Gene Network Reconstruction Research Articles

Reconstruction of gene networks using prior knowledge.

BackgroundReconstructing gene regulatory networks (GRNs) from expression data is a challenging task that has become essential to the understanding of complex regulatory mechanisms in cells. The major issues are the usually very high ratio of number of genes to sample size, and the noise in the available data. Integrating biological prior knowledge to the learning process is a natural and promising way to partially compensate for the lack of reliable expression data and to increase the accuracy of network reconstruction algorithms.ResultsIn this manuscript, we present PriorPC, a new algorithm based on the PC algorithm. PC algorithm is one of the most popular methods for Bayesian network reconstruction. The result of PC is known to depend on the order in which conditional independence tests are processed, especially for large networks. PriorPC uses prior knowledge to exclude unlikely edges from network estimation and introduces a particular ordering for the conditional independence tests. We show on synthetic data that the structural accuracy of networks obtained with PriorPC is greatly improved compared to PC.ConclusionPriorPC improves structural accuracy of inferred gene networks by using soft priors which assign to edges a probability of existence. It is robust to false prior which is not avoidable in the context of biological data. PriorPC is also fast and scales well for large networks which is important for its applicability to real data.Electronic supplementary materialThe online version of this article (doi:10.1186/s12918-015-0233-4) contains supplementary material, which is available to authorized users.

Combining Expression Data and Knowledge Ontology for Gene Clustering and Network Reconstruction

Many methods have been developed for reverse engineering gene networks from time series expression data. However, when the number of genes and the complexity of regulation increase, it becomes increasingly difficult to infer gene networks. To tackle this scalability problem, this study presents an approach with two phases: gene clustering and network reconstruction. To perform gene clustering, a hybrid method of data and knowledge-based clustering was developed to calculate both data and semantic similarity between genes. In the network reconstruction procedure, a Boolean network model that was inferred from the gene clusters was used to represent the network. A series of experiments were conducted to investigate the effect of the hybrid similarity measure in gene clustering and network reconstruction. The results prove the feasibility and effectiveness of the proposed approach.

Gene Network Reconstruction by Integration of Prior Biological Knowledge.

With the development of high-throughput genomic technologies, large, genome-wide datasets have been collected, and the integration of these datasets should provide large-scale, multidimensional, and insightful views of biological systems. We developed a method for gene association network construction based on gene expression data that integrate a variety of biological resources. Assuming gene expression data are from a multivariate Gaussian distribution, a graphical lasso (glasso) algorithm is able to estimate the sparse inverse covariance matrix by a lasso (L1) penalty. The inverse covariance matrix can be seen as direct correlation between gene pairs in the gene association network. In our work, instead of using a single penalty, different penalty values were applied for gene pairs based on a priori knowledge as to whether the two genes should be connected. The a priori information can be calculated or retrieved from other biological data, e.g., Gene Ontology similarity, protein-protein interaction, gene regulatory network. By incorporating prior knowledge, the weighted graphical lasso (wglasso) outperforms the original glasso both on simulations and on data from Arabidopsis. Simulation studies show that even when some prior knowledge is not correct, the overall quality of the wglasso network was still greater than when not incorporating that information, e.g., glasso.

Promoters of plant genes responsive to pathogen invasion

Gene networks controlling the plant resistance to different phytopathogens are rather complex; hundreds of genes can be involved in them. Infection causes considerable changes at molecular-genetic, biochemical, physiological, and morphological levels both locally (in the place of invasion) and systemically. The reconstruction of gene networks that are responsible for plant defense against pathogenic bacteria, fungi, and viruses is required for the elucidation of the underlying molecular mechanisms as well as for the development of new approaches to crop improvement. The transcriptional activity of genes involved in the defense mechanisms usually increases in response to the infection; therefore, the characteristics of their promoters is an important source of information for the detection of transcription factors that control their activity and for the search for new genes involved in the pathogen response. The data on promoters are required for the creation of plants that are resistant to phytopathogens by gene engineering techniques. The data on promoters of pathogen-sensitive genes with an experimentally verified expression pattern annotated in the TransGene Promoters (TGP) database are presented in the article. The TGP database can be used as a source of information for interpretation of transcriptome data and when planning genetically engineered experiments directed towards increasing plant resistance to pathogens of different origins.

