Integrative network biology: graph prototyping for co-expression cancer networks

Network-based analysis has been proven useful in biologically-oriented areas, e.g., to explore the dynamics and complexity of biological networks. Investigating a set of networks allows deriving general knowledge about the underlying topological and functional properties. The integrative analysis of networks typically combines networks from different studies that investigate the same or similar research questions. In order to perform an integrative analysis it is often necessary to compare the properties of matching edges across the data set. This identification of common edges is often burdensome and computational intensive. Here, we present an approach that is different from inferring a new network based on common features. Instead, we select one network as a graph prototype, which then represents a set of comparable network objects, as it has the least average distance to all other networks in the same set. We demonstrate the usefulness of the graph prototyping approach on a set of prostate cancer networks and a set of corresponding benign networks. We further show that the distances within the cancer group and the benign group are statistically different depending on the utilized distance measure.     

Unraveling gene function in agricultural species using gene co-expression networks

Co-expression networks have been shown to be a powerful tool for inferring a gene's function when little is known about it. With the advent of next generation sequencing technologies, the construction and analysis of co-expression networks is now possible in non-model species, including those with agricultural importance. Here, we review fundamental concepts in the construction and application of co-expression networks with a focus on agricultural crops. We survey past and current applications of co-expression network analysis in several agricultural species and provide perspective on important considerations that arise when analyzing network relationships. We conclude with a perspective on future directions and potential challenges of utilizing this powerful approach in crops. This article is part of a Special Issue entitled: Plant Gene Regulatory Mechanisms and Networks, edited by Dr. Erich Grotewold and Dr. Nathan Springer.     

Identification of biomarkers of chromophobe renal cell carcinoma by weighted gene co-expression network analysis

Background

Chromophobe renal cell carcinoma (ChRCC) is the second common subtype of non-clear cell renal cell carcinoma (nccRCC), which accounting for 4–5% of renal cell carcinoma (RCC). However, there is no effective bio-marker to predict clinical outcomes of this malignant disease. Bioinformatic methods may provide a feasible potential to solve this problem.

Methods

In this study, differentially expressed genes (DEGs) of ChRCC samples on The Cancer Genome Atlas database were filtered out to construct co-expression modules by weighted gene co-expression network analysis and the key module were identified by calculating module-trait correlations. Functional analysis was performed on the key module and candidate hub genes were screened out by co-expression and MCODE analysis. Afterwards, real hub genes were filter out in an independent dataset GSE15641 and validated by survival analysis.

Results

Overall 2215 DEGs were screened out to construct eight co-expression modules. Brown module was identified as the key module for the highest correlations with pathologic stage, neoplasm status and survival status. 29 candidate hub genes were identified. GO and KEGG analysis demonstrated most candidate genes were enriched in mitotic cell cycle. Three real hub genes (SKA1, ERCC6L, GTSE-1) were selected out after mapping candidate genes to GSE15641 and two of them (SKA1, ERCC6L) were significantly related to overall survivals of ChRCC patients.

Conclusions

In summary, our findings identified molecular markers correlated with progression and prognosis of ChRCC, which might provide new implications for improving risk evaluation, therapeutic intervention, and prognosis prediction in ChRCC patients.

     

Integrative inference of gene-regulatory networks in Escherichia coli using information theoretic concepts and sequence analysis

Background  

Although Escherichia coli is one of the best studied model organisms, a comprehensive understanding of its gene regulation is not yet achieved. There exist many approaches to reconstruct regulatory interaction networks from gene expression experiments. Mutual information based approaches are most useful for large-scale network inference.     

Towards a dynamic analysis of weighted networks in biogeography

An improvement to the Network Analysis Method (NAM) in Biogeography based on weighted inference and dynamic exploration of sympatry networks is proposed. Intricate distributions of species result in a reticulated structure of spatial associations. Species are geographically connected through sympatry links forming an overall natural network in biogeography. Spatial records are the signals that provide evidence to infer these sympatry links in the network. Punctual data are independent of a priori area determination. NAM is oriented to detect groups of species embedded into the global network that are internally sustained by sympatric cohesiveness but weakly connected (or disconnected) to outgroup entities. These groups, called units of co-occurrence (UCs), are segregated through the iterative removal of intermediary species according to their betweenness scores. Instances of analysis of the original NAM are improved through the following changes and extensions: (i) inference of weighted sympatry networks using new measures sensitive to the strength of overlap and topological resemblance between set of points; (ii) construction of a basal network discriminating major from minor sympatry associations; (iii) evaluation of the entire process of iterative removal of intermediary species for the selection of UCs found on different subnetworks; (iv) network partitioning based on the intrinsic cohesiveness of the UCs; (v) production of a graphical tool (cleavogram) depicting the structural changes of the network along the removal process. Improvements are tested using real and hypothetical data sets. Resolution of patterns is notably increased due to a more accurate recognition of allopatric patterns and the possibility of segregating spatially overlapped UCs. As in original NAM, spatial expressions of UCs are building blocks for biogeography supported by strictly endemic and connected species through sympatry paths.     

Constructing circular phylogenetic networks from weighted quartets using simulated annealing

In this paper, we present a heuristic algorithm based on the simulated annealing, SAQ-Net, as a method for constructing phylogenetic networks from weighted quartets. Similar to QNet algorithm, SAQ-Net constructs a collection of circular weighted splits of the taxa set. This collection is represented by a split network. In order to show that SAQ-Net performs better than QNet, we apply these algorithm to both the simulated and actual data sets containing salmonella, Bees, Primates and Rubber data sets. Then we draw phylogenetic networks corresponding to outputs of these algorithms using SplitsTree4 and compare the results. We find that SAQ-Net produces a better circular ordering and phylogenetic networks than QNet in most cases. SAQ-Net has been implemented in Matlab and is available for download at http://bioinf.cs.ipm.ac.ir/softwares/saq.net.     

