Omics Types Research Articles

Abstract 6029: Multi-omic landscape of squamous cell lung cancer

Abstract Patients with squamous cell lung cancer (SCC) have high unmet medical need. Knowledge of these tumors is limited, and a lack of targetable genomic drivers means patients have few treatment options. To provide a detailed analysis on the influence of genomic alterations to proteome-level changes in SCC, we previously integrated DNA copy number, somatic mutations, RNA-sequencing, and expression proteomics in a cohort of 108 SCC patients. A major finding was identification of three proteomic subtypes, two of which made up the majority (87%) of tumors: the “Inflamed” subtype was enriched for B-cell rich tertiary lymphoid structures (TLS), and the “Redox” subtype was enriched for redox pathways and NFE2L2/KEAP1 alterations but had significantly less immune infiltration. We hypothesized these proteomic subtypes would give rise to distinct metabolic signatures. Therefore, we performed untargeted metabolomics on 87 tumors from the same cohort using chromatographic separation on a HILIC column, followed by analysis on a Q Exactive HF mass spectrometer. This analysis yielded 7,344 features corresponding to 7,072 unannotated metabolites and 272 identified metabolites. Glutathione, a key redox metabolite, was anticorrelated with immune score (R = -0.44, padj = 0.004) calculated from our transcriptomic data with the ESTIMATE algorithm, and glutathione was elevated in the Redox proteomic subtype (0.58 log2 ratio, padj = 9.87E-04). Consensus clustering was next used to identify novel metabolomic subtypes of SCC. Surprisingly, none of the five metabolomic subtypes we identified corresponded to proteomic subtype or NFE2L2/KEAP1 alteration (Fisher’s Exact test p-values > 0.05). The fifth subtype had 332 metabolites (26 identified) differentially expressed (> 1.5 fold-change, padj < 0.05) with ascorbate and aldarate metabolism as the top enriched pathway (padj = 3.36E-04). Interestingly, this fifth metabolomic subtype had significantly higher DNp63-alpha (p = 2.40E-05), a primary transcript of delta-N p63 that is known to promote non-small cell lung cancer. Ongoing integrative analyses across omic types will determine how p53, p63, and p73 transcripts influence these metabolomic subtypes, how these transcripts relate to the poor immune infiltration in some SCC tumors, and if these transcripts relate to novel metabolic vulnerabilities in SCC. Citation Format: Paul Stewart, Ashley Lui, Eric Welsh, Dalia Ercan, Vanessa Rubio, Hayley Ackerman, Guohui Li, Bin Fang, Steven Eschrich, John Koomen, Elsa Flores, Eric Haura, Gina DeNicola. Multi-omic landscape of squamous cell lung cancer [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 6029.

MOPA: An integrative multi-omics pathway analysis method for measuring omics activity.

Pathways are composed of proteins forming a network to represent specific biological mechanisms and are often used to measure enrichment scores based on a list of genes in means to measure their biological activity. The pathway analysis is a de facto standard downstream analysis procedure in most genomic and transcriptomic studies. Here, we present MOPA (Multi-Omics Pathway Analysis), which is a multi-omics integrative method that scores individual pathways in a sample wise manner in terms of enriched multi-omics regulatory activity, which we refer to mES (multi-omics Enrichment Score). The mES score reflects the strength of regulatory relations between multi-omics in units of pathways. In addition, MOPA is able to measure how much each omics contribute to mES that may be used to observe what kind of omics are active in a pathway within a sample group (e.g., subtype, gender), which we refer to OCR (Omics Contribution Rate). Using nine different cancer types, 93 clinical features and three types of omics (i.e., gene expression, miRNA and methylation), MOPA was used to search for clinical features that were explainable in context of multi-omics. By evaluating the performance of MOPA, we showed that it yielded higher or at least equal performance compared to previous single and multi-omics pathway analysis tools. We find that the advantage of MOPA is the ability to explain pathways in terms of omics relation using mES and OCR. As one of the results, the TGF-beta signaling pathway was captured as an important pathway that showed distinct mES and OCR values specific to the CMS4 subtype in colon adenocarcinoma. The mES and OCR metrics suggested that the mRNA and miRNA expressions were significantly different from the other subtypes, which was concordant with previous studies. The MOPA software is available at https://github.com/jaeminjj/MOPA.

