Integrative Analysis Of Multi-omics Data Research Articles

Identification of an Immune Classification for Cervical Cancer and Integrative Analysis of Multiomics Data

Identification of an Immune Classification for Cervical Cancer and Integrative Analysis of Multiomics Data

Integrative Analysis of Multi-Omics Data with One-Class Logistic Regression Machine Learning Identifies Stemness Features in Glioblastoma

Integrative Analysis of Multi-Omics Data with One-Class Logistic Regression Machine Learning Identifies Stemness Features in Glioblastoma

Identification of Regulatory Modules That Stratify Lupus Disease Mechanism through Integrating Multi-Omics Data

Although recent advances in genetic studies have shed light on systemic lupus erythematosus (SLE), its detailed mechanisms remain elusive. In this study, using datasets on SLE transcriptomic profiles, we identified 750 differentially expressed genes (DEGs) in T and B lymphocytes and peripheral blood cells. Using transcription factor (TF) binding data derived from chromatin immunoprecipitation sequencing (ChIP-seq) experiments from the Encyclopedia of DNA Elements (ENCODE) project, we inferred networks of co-regulated genes (NcRGs) based on binding profiles of the upregulated DEGs by significantly enriched TFs. Modularization analysis of NcRGs identified co-regulatory modules among the DEGs and master TFs vital for each module. Remarkably, the co-regulatory modules stratified the common SLE interferon (IFN) signature and revealed SLE pathogenesis pathways, including the complement cascade, cell cycle regulation, NETosis, and epigenetic regulation. By integrative analyses of disease-associated genes (DAGs), DEGs, and enriched TFs, as well as proteins interacting with them, we identified a hierarchical regulatory cascade with TFs regulated by DAGs, which in turn regulates gene expression. Integrative analysis of multi-omics data provided valuable molecular insights into the molecular mechanisms of SLE.

Reconstruction and analysis of a Kluyveromyces marxianus genome-scale metabolic model

BackgroundKluyveromyces marxianus is a thermotolerant yeast with multiple biotechnological potentials for industrial applications, which can metabolize a broad range of carbon sources, including less conventional sugars like lactose, xylose, arabinose and inulin. These phenotypic traits are sustained even up to 45 °C, what makes it a relevant candidate for industrial biotechnology applications, such as ethanol production. It is therefore of much interest to get more insight into the metabolism of this yeast. Recent studies suggested, that thermotolerance is achieved by reducing the number of growth-determining proteins or suppressing oxidative phosphorylation. Here we aimed to find related factors contributing to the thermotolerance of K. marxianus.ResultsHere, we reported the first genome-scale metabolic model of Kluyveromyces marxianus, iSM996, using a publicly available Kluyveromyces lactis model as template. The model was manually curated and refined to include the missing species-specific metabolic capabilities. The iSM996 model includes 1913 reactions, associated with 996 genes and 1531 metabolites. It performed well to predict the carbon source utilization and growth rates under different growth conditions. Moreover, the model was coupled with transcriptomics data and used to perform simulations at various growth temperatures.ConclusionsK. marxianus iSM996 represents a well-annotated metabolic model of thermotolerant yeast, which provides a new insight into theoretical metabolic profiles at different temperatures of K. marxianus. This could accelerate the integrative analysis of multi-omics data, leading to model-driven strain design and improvement.

Knowledge-Guided Biclustering via Sparse Variational EM Algorithm.

A biclustering in the analysis of a gene expression data matrix, for example, is defined as a set of biclusters where each bicluster is a group of genes and a group of samples for which the genes are differentially expressed. Although many data mining approaches for biclustering exist in the literature, only few are able to incorporate prior knowledge to the analysis, which can lead to great improvements in terms of accuracy and interpretability, and all are limited in handling discrete data types. We propose a generalized biclustering approach that can be used for integrative analysis of multi-omics data with different data types. Our method is capable of utilizing biological information that can be represented by graph such as functional genomics and functional proteomics and accommodating a combination of continuous and discrete data types. The proposed method builds on a generalized Bayesian factor analysis framework and a variational EM approach is used to obtain parameter estimates, where the latent quantities in the loglikelihood are iteratively imputed by their conditional expectations. The biclusters are retrieved via the sparse estimates of the factor loadings and the conditional expectation of the latent factors. In order to obtain the sparse conditional expectation of the latent factors, a novel sparse variational EM algorithm is used. We demonstrate the superiority of our method over several existing biclustering methods in extensive simulation experiements and in integrative analysis of multi-omics data.

