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Journal articles on the topic 'Meta-omics'

Author: Grafiati

Published: 25 May 2024

Last updated: 25 July 2025

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1

Rusk, Nicole. "A meta-network of -omics." Nature Methods 5, no. 1 (2008): 25. http://dx.doi.org/10.1038/nmeth1165.

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Mackelprang, Rachel, Scott R. Saleska, Carsten Suhr Jacobsen, Janet K. Jansson, and Neslihan Taş. "Permafrost Meta-Omics and Climate Change." Annual Review of Earth and Planetary Sciences 44, no. 1 (2016): 439–62. http://dx.doi.org/10.1146/annurev-earth-060614-105126.

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Sathyanarayanan, Anita, Rohit Gupta, Erik W. Thompson, Dale R. Nyholt, Denis C. Bauer, and Shivashankar H. Nagaraj. "A comparative study of multi-omics integration tools for cancer driver gene identification and tumour subtyping." Briefings in Bioinformatics 21, no. 6 (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 e

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Tsoungos, Anastasios, Violeta Pemaj, Aleksandra Slavko, John Kapolos, Marina Papadelli, and Konstantinos Papadimitriou. "The Rising Role of Omics and Meta-Omics in Table Olive Research." Foods 12, no. 20 (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 approac

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João, C. Sequeira, Rocha Miguel, Madalena Alves M., and F. Salvador Andreia. "UPIMAPI, reCOGnizer and KEGGCharter: Bioinformatics tools for functional annotation and visualization of (meta)-omics datasets." Computational and Structural Biotechnology Journal 20 (April 11, 2022): 1798–810. https://doi.org/10.1016/j.csbj.2022.03.042.

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Omics and meta-omics technologies are powerful approaches to explore microorganisms’ functions, but the sheer size and complexity of omics datasets often turn the analysis into a challenging task. Software developed for omics and <em>meta</em>-omics analyses, together with knowledgebases encompassing information on genes, proteins, taxonomic and functional annotation, among other types of information, are valuable resources for analyzing omics data. Although several bioinformatics resources are available for <em>meta</em>-omics analyses, many require significant compu

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Kemmo Tsafack, Ulrich Kemmo, Kwang Woo Ahn, Anne E. Kwitek, and Chien-Wei Lin. "Meta-Analytic Gene-Clustering Algorithm for Integrating Multi-Omics and Multi-Study Data." Bioengineering 11, no. 6 (2024): 587. http://dx.doi.org/10.3390/bioengineering11060587.

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Gene pathways and gene-regulatory networks are used to describe the causal relationship between genes, based on biological experiments. However, many genes are still to be studied to define novel pathways. To address this, a gene-clustering algorithm has been used to group correlated genes together, based on the similarity of their gene expression level. The existing methods cluster genes based on only one type of omics data, which ignores the information from other types. A large sample size is required to achieve an accurate clustering structure for thousands of genes, which can be challengi

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Mallick, Himel, Ali Rahnavard, Lauren J. McIver, et al. "Multivariable association discovery in population-scale meta-omics studies." PLOS Computational Biology 17, no. 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,

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Johnson, David R., Damian E. Helbling, Yujie Men, and Kathrin Fenner. "Can meta-omics help to establish causality between contaminant biotransformations and genes or gene products?" Environmental Science: Water Research & Technology 1, no. 3 (2015): 272–78. http://dx.doi.org/10.1039/c5ew00016e.

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

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Darzi, Youssef, Gwen Falony, Sara Vieira-Silva, and Jeroen Raes. "Towards biome-specific analysis of meta-omics data." ISME Journal 10, no. 5 (2015): 1025–28. http://dx.doi.org/10.1038/ismej.2015.188.

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Cembrowska-Lech, Danuta, Adrianna Krzemińska, Tymoteusz Miller, et al. "An Integrated Multi-Omics and Artificial Intelligence Framework for Advance Plant Phenotyping in Horticulture." Biology 12, no. 10 (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 in

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Qin, Xiaofa. "Can inflammatory bowel disease really be solved by the multiple -omics and meta-omics analyses?" Immunology Letters 165, no. 2 (2015): 107–8. http://dx.doi.org/10.1016/j.imlet.2015.03.007.