Gene network inference using continuous time Bayesian networks: a comparative study and application to Th17 cell differentiation.

BackgroundDynamic aspects of gene regulatory networks are typically investigated by measuring system variables at multiple time points. Current state-of-the-art computational approaches for reconstructing gene networks directly build on such data, making a strong assumption that the system evolves in a synchronous fashion at fixed points in time. However, nowadays omics data are being generated with increasing time course granularity. Thus, modellers now have the possibility to represent the system as evolving in continuous time and to improve the models’ expressiveness.ResultsContinuous time Bayesian networks are proposed as a new approach for gene network reconstruction from time course expression data. Their performance was compared to two state-of-the-art methods: dynamic Bayesian networks and Granger causality analysis. On simulated data, the methods comparison was carried out for networks of increasing size, for measurements taken at different time granularity densities and for measurements unevenly spaced over time. Continuous time Bayesian networks outperformed the other methods in terms of the accuracy of regulatory interactions learnt from data for all network sizes. Furthermore, their performance degraded smoothly as the size of the network increased. Continuous time Bayesian networks were significantly better than dynamic Bayesian networks for all time granularities tested and better than Granger causality for dense time series. Both continuous time Bayesian networks and Granger causality performed robustly for unevenly spaced time series, with no significant loss of performance compared to the evenly spaced case, while the same did not hold true for dynamic Bayesian networks. The comparison included the IRMA experimental datasets which confirmed the effectiveness of the proposed method. Continuous time Bayesian networks were then applied to elucidate the regulatory mechanisms controlling murine T helper 17 (Th17) cell differentiation and were found to be effective in discovering well-known regulatory mechanisms, as well as new plausible biological insights.ConclusionsContinuous time Bayesian networks were effective on networks of both small and large size and were particularly feasible when the measurements were not evenly distributed over time. Reconstruction of the murine Th17 cell differentiation network using continuous time Bayesian networks revealed several autocrine loops, suggesting that Th17 cells may be auto regulating their own differentiation process.

Ensemble-based network aggregation improves the accuracy of gene network reconstruction.

Reverse engineering approaches to constructing gene regulatory networks (GRNs) based on genome-wide mRNA expression data have led to significant biological findings, such as the discovery of novel drug targets. However, the reliability of the reconstructed GRNs needs to be improved. Here, we propose an ensemble-based network aggregation approach to improving the accuracy of network topologies constructed from mRNA expression data. To evaluate the performances of different approaches, we created dozens of simulated networks from combinations of gene-set sizes and sample sizes and also tested our methods on three Escherichia coli datasets. We demonstrate that the ensemble-based network aggregation approach can be used to effectively integrate GRNs constructed from different studies – producing more accurate networks. We also apply this approach to building a network from epithelial mesenchymal transition (EMT) signature microarray data and identify hub genes that might be potential drug targets. The R code used to perform all of the analyses is available in an R package entitled “ENA”, accessible on CRAN (http://cran.r-project.org/web/packages/ENA/).

Algorithm for the Construction of a Global Enzymatic Network to be Used for Gene Network Reconstruction.

Relationships between genes are best represented using networks constructed from information of different types, with metabolic information being the most valuable and widely used for genetic network reconstruction. Other types of information are usually also available, and it would be desirable to systematically include them in algorithms for network reconstruction. Here, we present an algorithm to construct a global metabolic network that uses all available enzymatic and metabolic information about the organism. We construct a global enzymatic network (GEN) with a total of 4226 nodes (EC numbers) and 42723 edges representing all known metabolic reactions. As an example we use microarray data for Arabidopsis thaliana and combine it with the metabolic network constructing a final gene interaction network for this organism with 8212 nodes (genes) and 4606,901 edges. All scripts are available to be used for any organism for which genomic data is available.

