Zeitschriftenartikel: „Meta-omics“

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Rusk, Nicole. „A meta-network of -omics“. Nature Methods 5, Nr. 1 (Januar 2008): 25. http://dx.doi.org/10.1038/nmeth1165.

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Mackelprang, Rachel, Scott R. Saleska, Carsten Suhr Jacobsen, Janet K. Jansson und Neslihan Taş. „Permafrost Meta-Omics and Climate Change“. Annual Review of Earth and Planetary Sciences 44, Nr. 1 (29.06.2016): 439–62. http://dx.doi.org/10.1146/annurev-earth-060614-105126.

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Tsoungos, Anastasios, Violeta Pemaj, Aleksandra Slavko, John Kapolos, Marina Papadelli und Konstantinos Papadimitriou. „The Rising Role of Omics and Meta-Omics in Table Olive Research“. Foods 12, Nr. 20 (15.10.2023): 3783. http://dx.doi.org/10.3390/foods12203783.

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Table olives are often the result of fermentation, a process where microorganisms transform raw materials into the final product. The microbial community can significantly impact the organoleptic characteristics and safety of table olives, and it is influenced by various factors, including the processing methods. Traditional culture-dependent techniques capture only a fraction of table olives’ intricate microbiota, prompting a shift toward culture-independent methods to address this knowledge gap. This review explores recent advances in table olive research through omics and meta-omics approaches. Genomic analysis of microorganisms isolated from table olives has revealed multiple genes linked to technological and probiotic attributes. An increasing number of studies concern metagenomics and metabolomics analyses of table olives. The former offers comprehensive insights into microbial diversity and function, while the latter identifies aroma and flavor determinants. Although proteomics and transcriptomics studies remain limited in the field, they have the potential to reveal deeper layers of table olives’ microbiome composition and functionality. Despite the challenges associated with implementing multi-omics approaches, such as the reliance on advanced bioinformatics tools and computational resources, they hold the promise of groundbreaking advances in table olive processing technology.

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Sathyanarayanan, Anita, Rohit Gupta, Erik W. Thompson, Dale R. Nyholt, Denis C. Bauer und Shivashankar H. Nagaraj. „A comparative study of multi-omics integration tools for cancer driver gene identification and tumour subtyping“. Briefings in Bioinformatics 21, Nr. 6 (27.11.2019): 1920–36. http://dx.doi.org/10.1093/bib/bbz121.

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Abstract Oncogenesis and cancer can arise as a consequence of a wide range of genomic aberrations including mutations, copy number alterations, expression changes and epigenetic modifications encompassing multiple omics layers. Integrating genomic, transcriptomic, proteomic and epigenomic datasets via multi-omics analysis provides the opportunity to derive a deeper and holistic understanding of the development and progression of cancer. There are two primary approaches to integrating multi-omics data: multi-staged (focused on identifying genes driving cancer) and meta-dimensional (focused on establishing clinically relevant tumour or sample classifications). A number of ready-to-use bioinformatics tools are available to perform both multi-staged and meta-dimensional integration of multi-omics data. In this study, we compared nine different integration tools using real and simulated cancer datasets. The performance of the multi-staged integration tools were assessed at the gene, function and pathway levels, while meta-dimensional integration tools were assessed based on the sample classification performance. Additionally, we discuss the influence of factors such as data representation, sample size, signal and noise on multi-omics data integration. Our results provide current and much needed guidance regarding selection and use of the most appropriate and best performing multi-omics integration tools.

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Mallick, Himel, Ali Rahnavard, Lauren J. McIver, Siyuan Ma, Yancong Zhang, Long H. Nguyen, Timothy L. Tickle et al. „Multivariable association discovery in population-scale meta-omics studies“. PLOS Computational Biology 17, Nr. 11 (16.11.2021): e1009442. http://dx.doi.org/10.1371/journal.pcbi.1009442.

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It is challenging to associate features such as human health outcomes, diet, environmental conditions, or other metadata to microbial community measurements, due in part to their quantitative properties. Microbiome multi-omics are typically noisy, sparse (zero-inflated), high-dimensional, extremely non-normal, and often in the form of count or compositional measurements. Here we introduce an optimized combination of novel and established methodology to assess multivariable association of microbial community features with complex metadata in population-scale observational studies. Our approach, MaAsLin 2 (Microbiome Multivariable Associations with Linear Models), uses generalized linear and mixed models to accommodate a wide variety of modern epidemiological studies, including cross-sectional and longitudinal designs, as well as a variety of data types (e.g., counts and relative abundances) with or without covariates and repeated measurements. To construct this method, we conducted a large-scale evaluation of a broad range of scenarios under which straightforward identification of meta-omics associations can be challenging. These simulation studies reveal that MaAsLin 2’s linear model preserves statistical power in the presence of repeated measures and multiple covariates, while accounting for the nuances of meta-omics features and controlling false discovery. We also applied MaAsLin 2 to a microbial multi-omics dataset from the Integrative Human Microbiome (HMP2) project which, in addition to reproducing established results, revealed a unique, integrated landscape of inflammatory bowel diseases (IBD) across multiple time points and omics profiles.

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Adeleke, Bartholomew Saanu, und Olubukola Oluranti Babalola. „Meta-omics of endophytic microbes in agricultural biotechnology“. Biocatalysis and Agricultural Biotechnology 42 (Juli 2022): 102332. http://dx.doi.org/10.1016/j.bcab.2022.102332.

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Darzi, Youssef, Gwen Falony, Sara Vieira-Silva und Jeroen Raes. „Towards biome-specific analysis of meta-omics data“. ISME Journal 10, Nr. 5 (01.12.2015): 1025–28. http://dx.doi.org/10.1038/ismej.2015.188.

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Johnson, David R., Damian E. Helbling, Yujie Men und Kathrin Fenner. „Can meta-omics help to establish causality between contaminant biotransformations and genes or gene products?“ Environmental Science: Water Research & Technology 1, Nr. 3 (2015): 272–78. http://dx.doi.org/10.1039/c5ew00016e.

