Different Types Of Omics Data Research Articles

Network pharmacology based investigation of the effects of herbal ingredients on the immune dysfunction in heart disease

Dysregulated immune system has been implicated in the pathogenesis of various cardiovascular diseases. Therefore, development of pharmacological interventions targeting the immune system is promising. However, therapy with most common anti-inflammatory and immunomodulatory agents has proved challenging in the clinical translation. It has been proved that many herbal ingredients display definite therapeutic effects on preventing excessive inflammatory and immune responses. Here, we aim to systemically explore the immunomodulatory ability of herbal ingredients on the human heart tissue-specific immune dysfunction through a network pharmacology based approach. The approach matches gene expression data between herbal ingredients and human heart phenotype based on their immunological similarities. Firstly, 608 immunological signatures were produced from 304 transcriptional profiles of immunological cell state changes. Then, the immunological features of 28 human heart phenotypes and 102 herbal ingredients were constructed by calculating the enrichments of each immune signature in the transcriptional profiles of heart phenotypes and herbal ingredients, respectively. Finally, the likelihood that an herbal drug affects the immune system in a heart phenotype was qualified by calculating the immunological similarity between the herbal drug and the heart phenotype. This strategy integrating different types of OMICs data is expected to help create new opportunities for development of drugs targeting the immune dysfunction in heart disease.

IntegRATE: a desirability-based data integration framework for the prioritization of candidate genes across heterogeneous omics and its application to preterm birth

BackgroundThe integration of high-quality, genome-wide analyses offers a robust approach to elucidating genetic factors involved in complex human diseases. Even though several methods exist to integrate heterogeneous omics data, most biologists still manually select candidate genes by examining the intersection of lists of candidates stemming from analyses of different types of omics data that have been generated by imposing hard (strict) thresholds on quantitative variables, such as P-values and fold changes, increasing the chance of missing potentially important candidates.MethodsTo better facilitate the unbiased integration of heterogeneous omics data collected from diverse platforms and samples, we propose a desirability function framework for identifying candidate genes with strong evidence across data types as targets for follow-up functional analysis. Our approach is targeted towards disease systems with sparse, heterogeneous omics data, so we tested it on one such pathology: spontaneous preterm birth (sPTB).ResultsWe developed the software integRATE, which uses desirability functions to rank genes both within and across studies, identifying well-supported candidate genes according to the cumulative weight of biological evidence rather than based on imposition of hard thresholds of key variables. Integrating 10 sPTB omics studies identified both genes in pathways previously suspected to be involved in sPTB as well as novel genes never before linked to this syndrome. integRATE is available as an R package on GitHub (https://github.com/haleyeidem/integRATE).ConclusionsDesirability-based data integration is a solution most applicable in biological research areas where omics data is especially heterogeneous and sparse, allowing for the prioritization of candidate genes that can be used to inform more targeted downstream functional analyses.

Incorporating methylation genome information improves prediction accuracy for drug treatment responses

BackgroundAn accumulation of evidence has revealed the important role of epigenetic factors in explaining the etiopathogenesis of human diseases. Several empirical studies have successfully incorporated methylation data into models for disease prediction. However, it is still a challenge to integrate different types of omics data into prediction models, and the contribution of methylation information to prediction remains to be fully clarified.ResultsA stratified drug-response prediction model was built based on an artificial neural network to predict the change in the circulating triglyceride level after fenofibrate intervention. Associated single-nucleotide polymorphisms (SNPs), methylation of selected cytosine-phosphate-guanine (CpG) sites, age, sex, and smoking status, were included as predictors. The model with selected SNPs achieved a mean 5-fold cross-validation prediction error rate of 43.65%. After adding methylation information into the model, the error rate dropped to 41.92%. The combination of significant SNPs, CpG sites, age, sex, and smoking status, achieved the lowest prediction error rate of 41.54%.ConclusionsCompared to using SNP data only, adding methylation data in prediction models slightly improved the error rate; further prediction error reduction is achieved by a combination of genome, methylation genome, and environmental factors.

