Готові списки джерел за темами / Multiomics integration

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Добірка наукової літератури з теми "Multiomics integration"

Автор: Grafiati

Опубліковано: 21 грудня 2024

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Статті в журналах з теми "Multiomics integration"

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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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.

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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.

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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.

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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.

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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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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.

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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.

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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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Більше джерел

Дисертації з теми "Multiomics integration"

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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Iperi, Cristian. "Identification of B lymphocyte alterations in systemic lupus erythematosus and Sjögren syndrome using multiomics integration approach." Electronic Thesis or Diss., Brest, 2024. http://theses-scd.univ-brest.fr/2024/These-2024-SVS-Immunologie-IPERI_Cristian.pdf.

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Le métabolisme joue un rôle crucial dans l'orchestration et la régulation des processus immunologiques dans les cellules immunitaires, y compris les lymphocytes B.Cette branche, appelée immunométabolisme, étudie comment les altérations métaboliques influencent les réponses immunitaires et le développement de pathologies auto-immunes.Ce manuscrit traite spécifiquement du lupus érythémateux systémique (LES) et du syndrome de Sjögren (SjS) via une approche axée sur leurs altérations métaboliques dans les cellules B et leur environnement. L'intérêt pour ces maladies réside dans les mécanismes bien documentés de l'immunotolérance et le rôle du métabolisme dans leur maintien et exacerbation. Grâce aux données multiomiques de transcriptomique,métabolomique, méthylomique, cytométrie et aux données cliniques du consortium européen PRECISESADS, une analyse multi-omique des deux pathologies a été réalisée pour étudier leurs différences et similitudes. Cela a conduit au développement de BiomiX, un outil bioinformatique pour démocratiser ce type d'analyse. Ce travail de thèse a identifié une augmentation du récepteur LPAR6 dans les lymphocytes B, associée à une augmentation des acides lysophosphatidiques (LPA) dans le plasma, tant dans le LES que dans le SjS, ainsi qu'une activation particulière des voies de récupération des nucléotides chez les patients atteints de LES. La déplétion commune des nucléotides et du tryptophane, ainsi que les altérations du métabolisme du NAD, de l'adhésion cellulaire et de la voie WNT, sont également étudiées. Ces résultats ouvrent la voie à des thérapies potentielles pour le LES et le SjS basées sur la restauration du métabolisme cellulaire
Metabolism plays a crucial role in orchestrating and regulating immunological processes in immune cells, including B lymphocytes. This branch, called immunometabolism, studies how metabolic alterations influence immune responses and the development of autoimmune pathologies. This manuscript deals specifically with systemic lupus erythematosus (SLE) and Sjögren's syndrome (SjS) via an approach focusing on their metabolic alterations in B cells and their environment. The interest in these diseases lies in the well-documented mechanisms ofimmunotolerance and the role of metabolism in their maintenance and exacerbation. Using multi-omics data from transcriptomics, metabolomics, methylomics, flow cytometry and clinical data from the European PRECISESADS consortium, a multi-omics analysis of the two diseases was carried out to study their differences and similarities. This led to the development of BiomiX, a bioinformatics tool designed to democratize this type of analysis. This thesis work identified an increase in the LPAR6 receptor in B lymphocytes, associated with an increase in plasma lysophosphatidic acids (LPA) in both SLE and SjS, as well as a particular activation of nucleotide salvage pathways in SLE patients. Common nucleotide and tryptophan depletion, as well as alterations in NAD metabolism, cell adhesion and the WNT pathway, are also investigated. Theseresults pave the way for potential therapies for SLE and SjS based on restoration of cellular metabolism

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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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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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Частини книг з теми "Multiomics 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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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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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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Tarazona, Sonia, Leandro Balzano-Nogueira, and Ana Conesa. "Multiomics Data Integration in Time Series Experiments." In Comprehensive Analytical Chemistry, 505–32. Elsevier, 2018. http://dx.doi.org/10.1016/bs.coac.2018.06.005.

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Тези доповідей конференцій з теми "Multiomics integration"

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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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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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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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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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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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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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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Hong, J., L. Medzikovic, W. Sun, G. Ruffenach, B. Wong, C. J. Rhodes, A. J. Brownstein, et al. "Integrative Multiomics in the Lung Reveals a Protective Role of Asporin in Pulmonary Arterial Hypertension." In American Thoracic Society 2024 International Conference, May 17-22, 2024 - San Diego, CA. American Thoracic Society, 2024. http://dx.doi.org/10.1164/ajrccm-conference.2024.209.1_meetingabstracts.a7249.

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