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Qin, Xiaofa. „Can inflammatory bowel disease really be solved by the multiple -omics and meta-omics analyses?“ Immunology Letters 165, Nr. 2 (Juni 2015): 107–8. http://dx.doi.org/10.1016/j.imlet.2015.03.007.

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Cembrowska-Lech, Danuta, Adrianna Krzemińska, Tymoteusz Miller, Anna Nowakowska, Cezary Adamski, Martyna Radaczyńska, Grzegorz Mikiciuk und Małgorzata Mikiciuk. „An Integrated Multi-Omics and Artificial Intelligence Framework for Advance Plant Phenotyping in Horticulture“. Biology 12, Nr. 10 (30.09.2023): 1298. http://dx.doi.org/10.3390/biology12101298.

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This review discusses the transformative potential of integrating multi-omics data and artificial intelligence (AI) in advancing horticultural research, specifically plant phenotyping. The traditional methods of plant phenotyping, while valuable, are limited in their ability to capture the complexity of plant biology. The advent of (meta-)genomics, (meta-)transcriptomics, proteomics, and metabolomics has provided an opportunity for a more comprehensive analysis. AI and machine learning (ML) techniques can effectively handle the complexity and volume of multi-omics data, providing meaningful interpretations and predictions. Reflecting the multidisciplinary nature of this area of research, in this review, readers will find a collection of state-of-the-art solutions that are key to the integration of multi-omics data and AI for phenotyping experiments in horticulture, including experimental design considerations with several technical and non-technical challenges, which are discussed along with potential solutions. The future prospects of this integration include precision horticulture, predictive breeding, improved disease and stress response management, sustainable crop management, and exploration of plant biodiversity. The integration of multi-omics and AI holds immense promise for revolutionizing horticultural research and applications, heralding a new era in plant phenotyping.

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Kim, SungHwan, Dongwan Kang, Zhiguang Huo, Yongseok Park und George C. Tseng. „Meta-analytic principal component analysis in integrative omics application“. Bioinformatics 34, Nr. 8 (23.11.2017): 1321–28. http://dx.doi.org/10.1093/bioinformatics/btx765.

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Mannaa, Mohamed, Gil Han, Young-Su Seo und Inmyoung Park. „Evolution of Food Fermentation Processes and the Use of Multi-Omics in Deciphering the Roles of the Microbiota“. Foods 10, Nr. 11 (18.11.2021): 2861. http://dx.doi.org/10.3390/foods10112861.

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Food fermentation has been practised since ancient times to improve sensory properties and food preservation. This review discusses the process of fermentation, which has undergone remarkable improvement over the years, from relying on natural microbes and spontaneous fermentation to back-slopping and the use of starter cultures. Modern biotechnological approaches, including genome editing using CRISPR/Cas9, have been investigated and hold promise for improving the fermentation process. The invention of next-generation sequencing techniques and the rise of meta-omics tools have advanced our knowledge on the characterisation of microbiomes involved in food fermentation and their functional roles. The contribution and potential advantages of meta-omics technologies in understanding the process of fermentation and examples of recent studies utilising multi-omics approaches for studying food-fermentation microbiomes are reviewed. Recent technological advances in studying food fermentation have provided insights into the ancient wisdom in the practice of food fermentation, such as the choice of substrates and fermentation conditions leading to desirable properties. This review aims to stimulate research on the process of fermentation and the associated microbiomes to produce fermented food efficiently and sustainably. Prospects and the usefulness of recent advances in molecular tools and integrated multi-omics approaches are highlighted.

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Can, Handan, Sree K. Chanumolu, Barbara D. Nielsen, Sophie Alvarez, Michael J. Naldrett, Gülhan Ünlü und Hasan H. Otu. „Integration of Meta-Multi-Omics Data Using Probabilistic Graphs and External Knowledge“. Cells 12, Nr. 15 (04.08.2023): 1998. http://dx.doi.org/10.3390/cells12151998.

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Multi-omics has the promise to provide a detailed molecular picture of biological systems. Although obtaining multi-omics data is relatively easy, methods that analyze such data have been lagging. In this paper, we present an algorithm that uses probabilistic graph representations and external knowledge to perform optimal structure learning and deduce a multifarious interaction network for multi-omics data from a bacterial community. Kefir grain, a microbial community that ferments milk and creates kefir, represents a self-renewing, stable, natural microbial community. Kefir has been shown to have a wide range of health benefits. We obtained a controlled bacterial community using the two most abundant and well-studied species in kefir grains: Lentilactobacillus kefiri and Lactobacillus kefiranofaciens. We applied growth temperatures of 30 °C and 37 °C and obtained transcriptomic, metabolomic, and proteomic data for the same 20 samples (10 samples per temperature). We obtained a multi-omics interaction network, which generated insights that would not have been possible with single-omics analysis. We identified interactions among transcripts, proteins, and metabolites, suggesting active toxin/antitoxin systems. We also observed multifarious interactions that involved the shikimate pathway. These observations helped explain bacterial adaptation to different stress conditions, co-aggregation, and increased activation of L. kefiranofaciens at 37 °C.

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Myall, Ashleigh C., Simon Perkins, David Rushton, Jonathan David, Phillippa Spencer, Andrew R. Jones und Philipp Antczak. „An OMICs-based meta-analysis to support infection state stratification“. Bioinformatics 37, Nr. 16 (09.02.2021): 2347–55. http://dx.doi.org/10.1093/bioinformatics/btab089.