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Xu, Zhichao, and Peng Wei. "A novel statistical framework for meta-analysis of total mediation effect with high-dimensional omics mediators in large-scale genomic consortia." PLOS Genetics 20, no. 11 (2024): e1011483. http://dx.doi.org/10.1371/journal.pgen.1011483.

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Meta-analysis is used to aggregate the effects of interest across multiple studies, while its methodology is largely underexplored in mediation analysis, particularly in estimating the total mediation effect of high-dimensional omics mediators. Large-scale genomic consortia, such as the Trans-Omics for Precision Medicine (TOPMed) program, comprise multiple cohorts with diverse technologies to elucidate the genetic architecture and biological mechanisms underlying complex human traits and diseases. Leveraging the recent established asymptotic standard error of the R-squared (R2)-based mediation

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Mannaa, Mohamed, Gil Han, Young-Su Seo, and Inmyoung Park. "Evolution of Food Fermentation Processes and the Use of Multi-Omics in Deciphering the Roles of the Microbiota." Foods 10, no. 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 kn

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Kim, SungHwan, Dongwan Kang, Zhiguang Huo, Yongseok Park, and George C. Tseng. "Meta-analytic principal component analysis in integrative omics application." Bioinformatics 34, no. 8 (2017): 1321–28. http://dx.doi.org/10.1093/bioinformatics/btx765.

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Bu, Yongqi, Jiaxuan Liang, Zhen Li, Jianbo Wang, Jun Wang, and Guoxian Yu. "Cancer molecular subtyping using limited multi-omics data with missingness." PLOS Computational Biology 20, no. 12 (2024): e1012710. https://doi.org/10.1371/journal.pcbi.1012710.

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Diagnosing cancer subtypes is a prerequisite for precise treatment. Existing multi-omics data fusion-based diagnostic solutions build on the requisite of sufficient samples with complete multi-omics data, which is challenging to obtain in clinical applications. To address the bottleneck of collecting sufficient samples with complete data in clinical applications, we proposed a flexible integrative model (CancerSD) to diagnose cancer subtype using limited samples with incomplete multi-omics data. CancerSD designs contrastive learning tasks and masking-and-reconstruction tasks to reliably impute

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Can, Handan, Sree K. Chanumolu, Barbara D. Nielsen, et al. "Integration of Meta-Multi-Omics Data Using Probabilistic Graphs and External Knowledge." Cells 12, no. 15 (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

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Chialva, Matteo, Stefano Ghignone, Mara Novero, Wael N. Hozzein, Luisa Lanfranco, and Paola Bonfante. "Tomato RNA-seq Data Mining Reveals the Taxonomic and Functional Diversity of Root-Associated Microbiota." Microorganisms 8, no. 1 (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 so

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Myall, Ashleigh C., Simon Perkins, David Rushton, et al. "An OMICs-based meta-analysis to support infection state stratification." Bioinformatics 37, no. 16 (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 i

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Malacrinò, Antonino. "Meta-Omics Tools in the World of Insect-Microorganism Interactions." Biology 7, no. 4 (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 considerab

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Rodríguez, Elisa, Pedro A. García-Encina, Alfons J. M. Stams, Farai Maphosa, and Diana Z. Sousa. "Meta-omics approaches to understand and improve wastewater treatment systems." Reviews in Environmental Science and Bio/Technology 14, no. 3 (2015): 385–406. http://dx.doi.org/10.1007/s11157-015-9370-x.

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Robaina-Estévez, Semidán, and Jay Gutiérrez. "Applications of marine microbial community models in the nature-based economy." PLOS Sustainability and Transformation 3, no. 11 (2024): e0000145. http://dx.doi.org/10.1371/journal.pstr.0000145.

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Marine microorganisms are central to global ecological and biogeochemical systems, with their intricate interactions shaping community dynamics. While meta-omics data sets have revolutionized marine microbial ecology, they often provide fragmented insights, underscoring the need for advanced integrative modeling frameworks. In this review, we highlight the potential that community genome-scale metabolic models (cGEMs), in combination with meta-omics and environmental data sets, offer in advancing marine microbial ecology. We explore 3 key applications: quantifying marine ecosystem services, gu

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Tang, Haotian, Yanqing Huang, Didi Yuan, and Junwen Liu. "Atherosclerosis, gut microbiome, and exercise in a meta-omics perspective: a literature review." PeerJ 12 (April 4, 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

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Alaimo, Laura, Sara Boggio, Giovanni Catalano, et al. "Multi-Omics Classification of Intrahepatic Cholangiocarcinoma: A Systematic Review and Meta-Analysis." Cancers 16, no. 14 (2024): 2596. http://dx.doi.org/10.3390/cancers16142596.

