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Готові списки джерел за темами / Multiomics analysis

Добірка наукової літератури з теми "Multiomics analysis"

Автор: Grafiati

Опубліковано: 15 вересня 2021

Оновлено: 28 січня 2023

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Статті в журналах з теми "Multiomics analysis":

1

Lee, Jeongwoo, Do Young Hyeon, and Daehee Hwang. "Single-cell multiomics: technologies and data analysis methods." Experimental & Molecular Medicine 52, no. 9 (September 2020): 1428–42. http://dx.doi.org/10.1038/s12276-020-0420-2.

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Abstract Advances in single-cell isolation and barcoding technologies offer unprecedented opportunities to profile DNA, mRNA, and proteins at a single-cell resolution. Recently, bulk multiomics analyses, such as multidimensional genomic and proteogenomic analyses, have proven beneficial for obtaining a comprehensive understanding of cellular events. This benefit has facilitated the development of single-cell multiomics analysis, which enables cell type-specific gene regulation to be examined. The cardinal features of single-cell multiomics analysis include (1) technologies for single-cell isolation, barcoding, and sequencing to measure multiple types of molecules from individual cells and (2) the integrative analysis of molecules to characterize cell types and their functions regarding pathophysiological processes based on molecular signatures. Here, we summarize the technologies for single-cell multiomics analyses (mRNA-genome, mRNA-DNA methylation, mRNA-chromatin accessibility, and mRNA-protein) as well as the methods for the integrative analysis of single-cell multiomics data.

2

Dai, Ling-Yun, Rong Zhu, and Juan Wang. "Joint Nonnegative Matrix Factorization Based on Sparse and Graph Laplacian Regularization for Clustering and Co-Differential Expression Genes Analysis." Complexity 2020 (November 16, 2020): 1–10. http://dx.doi.org/10.1155/2020/3917812.

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The explosion of multiomics data poses new challenges to existing data mining methods. Joint analysis of multiomics data can make the best of the complementary information that is provided by different types of data. Therefore, they can more accurately explore the biological mechanism of diseases. In this article, two forms of joint nonnegative matrix factorization based on the sparse and graph Laplacian regularization (SG-jNMF) method are proposed. In the method, the graph regularization constraint can preserve the local geometric structure of data. L 2,1 -norm regularization can enhance the sparsity among the rows and remove redundant features in the data. First, SG-jNMF1 projects multiomics data into a common subspace and applies the multiomics fusion characteristic matrix to mine the important information closely related to diseases. Second, multiomics data of the same disease are mapped into the common sample space by SG-jNMF2, and the cluster structures are detected clearly. Experimental results show that SG-jNMF can achieve significant improvement in sample clustering compared with existing joint analysis frameworks. SG-jNMF also effectively integrates multiomics data to identify co-differentially expressed genes (Co-DEGs). SG-jNMF provides an efficient integrative analysis method for mining the biological information hidden in heterogeneous multiomics data.

3

Wang, Tzu-Hao, Cheng-Yang Lee, Tzong-Yi Lee, Hsien-Da Huang, Justin Bo-Kai Hsu, and Tzu-Hao Chang. "Biomarker Identification through Multiomics Data Analysis of Prostate Cancer Prognostication Using a Deep Learning Model and Similarity Network Fusion." Cancers 13, no. 11 (May 21, 2021): 2528. http://dx.doi.org/10.3390/cancers13112528.

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This study is to identify potential multiomics biomarkers for the early detection of the prognostic recurrence of PC patients. A total of 494 prostate adenocarcinoma (PRAD) patients (60-recurrent included) from the Cancer Genome Atlas (TCGA) portal were analyzed using the autoencoder model and similarity network fusion. Then, multiomics panels were constructed according to the intersected omics biomarkers identified from the two models. Six intersected omics biomarkers, TELO2, ZMYND19, miR-143, miR-378a, cg00687383 (MED4), and cg02318866 (JMJD6; METTL23), were collected for multiomics panel construction. The difference between the Kaplan–Meier curves of high and low recurrence-risk groups generated from the multiomics panel achieved p-value = 5.33 × 10−9, which is better than the former study (p-value = 5 × 10−7). Additionally, when evaluating the selected multiomics biomarkers with clinical information (Gleason score, age, and cancer stage), a high-performance prediction model was generated with C-index = 0.713, p-value = 2.97 × 10−15, and AUC = 0.789. The risk score generated from the selected multiomics biomarkers worked as an effective indicator for the prediction of PRAD recurrence. This study helps us to understand the etiology and pathways of PRAD and further benefits both patients and physicians with potential prognostic biomarkers when making clinical decisions after surgical treatment.