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Abstract Motivation A fundamental problem for disease treatment is that while antibiotics are a powerful counter to bacteria, they are ineffective against viruses. Often, bacterial and viral infections are confused due to their similar symptoms and lack of rapid diagnostics. With many clinicians relying primarily on symptoms for diagnosis, overuse and misuse of modern antibiotics are rife, contributing to the growing pool of antibiotic resistance. To ensure an individual receives optimal treatment given their disease state and to reduce over-prescription of antibiotics, the host response can in theory be measured quickly to distinguish between the two states. To establish a predictive biomarker panel of disease state (viral/bacterial/no-infection), we conducted a meta-analysis of human blood infection studies using machine learning. Results We focused on publicly available gene expression data from two widely used platforms, Affymetrix and Illumina microarrays as they represented a significant proportion of the available data. We were able to develop multi-class models with high accuracies with our best model predicting 93% of bacterial and 89% viral samples correctly. To compare the selected features in each of the different technologies, we reverse-engineered the underlying molecular regulatory network and explored the neighbourhood of the selected features. The networks highlighted that although on the gene-level the models differed, they contained genes from the same areas of the network. Specifically, this convergence was to pathways including the Type I interferon Signalling Pathway, Chemotaxis, Apoptotic Processes and Inflammatory/Innate Response. Availability Data and code are available on the Gene Expression Omnibus and github. Supplementary information Supplementary data are available at Bioinformatics online.

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Malacrinò, Antonino. „Meta-Omics Tools in the World of Insect-Microorganism Interactions“. Biology 7, Nr. 4 (27.11.2018): 50. http://dx.doi.org/10.3390/biology7040050.

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Microorganisms are able to influence several aspects of insects’ life, and this statement is gaining increasing strength, as research demonstrates it daily. At the same time, new sequencing technologies are now available at a lower cost per base, and bioinformatic procedures are becoming more user-friendly. This is triggering a huge effort in studying the microbial diversity associated to insects, and especially to economically important insect pests. The importance of the microbiome has been widely acknowledged for a wide range of animals, and also for insects this topic is gaining considerable importance. In addition to bacterial-associates, the insect-associated fungal communities are also gaining attention, especially those including plant pathogens. The use of meta-omics tools is not restricted to the description of the microbial world, but it can be also used in bio-surveillance, food safety assessment, or even to bring novelties to the industry. This mini-review aims to give a wide overview of how meta-omics tools are fostering advances in research on insect-microorganism interactions.

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Rodríguez, Elisa, Pedro A. García-Encina, Alfons J. M. Stams, Farai Maphosa und Diana Z. Sousa. „Meta-omics approaches to understand and improve wastewater treatment systems“. Reviews in Environmental Science and Bio/Technology 14, Nr. 3 (28.07.2015): 385–406. http://dx.doi.org/10.1007/s11157-015-9370-x.

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Chialva, Matteo, Stefano Ghignone, Mara Novero, Wael N. Hozzein, Luisa Lanfranco und Paola Bonfante. „Tomato RNA-seq Data Mining Reveals the Taxonomic and Functional Diversity of Root-Associated Microbiota“. Microorganisms 8, Nr. 1 (24.12.2019): 38. http://dx.doi.org/10.3390/microorganisms8010038.

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Next-generation approaches have enabled researchers to deeply study the plant microbiota and to reveal how microbiota associated with plant roots has key effects on plant nutrition, disease resistance, and plant development. Although early “omics” experiments focused mainly on the species composition of microbial communities, new “meta-omics” approaches such as meta-transcriptomics provide hints about the functions of the microbes when interacting with their plant host. Here, we used an RNA-seq dataset previously generated for tomato (Solanum lycopersicum) plants growing on different native soils to test the hypothesis that host-targeted transcriptomics can detect the taxonomic and functional diversity of root microbiota. Even though the sequencing throughput for the microbial populations was limited, we were able to reconstruct the microbial communities and obtain an overview of their functional diversity. Comparisons of the host transcriptome and the meta-transcriptome suggested that the composition and the metabolic activities of the microbiota shape plant responses at the molecular level. Despite the limitations, mining available next-generation sequencing datasets can provide unexpected results and potential benefits for microbiota research.

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Tang, Haotian, Yanqing Huang, Didi Yuan und Junwen Liu. „Atherosclerosis, gut microbiome, and exercise in a meta-omics perspective: a literature review“. PeerJ 12 (04.04.2024): e17185. http://dx.doi.org/10.7717/peerj.17185.

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Background Cardiovascular diseases are the leading cause of death worldwide, significantly impacting public health. Atherosclerotic cardiovascular diseases account for the majority of these deaths, with atherosclerosis marking the initial and most critical phase of their pathophysiological progression. There is a complex relationship between atherosclerosis, the gut microbiome’s composition and function, and the potential mediating role of exercise. The adaptability of the gut microbiome and the feasibility of exercise interventions present novel opportunities for therapeutic and preventative approaches. Methodology We conducted a comprehensive literature review using professional databases such as PubMed and Web of Science. This review focuses on the application of meta-omics techniques, particularly metagenomics and metabolomics, in studying the effects of exercise interventions on the gut microbiome and atherosclerosis. Results Meta-omics technologies offer unparalleled capabilities to explore the intricate connections between exercise, the microbiome, the metabolome, and cardiometabolic health. This review highlights the advancements in metagenomics and metabolomics, their applications in research, and examines how exercise influences the gut microbiome. We delve into the mechanisms connecting these elements from a metabolic perspective. Metagenomics provides insight into changes in microbial strains post-exercise, while metabolomics sheds light on the shifts in metabolites. Together, these approaches offer a comprehensive understanding of how exercise impacts atherosclerosis through specific mechanisms. Conclusions Exercise significantly influences atherosclerosis, with the gut microbiome serving as a critical intermediary. Meta-omics technology holds substantial promise for investigating the gut microbiome; however, its methodologies require further refinement. Additionally, there is a pressing need for more extensive cohort studies to enhance our comprehension of the connection among these element.

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Ding, Jessica, Montgomery Blencowe, Thien Nghiem, Sung-min Ha, Yen-Wei Chen, Gaoyan Li und Xia Yang. „Mergeomics 2.0: a web server for multi-omics data integration to elucidate disease networks and predict therapeutics“. Nucleic Acids Research 49, W1 (28.05.2021): W375—W387. http://dx.doi.org/10.1093/nar/gkab405.

