Single Omics Research Articles

Variable selection in omics data: A practical evaluation of small sample sizes.

In omics experiments, variable selection involves a large number of metabolites/ genes and a small number of samples (the n < p problem). The ultimate goal is often the identification of one, or a few features that are different among conditions- a biomarker. Complicating biomarker identification, the p variables often contain a correlation structure due to the biology of the experiment making identifying causal compounds from correlated compounds difficult. Additionally, there may be elements in the experimental design (blocks, batches) that introduce structure in the data. While this problem has been discussed in the literature and various strategies proposed, the over fitting problems concomitant with such approaches are rarely acknowledged. Instead of viewing a single omics experiment as a definitive test for a biomarker, an unrealistic analytical goal, we propose to view such studies as screening studies where the goal of the study is to reduce the number of features present in the second round of testing, and to limit the Type II error. Using this perspective, the performance of LASSO, ridge regression and Elastic Net was compared with the performance of an ANOVA via a simulation study and two real data comparisons. Interestingly, a dramatic increase in the number of features had no effect on Type I error for the ANOVA approach. ANOVA, even without multiple test correction, has a low false positive rates in the scenarios tested. The Elastic Net has an inflated Type I error (from 10 to 50%) for small numbers of features which increases with sample size. The Type II error rate for the ANOVA is comparable or lower than that for the Elastic Net leading us to conclude that an ANOVA is an effective analytical tool for the initial screening of features in omics experiments.

Combination of the fetal urinary metabolome and peptidome for the prediction of postnatal renal outcome in fetuses with PUV

Most of biomarker panels, extracted from single omics traits, still need improvement since they display a gray zone where prediction is uncertain. Here we verified whether a combination of omics traits, fetal urinary metabolites and peptides analyzed in the same sample, improved prediction of postnatal renal function in fetuses with posterior urethral valves (PUV) compared to individual omics traits.Using CE-MS, we explored the urinary metabolome of 13 PUV fetuses with end stage renal disease (ESRD) and 12 PUV fetuses without postnatal ESRD at 2 years postnatally. This allowed the selection of 24 differentially abundant metabolite features which were modelled into predictive classifiers, alone or in combination with 12 peptides previously identified as predictive of ESRD. Validation in 35 new fetuses showed that the combination of peptides and metabolites significantly outperformed the 24 metabolite features with increased AUC (0.987 vs 0.905), net reclassification improvement (36%) and better sensitivity accuracy (86% vs 60%). In addition, the two trait combination tended to improve, but without reaching statistical significance, the already high performances of the 12 peptide biomarkers (AUC 0.967, accuracy 80%).In conclusion, this study demonstrates the potential of cumulating different omics traits in biomarker research where single omics traits fall short. SignificanceAlthough increasingly proposed in disease-diagnosis and -prognosis because of their improved efficacy over single markers, panels of body fluid biomarkers based on single omics analysis still fail to display perfect accuracy, probably due to biological variability.Here, we hypothesized that combination of different omics traits allowed to better capture this biological variability. As proof of concept, we studied the added value of fetal urine metabolites and peptides using CE-MS, starting from the same urine sample, to predict postnatal renal outcome in fetuses with posterior urethral valves. We observed that the prognostic power of combined metabolite and peptide markers was clearly higher than that of metabolites alone and slightly, but non-significantly, improved compared to the peptides alone. To our knowledge, this report is the first to demonstrate that combining multiomics traits extracted from (fetal) urine samples displays clear promise for kidney disease stratification.