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Intrahepatic cholangiocarcinoma (ICC) is a heterogeneous disease characterized by a dismal prognosis. Various attempts have been made to classify ICC subtypes with varying prognoses, but a consensus has yet to be reached. This systematic review aims to gather relevant data on the multi-omics-based ICC classification. The PubMed, Embase, and Cochrane databases were searched for terms related to ICC and multi-omics analysis. Studies that identified multi-omics-derived ICC subtypes and investigated clinicopathological predictors of long-term outcomes were included. Nine studies, which included 91

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Ding, Jessica, Montgomery Blencowe, Thien Nghiem, et al. "Mergeomics 2.0: a web server for multi-omics data integration to elucidate disease networks and predict therapeutics." Nucleic Acids Research 49, W1 (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 consiste

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Taverna, Federico, Jermaine Goveia, Tobias K. Karakach, et al. "BIOMEX: an interactive workflow for (single cell) omics data interpretation and visualization." Nucleic Acids Research 48, W1 (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

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Tilocca, Bruno, Luisa Pieroni, Alessio Soggiu, et al. "Gut–Brain Axis and Neurodegeneration: State-of-the-Art of Meta-Omics Sciences for Microbiota Characterization." International Journal of Molecular Sciences 21, no. 11 (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).

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Dessì, Angelica, Roberta Pintus, Vassilios Fanos, and Alice Bosco. "Integrative Multiomics Approach to Skin: The Sinergy between Individualised Medicine and Futuristic Precision Skin Care?" Metabolites 14, no. 3 (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 microbiot

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Hoogstrate, Youri, Santoesha A. Ghisai, Maurice de Wit, et al. "The EGFRvIII transcriptome in glioblastoma: A meta-omics analysis." Neuro-Oncology 24, no. 3 (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

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Fritz, Joëlle V., Mahesh S. Desai, Pranjul Shah, Jochen G. Schneider, and Paul Wilmes. "From meta-omics to causality: experimental models for human microbiome research." Microbiome 1, no. 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, and Marie Joossens. "Meta-omics in Inflammatory Bowel Disease Research: Applications, Challenges, and Guidelines." Journal of Crohn's and Colitis 10, no. 6 (2016): 735–46. http://dx.doi.org/10.1093/ecco-jcc/jjw024.

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Stec, Krzysztof Franciszek, Luigi Caputi, Pier Luigi Buttigieg, et al. "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, and Matthew Wook Chang. "Meta‐Omics‐ and Metabolic Modeling‐Assisted Deciphering of Human Microbiota Metabolism." Biotechnology Journal 14, no. 9 (2019): 1800445. http://dx.doi.org/10.1002/biot.201800445.

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Tiong, Khong-Loon, Nardnisa Sintupisut, Min-Chin Lin, et al. "An integrated analysis of the cancer genome atlas data discovers a hierarchical association structure across thirty three cancer types." PLOS Digital Health 1, no. 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

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Nnyanzi, Lawrence Achilles, Akinyele Olumuyiwa Adisa, Kehinde Kazeem Kanmodi, et al. "Status of Omics Research Capacity on Oral Cancer in Africa: A Systematic Scoping Review Protocol." BioMedInformatics 3, no. 2 (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 sy

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Xu, Jing, and Yuejin Yang. "Gut microbiome and its meta-omics perspectives: profound implications for cardiovascular diseases." Gut Microbes 13, no. 1 (2021): 1936379. http://dx.doi.org/10.1080/19490976.2021.1936379.

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Mondot, Stanislas, and Patricia Lepage. "The human gut microbiome and its dysfunctions through the meta-omics prism." Annals of the New York Academy of Sciences 1372, no. 1 (2016): 9–19. http://dx.doi.org/10.1111/nyas.13033.

