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Relevant bibliographies by topics / Multi-omic analysis

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Academic literature on the topic 'Multi-omic analysis'

Author: Grafiati

Published: 4 May 2024

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Journal articles on the topic "Multi-omic analysis"

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Lancaster, Samuel M., Akshay Sanghi, Si Wu, and Michael P. Snyder. "A Customizable Analysis Flow in Integrative Multi-Omics." Biomolecules 10, no. 12 (November 27, 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 (October 25, 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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Boekel, Jorrit, John M. Chilton, Ira R. Cooke, Peter L. Horvatovich, Pratik D. Jagtap, Lukas Käll, Janne Lehtiö, Pieter Lukasse, Perry D. Moerland, and Timothy J. Griffin. "Multi-omic data analysis using Galaxy." Nature Biotechnology 33, no. 2 (February 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 (July 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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Sangaralingam, Ajanthah, Abu Z. Dayem Ullah, Jacek Marzec, Emanuela Gadaleta, Ai Nagano, Helen Ross-Adams, Jun Wang, Nicholas R. Lemoine, and Claude Chelala. "‘Multi-omic’ data analysis using O-miner." Briefings in Bioinformatics 20, no. 1 (August 4, 2017): 130–43. http://dx.doi.org/10.1093/bib/bbx080.

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von der Heyde, Silvia, Margarita Krawczyk, Julia Bischof, Thomas Corwin, Peter Frommolt, Jonathan Woodsmith, and Hartmut Juhl. "Clinically relevant multi-omic analysis of colorectal cancer." Journal of Clinical Oncology 38, no. 15_suppl (May 20, 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 (February 2023): AB210. http://dx.doi.org/10.1016/j.jaci.2022.12.654.

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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 (May 2021): 109085. http://dx.doi.org/10.1016/j.celrep.2021.109085.

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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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Beheshti, Ramin, Shane Stone, Desirae Chandran, and Steven D. Hicks. "Multi-Omic Profiles in Infants at Risk for Food Reactions." Genes 13, no. 11 (November 3, 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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Dissertations / Theses on the topic "Multi-omic analysis"

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Bilbrey, Emma A. "Seeding Multi-omic Improvement of Apple." The Ohio State University, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=osu1594907111820227.

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Thavamani, Abhishek [Verfasser], and Alfred [Akademischer Betreuer] Nordheim. "Integrated multi-omic analysis of HCC formation in the SRF-VP16iHep mouse model / Abhishek Thavamani ; Betreuer: Alfred Nordheim." Tübingen : Universitätsbibliothek Tübingen, 2018. http://d-nb.info/1173699864/34.

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VITRIOLO, ALESSANDRO. "MULTI-OMIC DECONVOLUTION OF THE REGULATORY NETWORKS UNDERLYING NEURODEVELOPMENTAL AND AUTISM SPECTRUM DISORDERS: A MULTIDIMENTIONAL ANALYSIS FOR A NEW DISEASE MODELLING PARADIGM." Doctoral thesis, Università degli Studi di Milano, 2019. http://hdl.handle.net/2434/609586.

