Journal articles: 'Multi-omic'

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Rappoport, Nimrod, Roy Safra, and Ron Shamir. "MONET: Multi-omic module discovery by omic selection." PLOS Computational Biology 16, no. 9 (September 15, 2020): e1008182. http://dx.doi.org/10.1371/journal.pcbi.1008182.

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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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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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Chu, Su, Mengna Huang, Rachel Kelly, Elisa Benedetti, Jalal Siddiqui, Oana Zeleznik, Alexandre Pereira, et al. "Integration of Metabolomic and Other Omics Data in Population-Based Study Designs: An Epidemiological Perspective." Metabolites 9, no. 6 (June 18, 2019): 117. http://dx.doi.org/10.3390/metabo9060117.

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It is not controversial that study design considerations and challenges must be addressed when investigating the linkage between single omic measurements and human phenotypes. It follows that such considerations are just as critical, if not more so, in the context of multi-omic studies. In this review, we discuss (1) epidemiologic principles of study design, including selection of biospecimen source(s) and the implications of the timing of sample collection, in the context of a multi-omic investigation, and (2) the strengths and limitations of various techniques of data integration across multi-omic data types that may arise in population-based studies utilizing metabolomic data.

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Demirel, Habibe Cansu, Muslum Kaan Arici, and Nurcan Tuncbag. "Computational approaches leveraging integrated connections of multi-omic data toward clinical applications." Molecular Omics 18, no. 1 (2022): 7–18. http://dx.doi.org/10.1039/d1mo00158b.

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Data integration approaches are crucial for transforming multi-omic data sets into clinically interpretable knowledge. This review presents a detailed and extensive guideline to catalog the recent computational multi-omic data integration methods.

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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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Daliri, Eric Banan-Mwine, Fred Kwame Ofosu, Ramachandran Chelliah, Byong H. Lee, and Deog-Hwan Oh. "Challenges and Perspective in Integrated Multi-Omics in Gut Microbiota Studies." Biomolecules 11, no. 2 (February 17, 2021): 300. http://dx.doi.org/10.3390/biom11020300.

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The advent of omic technology has made it possible to identify viable but unculturable micro-organisms in the gut. Therefore, application of multi-omic technologies in gut microbiome studies has become invaluable for unveiling a comprehensive interaction between these commensals in health and disease. Meanwhile, despite the successful identification of many microbial and host–microbial cometabolites that have been reported so far, it remains difficult to clearly identify the origin and function of some proteins and metabolites that are detected in gut samples. However, the application of single omic techniques for studying the gut microbiome comes with its own challenges which may be overcome if a number of different omics techniques are combined. In this review, we discuss our current knowledge about multi-omic techniques, their challenges and future perspective in this field of gut microbiome studies.

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Shaba, Enxhi, Lorenza Vantaggiato, Laura Governini, Alesandro Haxhiu, Guido Sebastiani, Daniela Fignani, Giuseppina Emanuela Grieco, Laura Bergantini, Luca Bini, and Claudia Landi. "Multi-Omics Integrative Approach of Extracellular Vesicles: A Future Challenging Milestone." Proteomes 10, no. 2 (April 22, 2022): 12. http://dx.doi.org/10.3390/proteomes10020012.

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In the era of multi-omic sciences, dogma on singular cause-effect in physio-pathological processes is overcome and system biology approaches have been providing new perspectives to see through. In this context, extracellular vesicles (EVs) are offering a new level of complexity, given their role in cellular communication and their activity as mediators of specific signals to target cells or tissues. Indeed, their heterogeneity in terms of content, function, origin and potentiality contribute to the cross-interaction of almost every molecular process occurring in a complex system. Such features make EVs proper biological systems being, therefore, optimal targets of omic sciences. Currently, most studies focus on dissecting EVs content in order to either characterize it or to explore its role in various pathogenic processes at transcriptomic, proteomic, metabolomic, lipidomic and genomic levels. Despite valuable results being provided by individual omic studies, the categorization of EVs biological data might represent a limit to be overcome. For this reason, a multi-omic integrative approach might contribute to explore EVs function, their tissue-specific origin and their potentiality. This review summarizes the state-of-the-art of EVs omic studies, addressing recent research on the integration of EVs multi-level biological data and challenging developments in EVs origin.

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Le Bras, Alexandra. "A multi-omic resource of mouse neutrophils." Lab Animal 50, no. 9 (August 25, 2021): 239. http://dx.doi.org/10.1038/s41684-021-00840-w.

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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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Hurst, Carolyn D., and Margaret A. Knowles. "Multi-omic profiling refines the molecular view." Nature Reviews Clinical Oncology 15, no. 4 (December 19, 2017): 203–4. http://dx.doi.org/10.1038/nrclinonc.2017.195.

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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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Chorna, Nataliya, and Filipa Godoy-Vitorino. "A Protocol for the Multi-Omic Integration of Cervical Microbiota and Urine Metabolomics to Understand Human Papillomavirus (HPV)-Driven Dysbiosis." Biomedicines 8, no. 4 (April 8, 2020): 81. http://dx.doi.org/10.3390/biomedicines8040081.

