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Journal articles on the topic 'Multi-omic analysis'

Author: Grafiati

Published: 4 May 2024

Last updated: 26 July 2025

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1

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

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The number of researchers using multi-omics is growing. Though still expensive, every year it is cheaper to perform multi-omic studies, often exponentially so. In addition to its increasing accessibility, multi-omics reveals a view of systems biology to an unprecedented depth. Thus, multi-omics can be used to answer a broad range of biological questions in finer resolution than previous methods. We used six omic measurements—four nucleic acid (i.e., genomic, epigenomic, transcriptomics, and metagenomic) and two mass spectrometry (proteomics and metabolomics) based—to highlight an analysis work

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

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Abstract Motivation The increasing availability of multi-omic data has enabled the discovery of disease biomarkers in different scales. Understanding the functional interaction between multi-omic biomarkers is becoming increasingly important due to its great potential for providing insights of the underlying molecular mechanism. Results Leveraging multiple biological network databases, we integrated the relationship between single nucleotide polymorphisms (SNPs), genes/proteins and metabolites, and developed an R package Multi-omic Network Explorer Tool (MoNET) for multi-omic network analysis.

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

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

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

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

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

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e16063 Background: Cancer is a highly heterogeneous disease, both intra- and inter-individually consisting of complex phenotypes and systems biology. Although genomic data has contributed greatly towards the identification of cancer-specific mutations and the progress of precision medicine, genomic alterations are only one of several important biological drivers of cancer. Furthermore, single-layer omics represent only a small piece of the cancer biology puzzle and provide only partial clues to connecting genotype with clinically relevant phenotypic data. A more integrated approach is urgently

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

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Davitavyan, Suren, Gevorg Martirosyan, Gohar Mkrtchyan, et al. "Integrated analysis of -omic landscapes in breast cancer subtypes." F1000Research 13 (June 3, 2024): 564. http://dx.doi.org/10.12688/f1000research.148778.1.

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The subtypes of breast cancer exhibit diverse histology, molecular features, therapeutic response, aggressiveness, and patient outcomes. Multi-omics high-throughput technologies, which are widely used in cancer research, generated waste amounts of multimodal omic datasets calling for new approaches of integrated analyses to uncover patterns of transcriptomic, genomic, and epigenetic changes in breast cancer subtypes and connect them to disease clinical characteristics. Here, we applied multi-layer self-organizing map (ml-SOM) algorithms to PAM50-classified TCGA breast cancer samples to disenta

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Tayrine, de Souza Rocha Pedro Augusto Farias de Souza Victor Hugo Santiago Costa Pinto Regiane Silva Kawasaki Francês Sintia Silva de Almeida Vinicius Augusto Carvalho de Abreu. "MULTIDIMENSIONAL VISUALIZATION FOR MULTI-OMICS DATA ANALYSIS." Revista ft 27, no. 124 (2023): 19. https://doi.org/10.5281/zenodo.8126719.

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High-throughput studies of biological systems are rapidly accumulating a large body of omic-scale data. The analysis of data that constitute various types of omics (Genomics, Metagenomics, Transcriptomics and Proteomics) from different experiments, play a role in the process of asking new questions that correlate between different combinations of these types of data. Thus, a systemic view is needed that addresses the use of the integration of these omics. The visualization of information or the use of a multidimensional view is increasingly relevant in the multi-omics theme. This article seeks

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Escriba-Montagut, Xavier, Yannick Marcon, Augusto Anguita-Ruiz, et al. "Federated privacy-protected meta- and mega-omics data analysis in multi-center studies with a fully open-source analytic platform." PLOS Computational Biology 20, no. 12 (2024): e1012626. https://doi.org/10.1371/journal.pcbi.1012626.

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The importance of maintaining data privacy and complying with regulatory requirements is highlighted especially when sharing omic data between different research centers. This challenge is even more pronounced in the scenario where a multi-center effort for collaborative omics studies is necessary. OmicSHIELD is introduced as an open-source tool aimed at overcoming these challenges by enabling privacy-protected federated analysis of sensitive omic data. In order to ensure this, multiple security mechanisms have been included in the software. This innovative tool is capable of managing a wide r

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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 (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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Miller, David T., Isidro Cortés-Ciriano, Nischalan Pillay, 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 (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 progr

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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 (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.