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Recent literature has highlighted that mutations causing neurodevelopmental syndromes are particularly enriched in genes related to chromatin regulation and synaptic functions. While the latter could be easily predicted, the former shed seeds to the flourishing of epigenomic studies focused on this type of disorders. Intriguingly, most of such disorders couple different shades of intellectual disabilities with peculiar cranio-facial features and systemic defects which are shared, opposite or unique across them. I have set up a dynamic framework of analysis, that encompasses the comparison of multiple disorders, cell types and cultures, to highlight cell-type specific and developmentally-relevant paths of transcriptional deregulation. Building on my lab’s expertise to harness potency and stability of induced pluripotent stem cells (iPSCs), I identified two main axes of development through which we could characterize on the one hand cerebral cortex related dysregulations and on the other hand cranio-facial features associated traits, peripheral nervous system- and cardiovascular system-related dysregulations. The former is based on the production of adult glutamatergic cortical neurons through ectopic expression of NGN2 in iPSCs and, in parallel, through production of brain organoids: 3D cultures obtained by neuronal differentiation and patterning via sequential exposure to small molecules. The latter is based on differentiation of iPSCs to neural crest stem cells (NCSCs) and mesenchymal stem cells (MSCs). I collected and standardised transcriptomic data coming from controls- and patient-derived iPSCs accounting for six disorders, NCSCs for five disorders and MSCs for two disorders; NGN2 neurons for two disorders and brain organoid for one disorder. During my research I helped define new standards for RNA-seq experiments tailored for differential-expression analysis and developed or implemented tools to make cross-disorder and cross-tissue comparisons in a connectable way. This work let me identify regulatory circuitries shared by all disorders or by subgroups characterized by shared phenotypes; symmetric deregulations in disorders caused by mutation of opposite histone modifiers; unexpected subgroups that will require further investigation. For most disorders, my work confirms previously published evidence that dysregulations identified at the pluripotent stage can be inherited and amplified in disease-relevant tissues in a tissue-specific fashion. Thus, I was capable of identifying disease-specific dysregulations at the pluripotent stage and in disease-relevant tissues; I drew conclusions on iPSCs cross-disorder transcriptional dysregulations through the definition of transcriptional modules; I implemented an analytical framework to boost the ability of identifying the effect of knocking down a certain gene on transcriptional and epigenetic landscapes; I identified sets of genes whose deregulation at the pluripotent stage reverberates and amplifies along development, funnelling and filtering several analyses to converge on a small set of actionable targets; I identified a small set of potential direct targets of PRC2 complex involved in brain development and on the onset of Weaver Syndrome; I identified BAZ1B-specific transcriptional dysregulations in NCSCs that confirm its importance for migration and craniofacial morphogenesis but more in general for chromatin remodelling and human evolution; I helped in the molecular characterization of YY1 mutations, which led to the identification of Gabriele-de Vries Syndrome; I contributed to the molecular characterization of Kabuki Syndrome in neural crest and adult cortical neurons.

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Carriot, Nathan. "Caractérisation de la production métabolique de biofilms marins. : Vers une application à l'étude de biofilms complexes in situ." Electronic Thesis or Diss., Toulon, 2022. http://www.theses.fr/2022TOUL0001.

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Le phénomène de biofouling est un processus naturel qui impacte toutes les surfaces immergées en milieu marin engendrant des problèmes économiques et écologiques majeurs à l’échelle planétaire. Il est notamment induit par la formation de biofilms marins correspondant à la colonisation des surfaces immergées par des bactéries s’organisant en communautés en s’entourant d’une matrice de substances polymériques extracellulaires (EPS). L’objectif de ce travail est l’utilisation et le développement de méthodologies permettant l’étude et la compréhension de l’étape précurseur de ce phénomène. La corrélation des données récoltées à partir des méthodes appliquées (métabolomique et réseau moléculaire, protéomique, dosages colorimétriques, microscopies, spectroscopies) permet une approche multi-échelles pour la caractérisation des biofilms. Ces développements visent, en premier lieu, à caractériser la production biochimique globale de biofilms in vitro pour ensuite analyser des biofilms naturels formés in situ. L’utilisation de ce large panel de techniques a permis de répondre à certaines questions scientifiques comme l’impact des nutriments (phosphates), d’une enzyme (quorum sensing) ou de l’hydrodynamisme sur la nature de biofilms formés
The phenomenon of biofouling is a natural process that impacts all the surfaces submerged in the marine environment, generating major economic and ecological problems on a global scale. It is induced by the formation of marine biofilms corresponding to the colonization of submerged surfaces by bacteria organizing in communities by surrounding themselves with a matrix of extracellular polymeric substances (EPS). The objective of this work is the use and development of methodologies to study and understand the precursor stage of this phenomenon. The correlation of the data collected from the applied methods (metabolomics and molecular network, proteomics, colorimetric assays, microscopies, spectroscopy) allows a multi-scale approach for the characterization of biofilms. These developments aim, first of all, to characterize the overall biochemical production of in vitro biofilms and then analyse natural biofilms formed in situ. The use of this wide range of techniques has made it possible to answer certain scientific questions such as the impact of nutrients (phosphates), an enzyme (quorum sensing) or hydrodynamics on the nature of formed biofilms

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Ciaccio, Roberto <1990&gt. "Multi-omic analyses of the MYCN network unveil new potential vulnerabilities in childhood neuroblastoma." Doctoral thesis, Alma Mater Studiorum - Università di Bologna, 2021. http://amsdottorato.unibo.it/9930/1/PhD%20thesis%20Ciaccio%20Roberto_2021.pdf.