4

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.

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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.

5

Rotroff, Daniel M., and Alison A. Motsinger-Reif. "Embracing Integrative Multiomics Approaches." International Journal of Genomics 2016 (2016): 1–5. http://dx.doi.org/10.1155/2016/1715985.

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As “-omics” data technology advances and becomes more readily accessible to address complex biological questions, increasing amount of cross “-omics” dataset is inspiring the use and development of integrative bioinformatics analysis. In the current review, we discuss multiple options for integrating data across “-omes” for a range of study designs. We discuss established methods for such analysis and point the reader to in-depth discussions for the various topics. Additionally, we discuss challenges and new directions in the area.

6

Nassar, Sam F., Khadir Raddassi, and Terence Wu. "Single-Cell Multiomics Analysis for Drug Discovery." Metabolites 11, no. 11 (October 25, 2021): 729. http://dx.doi.org/10.3390/metabo11110729.

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Given the heterogeneity seen in cell populations within biological systems, analysis of single cells is necessary for studying mechanisms that cannot be identified on a bulk population level. There are significant variations in the biological and physiological function of cell populations due to the functional differences within, as well as between, single species as a result of the specific proteome, transcriptome, and metabolome that are unique to each individual cell. Single-cell analysis proves crucial in providing a comprehensive understanding of the biological and physiological properties underlying human health and disease. Omics technologies can help to examine proteins (proteomics), RNA molecules (transcriptomics), and the chemical processes involving metabolites (metabolomics) in cells, in addition to genomes. In this review, we discuss the value of multiomics in drug discovery and the importance of single-cell multiomics measurements. We will provide examples of the benefits of applying single-cell omics technologies in drug discovery and development. Moreover, we intend to show how multiomics offers the opportunity to understand the detailed events which produce or prevent disease, and ways in which the separate omics disciplines complement each other to build a broader, deeper knowledge base.

7

Perkel, Jeffrey M. "Single-cell analysis enters the multiomics age." Nature 595, no. 7868 (July 19, 2021): 614–16. http://dx.doi.org/10.1038/d41586-021-01994-w.

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8

Marshall, John L., Beth N. Peshkin, Takayuki Yoshino, Jakob Vowinckel, Håvard E. Danielsen, Gerry Melino, Ioannis Tsamardinos, et al. "The Essentials of Multiomics." Oncologist 27, no. 4 (February 22, 2022): 272–84. http://dx.doi.org/10.1093/oncolo/oyab048.

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Abstract Within the last decade, the science of molecular testing has evolved from single gene and single protein analysis to broad molecular profiling as a standard of care, quickly transitioning from research to practice. Terms such as genomics, transcriptomics, proteomics, circulating omics, and artificial intelligence are now commonplace, and this rapid evolution has left us with a significant knowledge gap within the medical community. In this paper, we attempt to bridge that gap and prepare the physician in oncology for multiomics, a group of technologies that have gone from looming on the horizon to become a clinical reality. The era of multiomics is here, and we must prepare ourselves for this exciting new age of cancer medicine.

9

Campuzano, Susana, Rodrigo Barderas, Paloma Yáñez-Sedeño, and José M. Pingarrón. "Electrochemical biosensing to assist multiomics analysis in precision medicine." Current Opinion in Electrochemistry 28 (August 2021): 100703. http://dx.doi.org/10.1016/j.coelec.2021.100703.

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10

Xing, Lu, Tao Wu, Li Yu, Nian Zhou, Zhao Zhang, Yunjing Pu, Jinnan Wu, and Hong Shu. "Exploration of Biomarkers of Psoriasis through Combined Multiomics Analysis." Mediators of Inflammation 2022 (September 23, 2022): 1–25. http://dx.doi.org/10.1155/2022/7731082.