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Ohno, Satoshi, Saori Uematsu, and Shinya Kuroda. "Quantitative metabolic fluxes regulated by trans-omic networks." Biochemical Journal 479, no. 6 (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

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Khalyfa, Abdelnaby, Jose M. Marin, David Sanz-Rubio, Zhen Lyu, Trupti Joshi, and David Gozal. "Multi-Omics Analysis of Circulating Exosomes in Adherent Long-Term Treated OSA Patients." International Journal of Molecular Sciences 24, no. 22 (2023): 16074. http://dx.doi.org/10.3390/ijms242216074.

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Obstructive sleep apnea (OSA) is a highly prevalent chronic disease affecting nearly a billion people globally and increasing the risk of multi-organ morbidity and overall mortality. However, the mechanisms underlying such adverse outcomes remain incompletely delineated. Extracellular vesicles (exosomes) are secreted by most cells, are involved in both proximal and long-distance intercellular communication, and contribute toward homeostasis under physiological conditions. A multi-omics integrative assessment of plasma-derived exosomes from adult OSA patients prior to and after 1-year adherent

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Canela, Núria Anela. "A pioneering multi-omics data platform sheds light on the understanding of biological systems." Project Repository Journal 20, no. 1 (2024): 20–23. http://dx.doi.org/10.54050/prj2021863.

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A pioneering multi-omics data platform sheds light on the understanding of biological systems The GLOMICAVE project has developed an innovative multi-omics data analysis digital platform, relying on big data analytics and artificial intelligence and using large-scale publicly available and experimental omic datasets. The project aimed to maximise the utility of omic data at a massive level and discover new links between animal and vegetable genotype and phenotype, understanding biological systems as a whole.

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Nisbet, Laurence, Xueyi Shen, Naomi Wray, and Andrew McIntosh. "T47. A MULTI-OMIC QTL ANALYSIS OF MAJOR DEPRESSIVE DISORDER." European Neuropsychopharmacology 87 (October 2024): 181. http://dx.doi.org/10.1016/j.euroneuro.2024.08.357.

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Seguro, F., R. Bittencourt, EQ Crusoé, DLC Farias, IE Pereira, and CHC Xavier. "MULTI-OMIC ANALYSIS OF MULTIPLE MYELOMA BY T(11;14) STATUS." Hematology, Transfusion and Cell Therapy 46 (October 2024): S514—S515. http://dx.doi.org/10.1016/j.htct.2024.09.864.

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Oromendia, Ana, Dorina Ismailgeci, Michele Ciofii, et al. "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 (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 automate

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Towle-Miller, Lorin M., Jeffrey C. Miecznikowski, Fan Zhang, and David L. Tritchler. "SuMO-Fil: Supervised multi-omic filtering prior to performing network analysis." PLOS ONE 16, no. 8 (2021): e0255579. http://dx.doi.org/10.1371/journal.pone.0255579.

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Multi-omic analyses that integrate many high-dimensional datasets often present significant deficiencies in statistical power and require time consuming computations to execute the analytical methods. We present SuMO-Fil to remedy against these issues which is a pre-processing method for Supervised Multi-Omic Filtering that removes variables or features considered to be irrelevant noise. SuMO-Fil is intended to be performed prior to downstream analyses that detect supervised gene networks in sparse settings. We accomplish this by implementing variable filters based on low similarity across the

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Kogelman, Lisette J. A., Madeleine Ernst, Katrine Falkenberg, et al. "Multi-omics to predict changes during cold pressor test." BMC Genomics 23, no. 1 (2022): 759. https://doi.org/10.1186/s12864-022-08981-z.