The Personal Genome Project-UK, an open access resource of human multi-omics data

Integrative analysis of multi-omics data is a powerful approach for gaining functional insights into biological and medical processes. Conducting these multifaceted analyses on human samples is often complicated by the fact that the raw sequencing output is rarely available under open access. The Personal Genome Project UK (PGP-UK) is one of few resources that recruits its participants under open consent and makes the resulting multi-omics data freely and openly available. As part of this resource, we describe the PGP-UK multi-omics reference panel consisting of ten genomic, methylomic and transcriptomic data. Specifically, we outline the data processing, quality control and validation procedures which were implemented to ensure data integrity and exclude sample mix-ups. In addition, we provide a REST API to facilitate the download of the entire PGP-UK dataset. The data are also available from two cloud-based environments, providing platforms for free integrated analysis. In conclusion, the genotype-validated PGP-UK multi-omics human reference panel described here provides a valuable new open access resource for integrated analyses in support of personal and medical genomics.

A Radiogenomic Approach for Decoding Molecular Mechanisms Underlying Tumor Progression in Prostate Cancer.

Prostate cancer (PCa) is a genetically heterogeneous cancer entity that causes challenges in pre-treatment clinical evaluation, such as the correct identification of the tumor stage. Conventional clinical tests based on digital rectal examination, Prostate-Specific Antigen (PSA) levels, and Gleason score still lack accuracy for stage prediction. We hypothesize that unraveling the molecular mechanisms underlying PCa staging via integrative analysis of multi-OMICs data could significantly improve the prediction accuracy for PCa pathological stages. We present a radiogenomic approach comprising clinical, imaging, and two genomic (gene and miRNA expression) datasets for 298 PCa patients. Comprehensive analysis of gene and miRNA expression profiles for two frequent PCa stages (T2c and T3b) unraveled the molecular characteristics for each stage and the corresponding gene regulatory interaction network that may drive tumor upstaging from T2c to T3b. Furthermore, four biomarkers (ANPEP, mir-217, mir-592, mir-6715b) were found to distinguish between the two PCa stages and were highly correlated (average r = ± 0.75) with corresponding aggressiveness-related imaging features in both tumor stages. When combined with related clinical features, these biomarkers markedly improved the prediction accuracy for the pathological stage. Our prediction model exhibits high potential to yield clinically relevant results for characterizing PCa aggressiveness.

Microbial genes and pathways ininflammatory bowel disease.

Perturbations in the intestinal microbiome are implicated in inflammatory bowel disease (IBD). Studies of treatment-naive patients have identified microbial taxa associated with disease course and treatment efficacy. To gain a mechanistic understanding of how the microbiome affects gastrointestinal health, we need to move from census to function. Bacteria, including those that adhere to epithelial cells as well as several Clostridium species, can alter differentiation of T helper 17 cells and regulatory T cells. Similarly, microbial products such as short-chain fatty acids and sphingolipids also influence immune responses. Metagenomics and culturomics have identified strains of Ruminococcus gnavus and adherent invasive Escherichia coli that are linked to IBD and gut inflammation. Integrated analysis of multiomics data, including metagenomics, metatranscriptomics and metabolomics, with measurements of host response and culturomics, have great potential in understanding the role of the microbiome in IBD. In this Review, we highlight current knowledge of gut microbial factors linked to IBD pathogenesis and discuss how multiomics data from large-scale population studies in health and disease have been used to identify specific microbial strains, transcriptional changes and metabolic alterations associated with IBD.

Discovery of the Oncogenic Parp1, a Target of bcr-abl and a Potential Therapeutic, in mir-181a/PPFIA1 Signaling Pathway

miR-181a is downregulated in leukemia and affects its progression, drug resistance, and prognosis. However, the exact mechanism of its targets in leukemia, particularly in chronic myelogenous leukemia (CML), has not previously been established. Here, we use a multi-omics approach to demonstrate that protein tyrosine phosphatase, receptor type, f polypeptide, leukocyte common antigen (LAR) interacting protein (liprin), alpha 1 (PPFIA1) is a direct target for miR-181a in CML. Phospho-array assay shows that multiple phosphorylated proteins, particularly KIT signaling molecules, were downregulated in PPFIA1 inhibition. Additionally, PPFIA1 bound PARP1, a common molecule downstream of both PPFIA1 and BCR/ABL, to upregulate KIT protein through activation of nuclear factor kappa B (NF-κB)-P65 expression. Targeted inhibition of PPFIA1 and PARP1 downregulated c-KIT level, inhibited CML cell growth, and prolonged mouse survival. Overall, we report a critical regulatory miR-181a/PPFIA1/PARP1/NF-κB-P65/KIT axis in CML, and our preclinical study supports that targeted PPFIA1 and PARP1 may serve as a potential CML therapy.

