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Journal articles on the topic 'Omics techniques'

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Published: 4 June 2021

Last updated: 12 June 2025

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Akula, Siva Prasad, Raghava Naidu Miriyala, Hanuman Thota, Allam Appa Rao, and Srinubabu Gedela. "Techniques for integrating -omics data." Bioinformation 3, no. 6 (2009): 284–86. http://dx.doi.org/10.6026/97320630003284.

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Biedroń, Aleksandra, Paulina Malinowska, and Maciej Gawlik. "Utilizing of „omics” techniques in modern anti-doping analysis." Farmacja Polska 75, no. 7 (2019): 403–8. http://dx.doi.org/10.32383/farmpol/116128.

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Lancaster, Samuel M., Akshay Sanghi, Si Wu, and Michael P. Snyder. "A Customizable Analysis Flow in Integrative Multi-Omics." Biomolecules 10, no. 12 (2020): 1606. http://dx.doi.org/10.3390/biom10121606.

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The number of researchers using multi-omics is growing. Though still expensive, every year it is cheaper to perform multi-omic studies, often exponentially so. In addition to its increasing accessibility, multi-omics reveals a view of systems biology to an unprecedented depth. Thus, multi-omics can be used to answer a broad range of biological questions in finer resolution than previous methods. We used six omic measurements—four nucleic acid (i.e., genomic, epigenomic, transcriptomics, and metagenomic) and two mass spectrometry (proteomics and metabolomics) based—to highlight an analysis workflow on this type of data, which is often vast. This workflow is not exhaustive of all the omic measurements or analysis methods, but it will provide an experienced or even a novice multi-omic researcher with the tools necessary to analyze their data. This review begins with analyzing a single ome and study design, and then synthesizes best practices in data integration techniques that include machine learning. Furthermore, we delineate methods to validate findings from multi-omic integration. Ultimately, multi-omic integration offers a window into the complexity of molecular interactions and a comprehensive view of systems biology.

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Daliri, Eric Banan-Mwine, Fred Kwame Ofosu, Ramachandran Chelliah, Byong H. Lee, and Deog-Hwan Oh. "Challenges and Perspective in Integrated Multi-Omics in Gut Microbiota Studies." Biomolecules 11, no. 2 (2021): 300. http://dx.doi.org/10.3390/biom11020300.

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The advent of omic technology has made it possible to identify viable but unculturable micro-organisms in the gut. Therefore, application of multi-omic technologies in gut microbiome studies has become invaluable for unveiling a comprehensive interaction between these commensals in health and disease. Meanwhile, despite the successful identification of many microbial and host–microbial cometabolites that have been reported so far, it remains difficult to clearly identify the origin and function of some proteins and metabolites that are detected in gut samples. However, the application of single omic techniques for studying the gut microbiome comes with its own challenges which may be overcome if a number of different omics techniques are combined. In this review, we discuss our current knowledge about multi-omic techniques, their challenges and future perspective in this field of gut microbiome studies.

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Yoon, Sang Jun, Chae Bin Lee, Soon Uk Chae, Seong Jun Jo, and Soo Kyung Bae. "The Comprehensive “Omics” Approach from Metabolomics to Advanced Omics for Development of Immune Checkpoint Inhibitors: Potential Strategies for Next Generation of Cancer Immunotherapy." International Journal of Molecular Sciences 22, no. 13 (2021): 6932. http://dx.doi.org/10.3390/ijms22136932.

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In the past decade, immunotherapies have been emerging as an effective way to treat cancer. Among several categories of immunotherapies, immune checkpoint inhibitors (ICIs) are the most well-known and widely used options for cancer treatment. Although several studies continue, this treatment option has yet to be developed into a precise application in the clinical setting. Recently, omics as a high-throughput technique for understanding the genome, transcriptome, proteome, and metabolome has revolutionized medical research and led to integrative interpretation to advance our understanding of biological systems. Advanced omics techniques, such as multi-omics, single-cell omics, and typical omics approaches, have been adopted to investigate various cancer immunotherapies. In this review, we highlight metabolomic studies regarding the development of ICIs involved in the discovery of targets or mechanisms of action and assessment of clinical outcomes, including drug response and resistance and propose biomarkers. Furthermore, we also discuss the genomics, proteomics, and advanced omics studies providing insights and comprehensive or novel approaches for ICI development. The overview of ICI studies suggests potential strategies for the development of other cancer immunotherapies using omics techniques in future studies.

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Ahmad, Ashar, and Holger Fröhlich. "Integrating Heterogeneous omics Data via Statistical Inference and Learning Techniques." Genomics and Computational Biology 2, no. 1 (2016): 32. http://dx.doi.org/10.18547/gcb.2016.vol2.iss1.e32.

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Multi-omics studies are believed to provide a more comprehensive picture of a complex biological system than traditional studies with one omics data source. However, from a statistical point of view data integration implies non-trivial challenges. In this review, we highlight recent statistical inference and learning techniques that have been devised in this context. In the first part of our article, we focus on techniques to identify a relevant biological sub-system based on combined omics data. In the second part of our article we ask, in which way integrated omics data could be used for better personalized patient treatment in a supervised as well as unsupervised learning setting. Different classes of algorithms are discussed for both application tasks. Existing and future challenges for data integration methods are pointed out.

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Hu, Yisi, Shuyan Sun, Huizhong Fan, Wenliang Zhou, and Fuwen Wei. "Exploring marine endosymbiosis systems with omics techniques." Science China Life Sciences 64, no. 6 (2021): 1013–16. http://dx.doi.org/10.1007/s11427-021-1925-1.

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Ceciliani, F., P. Roccabianca, C. Giudice, and C. Lecchi. "Application of post-genomic techniques in dog cancer research." Molecular BioSystems 12, no. 9 (2016): 2665–79. http://dx.doi.org/10.1039/c6mb00227g.

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We present in this review the most recent achievement in the application of transcriptomics, proteomics and metabolomics to canine cancer research. The protocols to recover material suitable for omics analyses from formalin-fixed, paraffin-embedded tissues are highlighted, together with the potential of omics in veterinary cancer diagnostics.