Digitizing omics profiles by divergence from a baseline

Data collected from omics technologies have revealed pervasive heterogeneity and stochasticity of molecular states within and between phenotypes. A prominent example of such heterogeneity occurs between genome-wide mRNA, microRNA, and methylation profiles from one individual tumor to another, even within a cancer subtype. However, current methods in bioinformatics, such as detecting differentially expressed genes or CpG sites, are population-based and therefore do not effectively model intersample diversity. Here we introduce a unified theory to quantify sample-level heterogeneity that is applicable to a single omics profile. Specifically, we simplify an omics profile to a digital representation based on the omics profiles from a set of samples from a reference or baseline population (e.g., normal tissues). The state of any subprofile (e.g., expression vector for a subset of genes) is said to be "divergent" if it lies outside the estimated support of the baseline distribution and is consequently interpreted as "dysregulated" relative to that baseline. We focus on two cases: single features (e.g., individual genes) and distinguished subsets (e.g., regulatory pathways). Notably, since the divergence analysis is at the individual sample level, dysregulation can be analyzed probabilistically; for example, one can estimate the probability that a gene or pathway is divergent in some population. Finally, the reduction in complexity facilitates a more "personalized" and biologically interpretable analysis of variation, as illustrated by experiments involving tissue characterization, disease detection and progression, and disease-pathway associations.

The relationship between gut microbiota and diet and nutrition related diseases

The human gastro-intestinal tract is not only the habitat of gut microbiota, but also the main place that the body gets available nutrients. Therefore, the gut microbiota of human can be inseparable associated with the human nutrition. The common technologies used among gut microbiota research included metageonomic, metatranscriptomics, metaproteomics, and metabolomics. The research of gut microbiota based on above omics methods confirmed that diets were the main factors influencing the composition and expression of gut microbiota. The proportion, quantity, stable state, and metabolic changes of gut microbiota were closely related to obesity, diabetes mellitus, cardiovascular disease, and other nutritional-related diseases. Reasonable dietary intervention can adjust the disorders of gut microbiota, which can achieve prevention and treatment of obesity, diabetes, cardiovascular diseases, and other nutritional-related diseases. Although the single omics methods can be used to study the problems of some aspects of gut microbiota, the combination of multi-omics is needed to achieve the above objectives.

Single Cell Genetics and Epigenetics in Early Embryo: From Oocyte to Blastocyst.

Single cell technology has enormously changed the landscape of biomedical science, including single cell omics, gene editing, single cell imaging, single cell (embryo) manipulate, or non-invasive micro-test. Single cell technology also leads the research area of early embryo from basic research to reproductive medical application. We got the knowledge of programming/reprogramming and the epigenetics dynamics in the cell lineage differentiation. In the reproductive medicine, the genomic sequencing of embryo or polar body and the preimplantation genetic diagnosis rely on the single cell techniques. Those discoveries will improve the assisted reproductive technologies, human health, and livestock husbandry. In the future, the comprehensive atlas of cell state and lineage information can be generated for cellular systems by single-cell multi-omics approaches.

ChemDIS 2: an update of chemical-disease inference system.

Computational inference of affected functions, pathways and diseases for chemicals could largely accelerate the evaluation of potential effects of chemical exposure on human beings. Previously, we have developed a ChemDIS system utilizing information of interacting targets for chemical-disease inference. With the target information, testable hypotheses can be generated for experimental validation. In this work, we present an update of ChemDIS 2 system featured with more updated datasets and several new functions, including (i) custom enrichment analysis function for single omics data; (ii) multi-omics analysis function for joint analysis of multi-omics data; (iii) mixture analysis function for the identification of interaction and overall effects; (iv) web application programming interface (API) for programmed access to ChemDIS 2. The updated ChemDIS 2 system capable of analyzing more than 430 000 chemicals is expected to be useful for both drug development and risk assessment of environmental chemicals.Database URL: ChemDIS 2 is freely accessible via https://cwtung.kmu.edu.tw/chemdis

Network approaches to systems biology analysis of complex disease: integrative methods for multi-omics data.

In the past decade, significant progress has been made in complex disease research across multiple omics layers from genome, transcriptome and proteome to metabolome. There is an increasing awareness of the importance of biological interconnections, and much success has been achieved using systems biology approaches. However, because of the typical focus on one single omics layer at a time, existing systems biology findings explain only a modest portion of complex disease. Recent advances in multi-omics data collection and sharing present us new opportunities for studying complex diseases in a more comprehensive fashion, and yet simultaneously create new challenges considering the unprecedented data dimensionality and diversity. Here, our goal is to review extant and emerging network approaches that can be applied across multiple biological layers to facilitate a more comprehensive and integrative multilayered omics analysis of complex diseases.

