Готові списки джерел за темами / Multiomic integration

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Добірка наукової літератури з теми "Multiomic integration"

Автор: Grafiati

Опубліковано: 16 листопада 2024

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Статті в журналах з теми "Multiomic integration"

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.

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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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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.

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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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Ramos, Marcel, Ludwig Geistlinger, Sehyun Oh, Lucas Schiffer, Rimsha Azhar, Hanish Kodali, Ino de Bruijn, et al. "Multiomic Integration of Public Oncology Databases in Bioconductor." JCO Clinical Cancer Informatics, no. 4 (October 2020): 958–71. http://dx.doi.org/10.1200/cci.19.00119.

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PURPOSE Investigations of the molecular basis for the development, progression, and treatment of cancer increasingly use complementary genomic assays to gather multiomic data, but management and analysis of such data remain complex. The cBioPortal for cancer genomics currently provides multiomic data from > 260 public studies, including The Cancer Genome Atlas (TCGA) data sets, but integration of different data types remains challenging and error prone for computational methods and tools using these resources. Recent advances in data infrastructure within the Bioconductor project enable a novel and powerful approach to creating fully integrated representations of these multiomic, pan-cancer databases. METHODS We provide a set of R/Bioconductor packages for working with TCGA legacy data and cBioPortal data, with special considerations for loading time; efficient representations in and out of memory; analysis platform; and an integrative framework, such as MultiAssayExperiment. Large methylation data sets are provided through out-of-memory data representation to provide responsive loading times and analysis capabilities on machines with limited memory. RESULTS We developed the curatedTCGAData and cBioPortalData R/Bioconductor packages to provide integrated multiomic data sets from the TCGA legacy database and the cBioPortal web application programming interface using the MultiAssayExperiment data structure. This suite of tools provides coordination of diverse experimental assays with clinicopathological data with minimal data management burden, as demonstrated through several greatly simplified multiomic and pan-cancer analyses. CONCLUSION These integrated representations enable analysts and tool developers to apply general statistical and plotting methods to extensive multiomic data through user-friendly commands and documented examples.

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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.

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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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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.

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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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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.

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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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Culley, Christopher, Supreeta Vijayakumar, Guido Zampieri, and Claudio Angione. "A mechanism-aware and multiomic machine-learning pipeline characterizes yeast cell growth." Proceedings of the National Academy of Sciences 117, no. 31 (July 16, 2020): 18869–79. http://dx.doi.org/10.1073/pnas.2002959117.

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Metabolic modeling and machine learning are key components in the emerging next generation of systems and synthetic biology tools, targeting the genotype–phenotype–environment relationship. Rather than being used in isolation, it is becoming clear that their value is maximized when they are combined. However, the potential of integrating these two frameworks for omic data augmentation and integration is largely unexplored. We propose, rigorously assess, and compare machine-learning–based data integration techniques, combining gene expression profiles with computationally generated metabolic flux data to predict yeast cell growth. To this end, we create strain-specific metabolic models for 1,143Saccharomyces cerevisiaemutants and we test 27 machine-learning methods, incorporating state-of-the-art feature selection and multiview learning approaches. We propose a multiview neural network using fluxomic and transcriptomic data, showing that the former increases the predictive accuracy of the latter and reveals functional patterns that are not directly deducible from gene expression alone. We test the proposed neural network on a further 86 strains generated in a different experiment, therefore verifying its robustness to an additional independent dataset. Finally, we show that introducing mechanistic flux features improves the predictions also for knockout strains whose genes were not modeled in the metabolic reconstruction. Our results thus demonstrate that fusing experimental cues with in silico models, based on known biochemistry, can contribute with disjoint information toward biologically informed and interpretable machine learning. Overall, this study provides tools for understanding and manipulating complex phenotypes, increasing both the prediction accuracy and the extent of discernible mechanistic biological insights.

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Pratapa, Aditya, Lydia Hernandez, Bassem Ben Cheikh, Niyati Jhaveri, and Arutha Kulasinghe. "Abstract 5503: Ultrahigh-plex spatial phenotyping of head and neck cancer tissue uncovers multiomic signatures of immunotherapy response." Cancer Research 84, no. 6_Supplement (March 22, 2024): 5503. http://dx.doi.org/10.1158/1538-7445.am2024-5503.