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Zhang, Hong-Wei, Chao Lv, Li-Jun Zhang, et al. "Application of omics- and multi-omics-based techniques for natural product target discovery." Biomedicine & Pharmacotherapy 141 (September 2021): 111833. http://dx.doi.org/10.1016/j.biopha.2021.111833.

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Lana, Alessandro, Valentina Longo, Alessandra Dalmasso, Angelo D’Alessandro, Maria Teresa Bottero, and Lello Zolla. "Omics integrating physical techniques: Aged Piedmontese meat analysis." Food Chemistry 172 (April 2015): 731–41. http://dx.doi.org/10.1016/j.foodchem.2014.09.146.

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Eicher, Tara, Garrett Kinnebrew, Andrew Patt, et al. "Metabolomics and Multi-Omics Integration: A Survey of Computational Methods and Resources." Metabolites 10, no. 5 (2020): 202. http://dx.doi.org/10.3390/metabo10050202.

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As researchers are increasingly able to collect data on a large scale from multiple clinical and omics modalities, multi-omics integration is becoming a critical component of metabolomics research. This introduces a need for increased understanding by the metabolomics researcher of computational and statistical analysis methods relevant to multi-omics studies. In this review, we discuss common types of analyses performed in multi-omics studies and the computational and statistical methods that can be used for each type of analysis. We pinpoint the caveats and considerations for analysis methods, including required parameters, sample size and data distribution requirements, sources of a priori knowledge, and techniques for the evaluation of model accuracy. Finally, for the types of analyses discussed, we provide examples of the applications of corresponding methods to clinical and basic research. We intend that our review may be used as a guide for metabolomics researchers to choose effective techniques for multi-omics analyses relevant to their field of study.

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Pinu, Farhana R., David J. Beale, Amy M. Paten, et al. "Systems Biology and Multi-Omics Integration: Viewpoints from the Metabolomics Research Community." Metabolites 9, no. 4 (2019): 76. http://dx.doi.org/10.3390/metabo9040076.

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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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Ugidos, Manuel, Sonia Tarazona, José M. Prats-Montalbán, Alberto Ferrer, and Ana Conesa. "MultiBaC: A strategy to remove batch effects between different omic data types." Statistical Methods in Medical Research 29, no. 10 (2020): 2851–64. http://dx.doi.org/10.1177/0962280220907365.

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Diversity of omic technologies has expanded in the last years together with the number of omic data integration strategies. However, multiomic data generation is costly, and many research groups cannot afford research projects where many different omic techniques are generated, at least at the same time. As most researchers share their data in public repositories, different omic datasets of the same biological system obtained at different labs can be combined to construct a multiomic study. However, data obtained at different labs or moments in time are typically subjected to batch effects that need to be removed for successful data integration. While there are methods to correct batch effects on the same data types obtained in different studies, they cannot be applied to correct lab or batch effects across omics. This impairs multiomic meta-analysis. Fortunately, in many cases, at least one omics platform—i.e. gene expression— is repeatedly measured across labs, together with the additional omic modalities that are specific to each study. This creates an opportunity for batch analysis. We have developed MultiBaC (multiomic Multiomics Batch-effect Correction correction), a strategy to correct batch effects from multiomic datasets distributed across different labs or data acquisition events. Our strategy is based on the existence of at least one shared data type which allows data prediction across omics. We validate this approach both on simulated data and on a case where the multiomic design is fully shared by two labs, hence batch effect correction within the same omic modality using traditional methods can be compared with the MultiBaC correction across data types. Finally, we apply MultiBaC to a true multiomic data integration problem to show that we are able to improve the detection of meaningful biological effects.

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NAKANISHI, Toyofumi. "Applications of -omics Techniques for the Clinical Laboratory Tests." Journal of the Mass Spectrometry Society of Japan 64, no. 4 (2016): 117–20. http://dx.doi.org/10.5702/massspec.s16-25.

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Komatsu, Setsuko, Naoki Shirasaka, and Katsumi Sakata. "‘Omics’ techniques for identifying flooding–response mechanisms in soybean." Journal of Proteomics 93 (November 2013): 169–78. http://dx.doi.org/10.1016/j.jprot.2012.12.016.

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Szulc, Justyna, Anna Otlewska, Tomasz Ruman, et al. "Analysis of paper foxing by newly available omics techniques." International Biodeterioration & Biodegradation 132 (August 2018): 157–65. http://dx.doi.org/10.1016/j.ibiod.2018.03.005.

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Maroli, Amith S., Todd A. Gaines, Michael E. Foley, et al. "Omics in Weed Science: A Perspective from Genomics, Transcriptomics, and Metabolomics Approaches." Weed Science 66, no. 6 (2018): 681–95. http://dx.doi.org/10.1017/wsc.2018.33.

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AbstractModern high-throughput molecular and analytical tools offer exciting opportunities to gain a mechanistic understanding of unique traits of weeds. During the past decade, tremendous progress has been made within the weed science discipline using genomic techniques to gain deeper insights into weedy traits such as invasiveness, hybridization, and herbicide resistance. Though the adoption of newer “omics” techniques such as proteomics, metabolomics, and physionomics has been slow, applications of these omics platforms to study plants, especially agriculturally important crops and weeds, have been increasing over the years. In weed science, these platforms are now used more frequently to understand mechanisms of herbicide resistance, weed resistance evolution, and crop–weed interactions. Use of these techniques could help weed scientists to further reduce the knowledge gaps in understanding weedy traits. Although these techniques can provide robust insights about the molecular functioning of plants, employing a single omics platform can rarely elucidate the gene-level regulation and the associated real-time expression of weedy traits due to the complex and overlapping nature of biological interactions. Therefore, it is desirable to integrate the different omics technologies to give a better understanding of molecular functioning of biological systems. This multidimensional integrated approach can therefore offer new avenues for better understanding of questions of interest to weed scientists. This review offers a retrospective and prospective examination of omics platforms employed to investigate weed physiology and novel approaches and new technologies that can provide holistic and knowledge-based weed management strategies for future.