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Neuroblastoma is the first neurogenic-extracranial solid cancer occurring in infancy and childhood. The genetic aberration most commonly associated with a poor prognosis is MYCN gene’s amplification. We hypothesize that effective anti-MYC therapeutics can be developed by understanding the regulation and function of N-MYC in neuroblastoma. Since N-MYC is an intrinsically disordered protein, it is still challenging to target this transcription factor, however, the model is shifting significantly after discovering novel therapeutic targets that impact MYC-driven tumorigenesis. The following work explores how MYCN expression affects the induction and maintenance of neuroblastoma. By using different multi-omic approaches and many promising innovative techniques, we were able to identify and characterize new potential vulnerabilities of this pathology, which may work in concert with N-MYC for the instruction of a high-risk neuroblastoma phenotype. My studies’ first objective was to investigate whether and how N-MYC can regulate transcription of lncRNAs by comparing transcriptional profiles between non-amplified and MYCN-amplified neuroblastoma cells. Here, we singled out lncNB1, which is selectively higher expressed in high MYCN cells only and it is also firmly and almost uniquely transcribed in neuroblastoma among all types of cancers. Our data showed that N-MYC directly activates transcription of lncNB1, instructing a complex network of molecular interactions, ultimately resulting in increased N-MYC protein stability, reinforcing the N-MYC oncogenetic program. The second objective was to assess how high N-MYC expression may cooperate to establish a dynamic regulatory axis with the E2F3 transcription factor, impacting the development of the high-risk cancer phenotype. Taken together, our unbias screenings uncovered potential candidates that help to fill the knowledge gap in understanding what is the impact of N-MYC in childhood neuroblastoma, providing new opportunities for the development of specific treatments able to target the function of MYC oncoproteins in a context of MYCN gene amplification.

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Wang, Dongxue [Verfasser], Bernhard [Akademischer Betreuer] Küster, Bernhard [Gutachter] Küster, and Julien [Gutachter] Gagneur. "Comprehensive characterization of the human proteome by multi-omic analyses / Dongxue Wang ; Gutachter: Bernhard Küster, Julien Gagneur ; Betreuer: Bernhard Küster." München : Universitätsbibliothek der TU München, 2018. http://d-nb.info/1172415145/34.

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(10723641), Nathaphon Yu King Hing. "A Multi-Omic Characterization Of The Calvin-Benson-Bassham Cycle In Cyanobacteria." Thesis, 2021.

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Cyanobacteria are photosynthetic organisms with the potential to sustainably produce carbon-based end products by fixing carbon dioxide from the atmosphere. Optimizing the growth or biochemical production in cyanobacteria is an ongoing challenge in metabolic engineering. Rational design of metabolic pathways requires a deep understanding of regulatory mechanisms. Hence, a deeper understanding of photosynthetic regulation of the influence of the environment on metabolic fluxes provides exciting possibilities for enhancing the photosynthetic Calvin-Benson-Bassham cycle. One approach to study metabolic processes is to use omic-level techniques, such as proteomics and fluxomics, to characterize varying phenotypes that result from different environmental conditions or different genetic perturbations.

This dissertation examines the influence of light intensity on enzymatic abundances and the resulting Calvin-Benson-Bassham cycle fluxes using a combined proteomic and fluxomic approach in the model cyanobacteria Synechocystis sp. PCC 6803. The correlation between light intensity and enzymatic abundances is evaluated to determine which reactions are more regulated by enzymatic abundance. Additionally, carbon enrichment data from isotopic labelling experiments strongly suggest metabolite channeling as a flexible and light-dependent regulatory mechanism present in cyanobacteria. We propose and substantiate biological mechanisms that explains the formation of metabolite channels under specific redox conditions.

The same multi-omic approach was used to examine genetically modified cyanobacteria. Specifically, genetically engineered and conditionally growth-enhanced Synechocystis strains overexpressing the central Calvin-Benson-Bassham cycle enzymes FBP/SBPase or transketolase were evaluated. We examined the effect of the heterologous expression of each of these enzymes on the Calvin-Benson-Bassham cycle, as well as on adjacent central metabolic pathways. Using both proteomics and fluxomics, we demonstrate distinct increases in Calvin-Benson-Bassham cycle efficiency as a result of lowered oxidative pentose phosphate pathway activity. This work demonstrates the utility of a multi-omic approach in characterizing the differing phenotypes arising from environmental and genetic changes.

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Sharma, Supriya. "Integrative analysis of complex genomic and epigenomic maps." Thesis, 2018. https://hdl.handle.net/2144/27437.