Longitudinal multi-omics alterations response to 8-week risperidone monotherapy: Evidence linking cortical thickness, transcriptomics and epigenetics

Antipsychotic treatment-related alterations of cortical thickness (CT) and clinical symptoms have been previously corroborated, but less is known about whether the changes are driven by gene expression and epigenetic modifications. Utilizing a prospective design, we recruited 42 treatment-naive first-episode schizophrenia patients (FESP) and 38 healthy controls. Patients were scanned by TI weighted imaging before and after 8-week risperidone monotherapy. CT estimation was automatically performed with the FreeSurfer software package. Participants' peripheral blood genomic DNA methylation (DNAm) status, quantified by using Infinium® Human Methylation 450K BeadChip, was examined in parallel with T1 scanning. In total, CT measures from 118 subjects and genomic DNAm status from 114 subjects were finally collected. Partial least squares (PLS) regression was used to detect the spatial associations between longitudinal CT variations after treatment and cortical transcriptomic data acquired from the Allen Human Brain Atlas. Canonical correlation analysis (CCA) was then performed to identify multivariate associations between DNAm of PLS1 genes and patients' clinical improvement. We detected the significant PLS1 component (2,098 genes) related to longitudinal alterations of CT, and the PLS1 genes were significantly enriched in neurobiological processes, and dopaminergic- and cancer-related pathways. Combining Laplacian score and CCA analysis, we further linked DNAm of 33 representative genes from the 2,098 PLS1 genes with patients' reduction rate of clinical symptoms. This study firstly revealed that changes of CT and clinical behaviors after treatment may be transcriptionally and epigenetically underlied. We define a "three-step" roadmap which represents a vital step toward the exploration of treatment- and treatment response-related biomarkers on the basis of multiple omics rather than a single omics type as a strategy for advancing precise care.

Virtual histology of morphometric similarity network after risperidone monotherapy and imaging-epigenetic biomarkers for treatment response in first-episode schizophrenia.

Antipsychotic treatment has been conceived to alter brain connectivity, but it is unclear how the changes of network phenotypes relate to the underlying transcriptomics. Given DNA methylation (DNAm) may alter transcriptional levels, we further integrated an imaging-transcriptomic-epigenetic analysis to explore multi-omics treatment response biomarkers. Forty-two treatment-naive first-episode schizophrenia patients were scanned by TI weighted (T1W) imaging and DTI before and after 8-week risperidone monotherapy, and their peripheral blood genomic DNAm values were examined in parallel with MRI scanning. Morphometric similarity network (MSN) quantified with DTI and T1W data were used as a marker of treatment-related alterations in interareal cortical connectivity. We utilized partial least squares (PLS) to examine spatial associations between treatment-related MSN variations and cortical transcriptomic data obtained from the Allen Human Brain Atlas. Longitudinal MSN alterations were related to treatment response on cognitive function and general psychopathology symptoms, while DNAm values of 59 PLS1 genes were on negative and positive symptoms. Virtual-histology transcriptomic analysis linked the MSN alterations with the neurobiological, cellular and metabolic pathways or processes, and assigned MSN-related genes to multiple cell types, specifying neurons and glial cells as contributing most to the transcriptomic associations of longitudinal changes in MSN. We firstly reveal how brain-wide transcriptional levels and cell classes capture molecularly validated cortical connectivity alterations after antipsychotic treatment. Our findings represent a vital step towards the exploration of treatment response biomarkers on the basis of multiple omics rather than a single omics type as a strategy for advancing precise care.

