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Blutt, Sarah E., Cristian Coarfa, Josef Neu, and Mohan Pammi. "Multiomic Investigations into Lung Health and Disease." Microorganisms 11, no. 8 (August 19, 2023): 2116. http://dx.doi.org/10.3390/microorganisms11082116.
Повний текст джерелаАнотація:
Diseases of the lung account for more than 5 million deaths worldwide and are a healthcare burden. Improving clinical outcomes, including mortality and quality of life, involves a holistic understanding of the disease, which can be provided by the integration of lung multi-omics data. An enhanced understanding of comprehensive multiomic datasets provides opportunities to leverage those datasets to inform the treatment and prevention of lung diseases by classifying severity, prognostication, and discovery of biomarkers. The main objective of this review is to summarize the use of multiomics investigations in lung disease, including multiomics integration and the use of machine learning computational methods. This review also discusses lung disease models, including animal models, organoids, and single-cell lines, to study multiomics in lung health and disease. We provide examples of lung diseases where multi-omics investigations have provided deeper insight into etiopathogenesis and have resulted in improved preventative and therapeutic interventions.
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Demetci, Pinar, Rebecca Santorella, Björn Sandstede, William Stafford Noble, and Ritambhara Singh. "Single-Cell Multiomics Integration by SCOT." Journal of Computational Biology 29, no. 1 (January 1, 2022): 19–22. http://dx.doi.org/10.1089/cmb.2021.0477.
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Santiago, Raoul. "Multiomics integration: advancing pediatric cancer immunotherapy." Immuno Oncology Insights 04, no. 07 (August 5, 2023): 267–72. http://dx.doi.org/10.18609/ioi.2023.038.
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Valle, Filippo, Matteo Osella, and Michele Caselle. "Multiomics Topic Modeling for Breast Cancer Classification." Cancers 14, no. 5 (February 23, 2022): 1150. http://dx.doi.org/10.3390/cancers14051150.
Повний текст джерелаАнотація:
The integration of transcriptional data with other layers of information, such as the post-transcriptional regulation mediated by microRNAs, can be crucial to identify the driver genes and the subtypes of complex and heterogeneous diseases such as cancer. This paper presents an approach based on topic modeling to accomplish this integration task. More specifically, we show how an algorithm based on a hierarchical version of stochastic block modeling can be naturally extended to integrate any combination of ’omics data. We test this approach on breast cancer samples from the TCGA database, integrating data on messenger RNA, microRNAs, and copy number variations. We show that the inclusion of the microRNA layer significantly improves the accuracy of subtype classification. Moreover, some of the hidden structures or “topics” that the algorithm extracts actually correspond to genes and microRNAs involved in breast cancer development and are associated to the survival probability.
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Boroń, Dariusz, Nikola Zmarzły, Magdalena Wierzbik-Strońska, Joanna Rosińczuk, Paweł Mieszczański, and Beniamin Oskar Grabarek. "Recent Multiomics Approaches in Endometrial Cancer." International Journal of Molecular Sciences 23, no. 3 (January 22, 2022): 1237. http://dx.doi.org/10.3390/ijms23031237.
Повний текст джерелаАнотація:
Endometrial cancer is the most common gynecological cancers in developed countries. Many of the mechanisms involved in its initiation and progression remain unclear. Analysis providing comprehensive data on the genome, transcriptome, proteome, and epigenome could help in selecting molecular markers and targets in endometrial cancer. Multiomics approaches can reveal disturbances in multiple biological systems, giving a broader picture of the problem. However, they provide a large amount of data that require processing and further integration prior to analysis. There are several repositories of multiomics datasets, including endometrial cancer data, as well as portals allowing multiomics data analysis and visualization, including Oncomine, UALCAN, LinkedOmics, and miRDB. Multiomics approaches have also been applied in endometrial cancer research in order to identify novel molecular markers and therapeutic targets. This review describes in detail the latest findings on multiomics approaches in endometrial cancer.
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Ugidos, Manuel, Sonia Tarazona, José M. Prats-Montalbán, Alberto Ferrer, and Ana Conesa. "MultiBaC: A strategy to remove batch effects between different omic data types." Statistical Methods in Medical Research 29, no. 10 (March 4, 2020): 2851–64. http://dx.doi.org/10.1177/0962280220907365.
Повний текст джерелаАнотація:
Diversity of omic technologies has expanded in the last years together with the number of omic data integration strategies. However, multiomic data generation is costly, and many research groups cannot afford research projects where many different omic techniques are generated, at least at the same time. As most researchers share their data in public repositories, different omic datasets of the same biological system obtained at different labs can be combined to construct a multiomic study. However, data obtained at different labs or moments in time are typically subjected to batch effects that need to be removed for successful data integration. While there are methods to correct batch effects on the same data types obtained in different studies, they cannot be applied to correct lab or batch effects across omics. This impairs multiomic meta-analysis. Fortunately, in many cases, at least one omics platform—i.e. gene expression— is repeatedly measured across labs, together with the additional omic modalities that are specific to each study. This creates an opportunity for batch analysis. We have developed MultiBaC (multiomic Multiomics Batch-effect Correction correction), a strategy to correct batch effects from multiomic datasets distributed across different labs or data acquisition events. Our strategy is based on the existence of at least one shared data type which allows data prediction across omics. We validate this approach both on simulated data and on a case where the multiomic design is fully shared by two labs, hence batch effect correction within the same omic modality using traditional methods can be compared with the MultiBaC correction across data types. Finally, we apply MultiBaC to a true multiomic data integration problem to show that we are able to improve the detection of meaningful biological effects.
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Ramos, Marcel, Lucas Schiffer, Angela Re, Rimsha Azhar, Azfar Basunia, Carmen Rodriguez, Tiffany Chan, et al. "Software for the Integration of Multiomics Experiments in Bioconductor." Cancer Research 77, no. 21 (October 31, 2017): e39-e42. http://dx.doi.org/10.1158/0008-5472.can-17-0344.
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Hoang Anh, Nguyen, Jung Eun Min, Sun Jo Kim, and Nguyen Phuoc Long. "Biotherapeutic Products, Cellular Factories, and Multiomics Integration in Metabolic Engineering." OMICS: A Journal of Integrative Biology 24, no. 11 (November 1, 2020): 621–33. http://dx.doi.org/10.1089/omi.2020.0112.
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Kashima, Yukie, Yoshitaka Sakamoto, Keiya Kaneko, Masahide Seki, Yutaka Suzuki, and Ayako Suzuki. "Single-cell sequencing techniques from individual to multiomics analyses." Experimental & Molecular Medicine 52, no. 9 (September 2020): 1419–27. http://dx.doi.org/10.1038/s12276-020-00499-2.
Повний текст джерелаАнотація:
Abstract Here, we review single-cell sequencing techniques for individual and multiomics profiling in single cells. We mainly describe single-cell genomic, epigenomic, and transcriptomic methods, and examples of their applications. For the integration of multilayered data sets, such as the transcriptome data derived from single-cell RNA sequencing and chromatin accessibility data derived from single-cell ATAC-seq, there are several computational integration methods. We also describe single-cell experimental methods for the simultaneous measurement of two or more omics layers. We can achieve a detailed understanding of the basic molecular profiles and those associated with disease in each cell by utilizing a large number of single-cell sequencing techniques and the accumulated data sets.
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Bisht, Vartika, Katrina Nash, Yuanwei Xu, Prasoon Agarwal, Sofie Bosch, Georgios V. Gkoutos, and Animesh Acharjee. "Integration of the Microbiome, Metabolome and Transcriptomics Data Identified Novel Metabolic Pathway Regulation in Colorectal Cancer." International Journal of Molecular Sciences 22, no. 11 (May 28, 2021): 5763. http://dx.doi.org/10.3390/ijms22115763.
Повний текст джерелаАнотація:
Integrative multiomics data analysis provides a unique opportunity for the mechanistic understanding of colorectal cancer (CRC) in addition to the identification of potential novel therapeutic targets. In this study, we used public omics data sets to investigate potential associations between microbiome, metabolome, bulk transcriptomics and single cell RNA sequencing datasets. We identified multiple potential interactions, for example 5-aminovalerate interacting with Adlercreutzia; cholesteryl ester interacting with bacterial genera Staphylococcus, Blautia and Roseburia. Using public single cell and bulk RNA sequencing, we identified 17 overlapping genes involved in epithelial cell pathways, with particular significance of the oxidative phosphorylation pathway and the ACAT1 gene that indirectly regulates the esterification of cholesterol. These findings demonstrate that the integration of multiomics data sets from diverse populations can help us in untangling the colorectal cancer pathogenesis as well as postulate the disease pathology mechanisms and therapeutic targets.