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Modern healthcare research demands collaboration across disciplines to build preventive measures and innovate predictive capabilities for curing diseases. Along with the emergence of cutting-edge computational and statistical methodologies, data generation and analysis has become cheaper in the last ten years. However, the complexity of big data due to its variety, volume, and velocity creates new challenges for biologists, physicians, bioinformaticians, statisticians, and computer scientists. Combining data from complex multiple profiles is useful to better understand cellular functions and pathways that regulates cell function to provide insights that could not have been obtained using the individual profiles alone. However, current normalization and artifact correction methods are platform and data type specific, and may require both the training and test sets for any application (e.g. biomarker development). This often leads to over-fitting and reduces the reproducibility of genomic findings across studies. In addition, many bias correction and integration approaches require renormalization or reanalysis if additional samples are later introduced. The motivation behind this research was to develop and evaluate strategies for addressing data integration issues across data types and profiling platforms, which should improve healthcare-informatics research and its application in personalized medicine. We have demonstrated a comprehensive and coordinated framework for data standardization across tissue types and profiling platforms. This allows easy integration of data from multiple data generating consortiums. The main goal of this research was to identify regions of genetic-epigenetic co-ordination that are independent of tissue type and consistent across epigenomics profiling data platforms. We developed multi-‘omic’ therapeutic biomarkers for epigenetic drug efficacy by combining our biomarker regions with drug perturbation data generated in our previous studies. We used an adaptive Bayesian factor analysis approach to develop biomarkers for multiple HDACs simultaneously, allowing for predictions of comparative efficacy between the drugs. We showed that this approach leads to different predictions across breast cancer subtypes compared to profiling the drugs separately. We extended this approach on patient samples from multiple public data resources containing epigenetic profiling data from cancer and normal tissues (The Cancer Genome Atlas, TCGA; NIH Roadmap epigenomics data).

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Tassinari, Anna. "Multi-omic biomarker discovery and network analyses to elucidate the molecular mechanisms of lung cancer premalignancy." Thesis, 2017. https://hdl.handle.net/2144/27344.

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Lung cancer (LC) is the leading cause of cancer death in the US, claiming over 160,000 lives annually. Although CT screening has been shown to be efficacious in reducing mortality, the limited access to screening programs among high-risk individuals and the high number of false positives contribute to low survival rates and increased healthcare costs. As a result, there is an urgent need for preventative therapeutics and novel interception biomarkers that would enhance current methods for detection of early-stage LC. This thesis addresses this challenge by examining the hypothesis that transcriptomic changes preceding the onset of LC can be identified by studying bronchial premalignant lesions (PMLs) and the normal-appearing airway epithelial cells altered in their presence (i.e., the PML-associated airway field of injury). PMLs are the presumed precursors of lung squamous cell carcinoma (SCC) whose presence indicates an increased risk of developing SCC and other subtypes of LC. Here, I leverage high-throughput mRNA and miRNA sequencing data from bronchial brushings and lesion biopsies to develop biomarkers of PML presence and progression, and to understand regulatory mechanisms driving early carcinogenesis. First, I utilized mRNA sequencing data from normal-appearing airway brushings to build a biomarker predictive of PML presence. After verifying the power of the 200-gene biomarker to detect the presence of PMLs, I evaluated its capacity to predict PML progression and detect presence of LC (Aim 1). Next, I identified likely regulatory mechanisms associated with PML severity and progression, by evaluating miRNA expression and gene coexpression modules containing their targets in bronchial lesion biopsies (Aim2). Lastly, I investigated the preservation of the PML-associated miRNAs and gene modules in the airway field of injury, highlighting an emergent link between the airway field and the PMLs (Aim 3). Overall, this thesis suggests a multi-faceted utility of PML-associated genomic signatures as markers for stratification of high-risk smokers in chemoprevention trials, markers for early detection of lung cancer, and novel chemopreventive targets, and yields valuable insights into early lung carcinogenesis by characterizing mRNA and miRNA expression alterations that contribute to premalignant disease progression towards LC.
2020-01-25

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Gagliardi, Miriam. "Integrative analyses of multi-omic data applied to the study of a rare human disease, the ICF syndrome." Tesi di dottorato, 2016. http://www.fedoa.unina.it/11107/1/Tesi_Miriam_Gagliardi.pdf.