Multi-omics approaches for precision obesity management : Potentials and limitations of omics in precision prevention, treatment and risk reduction of obesity.

Obesity is amultifactorial chronic disease that cannot be addressed by simply promoting better diets and more physical activity. To date, not asingle country has successfully been able to curb the accumulating burden of obesity. One explanation for the lack of progress is that lifestyle intervention programs are traditionally implemented without acomprehensive evaluation of an individual's diagnostic biomarkers. Evidence from genome-wide association studies highlight the importance of genetic and epigenetic factors in the development of obesity and how they in turn affect the transcriptome, metabolites, microbiomes, and proteomes. The purpose of this review is to provide an overview of the different types of omics data: genomics, epigenomics, transcriptomics, proteomics, metabolomics and illustrate how amulti-omics approach can be fundamental for the implementation of precision obesity management. The different types of omics designs are grouped into two categories, the genotype approach and the phenotype approach. When applied to obesity prevention and management, each omics type could potentially help to detect specific biomarkers in people with risk profiles and guide healthcare professionals and decision makers in developing individualized treatment plans according to the needs of the individual before the onset of obesity. Integrating multi-omics approaches will enable aparadigm shift from the one size fits all approach towards precision obesity management, i.e. (1)precision prevention of the onset of obesity, (2)precision medicine and tailored treatment of obesity, and (3)precision risk reduction and prevention of secondary diseases related to obesity.

From Data Science to Bioscience: Emerging era of bioinformatics applications, tools and challenges

With the gradual technological changes in the current era, data science using soft computing and machine learning methods has played a tremendous role in various areas, including the biomedical and health sectors. The etymology of bioinformatics with several types of omics, such as genomics, transcriptomics, proteomics, and metabolomics was coined in the late twentieth century in the direction of the investigation and finding solutions for informatics processing in biological decision-making systems. Similarly, the data science era began and played a vital role in solving a large domain, particularly in biological problem solutions. In this conceptual article, an integrated machine learning-based framework for the prediction of outbreaks is proposed, followed by omics biological data processing and dimension reduction. Subsequently, various prominent benchmark tools have been shown related to problem solutions in data science and bioinformatics applications. In this study, the role of data science and soft computing approaches in bioinformatics disciplines with various associated applications is explored. Various simple soft computing techniques such as fuzzy logic, artificial neural networks, support vector machines and evolutionary computation techniques have been explained. Furthermore, complex optimization and dimension reduction techniques, such as artificial bee colony, ant colony optimization, formal concept analysis, and principal component analysis have been presented. Finally, active research zones in bioinformatics, as well as their current trends and challenges have been highlighted which will lead to future research directions.

Integrated Genetic and Omics Approaches for the Regulation of Nutritional Activities in Rice (Oryza sativa L.)

The primary considerations in rice (Oryza sativa L.) production evoke improvements in the nutritional quality as well as production. Rice cultivars need to be developed to tackle hunger globally with high yield and better nutrition. The traditional cultivation methods of rice to increase the production by use of non-judicious fertilizers to fulfill the nutritional requirement of the masses. This article provokes nutritional strategies by utilization of available omics techniques to increase the nutritional profiling of rice. Recent scientific advancements in genetic resources provide many approaches for better understanding the molecular mechanisms encircled in a specific trait for its up- or down-regulation for opening new horizons for marker-assisted breeding of new rice varieties. In this perspective, genome-wide association studies, genome selection (GS) and QTL mapping are all genetic analysis that help in precise augmentation of specific nutritional enrichment in rice grain. Implementation of several omics techniques are effective approaches to enhance and regulate the nutritional quality of rice cultivars. Advancements in different types of omics including genomics and pangenomics, transcriptomics, metabolomics, nutrigenomics and proteomics are also relevant to rice development initiatives. This review article compiles genes, locus, mutants and for rice yield and yield attribute enhancement. This knowledge will be useful for now and for the future regarding rice studies.