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Hatami, Elham, Hye-Won Song, Hongduan Huang, Zhiqi Zhang, Thomas McCarthy, Youngsook Kim, Ruifang Li, et al. "Integration of single-cell transcriptomic and chromatin accessibility on heterogenicity of human peripheral blood mononuclear cells utilizing microwell-based single-cell partitioning technology." Journal of Immunology 212, no. 1_Supplement (May 1, 2024): 1508_5137. http://dx.doi.org/10.4049/jimmunol.212.supp.1508.5137.
Повний текст джерелаАнотація:
Abstract Single-cell RNA sequencing (scRNA-Seq) deepens our understanding of cellular development and heterogeneity. However, limitations exist in unraveling cell states and gene regulatory programs. Chromatin state profiles assess gene expression potential and offer insights into transcriptional regulation. Integrated with gene expression data, chromatin accessibility region (CAR) profiles establish fundamental gene regulatory logic for cell fate. ATAC-seq (Assay for Transposase-Accessible Chromatin using Sequencing) is a highly potent approach for profiling genome-wide CARs. To investigate the power of the multiomics assay in identifying differentiated gene regulations, we conducted multiomic snATAC-seq+ snRNA-seq on PBMCs from two different donors, by utilizing the gentle and robust microwell-based single-cell partitioning technology. The assay showed high sensitivity and specificity metrics (>10,000 median unique fragments/cell, >0.7 fragments in peak score). Integrative analysis across donors revealed enriched transcription factor motifs and fragment coverage tracks in distinct cell types, correlating significantly with gene expression data. These findings highlight mRNA's intricate connections with CARs in immune cell development. Our study underscores the power of multiomic analysis in analyzing heterogeneity of PBMC cell populations and offers a toolkit to identify gene regulation specific to diverse cell types, enhancing our comprehension of epigenetic diversity.
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Chen, Tyrone, Al J. Abadi, Kim-Anh Lê Cao, and Sonika Tyagi. "multiomics: A user-friendly multi-omics data harmonisation R pipeline." F1000Research 10 (July 6, 2021): 538. http://dx.doi.org/10.12688/f1000research.53453.1.
Повний текст джерелаАнотація:
Data from multiple omics layers of a biological system is growing in quantity, heterogeneity and dimensionality. Simultaneous multi-omics data integration is a growing field of research as it has strong potential to unlock information on previously hidden biological relationships leading to early diagnosis, prognosis and expedited treatments. Many tools for multi-omics data integration are being developed. However, these tools are often restricted to highly specific experimental designs, and types of omics data. While some general methods do exist, they require specific data formats and experimental conditions. A major limitation in the field is a lack of a single or multi-omics pipeline which can accept data in an unrefined, information-rich form pre-integration and subsequently generate output for further investigation. There is an increasing demand for a generic multi-omics pipeline to facilitate general-purpose data exploration and analysis of heterogeneous data. Therefore, we present our R multiomics pipeline as an easy to use and flexible pipeline that takes unrefined multi-omics data as input, sample information and user-specified parameters to generate a list of output plots and data tables for quality control and downstream analysis. We have demonstrated application of the pipeline on two separate COVID-19 case studies. We enabled limited checkpointing where intermediate output is staged to allow continuation after errors or interruptions in the pipeline and generate a script for reproducing the analysis to improve reproducibility. A seamless integration with the mixOmics R package is achieved, as the R data object can be loaded and manipulated with mixOmics functions. Our pipeline can be installed as an R package or from the git repository, and is accompanied by detailed documentation with walkthroughs on two case studies. The pipeline is also available as Docker and Singularity containers.
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Li, Yongmei, Hao Zhuang, Xinran Zhang, Yuan Li, Yun Liu, Xianfu Yi, Guoxuan Qin, Wen Wei, and Ruibing Chen. "Multiomics Integration Reveals the Landscape of Prometastasis Metabolism in Hepatocellular Carcinoma." Molecular & Cellular Proteomics 17, no. 4 (January 25, 2018): 607–18. http://dx.doi.org/10.1074/mcp.ra118.000586.
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Wang, Xiangdong. "Clinical trans-omics: an integration of clinical phenomes with molecular multiomics." Cell Biology and Toxicology 34, no. 3 (April 24, 2018): 163–66. http://dx.doi.org/10.1007/s10565-018-9431-3.
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Antequera-González, Borja, Neus Martínez-Micaelo, Carlos Sureda-Barbosa, Laura Galian-Gay, M. Sol Siliato-Robles, Carmen Ligero, Artur Evangelista, and Josep M. Alegret. "Specific Multiomic Profiling in Aortic Stenosis in Bicuspid Aortic Valve Disease." Biomedicines 12, no. 2 (February 6, 2024): 380. http://dx.doi.org/10.3390/biomedicines12020380.
Повний текст джерелаАнотація:
Introduction and purpose: Bicuspid aortic valve (BAV) disease is associated with faster aortic valve degeneration and a high incidence of aortic stenosis (AS). In this study, we aimed to identify differences in the pathophysiology of AS between BAV and tricuspid aortic valve (TAV) patients in a multiomics study integrating metabolomics and transcriptomics as well as clinical data. Methods: Eighteen patients underwent aortic valve replacement due to severe aortic stenosis: 8 of them had a TAV, while 10 of them had a BAV. RNA sequencing (RNA-seq) and proton nuclear magnetic resonance spectroscopy (1H-NMR) were performed on these tissue samples to obtain the RNA profile and lipid and low-molecular-weight metabolites. These results combined with clinical data were posteriorly compared, and a multiomic profile specific to AS in BAV disease was obtained. Results: H-NMR results showed that BAV patients with AS had different metabolic profiles than TAV patients. RNA-seq also showed differential RNA expression between the groups. Functional analysis helped connect this RNA pattern to mitochondrial dysfunction. Integration of RNA-seq, 1H-NMR and clinical data helped create a multiomic profile that suggested that mitochondrial dysfunction and oxidative stress are key players in the pathophysiology of AS in BAV disease. Conclusions: The pathophysiology of AS in BAV disease differs from patients with a TAV and has a specific RNA and metabolic profile. This profile was associated with mitochondrial dysfunction and increased oxidative stress.
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Chen, Tyrone, Al J. Abadi, Kim-Anh Lê Cao, and Sonika Tyagi. "multiomics: A user-friendly multi-omics data harmonisation R pipeline." F1000Research 10 (August 2, 2023): 538. http://dx.doi.org/10.12688/f1000research.53453.2.
Повний текст джерелаАнотація:
Data from multiple omics layers of a biological system is growing in quantity, heterogeneity and dimensionality. Simultaneous multi-omics data integration is of immense interest to researchers as it has potential to unlock previously hidden biomolecular relationships leading to early diagnosis, prognosis, and expedited treatments. Many tools for multi-omics data integration are developed. However, these tools are often restricted to highly specific experimental designs, types of omics data, and specific data formats. A major limitation of the field is the lack of a pipeline that can accept data in unrefined form to preserve maximum biology in an individual dataset prior to integration. We fill this gap by developing a flexible, generic multi-omics pipeline called multiomics, to facilitate general-purpose data exploration and analysis of heterogeneous data. The pipeline takes unrefined multi-omics data as input, sample information and user-specified parameters to generate a list of output plots and data tables for quality control and downstream analysis. We have demonstrated its application on a sepsis case study. We enabled limited checkpointing functionality where intermediate output is staged to allow continuation after errors or interruptions in the pipeline and generate a script for reproducing the analysis to improve reproducibility. Our pipeline can be installed as an R package or manually from the git repository, and is accompanied by detailed documentation with walkthroughs on three case studies.
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Silberberg, Gilad, Clare Killick-Cole, Yaron Mosesson, Haia Khoury, Xuan Ren, Mara Gilardi, Daniel Ciznadija, Paolo Schiavini, Marianna Zipeto, and Michael Ritchie. "Abstract 854: A pharmaco-pheno-multiomic integration analysis of pancreatic cancer: A highly predictive biomarker model of biomarkers of Gemcitabine/Abraxane sensitivity and resistance." Cancer Research 83, no. 7_Supplement (April 4, 2023): 854. http://dx.doi.org/10.1158/1538-7445.am2023-854.