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Yamada, Ryo, Daigo Okada, Juan Wang, Tapati Basak, and Satoshi Koyama. "Interpretation of omics data analyses." Journal of Human Genetics 66, no. 1 (2020): 93–102. http://dx.doi.org/10.1038/s10038-020-0763-5.

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AbstractOmics studies attempt to extract meaningful messages from large-scale and high-dimensional data sets by treating the data sets as a whole. The concept of treating data sets as a whole is important in every step of the data-handling procedures: the pre-processing step of data records, the step of statistical analyses and machine learning, translation of the outputs into human natural perceptions, and acceptance of the messages with uncertainty. In the pre-processing, the method by which to control the data quality and batch effects are discussed. For the main analyses, the approaches are divided into two types and their basic concepts are discussed. The first type is the evaluation of many items individually, followed by interpretation of individual items in the context of multiple testing and combination. The second type is the extraction of fewer important aspects from the whole data records. The outputs of the main analyses are translated into natural languages with techniques, such as annotation and ontology. The other technique for making the outputs perceptible is visualization. At the end of this review, one of the most important issues in the interpretation of omics data analyses is discussed. Omics studies have a large amount of information in their data sets, and every approach reveals only a very restricted aspect of the whole data sets. The understandable messages from these studies have unavoidable uncertainty.

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Takahashi, Satoshi, Masamichi Takahashi, Shota Tanaka, et al. "A New Era of Neuro-Oncology Research Pioneered by Multi-Omics Analysis and Machine Learning." Biomolecules 11, no. 4 (2021): 565. http://dx.doi.org/10.3390/biom11040565.

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Although the incidence of central nervous system (CNS) cancers is not high, it significantly reduces a patient’s quality of life and results in high mortality rates. A low incidence also means a low number of cases, which in turn means a low amount of information. To compensate, researchers have tried to increase the amount of information available from a single test using high-throughput technologies. This approach, referred to as single-omics analysis, has only been partially successful as one type of data may not be able to appropriately describe all the characteristics of a tumor. It is presently unclear what type of data can describe a particular clinical situation. One way to solve this problem is to use multi-omics data. When using many types of data, a selected data type or a combination of them may effectively resolve a clinical question. Hence, we conducted a comprehensive survey of papers in the field of neuro-oncology that used multi-omics data for analysis and found that most of the papers utilized machine learning techniques. This fact shows that it is useful to utilize machine learning techniques in multi-omics analysis. In this review, we discuss the current status of multi-omics analysis in the field of neuro-oncology and the importance of using machine learning techniques.

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Yunusa, Y. R., and Z. D. Umar. "Effective microbial bioremediation via the multi-omics approach: An overview of trends, problems and prospects." UMYU Journal of Microbiology Research (UJMR) 6, no. 1 (2021): 127–45. http://dx.doi.org/10.47430/ujmr.2161.017.

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Techno-industrial advancements the world over had led to the generation of hazardous environmental pollutants. Microbial bioremediation offers the best alternative for the removal of these pollutants. The most recent advancements in microbial bioremediation were catalyzed by the advent of various tools that enable the study microbes at levels of sophisticated detail, including genome analysis tools (genomics), protocols for analyzing expressed proteins and enzymes or proteomes (proteomics), techniques of analyzing ribonucleic acids (RNAs) transcriptomes (transcriptomics), and tools for analyzing metabolic end products/metabolomes (metabolomics). The twenty first century is witnessing an outpour of developments in the application of omics approaches in effective microbial bioremediation, thus, this paper attempts to review some of the most significant insights gained from relatively recent studies over a period of two decades (2000-2020) in the applications of multi-OMICS in microbial bioremediation, including trends and cutting-edge researches. We aim to highlight, particularly, the challenges that need to be overcome before OMICs approaches are successfully enshrined in microbial bioremediation, especially in developing countries. The strategies for overcoming such challenges, and the prospects achieved were also outlined. In the coming years, we envision further researches involving the application of multi-OMICs approach in microbial bioremediation potentially revolutionizing this field, opening up research avenues, and leading to improvements in bioremediation of polluted environment. Keywords: Biodegradation, Bioremediation; Genomics; Multi-OMICs, OMICs techniques.

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Mangul, Serghei. "Interpreting and integrating big data in the life sciences." Emerging Topics in Life Sciences 3, no. 4 (2019): 335–41. http://dx.doi.org/10.1042/etls20180175.

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Abstract Recent advances in omics technologies have led to the broad applicability of computational techniques across various domains of life science and medical research. These technologies provide an unprecedented opportunity to collect the omics data from hundreds of thousands of individuals and to study the gene–disease association without the aid of prior assumptions about the trait biology. Despite the many advantages of modern omics technologies, interpretations of big data produced by such technologies require advanced computational algorithms. I outline key challenges that biomedical researches are facing when interpreting and integrating big omics data. I discuss the reproducibility aspect of big data analysis in the life sciences and review current practices in reproducible research. Finally, I explain the skills that biomedical researchers need to acquire to independently analyze big omics data.

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Subramanian, Indhupriya, Srikant Verma, Shiva Kumar, Abhay Jere, and Krishanpal Anamika. "Multi-omics Data Integration, Interpretation, and Its Application." Bioinformatics and Biology Insights 14 (January 2020): 117793221989905. http://dx.doi.org/10.1177/1177932219899051.

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To study complex biological processes holistically, it is imperative to take an integrative approach that combines multi-omics data to highlight the interrelationships of the involved biomolecules and their functions. With the advent of high-throughput techniques and availability of multi-omics data generated from a large set of samples, several promising tools and methods have been developed for data integration and interpretation. In this review, we collected the tools and methods that adopt integrative approach to analyze multiple omics data and summarized their ability to address applications such as disease subtyping, biomarker prediction, and deriving insights into the data. We provide the methodology, use-cases, and limitations of these tools; brief account of multi-omics data repositories and visualization portals; and challenges associated with multi-omics data integration.