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Background. Aberrant DNA methylation patterns are of increasing interest in the study of psoriasis mechanisms. This study aims to screen potential diagnostic indicators affected by DNA methylation for psoriasis based on bioinformatics using multiple machine learning algorithms and to preliminarily explore its molecular mechanisms. Methods. GSE13355, GSE14905, and GSE73894 were collected from the gene expression omnibus (GEO) database. Differentially expressed genes (DEGs) and differentially methylated region- (DMR-) genes between psoriasis and control samples were combined to obtain differentially expressed methylated genes. Subsequently, a protein-protein interaction (PPI) network was established to analyze the interaction between differentially expressed methylated genes. Moreover, the hub genes of psoriasis were screened by the least absolute shrinkage and selection operator (LASSO), Random Forest (RF), and Support Vector Machine (SVM), which were further performed single-gene gene set enrichment analysis (GSEA) to clarify the pathogenesis of psoriasis. The druggable genes were predicted using DGIdb. Finally, the expressions of hub genes in psoriasis lesions and healthy controls were detected by immunohistochemistry (IHC) and quantitative real-time PCR (RT-qPCR). Results. In this study, a total of 767 DEGs and 896 DMR-genes were obtained. Functional enrichment showed that they were significantly associated with skin development, skin barrier function, immune/inflammatory response, and cell cycle. The combined transcriptomic and DNA methylation data resulted in 33 differentially expressed methylated genes, of which GJB2 was the final identified hub gene for psoriasis, with robust diagnostic power. IHC and RT-qPCR showed that GJB2 was significantly higher in psoriasis samples than those in healthy controls. Additionally, GJB2 may be involved in the development and progression of psoriasis by disrupting the body’s immune system, mediating the cell cycle, and destroying the skin barrier, in addition to possibly inducing diseases related to the skeletal aspects of psoriasis. Moreover, OCTANOL and CARBENOXOLONE were identified as promising compounds through the DGIdb database. Conclusion. The abnormal expression of GJB2 might play a critical role in psoriasis development and progression. The genes identified in our study might serve as a diagnostic indicator and therapeutic target in psoriasis.

Більше джерел

Дисертації з теми "Multiomics analysis":

1

Richerd, Mathilde. "Développement d'un système de microfluidique de gouttes pour l'analyse d'échantillons complexes." Thesis, Université Paris sciences et lettres, 2021. http://www.theses.fr/2021UPSLS051.