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Abstract Background Targeted immune checkpoint inhibitors (ICI) with anti-PD-1/PD-L1 therapy offer durable treatment of mucosal head and neck squamous cell cancer (HNSCC), in both human papillomavirus-positive (HPV+) and negative (HPV-) patients. However, currently available biomarker signatures for targeted ICI therapies have limited predictive value. Our recent ultrahigh-plex profiling of HNSCC tissue with 100+ cancer hallmarks of tumor and immunobiology uncovered distinct spatial domains that serve as defining factors for clinical response and resistance. Methods Our unbiased analysis of whole-slide metastatic HNSCC tumors is from a clinical cohort of patients treated with Pembrolizumab/Nivolumab. The cohort consisted of patients with a range of outcomes from complete vs partial vs progressive disease responses to ICI therapy. We first characterized the tumor microenvironment using our ultrahigh-plex protein panel with 100+ antibodies on the PhenoCycler®-Fusion platform. To expand upon our biomarker discovery, we included multiomic cancer hallmarks with a multimodal protein/RNA detection panel. Targeted spatial RNA detection was performed to complement and augment the microenvironment characterization achieved by our protein panel. To further consolidate the multiomic data, we leveraged MaxFuse, a state-of-the art computational framework that integrates multimodal spatial and single-cell expression data. Results Our multiomic spatial phenotyping uncovered diverse tumor regions, each with distinct biomarker expression that is reflected across modalities including protein, RNA, and metabolic activity, indicating regions likely associated with resistance to immunotherapy. Our multiomic data integration also revealed spatial signatures associated with different tissue compartments, such as the tumor and non-tumor associated tertiary lymphoid structures. Conclusions We demonstrate a multi-pronged approach that incorporated both novel experimental and computational techniques for elucidating tumor microenvironment in HNSCC tissue prior to ICI-based immunotherapy. Our multiomic approach provides deeper characterization of the HNSCC at the transcriptomic and proteomic level incorporating depth across the entire transcriptome and single-cell spatial resolution of key protein determinants for predicting and furthering our understanding of immunotherapy response to ICI therapy. Citation Format: Aditya Pratapa, Lydia Hernandez, Bassem Ben Cheikh, Niyati Jhaveri, Arutha Kulasinghe. Ultrahigh-plex spatial phenotyping of head and neck cancer tissue uncovers multiomic signatures of immunotherapy response [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 5503.

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Signorelli, Mirko, Roula Tsonaka, Annemieke Aartsma-Rus, and Pietro Spitali. "Multiomic characterization of disease progression in mice lacking dystrophin." PLOS ONE 18, no. 3 (March 31, 2023): e0283869. http://dx.doi.org/10.1371/journal.pone.0283869.

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Duchenne muscular dystrophy (DMD) is caused by genetic mutations leading to lack of dystrophin in skeletal muscle. A better understanding of how objective biomarkers for DMD vary across subjects and over time is needed to model disease progression and response to therapy more effectively, both in pre-clinical and clinical research. We present an in-depth characterization of disease progression in 3 murine models of DMD by multiomic analysis of longitudinal trajectories between 6 and 30 weeks of age. Integration of RNA-seq, mass spectrometry-based metabolomic and lipidomic data obtained in muscle and blood samples by Multi-Omics Factor Analysis (MOFA) led to the identification of 8 latent factors that explained 78.8% of the variance in the multiomic dataset. Latent factors could discriminate dystrophic and healthy mice, as well as different time-points. MOFA enabled to connect the gene expression signature in dystrophic muscles, characterized by pro-fibrotic and energy metabolism alterations, to inflammation and lipid signatures in blood. Our results show that omic observations in blood can be directly related to skeletal muscle pathology in dystrophic muscle.

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Silberberg, Gilad, Bandana Vishwakarama, Brandon Walling, Chelsea Riveley, Alessandra Audia, Marianna Zipeto, Ido Sloma, Amy Wesa, and Michael Ritchie. "Abstract 3907: A pheno-multiomic integration analysis of primary samples of acute myeloid leukemia reveals biomarkers of cytarabine resistance." Cancer Research 82, no. 12_Supplement (June 15, 2022): 3907. http://dx.doi.org/10.1158/1538-7445.am2022-3907.