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Liu, Lin, and Hao Wang. "The Recent Applications and Developments of Bioinformatics and Omics Technologies in Traditional Chinese Medicine." Current Bioinformatics 14, no. 3 (2019): 200–210. http://dx.doi.org/10.2174/1574893614666190102125403.

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Background:Traditional Chinese Medicine (TCM) is widely utilized as complementary health care in China whose acceptance is still hindered by conventional scientific research methodology, although it has been exercised and implemented for nearly 2000 years. Identifying the molecular mechanisms, targets and bioactive components in TCM is a critical step in the modernization of TCM because of the complexity and uniqueness of the TCM system. With recent advances in computational approaches and high throughput technologies, it has become possible to understand the potential TCM mechanisms at the molecular and systematic level, to evaluate the effectiveness and toxicity of TCM treatments. Bioinformatics is gaining considerable attention to unearth the in-depth molecular mechanisms of TCM, which emerges as an interdisciplinary approach owing to the explosive omics data and development of computer science. Systems biology, based on the omics techniques, opens up a new perspective which enables us to investigate the holistic modulation effect on the body.Objective:This review aims to sum up the recent efforts of bioinformatics and omics techniques in the research of TCM including Systems biology, Metabolomics, Proteomics, Genomics and Transcriptomics.Conclusion:Overall, bioinformatics tools combined with omics techniques have been extensively used to scientifically support the ancient practice of TCM to be scientific and international through the acquisition, storage and analysis of biomedical data.

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Kiechle, Frederick L., Xinbo Zhang, and Carol A. Holland-Staley. "The -omics Era and Its Impact." Archives of Pathology & Laboratory Medicine 128, no. 12 (2004): 1337–45. http://dx.doi.org/10.5858/2004-128-1337-toeaii.

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Abstract Objective.—To review the advances in clinically useful molecular biologic techniques and to identify their applications, as presented at the 12th Annual William Beaumont Hospital DNA Symposium. Data Sources.—The 7 manuscripts submitted were reviewed and their major findings were compared with literature on the same or related topics. Study Selection.—Manuscripts address the use of molecular techniques in the detection of severe acute respiratory syndrome (SARS) and bacterial ribosome mutations, which may lead to ribosome-targeted drug resistance; pharmacogenomics as a clinical laboratory service and example of warfarin dosing using CYP2C9 mutation analysis; definition of the potential of cytosine arabinoside incorporation into DNA to disrupt transcription using an in vitro model of oligonucleotides; use of laser capture microdissection to isolate solid tumor cells free of nontumor cells; and molecular methods used to classify lymphomas. Data Synthesis.—Two current issues related to the use of molecular tests in the clinical laboratories are (1) decentralization of molecular-based testing to a variety of nonmolecular laboratories and (2) need for wider acceptance of molecular-based testing through its incorporation in clinical practice guidelines. Molecular methods have had a major impact on infectious disease through the rapid identification of new infectious agents, SARS, and the characterization of drug resistance. Pharmacogenomics identifies the genetic basis for heritable and interindividual variation in response to drugs. The incorporation of the nucleoside analog, cytosine arabinoside, into DNA leads to local perturbation of DNA structure and reduces the ability of transcription factors to bind to their specific DNA binding elements as measured by electrophoretic mobility shift assays. Laser capture microdissection of tumor cells can provide an adequate number of cells for whole genome amplification. Gene expression microassay profiles of various lymphomas have modified classification systems and predict prognosis and response to therapy. Conclusions.—The current -omics era will continue to emphasize the use of microarrays and database software for genomic, transcriptomic, and proteomic screening to search for a useful clinical assay. The number of molecular pathologic techniques will expand as additional disease-associated mutations are defined.

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Patterson, Eric L., Christopher Saski, Anita Küpper, Roland Beffa, and Todd A. Gaines. "Omics Potential in Herbicide-Resistant Weed Management." Plants 8, no. 12 (2019): 607. http://dx.doi.org/10.3390/plants8120607.

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The rapid development of omics technologies has drastically altered the way biologists conduct research. Basic plant biology and genomics have incorporated these technologies, while some challenges remain for use in applied biology. Weed science, on the whole, is still learning how to integrate omics technologies into the discipline; however, omics techniques are more frequently being implemented in new and creative ways to address basic questions in weed biology as well as the more practical questions of improving weed management. This has been especially true in the subdiscipline of herbicide resistance where important questions are the evolution and genetic basis of herbicide resistance. This review examines the advantages, challenges, potential solutions, and outlook for omics technologies in the discipline of weed science, with examples of how omics technologies will impact herbicide resistance studies and ultimately improve management of herbicide-resistant populations.

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ZHAO, Yan, and Yan-Yan LI. "Unintended effects assessment of genetically modified crops using omics techniques." Hereditas (Beijing) 35, no. 12 (2013): 1360–67. http://dx.doi.org/10.3724/sp.j.1005.2013.01360.

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Matuszewska, Eliza, Paweł Dereziński, Agnieszka Klupczyńska, et al. "Characterization of the selected honeybee products based on omics techniques." Journal of Medical Science 88, no. 2 (2019): 129–32. http://dx.doi.org/10.20883/jms.328.

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to comprehensively characterize honeybee venom, royal jelly, propolis, and pollen, by applying advanced analytical and bioinformatics methodologies. Honeybee products (HBP) contain many bioactive components with both beneficial and harmful effects on the human organism. Nevertheless, the overall composition of the HBP remains not fully investigated. Thus, this research is focused on complementary proteomic and metabolomic characterization of biologically active compounds derived from HBP, regarding their toxicological and pharmacological properties. The objectives of the study will be achieved by the application of up to date mass spectrometry techniques. Due to increasing interest in using of HBP in medicine, this project will contribute to improving the safety of HBP‑derived dietary supplements and drugs.

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Yuan Zhang, Yue Cheng, Kebin Jia, and Aidong Zhang. "Opportunities for computational techniques for multi-omics integrated personalized medicine." Tsinghua Science and Technology 19, no. 6 (2014): 545–58. http://dx.doi.org/10.1109/tst.2014.6961025.

