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Relevant bibliographies by topics / Meta-omics

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Journal articles

Academic literature on the topic 'Meta-omics'

Author: Grafiati

Published: 25 May 2024

Last updated: 14 June 2025

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Journal articles on the topic "Meta-omics"

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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2

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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3

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 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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4

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 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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5

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 challenging due to the cost of multi-omics data. Meta-analysis has been used to aggregate the data from multiple studies and improve the analysis results. We propose a computationally efficient meta-analytic gene-clustering algorithm that combines multi-omics datasets from multiple studies, using the fixed effects linear models and a modified weighted correlation network analysis framework. The simulation study shows that the proposed method outperforms existing single omic-based clustering approaches when multi-omics data and/or multiple studies are available. A real data example demonstrates that our meta-analytic method outperforms single-study based methods.

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6

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, 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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7

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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8

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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9

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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10

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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