Повний текст джерелаАнотація:
Abstract The overall survival of patients diagnosed with Pancreatic Cancer remains low. Initial responses to current therapeutic interventions are below 50%, leading to a high mortality rate shortly after diagnosis. To date, only a companion diagnostic, non-specific for pancreatic cancer, has been approved for this indication. A better understanding of the tumor cell biology and resistance mechanisms may shed light onto novel therapeutic targets that improve long-term outcome and improved patient stratification. In this study, we performed an exhaustive analysis to identify predictive biomarkers for gemcitabine/abraxane sensitivity using multiomics datasets. These datasets were integrated in a pharmaco-phenotypic-multiomic (PPMO) model predictive of therapeutic sensitivity or resistance, using sparse partial least squares (sPLS). Our results reveal major cellular discriminants in genomic variants, transcriptomics, and most pronouncedly in proteomics data. Tumors exhibiting Gemcitabine/Abraxane resistance associate with increased TPRV6 RNA expression, MUC13 protein expression, and USP42 mutation among others. Prospective application of the PPMO integration model was able to accurately predict Gemcitabine/Abraxane response profiles for 4/5 additional Pancreatic samples, therefore suggesting a potential application as a predictive diagnostic tool. Citation Format: Gilad Silberberg, Clare Killick-Cole, Yaron Mosesson, Haia Khoury, Xuan Ren, Mara Gilardi, Daniel Ciznadija, Paolo Schiavini, Marianna Zipeto, Michael Ritchie. A pharmaco-pheno-multiomic integration analysis of pancreatic cancer: A highly predictive biomarker model of biomarkers of Gemcitabine/Abraxane sensitivity and resistance [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 854.
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Mudadla, Tejaswi, Gayatri Sharma, Apoorva Mishra, and Shefali Gola. "Multifaceted Landscape ofOmics Data." Bio-Algorithms and Med-Systems 20, no. 1 (November 21, 2024): 22–36. http://dx.doi.org/10.5604/01.3001.0054.8093.
Повний текст джерелаАнотація:
<b>Objective:</b> This review aims to provide a comprehensive overview of omics fields – including genomics, epigenomics, transcriptomics, proteomics, metabolomics, single- -cell multiomics, microbiomics, and radiomics – and to highlight the significance of integrating these datasets to tackle complex biological questions in systems biology and precision medicine.<b>Methods:</b> The review analyzes current literature across various omics domains, focusing on their individual contributions to cellular functions and their integration challenges. It discusses successful integration examples and addresses issues like data heterogeneity across databases.<b>Results:</b> Omics integration significantly enhances our understanding of biological systems, with each field offering unique insights. Despite challenges with data inconsistencies, successful cases show the potential of integrated omics in advancing personalized medicine, drug discovery, and disease research.<b>Conclusions:</b> Advancing omics integration is essential for breakthroughs in personalized medicine and complex disease studies. Interdisciplinary collaboration will be crucial to overcoming data challenges and realizing the full potential of omics in biomedical advancements.
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Tesi, Niccolo’, Sven van der Lee, Marc Hulsman, Henne Holstege, and Marcel Reinders. "Bioinformatics Strategies for the Analysis and Integration of Large-Scale Multiomics Data." Journals of Gerontology: Series A 78, no. 4 (March 30, 2023): 659–62. http://dx.doi.org/10.1093/gerona/glad005.
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Taguchi, Y.-h., and Turki Turki. "Tensor-Decomposition-Based Unsupervised Feature Extraction Applied to Prostate Cancer Multiomics Data." Genes 11, no. 12 (December 11, 2020): 1493. http://dx.doi.org/10.3390/genes11121493.
Повний текст джерелаАнотація:
The large p small n problem is a challenge without a de facto standard method available to it. In this study, we propose a tensor-decomposition (TD)-based unsupervised feature extraction (FE) formalism applied to multiomics datasets, in which the number of features is more than 100,000 whereas the number of samples is as small as about 100, hence constituting a typical large p small n problem. The proposed TD-based unsupervised FE outperformed other conventional supervised feature selection methods, random forest, categorical regression (also known as analysis of variance, or ANOVA), penalized linear discriminant analysis, and two unsupervised methods, multiple non-negative matrix factorization and principal component analysis (PCA) based unsupervised FE when applied to synthetic datasets and four methods other than PCA based unsupervised FE when applied to multiomics datasets. The genes selected by TD-based unsupervised FE were enriched in genes known to be related to tissues and transcription factors measured. TD-based unsupervised FE was demonstrated to be not only the superior feature selection method but also the method that can select biologically reliable genes. To our knowledge, this is the first study in which TD-based unsupervised FE has been successfully applied to the integration of this variety of multiomics measurements.
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Jiang, Yuexu, Duolin Wang, Dong Xu, and Trupti Joshi. "IMPRes-Pro: A high dimensional multiomics integration method for in silico hypothesis generation." Methods 173 (February 2020): 16–23. http://dx.doi.org/10.1016/j.ymeth.2019.06.013.
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Pfau, Thomas, Mafalda Galhardo, Jake Lin, and Thomas Sauter. "IDARE2—Simultaneous Visualisation of Multiomics Data in Cytoscape." Metabolites 11, no. 5 (May 6, 2021): 300. http://dx.doi.org/10.3390/metabo11050300.
Повний текст джерелаАнотація:
Visual integration of experimental data in metabolic networks is an important step to understanding their meaning. As genome-scale metabolic networks reach several thousand reactions, the task becomes more difficult and less revealing. While databases like KEGG and BioCyc provide curated pathways that allow a navigation of the metabolic landscape of an organism, it is rather laborious to map data directly onto those pathways. There are programs available using these kind of databases as a source for visualization; however, these programs are then restricted to the pathways available in the database. Here, we present IDARE2 a cytoscape plugin that allows the visualization of multiomics data in cytoscape in a user-friendly way. It further provides tools to disentangle highly connected network structures based on common properties of nodes and retains structural links between the generated subnetworks, offering a straightforward way to traverse the splitted network. The tool is extensible, allowing the implementation of specialised representations and data format parsers. We present the automated reproduction of the original IDARE nodes using our tool and show examples of other data being mapped on a network of E. coli. The extensibility is demonstrated with two plugins that are available on github. IDARE2 provides an intuitive way to visualise data from multiple sources and allows one to disentangle the often complex network structure in large networks using predefined properties of the network nodes.
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Liu, Li, Jianjun Huang, Yan Liu, Xingshou Pan, Zhile Li, Liufang Zhou, Tengfang Lai, et al. "Multiomics Analysis of Transcriptome, Epigenome, and Genome Uncovers Putative Mechanisms for Dilated Cardiomyopathy." BioMed Research International 2021 (March 29, 2021): 1–29. http://dx.doi.org/10.1155/2021/6653802.
Повний текст джерелаАнотація:
Objective. Multiple genes have been identified to cause dilated cardiomyopathy (DCM). Nevertheless, there is still a lack of comprehensive elucidation of the molecular characteristics for DCM. Herein, we aimed to uncover putative molecular features for DCM by multiomics analysis. Methods. Differentially expressed genes (DEGs) were obtained from different RNA sequencing (RNA-seq) datasets of left ventricle samples from healthy donors and DCM patients. Furthermore, protein-protein interaction (PPI) analysis was then presented. Differentially methylated genes (DMGs) were identified between DCM and control samples. Following integration of DEGs and DMGs, differentially expressed and methylated genes were acquired and their biological functions were analyzed by the clusterProfiler package. Whole exome sequencing of blood samples from 69 DCM patients was constructed in our cohort, which was analyzed the maftools package. The expression of key mutated genes was verified by three independent datasets. Results. 1407 common DEGs were identified for DCM after integration of the two RNA-seq datasets. A PPI network was constructed, composed of 171 up- and 136 downregulated genes. Four hub genes were identified for DCM, including C3 ( degree = 24 ), GNB3 ( degree = 23 ), QSOX1 ( degree = 21 ), and APOB ( degree = 17 ). Moreover, 285 hyper- and 321 hypomethylated genes were screened for DCM. After integration, 20 differentially expressed and methylated genes were identified, which were associated with cell differentiation and protein digestion and absorption. Among single-nucleotide variant (SNV), C>T was the most frequent mutation classification for DCM. MUC4 was the most frequent mutation gene which occupied 71% across 69 samples, followed by PHLDA1, AHNAK2, and MAML3. These mutated genes were confirmed to be differentially expressed between DCM and control samples. Conclusion. Our findings comprehensively analyzed molecular characteristics from the transcriptome, epigenome, and genome perspectives for DCM, which could provide practical implications for DCM.
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Yun, Haiyang, Shabana Vohra, David Lara-Astiaso, and Brian J. P. Huntly. "Multiomics data integration to reveal chromatin remodeling and reorganization induced by gene mutational synergy." STAR Protocols 3, no. 4 (December 2022): 101770. http://dx.doi.org/10.1016/j.xpro.2022.101770.