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<strong>Background: </strong>The cold pressor test (CPT) is a widely used pain provocation test to investigate both pain tolerance and cardiovascular responses. We hypothesize, that performing multi-omic analyses during CPT gives the opportunity to home in on molecular mechanisms involved. Twenty-two females were phenotypically assessed before and after a CPT, and blood samples were taken. RNA-Sequencing, steroid profiling and untargeted metabolomics were performed. Each 'omic level was analyzed separately at both single-feature and systems-level (principal component [PCA] and partial least sq

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Rappoport, Nimrod, and Ron Shamir. "NEMO: cancer subtyping by integration of partial multi-omic data." Bioinformatics 35, no. 18 (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 impu

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Tan, Hexin, Xianghui Chen, Nan Liang, 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 (2019): 4819–34. http://dx.doi.org/10.1093/jxb/erz211.

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Suravajhala, Prashanth, Lisette J. A. Kogelman, and Haja N. Kadarmideen. "Multi-omic data integration and analysis using systems genomics approaches: methods and applications in animal production, health and welfare." Genetics Selection Evolution 48, no. 1 (2016): 38. https://doi.org/10.1186/s12711-016-0217-x.

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In the past years, there has been a remarkable development of high-throughput omics (HTO) technologies such as genomics, epigenomics, transcriptomics, proteomics and metabolomics across all facets of biology. This has spearheaded the progress of the systems biology era, including applications on animal production and health traits. However, notwithstanding these new HTO technologies, there remains an emerging challenge in data analysis. On the one hand, different HTO technologies judged on their own merit are appropriate for the identification of disease-causing genes, biomarkers for preventio

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He, Yuchen, Edrees H. Rashan, Vanessa Linke, et al. "Multi-Omic Single-Shot Technology for Integrated Proteome and Lipidome Analysis." Analytical Chemistry 93, no. 9 (2021): 4217–22. http://dx.doi.org/10.1021/acs.analchem.0c04764.

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Forny, Patrick, Ximena Bonilla, David Lamparter, et al. "INTEGRATED MULTI-OMIC ANALYSIS OF A RARE INBORN ERROR OF METABOLISM." Molecular Genetics and Metabolism 135, no. 4 (2022): 271–72. http://dx.doi.org/10.1016/j.ymgme.2022.01.039.

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Nuno, Kevin, Armon Azizi, Thomas Koehnke, M. Ryan Corces, and Ravi Majeti. "Multi-Omic Analysis Identifies Epigenetic Evolution in Relapsed Acute Myeloid Leukemia." Blood 136, Supplement 1 (2020): 13–14. http://dx.doi.org/10.1182/blood-2020-143141.

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Introduction: Acute myeloid leukemia (AML) is associated with a poor prognosis even with aggressive treatments including high dose chemotherapy. While most patients enter clinical remission, these remissions are often short-lived leading to chemotherapy-resistant relapsed disease that accounts for the majority of deaths. We undertook a meta-analysis of published datasets consisting of 142 genotyped paired diagnosis-relapse AML samples to understand the genetic evolution of AML between the two disease states. This analysis determined that a plurality of cases exhibited the same mutations at dia

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Teclemariam, Esei T., Melissa R. Pergande, and Stephanie M. Cologna. "Considerations for mass spectrometry-based multi-omic analysis of clinical samples." Expert Review of Proteomics 17, no. 2 (2020): 99–107. http://dx.doi.org/10.1080/14789450.2020.1724540.

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Ellenbogen, Yosef, Alexander Landry, Jeff Liu, Yiyan Wu, Federico Gaiti, and Gelareh Zadeh. "EPCO-51. A MULTI-OMIC ANALYSIS OF REGIONAL HETEROGENEITY IN GLIOBLASTOMA." Neuro-Oncology 26, Supplement_8 (2024): viii13. http://dx.doi.org/10.1093/neuonc/noae165.0050.

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Abstract Glioblastoma (GBM) is the most common malignant brain tumor in adults. A hallmark of GBM is its intratumoral heterogeneity as well as infiltration into the surrounding brain. The growing understanding of the cellular diversity and cellular state diversity within GBM necessitates a need for a more granular evaluation of the molecular landscape of this disease. This study aimed to investigate this using single cell RNA sequencing combined with single-cell ATAC sequencing and spatial transcriptomics in order to delineate cellular states and enriched pathways between malignant cells in di

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Hanson, Casey, Junmei Cairns, Liewei Wang, and Saurabh Sinha. "Principled multi-omic analysis reveals gene regulatory mechanisms of phenotype variation." Genome Research 28, no. 8 (2018): 1207–16. http://dx.doi.org/10.1101/gr.227066.117.