Single cell proteomics in biomedicine: High-dimensional data acquisition, visualization, and analysis.

New insights on cellular heterogeneity in the last decade provoke the development of a variety of single cell omics tools at a lightning pace. The resultant high-dimensional single cell data generated by these tools require new theoretical approaches and analytical algorithms for effective visualization and interpretation. In this review, we briefly survey the state-of-the-art single cell proteomic tools with a particular focus on data acquisition and quantification, followed by an elaboration of a number of statistical and computational approaches developed to date for dissecting the high-dimensional single cell data. The underlying assumptions, unique features, and limitations of the analytical methods with the designated biological questions they seek to answer will be discussed. Particular attention will be given to those information theoretical approaches that are anchored in a set of first principles of physics and can yield detailed (and often surprising) predictions.

Integrative FourD omics approach profiles the target network of the carbon storage regulatory system.

Multi-target regulators represent a largely untapped area for metabolic engineering and anti-bacterial development. These regulators are complex to characterize because they often act at multiple levels, affecting proteins, transcripts and metabolites. Therefore, single omics experiments cannot profile their underlying targets and mechanisms. In this work, we used an Integrative FourD omics approach (INFO) that consists of collecting and analyzing systems data throughout multiple time points, using multiple genetic backgrounds, and multiple omics approaches (transcriptomics, proteomics and high throughput sequencing crosslinking immunoprecipitation) to evaluate simultaneous changes in gene expression after imposing an environmental stress that accentuates the regulatory features of a network. Using this approach, we profiled the targets and potential regulatory mechanisms of a global regulatory system, the well-studied carbon storage regulatory (Csr) system of Escherichia coli, which is widespread among bacteria. Using 126 sets of proteomics and transcriptomics data, we identified 136 potential direct CsrA targets, including 50 novel ones, categorized their behaviors into distinct regulatory patterns, and performed in vivo fluorescence-based follow up experiments. The results of this work validate 17 novel mRNAs as authentic direct CsrA targets and demonstrate a generalizable strategy to integrate multiple lines of omics data to identify a core pool of regulator targets.

Multi-Omics Data Integration and Mapping of Altered Kinases to Pathways Reveal Gonadotropin Hormone Signaling in Glioblastoma.

Glioblastoma multiforme (GBM) is one of the most lethal brain tumors with an inadequately understood pathophysiology. Biomarkers that guide accurate diagnosis and treatment decisions would greatly support precision medicine for GBM. Previous studies of GBM have focused on signaling pathways such as epidermal growth factor receptor (EGFR), platelet-derived growth factor receptors (PDGFRs), notch, wnt, and others, identified with single omics technology platforms (genomics, transcriptomics, or proteomics), but not with their integrated use. In this context, we report here a multi-omics pathway view, expanded through integration of the expression data at transcriptomic and proteomic levels, followed by selection of a functionally related group of proteins such as kinases deregulated in GBM. By using this strategy, we observed a highly significant enrichment of the gonadotropin-releasing hormone (GnRH) signaling pathway that was not deciphered with single omics datasets. The curation of the GnRH pathway with extensive literature analysis brought about a comprehensive annotation of the pathway, which included several additional pathway members that were not previously annotated. A targeted search resulted in identification of additional nonkinase members of the pathway in the GBM multi-omics datasets. We found evidence of GnRH receptor expression in GBM and other cancers. We offer here an updated generic pathway map of GnRH signaling, show its enrichment in the context of GBM, and discuss its plausible cross-connectivity with EGFR, wnt, calcium, and focal adhesion kinase signaling pathways that were earlier shown to be the top deregulated pathways in GBM. In conclusion, this study demonstrates the promise of multi-omics research and analyses to better understand complex cancers and suggests continued efforts and research in this direction in the field of integrative biology.

Systems biology and genome-wide approaches to unveil the molecular players involved in the pre-germinative metabolism: implications on seed technology traits.

