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Relevant bibliographies by topics / Multi-omic analysis

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Journal articles

Academic literature on the topic 'Multi-omic analysis'

Author: Grafiati

Published: 4 May 2024

Last updated: 18 June 2025

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Journal articles on the topic "Multi-omic analysis"

1

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

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

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Li, Jin, Feng Chen, Hong Liang, and Jingwen Yan. "MoNET: an R package for multi-omic network analysis." Bioinformatics 38, no. 4 (2021): 1165–67. http://dx.doi.org/10.1093/bioinformatics/btab722.

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Abstract Motivation The increasing availability of multi-omic data has enabled the discovery of disease biomarkers in different scales. Understanding the functional interaction between multi-omic biomarkers is becoming increasingly important due to its great potential for providing insights of the underlying molecular mechanism. Results Leveraging multiple biological network databases, we integrated the relationship between single nucleotide polymorphisms (SNPs), genes/proteins and metabolites, and developed an R package Multi-omic Network Explorer Tool (MoNET) for multi-omic network analysis. This new tool enables users to not only track down the interaction of SNPs/genes with metabolome level, but also trace back for the potential risk variants/regulators given altered genes/metabolites. MoNET is expected to advance our understanding of the multi-omic findings by unveiling their transomic interactions and is likely to generate new hypotheses for further validation. Availability and implementation The MoNET package is freely available on https://github.com/JW-Yan/MONET. Supplementary information Supplementary data are available at Bioinformatics online.

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3

Boekel, Jorrit, John M. Chilton, Ira R. Cooke, et al. "Multi-omic data analysis using Galaxy." Nature Biotechnology 33, no. 2 (2015): 137–39. http://dx.doi.org/10.1038/nbt.3134.

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Morota, Gota. "30 Mutli-omic data integration in quantitative genetics." Journal of Animal Science 97, Supplement_2 (2019): 15. http://dx.doi.org/10.1093/jas/skz122.027.

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Abstract The advent of high-throughput technologies has generated diverse omic data including single-nucleotide polymorphisms, copy-number variation, gene expression, methylation, and metabolites. The next major challenge is how to integrate those multi-omic data for downstream analyses to enhance our biological insights. This emerging approach is known as multi-omic data integration, which is in contrast to studying each omic data type independently. I will discuss challenging issues in developing algorithms and methods for multi-omic data integration. The particular focus will be given to the potential for combining diverse types of FAANG data and the utility of multi-omic data integration in association analysis and phenotypic prediction.

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5

Sangaralingam, Ajanthah, Abu Z. Dayem Ullah, Jacek Marzec, et al. "‘Multi-omic’ data analysis using O-miner." Briefings in Bioinformatics 20, no. 1 (2017): 130–43. http://dx.doi.org/10.1093/bib/bbx080.

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6

von der Heyde, Silvia, Margarita Krawczyk, Julia Bischof, et al. "Clinically relevant multi-omic analysis of colorectal cancer." Journal of Clinical Oncology 38, no. 15_suppl (2020): e16063-e16063. http://dx.doi.org/10.1200/jco.2020.38.15_suppl.e16063.

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e16063 Background: Cancer is a highly heterogeneous disease, both intra- and inter-individually consisting of complex phenotypes and systems biology. Although genomic data has contributed greatly towards the identification of cancer-specific mutations and the progress of precision medicine, genomic alterations are only one of several important biological drivers of cancer. Furthermore, single-layer omics represent only a small piece of the cancer biology puzzle and provide only partial clues to connecting genotype with clinically relevant phenotypic data. A more integrated approach is urgently needed to unravel the underpinnings of molecular signatures and the phenotypic manifestation of cancer hallmarks. Methods: Here we characterize a colorectal cancer (CRC) cohort of 500 patients across multiple distinct omic data types. Across this CRC cohort, we defined clinically relevant whole genome sequencing based metrics such as micro-satellite-instability (MSI) status, and furthermore investigate gene expression at the transcript level using RNA-Seq, as well as at the proteomic level using tandem mass spectrometry. We further characterized a subgroup of 100 of these patients through 16s rRNA sequencing to identify associated microbiome profiles. Results: We combined these analyses with comprehensive clinical data to observe the impact of ascertained molecular signatures on the CRC patient cohort. Here, we report how patient survival correlates both with specific molecular events across individual omic data types, as well as with combined multi-omic analyses. Conclusions: This project highlights the utility of integrating multiple distinct data types to obtain a more comprehensive overview of the molecular mechanisms underpinning colo-rectal cancer. Furthermore, through combining identified aberrant molecular mechanisms with clinical reports, multi-omic data can be prioritized through their impact on patient cohort survival.

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Beheshti, Ramin, Steven Hicks, and Patrick Frangos. "Multi-omic Analysis Enhances Prediction Of Infantile Wheezing." Journal of Allergy and Clinical Immunology 151, no. 2 (2023): AB210. http://dx.doi.org/10.1016/j.jaci.2022.12.654.

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8

Hale, Andrew T., Lisa Bastarache, Diego M. Morales, John C. Wellons, David D. Limbrick, and Eric R. Gamazon. "Multi-omic analysis elucidates the genetic basis of hydrocephalus." Cell Reports 35, no. 5 (2021): 109085. http://dx.doi.org/10.1016/j.celrep.2021.109085.

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9

Henry, V. J., A. E. Bandrowski, A. S. Pepin, B. J. Gonzalez, and A. Desfeux. "OMICtools: an informative directory for multi-omic data analysis." Database 2014 (July 14, 2014): bau069. http://dx.doi.org/10.1093/database/bau069.

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10

Beheshti, Ramin, Shane Stone, Desirae Chandran, and Steven D. Hicks. "Multi-Omic Profiles in Infants at Risk for Food Reactions." Genes 13, no. 11 (2022): 2024. http://dx.doi.org/10.3390/genes13112024.

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Food reactions (FR) are multifactorial and impacted by medical, demographic, environmental, and immunologic factors. We hypothesized that multi-omic analyses of host-microbial factors in saliva would enhance our understanding of FR development. This longitudinal cohort study included 164 infants followed from birth through two years. The infants were identified as FR (n = 34) or non-FR (n = 130) using the Infant Feeding Practice II survey and medical record confirmation. Saliva was collected at six months for the multi-omic assessment of cytokines, mRNAs, microRNAs, and the microbiome/virome. The levels of one miRNA (miR-203b-3p, adj. p = 0.043, V = 2913) and one viral phage (Proteus virus PM135, adj. p = 0.027, V = 2955) were lower among infants that developed FRs. The levels of one bacterial phylum (Cyanobacteria, adj. p = 0.048, V = 1515) were higher among infants that developed FR. Logistical regression models revealed that the addition of multi-omic features (miR-203b-3p, Cyanobacteria, and Proteus virus PM135) improved predictiveness for future FRs in infants (p = 0.005, X2 = 12.9), predicting FRs with 72% accuracy (AUC = 0.81, sensitivity = 72%, specificity = 72%). The multi-omic analysis of saliva may enhance the accurate identification of infants at risk of FRs and provide insights into the host/microbiome interactions that predispose certain infants to FRs.

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