De-novo learning of genome-scale regulatory networks in S. cerevisiae.

De-novo reverse-engineering of genome-scale regulatory networks is a fundamental problem of biological and translational research. One of the major obstacles in developing and evaluating approaches for de-novo gene network reconstruction is the absence of high-quality genome-scale gold-standard networks of direct regulatory interactions. To establish a foundation for assessing the accuracy of de-novo gene network reverse-engineering, we constructed high-quality genome-scale gold-standard networks of direct regulatory interactions in Saccharomyces cerevisiae that incorporate binding and gene knockout data. Then we used 7 performance metrics to assess accuracy of 18 statistical association-based approaches for de-novo network reverse-engineering in 13 different datasets spanning over 4 data types. We found that most reconstructed networks had statistically significant accuracies. We also determined which statistical approaches and datasets/data types lead to networks with better reconstruction accuracies. While we found that de-novo reverse-engineering of the entire network is a challenging problem, it is possible to reconstruct sub-networks around some transcription factors with good accuracy. The latter transcription factors can be identified by assessing their connectivity in the inferred networks. Overall, this study provides the gene network reverse-engineering community with a rigorous assessment of the accuracy of S. cerevisiae gene network reconstruction and variability in performance of various approaches for learning both the entire network and sub-networks around transcription factors.

Review on statistical methods for gene network reconstruction using expression data

Network modeling has proven to be a fundamental tool in analyzing the inner workings of a cell. It has revolutionized our understanding of biological processes and made significant contributions to the discovery of disease biomarkers. Much effort has been devoted to reconstruct various types of biochemical networks using functional genomic datasets generated by high-throughput technologies. This paper discusses statistical methods used to reconstruct gene regulatory networks using gene expression data. In particular, we highlight progress made and challenges yet to be met in the problems involved in estimating gene interactions, inferring causality and modeling temporal changes of regulation behaviors. As rapid advances in technologies have made available diverse, large-scale genomic data, we also survey methods of incorporating all these additional data to achieve better, more accurate inference of gene networks.

Wigwams: identifying gene modules co-regulated across multiple biological conditions

Motivation: Identification of modules of co-regulated genes is a crucial first step towards dissecting the regulatory circuitry underlying biological processes. Co-regulated genes are likely to reveal themselves by showing tight co-expression, e.g. high correlation of expression profiles across multiple time series datasets. However, numbers of up- or downregulated genes are often large, making it difficult to discriminate between dependent co-expression resulting from co-regulation and independent co-expression. Furthermore, modules of co-regulated genes may only show tight co-expression across a subset of the time series, i.e. show condition-dependent regulation.Results: Wigwams is a simple and efficient method to identify gene modules showing evidence for co-regulation in multiple time series of gene expression data. Wigwams analyzes similarities of gene expression patterns within each time series (condition) and directly tests the dependence or independence of these across different conditions. The expression pattern of each gene in each subset of conditions is tested statistically as a potential signature of a condition-dependent regulatory mechanism regulating multiple genes. Wigwams does not require particular time points and can process datasets that are on different time scales. Differential expression relative to control conditions can be taken into account. The output is succinct and non-redundant, enabling gene network reconstruction to be focused on those gene modules and combinations of conditions that show evidence for shared regulatory mechanisms. Wigwams was run using six Arabidopsis time series expression datasets, producing a set of biologically significant modules spanning different combinations of conditions.Availability and implementation: A Matlab implementation of Wigwams, complete with graphical user interfaces and documentation, is available at: warwick.ac.uk/wigwams.Contact: k.j.denby@warwick.ac.ukSupplementary Data: Supplementary data are available at Bioinformatics online.