The pre-germinative metabolism is among the most fascinating aspects of seed biology. The early seed germination phase, or pre-germination, is characterized by rapid water uptake (imbibition), which directs a series of dynamic biochemical events. Among those are enzyme activation, DNA damage and repair, and use of reserve storage compounds, such as lipids, carbohydrates and proteins. Industrial seedling production and intensive agricultural production systems require seed stocks with high rate of synchronized germination and low dormancy. Consequently, seed dormancy, a quantitative trait related to the activation of the pre-germinative metabolism, is probably the most studied seed trait in model species and crops. Single omics, systems biology, QTLs and GWAS mapping approaches have unveiled a list of molecules and regulatory mechanisms acting at transcriptional, post-transcriptional and post-translational levels. Most of the identified candidate genes encode for regulatory proteins targeting ROS, phytohormone and primary metabolisms, corroborating the data obtained from simple molecular biology approaches. Emerging evidences show that epigenetic regulation plays a crucial role in the regulation of these mentioned processes, constituting a still unexploited strategy to modulate seed traits. The present review will provide an up-date of the current knowledge on seed pre-germinative metabolism, gathering the most relevant results from physiological, genetics, and omics studies conducted in model and crop plants. The effects exerted by the biotic and abiotic stresses and priming are also addressed. The possible implications derived from the modulation of pre-germinative metabolism will be discussed from the point of view of seed quality and technology.

Paired proteomics, transcriptomics and miRNomics in non-small cell lung cancers: known and novel signaling cascades.

High-throughput omics analyses are applied to elucidate molecular pathogenic mechanisms in cancer. Given restricted cohort sizes and contrasting large feature sets paired multi-omics analysis supports discovery of true positive deregulated signaling cascades. For lung cancer patients we measured from the same tissue biopsies proteomic- (6,183 proteins), transcriptomic- (34,687 genes) and miRNomic data (2,549 miRNAs). To minimize inter-individual variations case and control lung biopsies have been gathered from the same individuals.Considering single omics entities, 15 of 2,549 miRNAs (0.6%), 752 of 34,687 genes (2.2%) and 141 of 6,183 proteins (2.3%) were significantly deregulated. Multivariate analysis also revealed that effects in miRNA were smaller compared to genes and proteins indicating that expression changes of miRNAs might also have limited impact of pathogenicity. However, a new algorithm for modeling the complex mutual interactions of miRNAs and their target genes facilitated precise prediction of deregulation in cancer genes (92.3% accuracy, p=0.007). Lastly, deregulation of genes in cancer matched deregulation of proteins coded by the genes in 80% of cases.The resulting interaction network, which is based on quantitative analysis of the abundance of miRNAs, mRNAs and proteins each taken from the same lung cancer tissue and from the same autologous normal lung tissue confirms molecular pathological changes and further contributes to the discovery of altered signaling cascades in lung cancer.

Progress in omics research of Aspergillus niger

Aspergillus niger, as an important industrial fermentation strain, is widely applied in the production of organic acids and industrial enzymes. With the development of diverse omics technologies, the data of genome, transcriptome, proteome and metabolome of A. niger are increasing continuously, which declared the coming era of big data for the research in fermentation process of A. niger. The data analysis from single omics and the comparison of multi-omics, to the integrations of multi-omics based on the genome-scale metabolic network model largely extends the intensive and systematic understanding of the efficient production mechanism of A. niger. It also provides possibilities for the reasonable global optimization of strain performance by genetic modification and process regulation. We reviewed and summarized progress in omics research of A. niger, and proposed the development direction of omics research on this cell factory.

Increased Proportion of Variance Explained and Prediction Accuracy of Survival of Breast Cancer Patients with Use of Whole-Genome Multiomic Profiles.