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Abstract The overall survival of patients diagnosed with Acute Myeloid Leukemia (AML) remains low. While initial responses to therapy are favorable, the duration of response is short and overcoming therapeutic resistance has proven difficult. A better understanding of the tumor cell biology and resistance mechanisms may shed light onto novel therapeutic targets that improve long-term outcome. In this study, we performed an exhaustive analysis to include deep tumor phenotyping, drug sensitivity profiling and comprehensive omic characterization. These datasets were included in integrative pharmaco-phenotypic-multiomic analyses to identify targets and biomarkers associated with cellular phenotype and drug response. Our results reveal that the major cellular discriminant within the cellular phenotype is CD34 expression, which associates with a high PDK-mediated metabolic profile and cytarabine sensitivity. Tumors exhibiting cytarabine resistance associate with a CD34-negative cellular phenotype and molecular characteristics such as MYC copy number gain, and increased expression of SAMDH1, FBP1 and TYMP proteins. Citation Format: Gilad Silberberg, Bandana Vishwakarama, Brandon Walling, Chelsea Riveley, Alessandra Audia, Marianna Zipeto, Ido Sloma, Amy Wesa, Michael Ritchie. A pheno-multiomic integration analysis of primary samples of acute myeloid leukemia reveals biomarkers of cytarabine resistance [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 3907.

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Більше джерел

Дисертації з теми "Multiomic integration"

Bretones, Santamarina Jorge. "Integrated multiomic analysis, synthetic lethality inference and network pharmacology to identify SWI/SNF subunit-specific pathway alterations and targetable vulnerabilities." Electronic Thesis or Diss., université Paris-Saclay, 2024. http://www.theses.fr/2024UPASL049.

