185.
[http://dx.doi.org/10.1039/D0MO00041H] [PMID: 33347526]

© 2024 Bentham Science Publishers | Privacy Policy