Whole-genome multiomic profiles hold valuable information for the analysis and prediction of disease risk and progression. However, integrating high-dimensional multilayer omic data into risk-assessment models is statistically and computationally challenging. We describe a statistical framework, the Bayesian generalized additive model ((BGAM), and present software for integrating multilayer high-dimensional inputs into risk-assessment models. We used BGAM and data from The Cancer Genome Atlas for the analysis and prediction of survival after diagnosis of breast cancer. We developed a sequence of studies to (1) compare predictions based on single omics with those based on clinical covariates commonly used for the assessment of breast cancer patients (COV), (2) evaluate the benefits of combining COV and omics, (3) compare models based on (a) COV and gene expression profiles from oncogenes with (b) COV and whole-genome gene expression (WGGE) profiles, and (4) evaluate the impacts of combining multiple omics and their interactions. We report that (1) WGGE profiles and whole-genome methylation (METH) profiles offer more predictive power than any of the COV commonly used in clinical practice (e.g., subtype and stage), (2) adding WGGE or METH profiles to COV increases prediction accuracy, (3) the predictive power of WGGE profiles is considerably higher than that based on expression from large-effect oncogenes, and (4) the gain in prediction accuracy when combining multiple omics is consistent. Our results show the feasibility of omic integration and highlight the importance of WGGE and METH profiles in breast cancer, achieving gains of up to 7 points area under the curve (AUC) over the COV in some cases.

Editorial: Systems Biological Aspects of Pituitary Tumors.

The Editorial on the Research Topic Systems Biological Aspects of Pituitary Tumors Pituitary adenomas are a category of neoplasms with a high degree of heterogeneity that occur in the central regulatory organ pituitary, which plays important roles in the hypothalamus–pituitary-targeted organ axis systems that impact on important physiological functions of human body (1–3). Rapidly developed omics and systems biology (4–6) impact on treatment of pituitary adenomas and gradually change the paradigms from the traditional single-factor strategy to a multi-parameter systematic strategy. A pituitary adenoma is a complex, chronic, and whole-body disease that alters the genome, transcriptome, proteome, and metabolome and involves multiple factors, processes, and consequences (7–9). Pituitary adenomas gradually change in the model of predictive screening, diagnosis, and prognostic assessment of pituitary adenomas that previously only depended on changes of serum single-hormone change and pituitary imaging, and in the therapeutic model of cancer from general radiotherapy and chemotherapy to a personalized strategy (8, 9). This present issue focuses on systems biological aspects of pituitary adenomas, which contains four topics. (i) The first topic addressed vitamin D status and calcium–phosphate homeostasis in acromegaly patients (Halupczok-Żyla et al.). Vitamin D deficiency and alteration in calcium–phosphate balance are associated with a wide spectrum of diseases, such as cancer, diabetes, cardiovascular disease, and respiratory disease. Data demonstrate that acromegaly patients are at the higher risk of vitamin D deficiency and have a tendency to a lower level of calcium and higher level of inorganic phosphate. Data suggest the importance of inorganic calcium–phosphate homeostasis in the pathogenesis of acromegaly patients (growth hormone pituitary adenomas) from the systemic view point. (ii) The second topic addressed the alteration in the chemokine network in pituitary adenomas from a systemic view (Grizzi et al.). Chemokines are a category of inflammatory mediators that exert their roles through typical and atypical chemokine receptor signaling pathways. An alteration of chemokines and receptors is associated with cancer and inflammatory diseases. The chemokine network is proposed as the target of biomarker and new therapeutic approach for pituitary adenomas. (iii) The third topic addressed the proteomic variations in pituitary adenomas (Zhan and Wang). Proteomics is the key component of functional genomics and systems biology. Much progress has been achieved in pituitary adenoma proteomics to expand and enrich the systems biology analysis of pituitary adenomas. This topic emphasized the significance of variations in proteome and protein molecular networks for personalized and precise studies of pituitary adenomas. (iv) The fourth topic focused on molecular network variations in different subtypes of non-functional pituitary adenomas (Zhan and Long). That topic emphasized the integrative molecular networks derived from multiple omics data at the genome, transcriptome, proteome, peptidome, and metabolome levels, and on their variations in different subtypes of non-functional pituitary adenomas. These studies will benefit discovery of effective and reliable biomarkers and therapeutic targets for personalized and precise studies of highly heterogeneous non-functional pituitary adenomas. It is clear that the systems biological aspects of pituitary adenomas cover a very wide range from systemic concepts to analytic methods; from genome, transcriptome, proteome, and metabolome to interactome; from single omics to integrative omics; from panel analysis to molecular network analysis; from genetic feature to phenotype; and from common features to individual characteristics, to investigate the diversity of pituitary adenomas that include functional and non-functional pituitary adenomas and their subtypes (10–15). It must be clearly mentioned that this issue contains only a limited fraction of the very important systems biological aspects of pituitary adenomas. This research topic serves as a modest spur to induce researchers who study systems biology strategies to come forward with its valuable contributions to research and clinical practice of pituitary adenomas. From the point of view of systematic strategies in pituitary adenomas, it is necessary for future studies to systematically investigate variations in the genome, transcriptome, proteome, peptidome, and metabolome in pituitary adenoma tissue and body-fluids (cerebrospinal fluid, CSF; serum/plasma), and especially for different subtypes of pituitary adenomas (7, 9). Systems biology approaches will be used to integrate all experimental data and all clinical information of an individual and to propose corresponding molecular networks specific to a pituitary adenoma in order to achieve efficient prediction screening, early stage diagnosis, prognostic assessment, and individualized and precise prevention and therapy (16, 17). Future issues will collect different levels of omics studies, especially the integrative omics studies together with genetics and clinical information with developed advanced computational biology approaches.