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De nos jours, la communauté scientifique s'accorde sur la nécessité de diagnostics et de thérapies personnalisés pour les patients atteints de cancer, conçus par des études translationnelles combinant approches expérimentales et statistiques. Les défis actuels incluent la validation de modèles expérimentaux précliniques et leur profilage multi-omiques, ainsi que la conception de méthodes bioinformatiques et mathématiques dédiées pour identifier les combinaisons de médicaments optimales pour chaque patient.Cette thèse a visé à concevoir de telles approches statistiques pour analyser différents types de données à grande échelle et les intégrer afin d'identifier les vulnérabilités ciblables des lignées cancéreuses. Nous nous sommes focalisés sur les altérations du complexe de remodelage de la chromatine SWI/SNF, muté dans ~20 % des cancers, pour lesquels aucune thérapie efficace n'est disponible. Nous avons utilisé un panel de lignées cellulaires isogéniques HAP1 mutées pour les sous-unités du complexe SWI/SNF ou d'autres enzymes épigénétiques, pour lesquelles des données de transcriptomique, protéomique et de criblage de médicaments étaient disponibles.Nous avons travaillé sur quatre axes méthodologiques. Premièrement, nous avons conçu une méthodologie optimisée d'enrichissement pour détecter les voies de régulation différentiellement activées entre mutants et type sauvage. Ensuite, nous avons croisé les résultats des criblages de médicaments et les bases d'interaction gène-médicament, pour inférer des voies de régulation ciblables spécifiquement chez les lignées mutantes. Ensuite, la validation de ces cibles potentielles a été réalisée à l'aide d'une nouvelle méthode détectant la létalité synthétique à partir de données transcriptomiques et CRISPR de lignées cancéreuses indépendantes du projet DepMap. Enfin, en vue de l'optimisation de thérapies multi-agents, nous avons conçu une première représentation digitale des voies de régulation ciblables pour les tumeurs mutées SMARCA4, en construisant un réseau dirigé d'interaction protéine-protéine reliant les cibles inférées des analyses multi-omiques HAP1 et CRISPR DepMap. Nous avons utilisé la base de données OmniPath pour récupérer les interactions protéiques directes et ajouté les protéines liant celles présentes dans le réseau avec l'algorithme Neko.Ces développements méthodologiques ont été appliqués aux ensembles de données disponibles pour le panel HAP1. En utilisant notre méthodologie d'enrichissement optimisée, nous avons identifié le Métabolisme des protéines comme la catégorie de voies de régulation la plus fréquemment dérégulée dans les lignées SWI/SNF-KO. Ensuite, l'analyse de criblage de médicaments a révélé des médicaments cytotoxiques et épigénétiques ciblant sélectivement les mutants SWI/SNF, notamment les inhibiteurs de CBP/EP300 ou de la respiration mitochondriale, également identifiés comme létaux synthétiques par notre analyse CRISPR DepMap. Ces résultats ont été validés dans deux modèles expérimentaux isogéniques indépendants. L'analyse CRISPR DepMap a également été utilisée pour identifier des interactions létales synthétiques dans le glioblastome, qui se sont révélées pertinentes pour des lignées cellulaires dérivées de patients et sont en cours de validation.En résumé, nous avons développé des méthodes computationnelles pour intégrer des données d'expression multi-omiques avec des criblages de médicaments et des tests CRISPR, et identifié de nouvelles vulnérabilités chez les mutants SWI/SNF, qui ont été validées expérimentalement. Cette étude était limitée à l'identification de monothérapies efficaces. Pour l'avenir, nous proposons de concevoir des modèles mathématiques représentant les réseaux de protéines ciblables à l'aide d'équations différentielles et de les utiliser dans des procédures d'optimisation numérique et d'apprentissage automatique pour étudier les cibles médicamenteuses concomitantes et personnaliser les combinaisons de médicaments
Nowadays the cancer community agrees on the need for patient-tailored diagnostics and therapies, which calls for the design of translational studies combining experimental and statistical approaches. Current challenges include the validation of preclinical experimental models and their multi-omics profiling, along with the design of dedicated bioinformatics and mathematical pipelines (i.e. dimension reduction, multi-omics integration, mechanism-based digital twins) for identifying patient-specific optimal drug combinations.To address these challenges, we designed bioinformatics and statistical approaches to analyze various large-scale data types and integrate them to identify targetable vulnerabilities in cancer cell lines. We developed our pipeline in the context of alterations of the SWItch Sucrose Non-Fermentable (SWI/SNF) chromatin remodeling complex. SWI/SNF mutations occur in ~20% of all cancers, but such malignancies still lack efficient therapies. We leveraged a panel of HAP1 isogenic cell lines mutated for SWI/SNF subunits or other epigenetic enzymes for which transcriptomics, proteomics and drug screening data were available.We worked on four methodological axes, the first one being the design of an optimized pathway enrichment pipeline to detect pathways differentially activated in the mutants against the wild-type. We developed a pruning algorithm to reduce gene and pathway redundancy in the Reactome database and improve the interpretability of the results. We evidenced the bad performance of first-generation enrichment methods and proposed to combine the topology-based method ROntoTools with pre-ranked GSEA to increase enrichment performance .Secondly, we analyzed drug screens, processed drug-gene interaction databases to obtain genes and pathways targeted by effective drugs and integrated them with proteomics enrichment results to infer targetable vulnerabilities selectively harming mutant cell lines. The validation of potential targets was achieved using a novel method detecting synthetic lethality from transcriptomics and CRISPR data of independent cancer cell lines in DepMap, run for each studied epigenetic enzyme. Finally, to further inform multi-agent therapy optimization, we designed a first digital representation of targetable pathways for SMARCA4-mutated tumors by building a directed protein-protein interaction network connecting targets inferred from multi-omics HAP1 and DepMap CRISPR analyses. We used the OmniPath database to retrieve direct protein interactions and added the connecting neighboring genes with the Neko algorithm.These methodological developments were applied to the HAP1 panel datasets. Using our optimized enrichment pipeline, we identified Metabolism of proteins as the most frequently dysregulated pathway category in SWI/SNF-KO lines. Next, the drug screening analysis revealed cytotoxic and epigenetic drugs selectively targeting SWI/SNF mutants, including CBP/EP300 or mitochondrial respiration inhibitors, also identified as synthetic lethal by our Depmap CRISPR analysis. Importantly, we validated these findings in two independent isogenic cancer-relevant experimental models. The Depmap CRISPR analysis was also used in a separate project to identify synthetic lethal interactions in glioblastoma, which proved relevant for patient-derived cell lines and are being validated in dedicated drug screens.To sum up, we developed computational methods to integrate multi-omics expression data with drug screening and CRISPR assays and identified new vulnerabilities in SWI/SNF mutants which were experimentally revalidated. This study was limited to the identification of effective single agents. As a future direction, we propose to design mathematical models representing targetable protein networks using differential equations and their use in numerical optimization and machine learning procedures as a key tool to investigate concomitant druggable targets and personalize drug combinations

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Coronado, Zamora Marta. "Mapping natural selection through the drosophila melanogaster development following a multiomics data integration approach." Doctoral thesis, Universitat Autònoma de Barcelona, 2018. http://hdl.handle.net/10803/666761.