Sliced inverse regression for integrative multi-omics data analysis.

Advancement in next-generation sequencing, transcriptomics, proteomics and other high-throughput technologies has enabled simultaneous measurement of multiple types of genomic data for cancer samples. These data together may reveal new biological insights as compared to analyzing one single genome type data. This study proposes a novel use of supervised dimension reduction method, called sliced inverse regression, to multi-omics data analysis to improve prediction over a single data type analysis. The study further proposes an integrative sliced inverse regression method (integrative SIR) for simultaneous analysis of multiple omics data types of cancer samples, including MiRNA, MRNA and proteomics, to achieve integrative dimension reduction and to further improve prediction performance. Numerical results show that integrative analysis of multi-omics data is beneficial as compared to single data source analysis, and more importantly, that supervised dimension reduction methods possess advantages in integrative data analysis in terms of classification and prediction as compared to unsupervised dimension reduction methods.

Integrative analysis of multi-omics data reveals distinct impacts of DDB1-CUL4 associated factors in human lung adenocarcinomas

Many DDB1-CUL4 associated factors (DCAFs) have been identified and serve as substrate receptors. Although the oncogenic role of CUL4A has been well established, specific DCAFs involved in cancer development remain largely unknown. Here we infer the potential impact of 19 well-defined DCAFs in human lung adenocarcinomas (LuADCs) using integrative omics analyses, and discover that mRNA levels of DTL, DCAF4, 12 and 13 are consistently elevated whereas VBRBP is reduced in LuADCs compared to normal lung tissues. The transcriptional levels of DCAFs are significantly correlated with their gene copy number variations. SKIP2, DTL, DCAF6, 7, 8, 13 and 17 are frequently gained whereas VPRBP, PHIP, DCAF10, 12 and 15 are frequently lost. We find that only transcriptional level of DTL is robustly, significantly and negatively correlated with overall survival across independent datasets. Moreover, DTL-correlated genes are enriched in cell cycle and DNA repair pathways. We also identified that the levels of 25 proteins were significantly associated with DTL overexpression in LuADCs, which include significant decreases in protein level of the tumor supressor genes such as PDCD4, NKX2-1 and PRKAA1. Our results suggest that different CUL4-DCAF axis plays the distinct roles in LuADC development with possible relevance for therapeutic target development.

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.

MONGKIE: an integrated tool for network analysis and visualization for multi-omics data.

BackgroundNetwork-based integrative analysis is a powerful technique for extracting biological insights from multilayered omics data such as somatic mutations, copy number variations, and gene expression data. However, integrated analysis of multi-omics data is quite complicated and can hardly be done in an automated way. Thus, a powerful interactive visual mining tool supporting diverse analysis algorithms for identification of driver genes and regulatory modules is much needed.ResultsHere, we present a software platform that integrates network visualization with omics data analysis tools seamlessly. The visualization unit supports various options for displaying multi-omics data as well as unique network models for describing sophisticated biological networks such as complex biomolecular reactions. In addition, we implemented diverse in-house algorithms for network analysis including network clustering and over-representation analysis. Novel functions include facile definition and optimized visualization of subgroups, comparison of a series of data sets in an identical network by data-to-visual mapping and subsequent overlaying function, and management of custom interaction networks. Utility of MONGKIE for network-based visual data mining of multi-omics data was demonstrated by analysis of the TCGA glioblastoma data. MONGKIE was developed in Java based on the NetBeans plugin architecture, thus being OS-independent with intrinsic support of module extension by third-party developers.ConclusionWe believe that MONGKIE would be a valuable addition to network analysis software by supporting many unique features and visualization options, especially for analysing multi-omics data sets in cancer and other diseases.ReviewersThis article was reviewed by Prof. Limsoon Wong, Prof. Soojin Yi, and Prof. David Kreil.Electronic supplementary materialThe online version of this article (doi:10.1186/s13062-016-0112-y) 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.

Methods for the integration of multi-omics data: mathematical aspects.