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Disatham, Joshua, Lisa Brennan, Ales Cvekl, and Marc Kantorow. "Multiomics Analysis Reveals Novel Genetic Determinants for Lens Differentiation, Structure, and Transparency." Biomolecules 13, no. 4 (April 19, 2023): 693. http://dx.doi.org/10.3390/biom13040693.
Повний текст джерелаАнотація:
Recent advances in next-generation sequencing and data analysis have provided new gateways for identification of novel genome-wide genetic determinants governing tissue development and disease. These advances have revolutionized our understanding of cellular differentiation, homeostasis, and specialized function in multiple tissues. Bioinformatic and functional analysis of these genetic determinants and the pathways they regulate have provided a novel basis for the design of functional experiments to answer a wide range of long-sought biological questions. A well-characterized model for the application of these emerging technologies is the development and differentiation of the ocular lens and how individual pathways regulate lens morphogenesis, gene expression, transparency, and refraction. Recent applications of next-generation sequencing analysis on well-characterized chicken and mouse lens differentiation models using a variety of omics techniques including RNA-seq, ATAC-seq, whole-genome bisulfite sequencing (WGBS), chip-seq, and CUT&RUN have revealed a wide range of essential biological pathways and chromatin features governing lens structure and function. Multiomics integration of these data has established new gene functions and cellular processes essential for lens formation, homeostasis, and transparency including the identification of novel transcription control pathways, autophagy remodeling pathways, and signal transduction pathways, among others. This review summarizes recent omics technologies applied to the lens, methods for integrating multiomics data, and how these recent technologies have advanced our understanding ocular biology and function. The approach and analysis are relevant to identifying the features and functional requirements of more complex tissues and disease states.
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Brandão, Lucas André Cavalcanti, Paola Maura Tricarico, Rossella Gratton, Almerinda Agrelli, Luisa Zupin, Haissam Abou-Saleh, Ronald Moura, and Sergio Crovella. "Multiomics Integration in Skin Diseases with Alterations in Notch Signaling Pathway: PlatOMICs Phase 1 Deployment." International Journal of Molecular Sciences 22, no. 4 (February 3, 2021): 1523. http://dx.doi.org/10.3390/ijms22041523.
Повний текст джерелаАнотація:
The high volume of information produced in the age of omics was and still is an important step to understanding several pathological processes, providing the enlightenment of complex molecular networks and the identification of molecular targets associated with many diseases. Despite these remarkable scientific advances, the majority of the results are disconnected and divergent, making their use limited. Skin diseases with alterations in the Notch signaling pathway were extensively studied during the omics era. In the GWAS Catalog, considering only studies on genomics association (GWAS), several works were deposited, some of which with divergent results. In addition, there are thousands of scientific articles available about these skin diseases. In our study, we focused our attention on skin diseases characterized by the impairment of Notch signaling, this pathway being of pivotal importance in the context of epithelial disorders. We considered the pathologies of five human skin diseases, Hidradenitis Suppurativa, Dowling Degos Disease, Adams–Oliver Syndrome, Psoriasis, and Atopic Dermatitis, in which the molecular alterations in the Notch signaling pathway have been reported. To this end, we started developing a new multiomics platform, PlatOMICs, to integrate and re-analyze omics information, searching for the molecular interactions involved in the pathogenesis of skin diseases with alterations in the Notch signaling pathway.
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Aagaard, Kjersti, R. Harris, Ke Hao, Jia Chen, Chris Stodgell, Joel Dudley, Eric Schadt, and RIchard Miller. "345: Novel insights on molecular targets of environmental exposures during pregnancy using placental multiomics integration." American Journal of Obstetrics and Gynecology 212, no. 1 (January 2015): S182. http://dx.doi.org/10.1016/j.ajog.2014.10.391.
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Verhey, Theodore, Heewon Seo, and Sorana Morrissy. "EPCO-13. CNMF-SNS, A FRAMEWORK FOR UNSUPERVISED INTEGRATION OF MULTIOMICS DATA, IDENTIFIES INVASION AND RECURRENCE ASSOCIATED PROGRAMS IN GBM." Neuro-Oncology 25, Supplement_5 (November 1, 2023): v126. http://dx.doi.org/10.1093/neuonc/noad179.0476.
Повний текст джерелаАнотація:
Abstract Glioblastoma (GBM) is an aggressive cancer of the brain for which novel therapies are urgently needed. Heterogeneity in GBM underlies tumor evolution and resistance to therapy and involves genetic diversity of tumor cells, spatially distinct niches harbouring stem-like cells, and immune cells spanning anti- and pro-tumor states. No single profiling methodology can capture all these facets of tumor biology in the same space and time, necessitating multi-omics data collection and computational methods suited for multi-omic integration across and within cohorts. We therefore set out to develop a generalizable framework for integration that can bridge across cohorts and data types. Using consensus non-negative matrix factorization (cNMF), we discover gene expression programs (GEPs) corresponding to cell types and/or states in an unsupervised fashion. Rather than choosing a single rank (number of programs), we identify both high and low-resolution programs for each dataset, and then integrate programs from all datasets and ranks into a solution network space (SNS) on which graph algorithms identify communities of highly similar GEPs identified in all or some datasets. These communities are characterized by gene set enrichment analyses and association with sample metadata. SNS communities can be used to transfer sample metadata across datasets, enabling multi-omics and multi-cohort integration, even in the absence of shared samples or cells. We showcase the utility of cNMF-SNS in integrating multiomics datasets of GBM from single-cell to bulk RNA-Seq and mass spectrometry proteomics. We further demonstrate the power of cNMF-SNS to define and cross-annotate programs identified in 5 complementary mass spectrometry datasets of GBM, layering on survival, driver gene mutation status, clinical features, and imaging features. We demonstrate robust mapping of biologically and clinically relevant processes, highlighting how programs for tumor cell invasion and therapy resistance can be mined for novel therapeutic targets. cNMF-SNS is available at https://github.com/MorrissyLab/cNMF-SNS.
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Mengucci, Carlo, Lorenzo Nissen, Gianfranco Picone, Corinne Malpuech-Brugère, Caroline Orfila, Luigi Ricciardiello, Alessandra Bordoni, Francesco Capozzi, and Andrea Gianotti. "K-Clique Multiomics Framework: A Novel Protocol to Decipher the Role of Gut Microbiota Communities in Nutritional Intervention Trials." Metabolites 12, no. 8 (August 10, 2022): 736. http://dx.doi.org/10.3390/metabo12080736.
Повний текст джерелаАнотація:
The availability of omics data providing information from different layers of complex biological processes that link nutrition to human health would benefit from the development of integrated approaches combining holistically individual omics data, including those associated with the microbiota that impacts the metabolisation and bioavailability of food components. Microbiota must be considered as a set of populations of interconnected consortia, with compensatory capacities to adapt to different nutritional intake. To study the consortium nature of the microbiome, we must rely on specially designed data analysis tools. The purpose of this work is to propose the construction of a general correlation network-based explorative tool, suitable for nutritional clinical trials, by integrating omics data from faecal microbial taxa, stool metabolome (1H NMR spectra) and GC-MS for stool volatilome. The presented approach exploits a descriptive paradigm necessary for a true multiomics integration of data, which is a powerful tool to investigate the complex physiological effects of nutritional interventions.
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Huang, Hsuan-Ming, and Yi-Yu Shih. "Pushing CT and MR Imaging to the Molecular Level for Studying the “Omics”: Current Challenges and Advancements." BioMed Research International 2014 (March 13, 2014): 1–17. http://dx.doi.org/10.1155/2014/365812.
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During the past decade, medical imaging has made the transition from anatomical imaging to functional and even molecular imaging. Such transition provides a great opportunity to begin the integration of imaging data and various levels of biological data. In particular, the integration of imaging data and multiomics data such as genomics, metabolomics, proteomics, and pharmacogenomics may open new avenues for predictive, preventive, and personalized medicine. However, to promote imaging-omics integration, the practical challenge of imaging techniques should be addressed. In this paper, we describe key challenges in two imaging techniques: computed tomography (CT) and magnetic resonance imaging (MRI) and then review existing technological advancements. Despite the fact that CT and MRI have different principles of image formation, both imaging techniques can provide high-resolution anatomical images while playing a more and more important role in providing molecular information. Such imaging techniques that enable single modality to image both the detailed anatomy and function of tissues and organs of the body will be beneficial in the imaging-omics field.
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Khokhar, Manoj, Dipayan Roy, Sojit Tomo, Ashita Gadwal, Praveen Sharma, and Purvi Purohit. "Novel Molecular Networks and Regulatory MicroRNAs in Type 2 Diabetes Mellitus: Multiomics Integration and Interactomics Study." JMIR Bioinformatics and Biotechnology 3, no. 1 (February 23, 2022): e32437. http://dx.doi.org/10.2196/32437.