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La teoria de l'evolució de Charles Darwin proposa que les adaptacions dels organismes sorgeixen com a conseqüència del procés de la selecció natural. La selecció natural deixa una empremta característica en els patrons de variació genètica que pot detectar-se mitjançant mètodes estadístics d'anàlisi genòmica. Avui en dia podem inferir l'acció de la selecció natural en el genoma i fins i tot quantificar quina proporció de les noves variants genètiques que incorpora una espècie són adaptatives. L’era genòmica ha conduït a la situació paradoxal en la qual disposem de més informació sobre la selecció en el genoma que sobre el fenotip de l’organisme, l’objectiu principal de la selecció natural. El desenvolupament de les tecnologies de seqüenciació de nova generació (NGS, per les seves sigles en anglès) està proporcionant una gran quantitat de dades -òmiques, incrementant notablement la disponibilitat de sèries transcriptòmiques del desenvolupament. A diferència del genoma d'un organisme, el transcriptoma és un fenotip que varia al llarg de la vida i en diferents parts del cos. L'estudi d'un transcriptoma des d'una perspectiva genòmica-poblacional i espai-temporal és un enfocament prometedor per comprendre les bases genètiques i del desenvolupament del canvi fenotípic. Aquesta tesi és un projecte integrador de genòmica de poblacions i biologia evolutiva seguint un enfocament bioinformàtic. Es compon de tres passos seqüencials: (i) la comparativa d'un conjunt de mètodes de McDonald i Kreitman (MKT), un test per detectar selecció positiva recurrent en seqüències codificants a nivell molecular, utilitzant tant dades empíriques d'una població nord-americana de D. melanogaster i dades simulades, (ii) la inferència de les característiques del genoma que es correlacionen amb la tassa evolutiva dels gens codificadors de proteïnes, i (iii) la integració de patrons de variació genòmica amb anotacions de grans conjunts de dades espai-temporals del desenvolupament (evo-dev-omics). Com a resultat d'aquest enfocament hem dut a terme dos estudis diferents que integren els patrons de diversitat genòmica amb capes multiòmiques al llarg del desenvolupament, tant en el temps com en l'espai. En el primer estudi, donem una perspectiva global sobre com actua la selecció natural durant tot el cicle de vida de D. melanogaster, avaluant com els diferents règims de selecció actuen a través dels diferents estadis del desenvolupament. En el segon estudi, tracem un mapa exhaustiu de com la selecció actua sobre l'anatomia completa de l'embrió de D. melanogaster. En conjunt, els nostres resultats mostren que els gens expressats en el desenvolupament embrionari mitjà i tardà exhibeixen la major conservació a nivell de seqüència i una estructura gènica més complexa: són més llargs, contenen més exons i introns més llargs, codifiquen un gran nombre de isoformes i, de mitjana, tenen més expressió. El constrenyiment selectiu és ubic, especialment afectant els sistemes digestiu i nerviós. D'altra banda, els primers estadis del desenvolupament embrionari són els més divergents, i sembla ser degut a una menor eficàcia de la selecció natural sobre els gens d'efecte matern. A més, els gens expressats en aquestes primeres etapes tenen, de mitjana, els introns més curts, probablement degut a la necessitat d'expressar-se ràpidament i eficientment durant els cicles cel·lulars curts. L'adaptació es produeix en aquelles estructures que també mostren evidències d'adaptació en l'adult, el sistema immunològic i el sistema reproductiu. Finalment, els gens que s’expressen en una o unes poques estructures anatòmiques són evolutivament més joves i exhibeixen unes taxes d'evolució més altes, a diferència dels gens que s’expressen en totes o gairebé totes les estructures. La genòmica de poblacions ja no és una ciència teòrica, s’ha convertit en un camp interdisciplinari on la bioinformàtica, grans conjunts de dades -òmiques, models estadístics i evolutius i tècniques moleculars emergents s’integren per obtenir una visió sistèmica de les causes i les conseqüències de l’evolució. La integració de la genòmica de poblacions amb altres dades fenotípiques multiòmiques és un pas necessari per obtenir una visió global de com l’adaptació ocorre en la natura.
Charles Darwin's theory of evolution proposes that the adaptations of organisms arise because of the process of natural selection. Natural selection leaves a characteristic footprint on the patterns of genetic variation that can be detected by means of statistical methods of genomic analysis. Today, we can infer the action of natural selection in a genome and even quantify what proportion of the incorporated genetic variants in the populations are adaptive. The genomic era has led to the paradoxical situation in which much more evidence of selection is available on the genome than on the phenotype of the organism, the primary target of natural selection. The advent of next-generation sequencing (NGS) technologies is providing a vast amount of -omics data, especially increasing the breadth of available developmental transcriptomic series. In contrast to the genome of an organism, the transcriptome is a phenotype that varies during the lifetime and across different body parts. Studying a developmental transcriptome from a population genomic and spatio-temporal perspective is a promising approach to understand the genetic and developmental basis of the phenotypic change. This thesis is an integrative population genomics and evolutionary biology project following a bioinformatic approach. It is performed in three sequential steps: (i) the comparison of different variations of the McDonald and Kreitman test (MKT), a method to detect recurrent positive selection on coding sequences at the molecular level, using empirical data from a North American population of D. melanogaster and simulated data, (ii) the inference of the genome features correlated with the evolutionary rate of protein-coding genes, and (iii) the integration of patterns of genomic variation with annotations of large sets of spatio-temporal developmental data (evo-dev-omics). As a result of this approach, we have carried out two different studies integrating the patterns of genomic diversity with multiomics layers across developmental time and space. In the first study we give a global perspective on how natural selection acts during the whole life cycle of D. melanogaster, assessing whether different regimes of selection act through the developmental stages. In the second study, we draw an exhaustive map of selection acting on the complete embryo anatomy of D. melanogaster. Taking all together, our results show that genes expressed in mid- and late-embryonic development stages exhibit the highest sequence conservation and the most complex structure: they are larger, consist of more exons and longer introns, encode a large number of isoforms and, on average, are highly expressed. Selective constraint is pervasive, particularly on the digestive and nervous systems. On the other hand, earlier stages of embryonic development are the most divergent, which seems to be due to the diminished efficiency of natural selection on maternal-effect genes. Additionally, genes expressed in these first stages have on average the shortest introns, probably due to the need for a rapid and efficient expression during the short cell cycles. Adaptation is found in the structures that also show evidence of adaptation in the adult, the immune and reproductive systems. Finally, genes that are expressed in one or a few different anatomical structures are younger and have higher rates of evolution, unlike genes that are expressed in all or almost all structures. Population genomics is no longer a theoretical science, it has become an interdisciplinary field where bioinformatics, large functional -omics datasets, statistical and evolutionary models and emerging molecular techniques are all integrated to get a systemic view of the causes and consequences of evolution. The integration of population genomics with other phenotypic multiomics data is the necessary step to gain a global picture of how adaptation occurs in nature.