An integrative imputation method based on multi-omics datasets.

BackgroundIntegrative analysis of multi-omics data is becoming increasingly important to unravel functional mechanisms of complex diseases. However, the currently available multi-omics datasets inevitably suffer from missing values due to technical limitations and various constrains in experiments. These missing values severely hinder integrative analysis of multi-omics data. Current imputation methods mainly focus on using single omics data while ignoring biological interconnections and information imbedded in multi-omics data sets.ResultsIn this study, a novel multi-omics imputation method was proposed to integrate multiple correlated omics datasets for improving the imputation accuracy. Our method was designed to: 1) combine the estimates of missing value from individual omics data itself as well as from other omics, and 2) simultaneously impute multiple missing omics datasets by an iterative algorithm. We compared our method with five imputation methods using single omics data at different noise levels, sample sizes and data missing rates. The results demonstrated the advantage and efficiency of our method, consistently in terms of the imputation error and the recovery of mRNA-miRNA network structure.ConclusionsWe concluded that our proposed imputation method can utilize more biological information to minimize the imputation error and thus can improve the performance of downstream analysis such as genetic regulatory network construction.Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-016-1122-6) contains supplementary material, which is available to authorized users.

Dimension reduction techniques for the integrative analysis of multi-omics data.

State-of-the-art next-generation sequencing, transcriptomics, proteomics and other high-throughput ‘omics' technologies enable the efficient generation of large experimental data sets. These data may yield unprecedented knowledge about molecular pathways in cells and their role in disease. Dimension reduction approaches have been widely used in exploratory analysis of single omics data sets. This review will focus on dimension reduction approaches for simultaneous exploratory analyses of multiple data sets. These methods extract the linear relationships that best explain the correlated structure across data sets, the variability both within and between variables (or observations) and may highlight data issues such as batch effects or outliers. We explore dimension reduction techniques as one of the emerging approaches for data integration, and how these can be applied to increase our understanding of biological systems in normal physiological function and disease.

Abstract 3754: Predicting cancer phenotypes with mechanism-driven multi-omics data integration