Gene-centric multi-omics integration with convolutional encoders for cancer drug response prediction

Motivation: Tumor heterogeneity, including genetic and transcriptomic characteristics, can reduce the efficacy of anticancer pharmacological therapy, resulting in clinical variability in patient response to therapeutic medications. Multi-omics integration can allow in silico models to provide an additional perspective on a biological system. Methods:In this study, we propose a gene-centric multi-channel (GCMC) architecture to integrate multi-omics for predicting cancer drug response. GCMC transformed multi-omics profiles into a three-dimensional tensor with an additional dimension for omics types. GCMC’s convolutional encoders captures multi-omics profiles for each gene and yields gene-centric features to predict drug responses. Results:We evaluated GCMC on various datasets, including The Cancer Genome Atlas (TCGA) patients, patient-derived xenografts (PDX) mice models, and the Genomics of Drug Sensitivity in Cancer (GDSC) cell line datasets. GCMC achieved better performance than baseline models, including single-omics models, in more than 75% of 265 drugs from GDSC cell line datasets. Furthermore, as for the clinical applicability of GCMC, it achieved the best performance on TCGA and PDX datasets in terms of both AUPR and AUC. We also analyzed models’ capability of integrating multi-omics profiles by measuring the contribution ratio of omics types. GCMC can incorporate multi-omics profiles in various manners to enhance performance for each drug type. These results suggested that GCMC can improve performance and feature extraction capability by integrating multi-omics profiles in a gene-centric manner.

Network-based integration of multi-omics data for clinical outcome prediction in neuroblastoma

Multi-omics data are increasingly being gathered for investigations of complex diseases such as cancer. However, high dimensionality, small sample size, and heterogeneity of different omics types pose huge challenges to integrated analysis. In this paper, we evaluate two network-based approaches for integration of multi-omics data in an application of clinical outcome prediction of neuroblastoma. We derive Patient Similarity Networks (PSN) as the first step for individual omics data by computing distances among patients from omics features. The fusion of different omics can be investigated in two ways: the network-level fusion is achieved using Similarity Network Fusion algorithm for fusing the PSNs derived for individual omics types; and the feature-level fusion is achieved by fusing the network features obtained from individual PSNs. We demonstrate our methods on two high-risk neuroblastoma datasets from SEQC project and TARGET project. We propose Deep Neural Network and Machine Learning methods with Recursive Feature Elimination as the predictor of survival status of neuroblastoma patients. Our results indicate that network-level fusion outperformed feature-level fusion for integration of different omics data whereas feature-level fusion is more suitable incorporating different feature types derived from same omics type. We conclude that the network-based methods are capable of handling heterogeneity and high dimensionality well in the integration of multi-omics.

Uncertainty-aware dynamic integration for multi-omics classification of tumors.

Omics data are crucial for medical diagnosis as it contains intrinsic biomedical information. Multi-omics integrated analysis has become a new direction for scientists to explore life mechanisms. Nevertheless, the quality of complex omics data often varies greatly due to different samples or even different omics types, it is challenging to dynamically capture the uncertainty for different kinds of omics data. This paper proposes a uncertainty-aware dynamic integration framework for multi-omics classification. The framework consists of three modules: deep embedding, confidence prediction, and downstream tasks. The deep embedding module extract key information from multi-omics data to obtain a low-dimensional feature representation which is used to train downstream tasks. Combined with the deep embedding module, the confidence prediction module is used to dynamically capture the uncertainty of the data. We introduce "confidNet" to assign a confidence value for each type of omics data, which is used for dynamic integration between multi-omics. Compared with other integration methods, the proposed method can contain more crucial biomedical information in the obtained low-dimensional representation. Our framework realizes reliable integration among multiple omics, and it can still achieve high accuracy on small sample data sets. We have verified the effectiveness of the model in a large number of experiments. Our framework can be widely applied to high-dimensional omics data and has great potential to facilitate medical decision-making and biological analysis.