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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.

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Частини книг з теми "Multiomic integration"

Lee, Jae Jin, Philip Sell, and Hyungjin Eoh. "Multiomics Integration of Tuberculosis Pathogenesis." In Integrated Science, 937–67. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-15955-8_45.

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HajYasien, Ahmed. "Introduction to Multiomics Technology." In Machine Learning Methods for Multi-Omics Data Integration, 1–11. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-36502-7_1.

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Liu, Qian, Shujun Huang, Zhongyuan Zhang, Ted M. Lakowski, Wei Xu, and Pingzhao Hu. "Multiomics-Based Tensor Decomposition for Characterizing Breast Cancer Heterogeneity." In Machine Learning Methods for Multi-Omics Data Integration, 133–50. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-36502-7_8.

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Ning, Kang, and Yuxue Li. "Synthetic Biology-Related Multiomics Data Integration and Data Mining Techniques." In Synthetic Biology and iGEM: Techniques, Development and Safety Concerns, 31–38. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-2460-8_3.

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Kamar, Mohd Danish, Madhu Bala, Gaurav Prajapati, and Ratan Singh Ray. "Multiomics Data Integration in Understanding of Inflammation and Inflammatory Diseases." In Inflammation Resolution and Chronic Diseases, 235–43. Singapore: Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-0157-5_11.

