Academic literature on the topic 'Integrative multiomics'

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.

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.

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.

Abstract Post-transcriptional processing of RNAs plays important roles in a variety of physiological and pathological processes. These processes can be precisely controlled by a series of RNA binding proteins and cotranscriptionally regulated by transcription factors as well as histone modifications. With the rapid development of high-throughput sequencing techniques, multiomics data have been broadly used to study the mechanisms underlying the important biological processes. However, how to use these high-throughput sequencing data to elucidate the fundamental regulatory roles of post-transcriptional processes is still of great challenge. This review summarizes the regulatory mechanisms of post-transcriptional processes and the general principles and approaches to dissect these mechanisms by integrating multiomics data as well as public resources.

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.

Immunotherapy has been widely used in the treatment of lung cancer, and one of the most effective biomarkers for the prognosis of immunotherapy currently is tumor mutation burden (TMB). Although whole-exome sequencing (WES) could be utilized to assess TMB, several problems prevent its routine clinical application. To develop a simplified TMB prediction model, patients with lung adenocarcinoma (LUAD) in The Cancer Genome Atlas (TCGA) were randomly split into training and validation cohorts and categorized into the TMB-high (TMB-H) and TMB-low (TMB-L) groups, respectively. Based on the 610 differentially expressed genes, 50 differentially expressed miRNAs and 58 differentially methylated CpG sites between TMB-H and TMB-L patients, we constructed 4 predictive signatures and established TMB prediction model through machine learning methods that integrating the expression or methylation profiles of 7 genes, 7 miRNAs, and 6 CpG sites. The multiomics model exhibited excellent performance in predicting TMB with the area under curve (AUC) of 0.911 in the training cohort and 0.859 in the validation cohort. Besides, the significant correlation between the multiomics model score and TMB was observed. In summary, we developed a prognostic TMB prediction model by integrating multiomics data in patients with LUAD, which might facilitate the further development of quantitative real time-polymerase chain reaction- (qRT-PCR-) based TMB prediction assay.

Gene-environment (G×E) interaction is critical for understanding the genetic basis of complex disease beyond genetic and environment main effects. In addition to existing tools for interaction studies, penalized variable selection emerges as a promising alternative for dissecting G×E interactions. Despite the success, variable selection is limited in terms of accounting for multidimensional measurements. Published variable selection methods cannot accommodate structured sparsity in the framework of integrating multiomics data for disease outcomes. In this paper, we have developed a novel variable selection method in order to integrate multi-omics measurements in G×E interaction studies. Extensive studies have already revealed that analyzing omics data across multi-platforms is not only sensible biologically, but also resulting in improved identification and prediction performance. Our integrative model can efficiently pinpoint important regulators of gene expressions through sparse dimensionality reduction, and link the disease outcomes to multiple effects in the integrative G×E studies through accommodating a sparse bi-level structure. The simulation studies show the integrative model leads to better identification of G×E interactions and regulators than alternative methods. In two G×E lung cancer studies with high dimensional multi-omics data, the integrative model leads to an improved prediction and findings with important biological implications.

Rationale: GWAS (Genome-Wide Association Studies) have identified hundreds of genetic loci associated with atrial fibrillation (AF). However, these loci explain only a small proportion of AF heritability. Objective: To develop an approach to identify additional AF-related genes by integrating multiple omics data. Methods and Results: Three types of omics data were integrated: (1) summary statistics from the AFGen 2017 GWAS; (2) a whole blood EWAS (Epigenome-Wide Association Study) of AF; and (3) a whole blood TWAS (Transcriptome-Wide Association Study) of AF. The variant-level GWAS results were collapsed into gene-level associations using fast set-based association analysis. The CpG-level EWAS results were also collapsed into gene-level associations by an adapted SNP-set Kernel Association Test approach. Both GWAS and EWAS gene-based associations were then meta-analyzed with TWAS using a fixed-effects model weighted by the sample size of each data set. A tissue-specific network was subsequently constructed using the NetWAS (Network-Wide Association Study). The identified genes were then compared with the AFGen 2018 GWAS that contained more than triple the number of AF cases compared with AFGen 2017 GWAS. We observed that the multiomics approach identified many more relevant AF-related genes than using AFGen 2018 GWAS alone (1931 versus 206 genes). Many of these genes are involved in the development and regulation of heart- and muscle-related biological processes. Moreover, the gene set identified by multiomics approach explained much more AF variance than those identified by GWAS alone (10.4% versus 3.5%). Conclusions: We developed a strategy to integrate multiple omics data to identify AF-related genes. Our integrative approach may be useful to improve the power of traditional GWAS, which might be particularly useful for rare traits and diseases with limited sample size.

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.

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.

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.