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La microfluidique de gouttes est une technologie qui possède un très grand potentiel pour la miniaturisation et l’automatisation des méthodes bioanalytiques conventionnelles. En effet, les nombreuses fonctionnalités présentes en microfluidique de gouttes, telles que la fusion, le tri et l’encapsulation de cellules, permettent de reproduire des protocoles bioanalytiques standards sur de très petits volumes avec des avantages tels que la diminution de la consommation d’échantillon et de réactifs et la diminution du temps d’analyse. Au cours de cette thèse, nous avons développé les protocoles pour deux types d’analyse biologique utilisant la manipulation de particules magnétiques en gouttes de 100 nL.Le premier projet a porté sur l’analyse multiomique de cellules uniques. Nous avons conçu un protocole en microfluidique de gouttes permettant la séparation de l’ARNm et l’ADN d’un échantillon complexe : du mélange d’ADN et d’ARN pré-purifiés à une suspension de moins d’une dizaine de cellules entières. A chaque étape, les performances du système en gouttes ont été évaluées et les résultats comparés avec les extractions effectuées en tubes de manière non-microfluidique. Les résultats obtenus sont prometteurs car ils démontrent pour la première fois dans un tel dispositif la séparation séquentielle de l’ADN et de l’ARNm à partir de lysat cellulaire ou de quelques cellules entières encapsulées et lysées en gouttes.Le second projet concerne la préparation d’échantillon en gouttes pour l’analyse par spectrométrie de masse MALDI-TOF. Ce projet a été mené en collaboration avec des chercheurs du CEA Saclay qui ont montré qu’il y a un intérêt important à déposer les échantillons sur des plaques MALDI sous forme de gouttes pour augmenter la sensibilité de détection grâce à un effet de concentration. Nous avons mis en place un système automatisable qui permet de coupler la microfluidique de gouttes à la spectrométrie de masse MALDI-TOF : de la digestion enzymatique supportée en goutte au dépôt sur plaque avec pré-concentration de l’échantillon cristallisé. Le développement a été fait avec le lysozyme comme protéine modèle et notre protocole permet d’identifier la protéine par empreinte de masses peptidiques à partir de 200 fmol de protéine avec une bonne fiabilité
Droplet microfluidics is a technology with a huge potential for miniaturization and automation of conventional methods in bioanalysis. Indeed, droplet microfluidics has many functionalities, such as merging, sorting and cell encapsulation, that can reproduce manipulations in standard protocols in bioanalysis on very small volumes with advantages such as a low samples and reagents consumption and reduction of analysis duration. During this thesis, we developed protocols for two types of biological analysis, using magnetic particles manipulation in 100 nL droplets.The first project is about single cell multiomics analysis. We implemented a droplet microfluidics protocol for the separation of mRNA and DNA from complex samples: from a mix of pre-purified RNA and DNA to a suspension of less than ten cells. At each steps, the droplet system performances were evaluated and the results were compared to extractions made in tubes in a non-microfluidics way. The results are very promising since they demonstrate for the first time in such a system the sequential separation of DNA and RNA from cell lysate or a few cells encapsulated and lysed in droplets.The second project is about the sample preparation in droplet for MALDI-TOF mass spectrometry analysis. This project was built in collaboration researchers from CEA Saclay. They have shown that it is very interesting to deposit samples in droplets on MALDI plates to increase the detection sensitivity thanks to a focusing effect. We have implemented on an original integrated droplet microfluidic approach for protein analysis by MALDI-TOF MS: from in drop sample preparation by supported enzymatic digestion to on plate deposition and peptides local pre-concentration. Development was done with lysozyme as a model protein and thanks to our in-drop digestion and deposition protocol we identified this protein by mass fingerprint from 200 fmol protein with a good reliability

2

Bodily, Weston Reed. "Integrative Analysis to Evaluate Similarity Between BRCAness Tumors and BRCA Tumors." BYU ScholarsArchive, 2017. https://scholarsarchive.byu.edu/etd/6800.

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The term "BRCAness" is used to describe breast-cancer patients who lack a germline mutation in BRCA1 or BRCA2, yet who are believed to express characteristics similar to patients who do have a germline mutation in BRCA1 or BRCA2. Although it is hypothesized that BRCAness is related to deficiency in the homologous recombination repair (HRR) pathways, relatively little is understood about what drives BRCAness or what criteria should be used to assign patients to this category. We hypothesized that patients whose tumor carries a genomic or epigenomic aberration in BRCA1 or BRCA2 should be classified under the BRCAness category and that these tumors would exhibit downstream effects (additional mutations or gene-expression changes) similar to patients with germline BRCA1/2 mutations. To better understand BRCAness, we examined similarities and differences in gene-expression profiles and somatic-mutation "signatures" among 1054 breast-cancer patients from The Cancer Genome Atlas. First, we categorized patients into three categories: those who carried a germline BRCA1/2 mutation, those whose tumor carried a genomic aberration or DNA hypermethylation in BRCA1/2 (the BRCAness group), and those who fell into neither of the first two groups. Upon evaluating the gene-expression data in context of the PAM50 subtypes, we did not observe significant similarity between the germline BRCA1/2 and BRCAness groups, but we did observe enrichment within the basal subtype, especially for BRCAness tumors with hypermethylation of BRCA1/2. However, the gene-expression profiles were fairly heterogeneous; for example, BRCA1 patients differed significantly from BRCA2 patients. In agreement with prior findings, certain mutational signatures—especially "Signature 3"—were enriched for patients with germline BRCA1/2 mutations as well as for BRCAness patients. Furthermore, we observed significant similarity between germline BRCA1/2 patients and patients with germline mutations in PALB2, RAD51B, and RAD51C, genes that are key parts of the HRR pathway and that interact with BRCA1/2. Our findings suggest that the BRCAness category does have biological and clinical relevance but that the criteria for including patients in this category should be carefully defined, potentially including BRCA1/2 hypermethylation and homozygous deletions as well as germline mutations in PALB2, RAD51B, and RAD51C.