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Background Type 2 diabetes mellitus (T2DM) is a metabolic disorder with severe comorbidities. A multiomics approach can facilitate the identification of novel therapeutic targets and biomarkers with proper validation of potential microRNA (miRNA) interactions. Objective The aim of this study was to identify significant differentially expressed common target genes in various tissues and their regulating miRNAs from publicly available Gene Expression Omnibus (GEO) data sets of patients with T2DM using in silico analysis. Methods Using differentially expressed genes (DEGs) identified from 5 publicly available T2DM data sets, we performed functional enrichment, coexpression, and network analyses to identify pathways, protein-protein interactions, and miRNA-mRNA interactions involved in T2DM. Results We extracted 2852, 8631, 5501, 3662, and 3753 DEGs from the expression profiles of GEO data sets GSE38642, GSE25724, GSE20966, GSE26887, and GSE23343, respectively. DEG analysis showed that 16 common genes were enriched in insulin secretion, endocrine resistance, and other T2DM-related pathways. Four DEGs, MAML3, EEF1D, NRG1, and CDK5RAP2, were important in the cluster network regulated by commonly targeted miRNAs (hsa-let-7b-5p, hsa-mir-155-5p, hsa-mir-124-3p, hsa-mir-1-3p), which are involved in the advanced glycation end products (AGE)-receptor for advanced glycation end products (RAGE) signaling pathway, culminating in diabetic complications and endocrine resistance. Conclusions This study identified tissue-specific DEGs in T2DM, especially pertaining to the heart, liver, and pancreas. We identified a total of 16 common DEGs and the top four common targeting miRNAs (hsa-let-7b-5p, hsa-miR-124-3p, hsa-miR-1-3p, and has-miR-155-5p). The miRNAs identified are involved in regulating various pathways, including the phosphatidylinositol-3-kinase-protein kinase B, endocrine resistance, and AGE-RAGE signaling pathways.
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Marathe, Soumitra, Bhavuk Dhamija, Sushant Kumar, Nikita Jain, Sarbari Ghosh, Jai Prakash Dharikar, Sumana Srinivasan, et al. "Multiomics Analysis and Systems Biology Integration Identifies the Roles of IL-9 in Keratinocyte Metabolic Reprogramming." Journal of Investigative Dermatology 141, no. 8 (August 2021): 1932–42. http://dx.doi.org/10.1016/j.jid.2021.02.013.
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Mikulasova, Aneta, Enze Liu, Nathan Becker, Parvathi Sudha, Rafat Abonour, and Brian A. Walker. "Multiomics Data Integration in the Complete Myeloma Genome Reveals Frequent Centromeric Rearrangements and Their Epigenomic Consequences." Blood 144, Supplement 1 (November 5, 2024): 768. https://doi.org/10.1182/blood-2024-200043.
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Introduction: Myeloma has the most complex genomic architecture among blood cancers. Central to this complexity are structural variants (SVs), large-scale genomic changes with the capacity to disrupt chromatin organization. SVs can modify the epigenomic landscape near DNA breakpoints, leading to gene dysregulation without directly altering coding sequences or DNA copy number. Here, we elucidate SVs involving the previously hidden regions of the human genome, namely compact heterochromatin including centromeric regions. These dense and repetitive regions, traditionally dismissed as “junk DNA”, emerge as critical areas of investigation. Methods: Short-read whole-genome sequencing (srWGS) data from 23 CoMMpass cases were mapped to the CHM13v2 genome assembly by BWA-MEM. RNA-seq CoMMpass (N = 928) and control GSE148924 (N = 12) sets were processed by DESeq2 normalization, variance-stabilizing transformation, and limma batch correction. Gene under-/over-expression was assessed by Z-score (>2/<-2) and log2-fold change (>1/<-1). Patient-derived xenografts (PDXs, N = 13) and KMS27 and PCM6 cell lines were studied by multiomics: srWGS (Illumina), long-read HiFi WGS (PacBio), and Micro-C (CantanaBio); including cases with t(11;14), t(4;14), t(14;16), and hyperdiploidy. Raw reads were aligned to the CHM13v2 assembly. HiFi alignments were phased by WhatsHap. In-house algorithms were built for the analysis of SVs involving repetitive regions in srWGS and DNA methylation within the HiFi reads. Copy-number variants were assessed by CNVRobot. Micro-C data were processed with Juicer tools. Results: SVs affecting compact heterochromatin were detected in 61% of CoMMpass cases (14/23) and frequently involved (peri)centromeres of chromosomes 8, 12, 15, and 16. Breakpoints at partner loci often disrupted tumor-suppressor genes (CYLD, GAS8, UNC5D) and recombined near proto-oncogenes (MYC, NFKB1), which increased gene expression compared to control plasma cells. In a PDX case, we resolved the +1q as “jumping translocations” between 1q12 pericentromeric heterochromatin and 6q15, 15p11.2, and 18q11.2. The previous hg38 assembly could not resolve t(1;6) and t(1;15) due to DNA breakpoints being present in gaps of this reference genome. HiFi reads enabled breakpoint mapping at 1q (impossible by srWGS), and Micro-C data confirmed derivative chromosome der(1)t(1;18), der(6)t(1;6), and der(15)t(1;15) interactions, and were associated with loss of the partner chromosome arm. DNA methylation analysis of HiFi phased data showed significant hypomethylation of partner loci juxtaposed to the 1q pericentromere. We identified this pattern as a mark of compact heterochromatin rearrangements, confirming an “open chromatin” state, consistent with increased transcription in the CoMMpass data. Allele-specific DNA methylation changes were also observed in key chromosomal SVs involving super-enhancers, such as t(11;14) CCND1-IGH, t(4;14) NSD2-IGH, t(14;16) IGH-MAF, and t(6;8) TXNDC5-MYC. For example, in a t(11;14) PDX case, we detected hypomethylation (~2 kb) at the chr11 breakpoint, followed by 225 kb hypermethylation and subsequent hypomethylation spanning CCND1. Micro-C data showed interactions between these hypomethylated regions on chr11 and the IGH promoter through introduction of a de novo translocation-specific regulatory TAD loop absent in non-t(11;14) samples, allowing close interaction of the translocation breakpoint with CCND1. No methylation changes were observed in other SVs lacking super-enhancers or compact heterochromatin involvement. The level of compact heterochromatin breakage was not linked to global hypomethylation. However, global methylation analysis showed a higher proportion of methylated CpG sites in the t(4;14) group (71.8%, N = 4) compared to other molecular groups: t(11;14) (44.8%, N = 4), t(14;16) (46.1%, N = 2), and hyperdiploidy (34.1%, N = 4). This supports the role of NSD2 in forming H3K36me2 histone marks, essential for maintaining DNA methylation. Conclusions: Utilizing novel computational algorithms, complete genome assembly, and multiomics data integration, we provide the first genomic evidence of SVs affecting compact heterochromatin as common changes in the myeloma genome. These rearrangements alter the DNA topology, epigenomic code and gene expression in juxtaposed loci, suggesting a driving role in myeloma genome instability.
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Ma, Yawen, and Zhuo Xi. "Integrated Analysis of Multiomics Data Identified Molecular Subtypes and Oxidative Stress-Related Prognostic Biomarkers in Glioblastoma Multiforme." Oxidative Medicine and Cellular Longevity 2022 (September 22, 2022): 1–15. http://dx.doi.org/10.1155/2022/9993319.
Повний текст джерелаАнотація:
Glioblastoma multiforme (GBM) is a glioma in IV stage, which is one of the most common primary malignant brain tumors in adults. GBM has the characters of high invasiveness, high recurrence rate, and low survival rate and with a poor prognosis. GBM implicates various genetic changes and epigenetic and gene transcription disorders, which are crucial in developing GBM. With the progression and enhancement of high-throughput sequencing technologies, the acquirement and administering approaches of diverse biological omics data on distinctive levels are developing more advanced. However, the research of GBM with multiomics remains largely unknown. We identified GBM-related molecular subtypes by integrated multiomics data and exploring the connections of gene copy number variation (CNV) and methylation gene (MET) change data. The expression of CNV and MET genes was examined through cluster integration analysis. The present study confirmed three clusters (iC1, iC2, and iC3) with distinctive prognosis and molecule peculiarities. We also recognized three oxidative stress protecting molecules (OSMR, IGFBP6, and MYBPH) by contrasting gene expression, MET, and CNV in the three subtypes. OSMR, IGFBP6, and MYBPH were differentially expressed in the clusters, suggesting they might be recognized as characteristic markers for the three clusters in GBM. Through integrative investigation of genomics, epigenomics, and transcriptomics, we offer novel visions into the multilayered molecules of GBM and facilitate the accuracy remedy for GBM sufferers.