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Abstract The Mergeomics web server is a flexible online tool for multi-omics data integration to derive biological pathways, networks, and key drivers important to disease pathogenesis and is based on the open source Mergeomics R package. The web server takes summary statistics of multi-omics disease association studies (GWAS, EWAS, TWAS, PWAS, etc.) as input and features four functions: Marker Dependency Filtering (MDF) to correct for known dependency between omics markers, Marker Set Enrichment Analysis (MSEA) to detect disease relevant biological processes, Meta-MSEA to examine the consistency of biological processes informed by various omics datasets, and Key Driver Analysis (KDA) to identify essential regulators of disease-associated pathways and networks. The web server has been extensively updated and streamlined in version 2.0 including an overhauled user interface, improved tutorials and results interpretation for each analytical step, inclusion of numerous disease GWAS, functional genomics datasets, and molecular networks to allow for comprehensive omics integrations, increased functionality to decrease user workload, and increased flexibility to cater to user-specific needs. Finally, we have incorporated our newly developed drug repositioning pipeline PharmOmics for prediction of potential drugs targeting disease processes that were identified by Mergeomics. Mergeomics is freely accessible at http://mergeomics.research.idre.ucla.edu and does not require login.

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Taverna, Federico, Jermaine Goveia, Tobias K. Karakach, Shawez Khan, Katerina Rohlenova, Lucas Treps, Abhishek Subramanian et al. „BIOMEX: an interactive workflow for (single cell) omics data interpretation and visualization“. Nucleic Acids Research 48, W1 (11.05.2020): W385—W394. http://dx.doi.org/10.1093/nar/gkaa332.

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Abstract The amount of biological data, generated with (single cell) omics technologies, is rapidly increasing, thereby exacerbating bottlenecks in the data analysis and interpretation of omics experiments. Data mining platforms that facilitate non-bioinformatician experimental scientists to analyze a wide range of experimental designs and data types can alleviate such bottlenecks, aiding in the exploration of (newly generated or publicly available) omics datasets. Here, we present BIOMEX, a browser-based software, designed to facilitate the Biological Interpretation Of Multi-omics EXperiments by bench scientists. BIOMEX integrates state-of-the-art statistical tools and field-tested algorithms into a flexible but well-defined workflow that accommodates metabolomics, transcriptomics, proteomics, mass cytometry and single cell data from different platforms and organisms. The BIOMEX workflow is accompanied by a manual and video tutorials that provide the necessary background to navigate the interface and get acquainted with the employed methods. BIOMEX guides the user through omics-tailored analyses, such as data pretreatment and normalization, dimensionality reduction, differential and enrichment analysis, pathway mapping, clustering, marker analysis, trajectory inference, meta-analysis and others. BIOMEX is fully interactive, allowing users to easily change parameters and generate customized plots exportable as high-quality publication-ready figures. BIOMEX is open source and freely available at https://www.vibcancer.be/software-tools/biomex.

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Tilocca, Bruno, Luisa Pieroni, Alessio Soggiu, Domenico Britti, Luigi Bonizzi, Paola Roncada und Viviana Greco. „Gut–Brain Axis and Neurodegeneration: State-of-the-Art of Meta-Omics Sciences for Microbiota Characterization“. International Journal of Molecular Sciences 21, Nr. 11 (05.06.2020): 4045. http://dx.doi.org/10.3390/ijms21114045.

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Recent advances in the field of meta-omics sciences and related bioinformatics tools have allowed a comprehensive investigation of human-associated microbiota and its contribution to achieving and maintaining the homeostatic balance. Bioactive compounds from the microbial community harboring the human gut are involved in a finely tuned network of interconnections with the host, orchestrating a wide variety of physiological processes. These includes the bi-directional crosstalk between the central nervous system, the enteric nervous system, and the gastrointestinal tract (i.e., gut–brain axis). The increasing accumulation of evidence suggest a pivotal role of the composition and activity of the gut microbiota in neurodegeneration. In the present review we aim to provide an overview of the state-of-the-art of meta-omics sciences including metagenomics for the study of microbial genomes and taxa strains, metatranscriptomics for gene expression, metaproteomics and metabolomics to identify and/or quantify microbial proteins and metabolites, respectively. The potential and limitations of each discipline were highlighted, as well as the advantages of an integrated approach (multi-omics) to predict microbial functions and molecular mechanisms related to human diseases. Particular emphasis is given to the latest results obtained with these approaches in an attempt to elucidate the link between the gut microbiota and the most common neurodegenerative diseases, such as multiple sclerosis (MS), Alzheimer’s disease (AD), Parkinson’s disease (PD), and amyotrophic lateral sclerosis (ALS).

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Hoogstrate, Youri, Santoesha A. Ghisai, Maurice de Wit, Iris de Heer, Kaspar Draaisma, Job van Riet, Harmen J. G. van de Werken et al. „The EGFRvIII transcriptome in glioblastoma: A meta-omics analysis“. Neuro-Oncology 24, Nr. 3 (05.10.2021): 429–41. http://dx.doi.org/10.1093/neuonc/noab231.

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Abstract Background EGFR is among the genes most frequently altered in glioblastoma, with exons 2-7 deletions (EGFRvIII) being among its most common genomic mutations. There are conflicting reports about its prognostic role and it remains unclear whether and how it differs in signaling compared with wildtype EGFR. Methods To better understand the oncogenic role of EGFRvIII, we leveraged 4 large datasets into 1 large glioblastoma transcriptome dataset (n = 741) alongside 81 whole-genome samples from 2 datasets. Results The EGFRvIII/EGFR expression ratios differ strongly between tumors and range from 1% to 95%. Interestingly, the slope of relative EGFRvIII expression is near-linear, which argues against a more positive selection pressure than EGFR wildtype. An absence of selection pressure is also suggested by the similar survival between EGFRvIII-positive and -negative glioblastoma patients. EGFRvIII levels are inversely correlated with pan-EGFR (all wildtype and mutant variants) expression, which indicates that EGFRvIII has a higher potency in downstream pathway activation. EGFRvIII-positive glioblastomas have a lower CDK4 or MDM2 amplification incidence than EGFRvIII-negative (P = .007), which may point toward crosstalk between these pathways. EGFRvIII-expressing tumors have an upregulation of “classical” subtype genes compared to those with EGFR-amplification only (P = 3.873e−6). Genomic breakpoints of the EGFRvIII deletions have a preference toward the 3′-end of the large intron-1. These preferred breakpoints preserve a cryptic exon resulting in a novel EGFRvIII variant and preserve an intronic enhancer. Conclusions These data provide deeper insights into the complex EGFRvIII biology and provide new insights for targeting EGFRvIII mutated tumors.

