R tools for MicroRNA pathway analysis

In the early 2000s, microRNAs (miRNAs) were discovered as segments of a new class of highly conserved and small non-coding RNA molecules of 20-25 nucleotides that are transcribed from DNA.
They do not translation into proteins, rather they inhibit protein expression by binding to the 3’untranslated regions (3’ UTRs) of specific mRNA targets (that is/are complementary to them) and guiding their translational repression or complete degradation and gene silencing. With this, miRNAs provide a second level of regulation beyond primary gene expression. Integrative study of cellular pathways is pivotal to understanding the functions of individual genes and proteins in terms of systems and processes that contribute to normal physiology and to disease. "WikiPathways":http://wikipathways.org is an open, collaborative platform dedicated to the curation of biological pathways by and for the scientific community. The collection of pathways is publicly available to the researchers. The miRNA’s predicted by TargetScan in cardiomyocytes hypertrophy pathway has already been visualized on WikiPathways (WP1560). Since more studies investigate miRNAs using microarray technologies it would be desirable to be able to use information about miRNA’s in that analysis. One way to do that is to add the miRNA’s to all pathways. Therefore, we are integrating both validated and predicted miRNA information into biological pathways and making them available in WikiPathways. Initially, we focused on pathways related to the heart because miRNAs created a true revolution in the cardiovascular research field. The validated miRNAs have been downloaded from miRNA databases such as TarBase or miRTarbase. In order to link the validated miRNA targets to the genes in the pathways of our interest, we use "BridgeDb":http://www.bridgedb.org for identifier mapping. BridgeDb is a middleware between the relational databases, files and mapping services. BridgeDb is available in two forms. The first is a framework suitable for integration in Java applications. The other is based on Representational State Transfer (REST) webservices and is suitable for all other programming languages. The identifier mapping has been done in the R statistical environment as the connected Bioconductor repository has many pre-existing packages for microarray data analysis. For now we used the REST interface from R but we will also submit BridgeDb R package to Bioconductor.
Predicted miRNA targets by different prediction algorithms were verified by co evaluating miRNA and mRNA expression using microarray analysis. Quality control and normalization of the microarray datasets was done using the current functionality of the arrayanalysis.org web portal. Statistical analysis was done using Limma and the miRNAs were visualized in the pathways of interest using "PathVisio":http://www.pathvisio.org. Modules for statistical and pathway analysis have been developed which will be added to the "arrayanalysis.org":http://www.arrayanalysis.org portal. This also required connecting R to PathVisio, for which a new XMLRPC interface was developed. Through this PathVisio can be controlled by R scripts.
In conclusion, these R tools can help to integrate information about miRNAs with other knowledge about biological pathways and used for research purposes

Biological pathway analysis by ArrayUnlock and Ingenuity Pathway Analysis

<p>Abstract</p> <p>Background</p> <p>Once a list of differentially expressed genes has been identified from a microarray experiment, a subsequent post-analysis task is required in order to find the main biological processes associated to the experimental system. This paper describes two pathways analysis tools, ArrayUnlock and Ingenuity Pathways Analysis (IPA) to deal with the post-analyses of microarray data, in the context of the EADGENE and SABRE post-analysis workshop. Dataset employed in this study proceeded from an experimental chicken infection performed to study the host reactions after a homologous or heterologous secondary challenge with two species of <it>Eimeria</it>.</p> <p>Results</p> <p>Analysis of the same microarray data source employing both commercial pathway analysis tools in parallel let to identify several biological and/or molecular functions altered in the chicken <it>Eimeria maxima </it>infection model, including several immune system related pathways. Biological functions differentially altered in the homologous and heterologous second infection were identified. Similarly, the effect of the timing in a homologous second infection was characterized by several biological functions.</p> <p>Conclusion</p> <p>Functional analysis with ArrayUnlock and IPA provided information related to functional differences with the three comparisons of the chicken infection leading to similar conclusions. ArrayUnlock let an improvement of the annotations of the chicken genome adding InterPro annotations to the data set file. IPA provides two powerful tools to understand the pathway analysis results: the networks and canonical pathways that showed several pathways related to an adaptative immune response.</p