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Azad, Md, Sandeep Barwal, and Akhi Moni. "Exploring the impact of integrated breeding strategies in enhancing yield, nutritional quality, and stress tolerance in alfalfa." Plant Trends 1, no. 1 (2023): 1. http://dx.doi.org/10.5455/pt.2023.01.
Повний текст джерелаАнотація:
Yield, nutrition quality and stress tolerance are important traits for alfalfa improvement perspective. These qualitative and quantitative attributes are changed during its life cycle, no updated studies available on whether and how these traits are influenced by several environmental factors. Therefore, we updated the role of several breeding strategies for developing alfalfa yield, nutritional quality and biotic-abiotic stress tolerance in alfalfa. This study explored integrated breeding approaches would be suitable for the desire traits improvement in alfalfa. Subsequently, the integration of multiomics including genomics, transcriptomics, proteomics, metabolomics, and ionomics may facilitate the agronomic traits improvement and plant fitness in alfalfa. Furthermore, this study proposes integration of omics-system with top-down (phenotype to genotype) and bottom-up (genotype to phenotype) model that can be helpful to characterize or develop desire qualitative and qualitative traits in alfalfa. This updated study might be useful to alfalfa breeders and farmers for improving alfalfa through breeding programs.
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Chen, Xi, Yuan Wang, Antonio Cappuccio, Wan-Sze Cheng, Frederique Ruf Zamojski, Venugopalan D. Nair, Clare M. Miller, et al. "Mapping disease regulatory circuits at cell-type resolution from single-cell multiomics data." Nature Computational Science 3, no. 7 (July 25, 2023): 644–57. http://dx.doi.org/10.1038/s43588-023-00476-5.
Повний текст джерелаАнотація:
AbstractResolving chromatin-remodeling-linked gene expression changes at cell-type resolution is important for understanding disease states. Here we describe MAGICAL (Multiome Accessibility Gene Integration Calling and Looping), a hierarchical Bayesian approach that leverages paired single-cell RNA sequencing and single-cell transposase-accessible chromatin sequencing from different conditions to map disease-associated transcription factors, chromatin sites, and genes as regulatory circuits. By simultaneously modeling signal variation across cells and conditions in both omics data types, MAGICAL achieved high accuracy on circuit inference. We applied MAGICAL to study Staphylococcus aureus sepsis from peripheral blood mononuclear single-cell data that we generated from subjects with bloodstream infection and uninfected controls. MAGICAL identified sepsis-associated regulatory circuits predominantly in CD14 monocytes, known to be activated by bacterial sepsis. We addressed the challenging problem of distinguishing host regulatory circuit responses to methicillin-resistant and methicillin-susceptible S. aureus infections. Although differential expression analysis failed to show predictive value, MAGICAL identified epigenetic circuit biomarkers that distinguished methicillin-resistant from methicillin-susceptible S. aureus infections.
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Thompson, Kathryn, Benjamin Geller, Lubna Nousheen, Indira Krishnan, Shu Wang, Daniel Mendoza, Todd E. Druley, and Adam Sciambi. "A Multiomic, Single-Cell Measurable Residual Disease (scMRD) Assay for Simultaneous Assessment of DNA Mutations and Surface Immunophenotypes in Acute Myeloid Leukemia." Blood 144, Supplement 1 (November 5, 2024): 6168. https://doi.org/10.1182/blood-2024-204025.
Повний текст джерелаАнотація:
The small population of cancerous cells that remain following treatment, known as measurable residual disease (MRD), is the major cause of relapse in acute myeloid leukemia (AML). Usually, these refractory cells have gained additional resistance mutations or changed their surface immunophenotypes in ways that preclude detection and phasing by current gold standard flow cytometry or bulk next-generation sequencing assays. For this reason, a multiomic single-cell MRD (scMRD) assay could offer a more comprehensive indicator of relapse and the potential for faster response. Here, we present a new scMRD assay with a 0.01% limit of detection that provides single-cell clonal architecture and immunophenotyping to not only identify residual leukemia cells, but also identify putative DNA or protein targets for novel biomarker insights. The assay enables rare-cell detection on a standard Mission Bio Tapestri® run by adding (i) an upfront enrichment for blast cells, (ii) a DNA and protein panel specifically designed for characterization of clonal architecture in AML MRD hotspots and biomarkers, (iii) an automated analysis pipeline to evaluate single-cell multiomics output, and (iv) multiplexing for simultaneous analysis of up to three patient samples in a single run via germline variant identification. By utilizing Mission Bio's technology for sequencing single cells, this pipeline can identify and correlate co-occurring de novo variants, thereby reducing false positive rates over bulk assays that do not correlate variants. Furthermore, it can create phylogenetic trees of the detected MRD cells and present their surface protein signature and arm-level copy number. To demonstrate assay features and reproducibility, surrogate MRD samples were constructed using positive control cell lines (expressing CD34 and CD117 markers) or diseased cells spiked into healthy bone marrow cells before processing them with the scMRD assay. We detected the 0.01% cell line spike-ins in 20 of 20 samples tested, with an average blast enrichment of 30.8x. We further applied the scMRD assay to bone marrow aspirate samples from AML patients and achieved 0.01% diseased cell spike-in detection. The scMRD assay resolved the genotype clonal architecture, identifying multiple leukemic clones with co-occurring mutations and readily distinguishing pre-leukemic from leukemic clones. The integration of genotype and immunophenotype further enhanced MRD detection by identifying genotype-specific protein expression patterns. By combining high reproducibility with multiomics, this assay offers a potential scalable solution for comprehensive MRD detection that could possibly guide patient stratification and therapeutic decision-making in the future.
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Winders, Dafne Alves, Riley Graham, Xiangying Mao, Ilaria De Vito, Andrea O'Hara, Laure Turner, and Haythem Latif. "Abstract 4411: Enhancing scalability and consistency in clinical multiomics via an optimized fixed cell ATAC-seq method." Cancer Research 84, no. 6_Supplement (March 22, 2024): 4411. http://dx.doi.org/10.1158/1538-7445.am2024-4411.
Повний текст джерелаАнотація:
Abstract ATAC-seq has an emerging role in decoding mechanisms of gene regulation, offering valuable insights into pathology and treatment response in disease models. However, clinical adoption of ATAC-seq methods has been limited by logistical hurdles, including time-sensitive processing of fresh samples and compromised viability of cryopreserved cells. These constraints, compounded by changes in open chromatin regions (OCRs) following cryopreservation, introduce unintended bias and pose significant obstacles for the translational impact of ATAC-seq experiments. Here, we introduce an optimized fixed-cell ATAC-seq approach to overcome these limitations and unlock new sample types for ATAC-seq analyses. Our solution improves and simplifies the workflow from sample collection to clinical deliverable. This method enables ATAC-seq investigation of a diverse range of samples and facilitates the execution of complex experimental designs, including time course studies and high throughput screening.To demonstrate the effectiveness of this method, we compared our optimized fixed-cell method with traditional ATAC-seq preparations in both fresh and cryopreserved GM12878 cells in parallel. Human GM12878 cell line was obtained from Coriell Institute for Medical Research5. Remarkably, we observed consistent genome-wide patterns of OCR enrichment at key regulatory elements across the three sample preparation methods. We observed consistent OCR enrichment across the promoter region of known highly-expressed B-cell genes including CD48 and LCP1, underscoring this assay’s ability to detect chromatin changes at key genes in human disease models. To investigate the potential for multiomic analysis using this method, we prepared RNA-seq libraries from fixed-cell samples in parallel to ATAC-seq. We observed significantly elevated gene expression related to B-cell function and B-cell diseases, demonstrating our method’s compatibility with RNA-seq data collection and integration. This optimized fixed-cell ATAC-seq approach offers enhanced scalability and consistency over conventional methods and presents new opportunities for the multiomic analysis of chromatin and transcriptional activity genome-wide from a single sample in both clinical and research settings. Citation Format: Dafne Alves Winders, Riley Graham, Xiangying Mao, Ilaria De Vito, Andrea O'Hara, Laure Turner, Haythem Latif. Enhancing scalability and consistency in clinical multiomics via an optimized fixed cell ATAC-seq method [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 4411.
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Li, Xue, Lifeng Yang, and Xiong Jiao. "Deep learning-based multiomics integration model for predicting axillary lymph node metastasis in breast cancer." Future Oncology, July 25, 2023. http://dx.doi.org/10.2217/fon-2023-0070.