子宫内膜癌中的lncRNA-miRNA-mRNA ceRNA网络的整合分析

子宫内膜癌(uterine corpus endometrial carcinoma,UCEC)是危害女性健康的癌症之一,但其发生发展机制尚不完全清楚.基因的异常表达在细胞癌变过程中发挥着重要的作用.本研究利用生物信息学方法对UCEC中异常表达的基因进行网络调控分析,为UCEC的机制研究及预后治疗提供理论依据.首先,利...     

The architecture of weighted mutualistic networks

Several ecosystem services directly depend on mutualistic interactions. In species rich communities, these interactions can be studied using network theory. Current knowledge of mutualistic networks is based mainly on binary links; however, little is known about the role played by the weights of the interactions between species. What new information can be extracted by analyzing weighted mutualistic networks? In performing an exhaustive analysis of the topological properties of 29 weighted mutualistic networks, our results show that the generalist species, defined as those with a larger number of interactions in a network, also have the strongest interactions. Though most interactions of generalists are with specialists, the strongest interactions occur between generalists. As a result and by defining binary and weighted clustering coefficients for bipartite networks, we demonstrate that generalists form strongly‐interconnected groups of species. The existence of these strong clusters reinforces the idea that generalist species govern the coevolution of the whole community.     

Integration of miRNA weighted gene co-expression network and miRNA-mRNA co-expression analyses reveals potential regulatory functions of miRNAs in calf rumen development

This study aimed to explore the roles of microRNAs (miRNAs) in calf rumen development during early life. Rumen tissues were collected from 16 calves (8 at pre-weaning and 8 at post-weaning) for miRNA-sequencing, differential expression (DE), miRNA weighted gene co-expression network (WGCNA) and miRNA-mRNA co-expression analyses. 295 miRNAs were identified. Bta-miR-143, miR-26a, miR-145 and miR-27b were the most abundantly expressed. 122 miRNAs were significantly DE between the pre- and post-weaning periods and the most up- and down-regulated miRNAs were bta-miR-29b and bta-miR-493, respectively. Enrichment analyses of the target genes of DE miRNAs revealed important roles for miRNA in rumen developmental processes, immune system development, protein digestion and processes related to the extracellular matrix. WGCNA indicated that bta-miR-145 and bta-miR-199a-3p are important hub miRNAs in the regulation of these processes. Therefore, bta-miR-143, miR-29b, miR-145, miR-493, miR-26a and miR-199 family members might be key regulators of calf rumen development during early life.     

Enhancing biological relevance of a weighted gene co-expression network for functional module identification

Relationships among gene expression levels may be associated with the mechanisms of the disease. While identifying a direct association such as a difference in expression levels between case and control groups links genes to disease mechanisms, uncovering an indirect association in the form of a network structure may help reveal the underlying functional module associated with the disease under scrutiny. This paper presents a method to improve the biological relevance in functional module identification from the gene expression microarray data by enhancing the structure of a weighted gene co-expression network using minimum spanning tree. The enhanced network, which is called a backbone network, contains only the essential structural information to represent the gene co-expression network. The entire backbone network is decoupled into a number of coherent sub-networks, and then the functional modules are reconstructed from these sub-networks to ensure minimum redundancy. The method was tested with a simulated gene expression dataset and case-control expression datasets of autism spectrum disorder and colorectal cancer studies. The results indicate that the proposed method can accurately identify clusters in the simulated dataset, and the functional modules of the backbone network are more biologically relevant than those obtained from the original approach.     

Network aggregation improves gene function prediction of grapevine gene co-expression networks

Plant Molecular Biology - Aggregation across multiple networks highlights robust co-expression interactions and improves the functional connectivity of grapevine gene co-expression networks. In...     

Integrative analysis of gene expression and copy number alterations using canonical correlation analysis

Background  

With the rapid development of new genetic measurement methods, several types of genetic alterations can be quantified in a high-throughput manner. While the initial focus has been on investigating each data set separately, there is an increasing interest in studying the correlation structure between two or more data sets. Multivariate methods based on Canonical Correlation Analysis (CCA) have been proposed for integrating paired genetic data sets. The high dimensionality of microarray data imposes computational difficulties, which have been addressed for instance by studying the covariance structure of the data, or by reducing the number of variables prior to applying the CCA. In this work, we propose a new method for analyzing high-dimensional paired genetic data sets, which mainly emphasizes the correlation structure and still permits efficient application to very large data sets. The method is implemented by translating a regularized CCA to its dual form, where the computational complexity depends mainly on the number of samples instead of the number of variables. The optimal regularization parameters are chosen by cross-validation. We apply the regularized dual CCA, as well as a classical CCA preceded by a dimension-reducing Principal Components Analysis (PCA), to a paired data set of gene expression changes and copy number alterations in leukemia.     

Integrative analysis for finding genes and networks involved in diabetes and other complex diseases

We have developed an integrative analysis method combining genetic interactions, identified using type 1 diabetes genome scan data, and a high-confidence human protein interaction network. Resulting networks were ranked by the significance of the enrichment of proteins from interacting regions. We identified a number of new protein network modules and novel candidate genes/proteins for type 1 diabetes. We propose this type of integrative analysis as a general method for the elucidation of genes and networks involved in diabetes and other complex diseases.