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The multi-omic integration of microbiota data with metabolomics has gained popularity. This protocol is based on a human multi-omics study, integrating cervicovaginal microbiota, HPV status and neoplasia, with urinary metabolites. Indeed, to understand the biology of the infections and to develop adequate interventions for cervical cancer prevention, studies are needed to characterize in detail the cervical microbiota and understand the systemic metabolome. This article is a detailed protocol for the multi-omic integration of cervical microbiota and urine metabolome to shed light on the systemic effects of cervical dysbioses associated with Human Papillomavirus (HPV) infections. This methods article suggests detailed sample collection and laboratory processes of metabolomics, DNA extraction for microbiota, HPV typing, and the bioinformatic analyses of the data, both to characterize the metabolome, the microbiota, and joint multi-omic analyses, useful for the development of new point-of-care diagnostic tests based on these approaches.

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Ohno, Satoshi, Saori Uematsu, and Shinya Kuroda. "Quantitative metabolic fluxes regulated by trans-omic networks." Biochemical Journal 479, no. 6 (March 31, 2022): 787–804. http://dx.doi.org/10.1042/bcj20210596.

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Cells change their metabolism in response to internal and external conditions by regulating the trans-omic network, which is a global biochemical network with multiple omic layers. Metabolic flux is a direct measure of the activity of a metabolic reaction that provides valuable information for understanding complex trans-omic networks. Over the past decades, techniques to determine metabolic fluxes, including 13C-metabolic flux analysis (13C-MFA), flux balance analysis (FBA), and kinetic modeling, have been developed. Recent studies that acquire quantitative metabolic flux and multi-omic data have greatly advanced the quantitative understanding and prediction of metabolism-centric trans-omic networks. In this review, we present an overview of 13C-MFA, FBA, and kinetic modeling as the main techniques to determine quantitative metabolic fluxes, and discuss their advantages and disadvantages. We also introduce case studies with the aim of understanding complex metabolism-centric trans-omic networks based on the determination of metabolic fluxes.

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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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Chaddad, Ahmad, Paul Daniel, Siham Sabri, Christian Desrosiers, and Bassam Abdulkarim. "Integration of Radiomic and Multi-omic Analyses Predicts Survival of Newly Diagnosed IDH1 Wild-Type Glioblastoma." Cancers 11, no. 8 (August 10, 2019): 1148. http://dx.doi.org/10.3390/cancers11081148.

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Predictors of patient outcome derived from gene methylation, mutation, or expression are severely limited in IDH1 wild-type glioblastoma (GBM). Radiomics offers an alternative insight into tumor characteristics which can provide complementary information for predictive models. The study aimed to evaluate whether predictive models which integrate radiomic, gene, and clinical (multi-omic) features together offer an increased capacity to predict patient outcome. A dataset comprising 200 IDH1 wild-type GBM patients, derived from The Cancer Imaging Archive (TCIA) (n = 71) and the McGill University Health Centre (n = 129), was used in this study. Radiomic features (n = 45) were extracted from tumor volumes then correlated to biological variables and clinical outcomes. By performing 10-fold cross-validation (n = 200) and utilizing independent training/testing datasets (n = 100/100), an integrative model was derived from multi-omic features and evaluated for predictive strength. Integrative models using a limited panel of radiomic (sum of squares variance, large zone/low gray emphasis, autocorrelation), clinical (therapy type, age), genetic (CIC, PIK3R1, FUBP1) and protein expression (p53, vimentin) yielded a maximal AUC of 78.24% (p = 2.9 × 10−5). We posit that multi-omic models using the limited set of ‘omic’ features outlined above can improve capacity to predict the outcome for IDH1 wild-type GBM patients.

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Laccourreye, Paula, Concha Bielza, and Pedro Larrañaga. "Explainable Machine Learning for Longitudinal Multi-Omic Microbiome." Mathematics 10, no. 12 (June 9, 2022): 1994. http://dx.doi.org/10.3390/math10121994.

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Over the years, research studies have shown there is a key connection between the microbial community in the gut, genes, and immune system. Understanding this association may help discover the cause of complex chronic idiopathic disorders such as inflammatory bowel disease. Even though important efforts have been put into the field, the functions, dynamics, and causation of dysbiosis state performed by the microbial community remains unclear. Machine learning models can help elucidate important connections and relationships between microbes in the human host. Our study aims to extend the current knowledge of associations between the human microbiome and health and disease through the application of dynamic Bayesian networks to describe the temporal variation of the gut microbiota and dynamic relationships between taxonomic entities and clinical variables. We develop a set of preprocessing steps to clean, filter, select, integrate, and model informative metagenomics, metatranscriptomics, and metabolomics longitudinal data from the Human Microbiome Project. This study accomplishes novel network models with satisfactory predictive performance (accuracy = 0.648) for each inflammatory bowel disease state, validating Bayesian networks as a framework for developing interpretable models to help understand the basic ways the different biological entities (taxa, genes, metabolites) interact with each other in a given environment (human gut) over time. These findings can serve as a starting point to advance the discovery of novel therapeutic approaches and new biomarkers for precision medicine.

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Palsson, Bernhard, and Karsten Zengler. "The challenges of integrating multi-omic data sets." Nature Chemical Biology 6, no. 11 (October 18, 2010): 787–89. http://dx.doi.org/10.1038/nchembio.462.