3

Backman, Mattias [Verfasser], and Eckhard [Akademischer Betreuer] Wolf. "Bioinformatic analysis of multiomic data from the Munich MIDY Pig biobank / Mattias Backman ; Betreuer: Eckhard Wolf." München : Universitätsbibliothek der Ludwig-Maximilians-Universität, 2020. http://d-nb.info/121985204X/34.

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4

Proust, Lucas. "Analyse multiomique des peptides d’extraits de levure et de leurs impacts fonctionnels sur Streptococcus thermophilus." Thesis, Université Paris-Saclay (ComUE), 2018. http://www.theses.fr/2018SACLA032.

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Les bactéries lactiques sont largement utilisées en tant que ferments dans l'industrie laitière. Leur production s’effectue généralement dans des milieux semi-définis ou complexes dans lesquels certains nutriments peuvent être apportés par des extraits de levure (EXLs). Ce projet de thèse, qui associe deux partenaires industriels, les groupes Lesaffre et Sacco, ainsi que l’INRA, s’est focalisé sur l’effet des peptides de deux EXLs (EXL1 et EXL2) sur une souche industrielle de Streptococcus thermophilus, un levain lactique d’intérêt économique majeur. L’hypothèse sous-jacente était que ces peptides pourraient avoir un double rôle de nutrition et de régulation de fonctions cellulaires pouvant présenter un intérêt technologique. Afin d’explorer cette question, une stratégie expérimentale à deux niveaux a été élaborée : i) caractérisation et suivi cinétique de la fraction peptidique des deux EXLs par spectrométrie de masse (peptidomique) durant la fermentation de S. thermophilus en bioréacteurs, et ii) suivi cinétique parallèle du transcriptome et du protéome de la bactérie. L’objectif final était de croiser ces deux niveaux d’information afin de corréler des différences de contenu peptidique avec des différences d’activation de systèmes participant aux performances globales du levain.La caractérisation et le suivi du peptidome des EXLs en cours de fermentation a nécessité un important travail de développement méthodologique ayant abouti in fine à l’élaboration d’un outil analytique complet, combinant analyse peptidomique à haut-débit des échantillons et traitement bioinformatique et statistique des données. Cet outil a permis d’identifier environ 4000 peptides différents composant les deux EXLs. Le suivi cinétique a notamment permis de préciser la spécificité du transporteur d’oligopeptides de la bactérie (Ami). En particulier, il s’est avéré qu’une charge nette positive était le facteur prévalent pour le transport des peptides chez S. thermophilus. En complément de cette approche semi-quantitative, des analyses quantitatives ont été réalisées sur des fractions peptidiques des EXLs (dosages différentiels par HPLC des acides aminés avant et après hydrolyse). Elles ont notamment permis de révéler d’importantes différences de teneurs en oligopeptides entre les deux EXLs.En parallèle, le suivi transcriptomique et protéomique réalisé durant la croissance de la bactérie a révélé deux faits marquants. Le premier fait a trait à la surexpression dans l’EXL1 d’un locus génétique régulé par un mécanisme de quorum sensing utilisant un peptide phéromone comme signal moléculaire. Le deuxième fait marquant concerne diverses voies de biosynthèse (acides aminés et purines) différentiellement affectées par les deux EXLs. L’origine de ces dynamiques pourrait être au moins pour partie le fait de différences de contenu peptidique entre les deux substrats. Notamment, certaines voies de biosynthèse pourraient avoir été modulées différentiellement sous l’action de régulateurs centraux tels que CodY, dont l’activité est corrélée au contenu peptidique du milieu, ou encore YebC, un régulateur CodY-like dont le lien fonctionnel avec CodY reste encore inconnu chez S. thermophilus. Tous ces résultats ouvrent d’intéressantes perspectives pour mieux explorer le lien entre peptides et métabolisme bactérien. A terme, cette démarche pourrait se traduire par l’identification de biomarqueurs de performances dans les EXLs, et l’élaboration à façon de produits permettant de maximiser le potentiel technologique des ferments lactiques
Lactic acid bacteria are widely used as starters in dairy industry. They are generally produced in complex fermentation media containing a wide array of nutrients that can be provided by yeast extracts (YEs). The main goal of this thesis project, involving two industrial partners, Lesaffre and Sacco, as well as INRA, was to investigate the effect of the peptide fraction of two YEs (YE1 and YE2) on an industrial Streptococcus thermophilus strain, a major lactic acid starter. The underlying hypothesis of this whole project was that YE peptides could have a role in nutrition but also regulate cellular functions of technological relevance. In order to explore this question, a two-step strategy was elaborated: i) mass spectrometry characterization (peptidomics) of both YE peptide fractions and time course analysis of their relative abundance during the growth in bioreactors of S. thermophilus, and ii) parallel time course analysis of the strain transcriptome and proteome. The final objective was to cross these two levels of information in order to correlate differences of peptide content with differentially activated systems related to technological performances.YE peptidome characterization and kinetic analysis first required an important methodological development. It eventually resulted in a complete analytical tool that combines high throughput peptidomic analysis as well as bioinformatic and statistical data processing. This powerful tool was able to identify around 4,000 different peptides in both YEs. Then, the time course analysis also clarified the in vivo substrate specificities of the oligopeptide transport system of the bacterium (Ami). A peptide positive net charge notably turned out to be the leading factor governing peptide transport. In addition to this semi-quantitative approach, quantitative analyses were carried out on YE peptide fractions (differential HPLC analyses of amino acids before and after sample hydrolysis). They notably revealed significant differences in oligopeptides content between both YEs.Meanwhile, genome scale transcriptomic and proteomic analyses performed during the strain growth highlighted two significant events. The first one concerns the overexpression in YE1 of a quorum sensing-based genetic locus that uses a pheromone peptide as molecular signal. The second event relates to several biosynthesis pathways (amino acids and purines) that were differentially affected by both YEs. These dynamics could result from differences in peptide content between both substrates. In particular, some pathways could have been differentially modulated by central regulators such as CodY, whose activity is correlated to the medium peptide richness, or YebC, a CodY-like regulator whose functional link with CodY is still unknown in S. thermophilus. All these results open avenues for a better understanding of the interplay between peptides and bacterial metabolism. In the future, this whole approach could lead to the identification of performance biomarkers in YEs, which in turns may eventually translate into the conception of new customized products granting high technological performances to dairy starters

