WebPARE: web-computing for inferring genetic or transcriptional interactions

Summary: Inferring genetic or transcriptional interactions, when done successfully, may provide insights into biological processes or biochemical pathways of interest. Unfortunately, most computational algorithms require a certain level of programming expertise. To provide a simple web interface for users to infer interactions from time course gene expression data, we present WebPARE, which is based on the pattern recognition algorithm (PARE). For expression data, in which each type of interaction (e.g. activator target) and the corresponding paired gene expression pattern are significantly associated, PARE uses a non-linear score to classify gene pairs of interest into a few subclasses of various time lags. In each subclass, PARE learns the parameters in the decision score using known interactions from biological experiments or published literature. Subsequently, the trained algorithm predicts interactions of a similar nature. Previously, PARE was shown to infer two sets of interactions in yeast successfully. Moreover, several predicted genetic interactions coincided with existing pathways; this indicates the potential of PARE in predicting partial pathway components. Given a list of gene pairs or genes of interest and expression data, WebPARE invokes PARE and outputs predicted interactions and their networks in directed graphs

Inferring genetic interactions via a nonlinear model and an optimization algorithm

<p>Abstract</p> <p>Background</p> <p>Biochemical pathways are gradually becoming recognized as central to complex human diseases and recently genetic/transcriptional interactions have been shown to be able to predict partial pathways. With the abundant information made available by microarray gene expression data (MGED), nonlinear modeling of these interactions is now feasible. Two of the latest advances in nonlinear modeling used sigmoid models to depict transcriptional interaction of a transcription factor (TF) for a target gene, but do not model cooperative or competitive interactions of several TFs for a target.</p> <p>Results</p> <p>An S-shape model and an optimization algorithm (GASA) were developed to infer genetic interactions/transcriptional regulation of several genes simultaneously using MGED. GASA consists of a genetic algorithm (GA) and a simulated annealing (SA) algorithm, which is enhanced by a steepest gradient descent algorithm to avoid being trapped in local minimum. Using simulated data with various degrees of noise, we studied how GASA with two model selection criteria and two search spaces performed. Furthermore, GASA was shown to outperform network component analysis, the time series network inference algorithm (TSNI), GA with regular GA (GAGA) and GA with regular SA. Two applications are demonstrated. First, GASA is applied to infer a subnetwork of human T-cell apoptosis. Several of the predicted interactions are supported by the literature. Second, GASA was applied to infer the transcriptional factors of 34 cell cycle regulated targets in <it>S. cerevisiae</it>, and GASA performed better than one of the latest advances in nonlinear modeling, GAGA and TSNI. Moreover, GASA is able to predict multiple transcription factors for certain targets, and these results coincide with experiments confirmed data in YEASTRACT.</p> <p>Conclusions</p> <p>GASA is shown to infer both genetic interactions and transcriptional regulatory interactions well. In particular, GASA seems able to characterize the nonlinear mechanism of transcriptional regulatory interactions (TIs) in yeast, and may be applied to infer TIs in other organisms. The predicted genetic interactions of a subnetwork of human T-cell apoptosis coincide with existing partial pathways, suggesting the potential of GASA on inferring biochemical pathways.</p