Hierarchical structural component modeling of microRNA-mRNA integration analysis

BackgroundIdentification of multi-markers is one of the most challenging issues in personalized medicine era. Nowadays, many different types of omics data are generated from the same subject. Although many methods endeavor to identify candidate markers, for each type of omics data, few or none can facilitate such identification.ResultsIt is well known that microRNAs affect phenotypes only indirectly, through regulating mRNA expression and/or protein translation. Toward addressing this issue, we suggest a hierarchical structured component analysis of microRNA-mRNA integration (“HisCoM-mimi”) model that accounts for this biological relationship, to efficiently study and identify such integrated markers. In simulation studies, HisCoM-mimi showed the better performance than the other three methods. Also, in real data analysis, HisCoM-mimi successfully identified more gives more informative miRNA-mRNA integration sets relationships for pancreatic ductal adenocarcinoma (PDAC) diagnosis, compared to the other methods.ConclusionAs exemplified by an application to pancreatic cancer data, our proposed model effectively identified integrated miRNA/target mRNA pairs as markers for early diagnosis, providing a much broader biological interpretation.

Some Remarks on Prediction of Drug-Target Interaction with Network Models.

System-level understanding of the relationships between drugs and targets is very important for enhancing drug research, especially for drug function repositioning. The experimental methods used to determine drug-target interactions are usually time-consuming, tedious and expensive, and sometimes lack reproducibility. Thus, it is highly desired to develop computational methods for efficiently and effectively analyzing and detecting new drug-target interaction pairs. With the explosive growth of different types of omics data, such as genome, pharmacology, phenotypic, and other kinds of molecular networks, numerous computational approaches have been developed to predict Drug-Target Interactions (DTI). In this review, we make a survey on the recent advances in predicting drug-target interaction with network-based models from the following aspects: i) Available public data sources and benchmark datasets; ii) Drug/target similarity metrics; iii) Network construction; iv) Common network algorithms; v) Performance comparison of existing network-based DTI predictors.

Multi-omics facilitated variable selection in Cox-regression model for cancer prognosis prediction.

New developments in high-throughput genomic technologies have enabled the measurement of diverse types of omics biomarkers in a cost-efficient and clinically-feasible manner. Developing computational methods and tools for analysis and translation of such genomic data into clinically-relevant information is an ongoing and active area of investigation. For example, several studies have utilized an unsupervised learning framework to cluster patients by integrating omics data. Despite such recent advances, predicting cancer prognosis using integrated omics biomarkers remains a challenge. There is also a shortage of computational tools for predicting cancer prognosis by using supervised learning methods. The current standard approach is to fit a Cox regression model by concatenating the different types of omics data in a linear manner, while penalty could be added for feature selection. A more powerful approach, however, would be to incorporate data by considering relationships among omics datatypes. Here we developed two methods: a SKI-Cox method and a wLASSO-Cox method to incorporate the association among different types of omics data. Both methods fit the Cox proportional hazards model and predict a risk score based on mRNA expression profiles. SKI-Cox borrows the information generated by these additional types of omics data to guide variable selection, while wLASSO-Cox incorporates this information as a penalty factor during model fitting. We show that SKI-Cox and wLASSO-Cox models select more true variables than a LASSO-Cox model in simulation studies. We assess the performance of SKI-Cox and wLASSO-Cox using TCGA glioblastoma multiforme and lung adenocarcinoma data. In each case, mRNA expression, methylation, and copy number variation data are integrated to predict the overall survival time of cancer patients. Our methods achieve better performance in predicting patients' survival in glioblastoma and lung adenocarcinoma.

ROTS: An R package for reproducibility-optimized statistical testing.