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Patel, Rishi, R. Joseph Bender, Quanlin Li, Dana Pan, Richard Tuli, Michael J. Pishvaian, and Andrew Eugene Hendifar. "Multi-omic molecular profiling of pancreatic neuroendocrine tumors." Journal of Clinical Oncology 35, no. 15_suppl (May 20, 2017): e15685-e15685. http://dx.doi.org/10.1200/jco.2017.35.15_suppl.e15685.

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e15685 Multi-omic Molecular profiling of Pancreatic Neuroendocrine Tumors Authors: Rishi R Patel, Joseph Bender, Quanlin, Dana Pan, Lynn Matrisian, David Halverson, Emanuel Petricoin, Subha Madhavan, Richard Tuli, Michael Pishvaian, Andrew Hendifar; Samuel Oschin Comprehensive Cancer Institute, Cedars-Sinai Medical Center, Los Angeles, CA Background: Pancreatic Neuroendocrine Tumors (pNETs) are a rare malignancy with an incidence of 2 per 1,000,000. In 2016, the Pancreatic Cancer Action Network and Perthera initiated the Know Your Tumor (KYT) initiative in an effort to improve coordination across clinical spectrums in regards to multi-omic molecular profiling and clinical outcomes data pertaining to pancreatic tumors. We used data collected as part of the KYT effort to describe demographic, clinical and genomic data for pNETs. Methods: From 2015 - 2016, 15 patients with pNET were enrolled in the KYT program, which helped facilitate tissue acquisition, clinical data collection, and multi-omic molecular profiling. Using the data collected, we performed Fisher’s Exact to assess for statistical significance between genetic alterations and histology. Results: 11/15 of our patients were female. 8/15 had metastatic disease at the time of diagnosis, while 5/15 had locally advanced disease at the time of diagnosis. 29 genetic alterations were pathogenic. KMT2D and MEN1 were jointly found in 6/15 of our patients. 5/15 with pathogenic alterations in p53, 2/15 with DAXX, 5/15 with alteration in RB1, and 2/15 with alterations in PTEN and TSC2. 2 patients had pathologic alterations in mismatch repair genes, MLH1 and MSH1. Two genes had a statistically significant relationship to pNET histology. Alterations in MEN1 (p = 0.0097) and SPTA1 (p = 0.0333) were associated with high grade tumors (p = 0.0097). Of note, both of the patients under the age of 35 shared an alteration in ATR, which none of the other enrollees expressed. Conclusions: In PNETS, multi-omic profiling through the KYT program identified targetable alterations in several key pathways. Outcome data will be explored.

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Lu, Liangqun, and Bernie Daigle. "Multi-Omic PTSD Subgroup Identification and Clinical Characterization." Biological Psychiatry 87, no. 9 (May 2020): S9. http://dx.doi.org/10.1016/j.biopsych.2020.02.050.

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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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Oromendia, Ana, Dorina Ismailgeci, Michele Ciofii, Taylor Donnelly, Linda Bojmar, John Jyazbek, Arnaub Chatterjee, David Lyden, Kenneth H. Yu, and David Paul Kelsen. "Error-free, automated data integration of exosome cargo protein data with extensive clinical data in an ongoing, multi-omic translational research study." Journal of Clinical Oncology 38, no. 15_suppl (May 20, 2020): e16743-e16743. http://dx.doi.org/10.1200/jco.2020.38.15_suppl.e16743.

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e16743 Background: Major advances in understanding the biology of cancer have come from genomic analysis of tumor and normal tissue. Integrating extensive patient-related data with deep analysis of omic data is crucial to informing omic data interpretation. Currently, such integrations are a highly manual, asynchronous, and costly process as well as error-prone and time-consuming. To develop new blood assays that may detect very early stage PDAC, a multi-omic investigation with deep clinical annotation is needed. Using pilot data from an on-going study, we test a new platform allowing automated error-free integration of an extensive clinical database with extensive omic data. Methods: Demographic, clinical, family pedigree and pathology data were collected on the Rave EDC platform. Exosomes were purified from 46 plasma samples from 14 controls and 24 PDAC patients and cargo proteins were quantified via SILAC. The Rave Omics platform was used to ingest and integrate clinical and omic data, run quality checks and generate integrated clinical-omic datasets. Data fidelity was validated by systematically computing differences between corresponding values in the source flies with those present in the extracted data object (integrated data). The root mean squared error (RMSE) was calculated for numeric values in each sample. Additional validation was conducted by manual inspection to ascertain data integrity. Results: We demonstrated automatic integration, without human intervention, of a subset of the clinical data and all available SILAC data into an analysis-ready data object. Data transfer was completely faithful, with 100% concordance between the source and the integrated data without loss of features. All proteins (n = 1515) and clinical variables (n = 64) were imported. Their nomenclature and corresponding sample values (n = 69690) and clinical values (n = 2432) matched exactly between datasets. In all samples, the RMSE was exactly zero, indicating no deviation between data sources. Conclusions: We demonstrated that automatic, efficient, and reliable integration of clinical-omic data is achievable during an in-flight PDAC trial. Automatic exploratory analytics supporting biomarker discovery are currently being used to uncover associations between omic and clinical features. The Rave Omics platform is disease-agnostic and we plan to expand to trials of varying size, indication, and completion status where systematic, automated integration of clinical and (multi)omic data is needed.