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Islam, Mousona. "Strategic Short Note: Integration of Multiomics Approaches for Sustainable Crop Improvement." In IoT and AI in Agriculture, 149–53. Singapore: Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-1263-2_9.

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Zhang, Tianyu, Liwei Zhang, Philip R. O. Payne, and Fuhai Li. "Synergistic Drug Combination Prediction by Integrating Multiomics Data in Deep Learning Models." In Methods in Molecular Biology, 223–38. New York, NY: Springer US, 2020. http://dx.doi.org/10.1007/978-1-0716-0849-4_12.

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Sekar, Aishwarya, and Gunasekaran Krishnasamy. "Integrating Machine Learning Strategies with Multiomics to Augment Prognosis of Chronic Diseases." In Bioinformatics and Computational Biology, 87–97. Boca Raton: Chapman and Hall/CRC, 2023. http://dx.doi.org/10.1201/9781003331247-9.

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Lee, Hayan, Gilbert Feng, Ed Esplin, and Michael Snyder. "Predictive Signatures for Lung Adenocarcinoma Prognostic Trajectory by Multiomics Data Integration and Ensemble Learning." In Mathematical and Computational Oncology, 9–23. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-91241-3_2.

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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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Тези доповідей конференцій з теми "Multiomic integration"

Bhattacharyya, Rupam, Nicholas Henderson, and Veerabhadran Baladandayuthapani. "BaySyn: Bayesian Evidence Synthesis for Multi-system Multiomic Integration." In Pacific Symposium on Biocomputing 2023. WORLD SCIENTIFIC, 2022. http://dx.doi.org/10.1142/9789811270611_0026.

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Singhal, Pankhuri, Shefali S. Verma, Scott M. Dudek, and Marylyn D. Ritchie. "Neural network-based multiomics data integration in Alzheimer's disease." In GECCO '19: Genetic and Evolutionary Computation Conference. New York, NY, USA: ACM, 2019. http://dx.doi.org/10.1145/3319619.3321920.

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Bhat, Aadil Rashid, and Rana Hashmy. "Artificial Intelligence-based Multiomics Integration Model for Cancer Subtyping." In 2022 9th International Conference on Computing for Sustainable Global Development (INDIACom). IEEE, 2022. http://dx.doi.org/10.23919/indiacom54597.2022.9763283.

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Jiang, Yuexu, Yanchun Liang, Duolin Wang, Dong Xu, and Trupti Joshi. "IMPRes: Integrative MultiOmics pathway resolution algorithm and tool." In 2017 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2017. http://dx.doi.org/10.1109/bibm.2017.8218016.

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Wheelock, Åsa M. "Multiomics integration-based molecular characterizations in COPD and post-COVID." In RExPO23. REPO4EU, 2023. http://dx.doi.org/10.58647/rexpo.23033.

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Jagtap, Surabhi, Abdulkadir Celikkanat, Aurelic Piravre, Frederiuue Bidard, Laurent Duval, and Fragkiskos D. Malliaros. "Multiomics Data Integration for Gene Regulatory Network Inference with Exponential Family Embeddings." In 2021 29th European Signal Processing Conference (EUSIPCO). IEEE, 2021. http://dx.doi.org/10.23919/eusipco54536.2021.9616279.

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Koca, Mehmet Burak, and Fatih Erdoğan Sevilgen. "Comparative Analysis of Fusion Techniques for Integrating Single-cell Multiomics Datasets." In 2024 32nd Signal Processing and Communications Applications Conference (SIU). IEEE, 2024. http://dx.doi.org/10.1109/siu61531.2024.10601063.

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Singh, Satishkumar, Fouad Choueiry, Amber Hart, Anuvrat Sircar, Jiangjiang Zhu, and Lalit Sehgal. "Abstract 2351: Multiomics integration elucidates onco-metabolic modulators of drug resistance in lymphoma." 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.am2021-2351.

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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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Alkhateeb, Abedalrhman, Li Zhou, Ashraf Abou Tabl, and Luis Rueda. "Deep Learning Approach for Breast Cancer InClust 5 Prediction based on Multiomics Data Integration." In BCB '20: 11th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics. New York, NY, USA: ACM, 2020. http://dx.doi.org/10.1145/3388440.3415992.

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