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Schroeder, Frank, and Georg Pohnert. "Editorial overview: Omics techniques to map the chemistry of life." Current Opinion in Chemical Biology 36 (February 2017): v—vi. http://dx.doi.org/10.1016/j.cbpa.2017.02.014.

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Yuan, Lei, Fedrick C. Mgomi, Zhenbo Xu, Ni Wang, Guoqing He, and Zhenquan Yang. "Understanding of food biofilms by the application of omics techniques." Future Microbiology 16, no. 4 (2021): 257–69. http://dx.doi.org/10.2217/fmb-2020-0218.

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Biofilms constitute a protective barrier for foodborne pathogens to survive under stressful food processing conditions. Therefore, studies into the development and control of biofilms by novel techniques are vital for the food industry. In recent years, foodomics techniques have been developed for biofilm studies, which contributed to a better understanding of biofilm behavior, physiology, composition, as well as their response to antibiofilm methods at different molecular levels including genes, RNA, proteins and metabolic metabolites. Throughout this review, the main studies where foodomics tools used to explore the mechanisms for biofilm formation, dispersal and elimination were reviewed. The data summarized from relevant studies are important to design novel and appropriate biofilm elimination methods for enhancing food safety at any point of food processing lines.

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Komatsu, Setsuko, and Jesus V. Jorrin-Novo. "Plant Proteomic Research 3.0: Challenges and Perspectives." International Journal of Molecular Sciences 22, no. 2 (2021): 766. http://dx.doi.org/10.3390/ijms22020766.

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Hess, Moritz, Maren Hackenberg, and Harald Binder. "Exploring generative deep learning for omics data using log-linear models." Bioinformatics 36, no. 20 (2020): 5045–53. http://dx.doi.org/10.1093/bioinformatics/btaa623.

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Abstract Motivation Following many successful applications to image data, deep learning is now also increasingly considered for omics data. In particular, generative deep learning not only provides competitive prediction performance, but also allows for uncovering structure by generating synthetic samples. However, exploration and visualization is not as straightforward as with image applications. Results We demonstrate how log-linear models, fitted to the generated, synthetic data can be used to extract patterns from omics data, learned by deep generative techniques. Specifically, interactions between latent representations learned by the approaches and generated synthetic data are used to determine sets of joint patterns. Distances of patterns with respect to the distribution of latent representations are then visualized in low-dimensional coordinate systems, e.g. for monitoring training progress. This is illustrated with simulated data and subsequently with cortical single-cell gene expression data. Using different kinds of deep generative techniques, specifically variational autoencoders and deep Boltzmann machines, the proposed approach highlights how the techniques uncover underlying structure. It facilitates the real-world use of such generative deep learning techniques to gain biological insights from omics data. Availability and implementation The code for the approach as well as an accompanying Jupyter notebook, which illustrates the application of our approach, is available via the GitHub repository: https://github.com/ssehztirom/Exploring-generative-deep-learning-for-omics-data-by-using-log-linear-models. Supplementary information Supplementary data are available at Bioinformatics online.

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Chappell, Lia, Andrew J. C. Russell, and Thierry Voet. "Single-Cell (Multi)omics Technologies." Annual Review of Genomics and Human Genetics 19, no. 1 (2018): 15–41. http://dx.doi.org/10.1146/annurev-genom-091416-035324.

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Single-cell multiomics technologies typically measure multiple types of molecule from the same individual cell, enabling more profound biological insight than can be inferred by analyzing each molecular layer from separate cells. These single-cell multiomics technologies can reveal cellular heterogeneity at multiple molecular layers within a population of cells and reveal how this variation is coupled or uncoupled between the captured omic layers. The data sets generated by these techniques have the potential to enable a deeper understanding of the key biological processes and mechanisms driving cellular heterogeneity and how they are linked with normal development and aging as well as disease etiology. This review details both established and novel single-cell mono- and multiomics technologies and considers their limitations, applications, and likely future developments.

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Hernández-Vargas, Purificación, Manuel Muñoz, and Francisco Domínguez. "Identifying biomarkers for predicting successful embryo implantation: applying single to multi-OMICs to improve reproductive outcomes." Human Reproduction Update 26, no. 2 (2020): 264–301. http://dx.doi.org/10.1093/humupd/dmz042.

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Abstract BACKGROUND Successful embryo implantation is a complex process that requires the coordination of a series of events, involving both the embryo and the maternal endometrium. Key to this process is the intricate cascade of molecular mechanisms regulated by endocrine, paracrine and autocrine modulators of embryonic and maternal origin. Despite significant progress in ART, implantation failure still affects numerous infertile couples worldwide and fewer than 10% of embryos successfully implant. Improved selection of both the viable embryos and the optimal endometrial phenotype for transfer remains crucial to enhancing implantation chances. However, both classical morphological embryo selection and new strategies incorporated into clinical practice, such as embryonic genetic analysis, morphokinetics or ultrasound endometrial dating, remain insufficient to predict successful implantation. Additionally, no techniques are widely applied to analyse molecular signals involved in the embryo–uterine interaction. More reliable biological markers to predict embryo and uterine reproductive competence are needed to improve pregnancy outcomes. Recent years have seen a trend towards ‘omics’ methods, which enable the assessment of complete endometrial and embryonic molecular profiles during implantation. Omics have advanced our knowledge of the implantation process, identifying potential but rarely implemented biomarkers of successful implantation. OBJECTIVE AND RATIONALE Differences between the findings of published omics studies, and perhaps because embryonic and endometrial molecular signatures were often not investigated jointly, have prevented firm conclusions being reached. A timely review summarizing omics studies on the molecular determinants of human implantation in both the embryo and the endometrium will help facilitate integrative and reliable omics approaches to enhance ART outcomes. SEARCH METHODS In order to provide a comprehensive review of the literature published up to September 2019, Medline databases were searched using keywords pertaining to omics, including ‘transcriptome’, ‘proteome’, ‘secretome’, ‘metabolome’ and ‘expression profiles’, combined with terms related to implantation, such as ‘endometrial receptivity’, ‘embryo viability’ and ‘embryo implantation’. No language restrictions were imposed. References from articles were also used for additional literature. OUTCOMES Here we provide a complete summary of the major achievements in human implantation research supplied by omics approaches, highlighting their potential to improve reproductive outcomes while fully elucidating the implantation mechanism. The review highlights the existence of discrepancies among the postulated biomarkers from studies on embryo viability or endometrial receptivity, even using the same omic analysis. WIDER IMPLICATIONS Despite the huge amount of biomarker information provided by omics, we still do not have enough evidence to link data from all omics with an implantation outcome. However, in the foreseeable future, application of minimally or non-invasive omics tools, together with a more integrative interpretation of uniformly collected data, will help to overcome the difficulties for clinical implementation of omics tools. Omics assays of the embryo and endometrium are being proposed or already being used as diagnostic tools for personalised single-embryo transfer in the most favourable endometrial environment, avoiding the risk of multiple pregnancies and ensuring better pregnancy rates.