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Carrillo-Perez, Francisco, Juan Carlos Morales, Daniel Castillo-Secilla, Olivier Gevaert, Ignacio Rojas, and Luis Javier Herrera. "Machine-Learning-Based Late Fusion on Multi-Omics and Multi-Scale Data for Non-Small-Cell Lung Cancer Diagnosis." Journal of Personalized Medicine 12, no. 4 (April 8, 2022): 601. http://dx.doi.org/10.3390/jpm12040601.

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Differentiation between the various non-small-cell lung cancer subtypes is crucial for providing an effective treatment to the patient. For this purpose, machine learning techniques have been used in recent years over the available biological data from patients. However, in most cases this problem has been treated using a single-modality approach, not exploring the potential of the multi-scale and multi-omic nature of cancer data for the classification. In this work, we study the fusion of five multi-scale and multi-omic modalities (RNA-Seq, miRNA-Seq, whole-slide imaging, copy number variation, and DNA methylation) by using a late fusion strategy and machine learning techniques. We train an independent machine learning model for each modality and we explore the interactions and gains that can be obtained by fusing their outputs in an increasing manner, by using a novel optimization approach to compute the parameters of the late fusion. The final classification model, using all modalities, obtains an F1 score of 96.81±1.07, an AUC of 0.993±0.004, and an AUPRC of 0.980±0.016, improving those results that each independent model obtains and those presented in the literature for this problem. These obtained results show that leveraging the multi-scale and multi-omic nature of cancer data can enhance the performance of single-modality clinical decision support systems in personalized medicine, consequently improving the diagnosis of the patient.

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Rappoport, Nimrod, and Ron Shamir. "NEMO: cancer subtyping by integration of partial multi-omic data." Bioinformatics 35, no. 18 (January 30, 2019): 3348–56. http://dx.doi.org/10.1093/bioinformatics/btz058.

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Abstract Motivation Cancer subtypes were usually defined based on molecular characterization of single omic data. Increasingly, measurements of multiple omic profiles for the same cohort are available. Defining cancer subtypes using multi-omic data may improve our understanding of cancer, and suggest more precise treatment for patients. Results We present NEMO (NEighborhood based Multi-Omics clustering), a novel algorithm for multi-omics clustering. Importantly, NEMO can be applied to partial datasets in which some patients have data for only a subset of the omics, without performing data imputation. In extensive testing on ten cancer datasets spanning 3168 patients, NEMO achieved results comparable to the best of nine state-of-the-art multi-omics clustering algorithms on full data and showed an improvement on partial data. On some of the partial data tests, PVC, a multi-view algorithm, performed better, but it is limited to two omics and to positive partial data. Finally, we demonstrate the advantage of NEMO in detailed analysis of partial data of AML patients. NEMO is fast and much simpler than existing multi-omics clustering algorithms, and avoids iterative optimization. Availability and implementation Code for NEMO and for reproducing all NEMO results in this paper is in github: https://github.com/Shamir-Lab/NEMO. Supplementary information Supplementary data are available at Bioinformatics online.

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Rappoport, Nimrod, and Ron Shamir. "Multi-omic and multi-view clustering algorithms: review and cancer benchmark." Nucleic Acids Research 47, no. 2 (November 28, 2018): 1044. http://dx.doi.org/10.1093/nar/gky1226.

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Rappoport, Nimrod, and Ron Shamir. "Multi-omic and multi-view clustering algorithms: review and cancer benchmark." Nucleic Acids Research 46, no. 20 (October 8, 2018): 10546–62. http://dx.doi.org/10.1093/nar/gky889.

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Miller, David T., Isidro Cortés-Ciriano, Nischalan Pillay, Angela C. Hirbe, Matija Snuderl, Marilyn M. Bui, Katherine Piculell, et al. "Genomics of MPNST (GeM) Consortium: Rationale and Study Design for Multi-Omic Characterization of NF1-Associated and Sporadic MPNSTs." Genes 11, no. 4 (April 2, 2020): 387. http://dx.doi.org/10.3390/genes11040387.

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The Genomics of Malignant Peripheral Nerve Sheath Tumor (GeM) Consortium is an international collaboration focusing on multi-omic analysis of malignant peripheral nerve sheath tumors (MPNSTs), the most aggressive tumor associated with neurofibromatosis type 1 (NF1). Here we present a summary of current knowledge gaps, a description of our consortium and the cohort we have assembled, and an overview of our plans for multi-omic analysis of these tumors. We propose that our analysis will lead to a better understanding of the order and timing of genetic events related to MPNST initiation and progression. Our ten institutions have assembled 96 fresh frozen NF1-related (63%) and sporadic MPNST specimens from 86 subjects with corresponding clinical and pathological data. Clinical data have been collected as part of the International MPNST Registry. We will characterize these tumors with bulk whole genome sequencing, RNAseq, and DNA methylation profiling. In addition, we will perform multiregional analysis and temporal sampling, with the same methodologies, on a subset of nine subjects with NF1-related MPNSTs to assess tumor heterogeneity and cancer evolution. Subsequent multi-omic analyses of additional archival specimens will include deep exome sequencing (500×) and high density copy number arrays for both validation of results based on fresh frozen tumors, and to assess further tumor heterogeneity and evolution. Digital pathology images are being collected in a cloud-based platform for consensus review. The result of these efforts will be the largest MPNST multi-omic dataset with correlated clinical and pathological information ever assembled.