Functional Modeling Identifies Paralogous Solanesyl-diphosphate Synthases That Assemble the Side Chain of Plastoquinone-9 in Plastids

It is a little known fact that plastoquinone-9, a vital redox cofactor of photosynthesis, doubles as a precursor for the biosynthesis of a vitamin E analog called plastochromanol-8, the physiological significance of which has remained elusive. Gene network reconstruction, GFP fusion experiments, and targeted metabolite profiling of insertion mutants indicated that Arabidopsis possesses two paralogous solanesyl-diphosphate synthases, AtSPS1 (At1g78510) and AtSPS2 (At1g17050), that assemble the side chain of plastoquinone-9 in plastids. Similar paralogous pairs were detected throughout terrestrial plant lineages but were not distinguished in the literature and genomic databases from mitochondrial homologs involved in the biosynthesis of ubiquinone. The leaves of the atsps2 knock-out were devoid of plastochromanol-8 and displayed severe losses of both non-photoactive and photoactive plastoquinone-9, resulting in near complete photoinhibition at high light intensity. Such a photoinhibition was paralleled by significant damage to photosystem II but not to photosystem I. In contrast, in the atsps1 knock-out, a small loss of plastoquinone-9, restricted to the non-photoactive pool, was sufficient to eliminate half of the plastochromanol-8 content of the leaves. Taken together, these results demonstrate that plastochromanol-8 originates from a subfraction of the non-photoactive pool of plastoquinone-9. In contrast to other plastochromanol-8 biosynthetic mutants, neither the single atsps knock-outs nor the atsps1 atsps2 double knock-out displayed any defects in tocopherols accumulation or germination.

Incorporating prior knowledge into Gene Network Study

A major goal in genomic research is to identify genes that may jointly influence a biological response. From many years of intensive biomedical research, a large body of biological knowledge, or pathway information, has accumulated in available databases. There is a strong interest in leveraging these pathways to improve the statistical power and interpretability in studying gene networks associated with complex phenotypes. This prior information is a valuable complement to large-scale genomic data such as gene expression data generated from microarrays. However, it is a non-trivial task to effectively integrate available biological knowledge into gene expression data when reconstructing gene networks. In this article, we developed and applied a Lasso method from a Bayesian perspective, a method we call prior Lasso (pLasso), for the reconstruction of gene networks. In this method, we partition edges between genes into two subsets: one subset of edges is present in known pathways, whereas the other has no prior information associated. Our method assigns different prior distributions to each subset according to a modified Bayesian information criterion that incorporates prior knowledge on both the network structure and the pathway information. Simulation studies have indicated that the method is more effective in recovering the underlying network than a traditional Lasso method that does not use the prior information. We applied pLasso to microarray gene expression datasets, where we used information from the Pathway Commons (PC) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) as prior information for the network reconstruction, and successfully identified network hub genes associated with clinical outcome in cancer patients. The source code is available at http://nba.uth.tmc.edu/homepage/liu/pLasso.

Gene network reconstruction reveals cell cycle and antiviral genes as major drivers of cervical cancer

Although human papillomavirus (HPV) was identified as an etiological factor in cervical cancer, the key human gene drivers of this disease remain unknown. Here we apply an unbiased approach integrating gene expression and chromosomal aberration data. In an independent group of patients, we reconstruct and validate a gene regulatory meta-network, and identify cell cycle and antiviral genes that constitute two major sub-networks up-regulated in tumour samples. These genes are located within the same regions as chromosomal amplifications, most frequently on 3q. We propose a model in which selected chromosomal gains drive activation of antiviral genes contributing to episomal virus elimination, which synergizes with cell cycle dysregulation. These findings may help to explain the paradox of episomal HPV decline in women with invasive cancer who were previously unable to clear the virus.