5

Gressel, Saskia. "Multi-omics analysis of transcription kinetics in human cells." Doctoral thesis, 2019. http://hdl.handle.net/21.11130/00-1735-0000-0003-C183-E.

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Частини книг з теми "Multiomics analysis":

1

Taguchi, Y.-h. "Multiomics Data Analysis Using Tensor Decomposition Based Unsupervised Feature Extraction." In Intelligent Computing Theories and Application, 565–74. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-26763-6_54.

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2

Taguchi, Y. H. "Multiomics Data Analysis of Cancers Using Tensor Decomposition and Principal Component Analysis Based Unsupervised Feature Extraction." In Studies in Big Data, 1–17. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-9158-4_1.

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3

Ahrendt, Steven R., Stephen J. Mondo, Sajeet Haridas, and Igor V. Grigoriev. "MycoCosm, the JGI’s Fungal Genome Portal for Comparative Genomic and Multiomics Data Analyses." In Microbial Environmental Genomics (MEG), 271–91. New York, NY: Springer US, 2022. http://dx.doi.org/10.1007/978-1-0716-2871-3_14.

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4

González, Juan R., and Alejandro Cáceres. "Multiomic data analysis." In Omic Association Studies with R and Bioconductor, 315–52. Chapman and Hall/CRC, 2019. http://dx.doi.org/10.1201/9780429440557-11.

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5

Hassan, Muhammad Jawad, Muhammad Faheem, and Sabba Mehmood. "Emerging OMICS and Genetic Disease." In Omics Technologies for Clinical Diagnosis and Gene Therapy: Medical Applications in Human Genetics, 93–113. BENTHAM SCIENCE PUBLISHERS, 2022. http://dx.doi.org/10.2174/9789815079517122010010.