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Tiew, Pei Yee, Oliver W. Meldrum, and Sanjay H. Chotirmall. "Applying Next-Generation Sequencing and Multi-Omics in Chronic Obstructive Pulmonary Disease." International Journal of Molecular Sciences 24, no. 3 (February 3, 2023): 2955. http://dx.doi.org/10.3390/ijms24032955.

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Microbiomics have significantly advanced over the last decade, driven by the widespread availability of next-generation sequencing (NGS) and multi-omic technologies. Integration of NGS and multi-omic datasets allow for a holistic assessment of endophenotypes across a range of chronic respiratory disease states, including chronic obstructive pulmonary disease (COPD). Valuable insight has been attained into the nature, function, and significance of microbial communities in disease onset, progression, prognosis, and response to treatment in COPD. Moving beyond single-biome assessment, there now exists a growing literature on functional assessment and host–microbe interaction and, in particular, their contribution to disease progression, severity, and outcome. Identifying specific microbes and/or metabolic signatures associated with COPD can open novel avenues for therapeutic intervention and prognosis-related biomarkers. Despite the promise and potential of these approaches, the large amount of data generated by such technologies can be challenging to analyze and interpret, and currently, there remains a lack of standardized methods to address this. This review outlines the current use and proposes future avenues for the application of NGS and multi-omic technologies in the endophenotyping, prognostication, and treatment of COPD.

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Barry, Craig P., Rosemary Gillane, Gert H. Talbo, Manual Plan, Robin Palfreyman, Andrea K. Haber-Stuk, John Power, Lars K. Nielsen, and Esteban Marcellin. "Multi-omic characterisation of Streptomyces hygroscopicus NRRL 30439: detailed assessment of its secondary metabolic potential." Molecular Omics 18, no. 3 (2022): 226–36. http://dx.doi.org/10.1039/d1mo00150g.

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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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Xu, J., L. Pascual, N. Desplat, M. Faurobert, Y. Gibon, A. Moing, M. Maucourt, et al. "A MULTI-LEVEL OMIC APPROACH OF TOMATO FRUIT QUALITY." Acta Horticulturae, no. 1099 (September 2015): 793–800. http://dx.doi.org/10.17660/actahortic.2015.1099.100.

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Gao, Chao, Jialin Liu, April R. Kriebel, Sebastian Preissl, Chongyuan Luo, Rosa Castanon, Justin Sandoval, et al. "Iterative single-cell multi-omic integration using online learning." Nature Biotechnology 39, no. 8 (April 19, 2021): 1000–1007. http://dx.doi.org/10.1038/s41587-021-00867-x.

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Bhattacharya, Arjun, Yun Li, and Michael I. Love. "MOSTWAS: Multi-Omic Strategies for Transcriptome-Wide Association Studies." PLOS Genetics 17, no. 3 (March 8, 2021): e1009398. http://dx.doi.org/10.1371/journal.pgen.1009398.

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Traditional predictive models for transcriptome-wide association studies (TWAS) consider only single nucleotide polymorphisms (SNPs) local to genes of interest and perform parameter shrinkage with a regularization process. These approaches ignore the effect of distal-SNPs or other molecular effects underlying the SNP-gene association. Here, we outline multi-omics strategies for transcriptome imputation from germline genetics to allow more powerful testing of gene-trait associations by prioritizing distal-SNPs to the gene of interest. In one extension, we identify mediating biomarkers (CpG sites, microRNAs, and transcription factors) highly associated with gene expression and train predictive models for these mediators using their local SNPs. Imputed values for mediators are then incorporated into the final predictive model of gene expression, along with local SNPs. In the second extension, we assess distal-eQTLs (SNPs associated with genes not in a local window around it) for their mediation effect through mediating biomarkers local to these distal-eSNPs. Distal-eSNPs with large indirect mediation effects are then included in the transcriptomic prediction model with the local SNPs around the gene of interest. Using simulations and real data from ROS/MAP brain tissue and TCGA breast tumors, we show considerable gains of percent variance explained (1–2% additive increase) of gene expression and TWAS power to detect gene-trait associations. This integrative approach to transcriptome-wide imputation and association studies aids in identifying the complex interactions underlying genetic regulation within a tissue and important risk genes for various traits and disorders.

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Sardo, Emilia, Stefania Napolitano, Carminia Maria Della Corte, Davide Ciardiello, Antonio Raucci, Gianluca Arrichiello, Teresa Troiani, Fortunato Ciardiello, Erika Martinelli, and Giulia Martini. "Multi-Omic Approaches in Colorectal Cancer beyond Genomic Data." Journal of Personalized Medicine 12, no. 2 (January 18, 2022): 128. http://dx.doi.org/10.3390/jpm12020128.