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Application of the next-generation sequencing (NGS) technology has transformed epigenetic studies, generating large datasets that can be analyzed in different ways to answer a multitude of questions. Data integration is an essential step to understand intricate biological processes, such as the epigenetic control of gene regulation. Recent "multi-omic" studies proposed the intriguing possibility that the intragenic DNA methylation would play a role in processing of transcripts during transcription modulating the elongation or splicing. Indeed a kinetic model, in which epigenetic modifications affect the rate of transcriptional elongation, and/or a recruitment model, in which adaptor proteins bind to epigenetic modifications recruiting splicing factors have been proposed. Moreover, it was demonstrated that the intragenic methylation in highly transcribed genes is exclusively dependent on the DNMT3B function. However, whether a DNMT3B-dependent epigenetic regulatory network modulates exon usage and transcription of alternative isoforms remains to be determined. Through a large-scale integrative study we show that human DNMT3B germline mutations perturb its intragenic methyltransferase activity, affecting the relative abundance of transcript isoforms in the context of Immunodeficiency, Centromeric instability, Facial anomalies syndrome type-1 (ICF1). This correlates with changes of H3K4me3 and H3K27me3 at isoform-specific transcription start sites. Notably, the newly identified DNMT3B target genes might significantly contribute to ICF1 phenotype.

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Book chapters on the topic "Multi-omic analysis"

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Mason, Christopher E., Sandra G. Porter, and Todd M. Smith. "Characterizing Multi-omic Data in Systems Biology." In Systems Analysis of Human Multigene Disorders, 15–38. New York, NY: Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4614-8778-4_2.

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Yaneske, Elisabeth, and Claudio Angione. "A Data- and Model-Driven Analysis Reveals the Multi-omic Landscape of Ageing." In Bioinformatics and Biomedical Engineering, 145–54. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-56148-6_12.

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Tikunov, Andrey P., Jeremiah D. Tipton, Timothy J. Garrett, Sachi V. Shinde, Hong Jin Kim, David A. Gerber, Laura E. Herring, Lee M. Graves, and Jeffrey M. Macdonald. "Green Chemistry Preservation and Extraction of Biospecimens for Multi-omic Analyses." In Methods in Molecular Biology, 267–98. New York, NY: Springer US, 2022. http://dx.doi.org/10.1007/978-1-0716-1811-0_17.

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Gülfidan, Gizem, Kazım Yalçın Arga, Dilek Demircan Çeker, and Abdullah Karadağ. "Çoklu-Omik Veri Entegrasyonu: Yöntem, Araç Ve Sağlık Uygulamaları." In Moleküler Biyoloji ve Genetik, 687–706. Türkiye Bilimler Akademisi, 2023. http://dx.doi.org/10.53478/tuba.978-625-8352-48-1.ch26.

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The central dogma (basic assumption) of molecular biology is the assumption of the process of coding DNA, which contains the genetic material, transferring information to RNA and regulating protein synthesis according to the instructions in the code. Later, the concept of metabolites, i.e. products formed as a result of metabolic processes, was added when proteins begin to function by folding into active structures. In short, by adding the suffix -omic, the concepts that study these structures are derived. These are genomics, transcriptomics, proteomics, and metabolomics, respectively. However, later developments introduced the term “epi” into the literature, which plays a regulatory role without altering DNA, RNA, or protein sequences and is mostly dependent on environmental factors: Epigenomics, Epitranscriptomics, and Epiproteomics. The changes in these structures during normal and pathological processes have led to the acquisition of large amounts of data using high-throughput technologies. However, high performance information systems for analysis have not been able to be developed at the same pace. Information from a single omics analysis may not fully reflect the biological and clinical picture. Multi-omics data from multiple platforms, however, may offer significant potential for understanding the mechanisms underlying complex diseases and other clinical problems. New approaches to analyzing this Big Data have emerged, and the concept of multi-omics has entered the literature in recent years, allowing us to understand how they are affected by signal changes. In the coming years, healthcare will evolve from traditional methods to a process of “smart healthcare.” The 3 pillars of this process will be integrated multi-omics analysis (integrative multi-omics), personalized medicine (P-medicine), and artificial intelligence (AI). In this study, the developments, methods, and tools in the field of multi-omics analysis that are just starting to be talked about will be summarized.

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Conference papers on the topic "Multi-omic analysis"

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Fan, Ziling, Yuan Zhou, and Habtom W. Ressom. "MOTA: Multi-omic integrative analysis for biomarker discovery." In 2019 41st Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC). IEEE, 2019. http://dx.doi.org/10.1109/embc.2019.8857049.