IRIS: a method for reverse engineering of regulatory relations in gene networks

<p>Abstract</p> <p>Background</p> <p>The ultimate aim of systems biology is to understand and describe how molecular components interact to manifest collective behaviour that is the sum of the single parts. Building a network of molecular interactions is the basic step in modelling a complex entity such as the cell. Even if gene-gene interactions only partially describe real networks because of post-transcriptional modifications and protein regulation, using microarray technology it is possible to combine measurements for thousands of genes into a single analysis step that provides a picture of the cell's gene expression. Several databases provide information about known molecular interactions and various methods have been developed to infer gene networks from expression data. However, network topology alone is not enough to perform simulations and predictions of how a molecular system will respond to perturbations. Rules for interactions among the single parts are needed for a complete definition of the network behaviour. Another interesting question is how to integrate information carried by the network topology, which can be derived from the literature, with large-scale experimental data.</p> <p>Results</p> <p>Here we propose an algorithm, called inference of regulatory interaction schema (IRIS), that uses an iterative approach to map gene expression profile values (both steady-state and time-course) into discrete states and a simple probabilistic method to infer the regulatory functions of the network. These interaction rules are integrated into a factor graph model. We test IRIS on two synthetic networks to determine its accuracy and compare it to other methods. We also apply IRIS to gene expression microarray data for the <it>Saccharomyces cerevisiae </it>cell cycle and for human B-cells and compare the results to literature findings.</p> <p>Conclusions</p> <p>IRIS is a rapid and efficient tool for the inference of regulatory relations in gene networks. A topological description of the network and a matrix of gene expression profiles are required as input to the algorithm. IRIS maps gene expression data onto discrete values and then computes regulatory functions as conditional probability tables. The suitability of the method is demonstrated for synthetic data and microarray data. The resulting network can also be embedded in a factor graph model.</p

Maximization of negative correlations in time-course gene expression data for enhancing understanding of molecular pathways

Positive correlation can be diversely instantiated as shifting, scaling or geometric pattern, and it has been extensively explored for time-course gene expression data and pathway analysis. Recently, biological studies emerge a trend focusing on the notion of negative correlations such as opposite expression patterns, complementary patterns and self-negative regulation of transcription factors (TFs). These biological ideas and primitive observations motivate us to formulate and investigate the problem of maximizing negative correlations. The objective is to discover all maximal negative correlations of statistical and biological significance from time-course gene expression data for enhancing our understanding of molecular pathways. Given a gene expression matrix, a maximal negative correlation is defined as an activation–inhibition two-way expression pattern (AIE pattern). We propose a parameter-free algorithm to enumerate the complete set of AIE patterns from a data set. This algorithm can identify significant negative correlations that cannot be identified by the traditional clustering/biclustering methods. To demonstrate the biological usefulness of AIE patterns in the analysis of molecular pathways, we conducted deep case studies for AIE patterns identified from Yeast cell cycle data sets. In particular, in the analysis of the Lysine biosynthesis pathway, new regulation modules and pathway components were inferred according to a significant negative correlation which is likely caused by a co-regulation of the TFs at the higher layer of the biological network. We conjecture that maximal negative correlations between genes are actually a common characteristic in molecular pathways, which can provide insights into the cell stress response study, drug response evaluation, etc

TimeDelay-ARACNE: Reverse engineering of gene networks from time-course data by an information theoretic approach

<p>Abstract</p> <p>Background</p> <p>One of main aims of Molecular Biology is the gain of knowledge about how molecular components interact each other and to understand gene function regulations. Using microarray technology, it is possible to extract measurements of thousands of genes into a single analysis step having a picture of the cell gene expression. Several methods have been developed to infer gene networks from steady-state data, much less literature is produced about time-course data, so the development of algorithms to infer gene networks from time-series measurements is a current challenge into bioinformatics research area. In order to detect dependencies between genes at different time delays, we propose an approach to infer gene regulatory networks from time-series measurements starting from a well known algorithm based on information theory.</p> <p>Results</p> <p>In this paper we show how the ARACNE (Algorithm for the Reconstruction of Accurate Cellular Networks) algorithm can be used for gene regulatory network inference in the case of time-course expression profiles. The resulting method is called TimeDelay-ARACNE. It just tries to extract dependencies between two genes at different time delays, providing a measure of these dependencies in terms of mutual information. The basic idea of the proposed algorithm is to detect time-delayed dependencies between the expression profiles by assuming as underlying probabilistic model a stationary Markov Random Field. Less informative dependencies are filtered out using an auto calculated threshold, retaining most reliable connections. TimeDelay-ARACNE can infer small local networks of time regulated gene-gene interactions detecting their versus and also discovering cyclic interactions also when only a medium-small number of measurements are available. We test the algorithm both on synthetic networks and on microarray expression profiles. Microarray measurements concern <it>S. cerevisiae </it>cell cycle, <it>E. coli </it>SOS pathways and a recently developed network for in vivo assessment of reverse engineering algorithms. Our results are compared with ARACNE itself and with the ones of two previously published algorithms: Dynamic Bayesian Networks and systems of ODEs, showing that TimeDelay-ARACNE has good accuracy, recall and <it>F</it>-score for the network reconstruction task.</p> <p>Conclusions</p> <p>Here we report the adaptation of the ARACNE algorithm to infer gene regulatory networks from time-course data, so that, the resulting network is represented as a directed graph. The proposed algorithm is expected to be useful in reconstruction of small biological directed networks from time course data.</p

Is My Network Module Preserved and Reproducible?

