CoV-HiPathia

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====== HiPathia, a mechanistic model of signaling pathways ====== Mechanistic models offer a realistic framework to understand how signal transduction depends on changes in the expression of genes involved in signaling pathway circuits (see [[http://www.oncotarget.com/index.php?journal=oncotarget&page=article&op=view&path[]=14107&pubmed-linkout=1|Hidalgo et al., 2017]]). Interestingly, cell signaling circuits trigger relevant cell functionalities (e.g., proliferation, cell death, metabolic changes, etc.), which virtually allows to estimate not only **signaling activity profiles** but also a cell **functional activity profiles**, from **transcriptional profiles**. Moreover, a mechanistic model allows predicting the effect of interventions (gene knock-outs, drugs, etc.) over a given sample, opening the door to multiple applications for **research** or **therapeutic** purposes. The HiPathia web server integrates four different pathway analysis modules: * **Differential signaling** * **Prediction** * **Perturbation effect** * **Variant interpreter** {{ ::hipathiatools.png |http://hipathia.babelomics.org}} ===== Differential signaling ===== This module provides an estimation of the significant cell signaling activity changes across different conditions. To achieve so, signal value activities are estimated for each signaling circuit for the studied samples. Then, these signaling activity profiles can be **compared** according to the experimental design of the used dataset: * **Two group comparison**, to check which signaling circuits present a significant change among the two compared groups, for example Tumor vs Normal. * **Correlation with continuous variable** (e.g., the level of a metabolite, etc.), providing a correlation of each signaling path with this variable. See the [[differential_signaling|Differential signaling]] tool. ===== Prediction ===== HiPathia allows you to train, download and test a prediction model for your dataset. The machine learning module can be trained either to: * Discriminate between two different groups of samples * Predict the value of an unknown variable. In order to check how to use these tool please see [[Prediction|Prediction]]. ===== Perturbation effect ===== This module offers an interactive working environment to simulate the effect of different interventions (e.g. knock-out, over-expression, etc.) over the activity of signaling circuits in the pathways, as well as their potential functional consequences in the cell. Given a specific condition, corresponding to a specific gene expression profile, knock-outs, knock-downs or different types of inhibitions are simulated by reducing or setting to 0 the expression level of the gene or interest. Conversely, over-expressions or agonistic interventions can be simulated by increasing its gene expression level. Then, the new condition with the simulated interventions is compared to the original one and the circuits affected are reported. In this way a holistic view of the ultimate effects across all the signaling pathways can be obtained. The simulation of the intervention can be carried out in different ways: * Over one or several **genes**, * Simulating the effect of one or several **drugs**, by simulating the effect of the drug on the corresponding genes targeted, * A **combination** of the two previous scenarios. See more about the [[Perturbation effect|Perturbation effect]] tool. ===== Variant interpreter ===== This tool provides an estimation of the potential impact of genomic variation over cell signaling and consequently on cell functionality. The tool predicts the possible effect that gene disruptions produced by LoF (Loss of Function) variants can have over signaling circuits as well as the cellular functions triggered by them. This module of HiPathia mechanistic model would be a specific case of the **perturbation effect** analysis, allowing the possibility of visualizing the effects of such perturbation for several tissues separately. also in this tool, the estimation can be done for more than one sample. A knock-down simulation is carried out on genes harboring LoF variants in a condition that represent a normal tissue. Then a **perturbation effect** analysis is carried out by comparing the condition with the simulated KOs with the normal condition. Normal conditions are taken from the repository of gene expression profiles of normal tissues available in the [[https://gtexportal.org/home/|GTEx]] database or, alternatively, a gene expression profile can be provided by the user (custom tissue). Then, the signaling circuits potentially deregulated by the disruption of the gene/s provided by the user are detected and visualized. See the [[vafin|Variant interpreter]] tool.