Dynamical pathway analysis

<p>Abstract</p> <p>Background</p> <p>Although a great deal is known about one gene or protein and its functions under different environmental conditions, little information is available about the complex behaviour of biological networks subject to different environmental perturbations. Observing differential expressions of one or more genes between normal and abnormal cells has been a mainstream method of discovering pertinent genes in diseases and therefore valuable drug targets. However, to date, no such method exists for elucidating and quantifying the differential dynamical behaviour of genetic regulatory networks, which can have greater impact on phenotypes than individual genes.</p> <p>Results</p> <p>We propose to redress the deficiency by formulating the functional study of biological networks as a control problem of dynamical systems. We developed mathematical methods to study the stability, the controllability, and the steady-state behaviour, as well as the transient responses of biological networks under different environmental perturbations. We applied our framework to three real-world datasets: the SOS DNA repair network in <it>E. coli </it>under different dosages of radiation, the GSH redox cycle in mice lung exposed to either poisonous air or normal air, and the MAPK pathway in mammalian cell lines exposed to three types of HIV type I Vpr, a wild type and two mutant types; and we found that the three genetic networks exhibited fundamentally different dynamical properties in normal and abnormal cells.</p> <p>Conclusion</p> <p>Difference in stability, relative stability, degrees of controllability, and transient responses between normal and abnormal cells means considerable difference in dynamical behaviours and different functioning of cells. Therefore differential dynamical properties can be a valuable tool in biomedical research.</p

Elementary vectors and conformal sums in polyhedral geometry and their relevance for metabolic pathway analysis

A fundamental result in metabolic pathway analysis states that every flux mode can be decomposed into a sum of elementary modes. However, only a decomposition without cancelations is biochemically meaningful, since a reversible reaction cannot have different directions in the contributing elementary modes. This essential requirement has been largely overlooked by the metabolic pathway community. Indeed, every flux mode can be decomposed into elementary modes without cancelations. The result is an immediate consequence of a theorem by Rockafellar which states that every element of a linear subspace is a conformal sum (a sum without cancelations) of elementary vectors (support-minimal vectors). In this work, we extend the theorem, first to "subspace cones" and then to general polyhedral cones and polyhedra. Thereby, we refine Minkowski's and Carath\'eodory's theorems, two fundamental results in polyhedral geometry. We note that, in general, elementary vectors need not be support-minimal, in fact, they are conformally non-decomposable and form a unique minimal set of conformal generators. Our treatment is mathematically rigorous, but suitable for systems biologists, since we give self-contained proofs for our results and use concepts motivated by metabolic pathway analysis. In particular, we study cones defined by linear subspaces and nonnegativity conditions - like the flux cone - and use them to analyze general polyhedral cones and polyhedra. Finally, we review applications of elementary vectors and conformal sums in metabolic pathway analysis

Defining a robust biological prior from Pathway Analysis to drive Network Inference

Inferring genetic networks from gene expression data is one of the most challenging work in the post-genomic era, partly due to the vast space of possible networks and the relatively small amount of data available. In this field, Gaussian Graphical Model (GGM) provides a convenient framework for the discovery of biological networks. In this paper, we propose an original approach for inferring gene regulation networks using a robust biological prior on their structure in order to limit the set of candidate networks. Pathways, that represent biological knowledge on the regulatory networks, will be used as an informative prior knowledge to drive Network Inference. This approach is based on the selection of a relevant set of genes, called the "molecular signature", associated with a condition of interest (for instance, the genes involved in disease development). In this context, differential expression analysis is a well established strategy. However outcome signatures are often not consistent and show little overlap between studies. Thus, we will dedicate the first part of our work to the improvement of the standard process of biomarker identification to guarantee the robustness and reproducibility of the molecular signature. Our approach enables to compare the networks inferred between two conditions of interest (for instance case and control networks) and help along the biological interpretation of results. Thus it allows to identify differential regulations that occur in these conditions. We illustrate the proposed approach by applying our method to a study of breast cancer's response to treatment

Post-transcriptional knowledge in pathway analysis increases the accuracy of phenotypes classification

Motivation: Prediction of phenotypes from high-dimensional data is a crucial task in precision biology and medicine. Many technologies employ genomic biomarkers to characterize phenotypes. However, such elements are not sufficient to explain the underlying biology. To improve this, pathway analysis techniques have been proposed. Nevertheless, such methods have shown lack of accuracy in phenotypes classification. Results: Here we propose a novel methodology called MITHrIL (Mirna enrIched paTHway Impact anaLysis) for the analysis of signaling pathways, which has built on top of the work of Tarca et al., 2009. MITHrIL extends pathways by adding missing regulatory elements, such as microRNAs, and their interactions with genes. The method takes as input the expression values of genes and/or microRNAs and returns a list of pathways sorted according to their deregulation degree, together with the corresponding statistical significance (p-values). Our analysis shows that MITHrIL outperforms its competitors even in the worst case. In addition, our method is able to correctly classify sets of tumor samples drawn from TCGA. Availability: MITHrIL is freely available at the following URL: http://alpha.dmi.unict.it/mithril