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Scala, Giovanni, Antonio Federico, Vittorio Fortino, Dario Greco, and Barbara Majello. "Knowledge Generation with Rule Induction in Cancer Omics." International Journal of Molecular Sciences 21, no. 1 (2019): 18. http://dx.doi.org/10.3390/ijms21010018.

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The explosion of omics data availability in cancer research has boosted the knowledge of the molecular basis of cancer, although the strategies for its definitive resolution are still not well established. The complexity of cancer biology, given by the high heterogeneity of cancer cells, leads to the development of pharmacoresistance for many patients, hampering the efficacy of therapeutic approaches. Machine learning techniques have been implemented to extract knowledge from cancer omics data in order to address fundamental issues in cancer research, as well as the classification of clinically relevant sub-groups of patients and for the identification of biomarkers for disease risk and prognosis. Rule induction algorithms are a group of pattern discovery approaches that represents discovered relationships in the form of human readable associative rules. The application of such techniques to the modern plethora of collected cancer omics data can effectively boost our understanding of cancer-related mechanisms. In fact, the capability of these methods to extract a huge amount of human readable knowledge will eventually help to uncover unknown relationships between molecular attributes and the malignant phenotype. In this review, we describe applications and strategies for the usage of rule induction approaches in cancer omics data analysis. In particular, we explore the canonical applications and the future challenges and opportunities posed by multi-omics integration problems.

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González-Boja, Iranzu, Antonio Viúdez, Saioa Goñi, et al. "Omics Approaches in Pancreatic Adenocarcinoma." Cancers 11, no. 8 (2019): 1052. http://dx.doi.org/10.3390/cancers11081052.

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Pancreatic ductal adenocarcinoma, which represents 80% of pancreatic cancers, is mainly diagnosed when treatment with curative intent is not possible. Consequently, the overall five-year survival rate is extremely dismal—around 5% to 7%. In addition, pancreatic cancer is expected to become the second leading cause of cancer-related death by 2030. Therefore, advances in screening, prevention and treatment are urgently needed. Fortunately, a wide range of approaches could help shed light in this area. Beyond the use of cytological or histological samples focusing in diagnosis, a plethora of new approaches are currently being used for a deeper characterization of pancreatic ductal adenocarcinoma, including genetic, epigenetic, and/or proteo-transcriptomic techniques. Accordingly, the development of new analytical technologies using body fluids (blood, bile, urine, etc.) to analyze tumor derived molecules has become a priority in pancreatic ductal adenocarcinoma due to the hard accessibility to tumor samples. These types of technologies will lead us to improve the outcome of pancreatic ductal adenocarcinoma patients.

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Pietzner, Maik, Tim Kacprowski, and Nele Friedrich. "Empowering thyroid hormone research in human subjects using OMICs technologies." Journal of Endocrinology 238, no. 1 (2018): R13—R29. http://dx.doi.org/10.1530/joe-18-0117.

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OMICs subsume different physiological layers including the genome, transcriptome, proteome and metabolome. Recent advances in analytical techniques allow for the exhaustive determination of biomolecules in all OMICs levels from less invasive human specimens such as blood and urine. Investigating OMICs in deeply characterized population-based or experimental studies has led to seminal improvement of our understanding of genetic determinants of thyroid function, identified putative thyroid hormone target genes and thyroid hormone-induced shifts in the plasma protein and metabolite content. Consequently, plasma biomolecules have been suggested as surrogates of tissue-specific action of thyroid hormones. This review provides a brief introduction to OMICs in thyroid research with a particular focus on metabolomics studies in humans elucidating the important role of thyroid hormones for whole body metabolism in adults.

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Short, Ben. "Cell biologists expand their networks." Journal of Cell Biology 186, no. 3 (2009): 305–11. http://dx.doi.org/10.1083/jcb.200907093.

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High-throughput omics technologies generate huge datasets on the protein, transcript, lipid, and metabolite content of cells. By integrating and analyzing these data, systems biologists study complex networks of physical and functional interactions that go beyond the traditional focus on individual proteins or linear pathways. Many cell biologists have greeted these developments with healthy skepticism, complaining that long lists of genes or “hairballs” of interactions provide little insight into biological questions of genuine meaning. As omics techniques move beyond acquisition into hypothesis-driven applications, the chasm between systems biologists and cell biologists is narrowing and the benefits of working together are increasingly clear. While cell biologists need omics and computer analyses to extend their understanding of biological processes, omics scientists need cell biologists to help them interpret and use their vast amounts of data.

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Kasper, Claudia, David Ribeiro, André M. de Almeida, Catherine Larzul, Laurence Liaubet, and Eduard Murani. "Omics Application in Animal Science—A Special Emphasis on Stress Response and Damaging Behaviour in Pigs." Genes 11, no. 8 (2020): 920. http://dx.doi.org/10.3390/genes11080920.