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Fritz, Joëlle V., Mahesh S. Desai, Pranjul Shah, Jochen G. Schneider und Paul Wilmes. „From meta-omics to causality: experimental models for human microbiome research“. Microbiome 1, Nr. 1 (2013): 14. http://dx.doi.org/10.1186/2049-2618-1-14.

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Valles-Colomer, Mireia, Youssef Darzi, Sara Vieira-Silva, Gwen Falony, Jeroen Raes und Marie Joossens. „Meta-omics in Inflammatory Bowel Disease Research: Applications, Challenges, and Guidelines“. Journal of Crohn's and Colitis 10, Nr. 6 (22.01.2016): 735–46. http://dx.doi.org/10.1093/ecco-jcc/jjw024.

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Stec, Krzysztof Franciszek, Luigi Caputi, Pier Luigi Buttigieg, Domenico D'Alelio, Federico Matias Ibarbalz, Matthew B. Sullivan, Samuel Chaffron, Chris Bowler, Maurizio Ribera d'Alcalà und Daniele Iudicone. „Modelling plankton ecosystems in the meta-omics era. Are we ready?“ Marine Genomics 32 (April 2017): 1–17. http://dx.doi.org/10.1016/j.margen.2017.02.006.

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Sieow, Brendan Fu‐Long, Toni Juhani Nurminen, Hua Ling und Matthew Wook Chang. „Meta‐Omics‐ and Metabolic Modeling‐Assisted Deciphering of Human Microbiota Metabolism“. Biotechnology Journal 14, Nr. 9 (08.07.2019): 1800445. http://dx.doi.org/10.1002/biot.201800445.

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Dessì, Angelica, Roberta Pintus, Vassilios Fanos und Alice Bosco. „Integrative Multiomics Approach to Skin: The Sinergy between Individualised Medicine and Futuristic Precision Skin Care?“ Metabolites 14, Nr. 3 (07.03.2024): 157. http://dx.doi.org/10.3390/metabo14030157.

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The skin is a complex ecosystem colonized by millions of microorganisms, the skin microbiota, which are crucial in regulating not only the physiological functions of the skin but also the metabolic changes underlying the onset of skin diseases. The high microbial colonization together with a low diversity at the phylum level and a high diversity at the species level of the skin is very similar to that of the gastrointestinal tract. Moreover, there is an important communication pathway along the gut–brain–skin axis, especially associated with the modulation of neurotransmitters by the microbiota. Therefore, it is evident that the high complexity of the skin system, due not only to the genetics of the host but also to the interaction of the host with resident microbes and between microbe and microbe, requires a multi-omics approach to be deeply understood. Therefore, an integrated analysis, with high-throughput technologies, of the consequences of microbial interaction with the host through the study of gene expression (genomics and metagenomics), transcription (transcriptomics and meta-transcriptomics), and protein production (proteomics and meta-proteomics) and metabolite formation (metabolomics and lipidomics) would be useful. Although to date very few studies have integrated skin metabolomics data with at least one other ‘omics’ technology, in the future, this approach will be able to provide simple and fast tests that can be routinely applied in both clinical and cosmetic settings for the identification of numerous skin diseases and conditions. It will also be possible to create large archives of multi-omics data that can predict individual responses to pharmacological treatments and the efficacy of different cosmetic products on individual subjects by means of specific allotypes, with a view to increasingly tailor-made medicine. In this review, after analyzing the complexity of the skin ecosystem, we have highlighted the usefulness of this emerging integrated omics approach for the analysis of skin problems, starting with one of the latest ‘omics’ sciences, metabolomics, which can photograph the expression of the genome during its interaction with the environment.

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Tiong, Khong-Loon, Nardnisa Sintupisut, Min-Chin Lin, Chih-Hung Cheng, Andrew Woolston, Chih-Hsu Lin, Mirrian Ho, Yu-Wei Lin, Sridevi Padakanti und Chen-Hsiang Yeang. „An integrated analysis of the cancer genome atlas data discovers a hierarchical association structure across thirty three cancer types“. PLOS Digital Health 1, Nr. 12 (20.12.2022): e0000151. http://dx.doi.org/10.1371/journal.pdig.0000151.

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Cancer cells harbor molecular alterations at all levels of information processing. Genomic/epigenomic and transcriptomic alterations are inter-related between genes, within and across cancer types and may affect clinical phenotypes. Despite the abundant prior studies of integrating cancer multi-omics data, none of them organizes these associations in a hierarchical structure and validates the discoveries in extensive external data. We infer this Integrated Hierarchical Association Structure (IHAS) from the complete data of The Cancer Genome Atlas (TCGA) and compile a compendium of cancer multi-omics associations. Intriguingly, diverse alterations on genomes/epigenomes from multiple cancer types impact transcriptions of 18 Gene Groups. Half of them are further reduced to three Meta Gene Groups enriched with (1) immune and inflammatory responses, (2) embryonic development and neurogenesis, (3) cell cycle process and DNA repair. Over 80% of the clinical/molecular phenotypes reported in TCGA are aligned with the combinatorial expressions of Meta Gene Groups, Gene Groups, and other IHAS subunits. Furthermore, IHAS derived from TCGA is validated in more than 300 external datasets including multi-omics measurements and cellular responses upon drug treatments and gene perturbations in tumors, cancer cell lines, and normal tissues. To sum up, IHAS stratifies patients in terms of molecular signatures of its subunits, selects targeted genes or drugs for precision cancer therapy, and demonstrates that associations between survival times and transcriptional biomarkers may vary with cancer types. These rich information is critical for diagnosis and treatments of cancers.