A multi-layer inference approach to reconstruct condition-specific genes and their regulation

An important topic in systems biology is the reverse engineering of regulatory mechanisms through reconstruction of context-dependent gene networks. A major challenge is to identify the genes and the regulations specific to a condition or phenotype, given that regulatory processes are highly connected such that a specific response is typically accompanied by numerous collateral effects. In this study, we design a multi-layer approach that is able to reconstruct condition-specific genes and their regulation through an integrative analysis of large-scale information of gene expression, protein interaction and transcriptional regulation (transcription factor-target gene relationships). We establish the accuracy of our methodology against synthetic datasets, as well as a yeast dataset. We then extend the framework to the application of higher eukaryotic systems, including human breast cancer and Arabidopsis thaliana cold acclimation. Our study identified TACSTD2 (TROP2) as a target gene for human breast cancer and discovered its regulation by transcription factors CREB, as well as NFkB. We also predict KIF2C is a target gene for ER-/HER2- breast cancer and is positively regulated by E2F1. The predictions were further confirmed through experimental studies. The implementation and detailed protocol of the layer approach is available at http://www.egr.msu.edu/changroup/Protocols/Three-layer%20approach%20 to % 20reconstruct%20condition.html.

EGN: a wizard for construction of gene and genome similarity networks

BackgroundIncreasingly, similarity networks are being used for evolutionary analyses of molecular datasets. These networks are very useful, in particular for the analysis of gene sharing, lateral gene transfer and for the detection of distant homologs. Currently, such analyses require some computer programming skills due to the limited availability of user-friendly freely distributed software. Consequently, although appealing, the construction and analyses of these networks remain less familiar to biologists than do phylogenetic approaches.ResultsIn order to ease the use of similarity networks in the community of evolutionary biologists, we introduce a software program, EGN, that runs under Linux or MacOSX. EGN automates the reconstruction of gene and genome networks from nucleic and proteic sequences. EGN also implements statistics describing genetic diversity in these samples, for various user-defined thresholds of similarities. In the interest of studying the complexity of evolutionary processes affecting microbial evolution, we applied EGN to a dataset of 571,044 proteic sequences from the three domains of life and from mobile elements. We observed that, in Borrelia, plasmids play a different role than in most other eubacteria. Rather than being genetic couriers involved in lateral gene transfer, Borrelia’s plasmids and their genes act as private genetic goods, that contribute to the creation of genetic diversity within their parasitic hosts.ConclusionEGN can be used for constructing, analyzing, and mining molecular datasets in evolutionary studies. The program can help increase our knowledge of the processes through which genes from distinct sources and/or from multiple genomes co-evolve in lineages of cellular organisms.

Using graph theory to analyze gene network coherence

Using graph theory to analyze gene network coherence

Hub-centered gene network reconstruction using automatic relevance determination.

Network inference deals with the reconstruction of biological networks from experimental data. A variety of different reverse engineering techniques are available; they differ in the underlying assumptions and mathematical models used. One common problem for all approaches stems from the complexity of the task, due to the combinatorial explosion of different network topologies for increasing network size. To handle this problem, constraints are frequently used, for example on the node degree, number of edges, or constraints on regulation functions between network components. We propose to exploit topological considerations in the inference of gene regulatory networks. Such systems are often controlled by a small number of hub genes, while most other genes have only limited influence on the network's dynamic. We model gene regulation using a Bayesian network with discrete, Boolean nodes. A hierarchical prior is employed to identify hub genes. The first layer of the prior is used to regularize weights on edges emanating from one specific node. A second prior on hyperparameters controls the magnitude of the former regularization for different nodes. The net effect is that central nodes tend to form in reconstructed networks. Network reconstruction is then performed by maximization of or sampling from the posterior distribution. We evaluate our approach on simulated and real experimental data, indicating that we can reconstruct main regulatory interactions from the data. We furthermore compare our approach to other state-of-the art methods, showing superior performance in identifying hubs. Using a large publicly available dataset of over 800 cell cycle regulated genes, we are able to identify several main hub genes. Our method may thus provide a valuable tool to identify interesting candidate genes for further study. Furthermore, the approach presented may stimulate further developments in regularization methods for network reconstruction from data.