In many applications, one is interested in determining which of the properties of a network module change across conditions. For example, to validate the existence of a module, it is desirable to show that it is reproducible (or preserved) in an independent test network. Here we study several types of network preservation statistics that do not require a module assignment in the test network. We distinguish network preservation statistics by the type of the underlying network. Some preservation statistics are defined for a general network (defined by an adjacency matrix) while others are only defined for a correlation network (constructed on the basis of pairwise correlations between numeric variables). Our applications show that the correlation structure facilitates the definition of particularly powerful module preservation statistics. We illustrate that evaluating module preservation is in general different from evaluating cluster preservation. We find that it is advantageous to aggregate multiple preservation statistics into summary preservation statistics. We illustrate the use of these methods in six gene co-expression network applications including 1) preservation of cholesterol biosynthesis pathway in mouse tissues, 2) comparison of human and chimpanzee brain networks, 3) preservation of selected KEGG pathways between human and chimpanzee brain networks, 4) sex differences in human cortical networks, 5) sex differences in mouse liver networks. While we find no evidence for sex specific modules in human cortical networks, we find that several human cortical modules are less preserved in chimpanzees. In particular, apoptosis genes are differentially co-expressed between humans and chimpanzees. Our simulation studies and applications show that module preservation statistics are useful for studying differences between the modular structure of networks. Data, R software and accompanying tutorials can be downloaded from the following webpage: http://www.genetics.ucla.edu/labs/horvath/CoexpressionNetwork/ModulePreservation

Molecular Evolution of Major Epidermal Structure Genes and an Integrative Transcriptome Analysis of Chicken Epidermal Embryogenesis

Αlpha (α) and beta (β) keratins are the major structural proteins found in vertebrate epidermis and the β-keratins are only found in reptiles and birds. With the recent published 48 avian genomes, we searched and studied the molecular evolution of these gene families. We discovered that the expansion and contraction of different α- and β- keratins among the 48 phylogenetically diverse birds supports the importance of their role in the evolution of the feathers and the adaptation of birds to different ecological niches. Using a customized 44K microarray, we also performed transcriptome analysis on different epidermal regions (scutate scale, dorsal feather and wing feather) at important time points (day 8, 17 and 19) during chicken embryonic development. We profiled the differentially expressed α- and β-keratin genes in those comparison groups and demonstrated the important roles of α- and β-keratins in the development of the chicken epidermal appendages. MicroRNAs have been found to widely regulate many biological processes in animals. Here we also utilized the 44K microarray transcriptome data to profile the miRNA expression during chicken embryonic development. With the application of various bioinformatic tools, based on the differentially expressed miRNA genes and mRNAs, we identified highly possible target genes for epidermal development in the chicken, and provided a rational for future miRNA target validation. In previous studies, hundreds of genes (i.e., signaling pathway genes, structural genes, cell adhesion genes, etc.) have been associated with the morphogenesis of chicken epidermal structures as complex, interactive networks. A Weighted Gene Co-expression Network Analysis (WGCNA) using our microarray transcriptome data was performed to construct a co-expression network associated with traits. We identified two modules that were highly correlated with the developmental traits of the chicken scale and feather. The combination of traditional enrichment (KEGG and Gene Ontology) and novel enrichment (MSET and MeSH) analysis further demonstrated the important functional role of epidermal development related genes (EDRGs) and the hub genes to the development of scales and feathers. In the future, the discoveries of trait related modules will contribute to our understanding of the morphogenesis and differentiation of other epidermal appendages