Abstract Over the past decade technological advances have enabled molecular profiling of human cancers across distinct genomic domains and other “omes”. The availability of such multi-omics datasets has in turn enabled the discovery of cancer subtypes characterized by distinct molecular patterns within and across different data modalities. Despite promising beginnings and the wealth of data, most efforts so far have focused on the discovery of new molecular taxonomies, enumerating novel cancer subtypes, and only subsequently projecting them into a biological context by leveraging knowledge on genetic and epigenetic variations, genomic alterations, gene expression patterns, and, in general, cell pathophysiology. A paradigmatic approach to omics-based cancer classification usually entails the i) discovery of novel molecular subtypes; (ii) the biological contextualization of such subtypes and their correlation with clinical phenotypes; and (iii) the development of predictors to detect these subtypes. Nevertheless, the direct clinical utility of such taxonomies is less evident. Some of the molecular subtypes, for instance, might not portend any different clinical behavior, or the underlying molecular pathways might not be actionable. Ultimately, existing biological knowledge enters the analysis only a posteriori to characterize and “label” the novel subtypes, rather than being leveraged a priori to guide the discovery process itself. To overcome such nearly universal absence of mechanistic underpinnings for the omics-derived signatures and develop clinically useful biomarkers, we have proposed to develop mechanistic predictive models by incorporating gene network and signaling pathway information directly into the statistical learning process used to detect the cancer phenotypes. Unlike the paradigm described above, we used omics data and prior biological information to directly detect and predict the phenotypes. We now further extend this concept and leverage biological knowledge also to constrain multi-omics data integration, by implementing predictive rules that mechanistically aggregate measurements across distinct genomic modalities, reproducing the natural flow of biological information in the cell: from genome to phenotype, through epigenome, transcriptome and proteome. To illustrate our approach and its impact on computational learning and cancer classification, we analyze clinically relevant cancer phenotypes using independent training and testing data. To this end we build our novel predictors using the Top Scoring Pair (TSP) algorithm, a two-gene parameter-free classifier, and its multi-pair extension kTSP. We then compare the classification performance of predictors derived from a single omics modality to those constructed by integrating multi-omics data according to mechanistic and biologically meaningful rules, revealing increased accuracy with the integrated classifiers. Citation Format: Luigi Marchionni, Donald Geman. Predicting cancer phenotypes with mechanism-driven multi-omics data integration. [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr 3754. doi:10.1158/1538-7445.AM2015-3754

Identification of disease targets for precision medicine by integrative analysis of multi-omics data

With the development and improvement of high-throughput sequencing technologies, the acquisition and processing approaches of various biological omics data on different levels are becoming more mature. Despite several new disease-associated factors have been discovered based on single omics data analysis, identification of disease targets by integrative analysis of multi-omics data is still growing. Since life is a complex regulatory system in which the regulation of gene mutations, epigenetic alterations, abnormal gene expression as well as anomalous variations in signal pathway are related with the occurrence and development of diseases, it is obvious that finding therapeutic factors using single omics data analysis has its limitation. Systematical studies of clinical and pathological mechanisms and identification of optimal therapeutic targets through integrative analysis of multi-omics data from different levels and resources have become an important research direction of precision medicine, which would provide innovative perspectives on disease study and new theoretical basis for early diagnosis, personalized treatment and medicine guide. In this review, we introduce new technologies and research progresses in screening therapeutic targets using systematic omics such as genomics, transcriptomics and epigenomics, and also discusse new strategies and advantages of integrative analysis among them.

MicroRNA–mRNA interaction analysis to detect potential dysregulation in complex diseases

It is now recognized that genetic interactions (epistasis) are important sources of the hidden genetic variations and may play an important role in complex diseases. Identifying genetic interactions not only helps to explain part of the heritability of complex diseases, but also provides the clue to understand the underlying pathogenesis of complex diseases. Advances in high-throughput technologies enable simultaneous measurements of multiple genomic features from the same samples on a genome-wide scale, and different omics features are not acting in isolation but interact/crosstalk at multiple (within and across individual omics features) levels in complex networks. Therefore, genetic interaction needs to be accounted for across different omics features, potentially allowing an explanation of phenotype variation that single omics data cannot capture. In this study, we propose an analysis framework to detect the miRNA–mRNA interaction enrichment by incorporating principal components analysis and canonical correlation analysis. We demonstrate the advantages of our method by applying to miRNA and mRNA data on glioblastoma (GBM) generated by The Cancer Genome Atlas project. The results show that there are enrichments of the interactions between co-expressed miRNAs and gene pathways which are associated with GBM status. The biological functions of those identified genes and miRNAs have been confirmed to be associated with glioblastoma by independent studies. The proposed approach provides new insights in the regulatory mechanisms and an example for detecting interactions of multi-omics data on complex diseases.