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Increasing stress resilience of livestock is important for ethical and profitable meat and dairy production. Susceptibility to stress can entail damaging behaviours, a common problem in pig production. Breeding animals with increased stress resilience is difficult for various reasons. First, studies on neuroendocrine and behavioural stress responses in farm animals are scarce, as it is difficult to record adequate phenotypes under field conditions. Second, damaging behaviours and stress susceptibility are complex traits, and their biology is not yet well understood. Dissecting complex traits into biologically better defined, heritable and easily measurable proxy traits and developing biomarkers will facilitate recording these traits in large numbers. High-throughput molecular technologies (“omics”) study the entirety of molecules and their interactions in a single analysis step. They can help to decipher the contributions of different physiological systems and identify candidate molecules that are representative of different physiological pathways. Here, we provide a general overview of different omics approaches and we give examples of how these techniques could be applied to discover biomarkers. We discuss the genetic dissection of the stress response by different omics techniques and we provide examples and outline potential applications of omics tools to understand and prevent outbreaks of damaging behaviours.

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Razzaq, Muhammad Khuram, Muqadas Aleem, Shahid Mansoor, et al. "Omics and CRISPR-Cas9 Approaches for Molecular Insight, Functional Gene Analysis, and Stress Tolerance Development in Crops." International Journal of Molecular Sciences 22, no. 3 (2021): 1292. http://dx.doi.org/10.3390/ijms22031292.

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Plants are regularly exposed to biotic and abiotic stresses that adversely affect agricultural production. Omics has gained momentum in the last two decades, fueled by statistical methodologies, computational capabilities, mass spectrometry, nucleic-acid sequencing, and peptide-sequencing platforms. Functional genomics—especially metabolomics, transcriptomics, and proteomics—have contributed substantially to plant molecular responses to stress. Recent progress in reverse and forward genetics approaches have mediated high-throughput techniques for identifying stress-related genes. Furthermore, web-based genetic databases have mediated bioinformatics techniques for detecting families of stress-tolerant genes. Gene ontology (GO) databases provide information on the gene product’s functional features and help with the computational estimation of gene function. Functional omics data from multiple platforms are useful for positional cloning. Stress-tolerant plants have been engineered using stress response genes, regulatory networks, and pathways. The genome-editing tool, CRISPR-Cas9, reveals the functional features of several parts of the plant genome. Current developments in CRISPR, such as de novo meristem induction genome-engineering in dicots and temperature-tolerant LbCas12a/CRISPR, enable greater DNA insertion precision. This review discusses functional omics for molecular insight and CRISPR-Cas9-based validation of gene function in crop plants. Omics and CRISPR-Cas9 are expected to garner knowledge on molecular systems and gene function and stress-tolerant crop production.

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Meng, Chen, Oana A. Zeleznik, Gerhard G. Thallinger, Bernhard Kuster, Amin M. Gholami, and Aedín C. Culhane. "Dimension reduction techniques for the integrative analysis of multi-omics data." Briefings in Bioinformatics 17, no. 4 (2016): 628–41. http://dx.doi.org/10.1093/bib/bbv108.

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Gogolev, Yuri V., Sunny Ahmar, Bala Ani Akpinar, et al. "OMICs, Epigenetics, and Genome Editing Techniques for Food and Nutritional Security." Plants 10, no. 7 (2021): 1423. http://dx.doi.org/10.3390/plants10071423.

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The incredible success of crop breeding and agricultural innovation in the last century greatly contributed to the Green Revolution, which significantly increased yields and ensures food security, despite the population explosion. However, new challenges such as rapid climate change, deteriorating soil, and the accumulation of pollutants require much faster responses and more effective solutions that cannot be achieved through traditional breeding. Further prospects for increasing the efficiency of agriculture are undoubtedly associated with the inclusion in the breeding strategy of new knowledge obtained using high-throughput technologies and new tools in the future to ensure the design of new plant genomes and predict the desired phenotype. This article provides an overview of the current state of research in these areas, as well as the study of soil and plant microbiomes, and the prospective use of their potential in a new field of microbiome engineering. In terms of genomic and phenomic predictions, we also propose an integrated approach that combines high-density genotyping and high-throughput phenotyping techniques, which can improve the prediction accuracy of quantitative traits in crop species.

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Senevirathna, Jayan D. M., and Shuichi Asakawa. "Multi-Omics Approaches and Radiation on Lipid Metabolism in Toothed Whales." Life 11, no. 4 (2021): 364. http://dx.doi.org/10.3390/life11040364.

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Lipid synthesis pathways of toothed whales have evolved since their movement from the terrestrial to marine environment. The synthesis and function of these endogenous lipids and affecting factors are still little understood. In this review, we focused on different omics approaches and techniques to investigate lipid metabolism and radiation impacts on lipids in toothed whales. The selected literature was screened, and capacities, possibilities, and future approaches for identifying unusual lipid synthesis pathways by omics were evaluated. Omics approaches were categorized into the four major disciplines: lipidomics, transcriptomics, genomics, and proteomics. Genomics and transcriptomics can together identify genes related to unique lipid synthesis. As lipids interact with proteins in the animal body, lipidomics, and proteomics can correlate by creating lipid-binding proteome maps to elucidate metabolism pathways. In lipidomics studies, recent mass spectroscopic methods can address lipid profiles; however, the determination of structures of lipids are challenging. As an environmental stress, the acoustic radiation has a significant effect on the alteration of lipid profiles. Radiation studies in different omics approaches revealed the necessity of multi-omics applications. This review concluded that a combination of many of the omics areas may elucidate the metabolism of lipids and possible hazards on lipids in toothed whales by radiation.

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Muiño, Elena, Israel Fernández-Cadenas, and Adrià Arboix. "Contribution of “Omic” Studies to the Understanding of Cadasil. A Systematic Review." International Journal of Molecular Sciences 22, no. 14 (2021): 7357. http://dx.doi.org/10.3390/ijms22147357.