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Colorectal cancer (CRC) is one of the most frequent tumours and one of the major causes of morbidity and mortality globally. Its incidence has increased in recent years and could be linked to unhealthy dietary habits combined with environmental and hereditary factors, which can lead to genetic and epigenetic changes and induce tumour development. The model of CRC progression has always been based on a genomic, parametric, static and complex approach involving oncogenes and tumour suppressor genes. Recent advances in omics sciences have sought a paradigm shift to a multiparametric, immunological-stromal, and dynamic approach for a better understanding of carcinogenesis and tumour heterogeneity. In the present paper, we review the most important preclinical and clinical data and present recent discoveries in the field of transcriptomics, proteomics, metagenomics and radiomics in CRC disease.

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Ali, Johar, and Ome Kalsoom Afridi. "Omic or Multi-omics Approach Can Save The Mankind." Current Trends in OMICS 1, no. 1 (August 16, 2021): 01–07. http://dx.doi.org/10.32350/cto.11.01.

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The publication of the first draft of human genome, has led to the explosion of high throughput technologies including genomics, epigenomics, transcriptomic, proteomics, and metabolomics aiming to characterize the various biological molecules (DNA, RNA, proteins, and metabolites). These high throughput technologies collectively called as omics revolutionized medical research in the last two decades. The advent of next generation sequencing (NGS) reduced the time and economic cost of traditional sequencing and has led to the emergence of genomics as the first discipline of omics. Following the emergence of genomics, a number of projects such as The Cancer Genome Atlas (TCGA), 1000 Genome Project (1KGP), and the International Cancer Genome Consortium have been accomplished. These projects contributed significantly to the understanding of genetic variations in different cancers, for instance, TCGA produced over 2.5 petabytes of big data. Furthermore, the big data produced by these mega projects has been made publicly available to the clinicians and researchers to fast-track the diagnosis and prognosis of complex rare diseases. In developed countries, a multi-omics approach has been applied holistically to the clinical practice for the diagnosis and prognosis of various cancers and rare Mendelian diseases.

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Creasy, Heather Huot, Victor Felix, Jain Aluvathingal, Jonathan Crabtree, Olukemi Ifeonu, James Matsumura, Carrie McCracken, et al. "HMPDACC: a Human Microbiome Project Multi-omic data resource." Nucleic Acids Research 49, no. D1 (December 10, 2020): D734—D742. http://dx.doi.org/10.1093/nar/gkaa996.

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Abstract The Human Microbiome Project (HMP) explored microbial communities of the human body in both healthy and disease states. Two phases of the HMP (HMP and iHMP) together generated >48TB of data (public and controlled access) from multiple, varied omics studies of both the microbiome and associated hosts. The Human Microbiome Project Data Coordination Center (HMPDACC) was established to provide a portal to access data and resources produced by the HMP. The HMPDACC provides a unified data repository, multi-faceted search functionality, analysis pipelines and standardized protocols to facilitate community use of HMP data. Recent efforts have been put toward making HMP data more findable, accessible, interoperable and reusable. HMPDACC resources are freely available at www.hmpdacc.org.

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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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Burgess, Darren J. "CRISPR CAPTURE for multi-omic probing of genomic loci." Nature Reviews Genetics 18, no. 11 (October 3, 2017): 641. http://dx.doi.org/10.1038/nrg.2017.79.

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Sandri, Brian J., Adam Kaplan, Shane W. Hodgson, Mark Peterson, Svetlana Avdulov, LeeAnn Higgins, Todd Markowski, et al. "Multi-omic molecular profiling of lung cancer in COPD." European Respiratory Journal 52, no. 1 (May 24, 2018): 1702665. http://dx.doi.org/10.1183/13993003.02665-2017.

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Chronic obstructive pulmonary disease (COPD) is a known risk factor for developing lung cancer but the underlying mechanisms remain unknown. We hypothesise that the COPD stroma contains molecular mechanisms supporting tumourigenesis.We conducted an unbiased multi-omic analysis to identify gene expression patterns that distinguish COPD stroma in patients with or without lung cancer. We obtained lung tissue from patients with COPD and lung cancer (tumour and adjacent non-malignant tissue) and those with COPD without lung cancer for profiling of proteomic and mRNA (both cytoplasmic and polyribosomal). We used the Joint and Individual Variation Explained (JIVE) method to integrate and analyse across the three datasets.JIVE identified eight latent patterns that robustly distinguished and separated the three groups of tissue samples (tumour, adjacent and control). Predictive variables that associated with the tumour, compared to adjacent stroma, were mainly represented in the transcriptomic data, whereas predictive variables associated with adjacent tissue, compared to controls, were represented at the translatomic level. Pathway analysis revealed extracellular matrix and phosphatidylinositol-4,5-bisphosphate 3-kinase–protein kinase B signalling pathways as important signals in the tumour adjacent stroma.The multi-omic approach distinguishes tumour adjacent stroma in lung cancer and reveals two stromal expression patterns associated with cancer.