Metabolomic predictors of phenotypic traits can replace and complement measured clinical variables in population-scale expression profiling studies

Population-scale expression profiling studies can provide valuable insights into biological and disease-underlying mechanisms. The availability of phenotypic traits is essential for studying clinical effects. Therefore, missing, incomplete, or inaccurate phenotypic information can make analyses challenging and prevent RNA-seq or other omics data to be reused. A possible solution are predictors that infer clinical or behavioral phenotypic traits from molecular data. While such predictors have been developed based on different omics data types and are being applied in various studies, metabolomics-based surrogates are less commonly used than predictors based on DNA methylation profiles.In this study, we inferred 17 traits, including diabetes status and exposure to lipid medication, using previously trained metabolomic predictors. We evaluated whether these metabolomic surrogates can be used as an alternative to reported information for studying the respective phenotypes using expression profiling data of four population cohorts. For the majority of the 17 traits, the metabolomic surrogates performed similarly to the reported phenotypes in terms of effect sizes, number of significant associations, replication rates, and significantly enriched pathways.The application of metabolomics-derived surrogate outcomes opens new possibilities for reuse of multi-omics data sets. In studies where availability of clinical metadata is limited, missing or incomplete information can be complemented by these surrogates, thereby increasing the size of available data sets. Additionally, the availability of such surrogates could be used to correct for potential biological confounding. In the future, it would be interesting to further investigate the use of molecular predictors across different omics types and cohorts.

Widespread redundancy in -omics profiles of cancer mutation states

BackgroundIn studies of cellular function in cancer, researchers are increasingly able to choose from many -omics assays as functional readouts. Choosing the correct readout for a given study can be difficult, and which layer of cellular function is most suitable to capture the relevant signal remains unclear.ResultsWe consider prediction of cancer mutation status (presence or absence) from functional -omics data as a representative problem that presents an opportunity to quantify and compare the ability of different -omics readouts to capture signals of dysregulation in cancer. From the TCGA Pan-Cancer Atlas that contains genetic alteration data, we focus on RNA sequencing, DNA methylation arrays, reverse phase protein arrays (RPPA), microRNA, and somatic mutational signatures as -omics readouts. Across a collection of genes recurrently mutated in cancer, RNA sequencing tends to be the most effective predictor of mutation state. We find that one or more other data types for many of the genes are approximately equally effective predictors. Performance is more variable between mutations than that between data types for the same mutation, and there is little difference between the top data types. We also find that combining data types into a single multi-omics model provides little or no improvement in predictive ability over the best individual data type.ConclusionsBased on our results, for the design of studies focused on the functional outcomes of cancer mutations, there are often multiple -omics types that can serve as effective readouts, although gene expression seems to be a reasonable default option.

Transforming OMIC features for classification using siamese convolutional networks.

Modern biotechnologies have generated huge amount of OMIC data, among which transcriptomes and methylomes are two major OMIC types. Transcriptomes measure the expression levels of all the transcripts while methylomes depict the cytosine methylation levels across a genome. Both OMIC data types could be generated by array or sequencing. And some studies deliver many more features (the number of features is denoted as [Formula: see text]) for a sample than the number [Formula: see text] of samples in a cohort, which induce the "large [Formula: see text] small [Formula: see text]" paradigm. This study focused on the classification problem about OMIC with "large [Formula: see text] small [Formula: see text]" paradigm. A Siamese convolutional network was utilized to transform the OMIC features into a new space with minimized intra-class distances and maximized inter-class distances between the samples. The proposed feature engineering algorithm SiaCo was comprehensively evaluated using both transcriptome and methylome datasets. The experimental data showed that SiaCo generated SiaCo features with improved classification accuracies for binary classification problems, and achieved improvements on the independent test dataset. The individual SiaCo features did not show better inter-class discrimination powers than the original OMIC features. This may be due to that the Siamese convolutional network optimized the collective performances of the SiaCo features, instead of the individual feature's discrimination power. The inherent transformation nature of the Siamese twin network also makes the SiaCo features lack of interpretability. The source code of SiaCo is freely available at http://www.healthinformaticslab.org/supp/resources.php.