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Nnyanzi, Lawrence Achilles, Akinyele Olumuyiwa Adisa, Kehinde Kazeem Kanmodi, Timothy Olukunle Aladelusi, Afeez Abolarinwa Salami, Jimoh Amzat, Claudio Angione et al. „Status of Omics Research Capacity on Oral Cancer in Africa: A Systematic Scoping Review Protocol“. BioMedInformatics 3, Nr. 2 (06.04.2023): 327–38. http://dx.doi.org/10.3390/biomedinformatics3020022.

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Over the past decade, omics technologies such as genomics, epigenomics, transcriptomics, proteomics, and metabolomics have been used in the scientific understanding of diseases. While omics technologies have provided a useful tool for the diagnosis and treatment of diseases globally, there is a dearth of literature on the use of these technologies in Africa, particularly in the diagnosis and treatment of oral cancer. This systematic scoping review aims to present the status of the omics research capacity on oral cancer in Africa. The guidelines by the Joanna Brigg’s Institute for conducting systematic scoping reviews will be adopted for this review’s methodology and it will be reported using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) checklist. The literature that will be reviewed will be scooped out from PubMed, SCOPUS, Dentistry and Oral Sciences Source, AMED, CINAHL, and PsycInfo databases. In conclusion, the findings that will be obtained from this review will aid the in-depth understanding of the status of oral cancer omics research in Africa, as this knowledge is paramount for the enhancement of strategies required for capacity development and the prioritization of resources in the fight against oral cancer in Africa.

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Xu, Jing, und Yuejin Yang. „Gut microbiome and its meta-omics perspectives: profound implications for cardiovascular diseases“. Gut Microbes 13, Nr. 1 (01.01.2021): 1936379. http://dx.doi.org/10.1080/19490976.2021.1936379.

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Mondot, Stanislas, und Patricia Lepage. „The human gut microbiome and its dysfunctions through the meta-omics prism“. Annals of the New York Academy of Sciences 1372, Nr. 1 (04.03.2016): 9–19. http://dx.doi.org/10.1111/nyas.13033.

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Xu, H., M. Gu, X. Zheng, Y. Xia, Y. Qian, J. Guan, H. Yi, X. Li, W. Jia und S. Yin. „An integrated meta-omics based approach in pediatric obstructive sleep apnea syndrome“. Sleep Medicine 40 (Dezember 2017): e350. http://dx.doi.org/10.1016/j.sleep.2017.11.1032.

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Ternes, Dominik, Jessica Karta, Mina Tsenkova, Paul Wilmes, Serge Haan und Elisabeth Letellier. „Microbiome in Colorectal Cancer: How to Get from Meta-omics to Mechanism?“ Trends in Microbiology 28, Nr. 5 (Mai 2020): 401–23. http://dx.doi.org/10.1016/j.tim.2020.01.001.

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Ternes, Dominik, Jessica Karta, Mina Tsenkova, Paul Wilmes, Serge Haan und Elisabeth Letellier. „Microbiome in Colorectal Cancer: How to Get from Meta-omics to Mechanism?“ Trends in Microbiology 28, Nr. 8 (August 2020): 698. http://dx.doi.org/10.1016/j.tim.2020.05.013.

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Lee, Se Hee, Tae Woong Whon, Seong Woon Roh und Che Ok Jeon. „Unraveling microbial fermentation features in kimchi: from classical to meta-omics approaches“. Applied Microbiology and Biotechnology 104, Nr. 18 (04.08.2020): 7731–44. http://dx.doi.org/10.1007/s00253-020-10804-8.

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Dugat-Bony, Eric, Cécile Straub, Aurélie Teissandier, Djamila Onésime, Valentin Loux, Christophe Monnet, Françoise Irlinger et al. „Overview of a Surface-Ripened Cheese Community Functioning by Meta-Omics Analyses“. PLOS ONE 10, Nr. 4 (13.04.2015): e0124360. http://dx.doi.org/10.1371/journal.pone.0124360.

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Kaever, Alexander, Manuel Landesfeind, Kirstin Feussner, Burkhard Morgenstern, Ivo Feussner und Peter Meinicke. „Meta-Analysis of Pathway Enrichment: Combining Independent and Dependent Omics Data Sets“. PLoS ONE 9, Nr. 2 (28.02.2014): e89297. http://dx.doi.org/10.1371/journal.pone.0089297.

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Cocolin, Luca, Marios Mataragas, Francois Bourdichon, Agapi Doulgeraki, Marie-France Pilet, Balamurugan Jagadeesan, Kalliopi Rantsiou und Trevor Phister. „Next generation microbiological risk assessment meta-omics: The next need for integration“. International Journal of Food Microbiology 287 (Dezember 2018): 10–17. http://dx.doi.org/10.1016/j.ijfoodmicro.2017.11.008.

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Sales, Christopher M., und Patrick KH Lee. „Resource recovery from wastewater: application of meta-omics to phosphorus and carbon management“. Current Opinion in Biotechnology 33 (Juni 2015): 260–67. http://dx.doi.org/10.1016/j.copbio.2015.03.003.

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Obermayer, Alyssa, Li Dong, Qianqian Hu, Michael Golden, Jerald D. Noble, Paulo Rodriguez, Timothy J. Robinson, Mingxiang Teng, Aik-Choon Tan und Timothy I. Shaw. „DRPPM-EASY: A Web-Based Framework for Integrative Analysis of Multi-Omics Cancer Datasets“. Biology 11, Nr. 2 (08.02.2022): 260. http://dx.doi.org/10.3390/biology11020260.