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Анотація:

Multiomics also described as integrative omics is an analytical approach that combines data from multiple ‘omics’ approaches including genomics, transcriptomics, proteomics, metabolomics, epigenomics, metagenomics and Meta transcriptomics to answer the complex biological processes involved in rare genetic disorders. This omics approach is particularly helpful since it identifies biomarkers of disease progression and treatment progress by collective characterization and quantification of pools of biological molecules within and among the various types of cells to better understand and categorize the Mendelian and non- Mendelian forms of rare diseases. As compared to studies of a single omics type, multi-omics offers the opportunity to understand the flow of information that underlies the disease. A range of omics software and databases, for example WikiPathways, MixOmics, MONGKIE, GalaxyP, GalaxyM, CrossPlatform Commander, and iCluster are used for multi-omics data exploration and integration in rare disease analysis. Recent advances in the field of genetics and translational research have opened new treatment avenues for patients. The innovation in the next generation sequencing and RNA sequencing has improved the ability from diagnostics to detection of molecular alterations like gene mutations in specific disease types. In this chapter, we provide an overview of such omics technologies and focus on methods for their integration across multiple omics layers. The scrupulous understanding of rare genetic disorders and their treatment at the molecular level led to the concept of a personalized approach, which is one of the most significant advancements in modern research which enable researchers to better comprehend the flow of knowledge which underpins genetic diseases.

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Marín de Mas, Igor. "Multiomic Data Integration and Analysis via Model-Driven Approaches." In Comprehensive Analytical Chemistry, 447–76. Elsevier, 2018. http://dx.doi.org/10.1016/bs.coac.2018.07.005.

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Bangert, Patrick, Nandha Kumar Balasubramaniam, Carol E. Parker, and Christoph H. Borchers. "Pattern Recognition for Mass-Spectrometry-Based Proteomics." In Biomedical Engineering. IntechOpen, 2022. http://dx.doi.org/10.5772/intechopen.108422.

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Multiomic analysis comprises genomics, proteomics, and metabolomics leads to meaningful insights but necessitates sifting through voluminous amounts of complex data. Proteomics in particular focuses on the end product of gene expression – i.e., proteins. The mass spectrometric approach has proven to be a workhorse for the qualitative and quantitative study of protein interactions as well as post-translational modifications (PTMs). A key component of mass spectrometry (MS) is spectral data analysis, which is complex and has many challenges as it involves identifying patterns across a multitude of spectra in combination with the meta-data related to the origin of the spectrum. Artificial Intelligence (AI) along with Machine Learning (ML), and Deep Learning (DL) algorithms have gained more attention lately for analyzing the complex spectral data to identify patterns and to create networks of value for biomarker discovery. In this chapter, we discuss the nature of MS proteomic data, the relevant AI methods, and demonstrate their applicability. We also show that AI can successfully identify biomarkers and aid in the diagnosis, prognosis, and treatment of specific diseases.

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Xia, Yinglin. "Correlation and association analyses in microbiome study integrating multiomics in health and disease." In Progress in Molecular Biology and Translational Science, 309–491. Elsevier, 2020. http://dx.doi.org/10.1016/bs.pmbts.2020.04.003.

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Тези доповідей конференцій з теми "Multiomics analysis":

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Hristova, V., A. Watson, M. Glover, J. Wang, B. Angermann, S. Ashenden, A. Bornot, et al. "S90 Comprehensive multiomics analysis demonstrates surfactant dysregulation in COPD." In British Thoracic Society Winter Meeting 2021 Online, Wednesday 24 to Friday 26 November 2021, Programme and Abstracts. BMJ Publishing Group Ltd and British Thoracic Society, 2021. http://dx.doi.org/10.1136/thorax-2021-btsabstracts.96.