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Sureshbabu, V., A. Mallipatna, N. Guha, et al. "Integrated multi-omic analysis of human retinoblastoma identifies novel regulatory networks." Acta Ophthalmologica 93 (September 23, 2015): n/a. http://dx.doi.org/10.1111/j.1755-3768.2015.0544.

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Song, Yang, Zhe Wang, Guangji Zhang, et al. "Integrative Multi-Omic Analysis for Prognosis Stratification in Acute Myeloid Leukemia." Blood 142, Supplement 1 (2023): 5984. http://dx.doi.org/10.1182/blood-2023-173211.

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Acute myeloid leukemia (AML) is a heterogeneous hematopoietic malignancy with a dismal prognosis. European LeukemiaNet (ELN) is crucial for tailoring AML treatment individually. Various AML models correlated survival and clinical drug response with immune cell differentiation state by deconvoluting transcriptomics. However, few comprehensive molecular subtyping models integrate multi-omic profiles for prognostic and drug response prediction. Thus, this study aimed to integrate DNA methylation, genomics, transcriptomics and ex vivo drug sensitivity screening of AML patients and explore their ro

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Abdelhamid, Sultan S., Jacob Scioscia, Yoram Vodovotz, 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 (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 Medica

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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 (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,

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Crawford, John R., Yan Yuen Lo, Meghana S. Pagadala, et al. "TRLS-13. MULTI-OMICS AND FUNCTIONAL PRECISION MEDICINE FOR NEWLY DIAGNOSED AND RECURRENT PEDIATRIC CENTRAL NERVOUS SYSTEM TUMORS." Neuro-Oncology 26, Supplement_4 (2024): 0. http://dx.doi.org/10.1093/neuonc/noae064.166.

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Abstract BACKGROUND Comprehensive molecular characterization of pediatric brain tumors has led to a more refined diagnosis. However, the feasibility of performing multi-omic and functional precision medicine approaches using ex-vivo drug screening in the clinical setting is unknown. METHODS Patients with newly diagnosed or recurrent central nervous system tumors were enrolled in a feasibility study of multi-omic analysis including whole genome trio germline sequencing, tumor whole exome/RNA sequencing, RNA-based DiSCoVER analysis, methylation profiling, immunogenic potential analysis, and ex-v

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Sandri, Brian J., Adam Kaplan, Shane W. Hodgson, et al. "Multi-omic molecular profiling of lung cancer in COPD." European Respiratory Journal 52, no. 1 (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 polyriboso

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Martini, Lorenzo. "MODA - Multi-Omic Data Analysis." July 27, 2023. https://doi.org/10.5281/zenodo.8188656.

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This repository aims to not only share the code to obtain the results but gives step-by-step commented workflow notebooks, for the three different methods discussed in the paper. Specifically, the repo comprises of three sections, one for each pipeline (Monocle3/Cicero, Seurat/Signac, Scanpy/Episcanpy), consisting of the notebooks with the commented code and results. For each one it is also provided a HTML image to just look at them.

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Downing, Tim, and Nicos Angelopoulos. "A primer on correlation-based dimension reduction methods for multi-omics analysis." Journal of The Royal Society Interface 20, no. 207 (2023). http://dx.doi.org/10.1098/rsif.2023.0344.

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The continuing advances of omic technologies mean that it is now more tangible to measure the numerous features collectively reflecting the molecular properties of a sample. When multiple omic methods are used, statistical and computational approaches can exploit these large, connected profiles. Multi-omics is the integration of different omic data sources from the same biological sample. In this review, we focus on correlation-based dimension reduction approaches for single omic datasets, followed by methods for pairs of omics datasets, before detailing further techniques for three or more om

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Bardozzo, Francesco, Pietro Lió, and Roberto Tagliaferri. "Signal metrics analysis of oscillatory patterns in bacterial multi-omic networks." Bioinformatics, November 13, 2020. http://dx.doi.org/10.1093/bioinformatics/btaa966.