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CADASIL (Cerebral Autosomal Dominant Arteriopathy with Subcortical Infarcts and Leukoencephalopathy) is a small vessel disease caused by mutations in NOTCH3 that lead to an odd number of cysteines in the epidermal growth factor (EGF)-like repeat domain, causing protein misfolding and aggregation. The main symptoms are migraines, psychiatric disorders, recurrent strokes, and dementia. Omic technologies allow the massive study of different molecules for understanding diseases in a non-biased manner or even for discovering targets and their possible treatments. We analyzed the progress in understanding CADASIL that has been made possible by omics sciences. For this purpose, we included studies that focused on CADASIL and used omics techniques, searching bibliographic resources, such as PubMed. We excluded studies with other phenotypes, such as migraine or leukodystrophies. A total of 18 articles were reviewed. Due to the high prevalence of NOTCH3 mutations considered pathogenic to date in genomic repositories, one can ask whether all of them produce CADASIL, different degrees of the disease, or whether they are just a risk factor for small vessel disease. Besides, proteomics and transcriptomics studies found that the molecules that are significantly altered in CADASIL are mainly related to cell adhesion, the cytoskeleton or extracellular matrix components, misfolding control, autophagia, angiogenesis, or the transforming growth factor β (TGFβ) signaling pathway. The omics studies performed on CADASIL have been useful for understanding the biological mechanisms and could be key factors for finding potential drug targets.

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Kikuchi, Hiroaki, Hyun Jun Jung, Viswanathan Raghuram, et al. "Bayesian identification of candidate transcription factors for the regulation of Aqp2 gene expression." American Journal of Physiology-Renal Physiology 321, no. 3 (2021): F389—F401. http://dx.doi.org/10.1152/ajprenal.00204.2021.

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Abetted by the advent of systems biology-based (“-omics”) techniques in the 21st century, there has been a massive expansion of published data relevant to virtually every physiological question. The authors have developed a large-scale data integration approach based on the application of Bayes’' theorem. In the current work, they integrated 12 different -omics data sets to identify the transcription factors most likely to mediate vasopressin-dependent regulation of transcription of the aquaporin-2 gene.

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Villoutreix, Paul. "What machine learning can do for developmental biology." Development 148, no. 1 (2021): dev188474. http://dx.doi.org/10.1242/dev.188474.

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ABSTRACTDevelopmental biology has grown into a data intensive science with the development of high-throughput imaging and multi-omics approaches. Machine learning is a versatile set of techniques that can help make sense of these large datasets with minimal human intervention, through tasks such as image segmentation, super-resolution microscopy and cell clustering. In this Spotlight, I introduce the key concepts, advantages and limitations of machine learning, and discuss how these methods are being applied to problems in developmental biology. Specifically, I focus on how machine learning is improving microscopy and single-cell ‘omics’ techniques and data analysis. Finally, I provide an outlook for the futures of these fields and suggest ways to foster new interdisciplinary developments.

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Silverstein, Ana R., Melanie K. Flores, Brendan Miller, et al. "Mito-Omics and immune function: Applying novel mitochondrial omic techniques to the context of the aging immune system." Translational Medicine of Aging 4 (2020): 132–40. http://dx.doi.org/10.1016/j.tma.2020.08.001.

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Galler, Kerstin, Katharina Bräutigam, Christina Große, Jürgen Popp, and Ute Neugebauer. "Making a big thing of a small cell – recent advances in single cell analysis." Analyst 139, no. 6 (2014): 1237–73. http://dx.doi.org/10.1039/c3an01939j.

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All aspects of the characterisation of single cells are reviewed: from morphology to genetics and different omics-techniques to physiological, mechanical and electrical methods, including microfluidics and applications.

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Vargas-Gastélum, Lluvia, and Meritxell Riquelme. "The Mycobiota of the Deep Sea: What Omics Can Offer." Life 10, no. 11 (2020): 292. http://dx.doi.org/10.3390/life10110292.

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The deep sea (>1000 m below sea level) represents one of the most extreme environments of the ocean. Despite exhibiting harsh abiotic conditions such as low temperatures, high hydrostatic pressure, high salinity concentrations, a low input of organic matter, and absence of light, the deep sea encompasses a great fungal diversity. For decades, most knowledge on the fungal diversity of the deep sea was obtained through culture-dependent techniques. More recently, with the latest advances of high-throughput next generation sequencing platforms, there has been a rapid increment in the number of studies using culture-independent techniques. This review brings into the spotlight the progress of the techniques used to assess the diversity and ecological role of the deep-sea mycobiota and provides an overview on how the omics technologies have contributed to gaining knowledge about fungi and their activity in poorly explored marine environments. Finally, current challenges and suggested coordinated efforts to overcome them are discussed.

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Occhipinti, Annalisa, Filmon Eyassu, Thahira J. Rahman, Pattanathu K. S. M. Rahman, and Claudio Angione. "In silico engineering ofPseudomonasmetabolism reveals new biomarkers for increased biosurfactant production." PeerJ 6 (December 17, 2018): e6046. http://dx.doi.org/10.7717/peerj.6046.

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BackgroundRhamnolipids, biosurfactants with a wide range of biomedical applications, are amphiphilic molecules produced on the surfaces of or excreted extracellularly by bacteria includingPseudomonas aeruginosa. However,Pseudomonas putidais a non-pathogenic model organism with greater metabolic versatility and potential for industrial applications.MethodsWe investigate in silico the metabolic capabilities ofP. putidafor rhamnolipids biosynthesis using statistical, metabolic and synthetic engineering approaches after introducing key genes (RhlAandRhlB) fromP. aeruginosainto a genome-scale model ofP. putida. This pipeline combines machine learning methods with multi-omic modelling, and drives the engineeredP. putidamodel toward an optimal production and export of rhamnolipids out of the membrane.ResultsWe identify a substantial increase in synthesis of rhamnolipids by the engineered model compared to the control model. We apply statistical and machine learning techniques on the metabolic reaction rates to identify distinct features on the structure of the variables and individual components driving the variation of growth and rhamnolipids production. We finally provide a computational framework for integrating multi-omics data and identifying latent pathways and genes for the production of rhamnolipids inP. putida.ConclusionsWe anticipate that our results will provide a versatile methodology for integrating multi-omics data for topological and functional analysis ofP. putidatoward maximization of biosurfactant production.

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