Повний текст джерелаАнотація:
Aim: To develop a deep learning-based multiomics integration model. Materials & methods: Five types of omics data (mRNA, DNA methylation, miRNA, copy number variation and protein expression) were used to build a deep learning-based multiomics integration model via a deep neural network, incorporating an attention mechanism that adaptively considers the weights of multiomics features. Results: Compared with other methods, the deep learning-based multiomics integration model achieved remarkable results, with an area under the curve of 0.89 (95% CI: 0.863–0.910). Conclusion: The deep learning-based multiomics integration model achieved promising results and is an effective method for predicting axillary lymph node metastasis in breast cancer.
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Rönn, Tina, Alexander Perfilyev, Nikolay Oskolkov, and Charlotte Ling. "Predicting type 2 diabetes via machine learning integration of multiple omics from human pancreatic islets." Scientific Reports 14, no. 1 (June 25, 2024). http://dx.doi.org/10.1038/s41598-024-64846-3.
Повний текст джерелаАнотація:
AbstractType 2 diabetes (T2D) is the fastest growing non-infectious disease worldwide. Impaired insulin secretion from pancreatic beta-cells is a hallmark of T2D, but the mechanisms behind this defect are insufficiently characterized. Integrating multiple layers of biomedical information, such as different Omics, may allow more accurate understanding of complex diseases such as T2D. Our aim was to explore and use Machine Learning to integrate multiple sources of biological/molecular information (multiOmics), in our case RNA-sequening, DNA methylation, SNP and phenotypic data from islet donors with T2D and non-diabetic controls. We exploited Machine Learning to perform multiOmics integration of DNA methylation, expression, SNPs, and phenotypes from pancreatic islets of 110 individuals, with ~ 30% being T2D cases. DNA methylation was analyzed using Infinium MethylationEPIC array, expression was analyzed using RNA-sequencing, and SNPs were analyzed using HumanOmniExpress arrays. Supervised linear multiOmics integration via DIABLO based on Partial Least Squares (PLS) achieved an accuracy of 91 ± 15% of T2D prediction with an area under the curve of 0.96 ± 0.08 on the test dataset after cross-validation. Biomarkers identified by this multiOmics integration, including SACS and TXNIP DNA methylation, OPRD1 and RHOT1 expression and a SNP annotated to ANO1, provide novel insights into the interplay between different biological mechanisms contributing to T2D. This Machine Learning approach of multiOmics cross-sectional data from human pancreatic islets achieved a promising accuracy of T2D prediction, which may potentially find broad applications in clinical diagnostics. In addition, it delivered novel candidate biomarkers for T2D and links between them across the different Omics.
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Flynn, Emily, Ana Almonte-Loya, and Gabriela K. Fragiadakis. "Single-Cell Multiomics." Annual Review of Biomedical Data Science 6, no. 1 (May 9, 2023). http://dx.doi.org/10.1146/annurev-biodatasci-020422-050645.
Повний текст джерелаАнотація:
Single-cell RNA sequencing methods have led to improved understanding of the heterogeneity and transcriptomic states present in complex biological systems. Recently, the development of novel single-cell technologies for assaying additional modalities, specifically genomic, epigenomic, proteomic, and spatial data, allows for unprecedented insight into cellular biology. While certain technologies collect multiple measurements from the same cells simultaneously, even when modalities are separately assayed in different cells, we can apply novel computational methods to integrate these data. The application of computational integration methods to multimodal paired and unpaired data results in rich information about the identities of the cells present and the interactions between different levels of biology, such as between genetic variation and transcription. In this review, we both discuss the single-cell technologies for measuring these modalities and describe and characterize a variety of computational integration methods for combining the resulting data to leverage multimodal information toward greater biological insight. Expected final online publication date for the Annual Review of Biomedical Data Science, Volume 6 is August 2023. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.
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Williams, Amanda. "Multiomics data integration, limitations, and prospects to reveal the metabolic activity of the coral holobiont." FEMS Microbiology Ecology, April 23, 2024. http://dx.doi.org/10.1093/femsec/fiae058.
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Abstract Since their radiation in the Middle Triassic period ∼ 240 million years ago, stony corals have survived past climate fluctuations and five mass extinctions. Their long-term survival underscores the inherent resilience of corals, particularly when considering the nutrient-poor marine environments in which they have thrived. However, coral bleaching has emerged as a global threat to coral survival, requiring rapid advancements in coral research to understand holobiont stress responses and allow for interventions before extensive bleaching occurs. This review encompasses the potential, as well as the limits, of multiomics data applications when applied to the coral holobiont. Synopses for how different omics tools have been applied to date and their current restrictions are discussed, in addition to ways these restrictions may be overcome, such as recruiting new technology to studies, utilizing novel bioinformatics approaches, and generally integrating omics data. Lastly, this review presents considerations for the design of holobiont multiomics studies to support lab-to-field advancements of coral stress marker monitoring systems. Although much of the bleaching mechanism has eluded investigation to date, multiomic studies have already produced key findings regarding the holobiont's stress response, and have the potential to advance the field further.
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Hatamikia, Sepideh, Stephanie Nougaret, Camilla Panico, Giacomo Avesani, Camilla Nero, Luca Boldrini, Evis Sala, and Ramona Woitek. "Ovarian cancer beyond imaging: integration of AI and multiomics biomarkers." European Radiology Experimental 7, no. 1 (September 13, 2023). http://dx.doi.org/10.1186/s41747-023-00364-7.
Повний текст джерелаАнотація:
AbstractHigh-grade serous ovarian cancer is the most lethal gynaecological malignancy. Detailed molecular studies have revealed marked intra-patient heterogeneity at the tumour microenvironment level, likely contributing to poor prognosis. Despite large quantities of clinical, molecular and imaging data on ovarian cancer being accumulated worldwide and the rise of high-throughput computing, data frequently remain siloed and are thus inaccessible for integrated analyses. Only a minority of studies on ovarian cancer have set out to harness artificial intelligence (AI) for the integration of multiomics data and for developing powerful algorithms that capture the characteristics of ovarian cancer at multiple scales and levels. Clinical data, serum markers, and imaging data were most frequently used, followed by genomics and transcriptomics. The current literature proves that integrative multiomics approaches outperform models based on single data types and indicates that imaging can be used for the longitudinal tracking of tumour heterogeneity in space and potentially over time. This review presents an overview of studies that integrated two or more data types to develop AI-based classifiers or prediction models.Relevance statement Integrative multiomics models for ovarian cancer outperform models using single data types for classification, prognostication, and predictive tasks.Key points• This review presents studies using multiomics and artificial intelligence in ovarian cancer.• Current literature proves that integrative multiomics outperform models using single data types.• Around 60% of studies used a combination of imaging with clinical data.• The combination of genomics and transcriptomics with imaging data was infrequently used. Graphical Abstract
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Jeong, Yunhee, Jonathan Ronen, Wolfgang Kopp, Pavlo Lutsik, and Altuna Akalin. "scMaui: a widely applicable deep learning framework for single-cell multiomics integration in the presence of batch effects and missing data." BMC Bioinformatics 25, no. 1 (August 6, 2024). http://dx.doi.org/10.1186/s12859-024-05880-w.
Повний текст джерелаАнотація:
AbstractThe recent advances in high-throughput single-cell sequencing have created an urgent demand for computational models which can address the high complexity of single-cell multiomics data. Meticulous single-cell multiomics integration models are required to avoid biases towards a specific modality and overcome sparsity. Batch effects obfuscating biological signals must also be taken into account. Here, we introduce a new single-cell multiomics integration model, Single-cell Multiomics Autoencoder Integration (scMaui) based on variational product-of-experts autoencoders and adversarial learning. scMaui calculates a joint representation of multiple marginal distributions based on a product-of-experts approach which is especially effective for missing values in the modalities. Furthermore, it overcomes limitations seen in previous VAE-based integration methods with regard to batch effect correction and restricted applicable assays. It handles multiple batch effects independently accepting both discrete and continuous values, as well as provides varied reconstruction loss functions to cover all possible assays and preprocessing pipelines. We demonstrate that scMaui achieves superior performance in many tasks compared to other methods. Further downstream analyses also demonstrate its potential in identifying relations between assays and discovering hidden subpopulations.
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Wei, Lise, Dipesh Niraula, Evan D. H. Gates, Jie Fu, Yi Luo, Matthew J. Nyflot, Stephen R. Bowen, Issam M. El Naqa, and Sunan Cui. "Artificial intelligence (AI) and machine learning (ML) in precision oncology: a review on enhancing discoverability through multiomics integration." British Journal of Radiology, September 3, 2023. http://dx.doi.org/10.1259/bjr.20230211.