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Xu, H., M. Gu, X. Zheng, et al. "An integrated meta-omics based approach in pediatric obstructive sleep apnea syndrome." Sleep Medicine 40 (December 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, and Elisabeth Letellier. "Microbiome in Colorectal Cancer: How to Get from Meta-omics to Mechanism?" Trends in Microbiology 28, no. 5 (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, and Elisabeth Letellier. "Microbiome in Colorectal Cancer: How to Get from Meta-omics to Mechanism?" Trends in Microbiology 28, no. 8 (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, and Che Ok Jeon. "Unraveling microbial fermentation features in kimchi: from classical to meta-omics approaches." Applied Microbiology and Biotechnology 104, no. 18 (2020): 7731–44. http://dx.doi.org/10.1007/s00253-020-10804-8.

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Dugat-Bony, Eric, Cécile Straub, Aurélie Teissandier, et al. "Overview of a Surface-Ripened Cheese Community Functioning by Meta-Omics Analyses." PLOS ONE 10, no. 4 (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, and Peter Meinicke. "Meta-Analysis of Pathway Enrichment: Combining Independent and Dependent Omics Data Sets." PLoS ONE 9, no. 2 (2014): e89297. http://dx.doi.org/10.1371/journal.pone.0089297.

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Cocolin, Luca, Marios Mataragas, Francois Bourdichon, et al. "Next generation microbiological risk assessment meta-omics: The next need for integration." International Journal of Food Microbiology 287 (December 2018): 10–17. http://dx.doi.org/10.1016/j.ijfoodmicro.2017.11.008.

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Wen, Gang, and Limin Li. "MMOSurv: Meta-learning for few-shot survival analysis with multi-omics data." Bioinformatics, November 19, 2024. http://dx.doi.org/10.1093/bioinformatics/btae684.

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Abstract Motivation High-throughput techniques have produced a large amount of high-dimensional multi-omics data, which makes it promising to predict patient survival outcomes more accurately. Recent work has showed the superiority of multi-omics data in survival analysis. However, it remains challenging to integrate multi-omics data to solve few-shot survival prediction problem, with only a few available training samples, especially for rare cancers. Results In this work, we propose a meta-learning framework for multi-omics few-shot survival analysis, namely MMOSurv, which enables to learn an

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Sequeira, João C., Vítor Pereira, M. Madalena Alves, M. Alcina Pereira, Miguel Rocha, and Andreia F. Salvador. "MOSCA 2.0: A bioinformatics framework for metagenomics, metatranscriptomics and metaproteomics data analysis and visualization." Molecular Ecology Resources, August 4, 2024. http://dx.doi.org/10.1111/1755-0998.13996.

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AbstractThe analysis of meta‐omics data requires the utilization of several bioinformatics tools and proficiency in informatics. The integration of multiple meta‐omics data is even more challenging, and the outputs of existing bioinformatics solutions are not always easy to interpret. Here, we present a meta‐omics bioinformatics pipeline, Meta‐Omics Software for Community Analysis (MOSCA), which aims to overcome these limitations. MOSCA was initially developed for analysing metagenomics (MG) and metatranscriptomics (MT) data. Now, it also performs MG and metaproteomics (MP) integrated analysis

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Schiml, Valerie C., Francesco Delogu, Praveen Kumar, et al. "Integrative meta-omics in Galaxy and beyond." Environmental Microbiome 18, no. 1 (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-applica

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Muñoz-Benavent, Maria, Felix Hartkopf, Tim Van Den Bossche, et al. "gNOMO: a multi-omics pipeline for integrated host and microbiome analysis of non-model organisms." NAR Genomics and Bioinformatics 2, no. 3 (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 proce

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"Forecasting microbiome dynamics using integrated meta-omics." Nature Ecology & Evolution, November 13, 2023. http://dx.doi.org/10.1038/s41559-023-02248-w.

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Hör, Jens. "Advancing RNA phage biology through meta-omics." Nucleic Acids Research 53, no. 8 (2025). https://doi.org/10.1093/nar/gkaf314.

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Abstract Bacteriophages with RNA genomes are among the simplest biological entities on Earth. Since their discovery in the 1960s, they have been used as important models to understand the principal processes of life, including translation and the genetic code. While RNA phages were generally thought of as rare oddities in nature, meta-omics methods are rapidly changing this simplistic view by studying diverse biomes with unprecedented resolution. Metatranscriptomics dramatically expanded the number of known RNA phages from tens to tens of thousands, revealed their widespread abundance, and dis

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