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Abstract Motivation One of the branches of Systems Biology is focused on a deep understanding of underlying regulatory networks through the analysis of the biomolecules oscillations and their interplay. Synthetic Biology exploits gene or/and protein regulatory networks towards the design of oscillatory networks for producing useful compounds. Therefore, at different levels of application and for different purposes, the study of biomolecular oscillations can lead to different clues about the mechanisms underlying living cells. It is known that network-level interactions involve more than one ty

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Rau, Andrea, Regina Manansala, Michael J. Flister, et al. "Individualized multi-omic pathway deviation scores using multiple factor analysis." Biostatistics, August 6, 2020. http://dx.doi.org/10.1093/biostatistics/kxaa029.

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Summary Malignant progression of normal tissue is typically driven by complex networks of somatic changes, including genetic mutations, copy number aberrations, epigenetic changes, and transcriptional reprogramming. To delineate aberrant multi-omic tumor features that correlate with clinical outcomes, we present a novel pathway-centric tool based on the multiple factor analysis framework called padma. Using a multi-omic consensus representation, padma quantifies and characterizes individualized pathway-specific multi-omic deviations and their underlying drivers, with respect to the sampled pop

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Boyd, Samuel S., Chad Slawson, and Jeffrey A. Thompson. "AMEND 2.0: module identification and multi-omic data integration with multiplex-heterogeneous graphs." BMC Bioinformatics 26, no. 1 (2025). https://doi.org/10.1186/s12859-025-06063-x.

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Abstract Background Multi-omic studies provide comprehensive insight into biological systems by evaluating cellular changes between normal and pathological conditions at multiple levels of measurement. Biological networks, which represent interactions or associations between biomolecules, have been highly effective in facilitating omic analysis. However, current network-based methods lack generalizability to accommodate multiple data types across a range of diverse experiments. Results We present AMEND 2.0, an updated active module identification method which can analyze multiplex and/or heter

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Sajjad, Fatima, Ahmer Jalal, Amir Jalal, et al. "Multi‐omic analysis of dysregulated pathways in triple negative breast cancer." Asia-Pacific Journal of Clinical Oncology, June 20, 2024. http://dx.doi.org/10.1111/ajco.14095.

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AbstractThe aggressive characteristics of triple‐negative breast cancer (TNBC) and the absence of targeted medicines make TNBC a challenging clinical case. The molecular landscape of TNBC has been well‐understood thanks to recent developments in multi‐omic analysis, which have also revealed dysregulated pathways and possible treatment targets. This review summarizes the utilization of multi‐omic approaches in elucidating TNBC's complex biology and therapeutic avenues. Dysregulated pathways including cell cycle progression, immunological modulation, and DNA damage response have been uncovered i

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Hertzano, Ronna, and Anup Mahurkar. "Advancing discovery in hearing research via biologist-friendly access to multi-omic data." Human Genetics, March 2, 2022. http://dx.doi.org/10.1007/s00439-022-02445-w.

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AbstractHigh-throughput cell type-specific multi-omic analyses have advanced our understanding of inner ear biology in an unprecedented way. The full benefit of these data, however, is reached from their re-use. Successful re-use of data requires identifying the natural users and ensuring proper data democratization and federation for their seamless and meaningful access. Here we discuss universal challenges in access and re-use of multi-omic data, possible solutions, and introduce the gEAR (the gene Expression Analysis Resource, umgear.org)—a tool for multi-omic data visualization, sharing an

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Wu, Shengbo, Haonan Zhou, Danlei Chen, Yutong Lu, Yanni Li, and Jianjun Qiao. "Multi-omic analysis tools for microbial metabolites prediction." Briefings in Bioinformatics 25, no. 4 (2024). http://dx.doi.org/10.1093/bib/bbae264.