BackgroundMethods for the integrative analysis of multi-omics data are required to draw a more complete and accurate picture of the dynamics of molecular systems. The complexity of biological systems, the technological limits, the large number of biological variables and the relatively low number of biological samples make the analysis of multi-omics datasets a non-trivial problem.Results and ConclusionsWe review the most advanced strategies for integrating multi-omics datasets, focusing on mathematical and methodological aspects.

Integrative Analysis of Multi-omics Data for Discovery and Functional Studies of Complex Human Diseases.

Complex and dynamic networks of molecules are involved in human diseases. High-throughput technologies enable omics studies interrogating thousands to millions of makers with similar biochemical properties (eg, transcriptomics for RNA transcripts). However, a single layer of "omics" can only provide limited insights into the biological mechanisms of a disease. In the case of genome-wide association studies, although thousands of single nucleotide polymorphisms have been identified for complex diseases and traits, the functional implications and mechanisms of the associated loci are largely unknown. Additionally, the genomic variants alone are not able to explain the changing disease risk across the life span. DNA, RNA, protein, and metabolite often have complementary roles to jointly perform a certain biological function. Such complementary effects and synergistic interactions between omic layers in the life course can only be captured by integrative study of multiple molecular layers. Building upon the success in single-omics discovery research, population studies started adopting the multi-omics approach to better understanding the molecular function and disease etiology. Multi-omics approaches integrate data obtained from different omic levels to understand their interrelation and combined influence on the disease processes. Here, we summarize major omics approaches available in population research, and review integrative approaches and methodologies interrogating multiple omic layers, which enhance the gene discovery and functional analysis of human diseases. We seek to provide analytical recommendations for different types of multi-omics data and study designs to guide the emerging multi-omic research, and to suggest improvement of the existing analytical methods.

Integrated Transcriptomic-Proteomic Analysis Using a Proteogenomic Workflow Refines Rat Genome Annotation

Proteogenomic re-annotation and mRNA splicing information can lead to the discovery of various protein forms for eukaryotic model organisms like rat. However, detection of novel proteoforms using mass spectrometry proteomics data remains a formidable challenge. We developed EuGenoSuite, an open source multiple algorithmic proteomic search tool and utilized it in our in-house integrated transcriptomic-proteomic pipeline to facilitate automated proteogenomic analysis. Using four proteogenomic pipelines (integrated transcriptomic-proteomic, Peppy, Enosi, and ProteoAnnotator) on publicly available RNA-sequence and MS proteomics data, we discovered 363 novel peptides in rat brain microglia representing novel proteoforms for 249 gene loci in the rat genome. These novel peptides aided in the discovery of novel exons, translation of annotated untranslated regions, pseudogenes, and splice variants for various loci; many of which have known disease associations, including neurological disorders like schizophrenia, amyotrophic lateral sclerosis, etc. Novel isoforms were also discovered for genes implicated in cardiovascular diseases and breast cancer for which rats are considered model organisms. Our integrative multi-omics data analysis not only enables the discovery of new proteoforms but also generates an improved reference for human disease studies in the rat model.

Integrative analysis of multi-omics data for identifying multi-markers for diagnosing pancreatic cancer.

BackgroundmicroRNA (miRNA) expression plays an influential role in cancer classification and malignancy, and miRNAs are feasible as alternative diagnostic markers for pancreatic cancer, a highly aggressive neoplasm with silent early symptoms, high metastatic potential, and resistance to conventional therapies.MethodsIn this study, we evaluated the benefits of multi-omics data analysis by integrating miRNA and mRNA expression data in pancreatic cancer. Using support vector machine (SVM) modelling and leave-one-out cross validation (LOOCV), we evaluated the diagnostic performance of single- or multi-markers based on miRNA and mRNA expression profiles from 104 PDAC tissues and 17 benign pancreatic tissues. For selecting even more reliable and robust markers, we performed validation by independent datasets from the Gene Expression Omnibus (GEO) and the Cancer Genome Atlas (TCGA) data depositories. For validation, miRNA activity was estimated by miRNA-target gene interaction and mRNA expression datasets in pancreatic cancer.ResultsUsing a comprehensive identification approach, we successfully identified 705 multi-markers having powerful diagnostic performance for PDAC. In addition, these marker candidates annotated with cancer pathways using gene ontology analysis.ConclusionsOur prediction models have strong potential for the diagnosis of pancreatic cancer.

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.