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High-throughput transcriptomic and proteomic analyses are now routinely applied to study cancer biology. However, complex omics integration remains challenging and often time-consuming. Here, we developed DRPPM-EASY, an R Shiny framework for integrative multi-omics analysis. We applied our application to analyze RNA-seq data generated from a USP7 knockdown in T-cell acute lymphoblastic leukemia (T-ALL) cell line, which identified upregulated expression of a TAL1-associated proliferative signature in T-cell acute lymphoblastic leukemia cell lines. Next, we performed proteomic profiling of the USP7 knockdown samples. Through DRPPM-EASY-Integration, we performed a concurrent analysis of the transcriptome and proteome and identified consistent disruption of the protein degradation machinery and spliceosome in samples with USP7 silencing. To further illustrate the utility of the R Shiny framework, we developed DRPPM-EASY-CCLE, a Shiny extension preloaded with the Cancer Cell Line Encyclopedia (CCLE) data. The DRPPM-EASY-CCLE app facilitates the sample querying and phenotype assignment by incorporating meta information, such as genetic mutation, metastasis status, sex, and collection site. As proof of concept, we verified the expression of TP53 associated DNA damage signature in TP53 mutated ovary cancer cells. Altogether, our open-source application provides an easy-to-use framework for omics exploration and discovery.

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Gierse, Laurin, Alexander Meene, Daniel Schultz, Theresa Schwaiger, Claudia Karte, Charlotte Schröder, Haitao Wang et al. „A Multi-Omics Protocol for Swine Feces to Elucidate Longitudinal Dynamics in Microbiome Structure and Function“. Microorganisms 8, Nr. 12 (28.11.2020): 1887. http://dx.doi.org/10.3390/microorganisms8121887.

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Swine are regarded as promising biomedical models, but the dynamics of their gastrointestinal microbiome have been much less investigated than that of humans or mice. The aim of this study was to establish an integrated multi-omics protocol to investigate the fecal microbiome of healthy swine. To this end, a preparation and analysis protocol including integrated sample preparation for meta-omics analyses of deep-frozen feces was developed. Subsequent data integration linked microbiome composition with function, and metabolic activity with protein inventories, i.e., 16S rRNA data and expressed proteins, and identified proteins with corresponding metabolites. 16S rRNA gene amplicon and metaproteomics analyses revealed a fecal microbiome dominated by Prevotellaceae, Lactobacillaceae, Lachnospiraceae, Ruminococcaceae and Clostridiaceae. Similar microbiome compositions in feces and colon, but not ileum samples, were observed, showing that feces can serve as minimal-invasive proxy for porcine colon microbiomes. Longitudinal dynamics in composition, e.g., temporal decreased abundance of Lactobacillaceae and Streptococcaceae during the experiment, were not reflected in microbiome function. Instead, metaproteomics and metabolomics showed a rather stable functional state, as evident from short-chain fatty acids (SCFA) profiles and associated metaproteome functions, pointing towards functional redundancy among microbiome constituents. In conclusion, our pipeline generates congruent data from different omics approaches on the taxonomy and functionality of the intestinal microbiome of swine.

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Long, Nguyen Phuoc, Kyung Hee Jung, Nguyen Hoang Anh, Hong Hua Yan, Tran Diem Nghi, Seongoh Park, Sang Jun Yoon et al. „An Integrative Data Mining and Omics-Based Translational Model for the Identification and Validation of Oncogenic Biomarkers of Pancreatic Cancer“. Cancers 11, Nr. 2 (29.01.2019): 155. http://dx.doi.org/10.3390/cancers11020155.

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Substantial alterations at the multi-omics level of pancreatic cancer (PC) impede the possibility to diagnose and treat patients in early stages. Herein, we conducted an integrative omics-based translational analysis, utilizing next-generation sequencing, transcriptome meta-analysis, and immunohistochemistry, combined with statistical learning, to validate multiplex biomarker candidates for the diagnosis, prognosis, and management of PC. Experiment-based validation was conducted and supportive evidence for the essentiality of the candidates in PC were found at gene expression or protein level by practical biochemical methods. Remarkably, the random forests (RF) model exhibited an excellent diagnostic performance and LAMC2, ANXA2, ADAM9, and APLP2 greatly influenced its decisions. An explanation approach for the RF model was successfully constructed. Moreover, protein expression of LAMC2, ANXA2, ADAM9, and APLP2 was found correlated and significantly higher in PC patients in independent cohorts. Survival analysis revealed that patients with high expression of ADAM9 (Hazard ratio (HR)OS = 2.2, p-value < 0.001), ANXA2 (HROS = 2.1, p-value < 0.001), and LAMC2 (HRDFS = 1.8, p-value = 0.012) exhibited poorer survival rates. In conclusion, we successfully explore hidden biological insights from large-scale omics data and suggest that LAMC2, ANXA2, ADAM9, and APLP2 are robust biomarkers for early diagnosis, prognosis, and management for PC.

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Sompairac, Nicolas, Petr V. Nazarov, Urszula Czerwinska, Laura Cantini, Anne Biton, Askhat Molkenov, Zhaxybay Zhumadilov et al. „Independent Component Analysis for Unraveling the Complexity of Cancer Omics Datasets“. International Journal of Molecular Sciences 20, Nr. 18 (07.09.2019): 4414. http://dx.doi.org/10.3390/ijms20184414.

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Independent component analysis (ICA) is a matrix factorization approach where the signals captured by each individual matrix factors are optimized to become as mutually independent as possible. Initially suggested for solving source blind separation problems in various fields, ICA was shown to be successful in analyzing functional magnetic resonance imaging (fMRI) and other types of biomedical data. In the last twenty years, ICA became a part of the standard machine learning toolbox, together with other matrix factorization methods such as principal component analysis (PCA) and non-negative matrix factorization (NMF). Here, we review a number of recent works where ICA was shown to be a useful tool for unraveling the complexity of cancer biology from the analysis of different types of omics data, mainly collected for tumoral samples. Such works highlight the use of ICA in dimensionality reduction, deconvolution, data pre-processing, meta-analysis, and others applied to different data types (transcriptome, methylome, proteome, single-cell data). We particularly focus on the technical aspects of ICA application in omics studies such as using different protocols, determining the optimal number of components, assessing and improving reproducibility of the ICA results, and comparison with other popular matrix factorization techniques. We discuss the emerging ICA applications to the integrative analysis of multi-level omics datasets and introduce a conceptual view on ICA as a tool for defining functional subsystems of a complex biological system and their interactions under various conditions. Our review is accompanied by a Jupyter notebook which illustrates the discussed concepts and provides a practical tool for applying ICA to the analysis of cancer omics datasets.