Differential expression analysis is one of the most common types of analyses performed on various biological data (e.g. RNA-seq or mass spectrometry proteomics). It is the process that detects features, such as genes or proteins, showing statistically significant differences between the sample groups under comparison. A major challenge in the analysis is the choice of an appropriate test statistic, as different statistics have been shown to perform well in different datasets. To this end, the reproducibility-optimized test statistic (ROTS) adjusts a modified t-statistic according to the inherent properties of the data and provides a ranking of the features based on their statistical evidence for differential expression between two groups. ROTS has already been successfully applied in a range of different studies from transcriptomics to proteomics, showing competitive performance against other state-of-the-art methods. To promote its widespread use, we introduce here a Bioconductor R package for performing ROTS analysis conveniently on different types of omics data. To illustrate the benefits of ROTS in various applications, we present three case studies, involving proteomics and RNA-seq data from public repositories, including both bulk and single cell data. The package is freely available from Bioconductor (https://www.bioconductor.org/packages/ROTS).

Mini-Workshop: Recent Developments in Statistical Methods with Applications to Genetics and Genomics

Recent progress in high-throughput genomic technologies has revolutionized the field of human genetics and promises to lead to important scientific advances. With new improvements in massively parallel biotechnologies, it is becoming increasingly more efficient to generate vast amounts of information at the genomics, transcriptomics, proteomics, metabolomics etc. levels, opening up as yet unexplored opportunities in the search for the genetic causes of complex traits. Despite this tremendous progress in data generation, it remains very challenging to analyze, integrate and interpret these data. The resulting data are high-dimensional and very sparse, and efficient statistical methods are critical in order to extract the rich information contained in these data. The major focus of the mini-workshop, entitled “Recent Developments in Statistical Methods with Applications to Genetics and Genomics”, has been on integrative methods. Relevant research questions included the optimal study design for integrative genomic analyses; appropriate handling and pre-processing of different types of omics data; statistical methods for integration of multiple types of omics data; adjustment for confounding due to latent factors such as cell or tissue heterogeneity; the optimal use of omics data to enhance or make sense of results identified through genetic studies; and statistical and computational strategies for analysis of multiple types of high-dimensional data.

Object-oriented regression for building predictive models with high dimensional omics data from translational studies

Maturing omics technologies enable researchers to generate high dimension omics data (HDOD) routinely in translational clinical studies. In the field of oncology, The Cancer Genome Atlas (TCGA) provided funding support to researchers to generate different types of omics data on a common set of biospecimens with accompanying clinical data and has made the data available for the research community to mine. One important application, and the focus of this manuscript, is to build predictive models for prognostic outcomes based on HDOD. To complement prevailing regression-based approaches, we propose to use an object-oriented regression (OOR) methodology to identify exemplars specified by HDOD patterns and to assess their associations with prognostic outcome. Through computing patient’s similarities to these exemplars, the OOR-based predictive model produces a risk estimate using a patient’s HDOD. The primary advantages of OOR are twofold: reducing the penalty of high dimensionality and retaining the interpretability to clinical practitioners. To illustrate its utility, we apply OOR to gene expression data from non-small cell lung cancer patients in TCGA and build a predictive model for prognostic survivorship among stage I patients, i.e., we stratify these patients by their prognostic survival risks beyond histological classifications. Identification of these high-risk patients helps oncologists to develop effective treatment protocols and post-treatment disease management plans. Using the TCGA data, the total sample is divided into training and validation data sets. After building up a predictive model in the training set, we compute risk scores from the predictive model, and validate associations of risk scores with prognostic outcome in the validation data (P-value=0.015).

Integrative Exploratory Analysis of Two or More Genomic Datasets.

Exploratory analysis is an essential step in the analysis of high throughput data. Multivariate approaches such as correspondence analysis (CA), principal component analysis, and multidimensional scaling are widely used in the exploratory analysis of single dataset. Modern biological studies often assay multiple types of biological molecules (e.g., mRNA, protein, phosphoproteins) on a same set of biological samples, thereby creating multiple different types of omics data or multiassay data. Integrative exploratory analysis of these multiple omics data is required to leverage the potential of multiple omics studies. In this chapter, we describe the application of co-inertia analysis (CIA; for analyzing two datasets) and multiple co-inertia analysis (MCIA; for three or more datasets) to address this problem. These methods are powerful yet simple multivariate approaches that represent samples using a lower number of variables, allowing a more easily identification of the correlated structure in and between multiple high dimensional datasets. Graphical representations can be employed to this purpose. In addition, the methods simultaneously project samples and variables (genes, proteins) onto the same lower dimensional space, so the most variant variables from each dataset can be selected and associated with samples, which can be further used to facilitate biological interpretation and pathway analysis. We applied CIA to explore the concordance between mRNA and protein expression in a panel of 60 tumor cell lines from the National Cancer Institute. In the same 60 cell lines, we used MCIA to perform a cross-platform comparison of mRNA gene expression profiles obtained on four different microarray platforms. Last, as an example of integrative analysis of multiassay or multi-omics data we analyzed transcriptomic, proteomic, and phosphoproteomic data from pluripotent (iPS) and embryonic stem (ES) cell lines.