An S-System Parameter Estimation Method (SPEM) for Biological Networks

Advances in experimental biology, coupled with advances in computational power, bring new challenges to the interdisciplinary field of computational biology. One such broad challenge lies in the reverse engineering of gene networks, and goes from determining the structure of static networks, to reconstructing the dynamics of interactions from time series data. Here, we focus our attention on the latter area, and in particular, on parameterizing a dynamic network of oriented interactions between genes. By basing the parameterizing approach on a known power-law relationship model between connected genes (S-system), we are able to account for non-linearity in the network, without compromising the ability to analyze network characteristics. In this article, we introduce the S-System Parameter Estimation Method (SPEM). SPEM, a freely available R software package (http://www.picb.ac.cn/ClinicalGenomicNTW/temp3.html), takes gene expression data in time series and returns the network of interactions as a set of differential equations. The methods, which are presented and tested here, are shown to provide accurate results not only on synthetic data, but more importantly on real and therefore noisy by nature, biological data. In summary, SPEM shows high sensitivity and positive predicted values, as well as free availability and expansibility (because based on open source software). We expect these characteristics to make it a useful and broadly applicable software in the challenging reconstruction of dynamic gene networks.

Gene network reconstruction identifies the authentic trans‐prenyl diphosphate synthase that makes the solanesyl moiety of ubiquinone‐9 in Arabidopsis

Ubiquinone (coenzyme Q) is the generic name of a class of lipid-soluble electron carriers formed of a redox active benzoquinone ring attached to a prenyl side chain. The length of the latter varies among species, and depends upon the product specificity of a trans-long-chain prenyl diphosphate synthase that elongates an allylic diphosphate precursor. In Arabidopsis, this enzyme is assumed to correspond to an endoplasmic reticulum-located solanesyl diphosphate synthase, although direct genetic evidence was lacking. In this study, the reconstruction of the functional network of Arabidopsis genes linked to ubiquinone biosynthesis singled out an unsuspected solanesyl diphosphate synthase candidate--product of gene At2g34630--that, extraordinarily, had been shown previously to be targeted to plastids and to contribute to the biosynthesis of gibberellins. Green fluorescent protein (GFP) fusion experiments in tobacco and Arabidopsis, and complementation of a yeast coq1 knockout lacking mitochondrial hexaprenyl diphosphate synthase demonstrated that At2g34630 is also targeted to mitochondria. At2g34630 is the main--if not sole--contributor to solanesyl diphosphate synthase activity required for the biosynthesis of ubiquinone, as demonstrated by the dramatic (75-80%) reduction of the ubiquinone pool size in corresponding RNAi lines. Overexpression of At2g34630 gave up to a 40% increase in ubiquinone content compared to wild-type plants. None of the silenced or overexpressing lines, in contrast, displayed altered levels of plastoquinone. Phylogenetic analyses revealed that At2g34630 is the only Arabidopsis trans-long-chain prenyl diphosphate synthase that clusters with the Coq1 orthologs involved in the biosynthesis of ubiquinone in other eukaryotes.

Parmigene—a parallel R package for mutual information estimation and gene network reconstruction

Inferring large transcriptional networks using mutual information has been shown to be effective in several experimental setup. Unfortunately, this approach has two main drawbacks: (i) several mutual information estimators are prone to biases and (ii) available software still has large computational costs when processing thousand of genes. Here, we present parmigene (PARallel Mutual Information estimation for GEne NEtwork reconstruction), an R package that tries to fill the above gaps. It implements a mutual information estimator based on k-nearest neighbor distances that is minimally biased with respect to the other methods and uses a parallel computing paradigm to reconstruct gene regulatory networks. We test parmigene on in silico and real data. We show that parmigene gives more precise results than existing softwares with strikingly less computational costs. The parmigene package is available on the CRAN network at http://cran.r-project.org/web/packages/. chiara.romualdi@unipd.it