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Dhondalay, Gopal Krishna, Bryan J. Bunning, Kari C. Nadeau, and Sandra Andorf. "Multi-Omic Profiling Of Asthma Using High-Throughput Sequencing." Journal of Allergy and Clinical Immunology 143, no. 2 (February 2019): AB203. http://dx.doi.org/10.1016/j.jaci.2018.12.621.

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Dassi, Erik, Valentina Greco, Viktoryia Sidarovich, Paola Zuccotti, Natalia Arseni, Paola Scaruffi, Gian Paolo Tonini, and Alessandro Quattrone. "Multi-omic profiling of MYCN-amplified neuroblastoma cell-lines." Genomics Data 6 (December 2015): 285–87. http://dx.doi.org/10.1016/j.gdata.2015.11.012.

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Manor, Ohad, Niha Zubair, Matthew P. Conomos, Xiaojing Xu, Jesse E. Rohwer, Cynthia E. Krafft, Jennifer C. Lovejoy, and Andrew T. Magis. "A Multi-omic Association Study of Trimethylamine N-Oxide." Cell Reports 24, no. 4 (July 2018): 935–46. http://dx.doi.org/10.1016/j.celrep.2018.06.096.

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Abdelhamid, Sultan S., Jacob Scioscia, Yoram Vodovotz, Junru Wu, Anna Rosengart, Eunseo Sung, Syed Rahman, et al. "Multi-Omic Admission-Based Prognostic Biomarkers Identified by Machine Learning Algorithms Predict Patient Recovery and 30-Day Survival in Trauma Patients." Metabolites 12, no. 9 (August 23, 2022): 774. http://dx.doi.org/10.3390/metabo12090774.

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Admission-based circulating biomarkers for the prediction of outcomes in trauma patients could be useful for clinical decision support. It is unknown which molecular classes of biomolecules can contribute biomarkers to predictive modeling. Here, we analyzed a large multi-omic database of over 8500 markers (proteomics, metabolomics, and lipidomics) to identify prognostic biomarkers in the circulating compartment for adverse outcomes, including mortality and slow recovery, in severely injured trauma patients. Admission plasma samples from patients (n = 129) enrolled in the Prehospital Air Medical Plasma (PAMPer) trial were analyzed using mass spectrometry (metabolomics and lipidomics) and aptamer-based (proteomics) assays. Biomarkers were selected via Least Absolute Shrinkage and Selection Operator (LASSO) regression modeling and machine learning analysis. A combination of five proteins from the proteomic layer was best at discriminating resolvers from non-resolvers from critical illness with an Area Under the Receiver Operating Characteristic curve (AUC) of 0.74, while 26 multi-omic features predicted 30-day survival with an AUC of 0.77. Patients with traumatic brain injury as part of their injury complex had a unique subset of features that predicted 30-day survival. Our findings indicate that multi-omic analyses can identify novel admission-based prognostic biomarkers for outcomes in trauma patients. Unique biomarker discovery also has the potential to provide biologic insights.

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Beheshti, Ramin, E. Scott Halstead, Bryan Cusack, and Steven D. Hicks. "Multi-Omic Factors Associated with Frequency of Upper Respiratory Infections in Developing Infants." International Journal of Molecular Sciences 24, no. 2 (January 4, 2023): 934. http://dx.doi.org/10.3390/ijms24020934.

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Susceptibility to upper respiratory infections (URIs) may be influenced by host, microbial, and environmental factors. We hypothesized that multi-omic analyses of molecular factors in infant saliva would identify complex host-environment interactions associated with URI frequency. A cohort study involving 146 infants was used to assess URI frequency in the first year of life. Saliva was collected at 6 months for high-throughput multi-omic measurement of cytokines, microRNAs, transcripts, and microbial RNA. Regression analysis identified environmental (daycare attendance, atmospheric pollution, breastfeeding duration), microbial (Verrucomicrobia, Streptococcus phage), and host factors (miR-22-5p) associated with URI frequency (p < 0.05). These results provide pathophysiologic clues about molecular factors that influence URI susceptibility. Validation of these findings in a larger cohort could one day yield novel approaches to detecting and managing URI susceptibility in infants.

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Yang, Guang, Tao Lu, Daniel J. Weisenberger, and Gangning Liang. "The Multi-Omic Landscape of Primary Breast Tumors and Their Metastases: Expanding the Efficacy of Actionable Therapeutic Targets." Genes 13, no. 9 (August 29, 2022): 1555. http://dx.doi.org/10.3390/genes13091555.

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Breast cancer (BC) mortality is almost exclusively due to metastasis, which is the least understood aspect of cancer biology and represents a significant clinical challenge. Although we have witnessed tremendous advancements in the treatment for metastatic breast cancer (mBC), treatment resistance inevitably occurs in most patients. Recently, efforts in characterizing mBC revealed distinctive genomic, epigenomic and transcriptomic (multi-omic) landscapes to that of the primary tumor. Understanding of the molecular underpinnings of mBC is key to understanding resistance to therapy and the development of novel treatment options. This review summarizes the differential molecular landscapes of BC and mBC, provides insights into the genomic heterogeneity of mBC and highlights the therapeutically relevant, multi-omic features that may serve as novel therapeutic targets for mBC patients.