Group and sparse group partial least square approaches applied in genomics context.

The association between two blocks of 'omics' data brings challenging issues in computational biology due to their size and complexity. Here, we focus on a class of multivariate statistical methods called partial least square (PLS). Sparse version of PLS (sPLS) operates integration of two datasets while simultaneously selecting the contributing variables. However, these methods do not take into account the important structural or group effects due to the relationship between markers among biological pathways. Hence, considering the predefined groups of markers (e.g. genesets), this could improve the relevance and the efficacy of the PLS approach. We propose two PLS extensions called group PLS (gPLS) and sparse gPLS (sgPLS). Our algorithm enables to study the relationship between two different types of omics data (e.g. SNP and gene expression) or between an omics dataset and multivariate phenotypes (e.g. cytokine secretion). We demonstrate the good performance of gPLS and sgPLS compared with the sPLS in the context of grouped data. Then, these methods are compared through an HIV therapeutic vaccine trial. Our approaches provide parsimonious models to reveal the relationship between gene abundance and the immunological response to the vaccine. The approach is implemented in a comprehensive R package called sgPLS available on the CRAN. b.liquet@uq.edu.au Supplementary data are available at Bioinformatics online.

The promise of multi-omics and clinical data integration to identify and target personalized healthcare approaches in autism spectrum disorders.

Complex diseases are caused by a combination of genetic and environmental factors, creating a difficult challenge for diagnosis and defining subtypes. This review article describes how distinct disease subtypes can be identified through integration and analysis of clinical and multi-omics data. A broad shift toward molecular subtyping of disease using genetic and omics data has yielded successful results in cancer and other complex diseases. To determine molecular subtypes, patients are first classified by applying clustering methods to different types of omics data, then these results are integrated with clinical data to characterize distinct disease subtypes. An example of this molecular-data-first approach is in research on Autism Spectrum Disorder (ASD), a spectrum of social communication disorders marked by tremendous etiological and phenotypic heterogeneity. In the case of ASD, omics data such as exome sequences and gene and protein expression data are combined with clinical data such as psychometric testing and imaging to enable subtype identification. Novel ASD subtypes have been proposed, such as CHD8, using this molecular subtyping approach. Broader use of molecular subtyping in complex disease research is impeded by data heterogeneity, diversity of standards, and ineffective analysis tools. The future of molecular subtyping for ASD and other complex diseases calls for an integrated resource to identify disease mechanisms, classify new patients, and inform effective treatment options. This in turn will empower and accelerate precision medicine and personalized healthcare.

Integrated visualization of a multi-omics study of starvation in mouse intestine.

Our understanding of complex biological processes can be enhanced by combining different kinds of high-throughput experimental data, but the use of incompatible identifiers makes data integration a challenge. We aimed to improve methods for integrating and visualizing different types of omics data. To validate these methods, we applied them to two previous studies on starvation in mice, one using proteomics and the other using transcriptomics technology. We extended the PathVisio software with new plugins to link proteins, transcripts and pathways. A low overall correlation between proteome and transcriptome data was detected (Spearman rank correlation: 0.21). At the level of individual genes, correlation was highly variable. Many mRNA/protein pairs, such as fructose biphosphate aldolase B and ATP Synthase, show good correlation. For other pairs, such as ferritin and elongation factor 2, an interesting effect is observed, where mRNA and protein levels change in opposite directions, suggesting they are not primarily regulated at the transcriptional level. We used pathway diagrams to visualize the integrated datasets and found it encouraging that transcriptomics and proteomics data supported each other at the pathway level. Visualization of the integrated dataset on pathways led to new observations on gene-regulation in the response of the gut to starvation. Our methods are generic and can be applied to any multi-omics study. The PathVisio software can be obtained at http://www.pathvisio.org. Supplemental data are available at http://www.bigcat.unimaas.nl/data/jib-supplemental/ , including instructions on reproducing the pathway visualizations of this manuscript.