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Gardner, Lois, Dominic G. Rothwell, Caroline Dive, Kostas Kostarelos, and Marilena Hadjidemetriou. "Abstract 568: Nanonets for multiomics blood analysis and cancer biomarker discovery." In Proceedings: AACR Annual Meeting 2021; April 10-15, 2021 and May 17-21, 2021; Philadelphia, PA. American Association for Cancer Research, 2021. http://dx.doi.org/10.1158/1538-7445.am2021-568.

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Zounemat Kermani, nazanin, John Busby, Kai Sun, Ioannis Pandis, Gabrielle Gainsborough, Yike Guo, Ian Adcock, Tim Hardman, and Liam Heaney. "A data management and analysis platform for RASP-UK multiomics clinical datasets." In ERS International Congress 2021 abstracts. European Respiratory Society, 2021. http://dx.doi.org/10.1183/13993003.congress-2021.oa4058.

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Sen, Sidharth, Tyler McCubbin, Shannon K. King, Laura A. Greeley, Shuai Zeng, Cheyenne Baker, Rachel Mertz, et al. "A multiomics discriminatory analysis approach to identify drought-related signatures in maize nodal roots." In 2020 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2020. http://dx.doi.org/10.1109/bibm49941.2020.9313382.

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Mattie, Mike, Regis Perbost, Sarah Turcan, Corinne Danan, Frederick Locke, Sattva Neelapu, David Miklos, et al. "1454 Multiomics and multimodal analysis approach to construct a diffuse large B cell lymphoma atlas of tumor microenvironment for predictive modeling." In SITC 37th Annual Meeting (SITC 2022) Abstracts. BMJ Publishing Group Ltd, 2022. http://dx.doi.org/10.1136/jitc-2022-sitc2022.1454.

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Chen, Kezhong, Airong Yang, Shuangxiu Wu, Yuntao Nie, Haifeng Shen, Jian Bai, Lin Wu, Fan Yang, and Jun Wang. "Abstract 618: Spatio-temporal multiomics analysis reveals distinct molecular features in recurrent stage I non-small cell lung cancers after R0 tumor resection." In Proceedings: AACR Annual Meeting 2021; April 10-15, 2021 and May 17-21, 2021; Philadelphia, PA. American Association for Cancer Research, 2021. http://dx.doi.org/10.1158/1538-7445.am2021-618.

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Carl, Sarah, Juana Flores-Candia, Jeremy Staub, Jasmin D’Andrea, Brittney Ruedlinger, Kate Shapland, Jared Ehrhart, and Soner Altiok. "1438 Integrative analysis of single cell multiomics data using deep learning to identify immune related biomarkers in a patient derived 3D ex vivo tumoroid platform." In SITC 37th Annual Meeting (SITC 2022) Abstracts. BMJ Publishing Group Ltd, 2022. http://dx.doi.org/10.1136/jitc-2022-sitc2022.1438.

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Ooi, Aik, Pedro Mendez, Dalia Dhingra, Nigel Beard, and David Ruff. "Abstract 5910: Single-cell multiomic analysis of SNV, CNV, and protein expression." In Proceedings: AACR Annual Meeting 2020; April 27-28, 2020 and June 22-24, 2020; Philadelphia, PA. American Association for Cancer Research, 2020. http://dx.doi.org/10.1158/1538-7445.am2020-5910.

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Corselli, Mirko, Suraj Saksena, Margaret Nakamoto, Woodrow E. Lomas, Ian Taylor, and Pratip K. Chattopadhyay. "Abstract 2166: Deep characterization ofin vitrochronically stimulated T cells via single-cell multiomic analysis." In Proceedings: AACR Annual Meeting 2020; April 27-28, 2020 and June 22-24, 2020; Philadelphia, PA. American Association for Cancer Research, 2020. http://dx.doi.org/10.1158/1538-7445.am2020-2166.

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Bareche, Yacine, David Venet, Philippe Aftimos, Michail Ignatiadis, Martine Piccart, Francoise Rothe, and Christos Sotiriou. "Abstract 3698: Unraveling triple-negative breast cancer molecular heterogeneity using an integrative multiomic analysis." In Proceedings: AACR Annual Meeting 2018; April 14-18, 2018; Chicago, IL. American Association for Cancer Research, 2018. http://dx.doi.org/10.1158/1538-7445.am2018-3698.

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