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Tan, Hexin, Xianghui Chen, Nan Liang, Ruibing Chen, Junfeng Chen, Chaoyang Hu, Qi Li, et al. "Transcriptome analysis reveals novel enzymes for apo-carotenoid biosynthesis in saffron and allows construction of a pathway for crocetin synthesis in yeast." Journal of Experimental Botany 70, no. 18 (May 6, 2019): 4819–34. http://dx.doi.org/10.1093/jxb/erz211.

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Marshall, John, Takayuki Yoshino, Sun Young Rha, David N. Church, Anelisa Kruschewsky Coutinho, Carlos Alberto Sampaio-Filho, David James Gallagher, et al. "Multi-omics characterization of left-right colorectal cancer." Journal of Clinical Oncology 39, no. 15_suppl (May 20, 2021): 3542. http://dx.doi.org/10.1200/jco.2021.39.15_suppl.3542.

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3542 Background: Right (R) vs left (L) sided colorectal cancers are clinically distinguishable based on prognosis and response to certain therapies, but as of yet, limited data have emerged to explain these differences. The science of molecular testing has evolved rapidly. Enabled by improved technologies and computing power, it is now feasible to obtain to systematic multi-omic datasets covering DNA, RNA, proteins, phospho-proteins and metabolomics on large numbers of patients. Multi-omic analysis can further define disease specific subgroups but pre-analytic quality of the tissues (ischemia time) and comparison to normal tissue controls is paramount to optimize results. Methods: Following informed consent, 450 colorectal cancer primary tumors and paired normal tissues were collected following an SOP to minimize ischemia time, and were analyzed using comprehensive genomics, transcriptomics, proteomics, phosphoproteomics, morphology and annual clinical information. Right (C18.0,2,3) and left (C18.6,7) CRC tumors, normal tissue were compared using machine learning tools to unravel the molecular mechanisms that underpin these clinically distinguishable phenotypes as well as correlating with known genomic metrics such MSI and KRAS mutation status. Results: Through leveraging the tumor and paired normal patient samples, systematic differences between left and right tumor samples were observed including specific molecular events associated with these anatomical differences. The detailed results will be presented at the meeting. Conclusions: Progress in precision medicine requires the inclusion of multi-omics which in turn requires changes to our current SOPs of tissue collection. The ability to define molecular distinctions such as between R and L colon cancer will permit the rapid discovery of clinically useful prognostic and predictive markers, dramatically adding to our fundamental understanding to colon cancer biology. Future work will focus on the discovery of novel targets and signatures, creating innovative tools that depict multi-omic results for clinicians.

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Chantzichristos, Dimitrios, Per-Arne Svensson, Terence Garner, Camilla A. M. Glad, Brian Robert Walker, Ragnhildur Bergthorsdottir, Oskar Ragnarsson, et al. "MiR-122-5p: A Novel Biomarker of Glucocorticoid Action." Journal of the Endocrine Society 5, Supplement_1 (May 1, 2021): A89. http://dx.doi.org/10.1210/jendso/bvab048.178.

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Abstract Background: Glucocorticoids are among the most prescribed medications for various indications, and treatment with glucocorticoids is associated with increased morbidity and mortality. A biomarker allowing quantification of glucocorticoid action could improve treatment safety and efficacy. Objective: To identify and validate circulating biomarkers of glucocorticoid action using a clinical experimental study and multi-omic network analysis. Methods: In a randomized, controlled, crossover, single-blind trial, 10 subjects without endogenous glucocorticoid production (Addison’s disease) received intravenous hydrocortisone infusion in a circadian pattern (physiological glucocorticoid exposure) or isotonic saline (glucocorticoid withdrawal) over 22 hours. Food intake and sample collections were standardized during both treatment periods. The transcriptomes of peripheral blood mononuclear cells and adipose tissue, plasma miRNAome and serum metabolome were collected at 7 AM (end of infusion). These multi-omic data were compared between the two interventions, within and between subjects, using network analysis of higher order interactions along with statistical and machine learning approaches. Samples from 120 subjects with varying glucocorticoid exposure from independent studies were used for the replication of the miRNA findings. The study was pre-registered at ClinicalTrials.gov with identifier NCT02152553. Results: We identified a transcriptomic profile derived from both peripheral blood mononuclear cells and adipose tissue, and a multi-omic signature including genes, miRNAs and metabolites that were associated with glucocorticoid exposure. Within the multi-omic signature we identified a single microRNA (miR-122-5p, p=0.009) regulated by glucocorticoid exposure, which we then replicated as a novel biomarker of glucocorticoid action in 120 subjects from independent studies (0.01 ≤ p ≤ 0.05). Conclusions: The discovery of miR-122-5p as a novel circulating biomarker of glucocorticoid action may have a significant impact on clinical practice. Our data also improves the understanding of glucocorticoid action and may have impact on future studies on the mechanistic understanding for the role of glucocorticoids in the etiology of common diseases, such as cardiovascular disease and obesity.

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Tsai, Nicole Y., Derek S. Welsbie, and Xin Duan. "Live, die, or regenerate? New insights from multi-omic analyses." Neuron 110, no. 16 (August 2022): 2516–19. http://dx.doi.org/10.1016/j.neuron.2022.07.026.

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Journal articles on the topic 'Multi-omic'

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