Integrated visualization of a multi-omics study of starvation in mouse intestine

Summary Our understanding of complex biological processes can be enhanced by combining different kinds of high-throughput experimental data, but the use of incompatible identifiers makes data integration a challenge. We aimed to improve methods for integrating and visualizing different types of omics data. To validate these methods, we applied them to two previous studies on starvation in mice, one using proteomics and the other using transcriptomics technology. We extended the PathVisio software with new plugins to link proteins, transcripts and pathways. A low overall correlation between proteome and transcriptome data was detected (Spearman rank correlation: 0.21). At the level of individual genes, correlation was highly variable. Many mRNA/protein pairs, such as fructose biphosphate aldolase B and ATP Synthase, show good correlation. For other pairs, such as ferritin and elongation factor 2, an interesting effect is observed, where mRNA and protein levels change in opposite directions, suggesting they are not primarily regulated at the transcriptional level. We used pathway diagrams to visualize the integrated datasets and found it encouraging that transcriptomics and proteomics data supported each other at the pathway level. Visualization of the integrated dataset on pathways led to new observations on gene-regulation in the response of the gut to starvation. Our methods are generic and can be applied to any multi-omics study. The PathVisio software can be obtained at http://www.pathvisio.org. Supplemental data are available at http://www.bigcat.unimaas.nl/data/jib-supplemental/, including instructions on reproducing the pathway visualizations of this manuscript.

Identifying novel glioma associated pathways based on systems biology level meta-analysis

BackgroundWith recent advances in microarray technology, including genomics, proteomics, and metabolomics, it brings a great challenge for integrating this "-omics" data to analysis complex disease. Glioma is an extremely aggressive and lethal form of brain tumor, and thus the study of the molecule mechanism underlying glioma remains very important. To date, most studies focus on detecting the differentially expressed genes in glioma. However, the meta-analysis for pathway analysis based on multiple microarray datasets has not been systematically pursued.ResultsIn this study, we therefore developed a systems biology based approach by integrating three types of omics data to identify common pathways in glioma. Firstly, the meta-analysis has been performed to study the overlapping of signatures at different levels based on the microarray gene expression data of glioma. Among these gene expression datasets, 12 pathways were found in GeneGO database that shared by four stages. Then, microRNA expression profiles and ChIP-seq data were integrated for the further pathway enrichment analysis. As a result, we suggest 5 of these pathways could be served as putative pathways in glioma. Among them, the pathway of TGF-beta-dependent induction of EMT via SMAD is of particular importance.ConclusionsOur results demonstrate that the meta-analysis based on systems biology level provide a more useful approach to study the molecule mechanism of complex disease. The integration of different types of omics data, including gene expression microarrays, microRNA and ChIP-seq data, suggest some common pathways correlated with glioma. These findings will offer useful potential candidates for targeted therapeutic intervention of glioma.

Quantitative knowledge-based analysis in compound safety assessment

Introduction: Despite rapid progress in OMICs and computational technologies in compound safety assessment, drug failure rate due to toxicity is still unacceptably high. One reason for this is an inadequate interpretation of high-throughput preclinical data. Another reason is the poor mechanistic understanding of drug side effects as currently just a few compound targets are linked to specific adverse reactions.Areas covered: Current performance issues with statistical analysis of OMICs data or gene/protein/compound lists are discussed, illustrating potential advantages of knowledge-based approaches in prediction of human toxicity. The authors show several examples of quantitative functional analysis, including cross-tissue toxicity predictions and integrated analysis of different types of OMICs data. They also describe novel approaches linking compound targets and associated pathways to side effects. The reader will gain an update on the recent developments in knowledge-based analysis in toxicogenomics and computational methods correlating protein targets with adverse reactions.Expert opinion: Quantitative pathway analysis is a useful approach for deriving multi-variant predictive biomarkers for drug safety. However, more comprehensive studies are needed for direct comparison of performance between pathway- and gene-centric methods.