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Abstract How to resolve the metabolic dark matter of microorganisms has long been a challenging problem in discovering active molecules. Diverse omics tools have been developed to guide the discovery and characterization of various microbial metabolites, which make it gradually possible to predict the overall metabolites for individual strains. The combinations of multi-omic analysis tools effectively compensates for the shortcomings of current studies that focus only on single omics or a broad class of metabolites. In this review, we systematically update, categorize and sort out different an

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Nicol, Alexander James, Sai-Kit Lam, Jerry Chi Fung Ching, et al. "A multi-center, multi-organ, multi-omic prediction model for treatment-induced severe oral mucositis in nasopharyngeal carcinoma." La radiologia medica, November 21, 2024. http://dx.doi.org/10.1007/s11547-024-01901-z.

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Abstract Purpose Oral mucositis (OM) is one of the most prevalent and crippling treatment-related toxicities experienced by nasopharyngeal carcinoma (NPC) patients receiving radiotherapy (RT), posing a tremendous adverse impact on quality of life. This multi-center study aimed to develop and externally validate a multi-omic prediction model for severe OM. Methods Four hundred and sixty-four histologically confirmed NPC patients were retrospectively recruited from two public hospitals in Hong Kong. Model development was conducted on one institution (n = 363), and the other was reserved for exte

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Arehart, Christopher H., John D. Sterrett, Rosanna L. Garris, et al. "Poly-omic risk scores predict inflammatory bowel disease diagnosis." mSystems, December 14, 2023. http://dx.doi.org/10.1128/msystems.00677-23.

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ABSTRACT Inflammatory bowel disease (IBD) is characterized by complex etiology and a disrupted colonic ecosystem. We provide a framework for the analysis of multi-omic data, which we apply to study the gut ecosystem in IBD. Specifically, we train and validate models using data on the metagenome, metatranscriptome, virome, and metabolome from the Human Microbiome Project 2 IBD multi-omic database, with 1,785 repeated samples from 130 individuals (103 cases and 27 controls). After splitting the participants into training and testing groups, we used mixed-effects least absolute shrinkage and sele

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Mendelson, Jenna B., Jacob D. Sternbach, Michelle J. Doyle, et al. "A Multi-omic and Multi-Species Analysis of Right Ventricular Dysfunction." Journal of Heart and Lung Transplantation, October 2023. http://dx.doi.org/10.1016/j.healun.2023.09.020.

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Cai, Zhaoxiang, Rebecca C. Poulos, Adel Aref, Phillip J. Robinson, Roger R. Reddel, and Qing Zhong. "DeePathNet: a transformer-based deep learning model integrating multi-omic data with cancer pathways." Cancer Research Communications, November 11, 2024. http://dx.doi.org/10.1158/2767-9764.crc-24-0285.

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Abstract Multi-omic data analysis incorporating machine learning has the potential to significantly improve cancer diagnosis and prognosis. Traditional machine learning methods are usually limited to omic measurements, omitting existing domain knowledge, such as the biological networks that link molecular entities in various omic data types. Here we develop a Transformer-based explainable deep learning model, DeePathNet, which integrates cancer-specific pathway information into multi-omic data analysis. Using a variety of big datasets, including ProCan-DepMapSanger, CCLE, and TCGA, we demonstr

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Garmany, Ramin, J. Martijn Bos, David J. Tester, et al. "Multi-Omic Architecture of Obstructive Hypertrophic Cardiomyopathy." Circulation: Genomic and Precision Medicine, February 20, 2023. http://dx.doi.org/10.1161/circgen.122.003756.

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BACKGROUND: Hypertrophic cardiomyopathy (HCM) is characterized by asymmetric left ventricular hypertrophy. Currently, hypertrophy pathways responsible for HCM have not been fully elucidated. Their identification could serve as a nidus for the generation of novel therapeutics aimed at halting disease development or progression. Herein, we performed a comprehensive multi-omic characterization of hypertrophy pathways in HCM. METHODS: Flash-frozen cardiac tissues were collected from genotyped HCM patients (n=97) undergoing surgical myectomy and tissue from 23 controls. RNA sequencing and mass spec

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