Regulation of molecular dysfunction with Multi-Omics: A platform of personal care in diabetes syndrome

The present early screening and analytic tests for diabetes mellitus depend on the changes in the amount of glucose in the blood and autoantibody detection. However, innovative techniques have changed medical research relevance (Diabetes included). For instance, the introduction of genotyping arrays supported large-scale genome-wide relations studies and methods for evaluating international transcripts level, leading to integrated genetics. Additionally, other omics techniques like proteomics and metabolomics are currently being included in biological researchers' daily practices. This short review provides a comprehensive analysis of such omics technologies and concentrates on methods for their incorporation across different omics layers. Unlike a single omics type, multi-omics offers the opportunity to acknowledge the flow of information that triggers the disease. The inclusion of omics as a molecular term means an all-inclusive or international evaluation of various molecules. Most of the multi-omics studies focus on type 2 diabetes. Moreover, medical advancements are also expected to increase the level of anticipation concerning type 2 diabetes. Most data are also being incorporated with treatment responses, thereby offering the needed advancements for precision medicine in type 2 diabetes, which is currently the most prevalent chronic illness experienced in both the developed and developing nations in equal measure.

OmicsNet 2.0: a web-based platform for multi-omics integration and network visual analytics.

Researchers are increasingly seeking to interpret molecular data within a multi-omics context to gain a more comprehensive picture of their study system. OmicsNet (www.omicsnet.ca) is a web-based tool developed to allow users to easily build, visualize, and analyze multi-omics networks to study rich relationships among lists of ‘omics features of interest. Three major improvements have been introduced in OmicsNet 2.0, which include: (i) enhanced network visual analytics with eleven 2D graph layout options and a novel 3D module layout; (ii) support for three new ‘omics types: single nucleotide polymorphism (SNP) list from genetic variation studies; taxon list from microbiome profiling studies, as well as liquid chromatography–mass spectrometry (LC–MS) peaks from untargeted metabolomics; and (iii) measures to improve research reproducibility by coupling R command history with the release of the companion OmicsNetR package, and generation of persistent links to share interactive network views. We performed a case study using the multi-omics data obtained from a recent large-scale investigation on inflammatory bowel disease (IBD) and demonstrated that OmicsNet was able to quickly create meaningful multi-omics context to facilitate hypothesis generation and mechanistic insights.

Perseus plugin “Metis” for metabolic-pathway-centered quantitative multi-omics data analysis for static and time-series experimental designs

SummaryWe introduce Metis, a new plugin for the Perseus software aimed at analyzing quantitative multi-omics data based on metabolic pathways. Data from different omics types are connected through reactions of a genome-scale metabolic-pathway reconstruction. Metabolite concentrations connect through the reactants, while transcript, protein, and protein post-translational modification (PTM) data are associated through the enzymes catalyzing the reactions. Supported experimental designs include static comparative studies and time-series data. As an example for the latter, we combine circadian mouse liver multi-omics data and study the contribution of cycles of phosphoproteome and metabolome to enzyme activity regulation. Our analysis resulted in 52 pairs of cycling phosphosites and metabolites connected through a reaction. The time lags between phosphorylation and metabolite peak show non-uniform behavior, indicating a major contribution of phosphorylation in the modulation of enzymatic activity.

MultiBaC: an R package to remove batch effects in multi-omic experiments.