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Paixão, Douglas Antonio Alvaredo, Geizecler Tomazetto, Victoria Ramos Sodré, Thiago A. Gonçalves, Cristiane Akemi Uchima, Fernanda Büchli, Thabata Maria Alvarez et al. „Microbial enrichment and meta-omics analysis identify CAZymes from mangrove sediments with unique properties“. Enzyme and Microbial Technology 148 (August 2021): 109820. http://dx.doi.org/10.1016/j.enzmictec.2021.109820.

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Benevenuto, Rafael Fonseca, Hermoine Jean Venter, Caroline Bedin Zanatta, Rubens Onofre Nodari und Sarah Zanon Agapito-Tenfen. „Alterations in genetically modified crops assessed by omics studies: Systematic review and meta-analysis“. Trends in Food Science & Technology 120 (Februar 2022): 325–37. http://dx.doi.org/10.1016/j.tifs.2022.01.002.

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Boeri, Lucia, Francesca Donnaloja, Marzia Campanile, Lorenzo Sardelli, Marta Tunesi, Federica Fusco, Carmen Giordano und Diego Albani. „Using integrated meta-omics to appreciate the role of the gut microbiota in epilepsy“. Neurobiology of Disease 164 (März 2022): 105614. http://dx.doi.org/10.1016/j.nbd.2022.105614.

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Quemener, Maxence, Paraskevi Mara, Florence Schubotz, David Beaudoin, Wei Li, Maria Pachiadaki, Taylor R. Sehein et al. „Meta‐omics highlights the diversity, activity and adaptations of fungi in deep oceanic crust“. Environmental Microbiology 22, Nr. 9 (20.08.2020): 3950–67. http://dx.doi.org/10.1111/1462-2920.15181.

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Schiml, Valerie C., Francesco Delogu, Praveen Kumar, Benoit Kunath, Bérénice Batut, Subina Mehta, James E. Johnson et al. „Integrative meta-omics in Galaxy and beyond“. Environmental Microbiome 18, Nr. 1 (07.07.2023). http://dx.doi.org/10.1186/s40793-023-00514-9.

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Abstract Background ‘Omics methods have empowered scientists to tackle the complexity of microbial communities on a scale not attainable before. Individually, omics analyses can provide great insight; while combined as “meta-omics”, they enhance the understanding of which organisms occupy specific metabolic niches, how they interact, and how they utilize environmental nutrients. Here we present three integrative meta-omics workflows, developed in Galaxy, for enhanced analysis and integration of metagenomics, metatranscriptomics, and metaproteomics, combined with our newly developed web-application, ViMO (Visualizer for Meta-Omics) to analyse metabolisms in complex microbial communities. Results In this study, we applied the workflows on a highly efficient cellulose-degrading minimal consortium enriched from a biogas reactor to analyse the key roles of uncultured microorganisms in complex biomass degradation processes. Metagenomic analysis recovered metagenome-assembled genomes (MAGs) for several constituent populations including Hungateiclostridium thermocellum, Thermoclostridium stercorarium and multiple heterogenic strains affiliated to Coprothermobacter proteolyticus. The metagenomics workflow was developed as two modules, one standard, and one optimized for improving the MAG quality in complex samples by implementing a combination of single- and co-assembly, and dereplication after binning. The exploration of the active pathways within the recovered MAGs can be visualized in ViMO, which also provides an overview of the MAG taxonomy and quality (contamination and completeness), and information about carbohydrate-active enzymes (CAZymes), as well as KEGG annotations and pathways, with counts and abundances at both mRNA and protein level. To achieve this, the metatranscriptomic reads and metaproteomic mass-spectrometry spectra are mapped onto predicted genes from the metagenome to analyse the functional potential of MAGs, as well as the actual expressed proteins and functions of the microbiome, all visualized in ViMO. Conclusion Our three workflows for integrative meta-omics in combination with ViMO presents a progression in the analysis of ‘omics data, particularly within Galaxy, but also beyond. The optimized metagenomics workflow allows for detailed reconstruction of microbial community consisting of MAGs with high quality, and thus improves analyses of the metabolism of the microbiome, using the metatranscriptomics and metaproteomics workflows.

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„Forecasting microbiome dynamics using integrated meta-omics“. Nature Ecology & Evolution, 13.11.2023. http://dx.doi.org/10.1038/s41559-023-02248-w.

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Muñoz-Benavent, Maria, Felix Hartkopf, Tim Van Den Bossche, Vitor C. Piro, Carlos García-Ferris, Amparo Latorre, Bernhard Y. Renard und Thilo Muth. „gNOMO: a multi-omics pipeline for integrated host and microbiome analysis of non-model organisms“. NAR Genomics and Bioinformatics 2, Nr. 3 (05.08.2020). http://dx.doi.org/10.1093/nargab/lqaa058.

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Abstract The study of bacterial symbioses has grown exponentially in the recent past. However, existing bioinformatic workflows of microbiome data analysis do commonly not integrate multiple meta-omics levels and are mainly geared toward human microbiomes. Microbiota are better understood when analyzed in their biological context; that is together with their host or environment. Nevertheless, this is a limitation when studying non-model organisms mainly due to the lack of well-annotated sequence references. Here, we present gNOMO, a bioinformatic pipeline that is specifically designed to process and analyze non-model organism samples of up to three meta-omics levels: metagenomics, metatranscriptomics and metaproteomics in an integrative manner. The pipeline has been developed using the workflow management framework Snakemake in order to obtain an automated and reproducible pipeline. Using experimental datasets of the German cockroach Blattella germanica, a non-model organism with very complex gut microbiome, we show the capabilities of gNOMO with regard to meta-omics data integration, expression ratio comparison, taxonomic and functional analysis as well as intuitive output visualization. In conclusion, gNOMO is a bioinformatic pipeline that can easily be configured, for integrating and analyzing multiple meta-omics data types and for producing output visualizations, specifically designed for integrating paired-end sequencing data with mass spectrometry from non-model organisms.

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