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Ma, Tianle, and Aidong Zhang. "Multi-view Factorization AutoEncoder with Network Constraints for Multi-omic Integrative Analysis." In 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2018. http://dx.doi.org/10.1109/bibm.2018.8621379.

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Lund, Jim, Shannon Bailey, Muhammad Ekram, David Shahbazian, Lorenzo Memeo, Paul Hofman, Richard Williams, and Jeff Gulcher. "Abstract 1333: High-quality multi-omic analysis of FFPE samples." In Proceedings: AACR Annual Meeting 2020; April 27-28, 2020 and June 22-24, 2020; Philadelphia, PA. American Association for Cancer Research, 2020. http://dx.doi.org/10.1158/1538-7445.am2020-1333.

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Zhang, Aidong. "Deep Learning and Networks for Integrative Analysis of Multi-Omic Data." In 2018 IEEE 8th International Conference on Computational Advances in Bio and Medical Sciences (ICCABS). IEEE, 2018. http://dx.doi.org/10.1109/iccabs.2018.8541962.

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Barefoot, Megan E., Rency S. Varghese, Yuan Zhou, Cristina Di Poto, Alessia Ferrarini, and Habtom W. Ressom. "Multi-omic Pathway and Network Analysis to Identify Biomarkers for Hepatocellular Carcinoma." In 2019 41st Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC). IEEE, 2019. http://dx.doi.org/10.1109/embc.2019.8856576.

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Vasquez, Jacob M., and Joel Desharnais. "Abstract 2854: Multi-omic considerations: Exploring LBgard blood tubes for proteomic analysis." In Proceedings: AACR Annual Meeting 2020; April 27-28, 2020 and June 22-24, 2020; Philadelphia, PA. American Association for Cancer Research, 2020. http://dx.doi.org/10.1158/1538-7445.am2020-2854.

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Khalyfa, A., Z. Qiao, M. Raju, C. R. Shyu, A. Castro Grattoni, A. Ericsson, and D. Gozal. "Monocarboxylate Transporter-2 (MCT2) in Murine Model of Lung Cancer: A Multi-Omic Analysis." In American Thoracic Society 2021 International Conference, May 14-19, 2021 - San Diego, CA. American Thoracic Society, 2021. http://dx.doi.org/10.1164/ajrccm-conference.2021.203.1_meetingabstracts.a4695.

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Orlando, Krystal A., Jesse R. Raab, Jessica D. Lang, William P. Hendricks, Yemin Wang, David G. Huntsman, Jeffrey M. Trent, Joel S. Parker, and Bernard E. Weissman. "Abstract 4318: Identifying drivers of SMARCA4/BRG1-deficient SCCOHT tumorigenesis by integrative multi-omic analysis." In Proceedings: AACR Annual Meeting 2018; April 14-18, 2018; Chicago, IL. American Association for Cancer Research, 2018. http://dx.doi.org/10.1158/1538-7445.am2018-4318.

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Patel, Bhuvic, Thomas F. Barrett, Saad M. Khan, Riley Mullens, Aldrin K. Yim, Sangami Pugazenthi, Tatenda Mahlokozera, et al. "Multi-omic Analysis of Sporadic Vestibular Schwannoma Reveals a Nerve Injury-Like State and Novel Molecular Targets." In 33rd Annual Meeting North American Skull Base Society. Georg Thieme Verlag KG, 2024. http://dx.doi.org/10.1055/s-0044-1779830.

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Zwimpfer, Tibor A., Flavio Lombardo, Natalie Rimmer, Sandra Götze, Franziska Singer, Anne Bertolini, Céline Montavon, Christian Kurzeder, Francis Jacob, and Viola Heinzelmann-Schwarz. "#174 Integrated multi-omic and clinicopathological analysis of vulvar squamous cell carcinoma: identification of predictive biomarkers for personalized treatment." In ESGO 2023 Congress. BMJ Publishing Group Ltd, 2023. http://dx.doi.org/10.1136/ijgc-2023-esgo.773.

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Reports on the topic "Multi-omic analysis"

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Banfield, Jill. Multi-‘omic’ analyses of the dynamics, mechanisms, and pathways for carbon turnover in grassland soil under two climate regimes. Office of Scientific and Technical Information (OSTI), April 2019. http://dx.doi.org/10.2172/1504276.

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