MotivationBatch effects in omics datasets are usually a source of technical noise that masks the biological signal and hampers data analysis. Batch effect removal has been widely addressed for individual omics technologies. However, multi-omic datasets may combine data obtained in different batches where omics type and batch are often confounded. Moreover, systematic biases may be introduced without notice during data acquisition, which creates a hidden batch effect. Current methods fail to address batch effect correction in these cases.ResultsIn this article, we introduce the MultiBaC R package, a tool for batch effect removal in multi-omics and hidden batch effect scenarios. The package includes a diversity of graphical outputs for model validation and assessment of the batch effect correction.Availability and implementationMultiBaC package is available on Bioconductor (https://www.bioconductor.org/packages/release/bioc/html/MultiBaC.html) and GitHub (https://github.com/ConesaLab/MultiBaC.git). The data underlying this article are available in Gene Expression Omnibus repository (accession numbers GSE11521, GSE1002, GSE56622 and GSE43747).Supplementary information Supplementary data are available at Bioinformatics online.

Simultaneous Polar Metabolite and N-Glycan Extraction Workflow for Joint-Omics Analysis: A Synergistic Approach for Novel Insights into Diseases.

Bioinformatics and machine learning tools have made it possible to integrate data across different -omics platforms for novel multiomic insights into diseases. To synergistically process -omics data in an integrative manner, analyte extractions for each -omics type need to be done on the same set of clinical samples. Therefore, we introduce a simultaneous dual extraction method for generating both metabolomic (polar metabolites only) and glycomic (protein-derived N-glycans only) profiles from one sample with good extraction efficiency and reproducibility. As proof of the usefulness of the extraction and joint-omics workflow, we applied it on platelet samples obtained from a cohort study comprising 66 coronary heart disease (CHD) patients and 34 matched healthy community-dwelling controls. The metabolomics and N-glycomics data sets were subjected to block partial least-squares-discriminant analysis (block-PLS-DA) based on sparse generalized canonical correlation analysis (CCA) for identifying relevant mechanistic interactions between metabolites and glycans. This joint-omics investigation revealed intermodulative roles that protein-bound carbohydrates or glycoproteins and amino acids have in metabolic pathways and through intermediate protein dysregulations. It also suggested a protective role of the glyco-redox network in CHD, demonstrating proof-of-principle for a joint-omics analysis in providing new insights into disease mechanisms, as enabled by a simultaneous polar metabolite and protein-derived N-glycan extraction workflow.

Integrative omics approaches for biosynthetic pathway discovery in plants.

Covering: up to 2022With the emergence of large amounts of omics data, computational approaches for the identification of plant natural product biosynthetic pathways and their genetic regulation have become increasingly important. While genomes provide clues regarding functional associations between genes based on gene clustering, metabolome mining provides a foundational technology to chart natural product structural diversity in plants, and transcriptomics has been successfully used to identify new members of their biosynthetic pathways based on coexpression. Thus far, most approaches utilizing transcriptomics and metabolomics have been targeted towards specific pathways and use one type of omics data at a time. Recent technological advances now provide new opportunities for integration of multiple omics types and untargeted pathway discovery. Here, we review advances in plant biosynthetic pathway discovery using genomics, transcriptomics, and metabolomics, as well as recent efforts towards omics integration. We highlight how transcriptomics and metabolomics provide complementary information to link genes to metabolites, by associating temporal and spatial gene expression levels with metabolite abundance levels across samples, and by matching mass-spectral features to enzyme families. Furthermore, we suggest that elucidation of gene regulatory networks using time-series data may prove useful for efforts to unwire the complexities of biosynthetic pathway components based on regulatory interactions and events.

Omics technologies in personalized combination therapy for cardiovascular diseases: challenges and opportunities.

The primary purpose of 'omics' technologies is to understand the intricacy of genomics, proteomics, metabolomics and other molecular mechanisms to reveal the complex traits of human diseases. The significant use of omics technologies and their applications in medicine gear up the study of the pathogenesis of several disorders. The detection of biomarkers in the early onset of diseases is challenging; still, omics can discover novel molecular mechanisms and biomarkers. In this review, the different types of omics and their technologies are explicated and aimed to provide their emerging applications in cardiovascular precision medicine. These technologies significantly impact optimizing medical treatment for individuals to reach a higher level in precision medicine.