Повний текст джерелаАнотація:
Multiomics data including imaging radiomics and various types of molecular biomarkers have been increasingly investigated for better diagnosis and therapy in the era of precision oncology. Artificial intelligence (AI) including machine learning (ML) and deep learning (DL) techniques combined with the exponential growth of multiomics data may have great potential to revolutionize cancer subtyping, risk stratification, prognostication, prediction and clinical decision-making. In this article, we first present different categories of multiomics data and their roles in diagnosis and therapy. Second, AI-based data fusion methods and modeling methods as well as different validation schemes are illustrated. Third, the applications and examples of multiomics research in oncology are demonstrated. Finally, the challenges regarding the heterogeneity data set, availability of omics data, and validation of the research are discussed. The transition of multiomics research to real clinics still requires consistent efforts in standardizing omics data collection and analysis, building computational infrastructure for data sharing and storing, developing advanced methods to improve data fusion and interpretability, and ultimately, conducting large-scale prospective clinical trials to fill the gap between study findings and clinical benefits.
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Kesimoglu, Ziynet Nesibe, and Serdar Bozdag. "SUPREME: multiomics data integration using graph convolutional networks." NAR Genomics and Bioinformatics 5, no. 2 (March 29, 2023). http://dx.doi.org/10.1093/nargab/lqad063.
Повний текст джерелаАнотація:
Abstract To pave the road towards precision medicine in cancer, patients with similar biology ought to be grouped into same cancer subtypes. Utilizing high-dimensional multiomics datasets, integrative approaches have been developed to uncover cancer subtypes. Recently, Graph Neural Networks have been discovered to learn node embeddings utilizing node features and associations on graph-structured data. Some integrative prediction tools have been developed leveraging these advances on multiple networks with some limitations. Addressing these limitations, we developed SUPREME, a node classification framework, which integrates multiple data modalities on graph-structured data. On breast cancer subtyping, unlike existing tools, SUPREME generates patient embeddings from multiple similarity networks utilizing multiomics features and integrates them with raw features to capture complementary signals. On breast cancer subtype prediction tasks from three datasets, SUPREME outperformed other tools. SUPREME-inferred subtypes had significant survival differences, mostly having more significance than ground truth, and outperformed nine other approaches. These results suggest that with proper multiomics data utilization, SUPREME could demystify undiscovered characteristics in cancer subtypes that cause significant survival differences and could improve ground truth label, which depends mainly on one datatype. In addition, to show model-agnostic property of SUPREME, we applied it to two additional datasets and had a clear outperformance.
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Choudhary, Ratan Kumar, Sunil Kumar B. V., Chandra Sekhar Mukhopadhyay, Neeraj Kashyap, Vishal Sharma, Nisha Singh, Sina Salajegheh Tazerji, Roozbeh Kalantari, Pouneh Hajipour, and Yashpal Singh Malik. "Animal Wellness: The Power of Multiomics and Integrative Strategies." Veterinary Medicine International 2024, no. 1 (January 2024). http://dx.doi.org/10.1155/2024/4125118.
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The livestock industry faces significant challenges, with disease outbreaks being a particularly devastating issue. These diseases can disrupt the food supply chain and the livelihoods of those involved in the sector. To address this, there is a growing need to enhance the health and well‐being of livestock animals, ultimately improving their performance while minimizing their environmental impact. To tackle the considerable challenge posed by disease epidemics, multiomics approaches offer an excellent opportunity for scientists, breeders, and policymakers to gain a comprehensive understanding of animal biology, pathogens, and their genetic makeup. This understanding is crucial for enhancing the health of livestock animals. Multiomic approaches, including phenomics, genomics, epigenomics, metabolomics, proteomics, transcriptomics, microbiomics, and metaproteomics, are widely employed to assess and enhance animal health. High‐throughput phenotypic data collection allows for the measurement of various fitness traits, both discrete and continuous, which, when mathematically combined, define the overall health and resilience of animals, including their ability to withstand diseases. Omics methods are routinely used to identify genes involved in host‐pathogen interactions, assess fitness traits, and pinpoint animals with disease resistance. Genome‐wide association studies (GWAS) help identify the genetic factors associated with health status, heat stress tolerance, disease resistance, and other health‐related characteristics, including the estimation of breeding value. Furthermore, the interaction between hosts and pathogens, as observed through the assessment of host gut microbiota, plays a crucial role in shaping animal health and, consequently, their performance. Integrating and analyzing various heterogeneous datasets to gain deeper insights into biological systems is a challenging task that necessitates the use of innovative tools. Initiatives like MiBiOmics, which facilitate the visualization, analysis, integration, and exploration of multiomics data, are expected to improve prediction accuracy and identify robust biomarkers linked to animal health. In this review, we discuss the details of multiomics concerning the health and well‐being of livestock animals.
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Yu, Ying, Naixin Zhang, Yuanbang Mai, Luyao Ren, Qiaochu Chen, Zehui Cao, Qingwang Chen, et al. "Correcting batch effects in large-scale multiomics studies using a reference-material-based ratio method." Genome Biology 24, no. 1 (September 7, 2023). http://dx.doi.org/10.1186/s13059-023-03047-z.
Повний текст джерелаАнотація:
Abstract Background Batch effects are notoriously common technical variations in multiomics data and may result in misleading outcomes if uncorrected or over-corrected. A plethora of batch-effect correction algorithms are proposed to facilitate data integration. However, their respective advantages and limitations are not adequately assessed in terms of omics types, the performance metrics, and the application scenarios. Results As part of the Quartet Project for quality control and data integration of multiomics profiling, we comprehensively assess the performance of seven batch effect correction algorithms based on different performance metrics of clinical relevance, i.e., the accuracy of identifying differentially expressed features, the robustness of predictive models, and the ability of accurately clustering cross-batch samples into their own donors. The ratio-based method, i.e., by scaling absolute feature values of study samples relative to those of concurrently profiled reference material(s), is found to be much more effective and broadly applicable than others, especially when batch effects are completely confounded with biological factors of study interests. We further provide practical guidelines for implementing the ratio based approach in increasingly large-scale multiomics studies. Conclusions Multiomics measurements are prone to batch effects, which can be effectively corrected using ratio-based scaling of the multiomics data. Our study lays the foundation for eliminating batch effects at a ratio scale.
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Li, Chuan-Xing, Jing Gao, Zicheng Zhang, Lu Chen, Xun Li, Meng Zhou, and Åsa M. Wheelock. "Multiomics integration-based molecular characterizations of COVID-19." Briefings in Bioinformatics 23, no. 1 (December 2, 2021). http://dx.doi.org/10.1093/bib/bbab485.
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Abstract The coronavirus disease 2019 (COVID-19) pandemic, caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), rapidly became a global health challenge, leading to unprecedented social and economic consequences. The mechanisms behind the pathogenesis of SARS-CoV-2 are both unique and complex. Omics-scale studies are emerging rapidly and offer a tremendous potential to unravel the puzzle of SARS-CoV-2 pathobiology, as well as moving forward with diagnostics, potential drug targets, risk stratification, therapeutic responses, vaccine development and therapeutic innovation. This review summarizes various aspects of understanding multiomics integration-based molecular characterizations of COVID-19, which to date include the integration of transcriptomics, proteomics, genomics, lipidomics, immunomics and metabolomics to explore virus targets and developing suitable therapeutic solutions through systems biology tools. Furthermore, this review also covers an abridgment of omics investigations related to disease pathogenesis and virulence, the role of host genetic variation and a broad array of immune and inflammatory phenotypes contributing to understanding COVID-19 traits. Insights into this review, which combines existing strategies and multiomics integration profiling, may help further advance our knowledge of COVID-19.
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Shave, Steven, John C. Dawson, Abdullah M. Athar, Cuong Q. Nguyen, Richard Kasprowicz, and Neil O. Carragher. "Phenonaut; multiomics data integration for phenotypic space exploration." Bioinformatics, March 21, 2023. http://dx.doi.org/10.1093/bioinformatics/btad143.
Повний текст джерелаАнотація:
Abstract Summary Data integration workflows for multiomics data take many forms across academia and industry. Efforts with limited resources often encountered in academia can easily fall short of data integration best practices for processing and combining high content imaging, proteomics, metabolomics and other omics data. We present Phenonaut, a Python software package designed to address the data workflow needs of migration, control, integration, and auditability in the application of literature and proprietary techniques for data source and structure agnostic workflow creation. Availability and implementation Source code: https://github.com/CarragherLab/phenonaut, Documentation: https://carragherlab.github.io/phenonaut, PyPI package: https://pypi.org/project/phenonaut/ Supplementary information Supplementary data are available at Bioinformatics online.