Plant systems biology. Annual Plant Reviews, Volume 35

Many of us have been mired in arguments over the last few years about what systems biology really is, and what it is not. So first, let us specify what choice the editors have made when assembling this new book: they emphasize approaches taking advantage of ‘omics’ data sets to decipher the properties of their favourite systems. Most chapters have been written by and for wet-lab experimentalists, rather than mathematicians or computer scientists. Thus, the book does not describe quantitative modelling that studies the behaviour of a few connected components with high-resolution parameters, nor does it deal with synthetic biology. The book is structured in two parts. The first presents an overview of systems biology and contains four chapters. The two first introductory sections appropriately cover the basic notions of omics, network biology and data integration; however they are partly redundant and could have been advantageously merged. The third chapter aims at illustrating how specific projects in prokaryotic research have paved the way towards building system-level understanding. Unfortunately, this part rather misses its target: instead of highlighting showcase achievements that could instruct plant researchers, this long chapter – 70 pages out of a total of 350 – provides too many lengthy historical accounts about the incremental dissection of biological processes specific to bacteria, which are often only remotely linked to systems approaches, with the exception of data-driven models of regulatory networks. The fourth chapter is far more instructive and summarizes the collaborative work of several groups over the past few years to define functional modules involved in the early embryogenesis of the worm Caenorhabditis elegans. These elegant studies combine large-scale reverse genetics, yielding digital phenotypic signatures with gene expression and protein interaction data, and result in the unsupervised identification of functional modules at play during the initial embryonic divisions. This example is particularly relevant, because C. elegans and the plant model Arabidopsis thaliana are multicellular organisms of similar complexity, but the worm system-level research is arguably slightly ahead and should thus inspire plant scientists. The second part of the book is better balanced and focuses on plant research: although topics are always put into a broader perspective (e.g. in terms of software tools and resources listed), the examples pertain to plants in general, and often to Arabidopsis in particular. Briefly, the topics covered are the visualization and mining tools for the interpretation and integration of omics data; the characterization of transcription factors and cis-regulatory elements that together form transcriptional networks; the profiling of microRNA populations matched with their potential targets; the various technologies for proteome profiling; the metabolome, its relationship with the proteome, metabolome profiling techniques and downstream applications, the fingerprinting and modelling of networks including metabolite profiles; and the systematic analysis of the elemental composition, dubbed ionomics, applied to plants samples. The last two chapters each have a different outlook. The first describes how the root constitutes a fine tractable biological system to investigate and model the factors that drive the co-ordinated development of an organ. It reviews how the interaction between the different cell types can be analysed through transcriptional networks; how the kinematic analysis of root growth can be streamlined because the position, size and division of root cells can be relatively easily imaged; and how the modelling of the auxin transporter network can predict the phenotypes of corresponding mutants. The final chapter explores the interplay between components across the very different scales defining biological systems: molecules, regulatory mechanisms, organs (physiology), entire organisms (phenotype) and ecological niches (environment). It highlights the importance of variation and adaptation, which are rarely taken into consideration in experimental settings, and advocates the adoption of systems approaches to study ecosystems. To conclude, the book delivers an accurate and up-to-date review of the main progress made by plant researchers to capture, interpret and integrate different types of omics data, and to model plant biological processes taking into consideration multiple components of the system under scrutiny. However, the topics that the book does not address indicate indirectly what has yet to be tackled. As stated by one of the authors, plant biologists are still setting the stage for systems biology, but the transition between functional genomics and whole-system modelling will soon be over, and plant scientists must be prepared to reap the benefits of their efforts in that area. This volume of the Annual Plant Reviews series dedicated to Plant systems biology is a valuable contribution to this grand goal.